CN118521142A - Gate group flow remote regulation and control method based on fuzzy model - Google Patents
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Abstract
The invention discloses a remote control method for gate group flow based on a fuzzy model, which particularly relates to the technical field of remote control and comprises the following steps: the invention is beneficial to reducing frequent regulation and control of the gate opening, improving the precision of the gate opening and improving the regulation and control efficiency by constructing a fuzzy control model, comprehensively analyzing input variables in the fuzzy control model, determining the initial opening of the gate, or by retrieving similar hydrologic data from a historical database, determining the difference information and time sequence information of the hydrologic data through analyzing the real-time hydrologic data and the similar hydrologic data in the historical database, and taking the result of the comprehensive analysis of the difference information and the time sequence information as the basis for judging the initial opening of the gate, regulating the gate opening to the initial opening, and adopting different gate control methods to realize regulation of the gate opening according to the preset flow and the actual flow in PID control.
Description
Technical Field
The invention relates to the technical field of remote regulation and control, in particular to a fuzzy model-based brake group flow remote regulation and control method.
Background
The water yield of the irrigation area is affected by various factors, the irrigation area is mostly composed of natural river channels and artificial channels, the canal system structure is complex, the traditional water resource allocation model is input more and has uncertainty in precision, a large amount of researches and adjustment are needed, rapid response cannot be achieved, rapid scheduling cannot be achieved, the water resource allocation process has high hysteresis, nonlinearity and time variability, the problem of constrained combination optimization is solved, in the process of adjusting the water resource, the opening of a gate needs to be adjusted by combining with the change of hydrological data, the opening of the gate cannot be intelligently judged by the system in the face of hydrological data which is not stored in a historical database, and rapid response cannot be achieved due to the fact that the opening of the gate needs to be adjusted manually.
In order to solve the above-mentioned defect, a technical scheme is provided.
Disclosure of Invention
In order to overcome the above-mentioned drawbacks of the prior art, an embodiment of the present invention provides a method for remotely controlling a gate flow based on a fuzzy model, which solves the above-mentioned problems in the prior art.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a remote control method for gate group flow based on a fuzzy model comprises the following steps:
s1: determining the initial opening of the gate through comprehensively analyzing input variables in the fuzzy control model by constructing the fuzzy control model, and if the initial opening of the gate can be determined through the fuzzy control model, performing step S4;
s2: retrieving similar hydrologic data from a historical database, and judging whether the initial opening of the gate can be adjusted to the opening of the gate similar to the hydrologic data in the historical database;
s3: determining difference information and time sequence information of the hydrologic data through analysis of the real-time hydrologic data and similar hydrologic data in a historical database, and taking the result of comprehensive analysis of the difference information and the time sequence information as a basis for judging the initial opening of the gate;
S4: and adjusting the opening of the gate to the initial opening, and adjusting the opening of the gate by adopting different gate control methods according to the magnitude between the preset flow and the actual flow in the PID control.
In a preferred embodiment, constructing the fuzzy control model includes:
The fuzzy control model adopts a two-dimensional fuzzy controller to determine the initial opening of a gate, takes the water level deviation and the deviation change rate as input variables, takes the initial opening as output quantity, determines the range of the input variables and the input variable quantization factor, and ensures that the range of the input variables is correctly mapped onto the membership function of a fuzzy set in the fuzzification process;
the limit value of the input variable is the value range of the water level deviation and the deviation change rate, and the optimal input variable range of the water level deviation and the optimal input variable range of the deviation change rate are determined.
In a preferred embodiment, the comprehensive analysis of the input variables in the fuzzy control model includes:
The analysis result of the water level deviation of the input variable is represented by a water level deviation hidden safety coefficient, the water level deviation is obtained by collecting real-time hydrologic data and comparing a preset water level with the real-time water level, the water level deviation which is not in the optimal input variable range of the water level deviation in a unit time range is obtained, and the water level deviation hidden safety coefficient is determined by formula calculation;
Expressing an analysis result of the deviation change rate of the input variable by a hidden safety coefficient of the deviation change rate, constructing a differential equation of the water level by a mass conservation law, determining the deviation change rate according to the differential equation of the water level, obtaining the deviation change rate which is not in the optimal input variable range of the deviation change rate in a unit time range, and determining the hidden safety coefficient of the deviation change rate by formula calculation;
and obtaining a control evaluation coefficient of the fuzzy control model through weighted summation calculation according to the water level deviation hiding safety coefficient and the deviation change rate hiding safety coefficient.
