U-shaped channel flow measurement method, system and equipment

Abstract

The invention provides a U-shaped channel flow measurement method, system and device, and relates to the technical field of flow measurement, and the method comprises the steps: collecting channel parameters and water body parameters, calculating a flow influence index, a flow stability index and a water quality influence index, and calculating the area of a flowing cross section according to the water level and channel width data; and constructing a flow prediction model based on deep learning, inputting the area of the flowing cross section at the current moment, the flow influence index, the flow stability index and the water quality influence index into the flow prediction model, outputting predicted channel flow data by the model, comparing the flow data with a preset flow threshold value, and judging whether the flow is abnormal or not. According to the scheme, the flow condition of the U-shaped channel can be evaluated and predicted in real time, the flow monitoring efficiency of the U-shaped channel is improved, and agricultural production safety is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of flow measurement, and in particular to a method, system and equipment for measuring flow in a U-shaped channel. Background Art

[0002] In agricultural irrigation and water conservancy projects, accurate measurement of channel flow is of great significance to the efficient use of water resources and irrigation management. At present, most traditional channel flow measurement methods rely on simple physical measurement methods, such as buoy method, current meter method and weir method. Although these methods can reflect the channel flow to a certain extent, their measurement accuracy is often affected by the external environment, especially in the case of complex structures such as U-shaped channels. The combined effects of factors such as water level, water flow direction, turbidity, etc. make flow measurement have great uncertainty. In addition, traditional methods rely on a large number of manual operations, with low real-time and automation levels, which makes it difficult to meet the needs of modern agricultural irrigation for real-time monitoring and intelligent regulation.

[0003] In the existing technology, there is still a problem of insufficient research on the relationship between channel water quality parameters and flow, especially the potential impact of parameters such as turbidity and water temperature on flow, which lacks systematic analysis and quantitative models. In addition, with the rapid development of artificial intelligence technologies such as deep learning, some studies have attempted to apply machine learning to the fields of hydrological prediction and flow measurement, but the existing methods are mostly limited to linear regression or simple empirical formulas, and fail to fully utilize the advantages of deep learning in solving complex nonlinear problems. At the same time, the existing technology usually only predicts flow based on a single or a few parameters, ignoring the coupling effects and mutual influences between different parameters, resulting in low prediction accuracy, especially under non-ideal conditions, the prediction results may have large deviations. In summary, the existing technology still has a lot of room for improvement in terms of flow measurement accuracy, intelligence level, and adaptability to complex water characteristics.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0005] The object of the present invention is to provide a U-shaped channel flow measurement method, system and device to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: A U-shaped channel flow measurement method, the specific steps comprising: Step 1: Monitor and analyze the U-shaped channel, collect channel parameters and water body parameters, the channel parameters include channel slope and channel width, the water body parameters include the water level, flow rate, water temperature, turbidity value and flow direction of the channel water body at the current moment, and calculate the turbidity index according to the turbidity value; Step 2: Calculate the flow impact index based on the dimensionless processed channel slope, water level, flow velocity, water temperature, turbidity index and flow direction data, calculate the flow stability index based on the dimensionless processed flow velocity data, calculate the water quality impact index based on the dimensionless processed turbidity index and water temperature data, and calculate the flow cross-sectional area based on the water level and channel width data; Step 3: Obtain several groups of historical channel parameters, water body parameters and corresponding channel flows of the U-shaped channel, calculate the intermediate parameters of each group of historical data according to step 2, build a flow prediction model based on deep learning, take the intermediate parameters as input, take the corresponding channel flow as a label, train the model, and obtain a trained flow prediction model. The intermediate parameters include the area of ​​the flow cross section, flow impact index, flow stability index, and water quality impact index. The flow prediction model has the same architecture as the LSTM. Step 4: Input the current flow cross-sectional area, flow impact index, flow stability index, and water quality impact index into the flow prediction model, and the model outputs the predicted channel flow data; Step 5: Compare the flow data with the preset flow threshold. If the flow exceeds the threshold, it indicates that the flow is abnormal and the irrigation plan needs to be adjusted in time and an alarm needs to be sent to agricultural managers. At the same time, consider directing excess water to other crop areas. If the flow is within a reasonable range, continue to monitor the flow and record the data.

