Water conservancy equipment remote control method and system for water conservancy informatization

Abstract

The invention provides a water conservancy equipment remote control method and system for water conservancy informatization, and the method comprises the following steps: connecting water conservancy equipment to the Internet of Things, collecting equipment operation data through a sensor, and uploading the data to a cloud server; establishing a state monitoring model of the water conservancy equipment by applying a machine learning algorithm based on the collected equipment operation data; the water conservancy equipment is remotely controlled through a mobile application or a Web interface; in combination with real-time weather data, water demand prediction and equipment state information, an operation strategy of the water conservancy equipment is automatically adjusted; the information is presented to a manager through a data visualization technology; and feedback information of remote control and scheduling is collected regularly, and the system is optimized and adjusted. According to the invention, through real-time monitoring, accurate prediction, intelligent scheduling, data fusion, visual display and continuous optimization, efficient, stable and intelligent water conservancy equipment management is realized, and the operation efficiency and safety of a water conservancy project are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy informationization, and in particular to a water conservancy equipment remote control method and system for water conservancy informationization. Background Art

[0002] In traditional water conservancy project management, the monitoring and scheduling of water conservancy equipment (such as pumps, gates, valves, etc.) mainly rely on manual operation and regular maintenance. These devices are usually inspected and operated by on-site staff, and the equipment operation data is managed through manual recording and regular reporting. Although some water conservancy projects have begun to introduce simple remote monitoring systems to achieve remote monitoring of equipment status through basic sensors and communication networks, these systems are usually single-function and lack intelligent analysis and scheduling capabilities, making it difficult to cope with complex and changing water demand and equipment failures.

[0003] The existing technology has the following problems: Insufficient real-time monitoring. Traditional systems rely on manual inspections and manual records, making it difficult to achieve real-time monitoring of water conservancy equipment. Even if some basic remote monitoring systems are introduced, their monitoring data is often not comprehensive enough to detect equipment failures and abnormalities in a timely manner. Prediction and scheduling are not accurate. The existing system lacks advanced water demand prediction and equipment scheduling functions. Traditional scheduling methods are usually based on experience and fixed operation plans, and cannot dynamically adjust the operation strategy of the equipment, resulting in resource waste and unstable water supply. Limited data processing capabilities. Existing monitoring systems usually lack the ability to comprehensively analyze multi-source data, and cannot effectively integrate and process data from different sensors, resulting in low data utilization and difficulty in providing a scientific basis for management decisions. Lack of intelligent management. The existing system lacks intelligent analysis and optimization functions, and cannot automatically adjust the equipment operation strategy based on real-time and historical data, making it difficult to achieve energy saving and efficient management.

[0004] The above problems lead to a series of consequences: equipment failure response is not timely. Due to the lack of real-time monitoring and early warning functions, equipment failures cannot be discovered in time, resulting in extended downtime and increased equipment maintenance costs, which seriously affects the normal operation of water conservancy projects. Unstable water supply, inaccurate scheduling and waste of resources lead to unstable water supply, which is difficult to meet the actual water demand, especially during peak water use periods, which may lead to water shortages and affect residents' lives and industrial production. High operating costs. Due to the inability to achieve intelligent scheduling and energy-saving management, the energy consumption of equipment is high, the operating costs increase, and the operating pressure of water conservancy management departments increases. Low management efficiency, limited data processing capabilities and lack of intelligent management methods lead to low management efficiency, difficulty in making scientific decisions quickly, and affect the overall management level.

[0005] These problems need to be solved urgently to improve the management efficiency and operational safety of water conservancy projects. Summary of the invention

[0006] In order to overcome the shortcomings of the prior art, the present invention proposes a remote control method and system for water conservancy equipment for water conservancy informatization, which realizes efficient, stable and intelligent water conservancy equipment management through real-time monitoring, accurate prediction, intelligent scheduling, data fusion, visual display and continuous optimization, and greatly improves the operation efficiency and safety of water conservancy projects.

[0007] To achieve the above object, the present invention provides a remote control method for water conservancy equipment for water conservancy informatization, comprising the following steps:

[0008] Step 1: Connect all water conservancy equipment to the Internet of Things, collect equipment operation data through sensors, and upload the data to the cloud server;

[0009] Step 2: Based on the collected equipment operation data, a machine learning algorithm is applied to establish a condition monitoring model for water conservancy equipment to monitor the equipment operation status in real time and detect anomalies;

[0010] Step 3: Deploy a remote control system in the cloud server to remotely control the water conservancy equipment through mobile applications or web interfaces. The control commands are transmitted to the device end through secure encryption.

