An AI Agent-Based Intelligent Optimization Scheduling Method for Multi-Pump Gates in Plain River Networks

Abstract

The application discloses a plain river network multi-pump gate intelligent optimization scheduling method based on an AI Agent, relates to the technical field of water conservancy engineering scheduling, and comprises the following steps: S1, a multidimensional state perception system of the AI Agent is constructed, hydrological and hydraulic data of the plain river network and mechanical operation parameters, energy consumption characteristics, fault early warning threshold values and operation and maintenance cycle constraint data of each pump gate device are collected, and a multi-source perception data set is formed; through the construction of the AI Agent decision mechanism of the deep linkage between the full life cycle state of the pump gate device and the hydrological scheduling demand, the equipment multidimensional state data is integrated, the dynamic evaluation link of the equipment health level is added, the coupling constraint model is established, the scheduling strategy generated by the AI Agent can meet the hydrological core demand such as flood control, drainage and water supply, can accurately adapt to different health states of the pump gate device, the scheduling optimization of the plain river network multi-pump gate and the operation and maintenance guarantee of the equipment are cooperated, and the problems of excessive equipment wear, frequent faults and high operation and maintenance cost caused by the scheduling strategy ignoring the equipment state are solved.

Description

Technical Field

[0001] This invention relates to the field of water conservancy project scheduling technology, specifically to an intelligent optimization scheduling method for multiple pumping stations and gates in plain river networks based on AI Agent. Background Technology

[0002] The plain river network is a complex water network system distributed in plain areas, formed by numerous interconnected tributaries, lakes, depressions, and ditches. Its significant characteristics include high water density, strong connectivity, and the influence of both topography and human regulation on water flow direction. It is also susceptible to interference from natural factors such as rainfall, tides, and evaporation, as well as human activities such as industrial and agricultural water use and urban and rural sewage discharge, resulting in significant dynamics and complexity in its hydrological conditions. As the core carrier of the regional water ecological environment, the plain river network undertakes multiple functions, including flood control and drainage, water supply for industrial and agricultural production, urban and rural domestic water supply, navigation, and ecological conservation. It has irreplaceable strategic significance for maintaining regional water resource balance, ensuring stable production and living order, and promoting sustainable economic and social development. The multi-pump and sluice gate system in the plain river network refers to a collection of water flow control facilities scientifically deployed at key nodes of the water network system, consisting of multiple functionally complementary and spatially dispersed pump stations and sluice gates. Through the start-up and shutdown control of pumps and sluice gates, precise adjustment of opening degree, and coordinated operation of multiple devices, it achieves directional guidance, quantitative allocation, and dynamic regulation of river network water flow, serving as the core execution unit for water resource regulation in the plain river network. The optimized scheduling of multiple pump gates is a key link in the refined management of water resources in plain river networks. It can not only improve the ability to respond quickly to and cope with floods and reduce disaster losses, but also ensure regional water supply security and improve the efficiency of water resource recycling.

[0003] However, existing multi-pump and gate scheduling methods in plain river networks still have certain shortcomings. Most rely solely on hydrological and hydraulic data to formulate scheduling strategies, completely ignoring the operational status of the pump and gate equipment itself. They fail to incorporate key equipment information such as mechanical operating parameters, energy consumption characteristics, fault warnings, and maintenance cycles into scheduling decisions. This one-dimensional scheduling model results in scheduling strategies that lack adaptability to the status of pump and gate equipment, easily leading to pump and gate equipment operating under unreasonable high loads for extended periods. This causes excessive equipment wear and frequent failures, seriously affecting the stable and continuous execution of scheduling tasks and significantly increasing maintenance costs such as equipment repair and replacement. It is difficult to achieve the coordinated advancement of scheduling optimization goals and equipment maintenance assurance. Therefore, developing an AI Agent-based intelligent optimization scheduling method for multi-pump and gate systems in plain river networks is of great significance. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide an intelligent optimization scheduling method for multiple pumping and sluice gates in plain river networks based on AI Agent. It can build an AI Agent decision-making mechanism that deeply links the full life cycle status of pumping and sluice gate equipment with hydrological scheduling needs, integrate multi-dimensional status data of equipment, add a dynamic evaluation link for equipment health level and establish a coupling constraint model, so that the scheduling strategy generated by AI Agent can not only meet the core hydrological needs such as flood control, drainage and water supply, but also accurately adapt to different health states of pumping and sluice gate equipment, and realize the synergy between multi-pumping and sluice gate scheduling optimization and equipment operation and maintenance support in plain river networks.

[0005] To address the aforementioned technical problems, this invention provides the following technical solution: an intelligent optimization scheduling method for multiple pumping stations in plain river networks based on AI Agents, comprising the following steps:

[0006] S1. Construct a multi-dimensional state perception system for AI Agent, collect hydrological and hydraulic data of plain river network and mechanical operating parameters, energy consumption characteristics, fault warning thresholds and maintenance cycle constraints of various pump and gate equipment, and form a multi-source perception data set.

