Unit water resource intelligent scheduling system and method

Abstract

The invention relates to the technical field of power plant equipment operation and maintenance, provides an intelligent scheduling system and method for unit water resources, and aims to solve the problem of scheduling errors caused by low model precision in unit water resource scheduling. The system comprises a data acquisition unit, a data processing unit, a model detection unit, a model optimization unit and a model construction unit; comprehensive unit water resource scheduling basic data are collected, a deep learning model is used for training, construction and optimization of a scheduling model are achieved, a model detection unit carries out double comparison detection on a scheduling simulation result, the accuracy and compliance of a scheduling scheme are improved, and when the model precision is insufficient, the scheduling simulation result is subjected to double comparison detection. And the model optimization unit performs optimization by using a genetic algorithm. According to the method, the precision and adaptability of the scheduling model are improved, the accuracy of the scheduling scheme is improved, the intelligent level and scheduling efficiency of the system are improved, and optimal configuration and efficient utilization of resources are promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of power plant equipment operation and maintenance, and in particular to an intelligent water resource dispatching system and method for a unit. Background Art

[0002] The application of intelligent dispatching system of unit water resources in power plants is quite extensive and in-depth. By integrating advanced sensors, network technologies, etc., the intelligent dispatching system can collect relevant data of unit water resources in real time, such as water level, flow, water quality, etc., and perform real-time dispatching based on these data. This real-time monitoring and dispatching method greatly improves the efficiency and response speed of water resource management. The intelligent dispatching system can detect potential safety risks and problems in advance through real-time monitoring and analysis of unit water resource data, and take timely measures to deal with them. For example, in a hydropower station, the system can warn of natural disasters such as floods or droughts by monitoring key parameters such as water level and flow, and take corresponding dispatching measures to improve the safe and stable operation of the power station. By optimizing the dispatching strategy and resource allocation, the intelligent dispatching system can improve the stability and reliability of the entire power plant system. For example, in a thermal power plant, the system can improve the stable operation and efficient power generation of the unit by optimizing the dispatching of the cooling water system.

[0003] The intelligent water resource dispatching system for the unit has the following technical pain points in actual use. Water resource dispatching needs to consider multiple factors, including reservoir water storage, generator unit output, power grid load, hydraulic resource configuration, etc. The various factors considered in water resource dispatching are interrelated, which makes the model construction and optimization process complicated. When the accuracy of the model in the intelligent water resource dispatching system for the unit is too low, it will cause errors in the intelligent water resource dispatching of the unit. To solve this problem, the present invention provides a system and method for intelligent water resource dispatching of the unit. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a system and method for intelligent scheduling of water resources for a unit, which solves the problem that when the accuracy of the model in the intelligent scheduling system of water resources for a unit is too low, errors will occur in the intelligent scheduling of water resources for the unit.

[0005] In order to solve the above technical problems, the specific technical solutions of the present invention are as follows:

[0006] In a first aspect, the present invention provides a unit water resource intelligent scheduling system, comprising:

[0007] A data acquisition unit is used to obtain basic data of water resource scheduling of the unit and water resource scheduling demand information of the unit. The basic data of water resource scheduling of the unit includes basic water resource data, unit operation data, power grid load data, stability index of the power grid and water resource scheduling data;

[0008] A data processing unit is used to match the unit water resource scheduling demand information in a preset unit water resource scheduling model knowledge base to obtain the unit water resource scheduling model to be used, preprocess the unit water resource scheduling basic data to obtain the preprocessed unit water resource scheduling basic data, substitute the preprocessed unit water resource scheduling basic data into the unit water resource scheduling model to be used, and obtain the unit water resource scheduling simulation result;

[0009] The model detection unit is used to compare the unit water resource scheduling simulation result with the unit water resource scheduling demand information. If the unit water resource scheduling simulation result meets the unit water resource scheduling demand information, the unit water resource scheduling simulation result is compared with the stability index of the power grid to obtain the compliance comparison result of the unit water resource scheduling simulation result;

