Automatic Regulation System of Water Gate in Irrigation Area Based on Fuzzy Logic Control

Abstract

The present invention relates to the field of closed-loop control technology, specifically to an automatic regulation system for irrigation area water gates based on fuzzy logic control, the system comprising a load monitoring module, a gate parameter optimization module, an environmental response adjustment module, a water level threshold setting module, a flow prediction module, and a gate feedback control module. In the present invention, by integrating water resource flow and pressure data to accurately calculate the load difference between regions, the irrigation efficiency and response speed are significantly improved, the refined regional load analysis can accurately match water demand, avoid water resource waste, and enhance the system's adaptability to environmental changes. By real-time monitoring of environmental variables and adjusting gate operations, the optimal water level is maintained under constantly changing conditions to ensure agricultural demand and sustainable use of water resources. The predictive analysis of flow data enhances the preventive regulation of the system, enabling it to respond to future demand changes in advance, optimize resource allocation, and effectively improve water resource utilization and the automation level of irrigation area management.

Description

Technical Field

[0001] The invention relates to the technical field of closed-loop control, and in particular to an automatic regulating system for irrigation area water gates based on fuzzy logic control. Background Art

[0002] The field of closed-loop control technology is a type of technology that achieves automatic adjustment of systems through feedback mechanisms. It is widely used in various industrial and engineering systems that require precise control. The closed-loop control system consists of sensors, controllers, and actuators. The sensors collect the current state of the system in real time and feed it back to the controller. The controller calculates according to the set goals and real-time feedback, generates control instructions, and acts on the controlled object through the actuator to achieve dynamic adjustment of the target state. Closed-loop control technology has significant applications in industrial automation, process control, mechatronics, and other fields. It plays an important role in scenarios that require high-precision control and stability, and can ensure that the system always maintains the target state under external environmental changes or internal fluctuations.

[0003] Among them, the irrigation gate automation system is an automatic control system used for agricultural irrigation management. It automatically adjusts the irrigation gates through intelligent algorithms and automated equipment to meet the reasonable allocation of water resources in the irrigation area. The system automatically controls the opening and closing of the gates based on the real-time water usage, meteorological data and water level data in the irrigation area, realizes the precise supply and allocation of water resources, avoids water waste and improves irrigation efficiency. The application of this system can effectively save water resources, while reducing the cost and errors of manual management, and improving the intelligence and automation level of irrigation area management.

[0004] The existing technology is mainly limited in its response speed and accuracy to environmental changes. In traditional closed-loop control, the feedback loop between sensors and actuators can achieve basic automatic adjustment, but this system lacks sufficient flexibility and adaptability when dealing with complex or rapidly changing environmental conditions. For example, under extreme weather conditions, traditional systems cannot adjust water levels in a timely manner, resulting in insufficient or excessive irrigation, affecting crop growth and the effective use of water resources. Existing systems also show limitations in data processing and analysis capabilities, and cannot fully utilize the large amount of data collected for in-depth analysis or prediction, which limits their ability to optimize resource allocation and improve efficiency. Deficiencies in technology and operating modes lead to inefficient resource use and potential risks in irrigation management. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose an automatic regulation system for irrigation area sluice gates based on fuzzy logic control.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme: The automatic regulation system of irrigation area water gate based on fuzzy logic control comprises:

[0007] The load monitoring module analyzes the regional flow and pressure differences based on the water resource flow and pressure values ​​of the irrigation area, calculates the load differences between regions, integrates them into a load distribution parameter set, and obtains the load distribution status value;

[0008] The gate parameter optimization module extracts the parameter combination of gate opening and closing angle and duration based on the load distribution state value, selects the optimal angle and duration combination, and obtains the gate opening and closing parameter set;

[0009] The environmental response adjustment module extracts the current environmental factor change based on the gate opening and closing parameter set, analyzes the relationship between the opening and closing angle and the duration, matches the environmental factor with the opening and closing combination, and obtains the environmental response parameter set;

[0010] The water level threshold setting module extracts the current water level change value based on the environmental response parameter set, combines the real-time flow data, distributes the water level and flow, sets the threshold and applies the threshold to the distribution of regional load to obtain the water level load threshold;

[0011] The flow prediction module captures the flow data of the water level sampling point based on the water level load threshold, analyzes the flow change trend corresponding to the water level load, and obtains the flow distribution prediction value in combination with the flow change information;

[0012] The gate feedback control module analyzes the error value of water level and flow based on the flow distribution prediction value and real-time water level and flow data, adjusts the gate opening and closing in combination with the error value, and obtains an automatic balance control scheme for irrigation area water flow.

[0013] As a further scheme of the present invention, the load distribution state value includes a flow parameter set, a pressure parameter set, and a load difference parameter set; the gate opening and closing parameter set includes a screening angle parameter and a duration parameter; the environmental response parameter set includes an environmental factor change parameter and an opening and closing angle duration matching parameter; the water level load threshold includes a water level change parameter, a flow matching parameter, and a threshold setting parameter; the flow distribution prediction value includes a flow trend analysis parameter and a water level-flow relationship parameter; and the irrigation area water flow automatic balance control scheme includes an error analysis parameter and a gate adjustment parameter.

[0014] As a further solution of the present invention, the load monitoring module includes:

[0015] The data extraction submodule collects flow and pressure values ​​regionally based on the water resource flow and pressure values ​​in the irrigation area, locates invalid data, removes abnormal data, arranges the extracted flow and pressure values ​​in regional order, and generates a regional flow and pressure data set;

[0016] The difference analysis submodule analyzes the flow and pressure between regions based on the regional flow and pressure data set, calculates the regional parameter change ratio, sorts the difference values ​​between regions by weight, marks the regions with excessive fluctuation differences, and obtains regional load difference data;

[0017] Based on the regional load difference data, the distribution integration submodule calls the regional load difference value for multi-dimensional aggregation, screens the regional load value differences, classifies them according to the load value size, and arranges the regional load values ​​in order to generate a load distribution status value.