[0007] S2. Based on multi-source sensing data, establish a coupled constraint model between pump and gate equipment status and hydrological scheduling indicators to quantify the correlation between equipment health status, energy consumption level and hydrological scheduling needs.

[0008] S3 and AI Agent invoke the coupled constraint model to classify the health levels of each pump and gate device;

[0009] S4. Based on meeting the core needs of hydrological dispatching, and combined with equipment health level and energy consumption optimization target, generate an adaptive dispatching strategy.

[0010] S5. Issue scheduling strategies and collect equipment operation status data and river network hydrological response data after execution, and feed them back to the AI ​​Agent to iteratively optimize the model and scheduling strategies.

[0011] Furthermore, step S1, in constructing the multi-dimensional state perception system of the AI ​​Agent, includes the following steps:

[0012] Based on data type, the system is divided into a hydrological and hydraulic data acquisition module and an equipment status data acquisition module. The hydrological and hydraulic data acquisition module collects water level and flow data in real time for key sections of the plain river network, lakes, and nodes of the main and tributary water systems. The equipment status data acquisition module continuously collects motor operating current, bearing temperature, gate opening feedback value, energy consumption per unit time, fault diagnosis sensor data, and operation and maintenance record information for each pump and gate.

[0013] Outlier removal, missing value completion, and data standardization were performed on the two types of collected data to eliminate format differences and noise interference from different data sources.

[0014] The two types of preprocessed data are aligned and integrated according to timestamps through the data fusion interface to form a structured multi-source sensing data set.

[0015] Furthermore, step S2, in establishing the coupled constraint model of pump and gate equipment status and hydrological scheduling indicators, includes the following steps:

[0016] Based on multi-source sensing data, correlation analysis is used to explore the inherent correlation between equipment mechanical operating parameters, energy consumption characteristics and hydrological scheduling indicators such as water level control targets and flow regulation requirements, and to identify core influencing factors.

[0017] Based on the design rated parameters of pump gate equipment, operation and maintenance technical specifications, and the mandatory requirements for regional flood control, drainage, and water supply, the boundary conditions for equipment health status threshold constraints, energy consumption control constraints, and hydrological scheduling target constraints are clarified.

[0018] Machine learning algorithms are used to embed the aforementioned correlation patterns and constraint boundaries into the model framework. The model parameters are then trained and optimized using historical data. The coupled quantitative relationship between equipment status and hydrological scheduling indicators in the model is expressed by the following formula: ,in, For coupling quantization values, This is a comprehensive factor for equipment status. For hydrological dispatch demand factors, For equipment status weighting coefficients, This is the weighting coefficient for hydrological dispatch demand. For coupling interaction coefficients, For factor interaction operators, , Based on the operation and maintenance records and historical scheduling performance data of pump and gate equipment over the past five years, the determination was made using the entropy weight method. Based on the fitting analysis of equipment design parameters and actual operating data, and in conjunction with the calibration of water conservancy project scheduling technical specifications, the determination was made.

[0019] Furthermore, step S4, in generating the adaptive scheduling policy, includes the following steps:

[0020] Real-time assessment of hydrological scheduling needs for flood control, drainage, and water supply is conducted, and the needs are prioritized according to their urgency and importance.

[0021] For pump gate equipment with different health levels, analyze their operating load capacity respectively. Prioritize the allocation of control load to equipment with high health level, and limit the load or reserve maintenance window for equipment with fault warning level.

[0022] Based on the analysis results of demand priority and equipment carrying capacity, the start-stop status, opening degree and running time of pump gates are combined and optimized to form a scheduling strategy that includes single equipment operation instructions and multi-equipment collaborative linkage schemes.

[0023] Furthermore, step S5, when performing dynamic iterative optimization, includes the following steps:

[0024] Set the data feedback acquisition cycle, capture in real time the motor operating parameters, energy consumption change data and water level response value and flow change value of the corresponding area of ​​the river network after the pump gate equipment executes the scheduling strategy, and perform deviation analysis between the feedback data and the model prediction value;

[0025] Based on the results of the deviation analysis, the core parameters of the coupled constraint model are adjusted. At the same time, based on the new equipment status and hydrological conditions, the operation instructions of the original scheduling strategy are locally modified or optimized as a whole to form a new round of scheduling strategy.

[0026] Furthermore, the mechanical operating parameters of the pump gate equipment collected in step S1 include motor speed, output power, gate opening and closing speed, and seal operating status data. The hydrological and hydraulic data include real-time water level, instantaneous flow rate, cumulative runoff, and water flow direction data. All collected data are transmitted to the AI ​​Agent's data storage unit through an encrypted transmission protocol.

[0027] Furthermore, in step S3, when classifying the health level of the pump gate equipment, the comprehensive health value of the equipment is calculated based on the deviation of the equipment's mechanical operating parameters from the rated value, the proportion of energy consumption exceeding the standard, the time since the last maintenance, and the triggering status of the fault warning sensor, using the following formula: ,in, The overall health value of the equipment. These are the rated values ​​of the equipment's mechanical operating parameters. These are the actual values ​​of the equipment's mechanical operating parameters. The rated energy consumption of the equipment. This represents the actual energy consumption of the equipment. To preset the operation and maintenance cycle, This is the time since the last maintenance. This is the fault warning trigger flag; it is set to 1 when triggered and 0 when not triggered. The weights of each indicator are determined using the analytic hierarchy process (AHP) based on equipment manuals, maintenance statistics, and industry technical standards for different types of pump gates. The corresponding levels are divided into four categories: high load, normal, low load warning, and fault warning.

