CN101706299A - Coriolis mass flow transmitter based on DSP - Google Patents

Abstract

The invention relates to the field of flow detection, and is a DSP-based Coriolis mass flow transmitter, including an amplification filter circuit, an analog-to-digital converter 1, an analog-to-digital converter 2, a voltage reference source, a voltage follower, a current source, a differential Amplifier, analog-to-digital converter 3, analog drive module, digital signal processor DSP minimum system, externally expanded SRAM, externally expanded EEPROM, man-machine interface, 4-20mA current output and pulse output, power supply module and software. The analog drive module excites an exciter inside the Coriolis mass flow sensor, causing the flow tube to vibrate at its natural frequency. The two magnetoelectric sensors located on both sides of the flow tube output two sine wave signals. The two signals are amplified and filtered by two conditioning circuits with the same parameters. After being sampled and converted into The digital quantity is sent to the DSP through two multi-channel buffered serial ports of the DSP. DSP uses digital filtering to eliminate signal noise, adaptive lattice notch filter to calculate frequency, and then uses DTFT algorithm considering negative frequency to calculate phase difference, and finally obtains mass flow rate.

Description

A Coriolis Mass Flow Transmitter Based on DSP

technical field

The invention relates to the field of flow detection, and is a DSP-based Coriolis mass flow transmitter, in particular, a digital filter is used to filter the signal, an adaptive lattice notch filter is used to calculate the signal frequency, and then a negative The frequency of the DTFT algorithm is used to calculate the phase difference of the Coriolis mass flowmeter signal processing system and the analog drive system.

Background technique

The Coriolis mass flowmeter (hereinafter referred to as the Coriolis mass flowmeter) can directly measure the mass flow rate. It is one of the most rapidly developing flowmeters and has broad application prospects. The Coriolis mass flowmeter is composed of two parts, one is the sensor part, including the magnetoelectric speed sensor, exciter and temperature sensor, and the other is the transmitter, including the signal conditioning, processing part and driving part. As far as the signal conditioning and processing part is concerned, it is required to accurately measure the phase difference of the signals from the two flow (magnetoelectric speed) sensors and track changes in their frequency. Foreign countries take the lead in applying digital signal processing technology and digital signal processor (DSP) to Coriolis mass flowmeters.

(1) Method based on discrete Fourier transform

American Micro Motion company uses discrete Fourier transform (DFT) to process the output signal of Coriolis mass flow meter (Paul Romano.Coriolis mass flow rate meter having a substantially increased noise immunity.US Patent No.4934196, Jun.19, 1990), using the TMS series digital signal processor (DSP) as the processing core of the secondary instrument. When non-full-period sampling is performed, the calculation error of DFT cannot meet the requirements of instrument accuracy. Therefore, the ideas of rough measurement, fine measurement and frequency tracking are proposed. However, some key technologies are not disclosed. For example, when the frequency changes, how to collect zero-crossing points, etc. Hefei University of Technology developed a DFT-based signal processing system based on the ADSP series DSP with reference to its ideas, which solved the technical difficulties not described in the US patent, and made improvements in fine measurement and frequency tracking (Xu Kejun, Yu Cuixin et al. Coriolis mass flowmeter digital signal processing system, invention patent, patent number: ZL 00108414.3, application date: 2000.4.30., authorization announcement date: 2002.12.4.).

(2) Method based on signal amplitude

日本富士公司(Yoshimura Hiroyuki.Phase difference measuring apparatus for measuringphase difference between input signals.European patent application，EP0791807A2，27.08.1997；Yoshimura Hiroyuki.Phase difference measuring apparatus andflowmeter thereof.European patent application，EP 0702212A2，20.03.1996)对The signals from the two sensors in the Coriolis mass flowmeter are amplified, and the two signals are sent to the differential amplifier at the same time to obtain the difference between the signals of the two sensors. The multiplexer sends these three signals to the analog-to-digital converter sequentially, and sends them to the DSP for DFT to calculate the phase difference of the two sensor signals, and select one signal as a reference to compensate for the difference in the characteristics of each transmission channel. error. The problem with this method is: the phase difference of the two sensor signals is very small (generally less than 4 degrees), so the amplitude of the differential amplifier is very small, and it is very susceptible to noise interference; the compensation method takes too much time, because the calculation of the phase difference once Need to collect 6 signals.

(3) Method based on adaptive linear enhancement

A DSP-based digital signal processing system was designed in the US patent US-5555190 granted to H.V.Derby et al. on September 10, 1996 and the Chinese patent CN-1190461A granted on August 12, 1998. Adaptive Linear Enhancement (ALE) technique and apparatus to accurately determine the frequency and phase relationship between vibrating flow tubes to more accurately determine mass flow through a mass flow meter. Derby's patent has two embodiments, each of The signal processing link is composed of three parts: multi-decimation-filtering, frequency estimation/linear enhancement and phase difference calculation. The multi-decimation part of the two embodiments adopts (8:1 and 6:1 two-stage extraction), and the phase difference calculation adopts improved The Goertzel algorithm. The two embodiments are only different in the frequency estimation/linear enhancement part. The ALE of the first embodiment is realized by the adaptive notch filter (ANF) of the two parameter estimates simultaneously, and the second implementation The example uses four self-adaptive notch filters, and two notch filters are connected in series respectively in the left channel and the right channel. The shortcoming of Derby's patent is: for the first embodiment, the estimation of two parameters increases the complexity of the algorithm property; in the second embodiment, two cascaded ANFs are used, and the complexity of the algorithm is higher than that of the first embodiment, and, since the second-level ANF is based on the first-level ANF, only the first The second stage will converge after the first stage converges. Therefore, when the frequency changes greatly, the convergence speed will decrease instead. At the same time, the Goertzel algorithm used in the phase difference calculation part may overflow when it is implemented with fixed points.

In response to these problems, Hefei University of Technology designed a signal processing method and device, which takes into account the accuracy of frequency tracking and the convergence speed of the algorithm, without significantly increasing the complexity of the algorithm, and it is not easy to overflow when implemented with fixed points (Xu Kejun, Xu Wenfu. Coriolis mass flowmeter digital signal processing system based on AFF and SGA, invention patent, patent number: ZL03108944.5, application date: 2003.4.4., authorization announcement date: 2005.3.23.). The signal processing link consists of three parts: multi-decimation-filtering, frequency estimation/linear enhancement and phase difference calculation. The frequency estimation/linear enhancement part adopts the funnel type (Funnel) filter. The filter has two design parameters, which impose stronger restrictions on the zero and pole points, and the order is higher than that of the ANF of the same level. In the phase difference calculation part, the sliding Goertzel algorithm, which is easier to implement at fixed points and is very suitable for time-varying sinusoidal signals, is adopted. However, the convergence process of the SGA algorithm is still long, and the tracking accuracy is not high enough to meet the accuracy requirements of the Coriolis mass flowmeter; at the same time, when used in an actual system, the calculation amount is large and it is difficult to implement in real time.

Contents of the invention

In order to solve the above problems, the present invention adopts the following technical solutions: the Coriolis mass flow transmitter based on DSP includes an amplification filter circuit, an analog-to-digital converter 1, an analog-to-digital converter 2, a voltage reference source, a voltage follower, a current source, Differential amplifier, analog-to-digital converter 3, analog drive module, digital signal processor DSP minimum system, externally expanded static random access memory (SARAM), externally expanded EEPROM (electrically erasable programmable read-only memory), man-machine interface, 4-20mA current output and pulse output, power module and software module. The analog drive module excites the exciter inside the Coriolis mass flow sensor, causing the flow tube to vibrate at its natural frequency. The two magnetoelectric sensors located on both sides of the flow tube output two sine wave signals. The two signals are amplified and filtered by two conditioning circuits with the same parameters. After being sampled and converted into The digital quantity is sent to the DSP through two multi-channel buffered serial ports of the DSP. DSP uses digital filtering to eliminate signal noise, adaptive lattice notch filter to calculate frequency, and then uses negative frequency corrected discrete-time Fourier transform (DTFT) algorithm to calculate phase difference, and finally obtains mass flow rate.

The advantages of the patent of the present invention are: improving phase difference calculation accuracy, shortening convergence time, developing a system acceptable and applicable in the industry with DSP, and realizing digital signal processing algorithms in real time.

Description of drawings

Fig. 1 is the overall block diagram of the hardware of the system of the present invention.

Fig. 2 is the reference source of the signal conditioning and acquisition part of the system of the present invention.

Fig. 3 is a simulation drive of the system of the present invention.

Fig. 4 is the signal conditioning and acquisition circuit of the system of the present invention.

Fig. 5 is a temperature compensation circuit of the system of the present invention.

Fig. 6 is the DSP minimum system of the system of the present invention.

Fig. 7 is the selection of the externally expanded memory and the DSP startup mode of the system of the present invention.

Fig. 8 is the liquid crystal and keyboard circuit of the system of the present invention.

Figure 9 is the pulse output of the system of the present invention.

Fig. 10 is the 4-20mA current output of the system of the present invention.

Fig. 11 is an overall software block diagram of the system of the present invention.

