CN108362830A - The SCM Based infusion atmospheric monitoring system of one kind and method - Google Patents
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Abstract
The invention belongs to medical auxiliary equipment fields, disclose the SCM Based infusion atmospheric monitoring system of one kind and method, including air detection module, signal amplification module, A/D conversion modules, feedback module, identification module, one-chip computer module, filter circuit module, state estimation module, display module, input module, air monitering analysis module, alarm module.The present invention carries out air monitering by the infusion apparatus to transfusion outpatient, and bubble or large quantity of air is avoided to enter liquid-transport pipe-line, when excluding security risk, especially pressure transfusion, ensures patient safety；It can analyze and show the personal information of patient, treatment information, therapeutic process comparative analysis, simulation rehabilitation date etc. in real time simultaneously；The information system management for being conducive to be promoted hospital, enhances the safety of patient fluid infusion's process, improves the working efficiency of medical staff.
Description
Technical Field
The invention belongs to the field of medical auxiliary equipment, and particularly relates to a transfusion air monitoring system and method based on a single chip microcomputer.
Background
At present, medical equipment is developed more and more quickly, the requirement on medical auxiliary equipment is higher and more, infusion plays a very important role as conventional medical means in the treatment process of patients, however, the existing infusion apparatus lacks an air monitoring device, when air enters the infusion apparatus, blood vessels can be embolized along with liquid medicine entering blood vessels, so that heartbeat stops, extremely serious consequences are caused, and the existing infusion apparatus has huge potential safety hazards.
In summary, the problems of the prior art are as follows: the prior infusion apparatus lacks an air monitoring device and cannot meet the requirements of users.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a transfusion air monitoring system and method based on a single chip microcomputer.
The invention is realized in this way, a transfusion air monitoring system and method based on single chip microcomputer includes:
the air detection module is used for detecting air components;
the signal amplification module is connected with the air detection module and is used for amplifying the monitored air component signals;
the A/D conversion module is connected with the signal amplification module and is used for converting the analog signal into a digital signal;
the feedback module is connected with the A/D conversion module and is used for performing feedback control on the signal;
the identification module is connected with the feedback module and used for signal identification;
the singlechip module is connected with the identification module and is used for processing and storing signals;
the filter circuit module is connected with the singlechip module and is used for filtering signals and the state evaluation module is used for evaluating information states;
the wireless positioning method of the single chip microcomputer module specifically comprises the following steps:
the coordinate of the anchor node in the communication range of the node O to be positioned is Ai(xi,yi) Wherein i is 0,1, …, n (n is more than or equal to 4);
the method comprises the following steps: sampling a received signal r (t) by a node to be positioned to obtain a sampling signal r (N), wherein N is 0,1, …, N-1, N represents the number of subcarriers contained in an OFDM symbol, and simultaneously recording the number of a transmitting node of the received signal as Ai(xi,yi)；
Step two: from the sampled signal r (n), a cross-correlation value E is calculated:
step three: according to the logarithmic distance path loss model, the node to be positioned and the anchor node A are calculated according to the following formulaiThe distance between:
Pr(di)＝Pr(d0)-10·γlg(di)+Xσ；
wherein, Pr (d'i) Indicating a distance d from the transmitting endi' time derived cross-correlation value, Pr (d)0) Indicating distance from sender d0The cross-correlation value obtained at 1 meter, γ represents the path loss factor, lg (·) represents a logarithmic operation with a base of 10, XσObeying a Gaussian distribution with a mean value of 0 and a standard deviation of sigma;
calculating the distances d 'between each anchor node and the node O to be positioned by utilizing the formula'iThe coordinates of the corresponding anchor nodes are respectively Ai(xi,yi) Where i is 0,1,2, …, n;
step four: estimating the coordinates O (x, y) of the node to be positioned according to a self-adaptive distance correction algorithm;
the specific method of the second step comprises the following steps:
firstly, constructing a correlation window consisting of continuous sampling sequences with the length of l at the same sampling position in m continuous OFDM symbols, and then expressing a log-likelihood function Λ (τ) corresponding to the correlation window as follows:
wherein, the argument τ represents the starting point of the correlation window, and m represents the number of consecutive OFDM symbols;
and secondly, sliding the correlation window by the length of N + L sampling points to obtain the maximum value of the log-likelihood function Lambda (tau), wherein the sampling time corresponding to the maximum value is the initial position of the OFDM symbol
Wherein,representing the value of an independent variable tau when the function obtains the maximum value, representing a log-likelihood function by Λ (tau), representing the number of continuous OFDM symbols by m, representing the length of continuous sampling sequences at the same sampling position by L, representing a sampling signal by r (N), representing the number of subcarriers contained in the OFDM symbols by N, representing the number of sampling points of a cyclic prefix part in the OFDM symbols by L, and being a modulo operator by L;
thirdly, according to the starting position of the OFDM symbolCalculating a cross-correlation value E:
the fourth step specifically comprises:
firstly, selecting a differential correction point, determining a coordinate of a positioning intersection point and a plurality of positioning intersection points, and calculating the distance between the positioning intersection points;
from d'i(i-0, 1,2, …, n) selecting the anchor node A with the smallest distance value0For the difference correction point, the 3 smallest distance values are extracted from the remaining distance values, assuming that these 3 are distance values d'1、d′2And d'3The coordinates of the corresponding anchor nodes are respectively A1(x1,y1)、A2(x2,y2) And A3(x3,y3) Respectively with anchor nodes Ai(xi,yi) Is the center of a circle, d'iThree positioning circles i are made for the radius, wherein i is 1,2 and 3, 6 intersection conditions of the three positioning circles exist, two intersection points exist between the two circles, and the two intersection points are two equal real number intersection points or two unequal real number intersection points or two complex number intersection points; selecting a circle with a third positioning circle from two intersection points of the two positioning circlesThe intersection point with the smaller distance of the cardiac coordinate is used as a positioning intersection point to participate in the positioning of the node to be positioned; the three positioning intersection points and the number m 'of the plurality of positioning intersection points are determined by 3 positioning circles, and the coordinates of the positioning intersection points determined by the positioning circles 2 and 3 are A' (x)1,y1) And the coordinates of the positioning intersection point defined by the positioning circle 1 and the positioning circle 3 are B' (x)2,y2) The coordinate of the positioning intersection point defined by the positioning circle 1 and the positioning circle 2 is C' (x)3,y3) The distances between the positioning intersection points A 'and B', B 'and C', A 'and C' are d12、d23、d13：
Secondly, setting a threshold T, an individual difference coefficient correction coefficient omega and a parameter lambda (lambda > 0);
thirdly, according to the distances d between the three positioning intersection points12、d23And d13Judging whether d 'is needed'1、d′2、d′3Make a correction if d12<T、d23<T、d13<T, then do not need to be d'1、d′2、d′3Correcting, executing the fifth step, otherwise, d 'needs to be corrected'1、d′2、d′3Correcting and executing the fourth step;
fourthly, adjusting direction correction factors lambda of three measuring distances1、λ2And λ3D 'is corrected according to the following adaptive distance correction formula'1、d′2、d′3Obtaining a corrected distance d1、d2、d3：
Wherein d isiRepresenting the node to be positioned and the anchor node AiCorrected distance between d0iRepresenting a differential correction point A0And anchor node AiActual distance between, d'0iRepresenting a differential correction point A0And anchor node AiA measured distance therebetween, ω represents an individual difference coefficient correction coefficient, λiRepresents the directional correction factor, exp (-) represents the exponential function;
according to the corrected distance d1、d2、d3Re-solving the distance d between the three corrected positioning intersections12、d23、d13Returning to the third step;
fifthly, calculating the positioning coordinate O (x) of the node to be positioned according to the following formula0,y0)：
wherein alpha is1、α2、α3Respectively represent x'1、x′2、x′3weight of (1), beta1、β2、β3Are respectively y'1、y′2、y′3The weight of (c);
the display module is connected with the state evaluation module and used for displaying information;
the display module color information calibration method specifically comprises the following steps:
step one, selecting calibration color cards and calibration light sources, wherein the number of the calibration color cards is not less than 24Color samples according to the spectral reflectance rho of N color samples of the calibration color charti(lambda) and spectral intensity distribution of a calibration light sourceColor matching function in combination with CIE1931 standard chromaticity systemCalculating CIEXYZ tristimulus values (X) of N color samples of the calibration color chart under a CIE1931 standard chromaticity system through the following two formulasi,Yi,Zi)；
The CIEXYZ tristimulus value (X) of the calibration light source under the CIE1931 standard chromaticity system is calculated by the following formulaW,YW,ZW)；
Wherein, Δ λ is a spectrum sampling interval adopted in the calculation, 5nm is taken, i is a serial number of N color samples of the calibration color chart, and i is 1,2,3, …, N;
step two, the (X) obtained in the step onei,Yi,Zi) And (X)W,YW,ZW) Substituting the two formulas to calculate the coordinate of each color sample in the uniform color space CIELAB
Step three, respectively adopting a reference imaging system and an imaging system to be calibrated to image N color samples under a calibration light source, recording and acquiring color information of the digital image, and reading corresponding digital driving values (R) of each color sample in the two imaging systemsSi,GSi,BSi) And (R)Ti,GTi,BTi)；
Step four, for the imaging system to be calibrated, according to the CIELAB coordinates of the N color samples obtained in the step twoAnd the digital driving values (R) of the N color samples obtained in the third stepTi,GTi,BTi) Fitting the formula (R) by using a least square methodTi,GTi,BTi) Predict toMapping matrix M ofT，MTIs a 3 × 11 matrix;
step five, for the reference imaging system, obtaining N color sample CIELAB coordinates according to the step twoAnd the digital driving values (R) of the N color samples obtained in the third stepSi,GSi,BSi) Fitting out a product ofPredict to (R)Si,GSi,BSi) Mapping matrix H ofSI，HSIIs a 3 × 10 matrix;
step six, adopting the mapping matrix M obtained in the step four to the digital image obtained by the imaging system to be calibrated in any scene under any imaging environmentTFrom a digital drive value (R) per pixel by the following formulaTj',GTj',BTj') predict the corresponding CIELAB space coordinatesWherein j is 1,2,3, …, N' is the total number of pixels of the digital image acquired by the imaging system to be calibrated;
step seven, the CIELAB space coordinate of each pixel of the imaging system to be calibrated obtained in the step sixAdopting the mapping matrix H obtained in the step fiveSIThe calibrated digital drive value (R) for each pixel is predicted by the following equationSj',GSj',BSj') finishing color information calibration between the two imaging systems, so that a digital image acquired by the imaging system to be calibrated in a certain scene under any imaging environment has a digital drive value consistent with that of the reference imaging system;
the input module is connected with the display module and used for inputting information;
the air monitoring and analyzing module is connected with the filter circuit module and is used for detecting and analyzing the control components;
and the alarm module is connected with the air monitoring and analyzing module and used for alarming an abnormal signal.
The invention has the advantages and positive effects that: the air detection module detects the type of air, the air monitoring and analyzing module analyzes the air after signal conversion, and the alarm module can give an alarm to prompt when abnormal conditions occur, so that bubbles or a large amount of air is prevented from entering a transfusion pipeline, potential safety hazards are eliminated, and particularly, the safety of a patient is ensured during pressurized transfusion; meanwhile, the device is provided with a state evaluation module which can analyze and display personal information, treatment information, comparative analysis of treatment process, simulated rehabilitation date and the like of the patient in real time; is favorable for improving the information management of hospitals, enhances the safety of patients in the infusion process and improves the working efficiency of medical care personnel.
Drawings
FIG. 1 is a schematic structural diagram of a transfusion air monitoring system and method based on a single chip microcomputer according to an embodiment of the present invention;
in the figure: 1. an air detection module; 2. a signal amplification module; 3. an A/D conversion module; 4. a feedback module; 5. an identification module; 6. a single chip module; 7. a filter circuit module; 8. a state evaluation module; 9. a display module; 10. an input module; 11. an air monitoring and analyzing module; 12. and an alarm module.
Detailed Description
In order to further understand the contents, features and effects of the present invention, the following embodiments are illustrated and described in detail with reference to the accompanying drawings.
The structure of the present invention will be described in detail below with reference to the accompanying drawings.
