CN-121981572-A - Hydraulic engineering scheduling and resource management system based on optimization algorithm

CN121981572ACN 121981572 ACN121981572 ACN 121981572ACN-121981572-A

Abstract

The invention relates to the field of hydraulic engineering scheduling and resource management, in particular to a hydraulic engineering scheduling and resource management system based on an optimization algorithm, which comprises a data acquisition module, a data analysis module, a topology evolution analysis module, a scene recognition module, a decision optimization module, an execution control module and a feedback evaluation module, wherein the system acquires hydraulic engineering environment parameters in real time through a sensor network, extracts essential structural characteristics of a hydrologic system from high-dimensional data by using a topology data analysis method, constructs a hydrologic topological state space, analyzes dynamic evolution characteristics of the system, recognizes a system stable state set and generates a system stability evaluation index; the system adopts a hierarchical decision architecture to realize comprehensive closed-loop control, supports differentiated configuration of various application scenes, improves the prediction accuracy of extreme hydrologic events, and advances early warning time.

Inventors

Ju Jiashuai
LUAN JINKAI
JIAO WEN

Assignees

南京信息工程大学

Dates

Publication Date: 20260505
Application Date: 20260119

Claims (10)

1. The hydraulic engineering scheduling and resource management system based on the optimization algorithm is characterized by comprising the following components: the data acquisition module is used for acquiring hydraulic engineering environment parameters, wherein the environment parameters comprise water level data, flow data, rainfall data, soil moisture data, temperature data and meteorological image data; The data analysis module is in communication connection with the data acquisition module and is used for receiving the environmental parameters, preprocessing the environmental parameters and extracting topological features to generate a hydrologic topological feature descriptor, wherein the hydrologic topological feature descriptor comprises topological persistence features and topological structure complexity indexes; The topological evolution analysis module is in communication connection with the data analysis module and is used for constructing a hydrologic topological state space based on the hydrologic topological feature descriptor, analyzing the dynamic evolution characteristics of the hydrologic system, identifying the steady state set of the hydrologic system and generating a system stability assessment index; The scene recognition module is in communication connection with the topology evolution analysis module and is used for receiving the system stability evaluation index, matching the current hydrologic topological feature with a preset hydrologic scene feature fingerprint library and determining the scene type of the current hydrologic system; The decision optimization module is in communication connection with the scene recognition module and is used for constructing a parameterized decision space based on the scene type, and performing multi-objective optimization by adopting a reinforcement learning algorithm and a genetic algorithm under the condition of considering hydraulic engineering physical constraint, safety constraint and topology constraint to generate a hydraulic engineering scheduling scheme; the execution control module is in communication connection with the decision optimization module and is used for converting the hydraulic engineering scheduling scheme into a control instruction, sending the control instruction to hydraulic engineering control equipment and receiving execution feedback data of the control equipment; The feedback evaluation module is in communication connection with the execution control module and is used for receiving the execution feedback data, evaluating the deviation between the actual execution effect and the expected effect and feeding back the evaluation result to the decision optimization module for optimizing the parameter adjustment of the model.
2. The system of claim 1, wherein the data acquisition module comprises: the sensor network unit is used for collecting hydraulic engineering environment parameters through the distributed sensors; The edge computing unit is in communication connection with the sensor network unit and is used for carrying out preliminary filtration and space-time alignment on the collected environmental parameters; the data integration unit is in communication connection with the edge calculation unit and is used for integrating the filtered and aligned environment parameters into a data packet in a standard format; and the communication management unit is in communication connection with the data integration unit and is used for transmitting the data packet to the data analysis module through wired network, wireless network or satellite communication.
3. The system of claim 1, wherein the data analysis module comprises: the data preprocessing unit is used for carrying out outlier detection, missing value processing, noise filtering and data standardization on the received environmental parameters; the topological feature extraction unit is in communication connection with the data preprocessing unit and is used for constructing preprocessed environment parameters into a multidimensional point cloud, calculating topological features under different scales through continuous coherent analysis and generating hydrological topological feature descriptors; The feature library management unit is in communication connection with the topological feature extraction unit and is used for storing the generated hydrological topological feature descriptors in a classified manner according to geographic positions, time scales and hydrological types, and establishing feature indexes and a retrieval mechanism.
4. The system of claim 1, wherein the topology evolution analysis module comprises: A multi-scale time window unit for constructing short-term, medium-term and long-term time windows based on the hydrologic change rate, and capturing hydrologic topological feature changes of different time scales; The topological track construction unit is in communication connection with the multi-scale time window unit and is used for calculating topological feature distances of continuous time points and drawing an evolution track of the hydrologic system in a feature space; The state space analysis unit is in communication connection with the topological track construction unit and is used for identifying attractors of the hydrologic system, analyzing branch structures, and calculating stability evaluation indexes such as convergence rate, fluctuation amplitude, sensitivity index, critical threshold distance and the like of the system; The early warning mechanism unit is in communication connection with the state space analysis unit and is used for monitoring early warning indexes such as the change rate of a topological structure, the increase of complexity, the shrinkage of the state space, the weakening of the restoring force of a system and the like, and generating a warning signal when the indexes exceed a preset threshold value.
