Irrigation area water gate system with self-adaptive water level adjustment function

Abstract

The invention provides a self-adaptive water level adjustment irrigation area water gate system, and relates to the technical field of water conservancy projects, and the system comprises a water level monitoring module which is used for monitoring the water level change in an irrigation area in real time, and feeding back the water level information to a gate control module; and the gate control module is used for receiving the water level information fed back by the water level monitoring module according to a preset water level control strategy and automatically adjusting the opening degree of a water gate of the irrigation area so as to realize self-adaptive adjustment of the water level of the irrigation area. According to the invention, functions of monitoring, control, analysis, purification, remote monitoring, energy management and the like are integrated, intelligent adjustment of the water level of the irrigation area, effective purification of water quality and continuous optimization of energy consumption are realized, and the management efficiency and sustainability of the irrigation area are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy projects, and particularly to an irrigation district sluice system with adaptive water level regulation. Background Art

[0002] Traditional sluice systems rely on manual operation or simple mechanical control, making it difficult to achieve real-time and precise regulation of water level changes. For example, during the irrigation season, due to weather changes or changes in crop water requirements, the water level needs to be adjusted frequently. However, traditional systems may not be able to respond quickly to these changes, resulting in too high or too low water levels and affecting the irrigation effect.

[0003] In addition, under extreme weather conditions such as heavy rain, traditional sluice systems may not be able to adjust the water level in time, leading to flood overflow or damage to irrigation channels, posing a threat to agricultural production and irrigation district facilities. Traditional sluice systems lack intelligent management functions and remote monitoring means. This means that management personnel need to go to the site for inspection and operation in person, which is not only time-consuming and laborious, but also difficult to grasp the operation status of the sluice and the water level situation of the irrigation district in real time.

[0004] For example, in the case of a large irrigation district or complex terrain, management personnel may need to spend a lot of time making circuit inspections of each sluice point. And if a sluice fails or has abnormal conditions, management personnel may not be able to detect and handle it in time, resulting in the deterioration of the problem. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide an irrigation district sluice system with adaptive water level regulation, which can not only achieve precise control of the sluice, but also improve the automation level and emergency response ability of irrigation district management.

[0006] To solve the above technical problem, the technical solution of the present invention is as follows:

[0007] An irrigation district sluice system with adaptive water level regulation, comprising:

[0008] A water level monitoring module for real-time monitoring of water level changes in the irrigation district and feeding back the water level information to the gate control module;

[0009] A gate control module for receiving the water level information fed back by the water level monitoring module according to a preset water level control strategy and automatically adjusting the opening degree of the irrigation district sluice to achieve adaptive regulation of the irrigation district water level;

[0010] A data analysis module for collecting and real-time storing various data during the water operation process, including water level change data, gate opening degree data, flow rate data, water quality data, and analyzing various data through an optimization algorithm of cuckoo parasitic breeding behavior to continuously optimize and adjust the water level control strategy;

[0011] A purification processing module, which is set downstream of the sluice, is used to purify the water discharged through the sluice, and uses water quality monitoring and optimization algorithms to monitor water quality indicators in real time, adjusts the purification processing process according to water quality changes, and removes pollutants in the water;

[0012] A remote monitoring module is used to remotely monitor the water level changes inside and outside the irrigation area, the operating status of the main body of the sluice, and the working conditions of the purification processing module, and issues an alarm when an abnormal situation occurs;

[0013] An energy management module is used to provide power supply, monitor the energy consumption situation in real time, and continuously optimize the energy consumption by adjusting the equipment operation parameters.

[0014] Furthermore, the water level changes in the irrigation area are monitored in real time, and the water level information is fed back to the gate control module, including:

[0015] Install water level sensors at key positions in the irrigation area, including the entrance, exit, turning points of the channel, or places vulnerable to water level changes;

[0016] The water level sensors collect the water level data in the irrigation area in real time according to the preset sampling frequency, including the current water level height and water level change trend information;

[0017] Convert the water level data into electrical signals and transmit the electrical signals to the gate control module.

[0018] Furthermore, according to the preset water level control strategy, receive the water level information fed back by the water level monitoring module, and automatically adjust the opening of the irrigation area sluice to achieve the adaptive adjustment of the irrigation area water level, including:

[0019] Receive the water level information fed back by the water level monitoring module in real time, including the current water level height and water level change trend, and preset the relevant parameters and rules of the fuzzy control algorithm;

[0020] According to the water level information fed back by the water level monitoring module, use the deviation between the current water level and the set water level and the water level change trend as the input variables of the fuzzy control;

[0021] Define the fuzzy sets and membership functions, and through the membership functions, fuzzify the deviation and change trend to obtain the fuzzified input variables;

[0022] According to the preset fuzzy rules, perform inference operations on the fuzzified input variables to obtain the fuzzified output variables, that is, the adjustment amount of the gate opening;

[0023] Perform defuzzification processing on the fuzzified output variables to convert the fuzzified output variables into the gate opening adjustment amount;

[0024] According to the adjustment amount of the gate opening, send a control instruction to the gate actuator. The gate actuator automatically adjusts the opening of the gate according to the control instruction to achieve the adaptive adjustment of the water level in the irrigation area.

[0025] Furthermore, collect and store various data during the water operation process in real time, including water level change data, gate opening data, flow data, and water quality data, and analyze various data through an optimization algorithm based on the cuckoo's brood parasitism behavior to continuously optimize and adjust the water level control strategy, including:

[0026] Collect various data during the water operation process in real time, including water level change data, gate opening data, flow data, and water quality data, and set the parameters of the optimization algorithm based on the cuckoo's brood parasitism behavior, including population size, number of iterations, and number of nests;

[0027] Initialize the population, that is, generate a set of initial water level control strategies as the starting point of the algorithm;

[0028] According to the goal of water level control, evaluate each individual in the current population, calculate the score of each individual through the performance evaluation function to evaluate the advantages and disadvantages of each water level control strategy;

[0029] According to the cuckoo's brood parasitism behavior, select a part of the individuals for update to generate new water level control strategies;

[0030] Compare the performance evaluation function scores of the new water level control strategy and the current strategy to obtain the comparison result, and determine the final water level control strategy according to the comparison result;

[0031] Repeat the selection, generation, and comparison process until the preset number of iterations is reached, and adjust the water level in the irrigation area in real time according to the final water level control strategy.

[0032] Furthermore, purify the water discharged through the sluice, and use the water quality monitoring and optimization algorithm to monitor the water quality indicators in real time, and adjust the purification treatment process according to the water quality change to remove pollutants in the water, including:

[0033] Install purification treatment equipment downstream of the sluice, including filtration devices, sedimentation tanks, and disinfection devices, and set the initial water quality monitoring indicators and purification treatment process parameters according to the historical data of water quality monitoring, including filtration speed, disinfectant dosage, and sedimentation tank residence time;

[0034] Set the sampling frequency and monitoring point location of the water quality monitoring sensor. The water quality monitoring sensor collects the water quality data of the water discharged through the sluice in real time according to the preset sampling frequency and monitoring point location.

[0035] Compare the water quality data with the preset water quality standards and historical data to determine whether there is an abnormality in the current water quality, so as to obtain the comparison result;

[0036] Generate a water quality analysis report according to the comparison result, including the current values of various water quality indicators, the comparison with historical data, and whether the preset standards are met;

[0037] Calculate the adjusted purification process parameters according to the water quality analysis report, including the adjustment amount of the filtration speed of the filtration device, the adjustment amount of the dosage of the disinfectant, and the adjustment amount of the residence time of the sedimentation tank;

[0038] Formulate an adjustment strategy according to the purification process parameters, including the adjustment objectives, steps, and time nodes, and generate a control instruction according to the adjustment strategy, including the adjusted parameter values and the execution time;

[0039] Send the control instruction to the control unit of the purification treatment module, parse the instruction content, and adjust the operating state of the purification equipment according to the instruction content, including adjusting the filtration speed of the filtration device, increasing or decreasing the dosage of the disinfectant, and changing the residence time of the sedimentation tank, so as to remove pollutants in the water.