[0018] As a further solution of the present invention, the gate parameter optimization module includes:

[0019] The parameter extraction submodule identifies the opening and closing status of the gate in each area based on the load distribution state value, records the angle and opening and closing duration of the gate, standardizes the recorded data, sorts the standardized data by angle and duration, and generates a gate parameter data set;

[0020] The gate parameter optimization submodule analyzes the angle and duration values ​​in the data set according to the gate parameter data set, selects the parameter combination with high matching degree with the load state, adjusts the parameter combination through pattern matching and records the matching results, and generates the parameter combination optimization result;

[0021] The parameter selection submodule retrieves the parameter combination optimization result, determines the optimal matching angle and duration combination, adjusts the gate control parameters, inputs the control configuration, verifies the stability of the parameter set, and generates the gate opening and closing parameter set.

[0022] As a further solution of the present invention, the environmental response adjustment module includes:

[0023] The environmental factor analysis submodule collects key data including temperature, water level and flow rate through environmental monitoring based on the gate opening and closing parameter set, performs time series analysis on the data, removes abnormal values, partitions the remaining data, and obtains environmental factor analysis data;

[0024] The parameter matching submodule analyzes the environmental factors and the impact of environmental variables on the gate opening and closing angle and duration through the environmental factor analysis data, calculates the impact of each environmental factor change on the parameter adjustment, determines the optimal matching parameter setting according to the impact score, adjusts the parameters cyclically to capture the optimal combination, and obtains the parameter docking result;

[0025] The response parameter integration submodule selects angle and duration combinations that match the current environmental conditions from the parameter docking results, performs parameter adjustment tests, optimizes parameter settings through multiple adjustments and verifications, determines and solidifies the parameters as operating standards, and generates an environmental response parameter set.

[0026] As a further solution of the present invention, the water level threshold setting module includes:

[0027] The water level extraction submodule locates the water level change monitoring point based on the environmental response parameter set, extracts the water level change value in the monitoring area, continuously records the water level increase and decrease rate, extracts multiple key change nodes corresponding to the change rate, sorts the node values ​​in order, and obtains the current water level change characteristic value;

[0028] The flow matching submodule analyzes the node change value and the real-time flow data based on the current water level change characteristic value, calibrates according to a predetermined matching criterion, calls the matching criterion to perform distribution redistribution within the flow interval, and obtains a water level flow matching structure;

[0029] The load distribution submodule is based on the water level flow matching structure and adopts a fuzzy threshold dynamic adjustment method to calculate the distribution of flow between water level change nodes, set the upper and lower limits of the node threshold, apply the threshold to the regional load value, and distribute it to obtain the water level load threshold.

[0030] As a further solution of the present invention, the formula of the fuzzy threshold dynamic adjustment method is as follows:

[0031]

[0032] Get the distributed load value of the node matching the water level change, where: Indicates the distribution load value of the node matching the water level change. Represents the real-time traffic measured by the current node. Represents the traffic matching value adjusted by the upstream node. Represents the real-time water level measured by the current node. Represents the adjusted water level matching value of the downstream node. Represents the weight coefficient of traffic, represents the weight coefficient of the water level, Represents the dynamic adjustment weight coefficient, The lower threshold set for the node is used to control the minimum load.

[0033] As a further solution of the present invention, the traffic prediction module includes:

[0034] The flow data capture submodule applies fuzzy logic algorithm based on the water level load threshold, captures the flow data of the sampling point, removes abnormal values ​​and corrects errors, stores them in layers according to intervals, performs fuzzification processing, and generates a fuzzy flow data set;

[0035] The water level load analysis submodule divides the intervals according to the water level load based on the fuzzy flow data set, extracts the change trend and fluctuation characteristics, and generates a water level load and flow change feature set;

[0036] The flow distribution inference submodule adjusts characteristic parameters and calibrates trend data based on the water level load and flow change characteristic set, extracts distribution intervals, and performs numerical prediction to obtain flow distribution prediction values.

[0037] As a further solution of the present invention, the formula of the fuzzy logic algorithm is as follows:

[0038]

[0039] Calculate the traffic fuzzification value and generate the fuzzy traffic data set, where: represents the fuzzified flow value, Representative The weight of the data point, Representative The original flow value of the sampling point, Representative The water level value at each sampling point, Representative The load factor of each sampling point, The total number of sampling points.

[0040] As a further solution of the present invention, the gate feedback control module includes:

[0041] The error analysis submodule extracts the real-time water level and flow data based on the flow distribution prediction value, analyzes the real-time flow value and the prediction value, matches the flow difference with the current water level information, and generates a water level flow error value;

[0042] The parameter adjustment submodule sets the gate opening and closing adjustment parameters based on the water level and flow error value, sets the adjustment range for the area with large error, and makes fine adjustments for the area with low error. By comparing the opening and closing effects, matching parameter sets are selected and integrated to generate a gate adjustment parameter set.

[0043] Based on the gate adjustment parameter set, the opening and closing control submodule applies the adjustment parameters at each gate position, performs opening and closing operations item by item, monitors the water level and flow synchronously, gradually adjusts the opening and closing operation sequence of each area, and generates an automatic balance control plan for the irrigation area water flow.