[0007] Furthermore, the specific logic for collecting channel parameters is as follows: Channel slope refers to the inclination of the bottom of the channel. The angle from the horizontal plane to the bottom of the channel is measured using a level to determine the channel slope, which is recorded as ; The channel bottom width is measured by a laser rangefinder as the channel width data, denoted as ; The specific logic for collecting water parameters is as follows: The water level refers to the vertical height of the water surface relative to the bottom of the channel. The pressure sensor is placed at the bottom of the channel to measure the pressure exerted by the water column on it. The water level is calculated according to the following formula: ; in, is the water level, is the pressure data measured by the sensor, is the density of water, is the acceleration due to gravity; Collect a certain amount of channel water samples, collect the turbidity value of the water samples through the sensor, and calculate the turbidity index according to the following formula: ; in, is the turbidity index, is the turbidity value of the current water sample, is the maximum turbidity threshold of the channel water area, which is determined according to historical data and regulatory standards; the flow velocity of the channel water body is obtained using a water flow velocity meter and recorded as ; Use a temperature sensor to measure the water temperature, denoted as ; The flow direction of the channel water body is measured using a flow direction sensor. The flow direction is the angle relative to the true north direction, recorded as .

[0008] Furthermore, the flow impact index is calculated based on the following formula: ; in, is the traffic impact index, is the water level, is the flow rate, is the water temperature, The water temperature is ideal. is the turbidity index, is the flow direction, that is, the angle between the current flow direction of the channel water and the natural direction of the water flow, , is the channel slope, , , , and is the proportional coefficient, which is used to adjust the impact of each parameter on the flow impact index, and ; The flow stability index is calculated based on the following formula: ; in, is the flow stability index, is the standard deviation of flow rate, is the average flow velocity, is the proportionality coefficient, and greater than zero; The flow velocity measurement values ​​are collected multiple times in the time period before the current moment, and the measurement values ​​are measured at equal time intervals, and the average value and standard deviation of the flow velocity are calculated according to the following formula: ; ; in, is the average flow velocity, is the standard deviation of flow rate, is the total number of flow velocity data, It is Flow rate measurements; The water quality impact index is calculated based on the following formula: ; in, is the water quality impact index, is the water temperature, The water temperature is ideal. is the turbidity index, and is the preset scaling factor, and ; Calculate the cross-sectional area of ​​the flow based on the water level and channel width data ; in, is the area of ​​the flow cross section, is the channel width, For water level.

[0009] Further, several groups of historical channel parameters, water body parameters and corresponding channel flow of the U-shaped channel are obtained, wherein the channel parameters include channel slope and channel width, and the water body parameters include water level, flow velocity, water temperature, turbidity value and flow direction, and intermediate parameters of each group of historical data are calculated, wherein the intermediate parameters include the area of ​​the flow cross section, flow influence index, flow stability index and water quality influence index; A traffic prediction model is built based on deep learning. The intermediate parameters are used as input and the corresponding channel traffic is used as labels. The model is trained to obtain a trained traffic prediction model.

[0010] Furthermore, the area of ​​the flow cross section, flow impact index, flow stability index, and water quality impact index at the current moment are input into the flow prediction model, and the model outputs the predicted channel flow data, recorded as , compare the traffic data with the preset traffic threshold: Case A: When the predicted channel traffic Within the normal range, the system outputs information: channel flow is normal, the current flow is XX cubic meters per second; Case B: When the predicted channel traffic If the flow rate is higher than the upper threshold, the system will output the following information: Alarm: The channel flow rate is abnormally high. The current flow rate is XX cubic meters per second. Please check the water source and drainage conditions. Case C: When the predicted channel traffic If it is lower than the lower threshold, the system outputs the following information: Alarm: The channel flow is abnormally low, the current flow is XX cubic meters per second, please check the irrigation equipment and water supply; Case D: When the predicted channel traffic When there is a significant fluctuation compared with historical data and the deviation is 5%, the system outputs the message: Attention: The current flow rate changes significantly, please conduct further monitoring and analysis; The present invention further provides a U-shaped channel flow measurement system, wherein the U-shaped channel flow measurement system is used to perform the above-mentioned U-shaped channel flow measurement method, comprising: A data acquisition module is used to monitor and analyze the U-shaped channel, collect channel parameters and water body parameters, the channel parameters include channel slope and channel width, the water body parameters include the water level, flow rate, water temperature, turbidity value and flow direction of the channel water body at the current moment, and calculate the turbidity index according to the turbidity value; An index generation module is used to calculate the flow impact index based on the dimensionless processed channel slope, water level, flow velocity, water temperature, turbidity index and flow direction data, calculate the flow stability index based on the dimensionless processed flow velocity data, calculate the water quality impact index based on the dimensionless processed turbidity index and water temperature data, and calculate the area of ​​the flow cross section based on the water level and channel width data; A flow prediction model construction module is used to obtain several groups of historical channel parameters, water body parameters and corresponding channel flows of the U-shaped channel, calculate the intermediate parameters of each group of historical data according to step 2, build a flow prediction model based on deep learning, take the intermediate parameters as input, take the corresponding channel flow as a label, train the model, and obtain a trained flow prediction model. The intermediate parameters include the area of ​​the flow cross section, the flow impact index, the flow stability index, and the water quality impact index. The flow prediction model has the same architecture as the LSTM. The flow prediction module is used to input the area of ​​the flow cross section, flow impact index, flow stability index, and water quality impact index at the current moment into the flow prediction model, and the model outputs the predicted channel flow data; The feedback module is used to compare the flow data with the preset flow threshold. If the flow exceeds the threshold, it indicates that the flow is abnormal and the irrigation plan needs to be adjusted in time and an alarm needs to be sent to agricultural managers. At the same time, consider directing excess water to other crop areas. If the flow is within a reasonable range, continue to monitor the flow and record the data.