[0040] Further, formulate an adjustment strategy according to the purification process parameters, including the adjustment objectives, steps, and time nodes, and generate a control instruction according to the adjustment strategy, including the adjusted parameter values and the execution time, including:

[0041] Set the constraint conditions of the adjustment strategy, including the operating capacity of the purification equipment, energy consumption limit, and adjustment cost factors, and encode the parameters of the purification process, including filtration speed, disinfectant dosage, and sedimentation tank residence time, as individuals of the genetic algorithm, and each individual represents an adjustment strategy;

[0042] Randomly generate a group of initial individuals as the initial population of the genetic algorithm, and each individual contains a set of adjusted purification process parameters;

[0043] Define a fitness function according to the adjustment objectives and constraint conditions to evaluate the quality of each individual, that is, the effect of the adjustment strategy, so as to obtain the fitness function evaluation result;

[0044] Determine the parent individuals according to the fitness function evaluation result, and perform crossover operations on the parent individuals to generate new offspring individuals;

[0045] Perform mutation operations on the new offspring individuals and perform fitness scoring, and update the population according to the evaluation result;

[0046] Continuously repeat the process of determining parent individuals, generating new offspring through crossover, mutating the offspring, and updating the population according to fitness until the preset number of iterations is reached. Determine the final individual based on the fitness score of the individuals, and use the final individual as the adjustment strategy.

[0047] Decode the final individual into the parameter values of the purification process, including the adjustment amount of the filtration speed, the dosage of the disinfectant, and the adjustment amount of the residence time in the sedimentation tank extracted from the individual genes.

[0048] According to the parameter values of the purification process, combined with the real-time data of water quality monitoring and the water level changes in the irrigation area, formulate the execution time, and combine the parameter values of the purification process and the execution time into a control instruction.

[0049] Furthermore, remotely monitor the water level changes inside and outside the irrigation area, the operating status of the sluice main body, and the working conditions of the purification module, and send an alarm when an abnormal situation occurs, including:

[0050] Determine the alarm information template according to the triggering conditions. The template includes abnormal description, time, location, and countermeasures.

[0051] Real-time collect the water level, sluice status, and working data of the purification module through sensors inside and outside the irrigation area, and generate a real-time data monitoring report based on the working parameter data. The report includes the data values of each monitoring point and a preliminary judgment on whether an abnormal situation is triggered.

[0052] Compare the real-time data monitoring report with the set of triggering conditions. If the data of any monitoring point meets the triggering conditions of the abnormal situation, record the abnormal type and add the abnormal type to the abnormal event list.

[0053] When the abnormal event list is not empty, generate alarm information for each abnormal event according to the alarm information template, and send the alarm information through the preset alarm propagation channels.

[0054] The above solution of the present invention has at least the following beneficial effects:

[0055] The water level monitoring module continuously monitors the water level changes in the irrigation area and accurately feeds back the information to the gate control module, enabling the system to quickly respond to the water level changes, automatically adjust the sluice opening, and achieve the adaptive adjustment of the irrigation area water level. This greatly improves the accuracy and efficiency of water level adjustment and ensures the stable supply of irrigation water.

[0056] The data analysis module collects and stores various data in real time during the operation process, and analyzes the data using the optimization algorithm of the cuckoo brood parasitism behavior. This helps the system continuously learn and adapt to the water level change law of the irrigation area, optimize and adjust the water level control strategy, and make the water level control more scientific and reasonable.

[0057] The purification treatment module is set downstream of the sluice to purify the water discharged through the sluice. At the same time, water quality monitoring and optimization algorithms are used to monitor water quality indicators in real time, and the purification treatment process is adjusted according to water quality changes. This ensures that the discharged water quality meets environmental protection standards, effectively removes pollutants in the water, and protects the ecological environment of the irrigation area.

[0058] The remote monitoring module can remotely monitor the water level changes inside and outside the irrigation area, the operation status of the main body of the sluice, and the working conditions of the purification treatment module. In case of abnormal situations, the system can issue an alarm in a timely manner to remind the management personnel to take measures. This enhances the system's remote monitoring and emergency response capabilities, and improves the safety and reliability of irrigation area management.

[0059] The energy management module provides power supply and monitors the energy consumption in real time. By adjusting the operation parameters of the equipment, the system can continuously optimize the energy consumption and reduce the operation cost. This helps to improve the energy utilization efficiency and promote the sustainable development of the irrigation area. Description of the Drawings

[0060] Figure 1 It is a schematic diagram of an irrigation area sluice system with adaptive water level regulation provided by an embodiment of the present invention.

[0061] Figure 2 It is a schematic flow chart of a system information processing system based on power data according to a preset water level control strategy, receiving water level information fed back by the water level monitoring module, and automatically adjusting the opening degree of the irrigation area sluice to achieve adaptive regulation of the irrigation area water level provided by an embodiment of the present invention. Detailed Embodiment

[0062] Hereinafter, exemplary embodiments of the present disclosure will be described in more detail with reference to the drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art.

[0063] As Figure 1 shown, an embodiment of the present invention provides an irrigation area sluice system with adaptive water level regulation, including:

[0064] A water level monitoring module 11 for real-time monitoring of water level changes in the irrigation area and feeding back water level information to the gate control module;

[0065] A gate control module 12 for automatically adjusting the opening degree of the irrigation area sluice according to a preset water level control strategy, receiving the water level information fed back by the water level monitoring module, so as to achieve adaptive regulation of the irrigation area water level;

[0066] The data analysis module 13 is used to collect and store in real time various data during the water operation process, including water level change data, gate opening data, flow rate data, and water quality data, and analyze various data through the optimization algorithm of the cuckoo brood parasitism behavior to continuously optimize and adjust the water level control strategy;

[0067] The purification treatment module 14 is set downstream of the sluice and is used to purify the water discharged through the sluice, and uses the water quality monitoring and optimization algorithm to monitor the water quality indicators in real time, adjusts the purification treatment process according to the water quality change, and removes pollutants in the water;

[0068] The remote monitoring module 15 is used to remotely monitor the water level changes inside and outside the irrigation area, the operation status of the main body of the sluice, and the working conditions of the purification treatment module, and issue an alarm when an abnormal situation occurs;

[0069] The energy management module 16 is used to provide power supply, monitor the energy consumption situation in real time, and continuously optimize the energy consumption by adjusting the equipment operation parameters.

[0070] In the embodiment of the present invention, by real-time monitoring the water level changes in the irrigation area, the water level monitoring module 11 can ensure the accuracy and timeliness of the water level information. This will provide reliable data support for the gate control module, making the adaptive adjustment of the irrigation area water level more accurate and efficient. At the same time, real-time monitoring also helps to detect water level anomalies in a timely manner and provide early warnings for the management of the irrigation area.

[0071] The gate control module 12 automatically receives the water level information feedback by the water level monitoring module according to the preset water level control strategy, and adjusts the opening degree of the irrigation area sluice. This adaptive adjustment method not only improves the flexibility of water level control, but also reduces the intervention of manual operation and lowers the management cost. At the same time, the automatic adjustment can also ensure the stability of the irrigation area water level and meet the water use requirements of agricultural production.

[0072] The data analysis module 13 collects and stores in real time various data during the water operation process, providing rich data support for the scientific management of the irrigation area. By analyzing various data through the optimization algorithm of the cuckoo brood parasitism behavior, the system can continuously optimize and adjust the water level control strategy, making the management of the irrigation area more intelligent and refined. This will help improve the utilization efficiency of water resources and promote the sustainable development of the irrigation area.

[0073] The purification treatment module 14 is set downstream of the sluice and purifies the water discharged through the sluice, effectively removing pollutants in the water. At the same time, it uses the water quality monitoring and optimization algorithm to monitor the water quality indicators in real time and adjusts the purification treatment process according to the water quality change to ensure that the discharged water quality meets the environmental protection standards. This will help protect the ecological environment of the irrigation area and improve the water quality safety of the irrigation area.

[0074] The remote monitoring module 15 can remotely monitor the water level changes inside and outside the irrigation area, the operating status of the main sluice body, and the working conditions of the purification and treatment module. This remote monitoring method not only improves the convenience of irrigation area management but also enhances the real-time and accuracy of management. In case of abnormal situations, the system can promptly issue an alarm to remind the management personnel to take measures, ensuring the safety and reliability of irrigation area management.