[0011] Step 4: Combine real-time weather data, water demand forecasts, and equipment status information to develop intelligent scheduling algorithms that automatically adjust the operation strategy of water conservancy equipment to achieve energy-saving and efficient equipment management;

[0012] Step 5: Use data visualization technology to present equipment operating status, historical data, scheduling strategies and other information to managers to provide decision support;

[0013] Step 6: Regularly collect feedback information from remote control and scheduling, optimize and adjust the system, and continuously improve control accuracy and equipment operation efficiency.

[0014] Furthermore, the IoT system constructed in step 1 includes:

[0015] Sensors installed on various water conservancy equipment to collect equipment operation data;

[0016] The controller installed on each hydraulic equipment is used to receive and execute remote control commands;

[0017] The cloud data platform is used to receive, store and process data uploaded from sensors, and perform data analysis and monitoring.

[0018] The equipment condition monitoring model established in step 2 includes the following steps:

[0019] Data preprocessing: Clean, filter and standardize the collected raw data to ensure data quality and consistency;

[0020] Model training and validation: Use preprocessed data to train the machine learning model, and use cross-validation methods to evaluate model performance to ensure the accuracy and robustness of the monitoring model. The model is based on historical operating data and equipment status data. The algorithm learns the normal operating mode and fault characteristics of the equipment to establish a monitoring model that can detect equipment anomalies and warn of faults.

[0021] The remote device control functions implemented in step 3 include:

[0022] Design remote control interface to provide equipment status monitoring, control command sending and alarm processing functions;

[0023] Use secure transmission protocols such as SSL / TLS to ensure the security and reliability of control commands during transmission; the control interface allows managers to monitor and control water conservancy equipment in real time through mobile devices or computers, receive equipment status alarms and respond quickly, and control commands are transmitted to the device end through a secure channel to ensure the integrity and confidentiality of data transmission.

[0024] Furthermore, the intelligent scheduling algorithm developed in step 4 includes the following steps:

[0025] Data fusion and prediction: Integrate real-time weather data, water demand forecast data and equipment status data to form a comprehensive scheduling data source;

[0026] Scheduling algorithm optimization: Based on historical scheduling data and real-time data, the intelligent scheduling algorithm is continuously optimized to improve the scientificity and practicality of the scheduling strategy. The scheduling algorithm analyzes multi-source data, predicts changes in water demand, and automatically adjusts the operation strategy of water conservancy equipment based on equipment status and operating efficiency to achieve energy saving and efficient equipment management.

[0027] Furthermore, the data visualization and decision support performed in step 5 include:

[0028] Develop visual panels that use charts, graphs, and maps to display equipment operating status, historical data, and scheduling strategies, making it easier for managers to quickly understand and make decisions;

[0029] The integrated decision support system provides functions such as equipment failure warning, maintenance suggestions and optimization suggestions. The visualization panel displays key operating parameters and trend analysis through an intuitive graphical interface to help managers quickly identify problems and make decisions. The decision support system provides specific maintenance suggestions and optimization solutions based on real-time and historical data analysis to improve the efficiency and accuracy of equipment management.

[0030] Furthermore, the feedback and optimization mechanism implemented in step 6 includes:

[0031] Regularly collect system operation feedback data, analyze equipment control and scheduling effects, and identify improvement points;

[0032] Based on the feedback analysis results, the system is optimized and upgraded to improve the accuracy of remote control and the efficiency of equipment operation. The system collects and analyzes equipment operation data and user feedback, identifies deficiencies in control strategies and scheduling algorithms, and continuously optimizes and improves them to ensure long-term stable operation and efficient management of the system.

[0033] A remote control system for water conservancy equipment used for water conservancy informatization, applicable to the remote control method for water conservancy equipment used for water conservancy informatization, comprising:

[0034] Sensor module, used to collect equipment operation data;

[0035] A data acquisition module, connected to the sensor module signal, for acquiring equipment operation data;

[0036] A data processing module, connected to the data acquisition module by signal, for preprocessing and analyzing the collected data and establishing an equipment status monitoring model;

[0037] A control module, connected to the data processing module and the remote control module by signals, and used for sending control commands;

[0038] A remote control module, connected to the control module and the water conservancy equipment by signal, for receiving and executing control commands;

[0039] A cloud server, connected to the data processing module by signal, for storing and processing multi-source data and supporting the development and optimization of intelligent scheduling algorithms;

[0040] The user interface module is connected to the data processing module and the cloud server signal to display data visualization results and provide decision support.