5. The system of claim 1, wherein the scene recognition module comprises: The characteristic fingerprint library unit is used for storing preset hydrologic scene characteristic fingerprints, and the characteristic fingerprint library unit comprises typical topological characteristics of a normal operation scene, a flood risk scene, a drought risk scene, a water quality anomaly scene, a system fault scene and a season switching scene; The similarity calculation unit is in communication connection with the characteristic fingerprint library unit and is used for calculating the Neisseria distance between the current hydrologic topological characteristic and each scene characteristic in the characteristic fingerprint library; The scene judging unit is in communication connection with the similarity calculating unit and is used for determining the scene type of the current hydrologic system through a weighted voting mechanism based on a similarity calculating result; And the situation learning unit is in communication connection with the scene judging unit and is used for continuously updating the characteristic fingerprint library according to the new data, recording a scene transition path and predicting a scene which possibly appears in the future.
6. The system of claim 1, wherein the decision optimization module comprises: the decision space construction unit is used for defining a regulation and control parameter vector based on the current scene type and setting physical constraint, safety constraint, resource constraint and topology constraint; The objective function unit is in communication connection with the decision space construction unit and is used for setting the weight coefficients of the water supply guarantee rate, the flood control safety, the resource utilization efficiency and the topological stability based on the current scene type to construct a multi-objective optimization function; the optimization algorithm unit is in communication connection with the objective function unit and is used for generating a candidate scheduling scheme by using a topology-based reinforcement learning algorithm in a normal scene, a heuristic algorithm in an emergency scene and a multi-objective genetic algorithm in a recovery scene; the scheme evaluation unit is in communication connection with the optimization algorithm unit and is used for evaluating the potential influence of the candidate scheduling scheme on the topological structure of the hydrologic system and selecting the optimal scheduling scheme.
7. The system of claim 1, wherein the execution control module comprises: The instruction conversion unit is used for converting the hydraulic engineering scheduling scheme into a device-level control instruction sequence; the instruction verification unit is in communication connection with the instruction conversion unit and is used for checking the validity, the safety and the execution sequence of the control instruction; The execution management unit is in communication connection with the instruction verification unit and is used for arranging an execution plan according to the instruction priority and the dependency relationship and sending control instructions to hydraulic engineering control equipment in batches; The state monitoring unit is in communication connection with the execution management unit and is used for monitoring the execution state and the execution result of the control instruction in real time and collecting the execution feedback data.
8. The system of claim 1, wherein the feedback evaluation module comprises: The execution deviation calculation unit is used for comparing the actual execution effect with the expected effect and calculating an execution deviation index; The model adjusting unit is in communication connection with the execution deviation calculating unit and is used for adjusting parameters and weights of the decision optimization model based on the execution deviation index; The experience accumulation unit is in communication connection with the model adjustment unit and is used for establishing a decision-effect mapping library, recording historical decisions and effects thereof and forming a decision experience knowledge base; and the performance evaluation unit is in communication connection with the experience accumulation unit and is used for periodically evaluating the overall operation performance of the system and generating a system optimization suggestion.
9. The system of claim 1, wherein the system employs a hierarchical decision architecture comprising: the strategic layer is used for making a monthly and quarterly water resource overall allocation strategy, balancing the long-term supply and demand relationship, establishing a seasonal scheduling plan and optimizing a reservoir group cooperative operation mode; the tactical layer is in communication connection with the strategic layer and is used for adjusting the daily degree and Zhou Du scheduling parameters according to short-term prediction, balancing multi-target operation indexes and optimizing the water flow path and distribution proportion according to the demand fluctuation caused by weather change; And the operation layer is in communication connection with the tactical layer and is used for executing hour-level and real-time control instructions, responding to emergencies and abnormal conditions, and finely adjusting the operation parameters of the equipment to implement an emergency plan.
10. The system of claim 1, wherein the system has a differentiated configuration in the following application scenarios: the large-scale reservoir group joint scheduling scene strengthens the analysis of the topological relation of the reservoir group and optimizes the cooperative scheduling strategy among reservoirs; the urban water network intelligent management scene strengthens the monitoring of water quality safety topological characteristics, and optimizes water supply scheduling and pipe network pressure control; the water resource optimization scene of the agricultural irrigation area strengthens the analysis of the topological change of the soil moisture, and optimizes the irrigation time and water distribution; The hydropower station group power generation scheduling scene strengthens the modeling of the power station group operation topological structure, and balances the power generation benefit and flood control safety.