[0028] Furthermore, in step S4, when generating the scheduling strategy, the load of pumps and gates under high load conditions is distributed by transferring loads across regions and alternating operation in different time periods. The load transfer allocation ratio is determined by the following formula: ,in, For the first The load distribution ratio of the replacement pump gate, For the first The overall health value of the replacement pump gate. For the first The rated regulating flow rate of the replacement pump gate, To determine the total number of alternative pump gates participating in load transfer, the opening and closing timing and opening degree adjustment gradient of pump gates in normal condition are optimized to reduce energy consumption. Pump gates in fault warning condition are included in the temporary shutdown and maintenance plan and matched with the control scheme of alternative pump gates.

[0029] Furthermore, in the coupled constraint model established in step S2, the equipment operation and maintenance cycle constraint is transformed into a time window limit for the scheduling strategy. When the pump gate equipment is close to the preset operation and maintenance cycle, the model reduces the scheduling load allocation weight of the equipment.

[0030] Furthermore, the iterative optimization process in step S5 sets trigger conditions. When the deviation of equipment operating parameters in the feedback data exceeds a preset threshold, the river network hydrological response data fails to meet the scheduling target, or the health level of pump and gate equipment changes, the iterative optimization process of the model and scheduling strategy is initiated. If the trigger conditions are not met, regular optimization is performed at a fixed cycle.

[0031] Compared with existing technologies, this AI Agent-based intelligent optimization scheduling method for multiple pumping stations in plain river networks has the following advantages:

[0032] This invention constructs an AI Agent decision-making mechanism that deeply links the full lifecycle status of pump and gate equipment with hydrological scheduling needs. It integrates multi-dimensional status data of equipment, adds a dynamic assessment of equipment health levels, and establishes a coupled constraint model. This enables the scheduling strategies generated by the AI ​​Agent to not only meet the core hydrological needs such as flood control, drainage, and water supply, but also accurately adapt to different health states of pump and gate equipment. This achieves synergy between multi-pump and gate scheduling optimization and equipment operation and maintenance support in plain river networks. It solves the problems of excessive equipment wear, frequent failures, and high operation and maintenance costs caused by scheduling strategies ignoring equipment status in existing technologies. It ensures the stable and continuous execution of scheduling tasks and improves the scientificity, adaptability, and sustainability of multi-pump and gate scheduling.

[0033] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0035] Figure 1 The flowchart shows a method for intelligent optimization scheduling of multiple pumping stations in plain river networks based on AI Agent.

[0036] Figure 2 The flowchart shows the intelligent optimization scheduling method for multiple pumping stations in plain river networks based on AI Agent.

[0037] Figure 3 A flowchart for building a multi-dimensional state perception system for AI Agents. Detailed Implementation

[0038] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0039] This invention discloses an intelligent optimization scheduling method for multiple pumping stations in plain river networks based on AI Agent, aiming to solve the problems of excessive wear and tear, frequent failures, and high operation and maintenance costs caused by the neglect of pumping station equipment status in existing scheduling strategies, and to achieve synergy between scheduling optimization and equipment operation and maintenance assurance.

[0040] See Figure 1 and Figure 2 The complete technical solution of this method is as follows: First, a multi-dimensional state perception system for the AI ​​Agent is constructed, dividing the data into hydrological and hydraulic data acquisition modules and equipment status data acquisition modules according to data type. The former collects water level and flow data of key sections of the river network, lakes, and tributaries in real time, while the latter continuously collects motor operating current, bearing temperature, gate opening feedback values, energy consumption per unit time, fault diagnosis sensor data, and operation and maintenance records of each pump and gate. Outlier removal, missing value completion, and standardization are performed on both types of data, and then they are aligned and integrated according to timestamps to form a structured multi-source perception data set.

[0041] A coupled constraint model of pump and gate equipment status and hydrological scheduling indicators is established based on multi-source sensing data. Correlation analysis is used to explore the intrinsic relationship between equipment parameter energy consumption characteristics and hydrological scheduling indicators, clarify the constraint boundaries of equipment health status threshold energy consumption control and hydrological scheduling objectives, and use machine learning algorithms to embed correlation patterns and constraint boundaries and optimize parameters through training with historical data. At the same time, the equipment operation and maintenance cycle constraint is transformed into a scheduling time window limit.

[0042] The AI ​​Agent calls the model and, based on the deviation of the equipment's mechanical operating parameters from the rated value, the proportion of energy consumption exceeding the standard, the time since the last maintenance, and the fault warning trigger status, classifies the equipment into four health levels: high load, normal, low load, and fault warning.

