An open channel gate water metering precision metering method and system for water conservancy informatization

Abstract

The present application relates to the technical field of open channel water flow measurement, and particularly relates to a water conservancy informatization open channel gate water measurement accurate measurement method and system. The gate opening, upstream and downstream water depth, water flow and other water measurement data at each time of the gate, and the actual flow coefficient are collected. Because the silt in front of the gate raises the elevation of the gate sill, which affects the accuracy of the data and reduces the reliability of the regression model analysis, the differences in the gate opening, water flow and downstream water depth between the time points are compared, and the comparability of the silt at any two time points is calculated. Considering the change characteristics of the silt, the similarity characteristics of the comparability of the silt at adjacent time points are analyzed, and the difference in the downstream water depth is combined to quantify the final silt accumulation degree. Finally, based on considering the final silt accumulation degree, the simulation and actual flow coefficient deviation characteristics of the regression model in different water flow form scenarios are compared, the optimal model is selected, the stability and accuracy of the flow coefficient calculation are improved, and the reliability of future water flow calculation is ensured.

Description

Technical Field

[0001] This invention relates to the field of open channel water flow measurement technology, specifically to a water conservancy information-based method and system for accurate water measurement by open channel gates. Background Technology

[0002] Water flow measurement at open channel gates is crucial for water conservancy management. Firstly, accurate flow measurement helps optimize the scheduling of irrigation, drainage, and flood control projects, improving water resource utilization efficiency. Secondly, accurate flow data allows for the assessment of gate operation, timely detection of potential equipment malfunctions or flow anomalies, and thus ensures the safe operation of water conservancy facilities. Therefore, water flow measurement at open channel gates is not only fundamental to water conservancy facility management but also a vital means of ensuring the efficient use of water resources.

[0003] Current technologies for measuring gate flow typically involve collecting water flow data at the gate using sensing devices, followed by secondary processing. A regression model with a fitted flow coefficient is then used to replace the flow coefficient in the water flow formula, eliminating the interference of the flow coefficient on the accuracy of the formula and thus calculating the water flow. However, in actual hydraulic scenarios, the complex and constantly changing flow patterns, along with the continuous deposition of sediment and the gradual rise in the bottom elevation of the area in front of the gate, can lead to lower accuracy in some water flow data. Therefore, if all data are given the same contribution weight in the regression analysis, it will affect the accuracy of the regression analysis, resulting in poor fitting results and ultimately, inaccurate water flow calculations. Summary of the Invention

[0004] To address the technical problem in practical water conservancy scenarios where complex and constantly changing water flow patterns, coupled with continuous sediment deposition and a gradual rise in the bottom elevation of the area in front of the sluice gate, lead to low accuracy in some water measurement data, and consequently, using the same contribution weight for all data in regression analysis would affect the accuracy of the regression analysis, resulting in poor fitting results and ultimately inaccurate water flow calculations, this invention aims to provide a precise water measurement method and system for open channel gates in water conservancy information systems. The specific technical solution adopted is as follows: A method for accurate water measurement using gates in open channels in water conservancy information systems includes: Acquire water measurement data, which includes gate opening time series data, upstream water depth time series data and downstream water depth time series data at the gate, water flow time series data at the gate, and the actual flow coefficient at each time point; Based on the differences in gate opening, water flow, and downstream water depth at different times, the silt comparability between any two times is calculated. Based on the similarity between the silt comparability of adjacent times to the same time, the differences in upstream water depth between times are analyzed and combined with the silt comparability to obtain the final siltation degree at the gate at each time. In each flow pattern scenario, different regression models are used to fit the water volume data to obtain the simulated flow coefficient at each time point. Based on the final siltation degree at the gate at each time point, the deviation characteristics between the simulated flow coefficient and the actual flow coefficient under the regression model are compared to select the optimal model from all regression models. The optimal model for each flow pattern scenario is used to calculate the flow coefficient at each future moment, which is then used to calculate the water flow at the gate.

[0005] Furthermore, the method for obtaining the comparability of the sludge includes: For any two moments, calculate the first silt comparability factor based on the difference in gate opening between the two moments; For any two time points, analyze the differences in water flow between these two time points to determine the second silt comparability factor; For any two time points, a third silt comparability factor is obtained based on the difference in downstream water depth between these two time points. For any two moments, the normalized value of the product of the first silt comparability factor, the second silt comparability factor, and the third silt comparability factor between these two moments is taken as the silt comparability between these two moments.

[0006] Furthermore, the method for obtaining the first sludge comparability factor includes: The absolute value of the difference between the gate openings at these two times is negatively correlated and normalized, and this value is used as the first silt comparability factor between the two times.