[0010] A model optimization unit is used for optimizing the water resource scheduling model of the unit to be used by using a genetic algorithm based on the unit water resource scheduling historical data stored in the scheduling database and the compliance comparison result of the unit water resource scheduling simulation result when the compliance comparison result of the unit water resource scheduling simulation result is unqualified, so as to obtain the optimized water resource scheduling model of the unit to be used, use the optimized water resource scheduling model of the unit to be used to process the pre-processed unit water resource scheduling basic data, and perform compliance detection on the data processing result; if the detection result is compliant, use the optimized water resource scheduling model of the unit to be used to replace the water resource scheduling model of the unit to be used; if the detection result is uncompliant, reconstruct the water resource scheduling model of the unit to be used;

[0011] The model building unit is used to determine the data type corresponding to the unit water resource scheduling demand information according to the unit water resource scheduling demand information, and collect the real-time data in the unit water resource scheduling process according to the data type corresponding to the unit water resource scheduling demand information, retrieve the historical unit water resource scheduling process data corresponding to the data type corresponding to the unit water resource scheduling demand information, and use the historical unit water resource scheduling process data, the real-time data in the unit water resource scheduling process and the unit water resource scheduling demand information to train the deep learning model to obtain a reconstructed unit water resource scheduling model, and the reconstructed unit water resource scheduling model is tested by the model detection unit. If the test result is qualified, the reconstructed unit water resource scheduling model is used to process the unit water resource scheduling basic data and the unit water resource scheduling demand information.

[0012] Furthermore, in the unit water resource intelligent scheduling system provided by the present invention, the model detection unit is also used for:

[0013] Acquire the unit water resource scheduling simulation results from the data processing unit, and simultaneously acquire the unit water resource scheduling demand information from the data acquisition unit;

[0014] Compare the unit water resource scheduling simulation results with the unit water resource scheduling demand information to check whether the unit water resource scheduling simulation results meet the requirements in the demand information;

[0015] Based on the results of comparative analysis, determine whether the unit water resource scheduling simulation results meet the unit water resource scheduling demand information. If yes, proceed to the next step to compare with the stability index of the power grid. If not, return to the data processing unit or model optimization unit for model adjustment.

[0016] If the simulation results in the comparison result meet the requirement information, the compliance check will continue. If not, the model optimization or reconstruction process will be triggered.

[0017] Furthermore, in the unit water resource intelligent scheduling system provided by the present invention, the model optimization unit is also used for:

[0018] The result of data processing on the preprocessed basic data of water resources scheduling of the unit using the optimized water resources scheduling model of the unit to be used is obtained from the model optimization unit.

[0019] According to the actual needs of the unit's water resource scheduling, set compliance testing standards, including water balance, water quality requirements and scheduling efficiency indicators;

[0020] Compare the data processing results with the set compliance standards in detail to check whether various indicators meet the compliance requirements, including analyzing whether the water volume is balanced, whether the water quality meets the standards, and whether the scheduling efficiency meets the standards;

[0021] According to the results of the comparative test, determine whether the data processing results are compliant. If compliant, output the compliance test results and use the optimized water resource scheduling model of the unit to be used to replace the original model;

[0022] If it is not compliant, the non-compliant detection result is output and the reconstruction process of the water resource scheduling model of the unit to be used is triggered.

[0023] Furthermore, in the unit water resource intelligent scheduling system provided by the present invention, the model building unit is also used for:

[0024] The model building unit transmits the reconstructed unit water resource scheduling model to the model detection unit, and the model detection unit sets the detection standard according to the actual demand of the unit water resource scheduling;

[0025] The model detection unit uses the set detection standards to detect the reconstructed unit water resource scheduling model. The detection process includes simulating the actual water resource scheduling scenario and comparing the output results with the detection standards;

[0026] The model detection unit evaluates whether the reconstructed unit water resource scheduling model is qualified. If the detection result is qualified, the reconstructed unit water resource scheduling model can be used in the actual unit water resource scheduling; if the detection result is unqualified, the model construction unit needs to make corresponding adjustments and optimizations based on the feedback information, and then retransmit the model for detection.