[0041] Furthermore, the sensor module comprises:

[0042] Flow sensor, used to detect flow data of water conservancy equipment;

[0043] Pressure sensor, used to detect pressure data of water conservancy equipment;

[0044] Water quality sensor, used to detect water quality parameters.

[0045] Furthermore, the data processing module includes:

[0046] Data preprocessing unit, used to clean, filter and standardize the collected raw data to ensure data quality and consistency;

[0047] The machine learning unit is used to train and verify the machine learning model using the preprocessed data and establish an equipment status monitoring model.

[0048] The control module includes:

[0049] A remote control interface for receiving control commands from a user interface;

[0050] The secure transmission module adopts secure transmission protocols such as SSL / TLS to ensure the security and reliability of control commands during transmission.

[0051] Furthermore, the cloud server includes:

[0052] A data storage unit, used to store data uploaded from sensors and data acquisition modules;

[0053] The data processing unit is used to integrate and analyze multi-source data and support the development and optimization of intelligent scheduling algorithms.

[0054] Furthermore, the user interface module includes:

[0055] Visualization panel, which uses charts, graphs and maps to display equipment operation status, historical data and scheduling strategies;

[0056] Decision support system, providing functions such as equipment failure warning, maintenance suggestions and optimization suggestions.

[0057] Further including:

[0058] A feedback collection module, connected to the control module and the data processing module by signals, for regularly collecting system operation feedback data, analyzing equipment control and scheduling effects, and identifying improvement points;

[0059] The optimization module optimizes and upgrades the system based on the feedback analysis results to improve the accuracy of remote control and equipment operation efficiency.

[0060] Compared with the prior art, the present invention has the following beneficial effects:

[0061] 1. The present invention provides a remote control method and system for water conservancy equipment for water conservancy informationization. The system can monitor the operating status of water conservancy equipment in real time and detect equipment failures and abnormal conditions in a timely manner. Through the early warning function, managers can take quick action to reduce equipment downtime and improve equipment reliability and safety. Based on historical water use data and real-time weather data, the system can accurately predict future water demand. By reasonably arranging equipment operation, the system can ensure the stability of water supply and avoid waste of resources.

[0062] 2. The present invention provides a remote control method and system for water conservancy equipment for water conservancy informationization. The system adopts advanced intelligent scheduling algorithms, including genetic algorithms, particle swarm optimization algorithms, and deep reinforcement learning algorithms to dynamically adjust the operation strategies of water conservancy equipment. Through optimized scheduling, the system can achieve energy-saving and efficient equipment management and reduce operating costs. The system can integrate multi-source data (such as equipment status data, weather data, and water demand data) for comprehensive analysis and processing. The data processing module improves the accuracy and efficiency of data analysis through machine learning and deep learning algorithms, providing a scientific basis for equipment management.

[0063] 3. The present invention provides a remote control method and system for water conservancy equipment for water conservancy informationization. The system has feedback collection and continuous optimization functions. By regularly collecting operation data and feedback information, the effects of control strategies and scheduling algorithms are analyzed. The optimization module continuously improves based on feedback data to ensure long-term stable operation and efficient management of the system.

[0064] 4. The present invention provides a remote control method and system for water conservancy equipment for water conservancy informationization. The implementation of the system significantly improves the work efficiency of water conservancy management departments and ensures the water supply safety of urban residents. Through intelligent remote control and scheduling, the system reduces equipment maintenance costs and reduces the risk of water supply interruption, which has significant social and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0066] Figure 1 It is a schematic diagram of the steps of the present invention DETAILED DESCRIPTION

[0067] The technical solution of the present invention will be more clearly and completely explained below through description of preferred embodiments of the present invention in combination with the accompanying drawings.

[0068] like Figure 1 As shown, the present invention is specifically:

[0069] Step 1: Connect water conservancy equipment to the Internet of Things, collect equipment operation data through sensors, and upload the data to the cloud server;

[0070] Step 2: Based on the collected equipment operation data, a machine learning algorithm is applied to establish a condition monitoring model for water conservancy equipment to monitor the equipment operation status in real time and detect anomalies;

[0071] Step 3: Deploy a remote control system in the cloud server to remotely control the water conservancy equipment through mobile applications or web interfaces. The control commands are transmitted to the device end through secure encryption.