[0043] Based on meeting the core needs of hydrological dispatching, an adaptive dispatching strategy is generated by combining equipment health levels and energy consumption optimization objectives. First, the flood control, drainage, and water supply needs are prioritized according to urgency and importance. Then, the operational carrying capacity of equipment with different health levels is analyzed. Equipment with high health levels is given priority in load allocation, while equipment with fault warning levels has its load limited or maintenance windows reserved. This optimizes the opening degree and operating time of pumps and gates, forming a scheme for single-equipment operation commands and multi-equipment collaborative linkage.

[0044] Finally, the scheduling strategy is issued, and the equipment operation status data and river network hydrological response data are collected periodically and fed back to the AI ​​Agent. The feedback data is compared with the model predictions to analyze the discrepancies, adjust the core parameters of the model, and correct and optimize the scheduling strategy. When the deviation of equipment operation parameters exceeds the threshold, the hydrological response fails to meet the target, or the equipment health level changes, iterative optimization is initiated. If the triggering conditions are not met, regular optimization is performed at a fixed cycle.

[0045] Example 1

[0046] This embodiment is applied to a plain river network area. This area has a high water density and strong connectivity. Water flow direction is influenced by both topography and artificial regulation, making it susceptible to natural factors such as rainfall, tides, and evaporation, as well as human activities such as industrial and agricultural water use and urban and rural sewage discharge. The hydrological conditions in this area are highly dynamic and complex. The river network in this region undertakes multiple functions, including flood control, drainage, water supply for industrial and agricultural production, and water security for urban and rural life. Its key nodes are equipped with multiple functionally complementary and spatially dispersed pumping stations and sluice gates, forming a multi-pump and sluice gate water flow control facility complex. Previously, the multi-pump and sluice gate scheduling method used in this area relied solely on hydrological and hydraulic data to formulate strategies, ignoring the operational status of the pumping and sluice gate equipment itself. This resulted in some pumping and sluice gates operating under unreasonable high loads for extended periods, leading to excessive equipment wear and frequent failures, high maintenance costs, and affecting the stable and continuous execution of scheduling tasks. Therefore, this invention, based on AIAgent, provides an intelligent optimization scheduling method for multi-pump and sluice gates in plain river networks, applied to this area to achieve synergistic advancement of scheduling optimization and equipment operation and maintenance support.

[0047] See Figure 1 and Figure 2 The specific implementation process of this embodiment is as follows:

[0048] First, we will construct a multi-dimensional state perception system for AI agents. (See below) Figure 3 The sensing system is divided into a hydrological and hydraulic data acquisition module and an equipment status data acquisition module according to data types. The hydrological and hydraulic data acquisition module collects real-time water level and flow data for key sections, lakes, and tributary nodes of the plain river network. The collected hydrological and hydraulic data specifically includes real-time water level, instantaneous flow, cumulative runoff, and flow direction data. The equipment status data acquisition module continuously collects data on motor operating current, bearing temperature, gate opening feedback value, energy consumption per unit time, fault diagnosis sensor data, and maintenance records for each pump and gate device within the area. The mechanical operating parameters of the pump and gate devices include motor speed, output power, gate opening and closing speed, and seal operating status data.

[0049] The two types of collected data are processed separately. First, outliers are removed and missing values ​​are filled in. Then, data standardization is performed to eliminate format differences and noise interference between different data sources. Next, through a data fusion interface, the preprocessed hydrological and hydraulic data and equipment status data are aligned and integrated according to timestamps, ultimately forming a structured multi-source sensing data set. All collected and processed data are transmitted to the AI ​​Agent's data storage unit via an encrypted transmission protocol.

[0050] After completing the construction of the multi-source sensing data set, the next step was to establish a coupled constraint model between the status of pumping and sluice gate equipment and hydrological scheduling indicators. Based on the existing multi-source sensing data, correlation analysis was used to deeply explore the inherent correlation between equipment mechanical operating parameters, energy consumption characteristics, and hydrological scheduling indicators such as water level control targets and flow regulation requirements, thereby identifying the core influencing factors affecting scheduling effectiveness. Combining the design rated parameters of pumping and sluice gate equipment in the region, relevant operation and maintenance technical specifications, and the mandatory requirements for regional flood control, drainage, and water supply, the boundary conditions for equipment health status threshold constraints, energy consumption control constraints, and hydrological scheduling target constraints were clarified.

[0051] Machine learning algorithms are used to embed the aforementioned correlation patterns and explicit constraint boundaries into the model framework, and the model parameters are trained and optimized using historical operational data of pumping and sluice gate equipment in the region. In the specific implementation of this embodiment, the coupling and quantitative relationship between equipment status and hydrological scheduling indicators in the model is expressed by the following formula: ,in, For coupling quantization values, This is a comprehensive factor for equipment status. For hydrological dispatch demand factors, For equipment status weighting coefficients, This is the weighting coefficient for hydrological dispatch demand. For coupling interaction coefficients, This is the factor interaction operator. and Based on the operation and maintenance records and historical scheduling performance data of pump gate equipment in the area over the past five years, the determination was made using the entropy weight method. Based on the fitting analysis of equipment design parameters and actual operating data, and calibrated in accordance with the technical specifications for water conservancy project scheduling, the model is determined. Simultaneously, in this coupled constraint model, the equipment operation and maintenance cycle constraint is transformed into a time window limit for the scheduling strategy. When the pumping station equipment approaches its preset operation and maintenance cycle, the model automatically reduces the scheduling load allocation weight of that equipment.

