A method and device for correcting lockage flow of a water lock, medium and product

Abstract

The application discloses a sluice passing flow correction method, device, medium and product, relates to the technical field of hydraulic data correction, and the method comprises the steps of: determining the predicted value of the passing flow at the current moment based on the water level difference between the upstream and downstream at the current moment by using an empirical formula of the passing flow; determining the neighbor normalized relative deviation set at the current moment based on the water level difference between the upstream and downstream at the current moment and the optimal water level difference radius; determining the probability prediction value corresponding to each neighbor normalized relative deviation in the neighbor normalized relative deviation set at the current moment by using a probability prediction function; determining the error correction value at the current moment based on the probability prediction value corresponding to each neighbor normalized relative deviation in the neighbor normalized relative deviation set at the current moment by using an error correction formula; and determining the correction value of the passing flow of the sluice at the current moment based on the predicted value and the error correction value of the passing flow at the current moment. The application realizes the correction of the passing flow of the sluice.

Description

Technical Field

[0001] This application relates to the field of hydraulic data correction technology, and in particular to a method, device, medium and product for correcting the flow rate through a sluice gate. Background Technology

[0002] Accurate calculation of sluice gate discharge flow is a core foundation for water conservancy project scheduling, flood control and disaster reduction, and optimal water resource allocation. To achieve precise fitting of the sluice gate discharge curve, the relevant technologies mainly rely on the following two types of methods: One is fitting based on hydraulic theory formulas: Theoretical formulas are derived based on fundamental hydraulic principles (such as energy conservation and continuity equations) and the structural characteristics of the sluice gate (weir type, orifice shape). Different empirical formulas are required for different operating conditions (orifice flow, weir flow). After obtaining the initial formula, to obtain the optimal parameters that conform to the formula under the current operating conditions, the empirical coefficients in the formula need to be optimized using regression methods such as least squares through physical model experiments or field measurements to obtain the final empirical formula. The other is fitting based on machine learning methods: Machine learning methods such as support vector machines, random forests, and long short-term memory networks are directly used to fit the time-series relationship between water level, opening degree, and flow rate through historical data. Machine learning can input multi-source data to improve fitting accuracy.

[0003] However, the above methods have shortcomings: Using empirical formulas for fitting is the most common method in engineering, but its formulas are relatively fixed, its fitting ability is limited, and it cannot handle situations with large deviations; machine learning methods are less commonly used, parameter tuning is difficult, they are greatly affected by data, their internal principles have low interpretability, and the resulting models have poor credibility. Furthermore, both of these methods focus only on adjusting their own parameters to reduce fitting errors, and their ability to reduce errors is limited, failing to address computational deviations caused by external random factors. Summary of the Invention

[0004] The purpose of this application is to provide a method, device, medium, and product for correcting the flow rate through a sluice gate, so as to solve the problem of correcting the flow rate through a sluice gate.

[0005] To achieve the above objectives, this application provides the following solution.

[0006] Firstly, this application provides a method for correcting the flow rate through a sluice gate, including:

[0007] Obtain the current water level difference between upstream and downstream of the sluice gate and the optimal water level difference radius; the optimal water level difference radius is obtained by filtering from multiple preset water level difference radii.

[0008] Using the empirical formula for gate flow rate, the predicted value of the gate flow rate at the current moment is determined based on the difference between the upstream and downstream water levels at the current moment.

[0009] Based on the current upstream and downstream water level difference and the optimal water level difference radius, determine the neighbor normalized relative deviation set at the current moment;

[0010] Using a probability prediction function, determine the probability prediction value corresponding to each neighbor's normalized relative deviation in the current neighbor normalized relative deviation set;

[0011] Using the error correction formula, the error correction value at the current time is determined based on the probability prediction value corresponding to the normalized relative deviation of each neighbor in the normalized relative deviation set of the neighbors at the current time.

[0012] Based on the predicted and error correction values ​​of the current flow rate through the sluice gate, the correction value of the current flow rate through the sluice gate is determined.

[0013] In one embodiment, the process of selecting the optimal water level difference radius includes:

[0014] The time for screening the optimal water level difference radius is determined as the screening time, and any preset water level difference radius is determined as the current radius.