[0072] Step 4: Combine real-time weather data, water demand forecasts, and equipment status information to develop intelligent scheduling algorithms to automatically adjust the operation strategy of water conservancy equipment and achieve equipment management;

[0073] Step 5: Use data visualization technology to present equipment operating status, historical data, and scheduling strategy information to managers to provide decision support;

[0074] Step 6: Regularly collect feedback information from remote control and scheduling to optimize and adjust the system.

[0075] The IoT system built in step 1 includes:

[0076] Sensors installed on various water conservancy equipment to collect equipment operation data;

[0077] The controller installed on each hydraulic equipment is used to receive and execute remote control commands;

[0078] The cloud data platform is used to receive, store and process data uploaded from sensors, and perform data analysis and monitoring.

[0079] The equipment condition monitoring model established in step 2 is implemented through the following steps:

[0080] Data preprocessing: Clean, filter and standardize the collected raw data to ensure data quality and consistency;

[0081] Model training and validation: Use preprocessed data to train the machine learning model and use cross-validation method to evaluate model performance to ensure the accuracy and robustness of the monitoring model.

[0082] The detailed steps include:

[0083] Data cleaning: remove noisy data, process missing values ​​and outliers, and ensure data consistency and reliability.

[0084] Feature extraction: Extract key features from sensor data, such as flow, pressure, temperature, etc.

[0085] Algorithm selection: Select a machine learning algorithm suitable for water conservancy equipment data, such as random forest (RandomForest), support vector machine (SVM), deep learning algorithm (such as LSTM neural network), etc.

[0086] Model training: Use historical data to train the model and adjust hyperparameters to improve model performance.

[0087] Cross-validation: Use K-fold cross-validation method to evaluate the generalization ability of the model and avoid overfitting.

[0088] The remote device control functions implemented in step 3 include:

[0089] Design a user-friendly remote control interface to provide equipment status monitoring, control command sending and alarm processing functions;

[0090] Use secure transmission protocols such as SSL / TLS to ensure the security and reliability of control commands during transmission.

[0091] The detailed steps include:

[0092] Interface design: Develop an intuitive user interface, including device status display, control buttons, and alarm information display.

[0093] Command transmission: Control commands are transmitted through secure encrypted channels (such as SSL / TLS) to ensure that data is not tampered with or stolen during transmission.

[0094] Alarm processing: Set up a real-time alarm mechanism. When the equipment status is abnormal, send alarm information to the management personnel in time and provide processing suggestions.

[0095] The intelligent scheduling algorithm developed in step 4 is implemented by the following steps:

[0096] Data fusion and prediction: Integrate real-time weather data, water demand forecast data and equipment status data to form a comprehensive scheduling data source;

[0097] Scheduling algorithm optimization: Based on historical scheduling data and real-time data, the intelligent scheduling algorithm is continuously optimized to improve the scientificity and practicality of the scheduling strategy.

[0098] The detailed steps include:

[0099] Data Fusion and Prediction:

[0100] Data Fusion:

[0101] Real-time weather data: Collect and process meteorological data, including precipitation, temperature, humidity and other factors that affect water demand.

[0102] Water demand forecast data: Use historical water use data and forecast models to predict future water demand.

[0103] Equipment status data: Collect the operating status data of water conservancy equipment, including the equipment's operating efficiency, fault status, etc.

[0104] Comprehensive scheduling data source:

[0105] The above three types of data are integrated to form a comprehensive scheduling data source, providing comprehensive data support for the scheduling algorithm.

[0106] Scheduling algorithm optimization:

[0107] Preliminary scheduling strategy generation:

[0108] Time series forecasting model: Use the ARIMA (AutoRegressive Integrated Moving Average) model or LSTM (Long Short-Term Memory) neural network to perform time series forecasting on water demand and generate future water demand curves.

[0109] Equipment status assessment model: Utilize equipment status data to evaluate the operating efficiency and health status of each device through models such as random forest or support vector machine (SVM).

[0110] Scheduling strategy optimization:

[0111] Genetic Algorithm:

[0112] Define fitness function: Define fitness function based on energy saving, high efficiency, load balance and other goals.

[0113] Initialize population: Generate an initial scheduling solution population.

[0114] Selection, crossover and mutation operations: Generate a new scheduling scheme population by selecting individuals with high fitness for crossover and mutation.

[0115] Iterative optimization: Repeatedly iterate until the fitness function converges and the optimal scheduling strategy is obtained.

[0116] Particle Swarm Optimization (PSO):

[0117] Initialize particle swarm: randomly generate the initial scheduling solution particle swarm.