[0075] The energy management module 16 provides power supply and monitors the energy consumption in real time. By adjusting the operating parameters of the equipment, the system can continuously optimize the energy consumption and reduce the operating cost. This will help improve the energy utilization efficiency of the irrigation area and reduce energy waste.

[0076] In a preferred embodiment of the present invention, the real-time monitoring of the water level changes in the irrigation area and the feedback of the water level information to the gate control module may include:

[0077] Install water level sensors at key positions in the irrigation area, including the channel entrance, exit, turning points, or places vulnerable to water level changes;

[0078] The water level sensors collect the water level data in the irrigation area in real time according to the preset sampling frequency, including the current water level height and the water level change trend information;

[0079] Convert the water level data into an electrical signal and transmit the electrical signal to the gate control module.

[0080] In the embodiment of the present invention, according to the layout and topographical characteristics of the irrigation area, determine the key positions where water level sensors need to be installed. These positions are areas where the water level changes frequently or is easily affected by external factors. Then, select a suitable type of water level sensor (such as float type, pressure type, ultrasonic type, etc.) and install it according to the installation instructions of the sensor. Ensure that the sensor is firmly installed and its sensing part can accurately contact the water surface to accurately measure the water level. After the installation of the water level sensor is completed, configure the working parameters of the sensor to set its sampling frequency. The sampling frequency can be adjusted according to actual needs to ensure that the collected water level data is both accurate and timely. Each time the sensor samples, it measures the current water level height and calculates the water level change trend (such as rising, falling, or stable) through the built-in algorithm. The collected water level data and change trend information are stored in the memory of the sensor for subsequent transmission.

[0081] The water level sensor internally contains a signal conversion circuit that can convert the collected water level data into electrical signals (such as voltage, current, or frequency, etc.). These electrical signals are transmitted to the gate control module via wired or wireless means (such as cables, Bluetooth, Wi-Fi, etc.). During the transmission process, it is necessary to ensure the stability and accuracy of the signals to avoid data loss or mistransmission. After receiving the electrical signal, the gate control module will analyze and process it to obtain accurate water level information.

[0082] Suppose the irrigation area is a large-scale farmland irrigation system, which contains a main channel and multiple branch channels. In order to accurately control the water level in the irrigation area, water level sensors are installed at the entrance, exit of the main channel, and at the turning points of each branch channel.

[0083] At the entrance of the main channel, the water level sensor collects the current water level height and the change trend information at a frequency of once per second. When the water level rises, the sensor will detect the increase in the water level and convert this information into an electrical signal. The electrical signal is transmitted to the gate control module via wired means (such as cables). After receiving the electrical signal, the gate control module will analyze and obtain the water level height and the change trend information. If the water level height exceeds the preset threshold and shows an upward trend, the gate control module will automatically adjust the opening degree of the water gate to reduce the water inflow and thus control the water level.

[0084] By installing water level sensors at key positions in the irrigation area and setting appropriate sampling frequencies, it can ensure that the collected water level data is accurate and reliable. This provides accurate water level information for the gate control module, which helps to achieve more precise water level regulation. The water level sensor can monitor the water level changes in real time and transmit the change information to the gate control module in a timely manner. This enables the gate control module to quickly respond to the water level changes and adjust the opening degree of the water gate in a timely manner to maintain the water level stability in the irrigation area. Through an automated and intelligent water level monitoring and regulation system, it can reduce the intervention of manual operations and lower the management costs. At the same time, the system can also automatically optimize the water level control strategy based on historical data and real-time water level changes, improving the scientific and refined level of irrigation area management. By monitoring the water level changes in real time and adjusting the opening degree of the water gate in a timely manner, it can effectively avoid potential threats caused by too high or too low water levels to the irrigation area. This helps to ensure the safe operation of the irrigation area and reduce disaster accidents caused by abnormal water levels.

[0085] In a preferred embodiment of the present invention, according to the preset water level control strategy, receiving the water level information feedback by the water level monitoring module and automatically adjusting the opening degree of the water gates in the irrigation area to achieve the adaptive adjustment of the water level in the irrigation area may include:

[0086] Receiving the water level information feedback by the water level monitoring module in real time, including the current water level height, the water level change trend, and presetting the relevant parameters and rules of the fuzzy control algorithm;

[0087] According to the water level information fed back by the water level monitoring module, the deviation between the current water level and the set water level and the water level change trend are used as the input variables of fuzzy control;

[0088] Define fuzzy sets and membership functions. Through the membership functions, the deviation and change trend are fuzzified to obtain the fuzzified input variables;

[0089] According to the preset fuzzy rules, perform inference operations on the fuzzified input variables to obtain the fuzzified output variable, that is, the adjustment amount of the gate opening;

[0090] Perform defuzzification on the fuzzified output variable to convert the fuzzified output variable into the adjustment amount of the gate opening;

[0091] According to the adjustment amount of the gate opening, send a control command to the gate actuator. The gate actuator automatically adjusts the opening of the gate according to the control command to achieve the adaptive adjustment of the water level in the irrigation area.

[0092] In the embodiment of the present invention, the water level monitoring module collects water level data in real time through sensors, and transmits the current water level height and water level change trend (such as rising, falling, stable) to the control system by wired or wireless means. After receiving the data, the control system performs parsing and storage. At the same time, according to the actual situation and control requirements of the irrigation area, relevant parameters (such as the division of fuzzy sets, the shape of membership functions, etc.) and rules (such as "if the water level is higher than the set value and shows an upward trend, then reduce the gate opening") of the fuzzy control algorithm are preset.

[0093] Extract the current water level height from the stored water level data, compare it with the preset set water level, and calculate the water level deviation. At the same time, the water level change trend is also used as another input variable. These two input variables will be used as the input of the fuzzy control algorithm for subsequent fuzzification processing. According to the preset fuzzy set division (such as dividing the water level deviation into fuzzy sets such as "positive large", "positive medium", "positive small", "negative small", "negative medium", "negative large", etc.), define the corresponding membership functions. These membership functions map the water level deviation and change trend to the fuzzy sets to obtain the fuzzified input variables. The fuzzified input variables can more accurately describe the fuzziness of the water level deviation and change trend. According to the preset fuzzy rules (such as "if the water level deviation is positive large and the change trend is upward, then the adjustment amount of the gate opening is negative large"), perform inference operations on the fuzzified input variables. The result of the inference operation is a fuzzified output variable, that is, the adjustment amount of the gate opening. This adjustment amount indicates whether the gate opening needs to be increased or decreased and the adjustment amplitude.

[0094] Since the output variable after fuzzification is a fuzzy set, it needs to be converted into a specific numerical value, that is, the adjustment amount of the gate opening. This can be achieved through defuzzification methods (such as the centroid method, the maximum membership degree method, etc.). The result after defuzzification is a specific numerical value, indicating the specific amount by which the gate opening needs to be adjusted. According to the defuzzified adjustment amount of the gate opening, corresponding control instructions are generated and sent to the gate actuator by wired or wireless means. After receiving the control instructions, the gate actuator automatically adjusts the opening of the gate according to the content of the instructions. In this way, the water level in the irrigation area can be adaptively adjusted according to the feedback information of the water level monitoring module.

[0095] Assume that the set value of the water level in the irrigation area is 5 meters, the current water level height is 5.2 meters, and the water level is on the rise. The water level monitoring module feeds back this information to the control system in real time. After receiving the water level information, the water level deviation is calculated to be 0.2 meters (higher than the set value), and the water level change trend is identified as rising. Then, the water level deviation and the change trend are used as input variables for fuzzy control and fuzzified. Assume that the fuzzified input variables are "center" and "rising" respectively. According to the preset fuzzy rules, inference operations are performed on the fuzzified input variables, and the fuzzified output variable is obtained as "negative center", indicating that the gate opening needs to be reduced. Then, the fuzzified output variable is defuzzified to obtain a specific gate opening adjustment amount of -0.1 meters (indicating that the gate opening needs to be reduced by 0.1 meters). Finally, a control instruction is sent to the gate actuator, and the gate actuator automatically adjusts the opening of the gate according to the content of the instruction, so that the water level in the irrigation area gradually decreases to near the set value.