[0011] Compared with the prior art, the present invention has the following beneficial effects: This solution integrates multiple channel parameters and water body parameters to build a flow prediction model based on deep learning, which can evaluate and predict the flow conditions of U-shaped channels in real time. This method not only comprehensively considers factors such as channel slope, water level, flow velocity, water temperature, turbidity and flow direction, but also achieves accurate judgment of flow anomalies through scientific index calculation and weight analysis. The implementation of this method will significantly improve the flow monitoring efficiency of U-shaped channels, ensure agricultural production safety, provide agricultural managers with a scientific decision-making basis, and support them in rationally scheduling irrigation plans according to crop growth and meteorological changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 It is a schematic diagram of the overall method flow of the present invention; Figure 2 It is a schematic diagram of the system module of the present invention. DETAILED DESCRIPTION

[0013] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0014] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0015] Example: See also Figure 1 , the present invention provides a technical solution: A U-shaped channel flow measurement method, the specific steps comprising: Step 1: Monitor and analyze the U-shaped channel, collect channel parameters and water body parameters, the channel parameters include channel slope and channel width, the water body parameters include the water level, flow rate, water temperature, turbidity value and flow direction of the channel water body at the current moment, and calculate the turbidity index according to the turbidity value; In this embodiment, the specific logic for collecting channel parameters is as follows: Channel slope refers to the inclination of the bottom of the channel. The angle from the horizontal plane to the bottom of the channel is measured using a level to determine the channel slope, which is recorded as ; The channel bottom width is measured by a laser rangefinder as the channel width data, denoted as ; The specific logic for collecting water parameters is as follows: The water level refers to the vertical height of the water surface relative to the bottom of the channel. The pressure sensor is placed at the bottom of the channel to measure the pressure exerted by the water column on it. The water level is calculated according to the following formula: ; in, is the water level, is the pressure data measured by the sensor, is the density of water, is the acceleration due to gravity; Collect a certain amount of channel water samples, collect the turbidity value of the water samples through the sensor, and calculate the turbidity index according to the following formula: ; in, is the turbidity index, is the turbidity value of the current water sample, is the maximum turbidity threshold for the channel waters, determined based on historical data and regulatory standards; Use a water flow meter to obtain the flow velocity of the channel water body, recorded as ; Use the temperature sensor to measure the water temperature, recorded as ; The flow direction of the channel water body is measured using a flow direction sensor. The flow direction is the angle relative to the true north direction, recorded as .

[0016] The advantage of step 1 is that by systematically monitoring and analyzing multiple parameters of the U-shaped channel, the flow measurement can fully reflect the true condition of the water body. Compared with existing technologies, this step not only focuses on basic parameters such as water level and flow rate, but also integrates influencing factors such as water temperature, turbidity and flow direction to ensure the integrity and accuracy of the data. This comprehensive monitoring can effectively capture the changes in water bodies under different environmental and climatic conditions, thereby improving the reliability of flow measurement.