[0044] Compared with the prior art, the advantages and positive effects of the present invention are:

[0045] In the present invention, by integrating water resource flow and pressure data to calculate and optimize load differences between regions, the efficiency and response speed of irrigation are significantly improved. By refining regional load analysis, the water demand of each region can be matched more accurately, avoiding excessive or insufficient water supply, optimizing the opening and closing parameters of the gate, making water flow control more accurate, reducing resource waste, and increasing the adaptability of the system to environmental changes. By real-time monitoring of environmental variables and adjusting gate operation, it is ensured that the system can still maintain the optimal water level under changing environmental conditions, ensuring agricultural irrigation demand and sustainable use of water resources. The predictive analysis of flow data further enhances the system's preventive regulation capabilities, so that when facing future demand changes, the system can respond in advance, optimize resource allocation, and improve the ability of automatic water flow balance in the irrigation area, and achieve efficient management and adjustment of complex variables, thereby effectively improving water resource utilization efficiency and the automation level of irrigation area management. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a system flow chart of the present invention;

[0047] Figure 2 It is a schematic diagram of the system framework of the present invention;

[0048] Figure 3 It is a flow chart of the load monitoring module in the present invention;

[0049] Figure 4 It is a flow chart of the gate parameter optimization module in the present invention;

[0050] Figure 5 This is a flow chart of the environmental response adjustment module in the present invention;

[0051] Figure 6 This is a flow chart of a water level threshold setting module in the present invention;

[0052] Figure 7 It is a flow chart of the flow prediction module in the present invention;

[0053] Figure 8 It is a flow chart of the gate feedback control module in the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0055] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0056] Embodiment 1

[0057] See also Figure 1 to Figure 2 , the irrigation area sluice automatic regulation system based on fuzzy logic control includes:

[0058] The load monitoring module extracts the regional real-time flow and pressure data based on the water resource flow and pressure values ​​of the irrigation area, analyzes the differentiated regional flow and pressure values, calculates the load difference values ​​between regions, integrates the regional load values ​​into a load distribution parameter set, and obtains the load distribution status value;

[0059] The gate parameter optimization module extracts the parameter combination of gate opening and closing angle and opening and closing duration based on the load distribution state value, and compares it with the load distribution parameter set to select the optimal angle and duration combination to obtain the gate opening and closing parameter set;

[0060] The environmental response adjustment module extracts the change of current environmental factors based on the gate opening and closing parameter set, analyzes the environmental change and the opening and closing angle and duration parameters, and combines the differentiated environmental factors with the opening and closing combination to obtain the environmental response parameter set;

[0061] The water level threshold setting module extracts the current water level change value based on the environmental response parameter set, combines the water level change value with the real-time flow data, matches and distributes the water level change value and flow data, sets the threshold value and applies the threshold value to the distribution of regional load to obtain the water level load threshold value;

[0062] The flow prediction module captures the flow data of the water level sampling point based on the water level load threshold, extracts the flow change trend corresponding to the water level load, analyzes the flow data and water level load, and combines the flow change and water level load information to obtain the flow distribution prediction value;

[0063] The gate feedback control module analyzes the error value of water level and flow based on the flow distribution prediction value and real-time water level and flow data, adjusts the gate opening and closing based on the error value, applies the adjustment parameters to the current gate opening and closing operation, and obtains the automatic balance control plan for the irrigation area water flow.

[0064] The load distribution state value includes flow parameter set, pressure parameter set, and load difference parameter set. The gate opening and closing parameter set includes screening angle parameters and duration parameters. The environmental response parameter set includes environmental factor change parameters and opening and closing angle duration matching parameters. The water level load threshold includes water level change parameters, flow matching parameters, and threshold setting parameters. The flow distribution prediction value includes flow trend analysis parameters and water level-flow relationship parameters. The irrigation area water flow automatic balance control scheme includes error analysis parameters and gate adjustment parameters.

[0065] Specifically, if Figure 2 , 3 As shown, the load monitoring module includes:

[0066] The data extraction submodule collects flow and pressure values ​​regionally based on the water resource flow and pressure values ​​of the irrigation area, locates invalid data, removes abnormal data, and arranges the extracted flow and pressure values ​​in regional order to generate the regional flow and pressure data set. The execution process is as follows;

[0067] Based on the water resource flow and pressure values ​​in the irrigation area, regional collection of flow and pressure values ​​is carried out. By accurately measuring the flow and pressure values ​​in each area, the system will automatically screen and exclude abnormal or inappropriate data during data collection to ensure data accuracy and availability. During data collection, special attention will be paid to the correlation between flow and pressure values ​​and their changing trends. After preliminary analysis of the data, the data will be classified according to geographical and logical regions, and a regional flow and pressure data set will be established based on this. The data set will be used to further analyze and monitor the effectiveness of water resource management.

[0068] The difference analysis submodule analyzes the flow and pressure between regions based on the regional flow and pressure data set, calculates the regional parameter change ratio, sorts the difference values ​​between regions by weight, marks the areas with excessive fluctuation differences, and obtains the regional load difference data. The execution process is as follows;

[0069] Analysis of flow and pressure between regions is based on the formula:

[0070]

[0071] Calculate the regional parameter change ratio, where: represents the pressure difference, Represents the flow rate change, represents the flow resistance between regions;

[0072] Refer to the flow and pressure values ​​of water resources between irrigation areas, the flow resistance between areas Because the pipe size, length or material are different;

[0073] Suppose the flow rate in a region increases from 100 cubic meters / hour to 150 cubic meters / hour, the pressure increases from 200 Pa to 220 Pa, and the flow resistance 5000 ;

[0074] Then the regional parameter change ratio is: Cubic meters / hour;

[0075] Substituting the values ​​into the formula, we get: Pa;

[0076] This calculation process reveals the direct relationship between the pressure difference between regions and the flow change, and shows the influence of physical parameters on the system behavior through the calculation of flow resistance.