As shown in figure 1, the infusion air monitoring system and method based on the single chip microcomputer comprises an air detection module 1 for detecting air components;
the signal amplification module 2 is connected with the air detection module 1 and is used for amplifying the monitored air component signals;
the A/D conversion module 3 is connected with the signal amplification module 2 and is used for converting the analog signal into a digital signal;
a feedback module 4 connected with the A/D conversion module 3 and used for performing feedback control on the signal;
the identification module 5 is connected with the feedback module 4 and used for signal identification;
the singlechip module 6 is connected with the identification module 5 and used for processing and storing signals;
a filter circuit module 7 connected with the single chip module (MCS-51)6 and used for filtering signals and a state evaluation module 8 for evaluating information states;
a display module 9 connected with the state evaluation module 8 for displaying information;
an input module 10 connected with the display module 9 for inputting information;
the air monitoring and analyzing module 11 is connected with the filter circuit module 7 and used for detecting and analyzing the control components;
and the alarm module 12 is connected with the air monitoring and analyzing module 11 and is used for alarming an abnormal signal.
Further, the alarm module 12 includes an LED signal lamp and a buzzer alarm.
Further, the air detection module 1 includes a CO2 sensor for detecting a gas type, an N2 sensor for detecting nitrogen gas, and a temperature sensor for detecting a temperature.
The wireless positioning method of the single chip microcomputer module specifically comprises the following steps:
the coordinate of the anchor node in the communication range of the node O to be positioned is Ai(xi,yi) Wherein i is 0,1, …, n (n is more than or equal to 4);
the method comprises the following steps: sampling a received signal r (t) by a node to be positioned to obtain a sampling signal r (N), wherein N is 0,1, …, N-1, N represents the number of subcarriers contained in an OFDM symbol, and simultaneously recording the number of a transmitting node of the received signal as Ai(xi,yi)；
Step two: from the sampled signal r (n), a cross-correlation value E is calculated:
step three: according to the logarithmic distance path loss model, the node to be positioned and the anchor node A are calculated according to the following formulaiThe distance between:
Pr(di)＝Pr(d0)-10·γlg(di)+Xσ；
wherein, Pr (d'i) Representing distance d 'from transmitting end'iTime-derived cross-correlation value, Pr (d)0) Indicating distance from sender d0The cross-correlation value obtained at 1 meter, γ represents the path loss factor, lg (·) represents a logarithmic operation with a base of 10, XσObeying a Gaussian distribution with a mean value of 0 and a standard deviation of sigma;
calculating the distances d 'between each anchor node and the node O to be positioned by utilizing the formula'iThe coordinates of the corresponding anchor nodes are respectively Ai(xi,yi) Where i is 0,1,2, …, n;
step four: estimating the coordinates O (x, y) of the node to be positioned according to a self-adaptive distance correction algorithm;
the specific method of the second step comprises the following steps:
firstly, constructing a correlation window consisting of continuous sampling sequences with the length of l at the same sampling position in m continuous OFDM symbols, and then expressing a log-likelihood function Λ (τ) corresponding to the correlation window as follows:
wherein, the argument τ represents the starting point of the correlation window, and m represents the number of consecutive OFDM symbols;
and secondly, sliding the correlation window by the length of N + L sampling points to obtain the maximum value of the log-likelihood function Lambda (tau), wherein the sampling time corresponding to the maximum value is the initial position of the OFDM symbol
Wherein,representing the value of an independent variable tau when the function obtains the maximum value, representing a log-likelihood function by Λ (tau), representing the number of continuous OFDM symbols by m, representing the length of continuous sampling sequences at the same sampling position by L, representing a sampling signal by r (N), representing the number of subcarriers contained in the OFDM symbols by N, representing the number of sampling points of a cyclic prefix part in the OFDM symbols by L, and being a modulo operator by L;
thirdly, according to the starting position of the OFDM symbolCalculating a cross-correlation value E:
the fourth step specifically comprises:
firstly, selecting a differential correction point, determining a coordinate of a positioning intersection point and a plurality of positioning intersection points, and calculating the distance between the positioning intersection points;