[0017] In the scheme, this step can lay a solid foundation for subsequent flow assessment and model building. By accurately obtaining multi-dimensional data of channels and water bodies, the calculation accuracy of flow impact index, flow stability index and water quality impact index is enhanced, thereby improving the scientificity and credibility of the flow assessment model. This promotion effect not only improves the ability of real-time monitoring, but also provides more accurate data support for water resources management, and promotes the effective protection of the ecological environment of water bodies.

[0018] Step 2: Calculate the flow impact index based on the dimensionless processed channel slope, water level, flow velocity, water temperature, turbidity index and flow direction data, calculate the flow stability index based on the dimensionless processed flow velocity data, calculate the water quality impact index based on the dimensionless processed turbidity index and water temperature data, and calculate the flow cross-sectional area based on the water level and channel width data; In this embodiment, the flow impact index is calculated based on the following formula: ; in, is the traffic impact index, is the water level, is the flow rate, is the water temperature, The water temperature is ideal. is the turbidity index, is the flow direction, that is, the angle between the current flow direction of the channel water and the natural direction of the water flow, , is the channel slope, , , , and is the proportional coefficient, which is used to adjust the impact of each parameter on the flow impact index, and This is because water level is the direct factor affecting flow. As water level increases, flow usually increases significantly. Therefore, water level is of the highest importance in the calculation. is set to the maximum coefficient; flow velocity is another key factor affecting flow and an important component of flow. The square of flow velocity has a higher degree of influence on flow, so The value is lower than , but it is still an important parameter; water temperature will affect the physical properties of the water body, such as density and viscosity, and thus affect the flow state. Although the impact of water temperature on flow is smaller than that of flow velocity and water level, it still plays an important role; the impact of flow direction and turbidity on flow is more indirect. Flow direction may affect the distribution of water flow rather than directly change the flow, while turbidity mainly reflects water quality characteristics. Although they have a certain impact on flow, compared with the previous three, their impact is smaller; channel slope mainly affects the gravity drive and flow velocity of water flow, but its direct impact on flow is relatively small, so it is given Minimum weight.

[0019] An increase in water level usually means an increase in the volume of water. When increasing, The larger the flow rate Directly affects the size of the flow rate. The greater the flow rate, the greater the flow rate. Therefore, the increase in flow rate will lead to Increase; When it increases, it indicates that the deviation between the water temperature and the ideal temperature increases. When the temperature is too high or too low, it will affect the density and flow characteristics of the water body, resulting in a higher density and slower flow. The smaller the turbidity index is, the lower the fluidity of the fluid will be, thus affecting the flow rate. Therefore, when the turbidity index increases, the flow of the fluid is hindered. decreases; the steeper the slope, the stronger the gravity drive of the water flow, the greater the flow rate, and therefore The bigger, The bigger it is; When it increases, it means that the angle between the current flow direction of the channel water and the natural direction of the water flow increases, which is not conducive to the flow of water, causing obstruction of the water flow and reducing the flow efficiency. The smaller it is, the smaller it is. , , and There is a positive correlation, , , and There is a negative correlation.

[0020] The flow stability index is calculated based on the following formula: ; in, is the flow stability index, is the standard deviation of flow rate, is the average flow velocity, is the proportionality coefficient, and Greater than zero, used to adjust the flow stability index Sensitivity to flow rate fluctuations, larger values ​​will make It is more sensitive to changes in flow rate, that is, fluctuations in flow rate have a greater impact on the stability index, while smaller values ​​reduce this sensitivity.

[0021] Used to assess the volatility and stability of water flow, high A high value indicates that the flow velocity fluctuates greatly and is unstable, which may indicate that the water flow changes dramatically in different time periods or is affected by external factors, resulting in irregular flow velocity; a low value indicates that the flow velocity fluctuates greatly and is unstable, which may indicate that the water flow changes dramatically in different time periods or is affected by external factors, resulting in irregular flow velocity; A value of 0 indicates that the flow rate is relatively stable with small fluctuations.