[0077] The distribution integration submodule uses the regional load difference data to perform multi-dimensional aggregation, screens the regional load value differences, classifies them according to the load value size, and arranges the regional load values ​​in order to generate the load distribution status value. The execution process is as follows;

[0078] Based on the regional load difference data, the regional load difference values ​​are called for multi-dimensional aggregation, which involves the aggregation and classification of various values, and the load differences between regions are analyzed in detail. During the data integration process, the system ensures the efficiency of data integration through algorithm optimization. During the classification process, different regions will be classified into corresponding categories according to the size of the load values. This classification is to better analyze the performance of each region in resource allocation and utilization, and then optimize the resource allocation strategy. The load values ​​of each region are arranged in order to form a clear load distribution map and generate load distribution status values, which will provide decision support for water resources management.

[0079] Specifically, if Figure 2 , 4 As shown, the gate parameter optimization module includes:

[0080] The parameter extraction submodule identifies the opening and closing status of the gate in each area based on the load distribution state value, records the angle and opening and closing time of the gate, standardizes the recorded data, sorts the standardized data by angle and time, and generates the gate parameter data set. The execution process is as follows;

[0081] Based on the load distribution state value, first identify the gate opening and closing state of each area in the system, and detect the angle change and duration during the opening and closing state. Monitor this state data, collect angle change and duration data, and normalize the data according to a unified standard. In the normalization process, the angle data is divided by the interval setting method, and the minimum and maximum values ​​of each interval are used as the standardization benchmark to calculate the normalized value of the angle data. The angle value standardization formula is: Normalized angle value = (original angle value - minimum interval value) / (maximum interval value - minimum interval value). A unified benchmark is also set for duration, and the duration of each segment of data is normalized to a unified interval. Finally, the gates are classified according to the distribution of differentiated opening and closing angles and opening and closing durations, and the classified data are organized into parameterized data sets for subsequent processing.

[0082] The gate parameter optimization submodule analyzes the angle and duration values ​​in the data set according to the gate parameter data set, selects the parameter combination with high matching degree with the load state, adjusts the parameter combination through pattern matching and records the matching results, and generates the execution process of the parameter combination optimization result as follows;

[0083] Filter the parameter combination with high load state matching degree according to the formula:

[0084]

[0085] Calculate the matching degree of the parameter combination, where Indicates the matching degree of parameter combination, represents the opening and closing angle of gate i, Indicates the maximum opening and closing angle of the gate in the system. is the opening and closing duration of gate i, is the longest opening and closing time of the gate in the system;

[0086] The formula is used to measure the matching degree of the parameter combination. The total matching degree C is obtained by calculating the product of the normalized results of each gate angle and duration with respect to the maximum value, including 3 gates, namely:

[0087] Gate 1 has an angle of 30 degrees and a duration of 10 seconds;

[0088] Gate 2 has an angle of 45 degrees and lasts for 15 seconds;

[0089] Gate 3 has an angle of 60 degrees and a duration of 12 seconds;

[0090] Set the maximum opening and closing angle The maximum duration is 60 degrees. If the time interval is 15 seconds, the matching degree calculation process is as follows:

[0091] Gate 1 Matching Contribution: ;

[0092] Gate 2 Matching Contribution: ;

[0093] Gate 3 Matching Contribution: ;

[0094] The matching degree C is: ;

[0095] The result shows that the C value is 1.8833, which indicates the matching degree of the parameter combination. The larger the value, the higher the matching degree. This matching degree C can be used to screen the optimal angle and duration combination.

[0096] The parameter selection submodule retrieves the parameter combination optimization result, determines the optimal matching angle and duration combination, adjusts the gate control parameters, inputs the control configuration, verifies the stability of the parameter set, and generates the gate opening and closing parameter set. The execution process is as follows;

[0097] After obtaining the optimization results of various parameter combinations during the retrieval process, the parameters are set according to the screened optimal matching angle and duration combination, the optimal parameter combination adjustment is applied to the gate control system in real-time operation, and the data entry process is performed on the adjusted parameters. During the execution process, the optimal opening and closing angles and durations are first extracted according to the parameter optimization values ​​in the operation record module, the extracted optimal values ​​are input into the control module, and data updates are performed through the system parameter configuration interface. The parameters are integrated into the gate's operation control configuration to form a gate opening and closing parameter set, and the overall data is updated and solidified.

[0098] Specifically, if Figure 2 , 5 As shown, the environmental response adjustment module includes:

[0099] The environmental factor analysis submodule collects key data, including temperature, water level and flow rate, through environmental monitoring based on the gate opening and closing parameter set, performs time series analysis on the data, removes outliers, and partitions the remaining data. The execution process of obtaining environmental factor analysis data is as follows;

[0100] Based on the gate opening and closing parameter set, key environmental data including temperature, water level and flow rate are collected during the environmental monitoring process. Environmental variables are recorded regularly through data acquisition equipment, and the obtained data are stored in time series. Time series analysis technology is applied to the collected data to preliminarily screen abnormal data that deviates from the normal range, and the abnormal values ​​are removed from the data set to ensure data quality. For the processed valid data, partition processing is carried out according to geographical divisions and monitoring points to ensure that the data of each monitoring point can represent the environmental status of the area where it is located. By re-analyzing the partitioned data, key information reflecting the current environmental conditions is extracted. The generated environmental factor analysis data can be directly used in the subsequent parameter matching process to ensure the adaptability and accuracy of gate operations.