[0015] Obtain the sampling dataset; the sampling dataset includes multiple sampling data points from the time the sluice gate opens to the time of screening; the sampling data includes: the actual values ​​of the upstream and downstream water level difference and the flow rate through the sluice gate;

[0016] Using the empirical formula for gate flow rate, the predicted value of gate flow rate for each sampled data is determined based on the difference between upstream and downstream water levels in each sampled data.

[0017] The relative deviation of each sampled data is determined based on the predicted value and the actual value of the gate flow corresponding to each sampled data.

[0018] The relative deviation of each sampled data is normalized to obtain the normalized relative deviation of each sampled data.

[0019] Based on the normalized relative deviation of all sampled data in the sampled dataset, the proportion of normalized relative deviation under different values ​​is determined, thereby determining the maximum and minimum proportions of the sampled dataset.

[0020] Select any sampled data as the current data;

[0021] Using the upstream and downstream water level difference of the current data as the center and the current radius as the radius, filter in the sampled dataset to obtain the normalized relative deviation of the current data from multiple neighbors under the current radius;

[0022] Using a probability prediction function, based on the normalized relative deviation of the current data among its neighbors at the current radius, the maximum proportion of the sampled dataset, and the minimum proportion of the sampled dataset, the probability prediction value corresponding to the normalized relative deviation of the current data among its neighbors at the current radius is determined.

[0023] Using the error correction formula, the error correction value of the current data at the current radius is determined based on the probability prediction value corresponding to the normalized relative deviation of the current data at the current radius of multiple neighbors.

[0024] Based on the predicted value of the current gate flow and the error correction value under the current radius, the correction value of the current gate flow under the current radius is determined, thereby obtaining the correction value of the gate flow of all sampled data under the current radius in the sampled dataset;

[0025] The average deviation of the current radius is determined based on the corrected value of the gate flow rate and the actual value of the gate flow rate of all sampled data in the sampled dataset at the current radius;

[0026] The preset water level difference radius with the smallest average deviation is determined as the optimal water level difference radius.

[0027] In one embodiment, the empirical formula for the gate flow rate is:

[0028] ;

[0029] in, This is the predicted value of the flow rate through the gate; This is a comprehensive coefficient; The clear width of the sluice gate opening; To raise the gate height of the sluice gate; The difference in water levels between upstream and downstream; It is the exponential coefficient.

[0030] In one implementation, based on the upstream and downstream water level difference and the optimal water level difference radius at the current moment, the neighbor normalized relative deviation set at the current moment is determined, including:

[0031] Obtain historical datasets; historical datasets include multiple historical data points from the time the sluice gate was opened to the current time; historical data includes: upstream and downstream water level differences and normalized relative deviations;

[0032] Using the current upstream and downstream water level difference as the center and the optimal water level difference radius as the radius, filter through the historical dataset to obtain the normalized relative deviations of multiple neighbors of the current upstream and downstream water level difference under the optimal water level difference radius, thus obtaining the set of normalized relative deviations of neighbors at the current moment.

[0033] In one embodiment, a probability prediction function is used to determine the probability prediction value corresponding to each normalized relative deviation of the neighbors in the current time-normalized relative deviation set, including:

[0034] Based on all the normalized relative deviations in the historical dataset at the current moment, determine the proportion of the normalized relative deviations under different values ​​at the current moment, thereby determining the maximum and minimum proportions at the current moment;

[0035] Using a probabilistic prediction function, based on the normalized relative deviations of each neighbor at the current time, the maximum proportion at the current time, and the minimum proportion at the current time, the probabilistic prediction values ​​corresponding to the normalized relative deviations of each neighbor in the current time's neighbor normalized relative deviation set are determined; the probabilistic prediction function is:

[0036] ;

[0037] in, for The corresponding probability prediction value; The largest proportion; To be the minimum percentage; It is a natural constant; These are the fitting parameters; This is a relative deviation; for The corresponding relative deviation.

[0038] In one embodiment, the error correction formula is:

[0039] ;

[0040] in, This is the error correction value corresponding to the i-th upstream and downstream water level difference; The normalized relative deviation of the j-th neighbor corresponding to the i-th upstream and downstream water level difference; This represents the amount of relative deviation from the normalized neighbor order. for The corresponding probability prediction value.

[0041] In one embodiment, determining the correction value of the current passage flow of the sluice gate based on the predicted value and error correction value of the current passage flow includes:

[0042] The predicted flow rate and the error correction value of the current flow rate through the gate are summed to obtain the corrected flow rate of the current flow rate through the gate.