[0027] In a second aspect, the present invention provides a method for intelligent dispatching of water resources of a unit, which is applied to the intelligent dispatching system for water resources of a unit as described above, comprising:

[0028] Step S101, obtaining basic data of water resource scheduling of the unit and water resource scheduling demand information of the unit, the basic data of water resource scheduling of the unit includes basic water resource data, unit operation data, power grid load data, stability index of the power grid and water resource scheduling data;

[0029] Step S102, matching the unit water resource scheduling demand information in a preset unit water resource scheduling model knowledge base to obtain the unit water resource scheduling model to be used, preprocessing the unit water resource scheduling basic data to obtain the preprocessed unit water resource scheduling basic data, substituting the preprocessed unit water resource scheduling basic data into the unit water resource scheduling model to be used to obtain the unit water resource scheduling simulation result;

[0030] Step S103, comparing the unit water resource scheduling simulation result with the unit water resource scheduling demand information. If the unit water resource scheduling simulation result meets the unit water resource scheduling demand information, then comparing the unit water resource scheduling simulation result with the stability index of the power grid to obtain the compliance comparison result of the unit water resource scheduling simulation result;

[0031] Step S104, when the compliance comparison result of the unit water resource scheduling simulation result is unqualified, the water resource scheduling model of the unit to be used is optimized using a genetic algorithm based on the unit water resource scheduling historical data stored in the scheduling database and the compliance comparison result of the unit water resource scheduling simulation result to obtain an optimized water resource scheduling model of the unit to be used, and the pre-processed unit water resource scheduling basic data is processed using the optimized water resource scheduling model of the unit to be used, and the data processing result is subjected to compliance detection. If the detection result is compliant, the optimized water resource scheduling model of the unit to be used is used to replace the water resource scheduling model of the unit to be used. If the detection result is uncompliant, the water resource scheduling model of the unit to be used is reconstructed;

[0032] Step S105, according to the unit water resource scheduling demand information, determine the data type corresponding to the unit water resource scheduling demand information, and collect the real-time data in the unit water resource scheduling process according to the data type corresponding to the unit water resource scheduling demand information, retrieve the historical unit water resource scheduling process data corresponding to the data type corresponding to the unit water resource scheduling demand information, and use the historical unit water resource scheduling process data, the real-time data in the unit water resource scheduling process and the unit water resource scheduling demand information to train the deep learning model to obtain a reconstructed unit water resource scheduling model, and the reconstructed unit water resource scheduling model is tested through step S103. If the test result is qualified, the reconstructed unit water resource scheduling model is used to process the unit water resource scheduling basic data and the unit water resource scheduling demand information.

[0033] Furthermore, in the method for intelligently scheduling water resources of a unit provided by the present invention, step S103 comprises:

[0034] Acquire the unit water resource scheduling simulation result from step S101, and simultaneously acquire the unit water resource scheduling demand information from step S101;

[0035] Compare the unit water resource scheduling simulation results with the unit water resource scheduling demand information to check whether the unit water resource scheduling simulation results meet the requirements in the demand information;

[0036] Based on the results of the comparative analysis, it is determined whether the unit water resource scheduling simulation results meet the unit water resource scheduling demand information. If so, the next step is to compare with the stability index of the power grid. If not, the process returns to step S102 or step S104 to adjust the model.

[0037] If the simulation results in the comparison result meet the requirement information, the compliance check will continue. If not, the model optimization or reconstruction process will be triggered.

[0038] Furthermore, the method for intelligently scheduling water resources of a unit provided by the present invention, step S104 comprises:

[0039] From step S104, the result of data processing the pre-processed unit water resource scheduling basic data using the optimized unit water resource scheduling model to be used is obtained.

[0040] According to the actual needs of the unit's water resource scheduling, set compliance testing standards, including water balance, water quality requirements and scheduling efficiency indicators;

[0041] Compare the data processing results with the set compliance standards in detail to check whether various indicators meet the compliance requirements, including analyzing whether the water volume is balanced, whether the water quality meets the standards, and whether the scheduling efficiency meets the standards;

[0042] According to the results of the comparative test, determine whether the data processing results are compliant. If compliant, output the compliance test results and use the optimized water resource scheduling model of the unit to be used to replace the original model;

[0043] If it is not compliant, the non-compliant detection result is output and the reconstruction process of the water resource scheduling model of the unit to be used is triggered.

[0044] Furthermore, the method for intelligently scheduling water resources of a unit provided by the present invention, said step S105 comprises:

[0045] Step S105 transmits the reconstructed unit water resource scheduling model to step S103, and step S103 sets the detection standard according to the actual demand of the unit water resource scheduling;

[0046] Step S103 uses the set detection standard to detect the reconstructed unit water resource scheduling model, and the detection process includes simulating the actual water resource scheduling scenario and comparing the output result with the detection standard;

[0047] Step S103 evaluates whether the reconstructed unit water resource scheduling model is qualified. If the test result is qualified, the reconstructed unit water resource scheduling model can be used in the actual unit water resource scheduling; if the test result is unqualified, step S105 needs to make corresponding adjustments and optimizations based on the feedback information, and then retransmit the model for testing.