[0101] The parameter matching submodule analyzes data through environmental factors, analyzes the impact of environmental variables on the gate opening and closing angle and duration, calculates the impact of changes in each environmental factor on parameter adjustment, determines the optimal matching parameter settings based on the impact score, and adjusts parameters cyclically to capture the optimal combination. The execution process for obtaining parameter docking results is as follows;

[0102] Calculate the impact of changes in each environmental factor on parameter adjustment according to the formula:

[0103]

[0104] Calculate the impact of parameter adjustments , where , , Represent the changes of temperature, water level and flow rate respectively. , , is the corresponding adjustment coefficient;

[0105] This formula reflects the impact of environmental factors on gate operating parameters by directly linearly weighting changes in temperature, water level, and flow rate. Refer to an example where the temperature changes ℃, water level change Meters, flow rate change Meters / second, if the adjustment factor , , , then the parameter adjustment influence degree is calculated as follows:

[0106]

[0107] This result shows that the degree of influence of parameter adjustment under a given environmental change is 0.63, indicating that appropriate adjustments need to be made to the current parameter settings to adapt to environmental changes and improve the efficiency and safety of gate operations.

[0108] The response parameter integration submodule selects the angle and duration combination that matches the current environmental conditions from the parameter docking results, performs parameter adjustment tests, optimizes parameter settings through multiple adjustments and verifications, determines and solidifies the parameters as operating standards, and generates the execution process of the environmental response parameter set as follows;

[0109] After obtaining the parameter docking results, select the angle and duration combination that best matches the current environmental conditions. Through simulation testing, conduct multiple rounds of adjustment tests on the selected parameter combination. Each round of tests fine-tunes the parameters based on real-time data to capture the best operating parameter settings. Through continuous testing and adjustments, the effectiveness of the parameters is ultimately verified and the parameter settings are optimized to ensure that the selected parameter combination can demonstrate optimal performance under different environmental conditions. Through final testing and verification, the determined parameter combination is solidified as an operating standard to generate an environmental response parameter set, which provides standardized guidance for subsequent gate operations to ensure accuracy of operation and environmental adaptability.

[0110] Specifically, if Figure 2 , 6 As shown, the water level threshold setting module includes:

[0111] The water level extraction submodule locates the water level change monitoring point based on the environmental response parameter set, extracts the water level change value in the monitoring area, continuously records the water level increase and decrease rate, extracts multiple key change nodes corresponding to the change rate, sorts the node values ​​in order, and obtains the current water level change characteristic value. The execution process is as follows;

[0112] Locate the monitoring points, determine the key locations of water level changes based on the environmental response parameter set, continuously record water level changes through high-precision sensors, perform time series analysis on the obtained data to capture the rate of increase or deceleration of the water level, and pay special attention to the key nodes of acceleration or deceleration. The nodes are identified and recorded through algorithms, and all key nodes are extracted and sorted according to the size of their values. The sorting process uses a quick sorting algorithm to ensure efficiency. It not only captures the dynamic characteristics of water level changes, but also can quickly respond to environmental changes and organize node values ​​into current water level change characteristic values. The characteristic values ​​can describe the water level changes in the current water area in detail, providing accurate basic data for the next step of flow matching.

[0113] The flow matching submodule analyzes the node change value and real-time flow data based on the current water level change characteristic value, and calibrates according to the predetermined matching criteria, calls the matching criteria to perform distribution redistribution within the flow interval, and obtains the execution process of the water level flow matching structure as follows;

[0114] After receiving the characteristic value of water level change, the relationship between the node and the real-time flow data is analyzed, and the flow data is calibrated using the preset matching criteria. During the process, the volatility of the real-time data is referred to. The matching criteria include a variety of statistical methods and machine learning algorithms to ensure the accuracy and adaptability of the calibration. After the calibration is completed, the division of each flow interval is adjusted according to the distribution characteristics of the flow data. The purpose of redistribution is to ensure the representativeness of the data and the accuracy of water level monitoring. The adjusted flow data is reorganized to form a water level flow matching structure, which reflects the matching of the adjusted flow and water level changes.

[0115] The load distribution submodule is based on the water level flow matching structure and adopts the fuzzy threshold dynamic adjustment method to measure the flow distribution between water level change nodes, set the upper and lower limits of the node threshold, apply the threshold to the regional load value, and distribute it. The execution process of obtaining the water level load threshold is as follows;

[0116] The formula of the fuzzy threshold dynamic adjustment method is as follows:

[0117]

[0118] Get the distributed load value of the node matching the water level change, where: Indicates the distribution load value of the node matching the water level change. Represents the real-time traffic measured by the current node. Represents the traffic matching value adjusted by the upstream node. Represents the real-time water level measured by the current node. Represents the adjusted water level matching value of the downstream node. Represents the weight coefficient of traffic, represents the weight coefficient of the water level, Represents the dynamic adjustment weight coefficient, The lower threshold set for the node is used to control the minimum load;

[0119] Detailed explanation of the formula and the process of formula calculation and derivation:

[0120] Indicates the real-time flow rate calculated by the current node, which is the value directly obtained from the hydrological monitoring system. For example, the flow rate measured at a certain moment is 500 cubic meters per second.

[0121] It is the flow matching value adjusted by the upstream node, which is obtained based on the historical data and trend analysis of the upstream node. For example, the flow value obtained after analyzing the upstream hydrological data is 520 cubic meters per second.

[0122] The real-time water level height measured for the current node is obtained from the water level monitoring device, such as the measured water level is 3.2m;

[0123] is the adjusted water level matching value of the downstream node, which is obtained through the downstream water level control plan and real-time data adjustment, and is assumed to be 3.5m;

[0124] and They are the weight coefficients of flow and water level, which are determined based on the coefficient of variation of historical data and impact analysis. The weight coefficient setting must reflect the actual impact of flow and water level, such as It can be set to 0.6. It is set to 0.4, which indicates that the change of flow at this node has a slightly greater impact on the load than the change of water level;

[0125] To dynamically adjust the weight coefficient, adjust the coefficient to cope with the differences and liquidity between different nodes. For example, setting it to 0.2 means that limited adjustment flexibility is allowed;

[0126] It is the lower threshold set for the node, determined based on safety and operation standards. For example, it is set to 50 cubic meters per second, indicating the minimum load limit for each node.