[0118] Calculate fitness value: Evaluate the fitness value of each particle's scheduling solution.

[0119] Update particle speed and position: Update the particle speed and position according to the current particle position and speed, as well as the global optimal position.

[0120] Iterative optimization: Repeatedly iterate and update the global optimal position until convergence to obtain the optimal scheduling solution.

[0121] Real-time adjustment and optimization:

[0122] Deep Reinforcement Learning (DRL):

[0123] Construct environment and reward function: Construct water demand changes, equipment status, and scheduling strategies as the environment, and define energy-saving and high-efficiency goals as reward functions.

[0124] Training DRL model: Use algorithms such as DQN (Deep Q-Network) or PPO (Proximal Policy Optimization) to train the scheduling strategy of the agent in different environments.

[0125] Real-time adjustment: In actual operation, the intelligent agent dynamically adjusts the scheduling strategy based on real-time data and training models to achieve optimal equipment operation.

[0126] Through the above specific steps, it is possible to automatically analyze multi-source data, predict changes in water demand, and automatically adjust the operation strategy of water conservancy equipment based on equipment status and operating efficiency to achieve energy saving and efficient equipment management.

[0127] The data visualization and decision support performed in step 5 include:

[0128] Develop visual panels that use charts, graphs, and maps to display equipment operating status, historical data, and scheduling strategies, making it easier for managers to quickly understand and make decisions;

[0129] Integrated decision support system, providing functions such as equipment failure warning, maintenance suggestions and optimization suggestions.

[0130] The detailed steps include:

[0131] Visualization tools: Use front-end visualization tools such as D3.js and Chart.js to develop interactive data panels to display device operating status and key indicators in real time.

[0132] Graphical presentation: including line graphs, bar graphs, pie charts, and geographic information system (GIS) maps to display data in different dimensions.

[0133] Decision support: Based on data analysis results, generate equipment failure warnings, maintenance suggestions and scheduling optimization suggestions to assist managers in making decisions.

[0134] The feedback and optimization mechanisms implemented in step 6 include:

[0135] Regularly collect system operation feedback data, analyze equipment control and scheduling effects, and identify improvement points;

[0136] Based on the feedback analysis results, the system is optimized and upgraded to improve the accuracy of remote control and equipment operation efficiency.

[0137] The detailed steps include:

[0138] Feedback data collection: Regularly collect equipment operation data and user feedback information through sensors and control systems.

[0139] Data analysis: Use data mining techniques (such as association rule mining and cluster analysis) to analyze feedback data and identify bottlenecks and problems in system operation.

[0140] Optimization and upgrade: According to the analysis results, adjust the control strategy and scheduling algorithm, optimize the system parameters, and improve the system performance and reliability.

[0141] As a specific implementation method, the water conservancy equipment remote control system of the present invention includes: water conservancy equipment, a sensor module, a control module, a data acquisition module, a data processing module, a remote control module, a cloud server and a user interface module. All components are interconnected through a communication network, and the communication network can be wired or wireless.

[0142] System architecture:

[0143] Water conservancy equipment: including pumps, gates, valves and other equipment used in water conservancy projects.

[0144] Sensor module: installed on water conservancy equipment to collect equipment operation data, including flow, pressure, water quality and other parameters.

[0145] Data acquisition module: connected to the sensor module signal, used to obtain sensor data and transmit it to the data processing module.

[0146] Data processing module: connected to the data acquisition module signal, used to pre-process, analyze and monitor the data and establish an equipment status monitoring model.

[0147] Control module: connected to the data processing module and remote control module signals, used to send control commands.

[0148] Remote control module: connected to the control module and water conservancy equipment signals, used to receive and execute control commands.

[0149] Cloud server: connected to the data processing module signal, used to store and process multi-source data, and support the development and optimization of intelligent scheduling algorithms.

[0150] User interface module: connected to the data processing module and cloud server signals to display data visualization results and provide decision support.

[0151] Data Fusion and Prediction:

[0152] Real-time weather data fusion: Real-time weather data including precipitation, temperature, humidity, etc. are obtained from meteorological services. These data are transmitted to the data processing module through the data acquisition module for analysis and processing.

[0153] Water demand forecasting data fusion: Based on historical water use data, a time series forecasting model (such as an ARIMA model or LSTM neural network) is used to generate future water demand forecasts.

[0154] Equipment status data fusion: Real-time collection of operating status data of water conservancy equipment, including equipment operating efficiency, fault status, etc., and integration of the above data to form a comprehensive scheduling data source.