[0007] Furthermore, the method for obtaining the second sludge comparability factor includes: The absolute value of the difference in water flow between these two times is negatively correlated and normalized, and this value is used as the second silt comparability factor between these two times.

[0008] Furthermore, the method for obtaining the third sludge comparability factor includes: The absolute value of the difference between the downstream water depths at these two times is negatively correlated and normalized, and this value is used as the third silt comparability factor between the two times.

[0009] Furthermore, the method for obtaining the final sedimentation degree includes: Choose one time point as the test time point, and use the remaining other times points as comparison times for the test time point. Use the silt contrast between all comparison times point and the test time point as the target silt contrast. Among all comparison times, the two times closest to each comparison time are taken as the neighborhood times of each comparison time. The absolute value of the difference between the target sludge comparability of each comparison time and each neighborhood time is calculated as the deviation factor. The absolute value of the difference between the two deviation factors corresponding to each comparison time is negatively correlated and normalized, and then used as the confidence factor of the time to be tested for each comparison time. Multiply the confidence factor of the test time at each comparison time by the silt comparability between each comparison time and the test time, and use the normalized value of the product as the siltation weight of the test time at each comparison time. The normalized value of the difference in upstream water depth between the time to be measured and each comparison time is used as the relative siltation degree value of the time to be measured to each comparison time. By using the sedimentation degree weight of the comparison time to the test time, the relative sedimentation degree values ​​of the test time to the comparison time are weighted and fused, and the normalized value of the weighted result is used as the final sedimentation degree of the silt at the gate at the test time.

[0010] Furthermore, the method for obtaining the simulated flow coefficient includes: In each flow pattern scenario, at each time point, the corresponding water volume data is selected to calculate the ratio as the independent variable. Each regression model is used to fit the independent variable, and the dependent variable obtained from the fitting result is used as the simulated flow coefficient. The regression model includes at least power functions, exponential functions, logarithmic functions, binomial functions, etc.

[0011] Furthermore, the method for obtaining the optimal model includes: Based on the final sedimentation degree at the gate at each time point, the deviation characteristics between the simulated flow coefficient and the actual flow coefficient under each regression model are compared to obtain the determination coefficient corresponding to each regression model. The formula model of the determination coefficient includes: in, Indicates the coefficient of determination; Indicates the total number of moments; This represents the final degree of siltation at the lower gate at time i; This represents the actual flow coefficient at time i. This represents the average of the actual flow coefficients at all times. This represents the simulated flow coefficient at time i. Among all regression models for each water flow pattern scenario, the regression model with the largest coefficient of determination is selected as the optimal model.

[0012] Furthermore, the step of calculating the flow coefficient at each future moment using the optimal model under each flow pattern scenario, and then using it to calculate the water flow at the gate, includes: For each flow pattern scenario, the optimal model is used to replace the flow coefficient in the water measurement formula for each flow pattern scenario, thereby calculating the water flow value at the gate at each future time.

[0013] A water conservancy information-based open channel gate water measurement precision metering system includes a processor and a memory. The memory stores at least one instruction, at least one program, code set, or instruction set. When the processor loads and executes the at least one instruction, at least one program, code set, or instruction set, it implements the steps of the water conservancy information-based open channel gate water measurement precision metering method.

[0014] The present invention has the following beneficial effects: First, various water measurement data at the gate are collected, including the gate opening, upstream water depth, downstream water depth, and flow rate at each time point, as well as the actual flow coefficient (calculated using the water measurement formula) at each time point. Since siltation in front of the gate raises the actual elevation of the gate sill, affecting data accuracy and reducing the reliability of the regression model analysis, this invention needs to analyze the siltation situation at the gate at each time point to characterize the accuracy and reliability of the data at each time point. Siltation raises the riverbed in front of the gate, thus forcing the upstream water depth to rise under the same gate opening, flow rate, and downstream water depth conditions. Therefore, it is necessary to calculate the siltation comparability of other times relative to each historical time point. Thus, the differences in gate opening, flow rate, and downstream water depth between time points are compared to obtain the siltation comparability between any two time points. Considering that silt cannot suddenly disappear or change, the similarity characteristics of siltation comparability between adjacent time points are analyzed, and combined with the differences in upstream water depth between time points, the final siltation degree of the gate at each time point is quantified. Furthermore, considering the final siltation degree at the gate, the optimal model is selected by comparing the deviation characteristics between the simulated flow coefficient and the actual flow coefficient under the regression model in each flow pattern scenario. This model selection mechanism can choose the most suitable model for the current water conservancy scenario from numerous regression models, effectively improving the stability and accuracy of flow coefficient calculation, thereby ensuring the reliability of future water flow calculations. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of a water conservancy information-based method for accurate water measurement using open channel gates, provided in one embodiment of the present invention. Figure 2 A flowchart illustrating a method for obtaining sludge comparability according to an embodiment of the present invention; Figure 3 This is a system block diagram of a precise water measurement system for open channel gates in water conservancy, provided in one embodiment of the present invention. Figure 4 This is a schematic diagram of the system structure of a water conservancy information-based open channel gate water measurement system provided in one embodiment of the present invention. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a water conservancy information-based open channel gate precise water measurement method and system proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0019] The following description, in conjunction with the accompanying drawings, details the specific scheme of the water conservancy information-based open channel gate water measurement method and system provided by the present invention.