[0096] By comprehensively considering the water level deviation and the change trend through the fuzzy control algorithm, the gate opening can be adjusted more accurately, so that the water level in the irrigation area is maintained near the set value, improving the accuracy and stability of water level control. The fuzzy control algorithm can adaptively adjust the gate opening according to the feedback information of the water level monitoring module without manual intervention, enhancing the adaptive ability of the system. The automated and intelligent water level control system can reduce the intervention of manual operation, lower the management cost, and improve the efficiency of irrigation area management. By real-time monitoring the water level change and timely adjusting the gate opening, the potential threats caused by too high or too low water level to the irrigation area can be effectively avoided, ensuring the safe operation of the irrigation area.

[0097] In another preferred embodiment of the present invention, the calculation formula of the membership function is:

[0098]

[0099] where μ(h) represents the degree to which the current water level h belongs to the fuzzy set; h represents the current water level; h s represents the set water level; E 1 represents the left boundary threshold; δ represents the offset; E2 represents the right boundary threshold; k represents the slope adjustment parameter.

[0100] In an embodiment of the present invention, the set water level h is determined s , which is the target value of water level control. The left boundary threshold E is set 1 and the right boundary threshold E 2 . These two values define the acceptance range of water level deviation. The offset δ is set to fine-tune the flexibility of the boundary. The slope adjustment parameter k is set, which affects the change rate of the membership function within the boundary region. The current water level height h is obtained in real time through the water level monitoring module.

[0101] When h ≤ h s - E 1 - δ, μ(h) = 1. This means that the current water level is much lower than the set water level and completely belongs to the "low water level" fuzzy set.

[0102] When h s - E 1 - δ < h ≤ h s + E 2 + δ, a linear function is used to calculate μ(h). The specific formula is This formula calculates the degree to which the current water level belongs to the fuzzy set based on the magnitude of the water level deviation and the slope adjustment parameter k. The closer the water level is to the set water level, the closer the value of μ(h) is to 1; the farther the water level is from the set water level but still within the boundary range, the closer the value of μ(h) is to 0.

[0103] When h > h s + E 2 + δ, μ(h) = 0. This means that the current water level is much higher than the set water level and does not belong to the "low water level" fuzzy set at all.

[0104] By adjusting the offset δ and the slope adjustment parameter k, the membership function can better adapt to different water level control scenarios. This increases the flexibility of the water level control system, enabling it to respond more accurately to water level changes. The membership function adopts a linear change within the boundary region, which helps to smoothly transition the water level control strategy. When the water level approaches the set water level, the control strategy can be gradually adjusted to avoid sudden water level fluctuations, thereby enhancing the robustness of the system. The membership function provides a clear relationship between the input variables and output variables for the fuzzy control algorithm. This simplifies the design process of the control strategy, making it easier for engineers to understand and adjust the water level control system. By calculating the membership values in real time and inputting them into the fuzzy control algorithm, it can quickly respond to water level changes and adjust the gate opening. This improves the efficiency of water level control and helps to maintain the stability of the water level in the irrigation area. As an important part of the fuzzy control algorithm, the membership function provides strong support for the intelligent management of the irrigation area. Through an automated and intelligent water level control system, the intervention of manual operations can be reduced, and the scientific and refined level of irrigation area management can be improved.

[0105] In a preferred embodiment of the present invention, various types of data during the water operation process are collected and stored in real time, including water level change data, gate opening data, flow data, and water quality data, and through the optimization algorithm of the cuckoo's brood parasitism behavior, various types of data are analyzed to continuously optimize and adjust the water level control strategy, which may include:

[0106] Collect various types of data during the water operation process in real time, including water level change data, gate opening data, flow data, and water quality data, and set the parameters of the optimization algorithm of the cuckoo's brood parasitism behavior, including population size, number of iterations, and number of nests;

[0107] Initialize the population, that is, generate a set of initial water level control strategies as the starting point of the algorithm;

[0108] According to the goal of water level control, evaluate each individual in the current population, calculate the score of each individual through the performance evaluation function, so as to evaluate the advantages and disadvantages of each water level control strategy;

[0109] According to the cuckoo's brood parasitism behavior, select a part of the individuals for update to generate new water level control strategies;

[0110] Compare the performance evaluation function scores of the new water level control strategy and the current strategy to obtain a comparison result, and determine the final water level control strategy according to the comparison result;

[0111] Repeat the selection, generation, and comparison process until the preset number of iterations is reached, and adjust the water level in the irrigation area in real time according to the final water level control strategy.

[0112] In the embodiments of the present invention, a sensor network is deployed at key locations such as reservoirs and river channels. These sensors can monitor and collect data such as water level, gate opening, flow rate, and water quality in real time. The data is transmitted to the data center via wireless or wired means for storage and processing. In the algorithm implementation, the population size (i.e., the number of candidate water level control strategies), the number of iterations (the number of loops for the algorithm to run), and the number of nests (the number of attempts for the cuckoo to find a new nest) are set. These parameters are optimally selected according to the specific problem and computing resources. A set of initial water level control strategies is generated based on historical data. These strategies are (such as adjusting the water level at fixed time intervals). A performance evaluation function is defined, which scores each water level control strategy according to the objectives of water level control (such as maximizing irrigation efficiency, minimizing water resource waste, maintaining water quality, etc.). The scores of each strategy are calculated using this function.

[0113] Simulate the parasitic breeding behavior of cuckoos, that is, randomly select a part of the individuals (i.e., water level control strategies) for update. The update can be achieved by introducing random mutations and crossover methods to generate new candidate strategies. Calculate the scores of the performance evaluation function for the newly generated water level control strategies and compare them with the current strategies. Select the strategy with a higher score as the new best strategy. Loop through the above selection, generation, and comparison processes until the preset number of iterations is reached. Then, according to the finally determined best water level control strategy, the water level in the irrigation area is adjusted in real time.

[0114] Suppose there is a simple irrigation area system with the goal of maximizing irrigation efficiency while maintaining water quality. The execution process of (the algorithm) is as follows:

[0115] Randomly generate 10 water level control strategies (population size), and each strategy contains a series of rules on when to adjust the water level and how much to adjust. Evaluate each strategy using the performance evaluation function. For example, a strategy may get 80 points because it achieves a good balance between irrigation efficiency and water quality maintenance. Select 5 of these strategies for update (based on the parasitic breeding behavior of cuckoos). The updated strategies introduce more randomness. Calculate the scores of the performance evaluation function for the newly generated strategies and compare them with the current best strategy. Suppose a new strategy gets 85 points and becomes the new strategy. Repeat the above process until the preset number of iterations is reached. Finally, select the strategy with the highest score as the final water level control strategy.

[0116] By collecting and analyzing data in real time, and using optimization algorithms to adjust the water level control strategy, the utilization efficiency of water resources can be significantly improved, and waste can be reduced. The optimization algorithm can consider water quality factors to ensure that the water quality is maintained or improved during irrigation, thereby protecting the environment and crop health. Since the algorithm can adjust the water level control strategy in real time, it can adapt to different weather conditions, irrigation demands, and other changing factors. The entire process is automated, reducing the need for manual intervention. At the same time, the continuous learning and optimization of the algorithm make the water level control more intelligent. By optimizing water resource use, reducing waste and pollution, it helps to achieve sustainable agricultural development and environmental protection.

[0117] In another preferred embodiment of the present invention, the calculation formula of the performance evaluation function is:

[0118]

[0119] where f represents the total value of the performance evaluation function; w 1 , w 2 , w 3 , w 4 represent weight coefficients; W c represents the actual water level value in the irrigation area; W t represents the target water level; C represents a constant coefficient; n represents the number of times of the change in the gate opening; i represents an index variable; x i represents the change in the gate opening at the i-th time; T r represents the water level change response time; m represents the total number of water level measurements; D i represents the water level measurement value at the i-th time.