In a preferred embodiment, determining the initial opening of the gate based on a comprehensive analysis of the input variables in the fuzzy control model includes:
Setting a control evaluation coefficient threshold value, comparing the control evaluation coefficient with the control evaluation coefficient threshold value, generating a first signal if the control evaluation coefficient is larger than the control evaluation coefficient threshold value, determining the initial opening of the gate without a fuzzy control model, generating a second signal if the control evaluation coefficient is smaller than the control evaluation coefficient threshold value, and determining the initial opening of the gate with the fuzzy control model.
In a preferred embodiment, determining whether the initial opening of the gate can be adjusted to a gate opening that resembles the hydrologic data in the historical database includes:
when the matched hydrological data is directly inquired from the historical database, the initial opening of the gate is directly regulated and controlled to be the opening in the historical database;
when the matched hydrologic data is not queried in the historical database, the real-time hydrologic data is queried If the matched hydrologic data exist in the range of the gate opening, directly using a formula of a flow calculation library to calculate the initial opening of the gate opening;
If the matched hydrologic data exist, the corresponding opening value with the smallest difference value with the matched hydrologic data is taken as the initial opening of the gate, and the difference information and the time sequence information of the real-time hydrologic data are comprehensively analyzed to judge whether the corresponding opening value with the smallest difference value with the matched hydrologic data can be taken as the initial opening of the gate.
In a preferred embodiment, the comprehensively analyzing the difference information and the time sequence information of the real-time hydrologic data comprises:
The difference information of the real-time hydrologic data represents the similarity degree of the real-time hydrologic data and the stored hydrologic data in the historical database and the similarity degree of the time period in which the real-time hydrologic data is located;
The similarity degree of the hydrologic data is represented by hydrologic data similarity coefficients, real-time hydrologic data of the reservoir is obtained, the real-time hydrologic data comprise four hydrologic data of upstream water level, downstream water level, flow and flow velocity, and the hydrologic data similarity coefficients are determined through formula calculation;
The similarity of the time period where the real-time hydrologic data is located is represented by a time overlapping coefficient, a time point where the real-time hydrologic data is located and a time point corresponding to the matched hydrologic data in the historical database are obtained, the time point where the real-time hydrologic data is located is taken as a current time point, and the time point corresponding to the matched hydrologic data in the historical database is taken as a historical current time point;
setting a time interval, and determining a time sequence to a current time point and a time sequence to a historical current time point according to the length of the time interval;
Constructing a DTW distance matrix, wherein rows and columns of the matrix respectively correspond to a time sequence of a current time point and data points in the time sequence reaching a historical current time point, calculating local distances between the data points in each pair of sequences, gradually filling the matrix in a dynamic programming manner until the value of a right lower corner element of the matrix is obtained, and taking the value of the right lower corner element of the DTW distance matrix as a time overlap coefficient;
The time sequence information of the hydrologic data is represented by discrete accumulation coefficients, real-time hydrologic data in a unit time range is collected, a real-time hydrologic data matrix in the unit time range is constructed, the discrete coefficients of different hydrologic data are calculated by obtaining the average value and standard deviation of the different hydrologic data, and the discrete accumulation coefficients are determined by a formula.
In a preferred embodiment, taking the result of the comprehensive analysis of the difference information and the time sequence information as a basis for judging the initial opening degree of the gate, the method comprises the following steps:
Generating a data comparison evaluation coefficient through comprehensively analyzing the hydrologic data similarity coefficient, the time overlapping coefficient and the discrete accumulation coefficient;
Setting a data comparison evaluation coefficient threshold, comparing the data comparison evaluation coefficient with the data comparison evaluation coefficient threshold, generating a third signal which indicates that the opening of the gate corresponding to the hydrological data in the historical database cannot be used as the initial opening of the gate, calculating the initial opening of the gate by using a formula of a flow calculation library, and generating a fourth signal which indicates that the opening of the gate corresponding to the hydrological data in the historical database can be used as the initial opening of the gate if the data comparison evaluation coefficient is smaller than the data comparison evaluation coefficient threshold.
In a preferred embodiment, the adjustment of the gate opening is achieved by adopting different gate control methods, including:
regulating the gate according to the initial opening of the gate, regulating the gate to the initial opening, and regulating the gate through PID, wherein in the gate flow process control, a PID controller algorithm for controlling according to the deviation ratio (P), integral (I) and derivative (D) takes the difference between the preset flow and the actual flow as an error in PID control;
Setting a primary flow deviation threshold value, a secondary flow deviation threshold value and a tertiary flow deviation threshold value to realize rapid regulation of a gate, wherein the primary flow deviation threshold value is larger than the secondary flow deviation threshold value, and the secondary flow deviation threshold value is larger than the tertiary flow deviation threshold value;
When the difference between the preset flow and the actual flow is larger than the first-level flow deviation threshold, a PLC controller is used for executing a switching function, and the opening of the gate is adjusted through the switch;
When the difference between the preset flow and the actual flow is smaller than the primary flow deviation threshold value, and the difference between the preset flow and the actual flow is larger than the secondary flow deviation threshold value, the speed of the gate is adjusted by integrating the separated PID;
When the difference between the preset flow and the actual flow is smaller than the second-level flow deviation threshold value and the difference between the preset flow and the actual flow is larger than the third-level flow deviation threshold value, the effect of gradually reducing the motor speed is achieved by adding an integral term.