[0020] Please see Figure 1 The diagram illustrates a method flowchart for a precise water measurement method using open channel gates in water conservancy information systems, according to an embodiment of the present invention. The method includes the following steps: Step S1: Obtain water measurement data, which includes gate opening time series data, upstream water depth time series data and downstream water depth time series data at the gate, water flow time series data at the gate, and the actual flow coefficient at each time point.

[0021] With water resource management receiving increasing attention and the strictest water resource management system being implemented, accurate measurement of water flow in open channels has become a critical requirement. Open channel gates, as hydraulic structures with water measurement functions, encounter numerous challenges in practical applications. Their flow patterns are complex and constantly changing, and the water measurement conditions are extremely demanding. Furthermore, the downstream water flow is often unstable. These factors intertwine, making it difficult to reliably guarantee the accuracy of water measurement using open channel gates.

[0022] In this embodiment of the invention, water measurement data is first acquired. The types of water measurement data include: gate opening time series data (acquired based on the opening sensor installed on the gate opening mechanism), upstream water depth time series data and downstream water depth time series data at the gate (collected based on depth sensors installed upstream and downstream of the gate, such as pressure level gauges or ultrasonic level gauges), and water flow time series data at the gate (monitored based on the flow sensor installed on the main channel through which the water flows through the gate).

[0023] Then, the actual flow coefficient at each moment is calculated based on the water measurement formula for different water flow patterns. Here, we will illustrate the water measurement formula for two different water flow patterns: Gate-controlled free flow scenario: Where Q is the water flow rate; μ is the flow coefficient; B is the gate orifice width (obtainable from design drawings); e is the gate opening; g is the gravitational acceleration; and H is the upstream water depth. Gate-controlled submerged flow scenario: Where Q is the water flow rate; μ is the flow coefficient; B is the gate width; e is the gate opening; g is the gravitational acceleration; and Z is the water level difference between upstream and downstream (which can be calculated based on the upstream and downstream water depths). By substituting the required water measurement data into the water measurement formula, the flow coefficient at each time point under each water flow pattern scenario is calculated and recorded as the actual flow coefficient.

[0024] Existing technologies for measuring gate water flow use a regression model that fits the flow coefficient to replace the flow coefficient in the aforementioned water measurement formula. This is done to eliminate the interference of the flow coefficient on the accuracy of the water measurement formula and thus calculate the water flow. However, in real-world scenarios, water flow patterns are complex and constantly changing, and the sediment carried by the water flow is constantly deposited, which can lead to lower accuracy in some water measurement data, affecting the accuracy of regression analysis and resulting in inaccurate final water flow calculation. Therefore, it is necessary to analyze the siltation situation in subsequent processes to improve accuracy.

[0025] It should be noted that in this embodiment of the present invention, the time series data acquisition frequency is set to once per second, and the time series data length is set to 1 hour. The acquisition frequency and length can be adjusted according to the implementation scenario, and are not limited here.

[0026] Step S2: Based on the differences in gate opening, water flow, and downstream water depth at different times, calculate the silt comparability between any two times. Based on the similarity between the silt comparability of adjacent times and the same time, analyze the differences in upstream water depth between times and combine it with the silt comparability to obtain the final siltation degree at the gate at each time.