[0022] The flow velocity measurement values ​​are collected multiple times in the time period before the current moment, and the measurement values ​​are measured at equal time intervals, and the average value and standard deviation of the flow velocity are calculated according to the following formula: ; ; in, is the average flow velocity, is the standard deviation of flow rate, is the total number of flow velocity data, It is Flow rate measurements; The water quality impact index is calculated based on the following formula: ; in, is the water quality impact index, is the water temperature, The water temperature is ideal. is the turbidity index, and is the preset scaling factor, and This is because turbidity is one of the important parameters that affect water quality. Generally speaking, the higher the turbidity, the worse the water quality. Therefore, when calculating the water quality impact index, turbidity needs to be given a higher weight to emphasize the direct impact of turbidity on water quality. The impact of water temperature on water quality is also important, but usually, the impact of temperature on water quality may not be as direct and significant as turbidity. The turbidity index is expressed by an exponential function, which means that its impact shows a nonlinear growth with the increase of turbidity. This rapid growth characteristic requires a relatively large value to balance the dominant role of turbidity on the overall water quality.

[0023] when When it increases, it indicates that the water quality has become worse and the pollution level of the water body has become more serious. The bigger it is; When it increases, it means that the deviation between the temperature and the ideal temperature increases. Too high or too low temperature will cause physiological dysfunction of aquatic organisms and even cause biological death, thus affecting water quality. will also increase accordingly; that is, , and There is a positive correlation.

[0024] Calculate the cross-sectional area of ​​the flow based on the water level and channel width data ; in, is the area of ​​the flow cross section, is the channel width, is the water level; The advantage of step 2 is that the flow impact index, flow stability index and water quality impact index are calculated through dimensionless multidimensional data, thereby achieving a comprehensive evaluation of flow changes. This method not only overcomes the computational complexity caused by different dimensional parameters, but also effectively integrates various influencing factors to provide more scientific flow assessment results. Compared with existing technologies, this multi-index comprehensive analysis method can more accurately reflect the dynamic characteristics of water flow and provide more powerful data support for decision-making.

[0025] In the scheme, this step can significantly improve the accuracy and reliability of the flow assessment model. By systematically analyzing channel parameters and water body parameters and combining dimensionless processing, the model's sensitivity and adaptability to flow changes are enhanced. This not only helps to monitor flow dynamics in real time, but also provides a more solid data foundation for subsequent flow anomaly judgment, promoting the effectiveness and scientificity of the overall scheme in water resources management and emergency response.

[0026] Step 3: Obtain several groups of historical channel parameters, water body parameters and corresponding channel flows of the U-shaped channel, calculate the intermediate parameters of each group of historical data according to step 2, build a flow prediction model based on deep learning, take the intermediate parameters as input, take the corresponding channel flow as a label, train the model, and obtain a trained flow prediction model. The intermediate parameters include the area of ​​the flow cross section, flow impact index, flow stability index, and water quality impact index. The flow prediction model has the same architecture as the LSTM. In this embodiment, the area of ​​the flow cross section is the basic physical quantity of the flow rate, which directly affects the flow capacity of the water. The larger the cross-sectional area, the higher the volume and flow rate of the water flow at the same water level, which directly affects the flow rate. Therefore, the area of ​​the flow cross section is one of the key factors in evaluating the flow rate; the flow impact index is a comprehensive indicator that reflects the potential for change of the water flow under specific conditions by considering multiple factors such as water level, flow rate, water temperature, turbidity and flow direction. It can quantify the impact of various environmental factors on flow and help the model understand the dynamic changes of flow. The flow stability index mainly reflects the fluctuation of flow rate. The stability of flow rate has a direct impact on the fluctuation of flow. A stable flow rate usually means that the change of flow is relatively stable, while an unstable flow rate may indicate a drastic change in flow. By measuring the standard deviation and mean of flow rate, It can effectively reflect the reliability of flow. The water quality impact index reflects the quality characteristics of the water body by considering parameters such as water temperature and turbidity. Changes in water quality may affect the density and viscosity of the water flow, thereby indirectly affecting changes in flow. Good water quality is usually accompanied by more stable water flow conditions, so It is an important factor that cannot be ignored when evaluating traffic.

[0027] In summary, using these parameters as input enables the model to fully consider the multidimensional characteristics of flow, thereby improving the accuracy and reliability of flow prediction. By comprehensively analyzing the cross-sectional area and various influencing indices, the model can better capture the dynamic changes of water flow and its performance under different environmental conditions, thus providing a scientific basis for flow monitoring and management.

[0028] The advantage of step 3 is that by combining historical data and deep learning technology, an efficient flow prediction model is constructed. This model can use intermediate parameters as input to automatically learn and identify complex patterns of flow changes, thereby improving the accuracy and real-time performance of the prediction. The innovation of this method is that it can dynamically adapt to environmental changes and improve the flexibility of flow monitoring.