Fig. 12 is a flow chart of the main monitoring program of the system of the present invention.

Fig. 13 is a schematic diagram of the INT1.4 interrupt of the system of the present invention.

Fig. 14 is a schematic diagram of INT7.1 and INT7.2 interrupts of the system of the present invention.

Fig. 15 is a schematic diagram of the signal acquisition and transmission process of the system of the present invention.

Fig. 16 is a flow chart of the DMA interrupt service program of the system of the present invention.

Fig. 17 is the cyclic long array of the system of the present invention.

Fig. 18 is a flow chart of the algorithm processing of the system of the present invention.

Fig. 19 is a flow chart of the adaptive lattice notch algorithm of the system of the present invention.

Fig. 20 is a flow chart of the DTFT algorithm for negative frequency correction of the system of the present invention.

Fig. 21 is a flow chart of the averaging module of the system of the present invention.

Fig. 22 is a schematic diagram of the segments of the system of the present invention.

Fig. 23 is a flow chart of PWM wave output of the system of the present invention.

Detailed ways

The hardware block diagram of the system of the present invention is shown in Fig. 1 . The system of the present invention consists of a Coriolis mass flow sensor (including a magnetoelectric speed sensor A, a magnetoelectric speed sensor B, an exciter, a resistance temperature sensor RTD), an amplification filter circuit, an analog-to-digital converter ADC1, an analog-to-digital conversion ADC2, voltage reference source, voltage follower, current source, differential amplifier, analog-to-digital converter ADC3, analog drive module, digital signal processor DSP minimum system, external expansion SARAM, external expansion EEPROM, man-machine interface, 4 ~ 20mA Composed of current output, pulse output, and power module. The system of the present invention is divided into a plurality of circuit boards according to functions, the most important ones are analog boards and digital boards, the former includes sensor signal conditioning and acquisition circuits, analog drive circuits and temperature compensation circuits; the latter includes DSP minimum systems and externally expanded memory, etc. .

The working process of the system of the present invention is as follows: the analog driving module excites the vibrator inside the Coriolis mass flow sensor (hereinafter referred to as the Coriolis sensor), so that the flow tube vibrates at a natural frequency. Two magnetoelectric sensors located on both sides of the flow tube output two sine wave signals, and the two signals are amplified and filtered by two conditioning circuits with the same parameters, and then they are simultaneously sampled and converted into digital by two ADC1 and ADC2 of the same model. The amount is sent to the DSP through two multi-channel buffered serial ports McBSP of the DSP to calculate the phase difference of the two signals. DSP adopts multi-pump and self-adaptive lattice notch filter to digitally filter the signal, and then uses DTFT algorithm considering negative frequency to calculate the phase difference. Fluid temperature affects the flow tube angular modulus and thus the measured value, so temperature compensation is required. A constant current is generated to flow through the internal temperature sensor Pt100 of the Coriolis sensor. After the voltage at both ends of the Pt100 is amplified by the differential amplifier, it is sampled by ADC3 and converted into a digital quantity and sent to the DSP for temperature compensation calculation. Convert the phase difference into mass flow and add temperature compensation, and display the final value on LCD, and it is equipped with 4-20mA current output and pulse output.

Figure 2 shows the signal conditioning and acquisition part of the voltage reference source. U10 is the reference source, and its output passes through the voltage follower composed of operational amplifier U11 to improve the current output capability. C32 and C33 are input and output filter capacitors; capacitor C37 and the internal resistance of U10 form an RC filter to filter the output; capacitors C34, C35 and C36 are output filter capacitors; C30 and C38 are decoupling capacitors.

Figure 3 is an analog drive circuit. After the input signal LP+ is amplified by the non-inverting amplifier composed of U16A, R31, R34, it is sent to the band-pass filter composed of U16B, C51, R29, R27, C47, and R26 for filtering, and R32 and C55 are used to absorb the peak in the output signal of U16B Pulse, capacitors C48, C50, C52, and C53 are decoupling capacitors. One output signal of U16B is amplified by the non-inverting amplifier composed of U18B, R43, and R46, and then sent to an input terminal Y1 of the analog multiplier U19; Composed of R33, C56, D1, and D2, the input AC signal is rectified into a DC signal, and its value is proportional to the amplitude of the AC signal. Capacitors C49 and C54 are decoupling capacitors. The output TPE of the rectifier circuit is sent to the gain control circuit. The gain control circuit is composed of U18A, resistors R37, R39, R44, potentiometer W1, and capacitor C65. The output W decreases with the increase of the input, and vice versa; the capacitor C57, C62 is a decoupling capacitor. The output of U18A is sent to the other input terminal X1 of the analog multiplier U1, and the output of U19 is the product of X1 and Y1. When the gain control circuit controls X1, it can control the output of U19. When LP+ decreases, X1 increases; when LP+ increases, X1 decreases to stabilize the output of U19. R38 and R41 are current limiting resistors, and C58, C60, C64 and C66 are decoupling capacitors. C61, R40, R42 constitute a DC blocking circuit, the signal after DC blocking is amplified by the non-inverting amplifier composed of U20A, R45, R47, C59, C63 are decoupling capacitors, and the unused operational amplifier U20B is connected as a follower and the input is grounded. The output TPM of U20A is sent to the amplifying circuit with over-vibration protection composed of U21B, U21A and peripheral devices to avoid sensor damage caused by excessive driving voltage. The output TPE of the rectifier circuit is connected to the inverting input terminal of U21A. When TPE is less than the value set by the resistor R50 and the potentiometer W2, the output of U21A is close to the positive power supply voltage, the P-channel junction field effect transistor Q1 is turned on, and the resistor R48 Connect R55 to the ground in parallel, and the magnification is 6 times; on the contrary, when TPE is greater than the set value of resistor R50 and potentiometer W2, the output of U21A is close to the negative power supply voltage, Q1 is cut off, and the magnification is 1, which protects the sensor effect. Resistor R52 and capacitor C75 filter the input signal TPE, R51 is a current-limiting resistor, U21B, R49, R48, and R55 form a non-inverting amplifier, C73 and R54 are DC blocking circuits, which block DC input signal TPM, and capacitor C67 has a filtering effect. C68, C70, C74, and C76 are decoupling capacitors. The output signal of U21B passes through the DC blocking circuit composed of C72 and R53, and then the power amplifier composed of U22, R56 and R57 amplifies the voltage by 2 times and increases the output power to drive the exciter. coupling capacitance.

Figure 4 is the sensor signal conditioning, acquisition circuit. The signals RP+ and RP- are connected to the input terminal of U2 in a differential form, amplified by the instrument amplifier U2, and sent to the second-order low-pass filter composed of the operational amplifier U3A and peripheral resistors and capacitors, and then filtered by the second-order low-pass filter composed of R7 and U4. The clipping protection circuit feeds into the input of ADC U1. The input signals RP+ and RP- are connected into a differential form through R8 and R9, R2 sets the amplification factor of U2, C2, C6, C12, and C14 are decoupling capacitors, R5, R6, R1, R12, C5, and C10 are connected to the operational amplifier U3A Constitutes a second-order low-pass filter, C3, C9, C11, and C13 are decoupling capacitors, and capacitor C90 is used to filter out high-frequency interference generated by high-frequency switching devices in U1.

U1 is a differential input ADC1, the signal is connected to AIN0 (pin 6), and the other input terminal AIN1 (pin 7) is connected to a 2.5V reference source; the reference required by U1 is taken from an external 2.5V reference source, and VREF (pin 4) is connected to 2.5V Reference, VREFN (pin 3) is grounded; the data transmission interface of U1 is connected to the multi-channel buffer serial port McBSP of DSP: the clock SCLK (pin 18) is connected to MCLKXA, the data input DIN (pin 17) is connected to MDXA, and the data output DOUT (pin 16) ) is connected to MDRA, chip selection CS is connected to MFSXA, in which the serial resistors R3, R4, R10 and the corresponding pin input parasitic capacitance of U1 form an RC low-pass filter to filter out spikes; the control pins DRDY and SYNC are connected to the GPIO port of DSP ; Capacitors C7 and C8 are filter capacitors, and capacitors C1, C4, C15 and C17 are decoupling capacitors.

The other signal LP+, LP- is sent to the ADC2U6 of the same model as U1 through the conditioning circuit of the same principle and structure, and will not be described again. The clocks of U1 and U6 use the same clock source-the clock generated by the external active crystal oscillator U9.U9 The Schmitt trigger U5 is used to improve the driving ability, and then sent to the U1 and U6 clock input pins in two ways. The magnetic bead L1 and the capacitor C29 filter the power supply, and the capacitor C31 and the resistor R25 are used to filter out the high voltage in the clock signal. Frequency spikes, resistor R11 is also used to filter out spikes.