[0120] In the embodiment of the present invention, the actual water level value W c , the target water level W t , the change in the gate opening x i , the water level change response time T r , and the water level measurement value D i in the irrigation area are collected. The collected data is preprocessed, including data cleaning, missing value filling, and outlier processing, to ensure the accuracy and consistency of the data. According to the actual situation and requirements of the irrigation area, the weight coefficients w 1 , w 2 , w 3 , w 4 and the constant coefficient C are set. These parameters reflect the importance of different indicators in performance evaluation, determine the number of times n of the change in the gate opening and the total number of water level measurements m. Calculate the deviation between the actual water level value W c and the target water level W t , that is, |W c - W t |. For each change in the gate opening xi Square them and sum them up to obtain Directly use the collected water level change response time T r to calculate the average value of the water level measurement Calculate the square of the deviation between each water level measurement and the average value, and sum them up to obtain

[0121] Finally, calculate the standard deviation, that is Substitute the above calculation results into the formula of the performance evaluation function to calculate the total value f of the performance evaluation function.

[0122] The performance evaluation function comprehensively considers multiple factors such as water level deviation, change in gate opening, water level change response time, and stability of water level measurements, and can more comprehensively evaluate the effect of the water level control strategy. By continuously optimizing the weight coefficients and constant coefficients, the accuracy of water level control can be further improved. The weight coefficients in the performance evaluation function can be adjusted according to the actual situation and requirements of the irrigation area, making the water level control strategy more adaptable to different irrigation conditions and environmental changes. By minimizing the sum of the squares of the water level deviation and the change in gate opening, unnecessary water resource waste can be reduced and the utilization efficiency of water resources can be improved. By shortening the water level change response time, the water level can be adjusted faster to meet the irrigation demand and improve the irrigation efficiency. The performance evaluation function considers the stability of water level measurements, which helps to maintain the stability and consistency of water quality, thus ensuring the growth and quality of crops. The performance evaluation function provides a quantitative evaluation index for the water level control in the irrigation area, which helps to achieve intelligent management and improve the management efficiency and decision-making level.

[0123] In a preferred embodiment of the present invention, the water discharged through the sluice is purified, and a water quality monitoring and optimization algorithm is used to monitor water quality indicators in real time, and the purification process is adjusted according to water quality changes to remove pollutants in the water, which may include:

[0124] Install purification treatment equipment downstream of the sluice, including a filtration device, a sedimentation tank, and a disinfection device, and set initial water quality monitoring indicators and purification process parameters according to historical water quality monitoring data, including filtration speed, disinfectant dosage, and residence time in the sedimentation tank;

[0125] Set the sampling frequency and monitoring point positions of the water quality monitoring sensors. The water quality monitoring sensors collect the water quality data of the water discharged through the sluice in real time according to the preset sampling frequency and monitoring point positions.

[0126] Compare the water quality data with the preset water quality standards and historical data to determine whether there is an abnormality in the current water quality to obtain a comparison result;

[0127] Generate a water quality analysis report based on the comparison results, including the current values of various water quality indicators, the comparison with historical data, and whether the preset standards are met;

[0128] Calculate the adjusted purification process parameters based on the water quality analysis report, including the adjustment amount of the filtration speed of the filtration device, the adjustment amount of the dosage of the disinfectant, and the adjustment amount of the residence time in the sedimentation tank;

[0129] Formulate an adjustment strategy based on the purification process parameters, including the objectives, steps, and time nodes of the adjustment, and generate control instructions based on the adjustment strategy, including the parameter values to be adjusted and the execution time;

[0130] Send the control instructions to the control unit of the purification processing module, parse the instruction content, and adjust the operating state of the purification equipment according to the instruction content, including adjusting the filtration speed of the filtration device, increasing or decreasing the dosage of the disinfectant, and changing the residence time in the sedimentation tank to remove pollutants in the water.

[0131] In the embodiment of the present invention, physically, install necessary purification processing equipment downstream of the sluice, such as a filtration device, a sedimentation tank, and a disinfection device. Then, based on past water quality monitoring data, analyze and determine the initial water quality monitoring indicators (such as turbidity, pH value, dissolved oxygen, etc.) and the parameters of the purification process (such as the filtration speed is set to X m / s, the dosage of the disinfectant is set to Y mg / L, and the residence time in the sedimentation tank is set to Z minutes). Configure water quality monitoring sensors, set their sampling frequency (such as sampling once per hour) and the monitoring point location (such as 50 meters downstream of the sluice). The sensors automatically and continuously collect water quality data according to these settings, including the specific values of various water quality indicators. Compare the real-time collected water quality data with the preset water quality standards (such as national water quality standards) and historical data. Through algorithm analysis, judge whether the current water quality exceeds the normal range, whether there is an abnormality, and generate a comparison result.

[0132] Based on the comparison results, a water quality analysis report is automatically generated. The report details the current values of various water quality indicators, the comparison with historical data, and whether the preset standards are met. The report may also include trend analysis, speculation on the causes of anomalies, etc. Based on the information in the water quality analysis report, algorithms are used to calculate the parameters of the purification treatment process that need to be adjusted. For example, if the turbidity exceeds the standard, the filtration speed of the filtration device may need to be increased; if the microbial content is too high, the dosage of disinfectant may need to be increased. Based on the calculated adjustment parameters, a detailed adjustment strategy is formulated. The strategy includes the adjustment objectives (such as reducing the turbidity below X NTU), steps (such as first increasing the filtration speed and then adjusting the disinfectant dosage), and time nodes (such as execute immediately or within the next X hours). Then, specific control instructions are generated based on the strategy, including the parameter values to be adjusted and the execution time. The generated control instructions are sent to the control unit of the purification treatment module through the communication interface. The control unit analyzes the instruction content and adjusts the operating state of the purification equipment according to the instruction requirements. For example, adjust the filtration speed of the filtration device to the specified value, increase or decrease the dosage of disinfectant, change the residence time of the sedimentation tank, etc., to effectively remove pollutants in the water.

[0133] Suppose it is monitored that the turbidity of the water quality downstream of the sluice exceeds the standard, reaching 120 NTU, while the preset water quality standard is below 60 NTU. Perform the following steps:

[0134] The water quality monitoring sensor real-time collects data with a turbidity of 120 NTU, compares the collected data with the preset standard, finds that the turbidity exceeds the standard, generates a water quality analysis report, indicates that the turbidity exceeds the standard, and lists the comparison between historical data and the current value. Based on the report, it is calculated that the filtration speed of the filtration device needs to be increased by 20% to reduce the turbidity. A adjustment strategy is formulated with the goal of reducing the turbidity below 60 NTU, the step is to first increase the filtration speed, and the execution time is immediately. A control instruction is generated, requiring the filtration speed of the filtration device to be increased by 20% and executed immediately. The instruction is sent to the control unit of the purification treatment module, and the control unit analyzes the instruction content and adjusts the filtration speed of the filtration device to the specified value.

[0135] By real-time monitoring and automatically adjusting the parameters of the purification treatment process, it is possible to quickly respond to water quality changes, improve the water quality treatment efficiency, ensure that the treated water quality meets the preset standards, and guarantee the water use safety downstream. The automated treatment process reduces the need for manual intervention, lowers the labor cost, and can automatically adjust the treatment process parameters according to water quality changes to adapt to different water quality conditions and pollution situations.

[0136] In another preferred embodiment of the present invention, according to the parameters of the purification treatment process, an adjustment strategy is formulated, including the adjustment objectives, steps, and time nodes. And according to the adjustment strategy, control instructions are generated, including the parameter values to be adjusted and the execution time, which may include:

[0137] Set the constraint conditions for the adjustment strategy, including the operating capacity of the purification equipment, energy consumption limit, and adjustment cost factors, and encode the parameters of the purification process, including the filtration rate, disinfectant dosage, and sedimentation tank residence time, as individuals of the genetic algorithm. Each individual represents an adjustment strategy;

[0138] Randomly generate a group of initial individuals as the initial population of the genetic algorithm. Each individual contains a set of adjusted purification process parameters;

[0139] According to the adjustment target and constraint conditions, define a fitness function to evaluate the quality of each individual, that is, the effect of the adjustment strategy, to obtain the evaluation result of the fitness function;

[0140] According to the evaluation result of the fitness function, determine the parent individuals and perform crossover operations on the parent individuals to generate new offspring individuals;

[0141] Perform mutation operations on the new offspring individuals and conduct fitness scoring, and update the population according to the evaluation results;

[0142] Continuously repeat the process of determining parent individuals, crossing to generate new offspring, mutating offspring, and updating the population according to fitness until the preset number of iterations is reached. According to the fitness scores of the individuals, determine the final individuals and use the final individuals as the adjustment strategy;

[0143] Decode the final individuals into the parameter values of the purification process, including extracting the filtration rate adjustment amount, disinfectant dosage, and sedimentation tank residence time adjustment amount in the individual genes;

[0144] According to the parameter values of the purification process, combined with the real-time data of water quality monitoring and the water level changes in the irrigation area, formulate the execution time, and combine the parameter values of the purification process and the execution time into a control instruction.