from d'i(i-0, 1,2, …, n) selecting the anchor node A with the smallest distance value0For the difference correction point, the 3 smallest distance values are extracted from the remaining distance values, assuming that these 3 are distance values d'1、d′2And d'2The coordinates of the corresponding anchor nodes are respectively A1(x1,y1)、A2(x2,y2) And A3(x3,y3) Respectively with anchor nodes Ai(xi,yi) Is the center of a circle, d'iThree positioning circles i are made for the radius, wherein i is 1,2 and 3, 6 intersection conditions of the three positioning circles exist, two intersection points exist between the two circles, and the two intersection points are two equal real number intersection points or two unequal real number intersection points or two complex number intersection points; selecting one intersection point with a smaller distance from the center coordinates of the third positioning circle from two intersection points of the two positioning circles as a positioning intersection point to participate in positioning of the node to be positioned; the three positioning intersection points and the number m 'of the plurality of positioning intersection points are determined by 3 positioning circles, and the coordinates of the positioning intersection points determined by the positioning circles 2 and 3 are A' (x)1,y1) And the coordinates of the positioning intersection point defined by the positioning circle 1 and the positioning circle 3 are B' (x)2,y2) The coordinate of the positioning intersection point defined by the positioning circle 1 and the positioning circle 2 is C' (x)3,y3) The distances between the positioning intersection points A 'and B', B 'and C', A 'and C' are d12、d23、d13：
Secondly, setting a threshold T, an individual difference coefficient correction coefficient omega and a parameter lambda (lambda > 0);
thirdly, according to the distances d between the three positioning intersection points12、d23And d13Judging whether d 'is needed'1、d′2、d′3Make a correction if d12<T、d23<T、d13<T, then do not need to be d'1、d′2、d′3Correcting, executing the fifth step, otherwise, d 'needs to be corrected'1、d′2、d′3Correcting and executing the fourth step;
fourthly, adjusting direction correction factors lambda of three measuring distances1、λ2And λ3D 'is corrected according to the following adaptive distance correction formula'1、d′2、d′3Obtaining a corrected distance d1、d2、d3：
Wherein d isiRepresenting the node to be positioned and the anchor node AiCorrected distance between d0iRepresenting a differential correction point A0And anchor node AiActual distance between, d'0iRepresenting a differential correction point A0And anchor node AiA measured distance therebetween, ω represents an individual difference coefficient correction coefficient, λiRepresents the directional correction factor, exp (-) represents the exponential function;
according to the corrected distance d1、d2、d3Re-solving the distance d between the three corrected positioning intersections12、d23、d13Returning to the third step;
fifthly, calculating the positioning coordinate O (x) of the node to be positioned according to the following formula0,y0)：
wherein alpha is1、α2、α3Respectively represent x'1、x′2、x′3weight of (1), beta1、β2、β3Are respectively y'1、y′2、y′3The weight of (c).
The display module color information calibration method specifically comprises the following steps:
selecting a calibration color card and a calibration light source, wherein the calibration color card is not less than 24 color samples, and the spectral reflectance rho of the N color samples of the calibration color card is determinedi(lambda) and spectral intensity distribution of a calibration light sourceColor matching function in combination with CIE1931 standard chromaticity systemCalculating CIEXYZ tristimulus values (X) of N color samples of the calibration color chart under a CIE1931 standard chromaticity system through the following two formulasi,Yi,Zi)；
The CIEXYZ tristimulus value (X) of the calibration light source under the CIE1931 standard chromaticity system is calculated by the following formulaW,YW,ZW)；
Wherein, Δ λ is a spectrum sampling interval adopted in the calculation, 5nm is taken, i is a serial number of N color samples of the calibration color chart, and i is 1,2,3, …, N;
step two, the (X) obtained in the step onei,Yi,Zi) And (X)W,YW,ZW) Substituting the two formulas to calculate the coordinate of each color sample in the uniform color space CIELAB
Step three, respectively adopting a reference imaging system and an imaging system to be calibrated to image N color samples under a calibration light source, recording and acquiring color information of the digital image, and reading corresponding digital driving values (R) of each color sample in the two imaging systemsSi,GSi,BSi) And (R)Ti,GTi,BTi)；
Step four, for the imaging system to be calibrated, according to the CIELAB coordinates of the N color samples obtained in the step twoAnd the digital driving values (R) of the N color samples obtained in the third stepTi,GTi,BTi) Fitting the formula (R) by using a least square methodTi,GTi,BTi) Predict toMapping matrix M ofT，MTIs a 3 × 11 matrix;
step five, for the reference imaging system, obtaining N color sample CIELAB coordinates according to the step twoAnd the digital driving values (R) of the N color samples obtained in the third stepSi,GSi,BSi) Fitting out a product ofPredict to (R)Si,GSi,BSi) Mapping matrix H ofSI，HSIIs a 3 × 10 matrix;