[0048] The beneficial effects of the present invention are mainly reflected in the following aspects:

[0049] The present invention obtains comprehensive basic data on unit water resource scheduling through a data acquisition unit, including basic water resource data, unit operation data, power grid load data, power grid stability indicators and water resource scheduling data, which provides rich input information for the model and helps to improve the accuracy and adaptability of the model.

[0050] The model building unit uses historical unit water resources scheduling process data, real-time data, and unit water resources scheduling demand information to train the deep learning model. The deep learning model has strong nonlinear mapping and generalization capabilities, and can more accurately describe the complex relationships in the unit water resources scheduling process and improve the accuracy and reliability of the model.

[0051] The model detection unit performs a double comparative test on the unit water resource scheduling simulation results. First, it is compared with the unit water resource scheduling demand information to improve the model output to meet actual needs. Secondly, it is compared with the stability indicators of the power grid to improve the scheduling plan. It is feasible under the premise of stable operation of the power grid. The double comparison mechanism effectively avoids scheduling errors caused by insufficient model accuracy.

[0052] When the model detection unit finds that the scheduling simulation results do not meet the requirements, the model optimization unit will use genetic algorithms to optimize the model based on the historical data and compliance comparison results in the scheduling database to improve the accuracy and adaptability of the model and enhance the accuracy and compliance of the scheduling plan.

[0053] The present invention realizes real-time monitoring, analysis and scheduling of unit water resources through an intelligent scheduling system, greatly improving the efficiency and response speed of water resource management. The system can detect potential safety risks and problems in advance through real-time monitoring and analysis of unit water resource data, and take timely measures to respond, thereby improving the stability and reliability of the entire power plant system. Through continuous model detection and iteration mechanisms, the model is always kept at a high level of accuracy, avoiding scheduling errors caused by low model accuracy, and improving the intelligence level and scheduling efficiency of the system.

[0054] The present invention can improve the stable operation and efficient power generation of the unit, and improve the utilization efficiency and economic benefits of water resources by optimizing the scheduling strategy and resource allocation. The system can automatically adjust the scheduling plan according to actual needs and environmental changes, realize dynamic optimization of water resources, and reduce resource waste and environmental pollution.

[0055] In summary, the present invention provides an intelligent scheduling system and method for water resources of a unit, which effectively solves the scheduling error problem caused by low model accuracy in the prior art, improves the accuracy and compliance of the scheduling plan, enhances the intelligence level and scheduling efficiency of the system, and promotes the optimal allocation and efficient utilization of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0057] Figure 1 A schematic diagram of an intelligent water resource scheduling system and method for a unit provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. The technical solutions provided by the embodiments of the present invention are described in detail below in conjunction with the drawings.

[0059] In order to better understand the purpose of the present invention, the present invention is described in further detail below.

[0060] In a first aspect, the present invention provides a unit water resource intelligent scheduling system, comprising:

[0061] A data acquisition unit is used to obtain basic data of water resource scheduling of the unit and water resource scheduling demand information of the unit. The basic data of water resource scheduling of the unit includes basic water resource data, unit operation data, power grid load data, stability index of the power grid and water resource scheduling data;

[0062] In the present invention, the data acquisition unit is responsible for acquiring the basic data and scheduling demand information required for the unit water resource scheduling. The data acquisition unit mainly completes the following tasks:

[0063] Collect basic data on water resource scheduling of units:

[0064] Basic water resources data: including direct measurement data on water resources such as water level, flow, and water quality.

[0065] Unit operation data: involves key parameters such as the unit's operating status, output, efficiency, etc., reflecting the unit's working conditions.

[0066] Grid load data: indicates the current load situation of the grid, which is an important factor that must be considered when formulating water resource scheduling plans.

[0067] Grid stability indicators: various indicators to measure the stability of grid operation. Improving water resource scheduling will not have an adverse impact on the stable operation of the grid.