The invention has the technical effects and advantages that:
1. According to the invention, the condition of determining the initial opening of the gate by using the fuzzy control model is limited by comprehensively analyzing the value of the input variable in the fuzzy control model, so that the output quantity of the fuzzy control model is ensured to be more accurate, and the situation that the rapid scheduling of the gate cannot be performed due to inaccurate initial opening of the gate is avoided;
2. According to the invention, the gate is automatically regulated in a PID mode according to the input flow value which is expected to be regulated, the overflow target value is set according to each gate of the hub through the PID automatic regulating system, and the opening of each gate is automatically regulated according to the water storage amount or the water supply amount to carry out water distribution, so that frequent regulation and control on the gate are reduced, the accuracy of the opening of the gate is improved, and the regulation and control efficiency is improved.
Drawings
For the convenience of those skilled in the art, the present invention will be further described with reference to the accompanying drawings;
FIG. 1 is a flow chart of a remote control method for gate group flow based on fuzzy model according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment 1, fig. 1 shows a flow diagram of a remote control method for gate group flow based on a fuzzy model, which specifically includes the following steps:
s1: determining the initial opening of the gate through comprehensively analyzing input variables in the fuzzy control model by constructing the fuzzy control model, and if the initial opening of the gate can be determined through the fuzzy control model, performing step S4;
s2: retrieving similar hydrologic data from a historical database, and judging whether the initial opening of the gate can be adjusted to the opening of the gate similar to the hydrologic data in the historical database;
s3: determining difference information and time sequence information of the hydrologic data through analysis of the real-time hydrologic data and similar hydrologic data in a historical database, and taking the result of comprehensive analysis of the difference information and the time sequence information as a basis for judging the initial opening of the gate;
S4: and adjusting the opening of the gate to the initial opening, and adjusting the opening of the gate by adopting different gate control methods according to the magnitude between the preset flow and the actual flow in the PID control.
In the step 1, the fuzzy control model adopts a two-dimensional fuzzy controller to determine the initial opening of a gate, takes the water level deviation and the deviation change rate as input variables, takes the initial opening as output variables, determines the range of the input variables and the input variable quantization factors, and ensures that the range of the input variables is correctly mapped onto membership functions of a fuzzy set in the fuzzification process;
It should be noted that, the input variable of the one-dimensional fuzzy controller generally only includes the deviation of the controlled variable and the given value, the setting is simple, the reasoning operation time is short, but the dynamic performance of the system may not be ideal, the input variable of the two-dimensional fuzzy controller includes the deviation of the controlled variable and the given value and the variation of the deviation, the dynamic characteristic of the system can be reflected better, therefore, the control effect is better than that of the one-dimensional fuzzy controller, but correspondingly, the rule set and the reasoning process may be more complex, the input variable of the three-dimensional fuzzy controller includes the deviation of the controlled variable and the given value, the variation of the deviation and the variation rate of the deviation variation, and finer control can be realized, but more complex fuzzy control rules and reasoning processes and longer reasoning time are provided.
The quantization factors comprise information such as dividing intervals of fuzzy sets, shapes and parameters of membership functions, and the number, size and shape of the fuzzy sets are controlled by adjusting the quantization factors, so that the accuracy and effect of fuzzy reasoning are affected, and the quantization factors are selected to be adjusted according to specific application scenes and problem requirements so as to achieve optimal performance of the fuzzy control system.
The range of the input variable is the range of the water level deviation and the deviation change rate, which indicates that the water level deviation and the deviation change rate are in the allowable range, the performance of the gate in the actual work is analyzed and counted through the historical data, and the reasonable input variable range is determined based on the existing data distribution condition, for example: after setting the initial opening, an input variable with less gate opening adjustment is used as a more reasonable input variable, a water level deviation optimal input variable range and a deviation change rate optimal input variable range are determined, and the water level deviation optimal input variable range and the deviation change rate optimal input variable range are recorded as: And ；
It should be noted that, in the fuzzy control, the fuzzy rule is set according to expert knowledge or experience, and is used for mapping the fuzzy input to the fuzzy output, if the input variable exceeds the range set by the fuzzy rule, the fuzzy reasoning process may generate an uncertain or unreasonable result, so as to affect the performance and stability of the system, and avoid inaccurate matching or unstable control result of the fuzzy rule caused by too large or too small input data.