[0155] Scheduling algorithm optimization:

[0156] Preliminary scheduling strategy generation:

[0157] Time series forecasting model: Use the ARIMA model to forecast water demand and generate future water demand curves. For more complex data sets, LSTM neural networks can be used for modeling and forecasting.

[0158] Equipment status assessment model: Use the Random Forest or Support Vector Machine (SVM) algorithm to assess the operating efficiency and health status of the equipment.

[0159] Genetic Algorithm Optimization:

[0160] Define fitness function: Define fitness function based on energy saving, high efficiency, load balance and other goals.

[0161] Initialize population: Generate an initial scheduling solution population.

[0162] Selection, crossover and mutation operations: By selecting individuals with high fitness for crossover and mutation, a new scheduling scheme population is generated, and it is iterated repeatedly until the fitness function converges to obtain the optimal scheduling strategy.

[0163] Particle Swarm Optimization (PSO):

[0164] Initialize particle swarm: Randomly generate the initial scheduling solution particle swarm.

[0165] Calculate fitness value: Evaluate the scheduling solution fitness value of each particle.

[0166] Update particle speed and position: Update the particle speed and position according to the current particle position and speed, as well as the global optimal position, and iterate repeatedly until convergence to obtain the optimal scheduling plan.

[0167] Deep Reinforcement Learning (DRL):

[0168] Construct environment and reward function: Construct water demand changes, equipment status, and scheduling strategies as the environment, and define energy-saving and high-efficiency goals as reward functions.

[0169] Training DRL model: Use algorithms such as DQN (Deep Q-Network) or PPO (Proximal Policy Optimization) to train the scheduling strategy of the agent in different environments, dynamically adjust the scheduling strategy, and achieve optimal equipment operation.

[0170] Data Visualization and Decision Support:

[0171] Visualization panel: Use charts, graphs and maps to display equipment operation status, historical data and scheduling strategies. The user interface module provides an intuitive graphical interface to help managers quickly understand equipment operation status.

[0172] Decision support system: Provides equipment failure warnings, maintenance suggestions, and optimization suggestions. Based on real-time data and historical data analysis, the decision support system provides specific maintenance suggestions and optimization solutions to improve the efficiency and accuracy of equipment management.

[0173] Feedback and optimization mechanism:

[0174] Feedback data collection: The control module regularly collects system operation feedback data and transmits it to the data processing module.

[0175] Optimization module: Analyze feedback data, identify deficiencies in control strategies and scheduling algorithms, and perform continuous optimization and improvement to ensure long-term stable operation and efficient management of the system.

[0176] System operation process

[0177] Data collection and preprocessing:

[0178] The sensor module is installed on the water conservancy equipment to collect flow, pressure and water quality data in real time, and transmit it to the data processing module through the data acquisition module.

[0179] The data processing module cleans, filters and standardizes the raw data to ensure data quality and consistency.

[0180] Condition Monitoring and Prediction:

[0181] Use the preprocessed data to train the equipment condition monitoring model and evaluate the equipment health status through random forest or support vector machine.

[0182] Use time series forecasting models such as ARIMA or LSTM to generate future water demand forecasts.

[0183] Intelligent scheduling and control:

[0184] Based on the comprehensive scheduling data source, a preliminary scheduling strategy is generated using genetic algorithm or particle swarm optimization algorithm.

[0185] Dynamically adjust scheduling strategies through deep reinforcement learning algorithms (such as DQN or PPO) to optimize equipment operation.

[0186] Real-time control and feedback:

[0187] The control module sends control commands to the remote control module to execute control operations on the water conservancy equipment.

[0188] The system regularly collects feedback data, analyzes and optimizes it to ensure the effectiveness of the control strategy.

[0189] Data Visualization and Decision Support:

[0190] The user interface module displays equipment operating status, historical data and scheduling strategies, and helps managers make decisions through an intuitive graphical interface.

[0191] The decision support system provides equipment failure warnings, maintenance suggestions and optimization plans to improve equipment management efficiency.

[0192] Implementation Effect

[0193] Through the above-mentioned specific implementation methods, the water conservancy equipment remote control system of the present invention realizes efficient and intelligent equipment management, can monitor equipment status in real time, predict water demand, optimize scheduling strategies, and provide intuitive data visualization and decision support, thereby improving the operating efficiency and safety of water conservancy projects.