[0027] The accumulation of silt raised the riverbed in front of the sluice gate, causing the water flow to encounter greater resistance, which forced the upstream water level to rise under the same flow rate and sluice gate opening conditions. Given that gate opening directly affects water flow and level, different gate openings will lead to changes in water flow and velocity. This means that fluctuations in upstream water level are not entirely caused by siltation, but are also influenced by gate control. Therefore, it is essential to ensure that changes in upstream water level are directly correlated with siltation volume when gate openings are similar, demonstrating whether the rise in water level is due to silt accumulation rather than other factors (such as climate change). With consistent gate openings, water flow and water level are closely related. Changes in water flow directly affect the flow velocity and water level. When water flow is consistent, the transport capacity of the water flow and the upward trend of the river level are directly affected by silt accumulation. If the water flow differs significantly, changes in upstream water level are not only affected by siltation but may also be disturbed by water level fluctuations caused by flow fluctuations. Furthermore, downstream water depth also affects the expansion and velocity of the water flow, which is closely related to the upstream water level and determines the flow characteristics. If the downstream water depth varies significantly, the water flow will be subject to different restrictions or accelerations after passing through the gate, thus affecting water level changes and causing unnecessary deviations. Therefore, comparing water volume data at different times under the same conditions yields more reliable and accurate results. In other words, if two times have similar gate opening, flow rate, and downstream water depth, they can be considered comparable in terms of siltation levels. Thus, based on the differences in gate opening, flow rate, and downstream water depth at different times, the comparability of siltation between any two times can be calculated.

[0028] Preferably, in one embodiment of the present invention, the method for obtaining the comparability of sludge includes: Please see Figure 2 The diagram illustrates a method flowchart for obtaining sludge comparability according to an embodiment of the present invention, the method comprising the following steps: Step S201: For any two time points, calculate the first silt comparability factor based on the difference characteristics between the gate openings at these two time points.

[0029] The absolute value of the difference between the gate openings at these two moments is calculated. The smaller the absolute value, the more similar the gate openings are. Based on the previous analysis, it is known that only under this condition will the change in upstream water depth be directly related to the amount of silt deposition, and the comparability of silt between the two moments will be higher. Therefore, the absolute value of the difference is negatively correlated and normalized to correct the logical relationship, thus obtaining the first silt comparability factor between the two moments. The negative correlation mapping and normalization here can be performed using the formula... ,in, Let x represent an exponential function with the natural constant e as the base, and let x represent the independent variable.

[0030] Step S202: For any two time points, analyze the differences in water flow rates between the two time points to determine the second silt comparability factor.

[0031] Since changes in upstream water levels are affected not only by siltation but also by fluctuations in water flow, the absolute value of the difference in water flow between these two points in time is calculated. The smaller the absolute value of this difference, the more consistent the water flow at these two points, indicating a higher similarity in water transport capacity. Therefore, the rise in upstream water level is more likely to accurately reflect the impact of siltation, leading to a higher degree of comparability of the silt. Similarly, this absolute value of the difference is negatively correlated and normalized to correct the logical relationship, resulting in a second silt comparability factor between these two points in time. This negative correlation mapping and normalization can be performed using the formula... ,in, Let x represent an exponential function with the natural constant e as the base, and let x represent the independent variable.

[0032] Step S203: For any two time points, based on the difference in downstream water depth between these two time points, obtain the third silt comparability factor.

[0033] The accumulation of silt raises the riverbed in front of the sluice gate, causing the water flow to encounter greater resistance. If the water depth is different downstream, this change in water flow cannot be directly attributed to siltation. Only when the water depth is similar downstream can the water level change truly reflect the impact of siltation on the water flow.

[0034] Therefore, the absolute value of the difference between the downstream water depths at these two times is calculated. Based on the previous analysis, the smaller the absolute value of this difference, the more significant the comparability of siltation. Therefore, this absolute value is also subjected to negative correlation mapping and normalization to obtain the third silt comparability factor between these two times. The negative correlation mapping and normalization here can be performed using the formula... ,in, Let x represent an exponential function with the natural constant e as the base, and let x represent the independent variable.

[0035] Step S204: For any two time points, merge the first silt comparability factor, the second silt comparability factor, and the third silt comparability factor between these two time points to obtain the silt comparability between these two time points.

[0036] Based on the analysis and calculations in the aforementioned three steps, it is known that for any two time points, the first, second, and third silt comparability factors are all positively correlated with the degree of silt comparability between these two time points. Therefore, in this embodiment of the invention, the normalized value of the product of the first, second, and third silt comparability factors between these two time points is used as the silt comparability between these two time points. The higher the silt comparability, the more similar the gate opening, water flow, and downstream water depth are considered to be between the two time points. In this case, the rise in upstream water level between these two time points is more likely to be caused by silt accumulation. Therefore, when quantifying the final silt accumulation degree at the gate at each time point, the reference value of the time with higher silt comparability will be higher, effectively improving accuracy. Normalization is a technique well-known to those skilled in the art. The choice of normalization function can be linear normalization or standard normalization, etc., and the specific normalization method is not limited here.