[0145] In the embodiments of the present invention, the constraint conditions that the adjustment strategy needs to follow are clarified, such as the maximum operating capacity of the purification equipment, the upper limit of energy consumption, and the adjustment cost budget. Then, the key parameters (filtration rate, disinfectant dosage, sedimentation tank residence time) of the purification process are encoded to form individuals of the genetic algorithm. Each individual represents a possible adjustment strategy and contains the specific values of these parameters. Using a random number generator, a group of initial individuals are created, and these individuals constitute the initial population of the genetic algorithm. Each individual contains a set of adjusted purification process parameters, which are randomly selected within the range of constraint conditions.

[0146] Define a fitness function according to the goals of water quality treatment (such as reducing turbidity, removing microorganisms, etc.) and constraints. This function calculates the fitness value of each individual, that is, the effect of the adjustment strategy. The higher the fitness value, the better the strategy. By executing the fitness function, the evaluation results of each individual are obtained. According to the evaluation results of the fitness function, select individuals with higher fitness as parents. Then, perform crossover operations on the parent individuals, that is, exchange a part of their genes (parameter values) to generate new offspring individuals. The crossover operation helps to introduce new mutations in the parents and increase the diversity of the population. Perform mutation operations on the generated offspring individuals, that is, randomly change a part of their genes (parameter values). Then, perform fitness scoring on the mutated offspring individuals to evaluate their effects. Finally, according to the fitness scoring results, update the population, retain individuals with higher fitness, and eliminate individuals with lower fitness. Repeat the above process of determining parents, generating offspring through crossover, mutating offspring, and updating the population until the preset number of iterations is reached. In each iteration, new offspring individuals are generated and their fitness is evaluated. Finally, according to the fitness scores of the individuals, determine the optimal individual and use it as the final adjustment strategy.

[0147] Decode the finally determined individual and extract parameter values such as the adjustment amount of the filtration rate, the dosage of the disinfectant, and the adjustment amount of the residence time in the sedimentation tank. These parameter values are the final purification treatment process parameters. According to the decoded purification treatment process parameter values, combined with the current real-time water quality monitoring data and the water level changes in the irrigation area, formulate the specific execution time. Then, combine the purification treatment process parameter values and the execution time into a control instruction and send it to the purification treatment equipment for execution.

[0148] Suppose a water quality purification problem is being processed, and the goal is to reduce the turbidity to below 60 NTU. Perform the following steps:

[0149] Set the operating capacity of the purification equipment, energy consumption limit, and adjustment cost factors as constraints, and randomly generate a group of initial individuals, each of which contains a set of adjusted purification treatment process parameters. Evaluate each individual according to the fitness function and calculate their fitness values. Select individuals with higher fitness as parents and perform crossover operations on them to generate new offspring individuals. Perform mutation operations on the offspring individuals and evaluate their fitness. Then, update the population according to the fitness scoring results. Continuously repeat the above process until the preset number of iterations is reached. Finally, determine an individual with the highest fitness as the adjustment strategy, decode the final individual into specific purification treatment process parameter values. Combine the real-time water quality monitoring data and the water level changes in the irrigation area, formulate the execution time, and combine the parameter values and the execution time into a control instruction and send it to the purification treatment equipment.

[0150] By optimizing the adjustment strategy through genetic algorithms, the water quality indicators can be more efficiently reduced and the purification efficiency can be improved. Genetic algorithms can handle complex non-linear problems and adapt to different water quality conditions and purification equipment capabilities. Optimizing the adjustment strategy within the constraint conditions helps reduce energy consumption and costs, can automatically formulate and adjust the purification treatment strategy, reduce manual intervention, and improve decision-making efficiency. Combining real-time water quality monitoring data and the water level changes in the irrigation area can quickly respond and adjust the purification treatment strategy to ensure water quality safety.

[0151] In another preferred embodiment of the present invention, the calculation formula of the fitness function is:

[0152]

[0153] Wherein, F represents the value of the fitness function; w 1 represents the weight of the water quality compliance part; k represents the number of water quality indicators; I represents the index of the water quality indicator; W I represents the actual value of the water quality indicator; W g represents the target water quality standard value; g I represents the weight of the water quality indicator I; w 2 represents the weight of the purification efficiency; P r represents the amount of pollutants removed during the purification process; P max represents the theoretical maximum removal amount; w r represents the weight of the purification efficiency; w 3 represents the weight of the energy consumption control part; α represents the energy consumption penalty coefficient; M represents the number of devices; E j represents the energy consumption value of the jth device; j represents the index of the device; w 4 represents the weight of the device capacity constraint part; P k represents the current parameter value of the kth device; w a represents the weight of the device capacity constraint; w 5 represents the weight of the adjustment cost part; p represents the number of adjustment steps; l represents the index of the adjustment step; C l represents the cost of the lth adjustment step; x l represents whether the first adjustment step is executed. If x l = 1, it means the step is executed; if x l = 0, it means the step is not executed.

[0154] In the embodiment of the present invention, the number k of water quality indicators, the number M of devices, and the number p of adjustment steps are determined. The weights w 1 (water quality compliance), w 2 (purification efficiency), w 3 (energy consumption control), w 4 (device capacity constraint), w5 (Adjustment cost). Set the energy consumption penalty coefficient α and the weight w of the equipment capacity constraint a and the weight w of the purification efficiency r , obtain the actual value W of the water quality index I and the target water quality standard value W g . Record the amount of pollutants removed P r during the purification process and the theoretical maximum removal amount P max , collect the energy consumption values E of each device j and the current parameter values P k , determine the cost C of each adjustment step l and whether to execute x l . For each water quality index I, calculate the absolute difference |W I -W g | between the actual value and the target value, multiply it by the weight g of this index I , and sum to obtain the total value of the water quality compliance part, then multiply by the weight w 1 to obtain the contribution value of the water quality compliance part. Calculate the ratio of the amount of pollutants removed P r during the purification process to the theoretical maximum removal amount P max , multiply it by the weight w of the purification efficiency r and the total weight w 2 to obtain the contribution value of the purification efficiency part. For each device j, obtain its energy consumption value E j , multiply it by the energy consumption penalty coefficient α and sum to obtain the total energy consumption value, then multiply by the weight w 3 and take the negative value (because energy consumption needs to be controlled, so the contribution value is negative) to obtain the contribution value of the energy consumption control part. For each device k, calculate the difference between its current parameter value P k and the theoretical maximum value or constraint value (take the difference if it exceeds, otherwise it is 0), multiply it by the weight w of the equipment capacity constraint a and sum to obtain the total value of the equipment capacity constraint part, then multiply by the weight w 4 and take the negative value (because exceeding the capacity constraint is unfavorable) to obtain the contribution value of the equipment capacity constraint part. For each adjustment step l, obtain its cost C l and whether to execute x l .

[0155] If executed (x l = 1), then multiply the cost by the weight w of the adjustment cost 5 and sum to obtain the total value of the adjustment cost part; if not executed (x l = 0), then the contribution value is 0, to obtain the contribution value of the adjustment cost part (negative because the cost needs to be minimized). Add up the contribution values of the above parts to obtain the total value of the fitness function F

[0156] The fitness function comprehensively considers water quality compliance, purification efficiency, energy consumption control, equipment capacity constraints, and adjustment costs, achieving multi-objective optimization. By optimizing the water quality compliance part, it ensures that the purified water quality is closer to the target standard value, improving the purification effect. The optimization of the purification efficiency part helps to achieve a higher pollutant removal amount under limited resources, enhancing the utilization efficiency of the purification equipment. The optimization of the energy consumption control part helps to reduce the energy consumption during the purification process, lowering the operating costs and environmental impact. The optimization of the equipment capacity constraints part ensures that the equipment operates within a safe range, avoiding the risk of overload or damage. The optimization of the adjustment cost part helps to minimize the cost expenditure during the adjustment process while meeting other constraints. The implementation of the fitness function enables the purification process to be automatically and intelligently adjusted and optimized, reducing manual intervention and decision-making time.