[0194] As a specific embodiment: In a practical application, the water conservancy management department of a city introduces and cites the present invention to optimize and monitor the operation of the city's water supply, as follows:

[0195] The system includes multiple water conservancy equipment such as water pumps and gates, and is equipped with flow sensors, pressure sensors and water quality sensors. These sensors transmit data to the data acquisition module through a wireless network. The data acquisition module transmits real-time data to the data processing module for cleaning, filtering and standardization to ensure data quality.

[0196] The data processing module uses the random forest model to monitor the operating status of the equipment. By learning the historical operating data and fault data of the equipment, the model can evaluate the health status of the equipment in real time, identify abnormal operation and issue early warning signals. For example, when the pressure of a water pump is abnormal, the system will automatically issue an alarm to remind maintenance personnel to check and repair it.

[0197] At the same time, the system obtains real-time weather data from meteorological services, combines historical water use data, and uses the ARIMA model to predict water demand. The system analyzes weather changes and historical water use trends to generate future water demand curves to provide a basis for scheduling.

[0198] To optimize equipment operation, the system uses a genetic algorithm to generate a preliminary scheduling strategy. First, the fitness function is defined, taking into account goals such as energy saving, high efficiency, and load balancing. Then, the optimal scheduling solution is iteratively generated through selection, crossover, and mutation operations. The generated scheduling solution is further optimized through a deep reinforcement learning algorithm. The system trains the DQN model in a simulated environment, continuously adjusts the strategy, and improves operating efficiency.

[0199] The remote control module sends control commands to each pump according to the optimized scheduling strategy to perform switch operations and flow regulation. For example, when water demand increases, the system will automatically increase the operating frequency of some pumps; when demand decreases, it will reduce the operating frequency or shut down some pumps to ensure stable water supply and energy saving.

[0200] The user interface module displays the operating status and scheduling strategy of the equipment through charts and maps. Managers can view real-time data and historical trends on the interface, such as the flow change curve of the pump, the operating status and location distribution map of each device. When the equipment is abnormal, the interface will automatically pop up an alarm message, displaying specific maintenance suggestions and operation steps.

[0201] Through the real-time monitoring and intelligent scheduling of the system, the efficiency of urban water supply has been significantly improved. The system can detect equipment failures in time and issue early warnings, reduce downtime, and improve equipment reliability. Based on accurate water demand forecasts, the operation of water pumps is reasonably arranged to ensure stable water supply. By optimizing the scheduling strategy, energy saving and efficient management are achieved, reducing operating costs.

[0202] The implementation of this system not only improves the work efficiency of water management departments, but also ensures the water supply security of urban residents, and has significant social and economic benefits.

[0203] The above specific implementations are only descriptions of the preferred implementations of the present invention, and do not limit the protection scope of the present invention. Without departing from the design concept and spirit of the present invention, various modifications, substitutions and improvements made by ordinary technicians in this field to the technical solution of the present invention based on the text description and drawings provided by the present invention should all fall within the protection scope of the present invention. The protection scope of the present invention is determined by the claims.

Claims

1. A remote control method for water conservancy equipment used for water conservancy informatization, characterized in that: The following steps are involved: Step 1: Connect water conservancy equipment to the Internet of Things, collect equipment operation data through sensors, and upload the data to the cloud server; Step 2: Based on the collected equipment operation data, a machine learning algorithm is applied to establish a condition monitoring model for water conservancy equipment to monitor the equipment operation status in real time and detect anomalies; Step 3: Deploy a remote control system in the cloud server to remotely control the water conservancy equipment through mobile applications or web interfaces. The control commands are transmitted to the device end through secure encryption. Step 4: Combine real-time weather data, water demand forecasts, and equipment status information to develop intelligent scheduling algorithms to automatically adjust the operation strategy of water conservancy equipment and achieve equipment management; Step 5: Use data visualization technology to present equipment operating status, historical data, and scheduling strategy information to managers to provide decision support; Step 6: Regularly collect feedback information from remote control and scheduling to optimize and adjust the system.

2. A remote control method for water conservancy equipment for water conservancy informatization according to claim 1, characterized in that: The IoT system built in step 1 includes: Sensors installed on various water conservancy equipment to collect equipment operation data; The controller installed on each hydraulic equipment is used to receive and execute remote control commands; Cloud data platform, used to receive, store and process data uploaded from sensors, and perform data analysis and monitoring; The equipment condition monitoring model established in step 2 includes the following steps: Clean, filter and standardize the collected raw data to ensure data quality and consistency; Use the preprocessed data to train the machine learning model and use the cross-validation method to evaluate the model performance; The remote device control functions implemented in step 3 include: Design remote control interface to provide equipment status monitoring, control command sending and alarm processing functions; Use secure transmission protocols to ensure the security and reliability of control commands during transmission; the control interface allows managers to monitor and control water conservancy equipment in real time through mobile devices or computers, receive equipment status alarms and respond quickly, and control commands are transmitted to the device end through a secure channel.