[0037] Silt accumulation is influenced by various factors such as the natural environment and water flow, often exhibiting a slow accumulation or dispersion trend rather than a sudden and drastic change. This means that the degree of riverbed elevation and water flow resistance changes at adjacent moments do not show abrupt changes, maintaining a relatively consistent trend. Therefore, the comparability of siltation at adjacent moments relative to the same moment usually does not show significant abrupt changes and will have a high degree of similarity. At the same time, siltation raises the riverbed in front of the sluice gate, causing the upstream water level to rise. To determine the siltation situation at the sluice gate at each moment, the differences in upstream water depth between moments can be compared. Therefore, in this embodiment of the invention, based on the similarity of siltation comparability at adjacent moments relative to the same moment, the differences in upstream water depth between moments are analyzed and integrated with the siltation comparability to obtain the final siltation degree at the sluice gate at each moment.

[0038] Preferably, in one embodiment of the present invention, the method for obtaining the final sedimentation degree includes: For ease of explanation and illustration, we will select one time point as the time to be measured, and use the remaining times points as comparison times for the time to be measured. The silt contrast between all comparison times and the time to be measured will be used as the target silt contrast.

[0039] Among all comparison times, the two closest in time sequence to each comparison time are taken as the neighborhood times of each comparison time. The absolute value of the difference between the target sludge comparability of each comparison time and each neighborhood time is calculated as a bias factor. The smaller the bias factor, the more similar the target sludge comparability between the comparison time and the neighborhood times. Then, the absolute value of the difference between the two bias factors corresponding to each comparison time is calculated. The smaller the absolute value of the difference, the more consistent the sludge contrast of the three comparison times with the test time. This conforms to the characteristic described above that the sludge comparability usually does not undergo large abrupt changes and has a high similarity, so the confidence level is higher. Therefore, the absolute value of the difference is negatively correlated and normalized to obtain the confidence factor of each comparison time for the test time. The negative correlation mapping and normalization here can be performed using the formula... ,in, Let x represent an exponential function with the natural constant e as the base, and let x represent the independent variable.

[0040] The confidence factor for the target time at each comparison time is multiplied by the silt comparability between each comparison time and the target time. The normalized product is then used as the siltation weight for each comparison time relative to the target time. This siltation weight comprehensively considers both the reliability (confidence factor) of the comparison time for the target time and the silt comparability between the comparison time and the target time. Therefore, the larger this value, the higher the contribution of the comparison time to the siltation situation of the target time is considered in subsequent calculations. The normalization method here must ensure that the sum of the siltation weights for all comparison times at the target time is 1; the specific method is not limited here.

[0041] The increase in water depth is caused by the uplift of the riverbed. Therefore, the greater the upstream water depth at the time of measurement, the more severe the siltation is likely to be. Thus, the normalized value of the difference in upstream water depth between the time of measurement and each comparison time is used as the relative siltation degree value of the time of measurement relative to each comparison time. The larger the relative siltation degree value, the more severe the siltation at the time of measurement compared to the comparison time. Since the difference can be positive or negative, the normalization method can be... function.

[0042] By utilizing the sedimentation degree weights of the comparison times to the test time, the relative sedimentation degree values ​​of the test time to the comparison times are weighted and fused. The resulting weighted result is then normalized, that is, the sedimentation degree weight of the comparison times to the test time is multiplied by the relative sedimentation degree value of the test time to the comparison times to obtain a weighted sedimentation degree value. Finally, the sum of the weighted sedimentation degrees between the test time and all comparison times is normalized and taken as the final sedimentation degree at the gate at the test time. The larger this index is, the more severe the sedimentation at the gate at the test time, and the greater the impact on various water volume data. Normalization is a technique well-known to those skilled in the art. The choice of normalization function can be linear normalization or standard normalization, etc., and the specific normalization method is not limited here.

[0043] At this point, the final sedimentation degree at the lower gate at each moment can be obtained.

[0044] Step S3: Under each water flow pattern scenario, select water volume data and fit it with different regression models to obtain the simulated flow coefficient at each time point; based on the final siltation degree at the gate at each time point, compare the deviation characteristics between the simulated flow coefficient and the actual flow coefficient under the regression model to select the optimal model among all regression models.

[0045] The main reason for the different calculated water flow values ​​under the same water flow pattern is the difference in flow coefficient. In this embodiment of the invention, water measurement data can be selected and fitted with different regression models under different water flow pattern scenarios to obtain the simulated flow coefficient at each time. In step S1, various water measurement data at the gate are collected, and in step S2, the final siltation degree at the gate at each time is analyzed. Therefore, based on the final siltation degree at the gate at each time, the deviation characteristics between the simulated flow coefficient and the actual flow coefficient under the regression model can be compared to determine whether the regression model can fit the expected effect. The optimal model is selected from all regression models, so that the calculation formula of water flow obtained by regression analysis can be directly used in subsequent processes to eliminate the influence of the independent variable factor of the flow coefficient.