Figure 5 shows the temperature compensation circuit. The temperature sensor inside the sensor is Pt100. The constant current source is added to Pt100, and after differential amplification, it is sent to ADC3 for sampling and converted into digital quantity and sent to DSP. Pt100 is connected to RTD+ and RTD- in the figure, U23, R61, U24, R58, R59, R67, Q2, Q3 generate a constant current source and flow through Pt100. R62 and TP1 are used for testing and calibration, you can disconnect Pt100 and short-circuit TP1 for testing and calibration. The voltage at both ends of the Pt100 is amplified by the differential amplifier composed of U26A, U26B, R64, R63, and R65, and then sent to the differential input ADC3U25 input terminals AIN+ (3 pins) and AIN- (4 pins). R70, C88, R71, and C89 respectively constitute an RC low-pass filter; capacitors C80 and C87 are filter capacitors; R60, U27, R66, and U29 respectively constitute a limiting protection circuit; the reference source required by U25 is provided by U28, and C85 and C86 are The input and output filter capacitors, C84 and U25 internal resistors form an RC filter to filter the output of U28. The U25 digital interface is connected to the SPI port of the DSP, the clock SCLK (pin 1) is connected to SPICLKA, the chip select CS (pin 2) is connected to SPISTEA, the data input DIN (pin 10) is connected to SPISIMOA, and the data output DOUT (pin 9) is connected to SPISOMIA. Capacitors C79, C81, C82, and C83 are decoupling capacitors.

Figure 6 shows the minimum DSP system, including crystal oscillator, JTAG, and decoupling capacitors. The SPI, McBSP and multiple general-purpose input and output pins (GPIO) of the DSP (U1) are mainly used, and the RAM is also expanded, and the unused pins are properly handled. U2 is an active crystal oscillator, which provides clock for DSP, bead L3 and capacitor C34 filter the power supply, C35 and R7 filter out the peak pulse in the clock signal; pin 142 and pin 25 of DSP U1 are functional pins, which need to be reset separately In the valid and invalid states, R1 and R4 are pull-up resistors; DS2 is the DSP work indicator light, R22 is the current limiting resistor; the resistor R3 connected to pin 57 of U1 provides a path for the bias current, and pins 55 and 56 of U1 The pin is the internal ADC reference output, and capacitors C27 and C26 need to be connected in parallel to the ground; P1 is the JTAG interface, and R2, R5, and R6 are the pull-down and pull-up resistors required in the manual; the magnetic bead L1 isolates digital 3.3V and analog 3.3V , Magnetic bead L2 isolates digital 1.9V and analog 1.9V; capacitors C1~C25, C28~C33 are decoupling capacitors.

Figure 7 shows the selection of DSP external memory expansion and DSP startup mode. U4 is an external expansion RAM. When connecting with DSP, its address line and data line can be disrupted. Chip select CS (pin 6) is connected to XZCS7n of DSP, WE (pin 17) is connected to XWE0n, OE is connected to XRDn, C47 and C48 are decoupling capacitor. U5 is an external expansion EEPROM, connected to the GPIO port of DSP, C46 is a decoupling capacitor, R14 is a pull-down resistor; resistors R13, R18, R15, R19, R16, R20, R17, R21 and address lines XA15, XA14, XA13, XA12 Connection, whether R18, R19, R20, and R21 are soldered to the PCB determines how the DSP starts.

Figure 8 is the LCD and keyboard circuit. P1 is the liquid crystal interface, and D1 in series reduces the 3.3V power supply voltage to meet the power supply requirements of the liquid crystal. The 4th pin of P1 is the ground, and the 1st, 2nd and 3rd pins are the chip selection, clock and data input pins respectively, and the GPIO of the DSP Connected, because the level does not match the DSP level, the resistors R2~R8 form a voltage divider circuit to match the level; P3 is the keyboard interface, the fifth pin is the ground, and the rest are connected to the GPIO of the DSP. The resistors R1, R4, R9, R10 is a current-limiting resistor. Capacitors C1, C2, C3, and C4 are used for hardware debounce. When the system is powered off, D2, D3, D4, and D5 provide paths for the release of charges on the capacitors.

Figure 9 shows the pulse output circuit. When the input signal PulseIn is high level, Q4 is turned on, U1 is turned on, Q2 is turned off, Q1 and Q3 are turned off, and the output PulseOut is low level; when PulseIn is low level, Q4 is turned off, and U1 At the end, the base voltage of Q2 is clamped at 1.4V by D1 and D2, Q2 is turned on, and a voltage drop occurs on resistors R8 and R3, resulting in Q1 and Q3 being turned on, and the output PulseOut is high, and its voltage value is equal to (R6+ The voltage drop on R10) or (R7+R9), which is related to resistors R1, R8, R3, and R4. Resistor R11 is the base current-limiting resistor, R5 is the current-limiting resistor at the input of optocoupler U1, and R2 is the output of U1 End pull-up resistors, D1 and D2 are clamping diodes, resistors R1, R8, R3, and R4 determine the magnitude of the current flowing through R1, thereby determining the voltage value of the pulse output high level, resistors (R6+R10) and (R7+ The voltage drop on R9) is the voltage value of the pulse high level, and capacitors C1, C2, and C3 are decoupling capacitors.

Figure 10 is a 4 ~ 20mA output circuit. U3 is a DAC, and the flow calculation value (digital quantity) is converted into an analog voltage by U3 and sent to U2 for input, and U2 converts the voltage into a 4-20mA current output. R2 and U4 provide voltage reference source for U3, and C5 and C6 are decoupling capacitors. U2's pin 12 outputs 10V voltage; Q3 increases its current output capability; U1 is an LDO that provides 3.3V power; C3 and C2 are filter capacitors; R1 determines the relationship between U2's input voltage and output current; capacitor C4 stabilizes the circuit; Q1, Q2 and the operational amplifier in U2 form a voltage/current conversion circuit; capacitor C1 is a decoupling capacitor.

The software workflow of the patent of the present invention is:

(1) After the system is powered on, on the one hand, the analog drive circuit generates a drive signal to stimulate the vibration of the Coriolis flowmeter; on the other hand, the DSP completes the initialization of each part of the system, and then starts and synchronizes the sampling conversion of the two ADCs.

(2) After the data is converted, an interrupt is generated through GPIO; the interrupt service program transfers the converted result to the receiving pins of the two-way multi-channel buffered serial port McBSP, and then starts direct memory access (DMA) transmission without disturbing the CPU In the case of work, the data on the receiving pin of McBSP is transferred to the temporary array in the internal storage space through DMA; when the temporary array is full, two DMA receiving interrupts are generated, and the interrupt service routine transfers the data in the two temporary arrays Data is stored in two circular buffer arrays in the external SARAM.

(3) Take 500 points of new continuous data from the external loop array, and judge the size of the signal. If the signal is small, it means that the drive is not stable, and continue to wait; if the signal is greater than the set value, you can start calling the algorithm module .

(4) Preprocess the two channels of 500-point data signal filtering, filter out the noise and improve the quality of the enhanced signal; then, call the adaptive lattice notch filter on the two channels of filtered signals to estimate the basis of the two channels of sensor signals respectively frequency, and then average the frequency values of the two signals to obtain the instantaneous value of the fundamental frequency of the signal, average the frequency, and set a comparison range. If the frequency exceeds the range, the frequency is updated, so that a stable and accurate frequency value can be obtained, and the instantaneous density value can be obtained by combining the coefficient; finally, the DTFT algorithm with negative frequency correction is called to calculate the phase difference of the two signals, and the results are averaged, combined with The time difference is obtained from the frequency value, and combined with the set meter coefficient, the instantaneous flow value is obtained.

(5) Read the temperature sensor signal and convert it into a temperature value, and obtain the corresponding temperature compensation coefficient according to the material of the measured flowmeter, and perform temperature compensation on the instantaneous flow rate.

(6) Generate an interrupt at regular intervals of 1s through the CPU timer, and accumulate the instantaneous flow in the interrupt service program to obtain the accumulated flow; at the same time, set the output result flag bit.

(7) After obtaining the measurement results, the TMS320F28335 displays the measured values such as cumulative flow, density, and temperature through the LCD on the one hand; on the other hand, according to the obtained instantaneous flow, it outputs the corresponding 4-20mA current and PWM through the DA or ePWM module. Pulse, so that the upper computer or other secondary instruments can count.

(8) Finally check whether the keyboard flag is set, if set, call the keyboard service subroutine, if not, repeat the process of 2-8 to measure the flow in real time.

The main task of the digital signal processing system in the system of the present invention is to perform corresponding digital signal processing algorithms on real-time sampling data to complete flow measurement, and use hardware resources to write corresponding programs to expand functions to meet user requirements for instruments. In addition to measuring flow, the system of the present invention also needs to have functions such as pulse output, current output, instrument coefficient setting and man-machine interface (ie keyboard function and LCD display). In addition, in order to ensure the normal operation of the system, an error detection system is added to the system , Fault reset function. Therefore, the entire digital signal processing system has many functions, and the software design is complex. In order to facilitate the design and maintenance of the system, the software design adopts a modular design scheme, and the subroutines that complete specific functions or similar functions are combined into functions Module, and then called by a main monitoring program. Of course, these functional modules can also call each other. The interrupt module handles some special events and can interrupt the execution of the main program. The overall block diagram of the software is shown in Figure 11, mainly including the initialization module , signal acquisition module, algorithm module, interrupt module, result output module, man-machine interface module, EEPROM operation module, error module and watchdog reset module. Each function module generally includes a header file and a function file, so that The software is easy to manage and transplant.