[0043] Secondly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described sluice gate flow correction method.

[0044] Thirdly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method for correcting the flow rate through a sluice gate.

[0045] Fourthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method for correcting the flow rate through a sluice gate.

[0046] According to the specific embodiments provided in this application, the following technical effects are disclosed:

[0047] This application discloses a method, device, medium, and product for correcting the flow rate through a sluice gate. First, the upstream and downstream water level difference and the optimal water level difference radius of the sluice gate at the current moment are obtained. Then, using an empirical formula for flow rate through the sluice gate, the predicted value of the flow rate at the current moment is determined based on the upstream and downstream water level difference. Second, based on the upstream and downstream water level difference and the optimal water level difference radius, the set of normalized relative deviations of the neighbors at the current moment is determined. Subsequently, using a probability prediction function, the probability prediction value corresponding to each normalized relative deviation of the neighbors in the set of normalized relative deviations of the neighbors at the current moment is determined. Third, using an error correction formula, based on the probability prediction value corresponding to each normalized relative deviation of the neighbors in the set of normalized relative deviations of the neighbors at the current moment, the error correction value for the current moment is determined. Finally, based on the predicted value of the flow rate through the sluice gate and the error correction value, the corrected value of the flow rate through the sluice gate at the current moment is determined. This application adds an error correction value to the predicted value of the flow rate through the sluice gate obtained by the empirical formula for flow rate through the sluice gate, thus realizing the correction of the flow rate through the sluice gate. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is a schematic flowchart of a sluice gate flow correction method provided in an embodiment of this application.

[0050] Figure 2 The graph shows the probability density function of the relative deviation of the gate opening at a given value.

[0051] Figure 3 The graph shows the probability density function of the relative deviation of the gate opening for another value.

[0052] Figure 4 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0053] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0054] The purpose of this application is to provide a method, device, medium, and product for correcting the flow rate through a sluice gate, with the aim of correcting the flow rate through the sluice gate.

[0055] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0056] In one exemplary embodiment, such as Figure 1 As shown, a method for correcting the flow rate through a sluice gate is provided, which includes the following steps.

[0057] Step 1: Obtain the current water level difference between upstream and downstream of the sluice gate and the optimal water level difference radius; the optimal water level difference radius is obtained by filtering from multiple preset water level difference radii.

[0058] Specifically, the sluice gate is a sluice gate where the flow is through orifices.

[0059] As an optional implementation, the screening process for the optimal water level difference radius in step 10 includes the following steps.

[0060] Step 101: Determine the time when the optimal water level difference radius is selected as the selection time, and determine any preset water level difference radius as the current radius.

[0061] Step 102: Obtain the sampling dataset; the sampling dataset includes multiple sampling data points during the time period from the opening time of the sluice gate to the screening time; the sampling data includes the actual values ​​of the upstream and downstream water level difference and the flow rate through the sluice gate.

[0062] Step 103: Using the empirical formula for gate flow rate, determine the predicted value of the gate flow rate corresponding to each sampled data based on the upstream and downstream water level difference in each sampled data.

[0063] Step 104: Determine the relative deviation of each sampled data based on the predicted value and the actual value of the gate flow corresponding to each sampled data.

[0064] Specifically, the absolute values ​​of the predicted and actual flow rates for each sampled data point are subtracted to obtain the relative deviation of each sampled data point. Due to various environmental factors surrounding the sluice gate, random deviations occur when calculating the flow rate using the ideal empirical formula. Since these errors may be caused by multiple independent, uncontrolled, and random factors beyond human control, the relative deviations can be considered to follow a Gaussian distribution. Statistical analysis of the relative deviations of the formula for each gate opening in the actual flow rate shows that the deviation distribution for most openings follows a Gaussian distribution, verifying the correctness of the theory. The probability density function of the relative deviations for different gate openings is as follows: Figure 2 and Figure 3 As shown.

[0065] Step 105: Normalize the relative deviation of each sampled data to obtain the normalized relative deviation of each sampled data.

[0066] Specifically, the relative deviation values ​​are normalized to ensure that each relative deviation value maintains a certain proportion. For example, if the simplification range is 1, the relative deviation values ​​in the range of 0-1 are normalized to 0.5, the relative deviation values ​​in the range of 1-2 are normalized to 1.5, and so on, to obtain multiple normalized relative deviations.