The analysis result of the water level deviation of the input variable is represented by a water level deviation hiding safety coefficient, and the acquisition logic of the water level deviation hiding safety coefficient is as follows: the water level deviation is obtained by collecting real-time hydrological data and comparing a preset water level with the real-time water level, and the water level deviation is marked as follows: Wherein n=1, 2, 3 … … N, N is a positive integer, N represents the water level deviation collected in the unit time range;
It should be noted that, because the gate will always keep a certain opening, the water level may change in real time, and the water level at a certain moment is collected and cannot represent the overall situation of the upstream or downstream water level, and it is necessary to wait for the hydrologic data to stabilize and then determine the water level data.
Obtaining the water level deviation which is not in the water level deviation optimal input variable range in the unit time range, and marking as follows: wherein i=1, 2,3 … … I, I is a positive integer, I is the number of the water level deviation which is not in the optimal input variable range of the water level deviation in the unit time range;
The hidden safety coefficient of the water level deviation is calculated, and the calculation formula is as follows: ; wherein, The safety coefficient is hidden for the water level deviation,；
It should be noted that the water level is not always constant, and particularly in the case of occurrence of rainfall, snow melting, water drainage, or the like, the change in water level may be more remarkable, and therefore, the water level at each time in the unit time range does not necessarily satisfy the optimal input variable range set based on the fuzzy rule.
As can be seen from the formula, the greater the hidden safety coefficient of the water level deviation, the more the difference between the preset water level and the real-time water level exceeds the optimal input variable range of the water level deviation, the more inaccurate the obtained result is possibly, and the more unreasonable the initial opening degree is possibly determined by the fuzzy control model.
When the opening of the gate is controlled in the fuzzy control model, the deviation change rate represents a controlled variable, namely the change rate between the water level and an expected value, namely the change rate between the real-time water level and a preset water level, wherein the main function of the deviation change rate is to describe the change trend and the change rate of the system in the dynamic process, the analysis result of the deviation change rate of the input variable is represented by a deviation change rate hidden safety coefficient, and the acquisition logic of the deviation change rate hidden safety coefficient is as follows: constructing differential equation of water level by mass conservation law, assuming water level asThe water inflow isThe water flow isThe differential equation of the water level is expressed as: ; wherein S is the cross-sectional area of the reservoir, Representing the rate of change of the deviation;
It should be noted that, the cross-sectional area of the reservoir is determined through a map or a remote sensing image, then the measurement and calculation are performed by using tools such as a Geographic Information System (GIS), etc., the inflow water flow can be obtained through a hydrological measurement mode, the outflow water flow can be determined according to a hole flow calculation formula or a weir flow calculation formula, and the differential equation of the water level may be complex in practice and needs to be obtained through repeated verification by workers in the professional field, which is not described herein.
Determining a deviation change rate according to a differential equation of the water level, obtaining a deviation change rate which is not in an optimal input variable range of the deviation change rate within a unit time range, and marking as follows: Wherein m=1, 2,3 … … M, M is a positive integer, M is a number of a deviation change rate which is not in the deviation change rate optimal input variable range within a unit time range;
the hidden safety coefficient of the deviation change rate is calculated, and the calculation formula is as follows: ; wherein, The security coefficients are hidden for the rate of change of the deviations,。
As can be seen from the formula, the greater the hidden safety coefficient of the deviation change rate, the over-regulation or oscillation phenomenon may be generated in the fuzzy control system, and the output cannot be accurately regulated to be near the expected value, so that the control system is unstable, and the expected control effect cannot be realized.
According to the water level deviation hidden safety coefficient and the deviation change rate hidden safety coefficient, a control evaluation coefficient of the fuzzy control model is obtained through weighted summation calculation, and a calculation formula of the control evaluation coefficient is as follows: ; wherein, In order to control the evaluation coefficient,The water level deviation conceals the safety coefficient and the deviation change rate conceals the proportionality coefficient of the safety coefficient,Greater than 0.
As can be seen from the formula, the greater the water level deviation hiding safety coefficient and the deviation change rate hiding safety coefficient, the greater the control evaluation coefficient, which indicates that the real-time hydrologic data is unstable and may exceed or approach the range of the fuzzy control model, if the water level deviation and the deviation change rate are substituted as input variables into the two-dimensional fuzzy controller at this time, the obtained initial opening degree may be inaccurate.
Setting a control evaluation coefficient threshold value, comparing the control evaluation coefficient with the control evaluation coefficient threshold value, generating a first signal if the control evaluation coefficient is larger than the control evaluation coefficient threshold value, determining the initial opening of the gate without a fuzzy control model, generating a second signal if the control evaluation coefficient is smaller than the control evaluation coefficient threshold value, and determining the initial opening of the gate with the fuzzy control model.