step six, adopting the mapping matrix M obtained in the step four to the digital image obtained by the imaging system to be calibrated in any scene under any imaging environmentTFrom a digital drive value (R) per pixel by the following formulaTj',GTj',BTj') predict the corresponding CIELAB space coordinatesWherein j is 1,2,3, …, N' is the total number of pixels of the digital image acquired by the imaging system to be calibrated;
step seven, the CIELAB space coordinate of each pixel of the imaging system to be calibrated obtained in the step sixAdopting the mapping matrix H obtained in the step fiveSIThe calibrated digital drive value (R) for each pixel is predicted by the following equationSj',GSj',BSj') finishing color information calibration between the two imaging systems, so that a digital image acquired by the imaging system to be calibrated in a certain scene under any imaging environment has a digital drive value consistent with that of the reference imaging system;
the air detection module 1 is used for detecting air, the detected air data is amplified through the signal amplifier 2, the amplified signals are converted into digital signals through the A/D signal converter 3, the signals are fed back and adjusted through the feedback control module 4, the amplified signals are identified through the identification module 5, the identified signals are transmitted to the single chip microcomputer module 6 for data processing and storage, clutter is filtered out through the filter circuit module 6, only the waveform of the air signals is allowed to pass through, the detection and identification are carried out through the air monitoring and analyzing module 7, identification confirmation and analysis are carried out, and when the abnormal condition occurs, the air monitoring and analyzing module 7 triggers the LED signal indicator lamp and the buzzer alarm in the alarm module 12 to give an alarm after detecting the air electrical signals, and medical personnel are notified to process the alarm; in the using process, the information of the patient information state can be input through the input module 10, the information is stored by the single chip microcomputer module 6, various indexes and information are evaluated through the state evaluation module 8, and the information is displayed in real time through the display screen 9.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the present invention in any way, and all simple modifications, equivalent changes and modifications made to the above embodiment according to the technical spirit of the present invention are within the scope of the technical solution of the present invention.
Claims (4)
1. The utility model provides a transfusion air monitoring system based on singlechip which characterized in that, transfusion air monitoring system based on singlechip includes:
the air detection module is used for detecting air components;
the signal amplification module is connected with the air detection module and is used for amplifying the monitored air component signals;
the A/D conversion module is connected with the signal amplification module and is used for converting the analog signal into a digital signal;
the feedback module is connected with the A/D conversion module and is used for performing feedback control on the signal;
the identification module is connected with the feedback module and used for signal identification;
the singlechip module is connected with the identification module and is used for processing and storing signals;
the filter circuit module is connected with the singlechip module and is used for filtering signals and the state evaluation module is used for evaluating information states;
the wireless positioning method of the single chip microcomputer module specifically comprises the following steps:
the coordinate of the anchor node in the communication range of the node O to be positioned is Ai(xi,yi) Wherein i is 0,1, …, n (n is more than or equal to 4);
the method comprises the following steps: sampling a received signal r (t) by a node to be positioned to obtain a sampling signal r (N), wherein N is 0,1, …, N-1, N represents the number of subcarriers contained in an OFDM symbol, and simultaneously recording the number of a transmitting node of the received signal as Ai(xi,yi)；
Step two: from the sampled signal r (n), a cross-correlation value E is calculated:
step three: according to the logarithmic distance path loss model, the node to be positioned and the anchor node A are calculated according to the following formulaiThe distance between:
Pr(di)＝Pr(d0)-10·γlg(di)+Xσ；
wherein, Pr (d'i) Representing distance d 'from transmitting end'iTime-derived cross-correlation value, Pr (d)0) Indicating distance from sender d0The cross-correlation value obtained at 1 meter, γ represents the path loss factor, lg (·) represents a logarithmic operation with a base of 10, XσObeying a Gaussian distribution with a mean value of 0 and a standard deviation of sigma;
calculating the distances d 'between each anchor node and the node O to be positioned by utilizing the formula'iThe coordinates of the corresponding anchor nodes are respectively Ai(xi,yi) Where i is 0,1,2, …, n;
step four: estimating the coordinates O (x, y) of the node to be positioned according to a self-adaptive distance correction algorithm;
the specific method of the second step comprises the following steps:
firstly, constructing a correlation window consisting of continuous sampling sequences with the length of l at the same sampling position in m continuous OFDM symbols, and then expressing a log-likelihood function Λ (τ) corresponding to the correlation window as follows:
wherein, the argument τ represents the starting point of the correlation window, and m represents the number of consecutive OFDM symbols;
and secondly, sliding the correlation window by the length of N + L sampling points to obtain the maximum value of the log-likelihood function Lambda (tau), wherein the sampling time corresponding to the maximum value is the initial position of the OFDM symbol
Wherein,representing the value of an independent variable tau when the function obtains the maximum value, representing a log-likelihood function by Λ (tau), representing the number of continuous OFDM symbols by m, representing the length of continuous sampling sequences at the same sampling position by L, representing a sampling signal by r (N), representing the number of subcarriers contained in the OFDM symbols by N, representing the number of sampling points of a cyclic prefix part in the OFDM symbols by L, and being a modulo operator by L;
thirdly, according to the starting position of the OFDM symbolCalculating a cross-correlation value E:
the fourth step specifically comprises:
firstly, selecting a differential correction point, determining a coordinate of a positioning intersection point and a plurality of positioning intersection points, and calculating the distance between the positioning intersection points;
from d'i(i-0, 1,2, …, n) selecting the anchor node A with the smallest distance value0For the difference correction point, the 3 smallest distance values are extracted from the remaining distance values, assuming that these 3 are distance values d'1、d′2And d'3The coordinates of the corresponding anchor nodes are respectively A1(x1,y1)、A2(x2,y2) And A3(x3,y3) Respectively with anchor nodes Ai(xi,yi) Is the center of a circle, d'iThree positioning circles i are made for the radius, wherein i is 1,2 and 3, 6 intersection conditions of the three positioning circles exist, two intersection points exist between the two circles, and the two intersection points are two equal real number intersection points or two unequal real number intersection points or two complex number intersection points; selecting one intersection point with a smaller distance from the center coordinates of the third positioning circle from two intersection points of the two positioning circles as a positioning intersection point to participate in positioning of the node to be positioned; the three positioning intersection points and the number m 'of the plurality of positioning intersection points are determined by 3 positioning circles, and the coordinates of the positioning intersection points determined by the positioning circles 2 and 3 are A' (x)1,y1) And the coordinates of the positioning intersection point defined by the positioning circle 1 and the positioning circle 3 are B' (x)2,y2) The coordinate of the positioning intersection point defined by the positioning circle 1 and the positioning circle 2 is C' (x)3,y3) The distances between the positioning intersection points A 'and B', B 'and C', A 'and C' are d12、d23、d13：
Secondly, setting a threshold T, an individual difference coefficient correction coefficient omega and a parameter lambda (lambda > 0);
thirdly, according to the distances d between the three positioning intersection points12、d23And d13Judging whether d 'is needed'1、d′2、d′3Make a correction if d12<T、d23<T、d13<T, then do not need to be d'1、d′2、d′3Correcting, executing the fifth step, otherwise, d 'needs to be corrected'1、d′2、d′3Correcting and executing the fourth step;
fourthly, adjusting direction correction factors lambda of three measuring distances1、λ2And λ3D 'is corrected according to the following adaptive distance correction formula'1、d′2、d′3Obtaining a corrected distance d1、d2、d3：
Wherein d isiRepresenting the node to be positioned and the anchor node AiCorrected distance between d0iRepresenting a differential correction point A0And anchor node AiActual distance between, d'0iRepresenting a differential correction point A0And anchor node AiA measured distance therebetween, ω represents an individual difference coefficient correction coefficient, λiRepresents the directional correction factor, exp (-) represents the exponential function;
according to the corrected distance d1、d2、d3Re-solving the distance d between the three corrected positioning intersections12、d23、d13Returning to the third step;
fifthly, calculating the positioning coordinate O (x) of the node to be positioned according to the following formula0,y0)：
wherein alpha is1、α2、α3Respectively represent x'1、x′2、x′3weight of (1), beta1、β2、β3Are respectively y'1、y′2、y′3The weight of (c);
the display module is connected with the state evaluation module and used for displaying information;
the display module color information calibration method specifically comprises the following steps:
selecting a calibration color card and a calibration light source, wherein the calibration color card is not less than 24 color samples, and the spectral reflectance rho of the N color samples of the calibration color card is determinedi(lambda) and spectral intensity distribution of a calibration light sourceColor matching function in combination with CIE1931 standard chromaticity systemCalculating CIEXYZ tristimulus values (X) of N color samples of the calibration color chart under a CIE1931 standard chromaticity system through the following two formulasi,Yi,Zi)；
The CIEXYZ tristimulus value (X) of the calibration light source under the CIE1931 standard chromaticity system is calculated by the following formulaW,YW,ZW)；
Wherein, Δ λ is a spectrum sampling interval adopted in the calculation, 5nm is taken, i is a serial number of N color samples of the calibration color chart, and i is 1,2,3, …, N;
step two, the (X) obtained in the step onei,Yi,Zi) And (X)W,YW,ZW) Substituting the two formulas to calculate the coordinate of each color sample in the uniform color space CIELAB
Step three, respectively adopting a reference imaging system and an imaging system to be calibrated to image N color samples under a calibration light source, recording and acquiring color information of the digital image, and reading corresponding digital driving values (R) of each color sample in the two imaging systemsSi,GSi,BSi) And (R)Ti,GTi,BTi)；
Step four, for the imaging system to be calibrated, according to the CIELAB coordinates of the N color samples obtained in the step twoAnd the digital driving values (R) of the N color samples obtained in the third stepTi,GTi,BTi) Fitting the formula (R) by using a least square methodTi,GTi,BTi) Predict toMapping matrix M ofT，MTIs a 3 × 11 matrix;
step (ii) ofFifthly, for the reference imaging system, obtaining the CIELAB coordinates of the N color samples according to the step twoAnd the digital driving values (R) of the N color samples obtained in the third stepSi,GSi,BSi) Fitting out a product ofPredict to (R)Si,GSi,BSi) Mapping matrix H ofSI，HSIIs a 3 × 10 matrix;
step six, adopting the mapping matrix M obtained in the step four to the digital image obtained by the imaging system to be calibrated in any scene under any imaging environmentTFrom a digital drive value (R) per pixel by the following formulaTj',GTj',BTj') predict the corresponding CIELAB space coordinatesWherein j is 1,2,3, …, N' is the total number of pixels of the digital image acquired by the imaging system to be calibrated;
step seven, the CIELAB space coordinate of each pixel of the imaging system to be calibrated obtained in the step sixAdopting the mapping matrix H obtained in the step fiveSIThe calibrated digital drive value (R) for each pixel is predicted by the following equationSj',GSj',BSj') to finish the color information calibration between the two imaging systems, so that the imaging system to be calibrated can be in any imaging environmentA digital image acquired of a scene has digital drive values consistent with a reference imaging system;
the input module is connected with the display module and used for inputting information;
the air monitoring and analyzing module is connected with the filter circuit module and is used for detecting and analyzing the control components;
and the alarm module is connected with the air monitoring and analyzing module and used for alarming an abnormal signal.
2. The single-chip microcomputer based transfusion air monitoring system as claimed in claim 1, wherein the alarm module comprises an LED signal lamp and a buzzer alarm.
3. The single-chip microcomputer based infusate air monitoring system as claimed in claim 1, wherein the air detection module includes a CO for detecting a gas type2Sensor, N for detecting nitrogen2The sensor, be used for detecting the temperature sensor of temperature.
4. The single-chip microcomputer based transfusion air monitoring system according to claim 1, wherein the single-chip microcomputer based transfusion air monitoring method of the single-chip microcomputer based transfusion air monitoring system detects air through an air detection module, amplifies detected air data through a signal amplifier, converts an analog signal into a digital signal through an A/D signal converter after the amplified signal, performs feedback regulation of the signal through a feedback control module, performs identification of the amplified signal through an identification module, transmits the identified signal to the single-chip microcomputer module for data processing and storage, filters out noise waves through a filter circuit module, allows only air signal waveforms to pass through, performs detection identification through the air monitoring analysis module, performs identification confirmation and analysis, and triggers an LED signal indicator lamp and a buzzer alarm in the alarm module after the air monitoring analysis module detects the air electrical signal in case of abnormality Alarming and informing medical personnel to carry out treatment; in the use process, the information input can be carried out on the information state of the patient through the input module, the information is stored by the single chip microcomputer module, the evaluation of each index and information is carried out through the state evaluation module, and the real-time display is carried out through the display screen.
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