[0157] In a preferred embodiment of the present invention, remotely monitoring the water level changes inside and outside the irrigation area, the operating status of the sluice body, and the working conditions of the purification treatment module, and issuing an alarm when an abnormal situation occurs, may include:

[0158] According to the trigger conditions, determine the alarm information template, which includes abnormal description, time, location, and countermeasures;

[0159] Real-time collect the water level, sluice status, and working data of the purification treatment module through sensors inside and outside the irrigation area, and generate a real-time data monitoring report based on the working parameter data. The report includes the data values of each monitoring point and a preliminary judgment on whether an abnormal situation is triggered;

[0160] Compare the real-time data monitoring report with the set of trigger conditions. If the data of any monitoring point meets the trigger conditions of the abnormal situation, record the abnormal type and add the abnormal type to the abnormal event list;

[0161] When the abnormal event list is not empty, generate alarm information for each abnormal event according to the alarm information template, and send the alarm information through a preset alarm propagation channel.

[0162] In the embodiments of the present invention, a series of conditions are preset in advance, such as the water level exceeding the safety threshold, the abnormal state of the sluice (such as unable to close), the failure of the purification treatment module, etc. Create a structured template that contains placeholders for inserting abnormal descriptions, timestamps, specific locations, and recommended countermeasures. Store the designed template in the system configuration file for quick call when needed. Install water level sensors, sluice state monitors (such as opening sensors, motor state monitors), and working state monitors of the purification treatment module (such as flow sensors, water quality sensors) inside and outside the irrigation area. The sensors collect data regularly or in real-time and transmit it to the central processing unit via wired or wireless means. After receiving the data, the central processing unit performs necessary preprocessing (such as filtering, calibration), and generates a real-time data monitoring report containing the data values of each monitoring point and a preliminary abnormal judgment. Compare each monitoring point data value in the real-time data monitoring report with the preset set of trigger conditions one by one. If the data value meets a certain trigger condition, it is determined as abnormal, and the abnormal type (such as high water level, sluice failure, purification treatment module failure) is recorded. Add the abnormal type and its related information (such as time, location) to the abnormal event list.

[0163] Traverse the abnormal event list, fill in the alarm information template for each abnormal event, and generate specific alarm information. According to the preset alarm propagation channels (such as text messages, emails, APP push, audible and visual alarms, etc.), send the alarm information to relevant personnel or systems, and record the alarm sending time and receipt confirmation for subsequent tracking of the handling of abnormal events.

[0164] Suppose there are three monitoring points A, B, and C in the irrigation area, which monitor the water level, sluice state, and the working condition of the purification treatment module respectively:

[0165] Trigger conditions:

[0166] The water level exceeding 2 meters is abnormal; the sluice cannot be closed is abnormal; the flow rate of the purification treatment module being lower than 10 cubic meters per hour is abnormal.

[0167] Real-time data collection:

[0168] Monitoring point A: The water level is 2.1 meters.

[0169] Monitoring point B: The sluice state is normal.

[0170] Monitoring point C: The flow rate of the purification treatment module is 9 cubic meters per hour.

[0171] Real-time data monitoring report:

[0172] The water level at point A is abnormal, and it is preliminarily judged as a high water level.

[0173] The sluice state at point B is normal.

[0174] The purification processing module at point C is abnormal, initially judged to have too low a flow rate.

[0175] Abnormal event list:

[0176] Abnormal event 1: High water level at point A.

[0177] Abnormal event 2: Too low a flow rate in the purification processing module at point C.

[0178] According to the alarm information template, the following alarm information is generated:

[0179] "Alarm: The water level at point A exceeds 2 meters, at 10:00 on October 10, 2023, at point A in the irrigation area. Please immediately check and take drainage measures."

[0180] "Alarm: The flow rate in the purification processing module at point C is lower than 10 cubic meters per hour, at 10:00 on October 10, 2023, at point C in the irrigation area. Please immediately check and repair the fault."

[0181] The alarm information is sent to the irrigation area management personnel through text messages and APP push.

[0182] The real-time monitoring and alarm system can promptly detect and report abnormal situations, helping to prevent safety accidents caused by excessive water levels, sluice failures, or the ineffectiveness of purification processing modules. Through the automated alarm sending mechanism, it can ensure that relevant personnel can quickly receive abnormal information and take corresponding countermeasures to reduce losses. It can continuously monitor the operation status of the irrigation area, provide accurate data support for management personnel, help optimize the allocation and use of water resources, and improve the overall efficiency of the irrigation area. By recording abnormal events and handling situations, the system can provide valuable reference information for subsequent maintenance work, helping management personnel better understand the operation status and fault modes of equipment, and thus formulate more effective maintenance plans. The application of the real-time monitoring and alarm system is an important step in the intelligent management of irrigation areas, helping to promote the development of irrigation areas towards a more intelligent and efficient direction.

[0183] In a preferred embodiment of the present invention, power supply is provided, and the energy consumption situation is monitored in real time. By adjusting the equipment operation parameters and continuously optimizing the energy consumption, it may include:

[0184] Analyze and determine the capacity and configuration of the power supply system based on factors such as equipment power, operating time, and power demand stability. Design the architecture of the power supply system, including power input, distribution network, equipment power supply interfaces, etc., to ensure the stability and security of power supply. Select appropriate power equipment, such as transformers, switchgear, cables, etc., according to the system design requirements, and conduct procurement and installation. Commission and test the power supply system to ensure that all equipment operates normally and the power supply is stable and reliable. Select appropriate monitoring points, such as equipment input terminals, output terminals, or key component locations, according to the energy consumption characteristics and monitoring requirements of the equipment. Install energy consumption sensors, such as electricity meters, power sensors, etc., at the monitoring points to collect real-time energy consumption data of the equipment. Establish a data transmission network to transmit the collected energy consumption data to the data center in real time.

[0185] Clean and preprocess the collected energy consumption data to remove outliers and noise, ensuring the accuracy and reliability of the data. Use data analysis techniques to identify the energy consumption patterns of the equipment, such as normal operation patterns, abnormal energy consumption patterns, etc. Evaluate the energy consumption of the equipment and generate an energy consumption report, including total energy consumption, energy consumption distribution, energy consumption trends, etc., to provide data support for energy consumption optimization. Identify the key operating parameters that affect the energy consumption of the equipment, such as operating speed, load rate, temperature, etc., and set reasonable parameter ranges. According to the energy consumption evaluation results and the operating characteristics of the equipment, formulate parameter adjustment strategies, such as dynamically adjusting the operating speed, optimizing the load distribution, etc. Implement the parameter adjustment strategy, adjust the equipment operating parameters in real time, and verify the adjustment effect to ensure that the energy consumption is optimized.

[0186] Evaluate the effect of energy consumption optimization, compare the energy consumption data before and after optimization, and analyze whether the optimization effect meets the expected goals. Collect user feedback and opinions, improve and optimize according to the problems and deficiencies found, and continuously improve the energy consumption monitoring and optimization system. Continuously optimize the equipment operating parameters and energy consumption monitoring strategies according to the equipment operating conditions and energy consumption changes to achieve long-term stable optimization of energy consumption.

[0187] The above is the preferred implementation mode of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. An irrigation area sluice system with adaptive water level regulation, characterized in that: include: The water level monitoring module is used to monitor the water level changes in the irrigation area in real time and feed back the water level information to the gate control module; The gate control module is used to receive the water level information fed back by the water level monitoring module according to the preset water level control strategy, and automatically adjust the opening of the irrigation area water gate to achieve adaptive regulation of the irrigation area water level; The data analysis module is used to collect and store various data in the water operation process in real time, including water level change data, gate opening data, flow data, and water quality data. It also analyzes various data through the optimization algorithm of cuckoo parasitic breeding behavior to continuously optimize and adjust the water level control strategy; The purification module is located downstream of the sluice gate and is used to purify the water discharged through the sluice gate. It uses water quality monitoring and optimization algorithms to monitor water quality indicators in real time, adjust the purification process according to water quality changes, and remove pollutants in the water. Remote monitoring module, used to remotely monitor water level changes inside and outside the irrigation area, the operating status of the sluice body and the working status of the purification treatment module, and issue an alarm when an abnormal situation occurs; The energy management module is used to provide power supply and monitor energy consumption in real time, and continuously optimize energy consumption by adjusting equipment operating parameters.