[0052] After the coupling constraint model is established, the AI ​​Agent calls the model to perform health level classification of each pump and gate device. During the classification process, a comprehensive evaluation is conducted based on key indicators such as the deviation of equipment mechanical operating parameters from rated values, the proportion of energy consumption exceeding standards, the time since the last maintenance, and the trigger status of fault warning sensors. In the specific implementation of this embodiment, the comprehensive health value of the equipment is calculated using the following formula: ,in, The overall health value of the equipment. These are the rated values ​​of the equipment's mechanical operating parameters. These are the actual values ​​of the equipment's mechanical operating parameters. The rated energy consumption of the equipment. This represents the actual energy consumption of the equipment. To preset the operation and maintenance cycle, This is the time since the last maintenance. This is the fault warning trigger indicator; it is set to 1 when triggered and 0 when not triggered. The weights of each indicator were determined using the analytic hierarchy process (AHP), based on equipment manuals, operation and maintenance statistics, and industry technical standards for different types of pumping stations in the region. The values ​​are used to classify the equipment into four health levels: high load, normal, low load warning, and fault warning.

[0053] After classifying equipment health levels, adaptive scheduling strategies are generated based on core hydrological scheduling needs, equipment health levels, and optimal energy consumption targets. First, the hydrological scheduling needs for flood control, drainage, and water supply in the region are assessed in real time, and priorities are determined by urgency and importance. For pumping and sluice gate equipment classified into different health levels, their operational capacity is analyzed. Equipment with high health levels is prioritized for control load allocation, while equipment with fault warning levels has its load limited or maintenance windows reserved.

[0054] Based on the analysis results of demand priority and equipment carrying capacity, the start / stop status, opening degree, and running time of pump gates are combined and optimized to form a scheduling strategy that includes single-equipment operation commands and multi-equipment collaborative linkage schemes. For pump gates under high load, load distribution is carried out by transferring loads from pump gates with the same function across regions and alternating operation in different time periods. In the specific implementation of this embodiment, the load transfer allocation ratio is determined by the following formula: ,in, For the first The load distribution ratio of the replacement pump gate, For the first The overall health value of the replacement pump gate. For the first The rated regulating flow rate of the replacement pump gate, This refers to the total number of alternative pump gates participating in load transfer. For pump gates in normal operation, optimize their opening and closing timing and opening degree adjustment gradient to reduce energy consumption; for pump gates in fault warning state, include them in the temporary shutdown and maintenance plan and match the control scheme of alternative pump gates to ensure that scheduling tasks are not affected.

[0055] After the scheduling strategy is generated, it is issued and executed, while a dynamic iterative optimization process is initiated to continuously improve the model and scheduling strategy. A reasonable data feedback acquisition cycle is set to capture in real time the motor operating parameters, energy consumption changes, and water level response and flow changes of the corresponding river network areas after the pumping and sluice gate equipment executes the scheduling strategy. These feedback data are then compared with the model predictions to analyze the discrepancies. Based on the results of the discrepancy analysis, the core parameters of the coupled constraint model are adjusted in a timely manner. Simultaneously, based on the new equipment status and hydrological conditions, the operational instructions of the original scheduling strategy are locally modified or comprehensively optimized to form a new round of scheduling strategy.

[0056] The iterative optimization process sets clear trigger conditions. When the deviation of equipment operating parameters in the feedback data exceeds the preset threshold, the river network hydrological response data fails to meet the scheduling target, or the health level of pump and gate equipment changes, the iterative optimization process of the model and scheduling strategy is initiated. If the trigger conditions are not met, regular optimization is performed at fixed intervals to ensure that the scheduling strategy and model are always adapted to real-time operating conditions.

[0057] In summary, this embodiment applies an AI Agent-based intelligent optimization scheduling method for multiple pumping stations in plain river networks to the target plain river network area, constructing a multi-dimensional state perception system and achieving comprehensive collection and effective integration of hydrological and hydraulic data and equipment status data. By establishing a coupled constraint model, the correlation between equipment health status, energy consumption level, and hydrological scheduling needs is accurately quantified. Combined with scientific health level classification and load allocation calculation, a highly adaptable scheduling strategy is generated, and the scheduling effect is continuously improved through dynamic iterative optimization. This implementation process effectively solves the problems of excessive equipment wear, frequent failures, and high operation and maintenance costs caused by neglecting equipment status in traditional scheduling methods. The generated scheduling strategy not only meets the core hydrological needs such as regional flood control, drainage, and water supply, but also accurately adapts to different health states of pumping station equipment, realizing the coordinated advancement of multi-pumping station scheduling optimization and equipment operation and maintenance support in plain river networks.