According to the method, the condition that the initial opening of the gate is determined by the fuzzy control model is limited by comprehensively analyzing the values of the input variables in the fuzzy control model, so that the output quantity of the fuzzy control model is ensured to be more accurate, and the situation that the rapid scheduling of the gate cannot be performed due to inaccurate initial opening of the gate is avoided.
In embodiment 2, the initial opening of the gate is determined by the fuzzy control model, when the initial opening of the gate cannot be determined by the fuzzy control model, the real-time hydrologic data are compared with the data in the historical database, the initial opening of the gate is determined, and the accurate regulation and control of the gate are completed by adopting PID (proportion integration differentiation) regulation according to the initial opening of the gate.
In step 2, obtaining real-time hydrological data at the current time point, wherein the real-time hydrological data comprise gate opening, upstream water level, downstream water level, flow speed and the like, retrieving similar hydrological data from a historical database, and judging whether the initial opening of the gate can be adjusted to the gate opening of the similar hydrological data in the historical database;
Judging whether the initial opening degree of the gate can be adjusted to the gate opening degree of the similar hydrologic data in the historical database comprises the following conditions:
when the matched hydrological data is directly inquired from the historical database, the initial opening of the gate is directly regulated and controlled to be the opening in the historical database;
when the matched hydrologic data is not queried in the historical database, the real-time hydrologic data is queried If the matched hydrologic data exist in the range of the gate opening, directly using a formula of a flow calculation library to calculate the initial opening of the gate opening;
If the matched hydrologic data exist, taking a corresponding opening value with the smallest difference value with the matched hydrologic data as the initial opening of the gate, comprehensively analyzing the difference information and the time sequence information of the real-time hydrologic data, and judging whether the corresponding opening value with the smallest difference value with the matched hydrologic data can be taken as the initial opening of the gate;
It should be noted that, the history database includes data recorded during the running process of the gate, as the running time of the gate increases, the recorded data in the database increases, the history database can provide a reference basis for the past hydrologic situation for a decision maker, and the flow calculation database is a program or software library for calculating the flow of water, and generally includes a series of algorithms and models for estimating the flow according to hydrologic parameters such as water level, water flow speed, and the like, and adjusting the opening of the gate through the flow.
The difference information of the real-time hydrologic data indicates the similarity degree of the real-time hydrologic data and the hydrologic data stored in the historical database and the similarity degree of a time period where the real-time hydrologic data is located, and if the similarity degree of the real-time hydrologic data is higher and the time periods where the real-time water level data and the hydrologic data matched in the historical database are highly overlapped, the gate opening used in the historical database is indicated to be capable of better guiding the adjustment of the gate opening;
The time sequence information of the real-time hydrologic data indicates the change degree of the real-time hydrologic data in a unit time range, if the change degree is higher, the change of the hydrologic condition is indicated to be faster, the opening degree of the gate is determined through calculation of the flow calculation library, and the change of the hydrologic condition is responded in real time, if the change degree is lower, the hydrologic condition is indicated to be relatively stable, the gate is adjusted according to the matched hydrologic data recorded in the historical database, and because the opening degree in the historical data can be suitable for similar hydrologic conditions.
The similarity degree of the hydrologic data is represented by a hydrologic data similarity coefficient, and the acquisition logic of the hydrologic data similarity coefficient is as follows: obtaining real-time hydrological data of a reservoir, assuming an upstream water levelDownstream water levelFlow rate and flow rateFlow rate and velocity of flowFour kinds of hydrologic data, and marking the real-time hydrologic data of the reservoir as: where k=1, 2, 3, 4, k represents the number of different hydrologic data, the hydrologic data stored in the history database is marked as: ；
calculating hydrologic data similarity coefficient, wherein the calculation formula is as follows: ; wherein, Is the hydrologic data similarity coefficient.
As can be seen from the formula, the larger the hydrologic data similarity coefficient is, the more similar the real-time hydrologic data is to the hydrologic data stored in the history database, and the better the effect of using the hydrologic data recorded and matched in the history database to adjust the gate opening is.
The similarity of the time period where the real-time hydrologic data is located is represented by a time overlapping coefficient, and the acquisition logic of the time overlapping coefficient is as follows: acquiring a time point where the real-time hydrologic data is located and a time point corresponding to the hydrologic data matched in the historical database, taking the time point where the real-time hydrologic data is located as a current time point, and taking the time point corresponding to the hydrologic data matched in the historical database as a historical current time point;
setting a time interval, and determining a time sequence to a current time point and a time sequence to a historical current time point according to the length of the time interval;
It should be noted that, the time interval is set by a staff in the professional field, in order to stretch the time range, to help find similar change modes between different time points, so as to determine the similarity degree of the hydrologic data in time, the current time point and the historical current time point represent the ending time point of the time sequence, and the length of the time sequence is determined by the time interval.