Figure 12 is the main monitoring process of the system. The main monitoring program is the general dispatcher of the entire signal processing system, which calls the subroutines in each module to realize the functions required by the instrument. It is an endless loop program. Once the system is powered on, the main monitoring program runs automatically and enters a cycle of continuous calculation and processing. The basic process is: after the system is powered on, initialize it immediately; after initialization, turn on the ADC to convert the sampling data, and then call the calculation module to process the signal sampling sequence with a signal processing algorithm to calculate the sensor signal frequency and the phase difference between the two channels, time difference; then, according to the set instrument coefficient, calculate the instantaneous flow and cumulative flow; and average the results, and perform temperature compensation on the instantaneous flow and cumulative flow; then call the system output module, according to the calculated instantaneous flow , send out the corresponding pulse amount and the standard 4-20mA current; after the output is completed, the main monitoring program queries the keyboard operation flag bit for corresponding processing, and finally returns to start signal processing, flow calculation and output signal again, and continuously cycle.

After the system of the present invention is powered on, the software and hardware resources of each module need to be initialized before entering the normal flow measurement process. This module is responsible for the initialization of programmable devices and software resources in the system, mainly including the initialization of DSP chip TMS320F28335, the initialization of external programmable devices, the initialization of interrupt configuration and the initialization of software resources, etc.

The initialization of TMS320F28335 mainly includes system clock initialization and initialization of TMS320F28335 internal integrated function modules used by the system, such as watchdog, system external interface XINTF, ePWM1a in enhanced PWM module, timer, direct memory access DMA module, multi-channel buffer Serial port McBSP module, Flash memory and general digital multiplexing port GPIO, etc.

The initialization of external programmable devices includes the initialization of two ADC chips for collecting flow sensor signals, the initialization of ADC3 chip for collecting temperature sensor signals, and the LCD initialization. The initialization of the ADC chip is mainly the writing of control words such as the working mode of the chip. LCD initialization includes: control pin initialization, write mode command word and initialization display content, etc.

Interrupt configuration initialization includes PIE control register initialization, interrupt vector table initialization, and interrupt mapping used in the system.

Software resource initialization is mainly used for initialization of global data structures, arrays, parameters, etc. of each module. Some data in the software needs to be used continuously and should be configured as global variables; in addition, data shared between modules should also be configured as global variables. According to the different initialization methods, the initialization of global variables is mainly divided into two categories: one is to initialize through EEPROM, mainly the fixed deviation of instrument parameters and calibration, etc.; the other is to directly copy and initialize, mainly including reflecting system work State variables and constant variables to reduce program calculations and facilitate program calls, etc.

TMS320F28335 internally adopts a centralized peripheral interrupt expansion controller PIE to handle the priority and interrupt response of all on-chip peripherals and external pin interrupts. These interrupts are divided into 12 groups, and each group has 8 interrupts to multiplex one CPU-level interrupts are INT1-INT12. These 12 core-level interrupts have fixed priorities and are maskable. This system uses two core CPU-level interrupts: INT1 and INT7. INT1.4 is a peripheral interrupt source, which comes from the acquisition One of the ADCs of the two sensor signals indicates that the conversion data of the two ADCs is completed. As shown in Figure 13, the Ready signals of the two ADCs are connected to GPIO8 and GPIO10 of the DSP. Due to the requirements of this system, the two ADCs must be started at the same time Conversion, and the configuration of the two ADCs is the same, so the time to convert the data is the same, so the Ready signal should be valid at the same time. For simplicity, the Ready signal of one ADC is used to send an interrupt request through the GPIO port. If the interrupt is mapped in the interrupt configuration initialization, And PIE, CPU interrupt is enabled, the CPU will respond to the interrupt, inquire in the interrupt service program to determine whether the Ready signal of the other ADC is valid, if it is valid, execute the next step, if it is invalid, you can continue to wait, but as mentioned above The above-mentioned two ADCs act in the same way and will not wait too long, which not only reduces the number of interrupts, but also does not waste CPU time for querying, ensuring that the signals converted by the two ADCs are transferred in time, and also improves Improve the efficiency of the system. Since the ADC collects two sensor signals is the first step of the system, so in the peripheral interrupt, the interrupt should have the highest priority, so it is mapped to the INT1 of the CPU.

INT1.7 comes from the periodic interrupt trigger of CPU timer Time0, which is used to regularly calculate the accumulated flow, convert the frequency value of the PWM wave corresponding to the instantaneous flow, convert the voltage value of the DAC output corresponding to the instantaneous flow, and set the output flag. Prepare to call the output module. Since the interrupt is mainly used for the calculation of the total flow, the priority must be higher to ensure the correctness of the result, so the CPUTime0 timer is used, which can be mapped to the INT1 of the CPU.

In the system, some timing processes with low real-time requirements are not assigned processors, but delay functions are used for software timing, which is easy to implement. For example, timing keyboard scanning, timing LCD refresh, timing reading temperature value, timing flickering of LCD digital tube in setting mode, and GPIO simulation generating SPI communication timing, etc. INT7.1 and INT7.2 are composed of interrupts of DMA transfer channel 1 and transfer channel 2. As shown in Figure 14, the two-way DMA is used to transmit the Coriolis sensor signals received by the two-way ADC sampling conversion received by the multi-channel buffer serial port McBSP of the DSP. The DMA transfers the data to the internal RAM without disturbing the CPU. In the ping_buffer or pong_buffer buffer array, when the buffer array is full, a DMA reception interrupt will be generated, modify the DMA transfer address in the interrupt service routine, and read the data in the full ping_buffer or pong_buffer to the long circular buffer in the external SARAM array for algorithm calls. Because the entire digital signal processing system must ensure the real-time, continuity and correctness of the input signal, the DMA transmission of TMS320F28335 can realize the correct transfer of a large amount of real-time data without reducing the working efficiency of the CPU. The DMA receive interrupt has a lower priority than the ADC interrupt.

The signal acquisition module includes two parts: Coriolis sensor signal data acquisition and temperature sensor signal acquisition. The sensor signal data acquisition is mainly used to check the Coriolis frequency and the phase difference of the two channels. The acquisition process is divided into the acquisition transmission process and the signal reading process.

The digital signal processing of the Coriolis mass flowmeter is based on the signals of two sensors. The two signals must be sampled at the same time, and the real-time sampled data must be continuously saved for the digital signal processing algorithm to calculate the flow rate. value. In order to save a large amount of data in time, the process of signal acquisition and transmission as shown in Figure 15 is adopted.

Because this system has very high requirements on the accuracy of the algorithm, it is necessary to select an ADC with superior performance. This system chooses two ADCs to sample two sensor signals respectively. The ADC is a low-noise, Δ∑-type 24-bit ADC whose performance can meet the requirements of our algorithm, and has an SPI serial interface, which is easy to communicate with DSP transfers data. This system uses two multi-channel buffered serial ports McBSP to communicate with two ADs.

After the ADC is configured, the two ADCs start to convert synchronously. After the single data is converted, an interrupt is generated through GPIO8 (an interrupt is generated through the DRDY pin of one ADC, and the DRDY signal of another AD is queried in the interrupt service program). In the service program, the clock is provided for the data received by the McBSPa port and the b port, and the two 24-bit sampling data will arrive at the receiving registers of the McBSPa port and the b port. In the receiving buffer of McBSP, if McBSP is used to generate two interrupts to let the CPU read data at this time, the CPU utilization rate is too low, the sampling frequency is 2kHz, and the reading data will be interrupted once every 500ms. Time is spent on algorithms and other system functions, so that the whole system cannot be implemented at all. Therefore, we chose the DMA transfer function of McBSP to transfer the data to the internal RAM in time without disturbing the CPU, without losing or overwriting samples Data, improve CPU utilization.

Some DMAs in 28335 can serve ADC, McBSP, ePWM, XINTF and SARAM, so it is necessary to specify the DMA interrupt as the receiving interrupt of McBSPa/b.

Note that configuring DMA must be done before configuring McBSP and AD. In this way, once the McBSP receiving register has data, it will automatically start DMA transmission, and transfer the two signal data to the designated places pingL_buffer and pingR_buffer (buffer size is 40 points) in the DSP internal RAM respectively. When the buffer is full, the two channels of DMA transmission Generate interrupts respectively. There are two tasks in the DMA interrupt service program: 1. Modify the buffer address to pongL_buffer and pongR_buffer (buffer size is still 40 points) (if it is pong_buffer, modify the address to ping_buffer and use it alternately) to prevent the loss of AD sampling data ; Two, read out the two-way data from the internal buffer full of data, put it into a longer circular array located in the externally expanded SARAM, and record the head pointer, tail pointer and the length of the new data of the circular array, as The algorithm takes the data and prepares it. The flowchart of DMA interrupt service program is shown in Figure 16.

Signal reading refers to the process of reading continuous data from the cyclic long array channel_signal in the external SARAM. The structure of the cyclic long array is shown in Figure 17. The array is set with three flags, namely the head pointer Top_pointer, the tail pointer Tail_pointer, The length of the new data (the so-called new data refers to the sampling data that has not been used in the signal processing algorithm) Newda_len.