[0046] First, for each water flow pattern scenario, it is necessary to select water volume data and fit it with different regression models to obtain the simulated flow coefficient at each time point.

[0047] Preferably, in one embodiment of the present invention, the method for obtaining the simulated flow coefficient includes: In each flow pattern scenario, at each time point, the corresponding water volume data is selected to calculate the ratio as the independent variable. Each regression model is used to fit the independent variable, and the dependent variable obtained from the fitting result is used as the simulated flow coefficient. In this embodiment of the invention, the regression model includes at least power functions, exponential functions, logarithmic functions, binomial functions, etc.

[0048] For example, the water volume formula for a free-flow scenario controlled by a gate is: Where Q is the water flow rate; μ is the flow coefficient; B is the gate width; e is the gate opening; g is the gravitational acceleration; and H is the upstream water depth. In this flow pattern scenario, if the water measurement data includes the gate opening and the upstream water depth, then the independent variables are: The water volume formula for gate-controlled submerged flow scenarios is: Where Q is the water flow rate; μ is the flow coefficient; B is the gate width; e is the gate opening; g is the gravitational acceleration; and Z is the upstream and downstream water level difference, then the independent variable is... .

[0049] Finally, each regression model was used to fit the independent variables at all time points, and the dependent variable obtained from the fitting results was used as the simulated flow coefficient. Correspondingly, the simulated flow coefficient for the free flow scenario under gate control is: Where e is the gate opening and H is the upstream water depth; This represents the regression model; the simulated flow coefficient under the gate-controlled flooding scenario is: Where e is the gate opening degree; Z is the water level difference between upstream and downstream; This represents a regression model.

[0050] Thus, the simulated flow coefficients at each time point under each flow pattern scenario can be obtained. Given that the greater the final siltation degree at the gate at each time point, the more severe the siltation at the gate at that time point, and the gradual rise of the bottom elevation of the area in front of the gate, the siltation in front of the gate will raise the actual elevation of the gate bottom sill, thereby reducing the effective head at the same upstream water level. Therefore, the impact on various water volume data will be greater. In this case, the flow coefficient at that time point contributes less to the regression analysis to determine the accurate flow coefficient. Therefore, in this embodiment of the present invention, based on the final siltation degree at the gate at each time point under each flow pattern scenario, the deviation characteristics between the simulated flow coefficient and the actual flow coefficient under the regression model are compared to determine whether the result under the regression model can achieve the expected effect, and an optimal model is selected from all regression models.

[0051] Preferably, in one embodiment of the present invention, the method for obtaining the optimal model includes: Based on the foregoing analysis, it is known that the greater the final sedimentation degree at the gate at a given moment, the smaller the contribution percentage of the data at that moment. Therefore, the final sedimentation degree at the gate at each moment can be negatively correlated and normalized to correct the logical relationship and serve as the contribution weight. This negative correlation mapping and normalization can be performed using the formula... ,in, Let x represent an exponential function with the natural constant e as the base, and let x represent the independent variable.

[0052] In this embodiment of the invention, the coefficient of determination is used to measure the deviation characteristics. Therefore, the deviation characteristics between the simulated flow coefficient and the actual flow coefficient under each regression model are compared and combined with the contribution weight corresponding to each time point to obtain the coefficient of determination for each regression model. The formula model for the coefficient of determination includes: in, Indicates the coefficient of determination; Indicates the total number of moments; This represents the final degree of siltation at the lower gate at time i; This represents the actual flow coefficient at time i. This represents the average of the actual flow coefficients at all times. This represents the simulated flow coefficient at time i. This represents the contribution weight at time i.

[0053] Coefficient of determination The coefficient of determination represents the degree of agreement between the source data and the regression analysis results. Its value ranges from 0 to 1. The larger the value, the better the agreement. Therefore, in each water flow pattern scenario, among all regression models, the regression model corresponding to the largest coefficient of determination is taken as the optimal model for that water flow pattern scenario.

[0054] Step S4: Calculate the flow coefficient at each future moment using the optimal model for each flow pattern scenario, so as to calculate the water flow at the gate.

[0055] Based on the aforementioned steps, the optimal model for each water flow pattern scenario can be obtained. Therefore, in subsequent time series processes, the flow coefficient at future times can be calculated based on the optimal model, which can then be used to calculate the water flow at the gate.