Constructing a DTW distance matrix, wherein the DTW distance matrix is a two-dimensional matrix created by two time sequences, rows and columns of the matrix respectively correspond to data points in the two time sequences, for the data points in each pair of sequences, calculating local distances between the data points, gradually filling the matrix in a dynamic programming mode until the value of a right lower corner element of the matrix is obtained, namely the DTW distance of the whole sequence, taking the value of the right lower corner element of the DTW distance matrix as a time overlap coefficient, and marking the time overlap coefficient as:。
it should be noted that, the similarity matrix is filled by using a dynamic programming algorithm, the dynamic programming algorithm considers the distance between each data point and the accumulated distance on the path, and selects the path with the smallest accumulated distance as the best matching path, and in the process of filling the similarity matrix, the value of each element can be calculated by using a recursive or iterative mode.
The larger the time overlap factor, the lower the similarity between the time series of real-time hydrologic data and the time series of matched hydrologic data in the history database, indicating that the effect of using the matched hydrologic data recorded in the history database to adjust the gate opening may be worse.
The time sequence information of the hydrologic data is represented by discrete accumulation coefficients, and the acquisition logic of the discrete accumulation coefficients is as follows: collecting real-time hydrologic data in a unit time range, constructing a real-time hydrologic data matrix in the unit time range, and marking the real-time hydrologic data matrix in the unit time range as follows: J=1, 2 and 3 … … J, wherein J is a positive integer, and J represents the number of times of acquiring real-time hydrologic data in a unit time range;
Calculating discrete coefficients of different real-time hydrologic data, wherein the calculation formula is as follows: ，，， Wherein, the method comprises the steps of, wherein, 、、、Respectively represent the upstream water levelDownstream water levelFlow rate and flow rateFlow rate and velocity of flowDiscrete coefficients of four kinds of hydrologic data in a unit time range,、、、Respectively representing standard deviations of four hydrologic data of upstream water level, downstream water level, flow rate and flow velocity in a unit time range,、、、Respectively representing the average value of four hydrologic data of an upstream water level, a downstream water level, flow rate and flow velocity in a unit time range;
the calculation formula of the standard deviation of different real-time hydrologic data is as follows: ; the calculation formula of the average value of different real-time hydrologic data is as follows: ；
Calculating discrete accumulation coefficients, wherein the calculation formula is as follows: ; wherein, Is a discrete accumulation coefficient.
As can be seen from the formula, the larger the discrete accumulation coefficient, the higher the fluctuation degree of the real-time hydrologic data, which means that the effect of adjusting the gate opening by using the hydrologic data recorded and matched in the history database may be worse.
In step 3, comprehensively analyzing difference information and time sequence information of the real-time hydrologic data, establishing a data analysis model through hydrologic data similarity coefficients, time overlapping coefficients and discrete accumulation coefficients, and generating data comparison evaluation coefficients, wherein the calculation formula of the data comparison evaluation coefficients is as follows: ; wherein, The coefficients are evaluated for the comparison of the data,、、Is the proportional coefficient of the hydrologic data similarity coefficient, the time overlapping coefficient and the discrete accumulation coefficient,、、Greater than 0.
Setting a data comparison evaluation coefficient threshold, comparing the data comparison evaluation coefficient with the data comparison evaluation coefficient threshold, generating a third signal which indicates that the opening of the gate corresponding to the hydrological data in the historical database cannot be used as the initial opening of the gate, calculating the initial opening of the gate by using a formula of a flow calculation library, and generating a fourth signal which indicates that the opening of the gate corresponding to the hydrological data in the historical database can be used as the initial opening of the gate if the data comparison evaluation coefficient is smaller than the data comparison evaluation coefficient threshold.
In step 4, the gate is regulated and controlled according to the initial opening of the gate, the gate is regulated to the initial opening, and then the gate is regulated through PID, in the process of controlling the flow of the gate, the PID controller algorithm for controlling according to the proportion (P), integral (I) and derivative (D) of deviation takes the difference between the preset flow and the actual flow as the error in PID control, and the basic expression is: ; wherein, In order to achieve a proportional gain,The ratio of the allowable flow deviation is inversely related to the proportional opening degree, and defaults to，For the integration time constant, i.e. the system default adjustment time,A differential time constant, i.e., a system default flow sampling time; Is the output signal of the PID controller, Is the difference between the preset flow and the actual flow;
setting a primary flow deviation threshold Second order flow deviation thresholdThree-level flow deviation thresholdThe quick regulation of the gate is realized, namely, after the gate is regulated to the initial opening, the difference between the preset flow and the actual flow is calculated, wherein the larger the difference between the preset flow and the actual flow is, the larger the deviation between the actual gate opening and the gate opening required by the preset flow is, wherein,；
When the difference between the preset flow and the actual flowGreater than the primary flow deviation thresholdExecuting a switching function by using the PLC controller, and adjusting the opening of the gate through the switch;
when the difference between the preset flow and the actual flow Less than the primary flow deviation thresholdDifference between preset flow and actual flowGreater than the secondary flow deviation thresholdRegulating by integrating the separated PID to realize the speed regulation of the gate;
when the difference between the preset flow and the actual flow Less than the secondary flow deviation thresholdDifference between preset flow and actual flowGreater than the three-level flow deviation thresholdThe effect of gradually decreasing the motor speed is achieved by adding an integral term, once the gate reaches the specified position, the motor speed will become 0 to ensure that the gate stops at the target position.