[0029] Compared with the existing technology, the beneficial effect of this step is mainly reflected in its ability to accurately predict flow changes and detect anomalies. Traditional methods often rely on static models or single parameters and are difficult to cope with complex water flow changes. This step uses a deep learning model, which can more comprehensively capture the relationship between multi-dimensional parameters, improve the scientificity and reliability of flow monitoring, and effectively reduce overflow and pollution risks. In the scheme, the use of this step can promote the intelligent upgrade of the overall scheme and promote the modernization of water resources management. By realizing real-time prediction and monitoring of flow, it can provide a scientific basis for management decisions and ensure the ecological safety of water bodies and sustainable use of resources. In addition, the implementation of this method will provide reference and reference for technological innovation in related fields, and further promote the intelligent development of water conservancy projects.

[0030] Step 4: Input the current flow cross-sectional area, flow impact index, flow stability index, and water quality impact index into the flow prediction model, and the model outputs the predicted channel flow data; Step 5: Compare the flow data with the preset flow threshold. If the flow exceeds the threshold, it indicates that the flow is abnormal and the irrigation plan needs to be adjusted in time and an alarm needs to be sent to agricultural managers. At the same time, consider directing excess water to other crop areas. If the flow is within a reasonable range, continue to monitor the flow and record the data.

[0031] In this embodiment, the area of ​​the flow cross section at the current moment, the flow impact index, the flow stability index, and the water quality impact index are input into the flow prediction model, and the model outputs the predicted channel flow data, which is recorded as , compare the traffic data with the preset traffic threshold: Case A: When the predicted channel traffic Within the normal range, the system outputs information: channel flow is normal, the current flow is XX cubic meters per second; Case B: When the predicted channel traffic If the flow rate is higher than the upper threshold, the system will output the following information: Alarm: The channel flow rate is abnormally high. The current flow rate is XX cubic meters per second. Please check the water source and drainage conditions. Case C: When the predicted channel traffic If it is lower than the lower threshold, the system outputs the following information: Alarm: The channel flow is abnormally low, the current flow is XX cubic meters per second, please check the irrigation equipment and water supply; Case D: When the predicted channel traffic When there is a significant fluctuation compared with historical data and the deviation is 5%, the system outputs the message: Attention: The current flow rate changes significantly, please conduct further monitoring and analysis; Establish a data feedback mechanism, regularly evaluate the rationality of flow thresholds and the accuracy of flow prediction models, make adjustments based on actual irrigation effects and crop growth conditions, continuously optimize flow prediction models, and combine the latest meteorological data and farmland management experience to improve the model's predictive capabilities and the accuracy of management decisions.

[0032] The advantage of steps 4 and 5 is that by comparing the flow data with the preset flow threshold, abnormal flow conditions can be detected in a timely manner, thereby effectively preventing potential overflow risks. This dynamic monitoring mechanism makes water resource management more proactive and flexible. Compared with the existing technology that often relies on fixed monitoring cycles or delayed data analysis, it can respond more quickly to changes in water flow. This real-time monitoring and rapid response capabilities provide higher security and reliability for water resource management.

[0033] In the scheme, the adoption of this step can significantly enhance the practicality and effectiveness of the overall scheme. By comparing real-time flow data with thresholds, not only can abnormal conditions be quickly identified, but the corresponding emergency response procedures can also be initiated, thus providing timely information support for decision makers. This promotion effect ensures that water resources management can adapt to changes in a timely manner, effectively reduce ecological risks and resource waste caused by abnormal flow, and promote the scientific and refined management of the overall water body.