[0068] Water resource scheduling data: Past water resource scheduling records and historical data provide reference for model optimization and strategy formulation.

[0069] Obtaining the unit water resource scheduling demand information, including water demand, water quality requirements, scheduling targets, etc. within a specific time period, is an important basis for guiding the selection of water resource scheduling models and parameter settings. By collecting multi-dimensional and multi-type data, comprehensive information support is provided for unit water resource scheduling, which helps to more accurately describe and predict the actual situation of water resource scheduling. Rich basic data enables the scheduling model to better adapt to different scheduling scenarios and needs, and improves the applicability and accuracy of the model. Based on a detailed data foundation, a more scientific and reasonable unit water resource scheduling plan can be formulated to optimize resource allocation and improve scheduling efficiency and safety.

[0070] As the front end of the intelligent dispatching system, the data acquisition unit provides solid data support for subsequent data processing, model matching, simulation scheduling and optimization adjustment, and is a key link in promoting the intelligence of the system.

[0071] A data processing unit is used to match the unit water resource scheduling demand information in a preset unit water resource scheduling model knowledge base to obtain the unit water resource scheduling model to be used, preprocess the unit water resource scheduling basic data to obtain the preprocessed unit water resource scheduling basic data, substitute the preprocessed unit water resource scheduling basic data into the unit water resource scheduling model to be used, and obtain the unit water resource scheduling simulation result;

[0072] In the unit water resource intelligent scheduling system of the present invention, the data processing unit is responsible for converting the collected data into effective information that can be used for unit water resource scheduling, and generating scheduling simulation results through model calculation. The following is a detailed analysis of the specific functions and operation flow of the data processing unit:

[0073] The data processing unit mainly performs the following tasks:

[0074] According to the unit water resource scheduling demand information, search and match in the preset unit water resource scheduling model knowledge base to select the unit water resource scheduling model that best suits the current scheduling demand. This step improves the high degree of fit between the scheduling model and the actual scheduling demand, and provides an accurate basis for subsequent scheduling simulation.

[0075] The collected basic data of water resource dispatching of the units are cleaned, converted and normalized to eliminate noise, outliers and missing values ​​in the data and improve the quality and consistency of the data. At the same time, the data is formatted and standardized according to the requirements of the model to ensure that the data can be smoothly input into the model for calculation.

[0076] Substitute the preprocessed basic data of unit water resource scheduling into the selected unit water resource scheduling model, and generate the unit water resource scheduling simulation results through calculation and analysis of the model. These simulation results reflect the allocation and utilization of unit water resources under different scheduling strategies, providing an important basis for subsequent scheduling decisions.

[0077] The operation process of the data processing unit is as follows:

[0078] Receive the basic data and dispatch demand information of the unit water resources dispatch transmitted by the data acquisition unit. Search and match in the model knowledge base according to the dispatch demand information, and select the most suitable unit water resources dispatch model. Clean, convert and normalize the basic data to improve the quality and consistency of the data. Substitute the pre-processed data into the selected model for calculation to generate the unit water resources dispatch simulation results. Output the simulation results to the model detection unit for subsequent comparison detection and compliance judgment.

[0079] The data processing unit plays a vital role in the present invention. Through the data preprocessing step, the noise and outliers in the data are eliminated, the quality and consistency of the data are improved, and a reliable basis is provided for the subsequent model calculation. Through the model matching step, the high degree of fit between the scheduling model and the actual scheduling needs is improved, and the applicability and accuracy of the model are improved. The scheduling simulation results generated by the model calculation provide a scientific decision-making basis for the scheduling personnel, which is helpful to formulate a more reasonable and efficient unit water resource scheduling plan.

[0080] In summary, the data processing unit is an indispensable part of the unit's water resources intelligent scheduling system. It provides strong support for subsequent scheduling decisions through precise data processing and model calculation.

[0081] The model detection unit is used to compare the unit water resource scheduling simulation result with the unit water resource scheduling demand information. If the unit water resource scheduling simulation result meets the unit water resource scheduling demand information, the unit water resource scheduling simulation result is compared with the stability index of the power grid to obtain the compliance comparison result of the unit water resource scheduling simulation result;

[0082] In the unit water resource intelligent scheduling system of the present invention, the model detection unit is responsible for double comparison detection of the unit water resource scheduling simulation results to improve the accuracy and compliance of the scheduling plan. The following is a detailed analysis of the specific functions and operation process of the model detection unit:

[0083] The model detection unit mainly performs the following tasks: comparing the unit water resource scheduling simulation results with the unit water resource scheduling demand information, and checking whether the simulation results meet the various requirements in the scheduling demand, including water quantity, water quality, scheduling time, etc. This step improves the consistency between the scheduling plan and actual needs.