According to the embodiment, the gate is automatically adjusted in a PID mode according to the input flow value which is expected to be adjusted, the overflow target value is set according to each gate of the hinge through the PID automatic adjusting system, and the opening of each gate is automatically adjusted according to the water storage amount or the water supply amount to carry out water distribution, so that frequent regulation and control on the gate are reduced, the accuracy of the opening of the gate is improved, and the regulation and control efficiency is improved.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired or wireless means (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (8)
1. A remote control method for gate group flow based on a fuzzy model is characterized by comprising the following steps:
s1: determining the initial opening of the gate through comprehensively analyzing input variables in the fuzzy control model by constructing the fuzzy control model, and if the initial opening of the gate can be determined through the fuzzy control model, performing step S4;
s2: retrieving similar hydrologic data from a historical database, and judging whether the initial opening of the gate can be adjusted to the opening of the gate similar to the hydrologic data in the historical database;
s3: determining difference information and time sequence information of the hydrologic data through analysis of the real-time hydrologic data and similar hydrologic data in a historical database, and taking the result of comprehensive analysis of the difference information and the time sequence information as a basis for judging the initial opening of the gate;
S4: and adjusting the opening of the gate to the initial opening, and adjusting the opening of the gate by adopting different gate control methods according to the magnitude between the preset flow and the actual flow in the PID control.
2. The method for remotely regulating and controlling gate group flow based on a fuzzy model according to claim 1, wherein the constructing the fuzzy control model comprises the following steps:
The fuzzy control model adopts a two-dimensional fuzzy controller to determine the initial opening of a gate, takes the water level deviation and the deviation change rate as input variables, takes the initial opening as output quantity, determines the range of the input variables and the input variable quantization factor, and ensures that the range of the input variables is correctly mapped onto the membership function of a fuzzy set in the fuzzification process;
the limit value of the input variable is the value range of the water level deviation and the deviation change rate, and the optimal input variable range of the water level deviation and the optimal input variable range of the deviation change rate are determined.
3. The method for remotely controlling the gate group flow based on the fuzzy model according to claim 2, wherein the comprehensively analyzing the input variables in the fuzzy control model comprises the following steps:
The analysis result of the water level deviation of the input variable is represented by a water level deviation hidden safety coefficient, the water level deviation is obtained by collecting real-time hydrologic data and comparing a preset water level with the real-time water level, the water level deviation which is not in the optimal input variable range of the water level deviation in a unit time range is obtained, and the water level deviation hidden safety coefficient is determined by formula calculation;
Expressing an analysis result of the deviation change rate of the input variable by a hidden safety coefficient of the deviation change rate, constructing a differential equation of the water level by a mass conservation law, determining the deviation change rate according to the differential equation of the water level, obtaining the deviation change rate which is not in the optimal input variable range of the deviation change rate in a unit time range, and determining the hidden safety coefficient of the deviation change rate by formula calculation;
and obtaining a control evaluation coefficient of the fuzzy control model through weighted summation calculation according to the water level deviation hiding safety coefficient and the deviation change rate hiding safety coefficient.
4. The method for remotely controlling the gate group flow based on the fuzzy model according to claim 3, wherein the determining the initial opening of the gate according to the comprehensive analysis of the input variables in the fuzzy control model comprises:
Setting a control evaluation coefficient threshold value, comparing the control evaluation coefficient with the control evaluation coefficient threshold value, generating a first signal if the control evaluation coefficient is larger than the control evaluation coefficient threshold value, determining the initial opening of the gate without a fuzzy control model, generating a second signal if the control evaluation coefficient is smaller than the control evaluation coefficient threshold value, and determining the initial opening of the gate with the fuzzy control model.