[0034] See also Figure 2 , a U-shaped channel flow measurement system, comprising: A data acquisition module is used to monitor and analyze the U-shaped channel, collect channel parameters and water body parameters, the channel parameters include channel slope and channel width, the water body parameters include the water level, flow rate, water temperature, turbidity value and flow direction of the channel water body at the current moment, and calculate the turbidity index according to the turbidity value; An index generation module is used to calculate the flow impact index based on the dimensionless processed channel slope, water level, flow velocity, water temperature, turbidity index and flow direction data, calculate the flow stability index based on the dimensionless processed flow velocity data, calculate the water quality impact index based on the dimensionless processed turbidity index and water temperature data, and calculate the area of ​​the flow cross section based on the water level and channel width data; A flow prediction model construction module is used to obtain several groups of historical channel parameters, water body parameters and corresponding channel flows of the U-shaped channel, calculate the intermediate parameters of each group of historical data according to step 2, build a flow prediction model based on deep learning, take the intermediate parameters as input, take the corresponding channel flow as a label, train the model, and obtain a trained flow prediction model. The intermediate parameters include the area of ​​the flow cross section, the flow impact index, the flow stability index, and the water quality impact index. The flow prediction model has the same architecture as the LSTM. The flow prediction module is used to input the area of ​​the flow cross section, flow impact index, flow stability index, and water quality impact index at the current moment into the flow prediction model, and the model outputs the predicted channel flow data; The feedback module is used to compare the flow data with the preset flow threshold. If the flow exceeds the threshold, it indicates that the flow is abnormal and the irrigation plan needs to be adjusted in time and an alarm needs to be sent to agricultural managers. At the same time, consider directing excess water to other crop areas. If the flow is within a reasonable range, continue to monitor the flow and record the data.

[0035] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0036] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0037] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0038] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for measuring flow in a U-shaped channel, characterized in that: The specific steps include: Step 1: Monitor and analyze the U-shaped channel, collect channel parameters and water body parameters, the channel parameters include channel slope and channel width, the water body parameters include the water level, flow rate, water temperature, turbidity value and flow direction of the channel water body at the current moment, and calculate the turbidity index according to the turbidity value; Step 2: Calculate the flow impact index based on the dimensionless processed channel slope, water level, flow velocity, water temperature, turbidity index and flow direction data, calculate the flow stability index based on the dimensionless processed flow velocity data, calculate the water quality impact index based on the dimensionless processed turbidity index and water temperature data, and calculate the flow cross-sectional area based on the water level and channel width data; Step 3: Obtain several groups of historical channel parameters, water body parameters and corresponding channel flows of the U-shaped channel, calculate the intermediate parameters of each group of historical data according to step 2, build a flow prediction model based on deep learning, take the intermediate parameters as input, take the corresponding channel flow as a label, train the model, and obtain a trained flow prediction model. The intermediate parameters include the area of ​​the flow cross section, flow impact index, flow stability index, and water quality impact index. The flow prediction model has the same architecture as the LSTM. Step 4: Input the current flow cross-sectional area, flow impact index, flow stability index, and water quality impact index into the flow prediction model, and the model outputs the predicted channel flow data; Step 5: Compare the flow data with the preset flow threshold. If the flow exceeds the threshold, it indicates that the flow is abnormal and the irrigation plan needs to be adjusted in time and an alarm needs to be sent to agricultural managers. At the same time, consider directing excess water to other crop areas. If the flow is within a reasonable range, continue to monitor the flow and record the data.

2. A U-shaped channel flow measurement method according to claim 1, characterized in that: The specific logic for collecting channel parameters is as follows: Channel slope refers to the inclination of the bottom of the channel. The angle from the horizontal plane to the bottom of the channel is measured using a level to determine the channel slope, which is recorded as ; The channel bottom width is measured by a laser rangefinder as the channel width data, denoted as ; The specific logic for collecting water parameters is as follows: The water level refers to the vertical height of the water surface relative to the bottom of the channel. The pressure sensor is placed at the bottom of the channel to measure the pressure exerted by the water column on it. The water level is calculated according to the following formula: ； in, is the water level, is the pressure data measured by the sensor, is the density of water, is the acceleration due to gravity; Collect a certain amount of channel water samples, collect the turbidity value of the water samples through the sensor, and calculate the turbidity index according to the following formula: ； in, is the turbidity index, is the turbidity value of the current water sample, is the maximum turbidity threshold of the channel water area, which is determined according to historical data and regulatory standards; the flow velocity of the channel water body is obtained using a water flow velocity meter and recorded as ; Use a temperature sensor to measure the water temperature, denoted as ; The flow direction of the channel water body is measured using a flow direction sensor. The flow direction is the angle relative to the true north direction, recorded as .

3. A U-shaped channel flow measurement method according to claim 2, characterized in that: The flow impact index is calculated based on the following formula: ； in, is the traffic impact index, is the water level, is the flow rate, is the water temperature, The water temperature is ideal. is the turbidity index, is the flow direction, that is, the angle between the current flow direction of the channel water and the natural direction of the water flow, , is the channel slope, , , , and is the proportional coefficient, which is used to adjust the impact of each parameter on the flow impact index, and .