[0058] Example 2

[0059] This embodiment is applied to a plain river network area in the coastal region that is significantly affected by tides. This area has a crisscrossing river system that is directly connected to the sea. The direction of water flow is influenced by both artificial topographic control and tidal fluctuations, resulting in complex hydrological conditions characterized by the superposition of tidal periodic fluctuations and random rainfall. In addition to its functions of flood control, drainage, industrial and agricultural water supply, and urban and rural domestic water supply, the regional river network also needs to maintain estuary ecological flow and ensure navigation safety. The multi-pump and gate facilities deployed at key nodes have long faced problems such as sudden rises and falls in water levels and frequent fluctuations in equipment load caused by tidal alternation. Existing scheduling methods do not fully consider the dynamic adaptation relationship between tides and equipment status, leading to overload operation of pumps and gates during strong tides and energy waste during low tides, resulting in high equipment failure rates and maintenance costs. Therefore, based on the aforementioned embodiment, an AI Agent-based intelligent optimization scheduling method for multi-pump and gate systems in plain river networks, incorporating tidal collaborative control, is applied to this region.

[0060] See Figure 1 and Figure 2 The specific implementation process of this embodiment is as follows:

[0061] First, we construct a multi-dimensional state perception system for AI agents that integrates tidal collaborative perception. (See below) Figure 3Including the hydrological and hydraulic data acquisition module and equipment status data acquisition module provided in the aforementioned embodiments, a new tidal data acquisition module is added specifically for real-time acquisition of tidal level, tidal range, and tidal cycle data for estuary sections and nearshore areas. The hydrological and hydraulic data acquisition module simultaneously acquires water level and flow data of key river network sections, lakes, and tributary nodes at different tidal stages, focusing on capturing abrupt changes in hydrological parameters during the transition between high and low tides; the equipment status data acquisition module enhances the acquisition of pump and gate operating parameters at different tidal periods, including motor operating current and output power at high tide, gate opening feedback value at low tide, energy consumption per unit time, and the operating status of seals and fault diagnosis sensor data during tidal alternation.

[0062] The three types of collected data are processed separately. Hydrological and hydraulic data and equipment status data are processed according to the outlier removal, missing value completion, and data standardization process described in the previous embodiment. Tidal data is decomposed into tidal fundamental and harmonic components based on its periodic characteristics. The preprocessed three types of data are integrated by aligning the timestamps with the tidal phases through a data fusion interface to form a structured multi-source sensing data set containing tidal features. All data is transmitted to the AI ​​Agent's data storage unit via an encrypted transmission protocol.

[0063] Subsequently, a coupled constraint model of pump and gate equipment status and hydrological scheduling indicators incorporating tidal factors was established. Based on the correlation analysis of the aforementioned embodiments, the inherent correlation between tidal data and equipment operating parameters and hydrological scheduling indicators was further explored, incorporating tidal influence factors into the category of core influence factors. Combining the tidal adaptability design parameters, operation and maintenance technical specifications of regional pump and gate equipment, and the multiple needs of flood control, drainage, water supply, ecology, and navigation, the boundary conditions of health status threshold constraints, energy consumption control constraints, and multi-objective scheduling constraints of the equipment at different tidal stages were clarified.

[0064] Machine learning algorithms are used to embed correlation patterns and constraint boundaries into the model framework. The model parameters are trained and optimized using historical tidal data, equipment operation data, and scheduling effectiveness data. The coupling and quantitative relationship between equipment status and hydrological scheduling indicators in the model is expressed by the following formula: Simultaneously, the equipment operation and maintenance cycle constraint and the tidal cycle constraint are combined into a time window limit for the scheduling strategy. When the pump gate equipment is close to the preset operation and maintenance cycle or is about to enter the strong tide period, the model synchronously reduces the scheduling load allocation weight of the equipment.

[0065] The AI ​​Agent invokes the aforementioned coupled constraint model to classify the health level of pump gate equipment. Based on the evaluation indicators of the aforementioned embodiments, and combined with the equipment operation stability data at different tidal stages, a comprehensive calculation is performed. The comprehensive health value of the equipment is calculated using the following formula: Based on the calculated comprehensive health value, the equipment is divided into four levels: high load, normal, low load, early warning, and fault early warning. The optimal operating range of each piece of equipment at different stages of high tide, low tide, and slack tide is marked simultaneously.

[0066] Based on the above results, an adaptive scheduling strategy adapted to tidal rhythms is generated. First, the hydrological scheduling needs of regional flood control, drainage, water supply, ecological navigation are assessed in real time. Then, the priorities are determined by combining the tidal cycle rhythm and the stages of high tide, low tide, and slack tide with the urgency of the needs. During high tide, priority is given to ensuring drainage and estuary ecological flow needs. During low tide, priority is given to ensuring water supply and navigation needs. During slack tide, energy consumption optimization is emphasized.