When the DMA interrupt service program puts the data in the buffer into the circular array, Top_pointer++, Newda_len++, Newda_len records the existing sampling data, and judges the long read of Newda_len in the main program. When there are 500 points of data, it takes them from the circular array. Transfer 500 points to signalLCH[500] and signalRCH[500] and convert integer data to floating-point data to prepare for calling the algorithm. At this time, take a point out, Tail_pointer++, and call the algorithm program to calculate the flow after taking the data , Newda_len-500 after the calculation of 500 points, enter the next cycle. It can be seen that the head pointer Top_pointer is used to indicate that there is data written into the circular array, the tail pointer Tail_pointer is used to indicate that there is data read out of the circular array, and the new data length Newda_len is used to indicate valid data that can be used for the algorithm.

If Newda_len>Sum_len, it means that the speed of storing data is faster than the speed of fetching data, and the data will be overwritten before it can be fetched in time. At this time, it means that fetching data takes too long to do the algorithm, and the algorithm is too late to calculate. It is necessary to simplify the algorithm or reduce the sampling frequency. When an overflow error occurs, the flag overflow_error is set, and the error handling is to re-initialize the loop long array, so that Top_pointer=Rail_pointer, and it is necessary to improve the algorithm or reduce the sampling frequency.

The temperature signal acquisition is converted into digital quantity through ADC3 sampling directly after the analog signal is input into the conditioning circuit. ADC3 is a low-power, buffered 24-bit ΔΣ-type ADC, and uses SPI communication, and realizes data transmission and control through the SPI module port on the DSP. Since the temperature of the flow tube medium does not change much within a certain period of time, the real-time requirements for temperature information are not high, so it is not necessary to adopt a high SPI communication rate. We configure ADC3 to work in a single conversion mode, that is, to enable AD conversion. ADC3 After converting a piece of data, it becomes closed and dormant until the CPU checks that DOUT/RDY is valid and is free to fetch data. After reawakening ADC3, the next data can be converted. In this way, the system can read the temperature sensor signal when temperature information is needed, avoiding the waste of resources caused by collecting real-time data. The sampling rate of ADC3 is relatively low. At the same time, a delay software function can be used to read the temperature regularly, and wake up ADC3 to convert the next number, so that the temperature information can be updated at a fixed time.

The algorithm module contains most of the numerical operation subroutines performed by the system, mainly including signal preprocessing, estimating the fundamental frequency of sensor signals, calculating the phase difference and time difference of two signals, averaging the results, calculating instantaneous flow, flow temperature compensation. The processing flow of the entire algorithm module is shown in Figure 18.

Signal preprocessing refers to filtering the collected sensor signals to filter out unnecessary frequency components to lay the foundation for subsequent algorithms. There are two cases of signal preprocessing: one is, for the ΔΣ ADC, since the AD itself samples at a very high sampling frequency of 1.92MHz, and then has filtering and averaging processing inside, the digital data transmitted to the DSP at 2kHz The amount of noise is already relatively low, so software filtering only needs to filter out higher component frequencies, and simple FIR low-pass filtering can meet the requirements of subsequent algorithms; the other is, for other AD or audio processing chip Codec In other words, you need to use a multi-pump filter, that is, in order to enhance the suppression of noise, first use a higher sampling frequency of 16kHz to sample the output signal of the Coriolis flowmeter, and then use a multi-pump filter for anti-aliasing filtering And decimation, generally need to use two more decimation and one filter to achieve better results, and after the decimation, the amount of data is reduced, and the signal quality is improved without increasing the subsequent calculation burden. If there are other frequency interferences in the signal in actual application, a notch filter with adjustable trap depth and width can be designed by analyzing the frequency spectrum of the signal to specifically deal with the actual interference and improve the anti-interference ability of the entire algorithm.

The digital filtering formula is:

x(n)＝(b ₀ u(n)+b ₁ u(n-1)+...b _m u(nm)-a ₁ x(n-1)-...-a _k x(nk))/a ₀ (1)

In the formula, u and x represent the original signal sequence obtained by sampling and the signal sequence after digital filtering respectively, {b ₀ ,..., b _m , a ₀ ,..., a _k } are filter coefficients, m and k are filter coefficients of order. The filter coefficient can be set through the system panel according to the site conditions.

The preprocessed signal can call the adaptive lattice notch filter to estimate the fundamental frequency of the signal. Due to the inconsistency of the two flow tubes of the Coriolis mass flowmeter, there is a deviation in the two sensor signals. In order to improve the calculation of the phase difference The accuracy of the two signals is passed through two different adaptive lattice notch filters to estimate the frequencies of the two signals respectively, and then a compromised frequency value is obtained on average and passed to the subsequent algorithm.

The lattice IIR notch filter is formed by cascading two lattice filters with all poles and all zeros, and its transfer function is shown in formula (2).

$\mathrm{<span\; class="notranslate">\; h</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; z</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; z</span>}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; z</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; z</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; k</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; z</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 2</span>}}}{\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; z</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; z</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$

H ₁ (z) contributes to the pole part of the entire lattice notch filter, which is equivalent to the AR model, while H ₂ (z) contributes to the zero part of the entire lattice IIR notch filter, which is a lattice FIR filter. In order to reduce the computational burden, the zero point is fixed on the unit circle, so that only one parameter can be adjusted to achieve the purpose of adaptive notch. Fix the zero point on the unit circle, that is, let k ₁ =1, k ₀ converges to -cosω after a period of self-adaptation, ω is the normalized frequency of the signal, α determines the width of the trap, and its adaptive algorithm is as follows ( 3) to formula (10).

$\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; the\; y</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; k</span>}}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&rho;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; z</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&rho;</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; z</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 2</span>}}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; )</span>$

${\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; k</span>}}}_{\mathrm{<span\; class="notranslate">\; 0</span>}\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; \&gamma;</span>}{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; k</span>}}}_{\mathrm{<span\; class="notranslate">\; 0</span>}\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&gamma;</span>}<span\; class="notranslate">\; )</span>{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; k</span>}}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; )</span>$

D(n)＝λ(n)D(n-1)+2(1-λ(n))s ² (n-1) (5)

${\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; k</span>}}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; C</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; D.</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 6</span>}<span\; class="notranslate">\; )</span>$

C(n)＝λ(n)C(n-1)+(1-λ(n))s(n-1)[s(n)+s(n-2)] (7)

$\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; arccos</span>}<span\; class="notranslate">\; (</span><span\; class="notranslate">\; -</span>{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; k</span>}}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 8</span>}<span\; class="notranslate">\; )</span>$

${\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; k</span>}}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\left\{\begin{array}{cc}{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; k</span>}}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>& <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; \&le;</span>{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; k</span>}}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&le;</span>\mathrm{<span\; class="notranslate">\; 1</span>}\\ \mathrm{<span\; class="notranslate">\; 1</span>}& {\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; k</span>}}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ></span>\mathrm{<span\; class="notranslate">\; 1</span>}\\ <span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}& {\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; k</span>}}}_{\mathrm{<span\; class="notranslate">\; 0</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; <</span><span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}\end{array}\right.<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 9</span>}<span\; class="notranslate">\; )</span>$

$\mathrm{<span\; class="notranslate">\; x</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 2</span>}{\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; k</span>}}}_{\mathrm{<span\; class="notranslate">\; os</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 10</span>}<span\; class="notranslate">\; )</span>$

是k₀(n)的估计值，

是

经过平滑处理后的值，γ＝0.5，y(n)-x(n)即为去除噪声后的增强信号。由于科氏流量计流体的密度反映为频率的变化，所以需要及时跟踪流体信号变化的频率，通过大量的仿真研究，发现通过调整ρ和λ的终值，适当地加大陷波器陷阱的宽度，也能够在保证精度的同时实现对频率变化的跟踪，如式(11)和式(12)所示。in,

is an estimate of k ₀ (n),

yes

The value after smoothing, γ=0.5, y(n)-x(n) is the enhanced signal after removing the noise. Since the density of the Coriolis flowmeter fluid is reflected as a change in frequency, it is necessary to track the frequency of the fluid signal change in time. Through a large number of simulation studies, it is found that by adjusting the final values of ρ and λ, the width of the trap trap can be appropriately increased. , can also realize the tracking of frequency changes while ensuring the accuracy, as shown in equations (11) and (12).

ρ(n)＝0.98-0.195×0.99 ^(n-1) (11)

λ(n)=0.98-0.05×0.99 ^(n-1) (12)

The flowchart of the adaptive lattice notch algorithm is shown in Figure 19. Note that the lattice notch filter has a filtering function and can obtain an enhanced signal with only the signal frequency. However, due to the two IIR filters used by the two signals, the phase in the enhanced signal has changed, so in the subsequent algorithm No boost signal is used, adaptive lattice notch is only used to estimate the signal frequency.