Description

Hydraulic engineering scheduling and resource management system based on optimization algorithm Technical Field The invention relates to the field of hydraulic engineering scheduling and resource management, in particular to a hydraulic engineering scheduling and resource management system based on an optimization algorithm. Background The hydraulic engineering scheduling and the resource management are key links for guaranteeing national water safety and sustainable utilization of water resources. Traditional hydraulic engineering scheduling mainly depends on experience rules and simplified models, lacks deep understanding of the complexity of a hydrologic system, and has obvious limitations particularly when facing challenges such as extreme weather events, multi-objective scheduling requirements, water resource shortage and the like. In the prior art, hydraulic engineering scheduling is generally performed by adopting a method of combining a hydrologic model and a statistical model, such as a statistical model based on rainfall-runoff relation, a distributed hydrologic model based on a physical process and the like. These methods typically treat the hydrologic parameters as independent variables, which makes it difficult to capture complex high-dimensional topological relationships in the hydrologic system, resulting in limited system prediction accuracy, particularly when dealing with anomalies and extreme cases. In addition, the traditional scheduling system is mostly based on a threshold trigger mechanism, and often reacts when the problem is obvious, and lacks early warning and preventive scheduling capability. Meanwhile, most of decision optimization of the existing system is based on preset rules or simplified models, adaptability is poor, autonomous learning and optimization are difficult according to actual conditions, water resources cannot be fully utilized, and water safety is guaranteed. Therefore, development of a hydraulic engineering scheduling and resource management system capable of deeply understanding essential characteristics of a hydrologic system, having early warning capability and self-adaptive decision making capability is needed to improve water resource utilization efficiency and hydraulic engineering operation safety. Disclosure of Invention The invention mainly aims to provide a hydraulic engineering scheduling and resource management system based on an optimization algorithm, which extracts essential structural features of a hydrologic system from high-dimensional data by introducing a topology data analysis method, realizes deep understanding of dynamic evolution of the system, and builds a more intelligent and more robust decision-making optimization framework based on a topology view angle. The invention provides a hydraulic engineering scheduling and resource management system based on an optimization algorithm, which comprises the following steps: the data acquisition module is used for acquiring hydraulic engineering environment parameters, wherein the environment parameters comprise water level data, flow data, rainfall data, soil moisture data, temperature data and meteorological image data; The data analysis module is in communication connection with the data acquisition module and is used for receiving the environmental parameters, preprocessing the environmental parameters and extracting topological features to generate a hydrologic topological feature descriptor, wherein the hydrologic topological feature descriptor comprises topological persistence features and topological structure complexity indexes; The topological evolution analysis module is in communication connection with the data analysis module and is used for constructing a hydrologic topological state space based on the hydrologic topological feature descriptor, analyzing the dynamic evolution characteristics of the hydrologic system, identifying the steady state set of the hydrologic system and generating a system stability assessment index; The scene recognition module is in communication connection with the topology evolution analysis module and is used for receiving the system stability evaluation index, matching the current hydrologic topological feature with a preset hydrologic scene feature fingerprint library and determining the scene type of the current hydrologic system; The decision optimization module is in communication connection with the scene recognition module and is used for constructing a parameterized decision space based on the scene type, and performing multi-objective optimization by adopting a reinforcement learning algorithm and a genetic algorithm under the condition of considering hydraulic engineering physical constraint, safety constraint and topology constraint to generate a hydraulic engineering scheduling scheme; the execution control module is in communication connection with the decision optimization module and is used for converting the hydraulic engineering schedul