[0067] For pump gate equipment with different health levels, their operational load capacity during each tidal phase is analyzed. High-health-level equipment is prioritized for control load allocation during critical high and low tide periods. Equipment in low-load warning state only participates in auxiliary control during slack tide periods, and equipment in fault warning state is scheduled for maintenance during periods of strong tide. Combining demand priority, tidal rhythm, and equipment load capacity, the opening size and operating time of pump gates in start / stop states are optimized to form single-equipment operation commands and multi-equipment collaborative linkage schemes. For pump gates under high load conditions, a load sharing method of cross-regional transfer of load from pump gates with the same function, with alternating operation in different time periods, is adopted. The load transfer allocation ratio is determined by the following formula: At the same time, the load allocation ratio is dynamically adjusted according to the flow demand at different tidal stages to ensure the scheduling response speed during high and low tides and the energy consumption optimization target during low tide.

[0068] After the scheduling strategy is issued and executed, a dynamic iterative optimization process integrating tidal prediction is initiated. Based on the data feedback collection and deviation analysis of the aforementioned embodiments, a dynamic feedback collection cycle is set by incorporating short-term tidal prediction data, shortening the collection cycle during high and low tide periods and appropriately extending the cycle during low tide periods. Real-time data on changes in operating parameters and energy consumption of pump and gate equipment after the execution of the scheduling strategy, as well as river network hydrological response data, are captured, and deviation analysis is performed by combining tidal prediction values ​​with model prediction values.

[0069] The iterative optimization process sets trigger conditions. In addition to the aforementioned embodiment where the equipment operating parameter deviation exceeds the threshold and the hydrological response fails to reach the target equipment health level change, a new trigger condition for sudden tidal condition changes is added: when the rate of tidal change exceeds the set range, an emergency optimization process is initiated. Based on the deviation analysis results and tidal prediction data, the core parameters of the coupled constraint model are adjusted in a timely manner. Based on the new equipment status, hydrological conditions, and tidal change trends, the original scheduling strategy is locally modified or optimized overall, forming a new round of scheduling strategies adapted to dynamic tidal changes.

[0070] In summary, this embodiment, based on the aforementioned embodiments, optimizes the multi-dimensional state perception system by adding a tidal data acquisition module, establishes a coupled constraint model incorporating tidal factors, generates a scheduling strategy adapted to tidal rhythms, and conducts dynamic iterative optimization. This effectively solves the problems of delayed response, large load fluctuations, energy waste, and frequent failures of pump and gate scheduling in plain river network areas affected by tides. This implementation process achieves deep coordination between the equipment status under tidal hydrological conditions and multi-objective scheduling requirements. The generated scheduling strategy not only meets multiple core needs such as flood control, drainage, water supply, ecological navigation, etc., but also adapts to different health states of pump and gate equipment and tidal cycle changes, ensuring stable execution of scheduling tasks during strong tides and energy consumption optimization during slack tides.

[0071] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for intelligent optimization scheduling of multiple pumping stations and sluices in plain river networks based on AI Agent, characterized in that, The method includes the following steps: S1. Construct a multi-dimensional state perception system for AI Agent, collect hydrological and hydraulic data of plain river network and mechanical operating parameters, energy consumption characteristics, fault warning thresholds and maintenance cycle constraints of various pump and gate equipment, and form a multi-source perception data set. S2. Based on multi-source sensing data, establish a coupled constraint model between pump and gate equipment status and hydrological scheduling indicators to quantify the correlation between equipment health status, energy consumption level and hydrological scheduling needs. S3 and AI Agent invoke the coupled constraint model to classify the health levels of each pump and gate device; S4. Based on meeting the core needs of hydrological dispatching, and combined with equipment health level and energy consumption optimization target, generate an adaptive dispatching strategy. S5. Issue scheduling strategies and collect equipment operation status data and river network hydrological response data after execution, and feed them back to AIAgent to iteratively optimize the model and scheduling strategies.

2. The intelligent optimization scheduling method for multiple pumping stations in plain river networks based on AI Agent as described in claim 1, characterized in that, Step S1, in constructing the multi-dimensional state perception system of the AI ​​Agent, includes the following steps: Based on data type, the system is divided into a hydrological and hydraulic data acquisition module and an equipment status data acquisition module. The hydrological and hydraulic data acquisition module collects water level and flow data in real time for key sections of the plain river network, lakes, and nodes of the main and tributary water systems. The equipment status data acquisition module continuously collects motor operating current, bearing temperature, gate opening feedback value, energy consumption per unit time, fault diagnosis sensor data, and operation and maintenance record information for each pump and gate. Outlier removal, missing value completion, and data standardization were performed on the two types of collected data to eliminate format differences and noise interference from different data sources. The two types of preprocessed data are aligned and integrated according to timestamps through the data fusion interface to form a structured multi-source sensing data set.

3. The intelligent optimization scheduling method for multiple pumping stations in plain river networks based on AI Agent as described in claim 1, characterized in that, Step S2, in establishing the coupled constraint model of pump and gate equipment status and hydrological scheduling indicators, includes the following steps: Based on multi-source sensing data, correlation analysis is used to explore the inherent correlation between equipment mechanical operating parameters, energy consumption characteristics and hydrological scheduling indicators such as water level control targets and flow regulation requirements, and to identify core influencing factors. Based on the design rated parameters of pump gate equipment, operation and maintenance technical specifications, and the mandatory requirements for regional flood control, drainage, and water supply, the boundary conditions for equipment health status threshold constraints, energy consumption control constraints, and hydrological scheduling target constraints are clarified. Machine learning algorithms are used to embed the aforementioned correlation patterns and constraint boundaries into the model framework. The model parameters are then trained and optimized using historical data. The coupled quantitative relationship between equipment status and hydrological scheduling indicators in the model is expressed by the following formula: ,in, For coupling quantization values, This is a comprehensive factor for equipment status. For hydrological dispatch demand factors, For equipment status weighting coefficients, This is the weighting coefficient for hydrological dispatch demand. For coupling interaction coefficients, This is the factor interaction operator.