The adaptive lattice notch algorithm can be used to track the changing frequency, and the tracking speed is fast and the tracking accuracy is high. The calculation amount of the whole algorithm is small. The time for one channel of adaptive lattice filtering to obtain a frequency value is 10.5μs, and the tracking accuracy is excellent. at 0.01%. And by adjusting the final value of the parameters and changing the step size, the speed and accuracy of the grid pattern tracking frequency can be adjusted. In this way, the algorithm can be flexibly applied to sudden and sudden changes in signals, such as two-phase flow, batch flow and other applications.

After the DTFT_omega frequency value is obtained, it is passed to the DTFT algorithm module together with 500 points of signal data to calculate the phase difference between the two signals. For a steady flow signal, we believe that within a certain period of time, for example, the data of 4000 points within 2s remains basically unchanged, and the data length is continuously increased from the first sampling point to calculate the Fourier coefficient at the specified frequency, 4000 points Then restart the calculation to prevent the overflow of the value. At the same time, because there is a process of DTFT convergence, we only save the data of the next 2500 points among the 4000 points for the subsequent average processing. At the same time, in order to improve the calculation accuracy, a negative frequency correction is performed on the obtained phase difference.

The phase difference measurement method based on DTFT spectrum analysis ignores the negative frequency components in the spectrum when calculating the phase difference. When the signal frequency is very low or the number of sampling points is small, the influence of the negative frequency component is significantly increased, resulting in a significant decrease in the phase difference measurement accuracy of the DTFT recursive algorithm, or even impossible measurement. This is also one of the main reasons why the sliding Goertzel algorithm has a long convergence process when calculating the phase difference. To this end, a negative frequency correction DTFT recursive algorithm is used to measure the phase difference, and its derivation formula is shown in formula (13) to formula (23).

s ₁ (t)＝A ₁ cos(2πf ₀ t+θ ₁ ) (13-1)

s ₂ (t)＝A ₂ cos(2πf ₀ t+θ ₂ ) (13-2)

s ₁ (n)＝A ₁ cos(ωt+θ ₁ ) (14-1)

s ₂ (n)=A ₂ cos(ωt+θ ₂ ), n=0, 1, ..., N-1, ω=2πf ₀ /f _s (14-2)

${\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}\mathrm{<span\; class="notranslate">\; N</span>}}<span\; class="notranslate">\; (</span>\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; <figure-callout\; id="1"\; label="A"\; filenames="H2009101855608E0000012.png,H2009101855608E0000021.png"\; state="\{\{state\}\}">A</figure-callout></span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}\mathrm{<span\; class="notranslate">\; cos</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&omega;n</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&theta;</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; j</span>}\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\frac{{\mathrm{<span\; class="notranslate">\; <figure-callout\; id="1"\; label="A"\; filenames="H2009101855608E0000012.png,H2009101855608E0000021.png"\; state="\{\{state\}\}">A</figure-callout></span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&omega;n</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&theta;</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; j</span>}\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; \&omega;n</span>}}}<span\; class="notranslate">\; +</span>\underset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\frac{{\mathrm{<span\; class="notranslate">\; <figure-callout\; id="1"\; label="A"\; filenames="H2009101855608E0000012.png,H2009101855608E0000021.png"\; state="\{\{state\}\}">A</figure-callout></span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&omega;n</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; \&theta;</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; j</span>}\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; \&omega;n</span>}}}$

$<span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; A</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{\mathrm{<span\; class="notranslate">\; j</span>}{\mathrm{<span\; class="notranslate">\; \&theta;</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}\underset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; -</span>\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; <figure-callout\; id="1"\; label="+\; A"\; filenames="H2009101855608E0000012.png,H2009101855608E0000021.png"\; state="\{\{state\}\}">+</figure-callout></span><figure-callout\; id="1"\; label="+\; A"\; filenames="H2009101855608E0000012.png,H2009101855608E0000021.png"\; state="\{\{state\}\}">\; </figure-callout>\frac{{\mathrm{<span\; class="notranslate">\; </span>}}_{}}{}\frac{{\mathrm{<span\; class="notranslate">A</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}{\mathrm{<span\; class="notranslate">\; 2</span>}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; j</span>}{\mathrm{<span\; class="notranslate">\; \&theta;</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}}\underset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; +</span>\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 15</span>}<span\; class="notranslate">\; )</span>$

${\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; N</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; j\&omega;n</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 16</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

${\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 0</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; j\&omega;n</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; N</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; e</span>}}^{<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; j\&omega;N</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 16</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$

c ₁ = sinα ₁ sinα ₂ cos(α ₁ -α ₃ )+sinα ₃ sinα ₄ cos(α ₄ -α ₂ ) (18)

c ₂ = sinα ₁ sinα ₂ sin(α ₁ -α ₃ )-sinα ₃ sinα ₄ cos(α ₄ -α ₂ ) (19)

c ₃ ＝sinα ₁ sinα ₂ sin(α ₁ -α ₃ )+sinα ₃ sinα ₄ sin(α ₄ -α ₂ ) (20)

c ₄ ＝sinα ₁ sinα ₂ cos(α ₁ -α ₃ )-sinα ₃ sinα ₄ cos(α ₄ -α ₂ ) (21)

${\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; -</span>\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; =</span>\frac{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; +</span>\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; =</span>\frac{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; -</span>\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}}_{\mathrm{<span\; class="notranslate">\; 4</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&omega;</span>}<span\; class="notranslate">\; +</span>\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; twenty\; two</span>}<span\; class="notranslate">\; )</span>$

${\mathrm{<span\; class="notranslate">\; <figure-callout\; id="1"\; label="m"\; filenames="H2009101855608E0000012.png,H2009101855608E0000021.png"\; state="\{\{state\}\}">m</figure-callout></span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; N</span>}{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; sin</span>}\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; -</span>{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; sin</span>}\mathrm{<span\; class="notranslate">\; \&alpha;</span>}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; twenty\; four</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

${\mathrm{<span\; class="notranslate">\; <figure-callout\; id="2"\; label="m"\; filenames="H2009101855608E0000012.png,H2009101855608E0000021.png"\; state="\{\{state\}\}">m</figure-callout></span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; N</span>}{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; sin</span>}\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; sin</span>}\mathrm{<span\; class="notranslate">\; \&alpha;</span>}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; sin</span>}\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}\mathrm{<span\; class="notranslate">\; sin</span>}\mathrm{<span\; class="notranslate">\; \&alpha;</span>}\mathrm{<span\; class="notranslate">\; cos</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&alpha;</span>}<span\; class="notranslate">\; -</span>\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; twenty\; four</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$

${\mathrm{<span\; class="notranslate">\; <figure-callout\; id="3"\; label="m"\; filenames="H2009101855608E0000012.png,H2009101855608E0000031.png"\; state="\{\{state\}\}">m</figure-callout></span>}}_{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; sin</span>}\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}\mathrm{<span\; class="notranslate">\; sin</span>}\mathrm{<span\; class="notranslate">\; \&alpha;</span>}\mathrm{<span\; class="notranslate">\; sin</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&alpha;</span>}<span\; class="notranslate">\; -</span>\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; twenty\; four</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; )</span>$

${\mathrm{<span\; class="notranslate">\; <figure-callout\; id="4"\; label="m"\; filenames="H2009101855608E0000012.png,H2009101855608E0000031.png"\; state="\{\{state\}\}">m</figure-callout></span>}}_{\mathrm{<span\; class="notranslate">\; 4</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; N</span>}{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; sin</span>}\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; sin</span>}\mathrm{<span\; class="notranslate">\; \&alpha;</span>}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; N</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 2</span>}\mathrm{<span\; class="notranslate">\; sin</span>}\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}\mathrm{<span\; class="notranslate">\; sin</span>}\mathrm{<span\; class="notranslate">\; \&alpha;</span>}\mathrm{<span\; class="notranslate">\; cos</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&alpha;</span>}<span\; class="notranslate">\; -</span>\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; twenty\; four</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; )</span>$

$\mathrm{<span\; class="notranslate">\; \&Delta;t</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; \&Delta;\&theta;</span>}<span\; class="notranslate">\; /</span>\stackrel{<span\; class="notranslate">\; ^</span>}{\mathrm{<span\; class="notranslate">\; \&omega;</span>}}<span\; class="notranslate">\; *</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 25</span>}<span\; class="notranslate">\; )</span>$

To sum up, the calculation steps of the DTFT recursive algorithm considering the influence of negative frequency are as follows:

由格型陷波滤波器估计频率；(1) Calculate the estimated value of ω

Estimate frequency by lattice notch filter;

处的DTFT，求出tanφ1和tanφ2，

(2) Apply DTFT recursive algorithm (as shown in formula (15)) to calculate s1(n) and s2(n) in

DTFT at the place, find tanφ1 and tanφ2,

N求出m1～m4，并同tanφ和tanφ一起代入式(25)，求得相位差和时间差。(3) by

N finds m1 ~ m4, and substitutes into formula (25) together with tanφ and tanφ, obtains phase difference and time difference.

(4) When applied to the actual system, with the continuous increase of N, the DTFT recursive algorithm also has the problem of numerical overflow. Therefore, it is necessary to set the maximum value for the number of sampling points. If the maximum value is exceeded, all variables are cleared and recalculated. Due to this The phase difference measurement method of the invention patent converges quickly during calculation, so the impact of recalculation is very small. Through experiments, it is found that when the maximum value is set to 2000, it can not only converge to a high precision, but also meet the real-time requirements, and can quickly track when the phase difference changes.