[0056] Preferably, in one embodiment of the present invention, the flow coefficient at each future time step is calculated using the optimal model for each flow pattern scenario, thereby used to calculate the water flow at the gate, including: For each flow pattern scenario, the optimal model is used to replace the flow coefficient in the water measurement formula for each flow pattern scenario, thereby calculating the water flow value at the gate at each future time.

[0057] For example, the water volume formula for a scenario where a gate control system replaces a free-flowing system is: Where Q is the water flow rate; B is the gate opening width; e is the gate opening degree; g is the acceleration due to gravity; and H is the upstream water depth. This represents the optimal model; the water volume formula for the gate-controlled submerged flow scenario is: Where Q is the water flow rate; μ is the flow coefficient; B is the gate width; e is the gate opening; g is the acceleration due to gravity; and Z is the water level difference between upstream and downstream. This represents the optimal model.

[0058] In summary, various water measurement data at the gate were first collected, including the gate opening, upstream water depth, downstream water depth, and flow rate at each time point, as well as the actual flow coefficient (calculated using the water measurement formula) at each time point. Given that siltation in front of the gate raises the actual elevation of the gate sill, affecting data accuracy and reducing the reliability of the regression model analysis, this embodiment of the invention requires analyzing the siltation situation at the gate at each time point to characterize the accuracy and reliability of the data at each time point. Siltation raises the riverbed in front of the gate, thus forcing the upstream water depth to increase under the same gate opening, flow rate, and downstream water depth conditions. Therefore, it is necessary to calculate the siltation comparability of other times relative to each historical time point. Thus, the differences in gate opening, flow rate, and downstream water depth between time points were compared to obtain the siltation comparability between any two time points. Considering that silt cannot suddenly disappear or change, the similarity characteristics of siltation comparability between adjacent time points were analyzed, and combined with the differences in downstream water depth between time points, the final siltation degree of the gate at each time point was quantified. Furthermore, considering the final siltation degree at the gate, the optimal model is selected by comparing the deviation characteristics between the simulated flow coefficient and the actual flow coefficient under the regression model in each flow pattern scenario. This model selection mechanism can choose the most suitable model for the current water conservancy scenario from numerous regression models, effectively improving the stability and accuracy of flow coefficient calculation, thereby ensuring the reliability of future water flow calculations.

[0059] This invention also provides a precise water metering system for open channel gates in water conservancy information systems. Please refer to [link / reference]. Figure 3 The diagram shows a system block diagram, including a data acquisition module 301 for implementing step S1 in the above method embodiment; a siltation analysis module 302 for implementing step S2 in the above method embodiment; a model fitting and screening module 303 for implementing step S3 in the above method embodiment; and a water flow calculation module 304 for implementing step S4 in the above method embodiment.

[0060] It should be noted that the system provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer equipment can be divided into different functional modules to complete all or part of the functions described above. In addition, the water conservancy informatization open channel gate water measurement accuracy system and the water conservancy informatization open channel gate water measurement accuracy method embodiment provided in the above embodiments belong to the same concept, and their specific implementation process can be found in the method embodiment, which will not be repeated here.

[0061] Please see Figure 4 This document illustrates a schematic diagram of the system structure of a water conservancy information-based open channel gate water measurement system according to an embodiment of the present invention. The system includes a processor 400, a memory 401, a bus 402, and a communication interface 403. The processor 400, communication interface 403, and memory 401 are connected via the bus 402. The memory 401 may include a high-speed random access memory, and the bus 402 may be an ISA bus, PCI bus, or EISA bus, etc. The processor 400 may be an integrated circuit chip with signal processing capabilities. The memory 401 stores at least one instruction, at least one program, code set, or instruction set. When the processor loads and executes the at least one instruction, at least one program, code set, or instruction set, it implements the steps in a water conservancy information-based open channel gate water measurement method.