2. The irrigation area sluice system with adaptive water level regulation according to claim 1, characterized in that: Real-time monitoring of water level changes in the irrigation area and feedback of water level information to the gate control module, including: Install water level sensors at key locations in the irrigation area, including channel entrances, exits, bends, or places susceptible to water level changes; The water level sensor collects water level data in the irrigation area in real time according to the preset sampling frequency, including the current water level height and water level change trend information; The water level data is converted into electrical signals and transmitted to the gate control module.

3. The irrigation area sluice system with adaptive water level regulation according to claim 2, characterized in that: According to the preset water level control strategy, the water level information fed back by the water level monitoring module is received, and the opening of the irrigation area sluice gate is automatically adjusted to achieve adaptive regulation of the irrigation area water level, including: Receive water level information fed back by the water level monitoring module in real time, including the current water level height and water level change trend, and preset relevant parameters and rules of the fuzzy control algorithm; According to the water level information fed back by the water level monitoring module, the deviation between the current water level and the set water level and the water level change trend are used as input variables of fuzzy control; Define fuzzy sets and membership functions, and use membership functions to fuzzify deviations and change trends to obtain fuzzified input variables; According to the preset fuzzy rules, the fuzzified input variables are inferred and the fuzzified output variables, i.e. the adjustment amount of the gate opening, are obtained; Defuzzification is performed on the fuzzy output variables, and the fuzzy output variables are converted into gate opening adjustment values; According to the gate opening adjustment amount, a control instruction is sent to the gate actuator, and the gate actuator automatically adjusts the gate opening according to the control instruction to achieve adaptive adjustment of the water level in the irrigation area.

4. The irrigation area sluice system with adaptive water level regulation according to claim 3 is characterized in that: Collect and store various data in the water operation process in real time, including water level change data, gate opening data, flow data, water quality data, and analyze various data through the optimization algorithm of cuckoo parasitic breeding behavior to continuously optimize and adjust the water level control strategy, including: Collect various data in real time during the water operation process, including water level change data, gate opening data, flow data and water quality data, and set the parameters of the optimization algorithm for cuckoo parasitic breeding behavior, including population size, number of iterations, and number of nests; Initialize the population, that is, generate a set of initial water level control strategies as the starting point of the algorithm.

5. The irrigation area sluice system with adaptive water level regulation according to claim 4, characterized in that: Collect and store various data in the water operation process in real time, including water level change data, gate opening data, flow data, water quality data, and analyze various data through the optimization algorithm of cuckoo parasitic breeding behavior to continuously optimize and adjust the water level control strategy, including: According to the goal of water level control, each individual in the current population is evaluated, and the score of each individual through the performance evaluation function is calculated to evaluate the pros and cons of each water level control strategy; According to the parasitic reproduction behavior of cuckoos, a part of individuals are selected for updating to generate a new water level control strategy; Compare the performance evaluation function scores of the new water level control strategy and the current strategy to obtain a comparison result, and determine the final water level control strategy based on the comparison result; The selection, generation and comparison process is repeated until the preset number of iterations is reached, and the water level in the irrigation area is adjusted in real time according to the final water level control strategy.

6. The irrigation area sluice system with adaptive water level regulation according to claim 5, characterized in that: Purify the water discharged through the sluice gates, and use water quality monitoring and optimization algorithms to monitor water quality indicators in real time, adjust the purification process according to water quality changes, and remove pollutants in the water, including: Install purification equipment downstream of the sluice gate, including filtration devices, sedimentation tanks, and disinfection devices, and set initial water quality monitoring indicators and purification process parameters based on historical water quality monitoring data, including filtration speed, disinfectant dosage, and sedimentation tank residence time; The sampling frequency and monitoring point location of the water quality monitoring sensor are set. The water quality monitoring sensor collects water quality data of the water discharged through the sluice in real time according to the preset sampling frequency and monitoring point location; Compare the water quality data with the preset water quality standards and historical data to determine whether the current water quality is abnormal, so as to obtain the comparison results; Based on the comparison results, a water quality analysis report is generated, including the current value of each water quality indicator, the comparison with historical data, and whether it meets the preset standards.

7. The irrigation area sluice system with adaptive water level regulation according to claim 6, characterized in that: Purify the water discharged through the sluice gates, and use water quality monitoring and optimization algorithms to monitor water quality indicators in real time, adjust the purification process according to water quality changes, and remove pollutants in the water, including: According to the water quality analysis report, calculate the adjusted purification process parameters, including the adjustment of the filtration speed of the filtration device, the adjustment of the amount of disinfectant added, and the adjustment of the residence time of the sedimentation tank; Formulate adjustment strategies based on purification process parameters, including adjustment targets, steps, and time nodes, and generate control instructions based on the adjustment strategies, including adjustment parameter values ​​and execution time; The control instructions are sent to the control unit of the purification treatment module, and the instruction content is parsed. According to the instruction content, the operating status of the purification equipment is adjusted, including adjusting the filtration speed of the filtration device, increasing or decreasing the amount of disinfectant, and changing the residence time of the sedimentation tank to remove pollutants in the water.

8. The irrigation area sluice system with adaptive water level regulation according to claim 7, characterized in that: According to the purification process parameters, formulate adjustment strategies, including adjustment targets, steps, and time nodes, and generate control instructions according to the adjustment strategies, including adjustment parameter values ​​and execution time, including: Set the constraints of the adjustment strategy, including the operating capacity of the purification equipment, energy consumption limit, and adjustment cost factors, and encode the parameters of the purification process, including filtration speed, disinfectant dosage, and sedimentation tank residence time, into individuals of the genetic algorithm, each of which represents an adjustment strategy; A group of initial individuals are randomly generated as the initial population of the genetic algorithm, each of which contains a group of adjusted purification process parameters; According to the adjustment objectives and constraints, a fitness function is defined to evaluate the pros and cons of each individual, that is, the effect of the adjustment strategy, to obtain the fitness function evaluation result; According to the fitness function evaluation results, the parent individuals are determined, and crossover operations are performed on the parent individuals to generate new offspring individuals; Perform mutation operations on the new offspring individuals, perform fitness scoring, and update the population based on the evaluation results; Repeat the process of determining parent individuals, crossover to generate new offspring, offspring mutation, and updating the population according to fitness until the preset number of iterations is reached. The final individual is determined according to the individual fitness score and used as the adjustment strategy. Decoding the final individual into purification process parameter values, including extracting the filtration speed adjustment, disinfectant dosage, and sedimentation tank residence time adjustment in the individual gene; According to the purification process parameter values, combined with the real-time data of water quality monitoring and the water level changes in the irrigation area, the execution time is determined, and the purification process parameter values ​​and the execution time are combined into a control instruction.

9. The irrigation area sluice system with adaptive water level regulation according to claim 8, characterized in that: Remotely monitor water level changes inside and outside the irrigation area, the operating status of the sluice body, and the working status of the purification treatment module, and issue an alarm when an abnormal situation occurs, including: According to the triggering conditions, determine the alarm information template, which includes the abnormal description, time, location and response measures; The sensors inside and outside the irrigation area collect the working data of water level, sluice status and purification treatment module in real time, and generate real-time data monitoring report based on the working parameter data. The report contains the data value of each monitoring point and the preliminary judgment on whether an abnormal situation is triggered; Compare the real-time data monitoring report with the trigger condition set. If the data of any monitoring point meets the trigger condition of the abnormal situation, record the abnormal type and add the abnormal type to the abnormal event list; When the abnormal event list is not empty, alarm information is generated for each abnormal event according to the alarm information template, and the alarm information is sent through the preset alarm propagation channel.

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