[0127] Calculation of the absolute value of flow difference: Cubic meters per second;

[0128] Calculation of absolute value of water level difference: m;

[0129] Substitute the weight coefficient to calculate the adjusted value: ;

[0130] Add dynamic adjustment weight calculation: ;

[0131] Add the lower threshold to get the load value: Cubic meters per second;

[0132] The result shows that the load value that the current node should be adjusted to is 53.48 cubic meters per second, which represents the minimum operating standard for maintaining the node under given water level and flow conditions. This value is the basis for decision-making for operators, indicating how to adjust the node to ensure safe operation.

[0133] Specifically, if Figure 2 , 7 As shown, the traffic prediction module includes:

[0134] The flow data capture submodule applies fuzzy logic algorithm based on water level load threshold, captures flow data of sampling points, removes outliers and corrects errors, stores them in layers according to intervals, performs fuzzification, and generates fuzzy flow data sets. The execution process is as follows;

[0135] The formula of the fuzzy logic algorithm is as follows:

[0136]

[0137] Calculate the traffic fuzzification value and generate the fuzzy traffic data set, where: represents the fuzzified flow value, Representative The weight of the data point, Representative The original flow value of the sampling point, Representative The water level value at each sampling point, Representative The load factor of each sampling point, Total number of sampling points;

[0138] Detailed explanation of the formula and the process of formula calculation and derivation:

[0139] Weight Dynamically adjusted by the membership function, determined by the credibility of the sampling point data, the weight is adjusted from 0 to 1 according to the membership value, and the specific value is obtained by the data credibility score. The credibility scores of the sampling points are set as follows: , , ;

[0140] Original flow value The samples are collected directly through on-site sampling instruments in cubic meters per second. The sampling values ​​are , , ;

[0141] Water level value Through real-time monitoring of the water level meter, the water level values ​​are collected in meters. , , ;

[0142] Load Factor It is calculated from the ratio of load pressure to design flow rate. The unit is dimensionless and the value range is 0 to 1. The monitoring pressures are 0.85, 0.88, and 0.83 respectively. The design flow rate ratio coefficients are 1.0, 1.0, and 1.0 respectively. The calculation results are: , , ;

[0143] Calculate the flow correction for each item:

[0144] Item 1: ;

[0145] Item 2: ;

[0146] Item 3: ;

[0147] Weighted sum numerator: ;

[0148] Sum of weights: ;

[0149] Calculate the fuzzy flow value:

[0150]

[0151] The result shows that the fuzzy flow value is 2.47, which represents the representative flow data obtained after the sampling point data is corrected and can be used as the basic storage value of the flow fuzzy data set.

[0152] The water level load analysis submodule is based on the fuzzy flow data set, divides the intervals according to the water level load, extracts the change trend and fluctuation characteristics, and generates the water level load and flow change feature set. The execution process is as follows;

[0153] Based on the fuzzy flow data set, the change trend and fluctuation characteristics of the flow data are analyzed in detail. The uncertainty information in the fuzzy data is processed by fuzzy logic, and the data interval is divided according to the numerical range of the water level load. First, the load interval boundary is calculated and a reasonable membership function is defined to determine the fuzzy membership of the flow data in different intervals. By setting the interval fluctuation amplitude, the key change points are extracted, the data trend within the interval is analyzed in sections, and the key trend characteristic values ​​are calculated by the fluctuation amplitude and the change rate. In this process, fuzzy logic assists in identifying the nonlinear impact of load fluctuations on flow changes, fuzzy fusion of the change rate and amplitude value, and the extracted characteristic parameters are classified according to the trend direction and corresponding rules are established. Finally, by integrating the correlation between the characteristics and trend data, the corresponding structure of the load and flow characteristics is generated, and the water level load and flow change characteristic set is generated, which provides an important input basis for subsequent distribution inference.

[0154] The flow distribution inference submodule adjusts the characteristic parameters and calibrates the trend data based on the water level load and flow change feature set, extracts the distribution interval, and performs numerical prediction to obtain the flow distribution prediction value. The execution process is as follows;

[0155] Based on the water level load and flow change feature set, the feature parameters are adjusted and the trend data is calibrated. The advantages of fuzzy logic in data distribution prediction are utilized to improve the prediction accuracy through dynamic optimization processing of the distribution interval. The distribution center is calculated using the rate of change of trend characteristics and the position of key nodes. The values ​​in the flow interval are interpolated and adjusted, and the distribution interval data is redistributed. Fuzzy logic flexibly processes the interval fluctuation characteristics by setting fuzzy rules, making the construction of the distribution matrix more practical. The constructed distribution model combines the nonlinear correlation of the water level load trend to continuously optimize and adjust the distribution center position and fluctuation amplitude. After completing the distribution matrix calculation, the flow distribution prediction value is generated according to the calculated distribution results, and finally the flow distribution prediction value is obtained, providing more reliable flow input data for the gate control system.