4. A U-shaped channel flow measurement method according to claim 3, characterized in that: The flow stability index is calculated based on the following formula: ； in, is the flow stability index, is the standard deviation of flow rate, is the average flow velocity, is the proportionality coefficient, and greater than zero; The flow velocity measurement values ​​are collected multiple times in the time period before the current moment, and the measurement values ​​are measured at equal time intervals, and the average value and standard deviation of the flow velocity are calculated according to the following formula: ； ； in, is the average flow velocity, is the standard deviation of flow rate, is the total number of flow rate data, It is A flow rate measurement.

5. A U-shaped channel flow measurement method according to claim 4, characterized in that: The water quality impact index is calculated based on the following formula: ； in, is the water quality impact index, is the water temperature, The water temperature is ideal. is the turbidity index, and is the preset scaling factor, and ; Calculate the area of ​​the flow cross section based on the water level and channel width data: ； in, is the area of ​​the flow cross section, is the channel width, For water level.

6. A U-shaped channel flow measurement method according to claim 1, characterized in that: The area of ​​the flow cross section, flow impact index, flow stability index, and water quality impact index at the current moment are input into the flow prediction model, and the model outputs the predicted channel flow data, recorded as , compare the traffic data with the preset traffic threshold: Case A: When the predicted channel traffic Within the normal range, the system outputs information: channel flow is normal, the current flow is XX cubic meters per second; Case B: When the predicted channel traffic If the flow rate is higher than the upper threshold, the system will output the following information: Alarm: The channel flow rate is abnormally high. The current flow rate is XX cubic meters per second. Please check the water source and drainage conditions. Case C: When the predicted channel traffic If it is lower than the lower threshold, the system outputs the following information: Alarm: The channel flow is abnormally low, the current flow is XX cubic meters per second, please check the irrigation equipment and water supply; Case D: When the predicted channel traffic When there is a significant fluctuation compared to historical data and the deviation is 5%, the system outputs the message: Note: The current flow rate changes significantly, please conduct further monitoring and analysis.

7. A U-shaped channel flow measurement system, characterized in that: The U-shaped channel flow measurement system is used to implement the U-shaped channel flow measurement method according to any one of claims 1 to 6, comprising: A data acquisition module is used to monitor and analyze the U-shaped channel, collect channel parameters and water body parameters, the channel parameters include channel slope and channel width, the water body parameters include the water level, flow rate, water temperature, turbidity value and flow direction of the channel water body at the current moment, and calculate the turbidity index according to the turbidity value; An index generation module is used to calculate the flow impact index based on the dimensionless processed channel slope, water level, flow velocity, water temperature, turbidity index and flow direction data, calculate the flow stability index based on the dimensionless processed flow velocity data, calculate the water quality impact index based on the dimensionless processed turbidity index and water temperature data, and calculate the area of ​​the flow cross section based on the water level and channel width data; The flow prediction model construction module is used to obtain several groups of historical channel parameters, water body parameters and corresponding channel flows of the U-shaped channel, calculate the intermediate parameters of each group of historical data according to the index generation module, build a flow prediction model based on deep learning, take the intermediate parameters as input, take the corresponding channel flow as a label, train the model, and obtain a trained flow prediction model. The intermediate parameters include the area of ​​the flow cross section, the flow impact index, the flow stability index, and the water quality impact index. The flow prediction model has the same architecture as the LSTM. The flow prediction module is used to input the area of ​​the flow cross section, flow impact index, flow stability index, and water quality impact index at the current moment into the flow prediction model, and the model outputs the predicted channel flow data; The feedback module is used to compare the flow data with the preset flow threshold. If the flow exceeds the threshold, it indicates that the flow is abnormal and the irrigation plan needs to be adjusted in time and an alarm needs to be sent to agricultural managers. At the same time, consider directing excess water to other crop areas. If the flow is within a reasonable range, continue to monitor the flow and record the data.

8. An electronic device, characterized in that: The device comprises at least one processor and a memory connected to the processor, wherein the memory is used to store computer programs or instructions, and the processor is used to execute the computer programs or instructions so that the electronic device can implement the U-shaped channel flow measurement method according to any one of claims 1 to 6.

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