The DTFT algorithm and the sliding Goertzal algorithm, which take into account the influence of negative frequency, are consistent in principle. They both realize the fast calculation of the Fourier coefficient at the specified frequency by continuously increasing the length of the calculation sequence. Considering the calculation of the phase difference of the Corian flowmeter Due to the real-time requirements, the Fourier coefficient needs to be calculated every sampling point. The recursive algorithm of DTFT has a smaller calculation amount than the sliding Goertzal algorithm, and can converge quickly, with high calculation accuracy.

The flow chart of the whole algorithm is shown in Figure 20.

To prevent the statistical variability of the data from affecting the phase difference results, several results must be averaged. The signal of Coriolis flowmeter is weak when the flow rate is small, the signal-to-noise ratio is reduced, and the signal quality is relatively poor. The phase difference calculated by DTFT fluctuates relatively large, and averaging measures must be taken in order to output a stable phase difference value.

We adopt the following weighted average method to rearrange the results:

$\mathrm{<span\; class="notranslate">\; \&Sigma;\&lambda;</span>}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&Sigma;\&lambda;</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; *</span>\mathrm{<span\; class="notranslate">\; \&lambda;</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 28</span>}<span\; class="notranslate">\; )</span>$

个点数在平均。λ值小的话，表示平均的点数少，当相位差发生变化时，算法的响应速度会很快，但是由于平均点数少，数据的波动克服能力小，反之，如果λ值取小的话，表示平均的点数增加，这样可以很好的克服数据的波动性，输出平稳的结果，但如果流量出现突变，这时需要计算那么多点才能反映流量的变化，这仍会造成测量误差，因此还需采取设置门限值的方法来解决。设置一个相位差突变门限phase_limit，如果连续计算出N个相位差值波动幅度都超过了phase_limit，则对这N个相位差值和之后算出的结果平均，而前面的值都不参加平均，从而快速的反应流量变化。N一般取5至10，理论上，门限phase_limit选择的越小对微小突变越敏感，但实际中门限值也不能取太小，因为本身DTFT算法就会在一定范围内波动。平均模块的具体流程图如图21所示。In the formula, λ is the weighted average forgetting factor, which is close to 1, which is equivalent to

points on average. If the value of λ is small, it means that the number of average points is small. When the phase difference changes, the response speed of the algorithm will be very fast, but because of the small number of average points, the ability to overcome data fluctuations is small. On the contrary, if the value of λ is small, it means that the average The number of points increases, which can well overcome the volatility of the data and output a stable result, but if the flow rate changes suddenly, so many points need to be calculated to reflect the change of the flow rate, which will still cause measurement errors, so further measures are needed Set the threshold method to solve it. Set a phase difference mutation threshold phase_limit. If the fluctuation range of N phase difference values calculated continuously exceeds the phase_limit, then the N phase difference values and the subsequent calculated results are averaged, and the previous values do not participate in the average, so as to quickly Response to flow changes. N generally ranges from 5 to 10. In theory, the smaller the threshold phase_limit is, the more sensitive it is to small mutations. However, in practice, the threshold value should not be too small, because the DTFT algorithm itself will fluctuate within a certain range. The specific flow chart of the averaging module is shown in Figure 21.

The performance of the Coriolis mass flowmeter will be affected by the environment, and the influence of temperature is more prominent. The change of temperature will cause the measurement error of the mass flowmeter on the flow and density. In the actual high-precision use occasions, the temperature effect must be considered. And temperature compensation for mass flow.

The elastic modulus of the sensitive tube material in the flowmeter changes with the temperature. It is generally believed that the temperature coefficient of the elastic modulus of the material is determined, and the temperature can be detected timely and digitally compensated by the computer. The material of the primary instrument sensitive tube of the flowmeter we selected is 316L, and the temperature coefficient of the flow rate slope is 5.41%/100°C according to the data. Output an instantaneous flow every 2s, read the current temperature value, and get the compensation coefficient according to the temperature. The calculation of the instantaneous flow and temperature compensation are shown in formula (4).

The system output module is the remote signal transmission operation module, which includes two sub-operations, namely 4-20mA current output operation and pulse output operation.

The basic principle of 4-20mA current output is: since one SPI module of 28335 has been occupied, the internal general-purpose GPIO port is used to simulate the SPI timing to send 12-bit serial data to the DA7513 converter, and the digital quantity is transferred to the DA7513 converter by D/A. The analog voltage is converted, and then through a V/I conversion circuit, the voltage is converted into a 4-20mA current. Therefore, the 4-20mA current output software operation is: write the corresponding data to the DA7513 according to the instantaneous flow estimation result. The estimated voltage formula is shown in formula (5) and formula (6).

$\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 16.0</span>}}{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 100.0</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 60.0</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0.0</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 60.0</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; \&times;</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; q</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0.0</span>}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 60.0</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 4.0</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 30</span>}<span\; class="notranslate">\; )</span>$

$\mathrm{<span\; class="notranslate">\; u</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 2.462</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 0.4873</span>}}{\mathrm{<span\; class="notranslate">\; 16</span>}}<span\; class="notranslate">\; \&times;</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; I</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 4.0</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; 0.4873</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 31</span>}<span\; class="notranslate">\; )</span>$

The PWM output function is realized by the comparison output function of one PWM in the ePWM module of DSP, and the output pulse range is 10Hz-5kHz. Since the Coriolis mass flowmeter can directly give the mass of the fluid, the number of PWM wave output pulses corresponds to the mass. The flow rate range that this system can measure is 1g/s-500g/s, so the number of PWM pulses output in 1s is required It is 1-500, that is, one pulse represents 1g, that is to say, the PWM pulse frequency is 1Hz-500Hz. Since the 28335PWM module can output a minimum frequency of 8.9Hz (150000000/2/128/65536), the PWM pulse frequency is expanded by 10 times, that is, 10 pulses represent 1g, and the pulse frequency becomes 10Hz-5kHz.

Since the instantaneous flow calculated by the algorithm is a floating-point number, it is necessary to round to convert the values of the period register and the comparison register. Rounding will cause the value of the period register to increase, which may cause an extra TBCLK to be counted every time a PWM pulse is output. In order to ensure Finally, PWM does not output more than one pulse, and the number of TBCLK accumulated in 1s is required to be less than the comparison register value, that is, f _PWM <PRD/2 is required, and the specific frequency division is shown in Figure 22.

In order to minimize the impact of integer truncation on the number of output PWM pulses, an intermediate variable middle_fre is set to accumulate the current PWM frequency value and the fractional part lost after rounding the previous frequency value, and then round up middle_fre to obtain this time Output the frequency value of PWM, so the flow chart of PWM output function is shown in Figure 23.

The system expands the EEPROM model 93LC66 with a size of 256×16, which is used to save the measurement results, instrument coefficients and other data that need to be saved when the power is turned off. Through the multi-function multiplexing GPIO port on the DSP to simulate the serial timing to read, write and control the data of EEPROM, the typical data writing time is 10ms, so reading and writing EEPRM is used in two occasions: first: for system initialization The global variable initialization in; the second: cooperate with the keyboard to save the parameters set by the keyboard and the result data to be saved.

After configuring the GPIO port of the EEPROM, before writing data, you should first send the control word write enable. If you write data at a fixed address, it is best to erase the original data on the address first, and then specify the 8-bit address, 16 Bit data, write the data to be saved, and also need to specify the address when reading. Three kinds of data need to be saved in this system: instrument coefficient K, zero point fixed deviation φ, accumulated flow Q to be saved. And assign a fixed address to the three kinds of data. The key in the process of reading and writing EPROM is the transformation of the data format. When writing data, the floating-point number needs to be converted into 32-bit integer data, and when reading data, the 32-bit integer data needs to be converted into floating-point data.

When the instrument starts to work, it is necessary to set the instrument coefficient K and zero point fixed deviation φ through the keyboard. At this time, the two parameters need to be saved. When the program starts to run and enters the initialization stage, these two data need to be read out and assigned to the corresponding variables. , and then start the algorithm process, the entire measurement process does not need to use EEPROM to operate, after the measurement is completed, query whether to save the accumulated flow flag, if the flag is set, you need to write the result data to the specified address of the EEPROM.

There may be some abnormalities during the measurement process, such as measurement results out of range, array out of bounds, data coverage, etc. In order to facilitate monitoring of these errors and facilitate debugging, an error module is added to the system software to mark and Record the abnormal conditions generated during the execution of the program. The setting of the error module is very simple. Set a global variable to record the error code, and use macros to define some error code flags and error handling interface functions. In this way, other modules only need to include the error module. If an error occurs, you can call the macro definition function to report the error status and set the error flag, so that you can clearly know whether there is an error and what type of error occurred during operation, which is convenient for monitoring and debugging. The error module is only responsible for recording error information , the error handling also needs specific functions in each module to handle.