[0067] Step 106: Based on the normalized relative deviation of all sampled data in the sampled dataset, determine the proportion of normalized relative deviation under different values, thereby determining the maximum and minimum proportions of the sampled dataset.

[0068] Specifically, assuming the sampled dataset includes 100 sampled data points, and the number of normalized relative deviations with a value of 0.3 is 20, then the proportion of normalized relative deviations with a value of 0.3 is 20%. The maximum value among all proportions corresponding to the sampled dataset is determined as the maximum proportion, and the minimum value among all proportions corresponding to the sampled dataset is determined as the minimum proportion.

[0069] Step 107: Determine any sampled data as the current data.

[0070] Step 108: Using the upstream and downstream water level difference of the current data as the center and the current radius as the radius, filter in the sampled dataset to obtain the normalized relative deviation of the current data from multiple neighbors within the current radius.

[0071] Specifically, the normalized relative deviation of the upstream and downstream water level difference in the sampled data within the range centered on the upstream and downstream water level difference of the current data and with the current radius as the radius is determined as the normalized relative deviation of the current data among multiple neighbors under the current radius.

[0072] Step 109: Using the probability prediction function, based on the normalized relative deviation of the current data among its neighbors at the current radius, the maximum proportion of the sampled dataset, and the minimum proportion of the sampled dataset, determine the probability prediction value corresponding to the normalized relative deviation of the current data among its neighbors at the current radius.

[0073] Step 110: Using the error correction formula, determine the error correction value of the current data at the current radius based on the probability prediction value corresponding to the normalized relative deviation of the current data at the current radius of multiple neighbors.

[0074] Step 111: Based on the predicted value of the current gate flow and the error correction value under the current radius, determine the correction value of the current gate flow under the current radius, thereby obtaining the correction value of the gate flow of all sampled data under the current radius in the sampled dataset.

[0075] Step 112: Determine the average deviation of the current radius based on the corrected value of the gate flow rate and the actual value of the gate flow rate of all sampled data in the sampled dataset at the current radius.

[0076] Specifically, the formula for calculating the average deviation is:

[0077] ;

[0078] in, The average deviation; This is the correction value for the gate flow rate of the d-th sampled data in the sampled dataset; This represents the actual value of the gate flow rate for the d-th sampled data in the sampled dataset. This represents the total number of sampled data points in the sampled dataset.

[0079] Step 113: Determine the preset water level difference radius with the smallest average deviation as the optimal water level difference radius.

[0080] Step 2: Using the empirical formula for gate flow rate, determine the predicted value of the gate flow rate at the current moment based on the upstream and downstream water level difference.

[0081] As an optional implementation method, the empirical formula for gate throughput is:

[0082] ;

[0083] in, This is the predicted value of the flow rate through the gate; This is a comprehensive coefficient; The clear width of the sluice gate opening; To raise the gate height of the sluice gate; The difference in water levels between upstream and downstream; It is the exponential coefficient.

[0084] Specifically, and The data was obtained by fitting multiple sets of data and using the least squares method. The fitted data included: the actual value of the flow rate through the sluice gate, the net width of the sluice gate opening, the gate opening height of the sluice gate, and the water level difference between the upstream and downstream sides.

[0085] Step 3: Based on the current upstream and downstream water level difference and the optimal water level difference radius, determine the current neighbor normalized relative deviation set.

[0086] As an optional implementation, step 3 includes the following steps.

[0087] Step 31: Obtain historical dataset; the historical dataset includes multiple historical data points from the time the sluice gate was opened to the current time; the historical data includes: upstream and downstream water level difference and normalized relative deviation.

[0088] Step 32: Using the current upstream and downstream water level difference as the center and the optimal water level difference radius as the radius, filter the historical dataset to obtain the normalized relative deviations of the upstream and downstream water level difference at the current time under the optimal water level difference radius, thus obtaining the set of normalized relative deviations of the neighbors at the current time.

[0089] Specifically, the normalized relative deviation of the upstream and downstream water level difference in the historical dataset, which is located within the range of the current upstream and downstream water level difference as the center and the radius of the optimal water level difference as the radius, is determined as the normalized relative deviation of the upstream and downstream water level difference at the current time within the radius of the optimal water level difference.