[0156] Specifically, if Figure 2 , 8 As shown, the gate feedback control module includes:

[0157] The error analysis submodule extracts the real-time water level and flow data based on the flow distribution prediction value, analyzes the real-time flow value and the prediction value, matches the flow difference with the current water level information, and generates the water level flow error value. The execution process is as follows;

[0158] Extract real-time water level and flow data, analyze the real-time flow value and predicted value, and calculate the flow difference corresponding to the current water level information according to the formula:

[0159]

[0160] Calculate the water level flow error value, where: Represents the water level flow error value, Represents the real-time traffic value, Represents the predicted flow value;

[0161] Obtained through real-time monitoring and For example, if the real-time flow is 500 cubic meters per second and the predicted flow is 485 cubic meters per second, the calculation is: Cubic meters per second;

[0162] The result shows that there is an error between the current actual flow and the predicted flow, and adjustment is needed to achieve the predicted equilibrium state.

[0163] The parameter adjustment submodule sets the gate opening and closing adjustment parameters based on the water level and flow error value, sets the adjustment range for the area with large error, and makes fine adjustments for the area with low error. By comparing the opening and closing effects, the matching parameter sets are screened and integrated to generate the gate adjustment parameter set. The execution process is as follows;

[0164] Adjustment parameters are set based on the water level and flow error values, and adjustment ranges are set for areas with large errors. The error value of each area is first obtained by comparing the real-time data with the predicted value. A large adjustment range or fine-tuning is set according to the size of the error. For example, large adjustments are performed in areas where the error exceeds 20 cubic meters per second, while fine-tuning is performed in areas where the error is below 5 cubic meters per second. The process of setting the adjustment parameters refers to the volatility of real-time data and the accuracy of the prediction model to ensure that the opening and closing parameters of the gate can effectively respond to error changes and optimize the water level control strategy. By comparing the opening and closing effects, matching parameter sets are screened and integrated to generate a gate adjustment parameter set. The adjusted parameter set will be used again for comparative analysis of real-time data to verify the effectiveness of the adjustment.

[0165] The opening and closing control submodule applies the adjustment parameters at each gate position based on the gate adjustment parameter set, implements the opening and closing operations one by one, monitors the water level and flow synchronously, and gradually adjusts the opening and closing operation sequence of each area to generate the execution process of the automatic balance control scheme of irrigation area water flow as follows;

[0166] Based on the gate adjustment parameter set, the adjustment parameters are applied at each gate position to perform opening and closing operations. The detailed execution process includes adjusting the specific opening and closing instructions of each gate, such as increasing the gate opening in the area with a large flow deviation by 5%, and adjusting the gate opening in the area with a small flow deviation to 1%. When implementing these operations, the water level and flow are monitored synchronously to ensure that each adjustment is based on the latest real-time data, and gradually adjust the opening and closing operation sequence of each area to eventually form an automatic balancing and control scheme for irrigation area water flow. This scheme optimizes overall water resources management through real-time feedback adjustment, improves the operating efficiency of water conservancy facilities, and ensures the rational allocation and utilization of water resources. The implementation result of this control scheme can effectively balance the water flow in each area, thereby achieving the predetermined management goals.

[0167] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. The automatic regulation system of irrigation area sluice gate based on fuzzy logic control is characterized by: The system comprises: The load monitoring module analyzes the regional flow and pressure differences based on the water resource flow and pressure values ​​of the irrigation area, calculates the load differences between regions, integrates them into a load distribution parameter set, and obtains the load distribution status value; The gate parameter optimization module extracts the parameter combination of gate opening and closing angle and duration based on the load distribution state value, selects the optimal angle and duration combination, and obtains the gate opening and closing parameter set; The environmental response adjustment module extracts the current environmental factor change based on the gate opening and closing parameter set, analyzes the relationship between the opening and closing angle and the duration, matches the environmental factor with the opening and closing combination, and obtains the environmental response parameter set; The water level threshold setting module extracts the current water level change value based on the environmental response parameter set, combines the real-time flow data, distributes the water level and flow, sets the threshold and applies the threshold to the distribution of regional load to obtain the water level load threshold; The flow prediction module captures the flow data of the water level sampling point based on the water level load threshold, performs fuzzy inference on the flow change of the water level sampling point in combination with fuzzy logic, analyzes the flow change trend corresponding to the water level load, classifies and sorts the flow change trend according to the inference result, and performs fuzzy logic adjustment and analysis on the classified data in combination with the flow change information to obtain the flow distribution prediction value; The gate feedback control module analyzes the error value of water level and flow based on the flow distribution prediction value and real-time water level and flow data, adjusts the gate opening and closing in combination with the error value, and obtains an automatic balance control scheme for irrigation area water flow.

2. The automatic regulation system for irrigation area sluice gates based on fuzzy logic control according to claim 1 is characterized in that: The load distribution state value includes a flow parameter set, a pressure parameter set, and a load difference parameter set; the gate opening and closing parameter set includes a screening angle parameter and a duration parameter; the environmental response parameter set includes an environmental factor change parameter and an opening and closing angle duration matching parameter; the water level load threshold includes a water level change parameter, a flow matching parameter, and a threshold setting parameter; the flow distribution prediction value includes a flow trend analysis parameter and a water level-flow relationship parameter; and the irrigation area water flow automatic balance control scheme includes an error analysis parameter and a gate adjustment parameter.

3. The automatic regulation system for irrigation area sluice gates based on fuzzy logic control according to claim 1 is characterized in that: The load monitoring module comprises: The data extraction submodule collects flow and pressure values ​​regionally based on the water resource flow and pressure values ​​in the irrigation area, locates invalid data, removes abnormal data, arranges the extracted flow and pressure values ​​in regional order, and generates a regional flow and pressure data set; The difference analysis submodule analyzes the flow and pressure between regions based on the regional flow and pressure data set, calculates the regional parameter change ratio, sorts the difference values ​​between regions by weight, marks the regions with excessive fluctuation differences, and obtains regional load difference data; Based on the regional load difference data, the distribution integration submodule calls the regional load difference value for multi-dimensional aggregation, screens the regional load value differences, classifies them according to the load value size, and arranges the regional load values ​​in order to generate a load distribution status value.