[0062] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0063] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for accurate water measurement using gates in open channels in water conservancy information systems, characterized in that, The method includes: Acquire water measurement data, which includes gate opening time series data, upstream water depth time series data and downstream water depth time series data at the gate, water flow time series data at the gate, and the actual flow coefficient at each time point; Based on the differences in gate opening, water flow, and downstream water depth at different times, the silt comparability between any two times is calculated. Then, based on the similarity between the silt comparability of adjacent times to the same time, the differences in upstream water depth between times are analyzed and combined with the silt comparability to obtain the final silt deposition degree at the gate at each time point, including: Choose one time point as the test time point, and use the remaining other times points as comparison times for the test time point. Use the silt contrast between all comparison times point and the test time point as the target silt contrast. Among all comparison times, the two times closest to each comparison time are taken as the neighborhood times of each comparison time. The absolute value of the difference between the target sludge comparability of each comparison time and each neighborhood time is calculated as the deviation factor. The absolute value of the difference between the two deviation factors corresponding to each comparison time is negatively correlated and normalized, and then used as the confidence factor of the time to be tested for each comparison time. Multiply the confidence factor of the test time at each comparison time by the silt comparability between each comparison time and the test time, and use the normalized value of the product as the siltation weight of the test time at each comparison time. The normalized value of the difference in upstream water depth between the time to be measured and each comparison time is used as the relative siltation degree value of the time to be measured to each comparison time. Using the sedimentation degree weight of the comparison time to the test time, the relative sedimentation degree values ​​of the test time to the comparison time are weighted and fused, and the normalized value of the weighted result is used as the final sedimentation degree of the silt at the gate at the test time. In each flow pattern scenario, different regression models are used to fit the water volume data to obtain the simulated flow coefficient at each time point. Based on the final siltation degree at the gate at each time point, the deviation characteristics between the simulated flow coefficient and the actual flow coefficient under the regression model are compared to select the optimal model from all regression models. The optimal model for each flow pattern scenario is used to calculate the flow coefficient at each future moment, which is then used to calculate the water flow at the gate.

2. The method for accurate water measurement using open channel gates in water conservancy information systems according to claim 1, characterized in that, The methods for obtaining the comparability of the silt include: For any two moments, calculate the first silt comparability factor based on the difference in gate opening between the two moments; For any two time points, analyze the differences in water flow between these two time points to determine the second silt comparability factor; For any two time points, a third silt comparability factor is obtained based on the difference in downstream water depth between these two time points. For any two moments, the normalized value of the product of the first silt comparability factor, the second silt comparability factor, and the third silt comparability factor between these two moments is taken as the silt comparability between these two moments.

3. The method for accurate water measurement using open channel gates in water conservancy information systems according to claim 2, characterized in that, The method for obtaining the first sludge comparability factor includes: The absolute value of the difference between the gate openings at these two times is negatively correlated and normalized, and this value is used as the first silt comparability factor between the two times.

4. The method for accurate water measurement using open channel gates in water conservancy information systems according to claim 2, characterized in that, The method for obtaining the second sludge comparability factor includes: The absolute value of the difference in water flow between these two times is negatively correlated and normalized, and this value is used as the second silt comparability factor between these two times.

5. The method for accurate water measurement using open channel gates in water conservancy information systems according to claim 2, characterized in that, The method for obtaining the third sludge comparability factor includes: The absolute value of the difference between the downstream water depths at these two times is negatively correlated and normalized, and this value is used as the third silt comparability factor between the two times.

6. The method for accurate water measurement using open channel gates in water conservancy information systems according to claim 1, characterized in that, The method for obtaining the simulated flow coefficient includes: In each flow pattern scenario, at each time point, the corresponding water volume data is selected to calculate the ratio as the independent variable. Each regression model is used to fit the independent variable, and the dependent variable obtained from the fitting result is used as the simulated flow coefficient. The regression model includes at least a power function, an exponential function, a logarithmic function, and a binomial function.

7. The method for accurate water measurement using open channel gates in water conservancy information systems according to claim 1, characterized in that, The method for obtaining the optimal model includes: Based on the final sedimentation degree at the gate at each time point, the deviation characteristics between the simulated flow coefficient and the actual flow coefficient under each regression model are compared to obtain the determination coefficient corresponding to each regression model. The formula model of the determination coefficient includes: in, Indicates the coefficient of determination; Indicates the total number of moments; This represents the final degree of siltation at the lower gate at time i. This represents the actual flow coefficient at time i. This represents the average of the actual flow coefficients at all times. This represents the simulated flow coefficient at time i. Among all regression models for each water flow pattern scenario, the regression model with the largest coefficient of determination is selected as the optimal model.

8. The method for accurate water measurement using open channel gates in water conservancy information systems according to claim 1, characterized in that, The process of calculating the flow coefficient at each future moment using the optimal model under each flow pattern scenario, and then using it to calculate the water flow at the gate, includes: For each flow pattern scenario, the optimal model is used to replace the flow coefficient in the water measurement formula for each flow pattern scenario, thereby calculating the water flow value at the gate at each future time.

9. A precise water measurement system for open channel gates in water conservancy information systems, characterized in that, It includes a processor and a memory, the memory storing at least one instruction, at least one program, code set or instruction set, and when the at least one instruction, at least one program, code set or instruction set is loaded and executed by the processor, it implements the steps of the water conservancy information open channel gate water measurement accurate measurement method as described in any one of claims 1-8.

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