[0090] Step 4: Using the probability prediction function, determine the probability prediction value corresponding to each neighbor's normalized relative deviation in the current neighbor normalized relative deviation set.

[0091] As an optional implementation, step 4 includes the following steps.

[0092] Step 41: Based on all the normalized relative deviations in the historical dataset at the current moment, determine the proportion of the normalized relative deviations under different values ​​at the current moment, thereby determining the maximum and minimum proportions at the current moment.

[0093] Step 42: Using the probability prediction function, based on the normalized relative deviations of each neighbor at the current time, the maximum proportion at the current time, and the minimum proportion at the current time, determine the probability prediction value corresponding to the normalized relative deviations of each neighbor in the current time's neighbor normalized relative deviation set; the probability prediction function is:

[0094] ;

[0095] in, for The corresponding probability prediction value; The largest proportion; To be the minimum percentage; It is a natural constant; These are the fitting parameters; This is a relative deviation; for The corresponding relative deviation.

[0096] Specifically, It was obtained by fitting using the Levenberg-Marquardt (LM) algorithm.

[0097] Step 5: Using the error correction formula, determine the error correction value for the current time based on the probability prediction value corresponding to the normalized relative deviation of each neighbor in the current time's neighbor normalized relative deviation set.

[0098] As an optional implementation method, the error correction formula is:

[0099] ;

[0100] in, This is the error correction value corresponding to the i-th upstream and downstream water level difference; The normalized relative deviation of the j-th neighbor corresponding to the i-th upstream and downstream water level difference; This represents the amount of relative deviation from the normalized neighbor order. for The corresponding probability prediction value.

[0101] Step 6: Based on the predicted value and error correction value of the current flow rate through the sluice gate, determine the correction value of the current flow rate through the sluice gate.

[0102] As an optional implementation, step 6 includes:

[0103] Step 61: Sum the predicted value and error correction value of the current flow rate through the gate to obtain the corrected value of the current flow rate through the gate.

[0104] Specifically, the formula for calculating the correction value of the gate flow rate is as follows:

[0105] ;

[0106] in, This is a correction value for the flow rate through the gate; This is the error correction value.

[0107] In one exemplary embodiment, a computer device is provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a sluice gate flow correction method.

[0108] In one exemplary embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements a method for correcting the flow rate through a sluice gate.

[0109] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements a sluice gate flow correction method.

[0110] In one exemplary embodiment, a computer device is provided, which may be a server or a terminal, and its internal structure diagram may be as follows. Figure 4 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media to run. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network. When the computer program is executed by the processor, it implements a method for correcting the flow rate through a sluice gate.

[0111] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0112] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0113] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0114] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0115] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0116] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for correcting the flow rate through a sluice gate, characterized in that, The method for correcting the flow rate through the sluice gate includes: Obtain the current water level difference between upstream and downstream of the sluice gate and the optimal water level difference radius; the optimal water level difference radius is obtained by filtering from multiple preset water level difference radii. Using the empirical formula for gate flow rate, the predicted value of the gate flow rate at the current moment is determined based on the difference between the upstream and downstream water levels at the current moment. Based on the current upstream and downstream water level difference and the optimal water level difference radius, determine the neighbor normalized relative deviation set at the current moment; Using a probability prediction function, determine the probability prediction value corresponding to each neighbor's normalized relative deviation in the current neighbor normalized relative deviation set; Using the error correction formula, the error correction value at the current time is determined based on the probability prediction value corresponding to the normalized relative deviation of each neighbor in the normalized relative deviation set of the neighbors at the current time. Based on the predicted value and error correction value of the current flow rate through the sluice gate, determine the correction value of the current flow rate through the sluice gate. The process of selecting the optimal water level difference radius includes: The time for screening the optimal water level difference radius is determined as the screening time, and any preset water level difference radius is determined as the current radius. Obtain the sampling dataset; the sampling dataset includes multiple sampling data points from the time the sluice gate opens to the time of screening; the sampling data includes: the actual values ​​of the upstream and downstream water level difference and the flow rate through the sluice gate; Using the empirical formula for gate flow rate, the predicted value of gate flow rate for each sampled data is determined based on the difference between upstream and downstream water levels in each sampled data. The relative deviation of each sampled data is determined based on the predicted value and the actual value of the gate flow corresponding to each sampled data. The relative deviation of each sampled data is normalized to obtain the normalized relative deviation of each sampled data. Based on the normalized relative deviation of all sampled data in the sampled dataset, the proportion of normalized relative deviation under different values ​​is determined, thereby determining the maximum and minimum proportions of the sampled dataset. Select any sampled data as the current data; Using the upstream and downstream water level difference of the current data as the center and the current radius as the radius, filter in the sampled dataset to obtain the normalized relative deviation of the current data from multiple neighbors under the current radius; Using a probability prediction function, based on the normalized relative deviation of the current data among its neighbors at the current radius, the maximum proportion of the sampled dataset, and the minimum proportion of the sampled dataset, the probability prediction value corresponding to the normalized relative deviation of the current data among its neighbors at the current radius is determined. Using the error correction formula, the error correction value of the current data at the current radius is determined based on the probability prediction value corresponding to the normalized relative deviation of the current data at the current radius of multiple neighbors. Based on the predicted value of the current gate flow and the error correction value under the current radius, the correction value of the current gate flow under the current radius is determined, thereby obtaining the correction value of the gate flow of all sampled data under the current radius in the sampled dataset; The average deviation of the current radius is determined based on the corrected value of the gate flow rate and the actual value of the gate flow rate of all sampled data in the sampled dataset at the current radius; The preset water level difference radius with the smallest average deviation is determined as the optimal water level difference radius.