3. A remote control method for water conservancy equipment for water conservancy informatization according to claim 1, characterized in that: The intelligent scheduling algorithm developed in step 4 includes the following steps: Integrate real-time weather data, water demand forecast data and equipment status data to form a comprehensive dispatch data source; Based on historical scheduling data and real-time data, the intelligent scheduling algorithm is optimized to improve the scientificity and practicality of the scheduling strategy. The scheduling algorithm analyzes multi-source data to predict changes in water demand, and automatically adjusts the operating strategy of water conservancy equipment based on equipment status and operating efficiency to achieve equipment management.

4. A remote control method for water conservancy equipment for water conservancy informatization according to claim 1, characterized in that: The data visualization and decision support performed in step 5 include: Develop visual panels that use charts, graphs, and maps to display equipment operating status, historical data, and scheduling strategies, making it easier for managers to quickly understand and make decisions; The integrated decision support system provides equipment failure warning, maintenance suggestions and optimization suggestions. The visualization panel displays key operating parameters and trend analysis through an intuitive graphical interface to help managers quickly identify problems and make decisions.

5. A remote control method for water conservancy equipment for water conservancy informatization according to claim 1, characterized in that: The feedback and optimization mechanisms implemented in step 6 include: Regularly collect system operation feedback data, analyze equipment control and scheduling effects, and identify improvement points; Based on the feedback analysis results, the system is optimized and upgraded to improve the accuracy of remote control and the efficiency of equipment operation. The system collects and analyzes equipment operation data and user feedback, identifies deficiencies in control strategies and scheduling algorithms, and continuously optimizes and improves them.

6. A remote control system for water conservancy equipment used for water conservancy informatization, applicable to a remote control method for water conservancy equipment used for water conservancy informatization according to any one of claims 1 to 5, characterized in that: include: Sensor module, used to collect equipment operation data; A data acquisition module, connected to the sensor module signal, for acquiring equipment operation data; The data processing module is connected to the data acquisition module signal, and is used to pre-process and analyze the collected data and establish an equipment status monitoring model; A control module, connected to the data processing module and the remote control module by signals, for sending control commands; A remote control module is connected to the control module and the water conservancy equipment signals to receive and execute control commands; The cloud server is connected to the data processing module signal, used to store and process multi-source data, and supports the development and optimization of intelligent scheduling algorithms; The user interface module is connected to the data processing module and the cloud server signal to display data visualization results and provide decision support.

7. A remote control system for water conservancy equipment used for water conservancy informatization according to claim 6, characterized in that: The sensor module includes: Flow sensor, used to detect flow data of water conservancy equipment; Pressure sensor, used to detect pressure data of water conservancy equipment; Water quality sensor, used to detect water quality parameters.

8. A remote control system for water conservancy equipment used for water conservancy informatization according to claim 6, characterized in that: The data processing module includes: Data preprocessing unit, used to clean, filter and standardize the collected raw data to ensure data quality and consistency; The machine learning unit is used to train and verify the machine learning model using the preprocessed data and establish an equipment status monitoring model. The control module includes: A remote control interface for receiving control commands from a user interface; The secure transmission module adopts a secure transmission protocol to ensure the security and reliability of control commands during transmission.

9. A remote control system for water conservancy equipment used for water conservancy informatization according to claim 6, characterized in that: The cloud server includes: A data storage unit, used to store data uploaded from sensors and data acquisition modules; The data processing unit is used to integrate and analyze multi-source data and support the development and optimization of intelligent scheduling algorithms.

10. A remote control system for water conservancy equipment used for water conservancy informatization according to claim 6, characterized in that: The user interface module includes: Visualization panel, which uses charts, graphs and maps to display equipment operation status, historical data and scheduling strategies; Decision support system, providing functions such as equipment failure warning, maintenance suggestions and optimization suggestions. Further including: A feedback collection module, connected to the control module and the data processing module by signals, for regularly collecting system operation feedback data, analyzing equipment control and scheduling effects, and identifying improvement points; The optimization module optimizes and upgrades the system based on the feedback analysis results to improve the accuracy of remote control and equipment operation efficiency.

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