The man-machine interface module of the system is composed of LCD and keyboard, which is used to realize the local display of the instrument measurement results and the modification of the instrument parameters. Its software implementation includes LCD operation subroutines and keyboard processing subroutines.

Claims (9)

1. A Coriolis mass flow transmitter based on DSP comprises an amplifying and filtering circuit, an analog-to-digital converter 1(ADC1), an analog-to-digital converter 2(ADC2), a voltage reference source, a voltage follower, a current source, a differential amplifier, an analog-to-digital converter 3(ADC3), an analog driving module, a digital signal processor DSP minimum system, an external-expansion SARAM (static random access memory), an external-expansion EEPROM (electrically erasable and programmable read only memory), a man-machine interface, 4-20 mA current output and pulse output, a power supply module and software, and is characterized in that: the analog driving module excites a vibration exciter in the Coriolis flow sensor to enable the flow tube to vibrate at the natural frequency; two magnetoelectric sensors positioned at two sides of the flow tube output two paths of sine wave signals, the two paths of sine wave signals are amplified and filtered by two paths of conditioning circuits with the same parameters, then are simultaneously sampled by two ADCs 1 and ADCs 2 with the same model, and are converted into digital quantity, and the digital quantity is sent into a DSP through two multi-channel buffer serial ports McBSP of the DSP to calculate the phase difference of the two paths of signals; the DSP adopts digital filtering to filter the signal, the adaptive lattice notch filter calculates the frequency of the signal, and then adopts a Discrete Time Fourier Transform (DTFT) algorithm considering negative frequency to calculate the phase difference; the fluid temperature influences the angular elastic modulus of the flow tube, constant current is generated to flow through the temperature sensor in the Coriolis mass flow sensor, the output voltage of the temperature sensor is amplified by the differential amplifier, sampled by the ADC3 and converted into digital quantity, and the digital quantity is sent to the DSP for carrying out temperature compensation related calculation; converting the phase difference into mass flow, adding temperature compensation, displaying the final value on a Liquid Crystal Display (LCD), and matching with 4-20 mA current output and pulse output.

2. The DSP-based coriolis mass flow transmitter of claim 1 characterized in that: after the system is powered on, on one hand, the analog driving circuit generates a driving signal to excite the vibration of the Coriolis flowmeter, on the other hand, the DSP completes the initialization of each part of the system, and then, the sampling conversion of the two paths of ADCs is started and synchronized; after the data is converted, generating interruption through GPIO; the interrupt service program transmits the conversion result to the receiving pins of the two multi-channel buffer serial ports McBSP, then starts Direct Memory Access (DMA) transmission, and transmits the data on the receiving pins of the McBSP to a temporary array of an internal storage space through the DMA under the condition of not disturbing the work of a CPU; when the temporary arrays are full, two DMA receiving interrupts are generated, and the interrupt service program stores the data in the two temporary arrays into two circular buffer arrays in the external SARAM; acquiring 500 points of new continuous data from the external circulation array, judging the size of the signal, if the signal is small, indicating that the driving is not stable, and continuing to wait; if the signal is greater than the set value, the algorithm module may begin to be called.

3. The DSP-based coriolis mass flow transmitter of claim 1 characterized in that: the two paths of 500 point data signals are filtered and preprocessed, noise is filtered, and signal quality is improved; then, calling an adaptive lattice type notch filter for the two paths of filtered signals to respectively estimate fundamental frequencies of the two paths of sensor signals, then averaging frequency values of the two paths of signals to obtain a signal fundamental frequency instantaneous value, averaging the frequency, setting a comparison range, and updating the frequency value if the variation amplitude of the obtained average frequency value is in the comparison range, and if the variation amplitude exceeds the comparison range, updating the frequency value, so that a stable and accurate frequency value can be obtained, and an instantaneous density value can be obtained by combining coefficients; and finally, calculating the phase difference of the two paths of signals by using a DTFT algorithm taking account of negative frequency influence, averaging the result, obtaining the time difference by combining frequency values, and further obtaining the instantaneous flow value by combining set instrument coefficients.

4. The DSP-based coriolis mass flow transmitter of claim 1 characterized in that: after the instantaneous flow value is obtained, reading a signal of the temperature sensor and converting the signal into a temperature value, obtaining a corresponding temperature compensation coefficient according to the material of the measured flowmeter, and performing temperature compensation on the instantaneous flow; generating interruption by timing 2s through a CPU timer, and accumulating instantaneous flow in an interruption service program to obtain accumulated flow; meanwhile, setting an output result flag bit; after the measurement result is obtained, on one hand, the DSP displays the measurement values of the accumulated flow, the density, the temperature and the like through the LCD; and on the other hand, corresponding 4-20 mA current and PWM pulse are output outwards through a digital-to-analog converter DAC and an ePWM module according to the obtained instantaneous flow, so that an upper computer or other secondary instruments can count.

5. The DSP-based coriolis mass flow transmitter of claim 1 characterized in that: the signals RP + and RP-are connected to the input end of the U2 in a differential mode, are amplified by an instrument amplifier U2 and then are sent to a second-order low-pass filter composed of an operational amplifier U3A, a peripheral resistor and a capacitor for filtering, and then are sent to the input end of an ADC U1 through an amplitude limiting protection circuit composed of R7 and U4; input signals RP + and RP-are connected in a differential mode through R8 and R9, R2 sets the amplification factor of U2, C2, C6, C12 and C14 are decoupling capacitors, R5, R6, R1, R12, C5 and C10 and operational amplifier U3A form a second-order low-pass filter, C3, C9, C11 and C13 are decoupling capacitors, and a capacitor C90 is used for filtering high-frequency interference generated by high-frequency switching devices in U1; u1 is differential input ADC1, signal is connected to AIN0 (pin 6), another input end AIN1 (pin 7) is connected to 2.5V reference source; the reference required by U1 is taken from an external 2.5V reference source, VREF (pin 4) is connected with 2.5V reference, and VREFN (pin 3) is grounded; the data transmission interface of U1 is connected with a clock SCLK (pin 18) connected with MCLKXA, the data input DIN (pin 17) connected with MDXA, the data output DOUT (pin 16) connected with MDRA, the chip select CS connected with MFSXA, wherein the series-connected resistors R3, R4, R10 and corresponding pins of U1 are input with parasitic capacitors to form RC low-pass filter to filter spike pulse; the control pins DRDY and SYNC are connected to the GPIO port of the DSP; the capacitors C7 and C8 are filter capacitors, and the capacitors C1, C4, C15 and C17 are decoupling capacitors.

6. The DSP-based coriolis mass flow transmitter of claim 1 characterized in that: two ADC synchronous conversion paths, after single data is converted, interruption is generated through GPIO8, a clock is provided for data received by the McBSPa port and the b port in an interruption service program, two paths of 24-bit sampling data can reach receiving registers of the McBSPa port and the b port, the high bit is received in DRR2, the low bit is in DRR1, 32-bit shaping data is stored in a receiving buffer of the McBSP, the DMA transmission function of the McBSP is adopted, the data is timely transmitted to an internal RAM without disturbing a CPU, the sampling data cannot be lost or covered, and the utilization rate of the CPU is improved.

7. The DSP-based coriolis mass flow transmitter of claim 1 characterized in that: signal preprocessing is divided into two cases: one is, for delta-sigma type ADC, because ADC itself samples with very high sampling frequency 1.92MSPS, then the inside has the processing of filtering average, so the digital quantity noise transmitted to DSP with the data output rate of 2kSPS is already lower, only need to filter out the higher component frequency with the software filtering, the simple FIR low-pass filtering can meet the requirement of the follow-up algorithm; the other is that, if data is transmitted to the DSP at a higher data output rate, an extra decimation filter is needed, i.e. to enhance the suppression of noise, the data is first transmitted to the DSP at a higher data output rate (e.g. 16kSPS), and then the extra decimation filter is used to perform anti-aliasing filtering and decimation, and generally two-stage extra decimation filtering is needed to achieve a better effect, and after decimation, the data amount is reduced, and the signal quality is improved without increasing the subsequent calculation load.

8. The DSP-based coriolis mass flow transmitter of claim 1 characterized in that: the preprocessed signals can call self-adaptive lattice notch filtering to estimate the fundamental frequency of the signals, the two sensor signals have deviation due to the inconsistency of two flow tubes of the Coriolis mass flowmeter, in order to improve the accuracy of calculating the phase difference, the two signals respectively pass through two different self-adaptive lattice notch filters to respectively estimate the frequencies of the two signals, and then a compromise frequency value obtained on average is transmitted to a later algorithm.

9. The DSP-based coriolis mass flow transmitter of claim 1 characterized in that: in an actual system, along with the continuous increase of the number N of sampling points, the DTFT recursion algorithm also has the problem of numerical value overflow, so that the maximum value needs to be set for the number of the sampling points, all variables are cleared and recalculated after exceeding the maximum value, and the phase difference measurement method disclosed by the invention has the advantages that the convergence is fast during calculation, so the influence caused by recalculation is small; experiments show that when the maximum value is set to 4000, the method can be converged to high precision, can meet real-time requirements, and can be quickly tracked when the phase difference changes.

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