4. The automatic regulation system for irrigation area sluice gates based on fuzzy logic control according to claim 1 is characterized in that: The gate parameter optimization module includes: The parameter extraction submodule identifies the opening and closing status of the gate in each area based on the load distribution state value, records the angle and opening and closing duration of the gate, standardizes the recorded data, sorts the standardized data by angle and duration, and generates a gate parameter data set; The gate parameter optimization submodule analyzes the angle and duration values ​​in the data set according to the gate parameter data set, selects the parameter combination with high matching degree with the load state, adjusts the parameter combination through pattern matching and records the matching results, and generates the parameter combination optimization result; The parameter selection submodule retrieves the parameter combination optimization result, determines the optimal matching angle and duration combination, adjusts the gate control parameters, inputs the control configuration, verifies the stability of the parameter set, and generates the gate opening and closing parameter set.

5. The automatic regulation system of irrigation area sluice gate based on fuzzy logic control according to claim 1 is characterized in that: The environmental response adjustment module includes: The environmental factor analysis submodule collects key data including temperature, water level and flow rate through environmental monitoring based on the gate opening and closing parameter set, performs time series analysis on the data, removes abnormal values, partitions the remaining data, and obtains environmental factor analysis data; The parameter matching submodule analyzes the environmental factors and the impact of environmental variables on the gate opening and closing angle and duration through the environmental factor analysis data, calculates the impact of each environmental factor change on the parameter adjustment, determines the optimal matching parameter setting according to the impact score, adjusts the parameters cyclically to capture the optimal combination, and obtains the parameter docking result; The response parameter integration submodule selects angle and duration combinations that match the current environmental conditions from the parameter docking results, performs parameter adjustment tests, optimizes parameter settings through multiple adjustments and verifications, determines and solidifies the parameters as operating standards, and generates an environmental response parameter set.

6. The automatic regulation system of irrigation area sluice gate based on fuzzy logic control according to claim 1 is characterized in that: The water level threshold setting module includes: The water level extraction submodule locates the water level change monitoring point based on the environmental response parameter set, extracts the water level change value in the monitoring area, continuously records the water level increase and decrease rate, extracts multiple key change nodes corresponding to the change rate, sorts the node values ​​in order, and obtains the current water level change characteristic value; The flow matching submodule analyzes the node change value and the real-time flow data based on the current water level change characteristic value, performs calibration according to a predetermined matching criterion, calls the matching criterion to perform distribution redistribution within the flow interval, and obtains a water level flow matching structure; The load distribution submodule is based on the water level flow matching structure and adopts a fuzzy threshold dynamic adjustment method to measure the distribution of flow between water level change nodes, set the upper and lower limits of the node threshold, apply the threshold to the regional load value, and distribute it to obtain the water level load threshold.

7. The automatic regulation system for irrigation area sluice gates based on fuzzy logic control according to claim 6 is characterized in that: The formula of the fuzzy threshold dynamic adjustment method is as follows: Get the distributed load value of the node matching the water level change, where: Indicates the distribution load value of the node matching the water level change. Represents the real-time traffic measured by the current node. Represents the traffic matching value adjusted by the upstream node. Represents the real-time water level measured by the current node. Represents the adjusted water level matching value of the downstream node. Represents the weight coefficient of traffic, represents the weight coefficient of the water level, Represents the dynamic adjustment weight coefficient, The lower threshold set for the node is used to control the minimum load.

8. The automatic regulation system for irrigation area sluice gates based on fuzzy logic control according to claim 1 is characterized in that: The traffic prediction module includes: The flow data capture submodule applies fuzzy logic algorithm based on the water level load threshold, captures the flow data of the sampling point, removes abnormal values ​​and corrects errors, stores them in layers according to intervals, performs fuzzification processing, and generates a fuzzy flow data set; The water level load analysis submodule divides the intervals according to the water level load based on the fuzzy flow data set, extracts the change trend and fluctuation characteristics, and generates a water level load and flow change feature set; The flow distribution inference submodule adjusts characteristic parameters and calibrates trend data based on the water level load and flow change characteristic set, extracts distribution intervals, and performs numerical prediction to obtain flow distribution prediction values.

9. The automatic regulation system for irrigation area water gates based on fuzzy logic control according to claim 8 is characterized in that: The formula of the fuzzy logic algorithm is as follows: Calculate the traffic fuzzification value and generate the fuzzy traffic data set, where: represents the fuzzified flow value, Representative The weight of the data point, Representative The original flow value of the sampling point, Representative The water level value at each sampling point, Representative The load factor of each sampling point, Total number of sampling points.

10. The automatic regulation system of irrigation area sluice gate based on fuzzy logic control according to claim 1 is characterized in that: The gate feedback control module comprises: The error analysis submodule extracts the real-time water level and flow data based on the flow distribution prediction value, analyzes the real-time flow value and the prediction value, matches the flow difference with the current water level information, and generates a water level flow error value; The parameter adjustment submodule sets the gate opening and closing adjustment parameters based on the water level and flow error value, sets the adjustment range for the area with large error, and makes fine adjustments for the area with low error. By comparing the opening and closing effects, matching parameter sets are selected and integrated to generate a gate adjustment parameter set. Based on the gate adjustment parameter set, the opening and closing control submodule applies the adjustment parameters at each gate position, performs opening and closing operations item by item, monitors the water level and flow synchronously, gradually adjusts the opening and closing operation sequence of each area, and generates an automatic balance control plan for the irrigation area water flow.

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