4. The intelligent optimization scheduling method for multiple pumping stations in plain river networks based on AI Agent as described in claim 1, characterized in that, Step S4, in generating the adaptive scheduling policy, includes the following steps: Real-time assessment of hydrological scheduling needs for flood control, drainage, and water supply is conducted, and the needs are prioritized according to their urgency and importance. For pump gate equipment with different health levels, analyze their operating load capacity respectively. Prioritize the allocation of control load to equipment with high health level, and limit the load or reserve maintenance window for equipment with fault warning level. Based on the analysis results of demand priority and equipment carrying capacity, the start-stop status, opening degree and running time of pump gates are combined and optimized to form a scheduling strategy that includes single equipment operation instructions and multi-equipment collaborative linkage schemes.

5. The intelligent optimization scheduling method for multiple pumping stations in plain river networks based on AI Agent as described in claim 1, characterized in that, Step S5, when performing dynamic iterative optimization, includes the following steps: Set the data feedback acquisition cycle, capture in real time the motor operating parameters, energy consumption change data and water level response value and flow change value of the corresponding area of ​​the river network after the pump gate equipment executes the scheduling strategy, and perform deviation analysis between the feedback data and the model prediction value; Based on the results of the deviation analysis, the core parameters of the coupled constraint model are adjusted. At the same time, based on the new equipment status and hydrological conditions, the operation instructions of the original scheduling strategy are locally modified or optimized as a whole to form a new round of scheduling strategy.

6. The intelligent optimization scheduling method for multiple pumping stations in plain river networks based on AI Agent as described in claim 1, characterized in that, The mechanical operating parameters of the pump gate equipment collected in step S1 include motor speed, output power, gate opening and closing speed, and seal operating status data. The hydrological and hydraulic data include real-time water level, instantaneous flow rate, cumulative runoff, and water flow direction data. All collected data are transmitted to the data storage unit of the AI ​​Agent through an encrypted transmission protocol.

7. The intelligent optimization scheduling method for multiple pumping stations in plain river networks based on AI Agent as described in claim 1, characterized in that, In step S3, when classifying the health level of the pump gate equipment, the comprehensive health value of the equipment is calculated using the following formula, based on the deviation of the equipment's mechanical operating parameters from the rated values, the proportion of energy consumption exceeding the standard, the time since the last maintenance, and the triggering status of the fault warning sensors: ,in, The overall health value of the equipment. These are the rated values ​​of the equipment's mechanical operating parameters. These are the actual values ​​of the equipment's mechanical operating parameters. The rated energy consumption of the equipment. This represents the actual energy consumption of the equipment. To preset the operation and maintenance cycle, This is the time since the last maintenance. This is a fault warning trigger indicator. Assign weights to each indicator, and calculate the results. It is divided into four levels: high load state, normal state, low load warning state, and fault warning state.

8. The intelligent optimization scheduling method for multiple pumping stations in plain river networks based on AI Agent according to claim 1, characterized in that, In step S4, when generating the scheduling strategy, the load distribution for pump gates under high load conditions is carried out by cross-regional transfer of load from pump gates with the same function and alternating operation in different time periods. The load transfer distribution ratio is determined by the following formula: ,in, For the first The load distribution ratio of the replacement pump gate, For the first The overall health value of the replacement pump gate. For the first The rated regulating flow rate of the replacement pump gate, To determine the total number of alternative pump gates participating in load transfer, the opening and closing timing and opening degree adjustment gradient of pump gates in normal condition are optimized to reduce energy consumption. Pump gates in fault warning condition are included in the temporary shutdown and maintenance plan and matched with the control scheme of alternative pump gates.

9. The intelligent optimization scheduling method for multiple pumping stations in plain river networks based on AI Agent as described in claim 1, characterized in that, In the coupled constraint model established in step S2, the equipment operation and maintenance cycle constraint is transformed into a time window limit for the scheduling strategy. When the pump gate equipment is close to the preset operation and maintenance cycle, the model reduces the scheduling load allocation weight of the equipment.

10. The intelligent optimization scheduling method for multiple pumping stations in plain river networks based on AI Agent according to claim 1, characterized in that, The iterative optimization process in step S5 sets trigger conditions. When the deviation of equipment operating parameters in the feedback data exceeds the preset threshold, the river network hydrological response data fails to meet the scheduling target, or the health level of pump and gate equipment changes, the iterative optimization process of the model and scheduling strategy is initiated. If the trigger conditions are not met, regular optimization is performed at a fixed cycle.

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