5. The method for remotely controlling the gate group flow based on the fuzzy model according to claim 1, wherein the step of judging whether the initial opening of the gate can be adjusted to the opening of the gate similar to the hydrologic data in the history database comprises the steps of:
when the matched hydrological data is directly inquired from the historical database, the initial opening of the gate is directly regulated and controlled to be the opening in the historical database;
when the matched hydrologic data is not queried in the historical database, the real-time hydrologic data is queried If the matched hydrologic data exist in the range of the gate opening, directly using a formula of a flow calculation library to calculate the initial opening of the gate opening;
If the matched hydrologic data exist, the corresponding opening value with the smallest difference value with the matched hydrologic data is taken as the initial opening of the gate, and the difference information and the time sequence information of the real-time hydrologic data are comprehensively analyzed to judge whether the corresponding opening value with the smallest difference value with the matched hydrologic data can be taken as the initial opening of the gate.
6. The method for remotely controlling the gate group flow based on the fuzzy model according to claim 5, wherein the comprehensively analyzing the difference information and the time sequence information of the real-time hydrologic data comprises the following steps:
The difference information of the real-time hydrologic data represents the similarity degree of the real-time hydrologic data and the stored hydrologic data in the historical database and the similarity degree of the time period in which the real-time hydrologic data is located;
The similarity degree of the hydrologic data is represented by hydrologic data similarity coefficients, real-time hydrologic data of the reservoir is obtained, the real-time hydrologic data comprise four hydrologic data of upstream water level, downstream water level, flow and flow velocity, and the hydrologic data similarity coefficients are determined through formula calculation;
The similarity of the time period where the real-time hydrologic data is located is represented by a time overlapping coefficient, a time point where the real-time hydrologic data is located and a time point corresponding to the matched hydrologic data in the historical database are obtained, the time point where the real-time hydrologic data is located is taken as a current time point, and the time point corresponding to the matched hydrologic data in the historical database is taken as a historical current time point;
setting a time interval, and determining a time sequence to a current time point and a time sequence to a historical current time point according to the length of the time interval;
Constructing a DTW distance matrix, wherein rows and columns of the matrix respectively correspond to a time sequence of a current time point and data points in the time sequence reaching a historical current time point, calculating local distances between the data points in each pair of sequences, gradually filling the matrix in a dynamic programming manner until the value of a right lower corner element of the matrix is obtained, and taking the value of the right lower corner element of the DTW distance matrix as a time overlap coefficient;
The time sequence information of the hydrologic data is represented by discrete accumulation coefficients, real-time hydrologic data in a unit time range is collected, a real-time hydrologic data matrix in the unit time range is constructed, the discrete coefficients of different hydrologic data are calculated by obtaining the average value and standard deviation of the different hydrologic data, and the discrete accumulation coefficients are determined by a formula.
7. The method for remotely controlling the traffic of a gate based on a fuzzy model according to claim 6, wherein the step of determining the initial opening of the gate based on the result of the comprehensive analysis of the difference information and the time sequence information comprises:
Generating a data comparison evaluation coefficient through comprehensively analyzing the hydrologic data similarity coefficient, the time overlapping coefficient and the discrete accumulation coefficient;
Setting a data comparison evaluation coefficient threshold, comparing the data comparison evaluation coefficient with the data comparison evaluation coefficient threshold, generating a third signal which indicates that the opening of the gate corresponding to the hydrological data in the historical database cannot be used as the initial opening of the gate, calculating the initial opening of the gate by using a formula of a flow calculation library, and generating a fourth signal which indicates that the opening of the gate corresponding to the hydrological data in the historical database can be used as the initial opening of the gate if the data comparison evaluation coefficient is smaller than the data comparison evaluation coefficient threshold.
8. The remote control method for gate group flow based on fuzzy model of claim 1, wherein the adjustment of the gate opening is realized by adopting different gate control methods, comprising:
regulating the gate according to the initial opening of the gate, regulating the gate to the initial opening, and regulating the gate through PID, wherein in the gate flow process control, a PID controller algorithm for controlling according to the deviation ratio (P), integral (I) and derivative (D) takes the difference between the preset flow and the actual flow as an error in PID control;
Setting a primary flow deviation threshold value, a secondary flow deviation threshold value and a tertiary flow deviation threshold value to realize rapid regulation of a gate, wherein the primary flow deviation threshold value is larger than the secondary flow deviation threshold value, and the secondary flow deviation threshold value is larger than the tertiary flow deviation threshold value;
When the difference between the preset flow and the actual flow is larger than the first-level flow deviation threshold, a PLC controller is used for executing a switching function, and the opening of the gate is adjusted through the switch;
When the difference between the preset flow and the actual flow is smaller than the primary flow deviation threshold value, and the difference between the preset flow and the actual flow is larger than the secondary flow deviation threshold value, the speed of the gate is adjusted by integrating the separated PID;
When the difference between the preset flow and the actual flow is smaller than the second-level flow deviation threshold value and the difference between the preset flow and the actual flow is larger than the third-level flow deviation threshold value, the effect of gradually reducing the motor speed is achieved by adding an integral term.
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