2. The sluice gate flow correction method according to claim 1, characterized in that, The empirical formula for gate flow rate is: ； in, This is the predicted value of the flow rate through the gate; This is a comprehensive coefficient; The clear width of the sluice gate opening; To raise the gate height of the sluice gate; The difference in water levels between upstream and downstream; It is the exponential coefficient.

3. The sluice gate flow correction method according to claim 1, characterized in that, Based on the current upstream and downstream water level difference and the optimal water level difference radius, determine the current neighbor normalized relative deviation set, including: Obtain historical datasets; historical datasets include multiple historical data points from the time the sluice gate was opened to the current time; historical data includes: upstream and downstream water level differences and normalized relative deviations; Using the current upstream and downstream water level difference as the center and the optimal water level difference radius as the radius, filter through the historical dataset to obtain the normalized relative deviations of multiple neighbors of the current upstream and downstream water level difference under the optimal water level difference radius, thus obtaining the set of normalized relative deviations of neighbors at the current moment.

4. The sluice gate flow correction method according to claim 1, characterized in that, Using a probabilistic prediction function, determine the probabilistic prediction values ​​corresponding to the normalized relative deviations of each neighbor in the current time-to-time neighborhood normalized relative deviation set, including: Based on all the normalized relative deviations in the historical dataset at the current moment, determine the proportion of the normalized relative deviations under different values ​​at the current moment, thereby determining the maximum and minimum proportions at the current moment; Using a probabilistic prediction function, based on the normalized relative deviations of each neighbor at the current time, the maximum proportion at the current time, and the minimum proportion at the current time, the probabilistic prediction values ​​corresponding to the normalized relative deviations of each neighbor in the current time's neighbor normalized relative deviation set are determined; the probabilistic prediction function is: ； in, for The corresponding probability prediction value; The largest proportion; To be the minimum percentage; It is a natural constant; These are the fitting parameters; This is a relative deviation; for The corresponding relative deviation.

5. The sluice gate flow correction method according to claim 4, characterized in that, The error correction formula is: ； in, This is the error correction value corresponding to the i-th upstream and downstream water level difference; The normalized relative deviation of the j-th neighbor corresponding to the i-th upstream and downstream water level difference; This represents the amount of relative deviation from the normalized neighbor order. for The corresponding probability prediction value.

6. The method for correcting the flow rate through a sluice gate according to claim 1, characterized in that, Based on the predicted and error correction values ​​of the current flow rate through the sluice gate, determine the correction value of the current flow rate through the sluice gate, including: The predicted flow rate and the error correction value of the current flow rate through the gate are summed to obtain the corrected flow rate of the current flow rate through the gate.

7. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the sluice gate flow correction method according to any one of claims 1-6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the sluice gate flow correction method according to any one of claims 1-6.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the sluice gate flow correction method according to any one of claims 1-6.

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