[0084] If the simulation results meet the dispatch demand information, the model detection unit will further compare the simulation results with the stability index of the power grid to evaluate whether the dispatch plan will have an adverse impact on the stable operation of the power grid. This step improves the dispatch plan to meet the actual needs while also meeting the safety and stability requirements of the power grid operation.

[0085] The operation process of the model detection unit is as follows:

[0086] Receive the unit water resource scheduling simulation results from the data processing unit. Compare the simulation results with the unit water resource scheduling demand information to check whether the simulation results meet the requirements in the demand information. According to the results of the demand comparison, if the simulation results meet the scheduling demand information, proceed to the next step of compliance detection; if not, feedback is given to the data processing unit or the model optimization unit to adjust the model or re-simulate. Compare the simulation results that meet the scheduling requirements with the stability indicators of the power grid to evaluate the compliance of the scheduling plan. According to the results of the compliance test, output the compliance comparison results of the unit water resource scheduling simulation results. If compliant, the scheduling plan can be passed; if not, further adjustment and optimization are required.

[0087] The model detection unit plays an indispensable role in the present invention:

[0088] Through the demand comparison step, the consistency between the dispatching plan and the actual demand is improved, avoiding resource waste or operational risks caused by dispatching errors. Through the compliance detection step, the impact of the dispatching plan on the stable operation of the power grid is evaluated, and the dispatching plan is improved to meet the safety and stability requirements of the power grid operation while meeting the actual demand. Through the automated and intelligent comparison and detection process, the possibility of manual intervention and misjudgment is reduced, and the efficiency and accuracy of dispatching decisions are improved. The model detection unit is a key link in ensuring the accuracy and compliance of the dispatching plan in the intelligent water resource dispatching system of the unit. It improves the scientificity, rationality and safety of the dispatching plan through a double comparison and detection process.

[0089] A model optimization unit is used for optimizing the water resource scheduling model of the unit to be used by using a genetic algorithm based on the unit water resource scheduling historical data stored in the scheduling database and the compliance comparison result of the unit water resource scheduling simulation result when the compliance comparison result of the unit water resource scheduling simulation result is unqualified, so as to obtain the optimized water resource scheduling model of the unit to be used, use the optimized water resource scheduling model of the unit to be used to process the pre-processed unit water resource scheduling basic data, and perform compliance detection on the data processing result; if the detection result is compliant, use the optimized water resource scheduling model of the unit to be used to replace the water resource scheduling model of the unit to be used; if the detection result is uncompliant, reconstruct the water resource scheduling model of the unit to be used;

[0090] In the unit water resource intelligent dispatching system, the model optimization unit is an important component to improve the accuracy and effectiveness of the dispatching model. When the compliance comparison result of the unit water resource dispatching simulation result is unqualified, the model optimization unit will intervene and optimize the model based on historical data and genetic algorithms. The following is a detailed analysis of the model optimization unit:

[0091] The model optimization unit is mainly responsible for: When the compliance comparison result of the unit water resource scheduling simulation result is unqualified, the model optimization unit will use the genetic algorithm to optimize the water resource scheduling model of the unit to be used based on the unit water resource scheduling historical data stored in the scheduling database and the compliance comparison result.

[0092] The optimized model is used to process the pre-processed basic data of unit water resource scheduling, and the processing results are checked for compliance to improve the ability of the optimized model to meet scheduling requirements and grid stability indicators.

[0093] According to the compliance test results, if the optimized model is qualified, the original water resource scheduling model of the unit to be used will be replaced; if it is unqualified, the model reconstruction process will be triggered.

[0094] The operation process of the model optimization unit is as follows:

[0095] Receive the compliance comparison results of the unit water resource scheduling simulation results from the model detection unit. If the result is unqualified, start the model optimization process. Obtain the unit water resource scheduling historical data from the scheduling database as the input data for genetic algorithm optimization. Use the genetic algorithm to optimize the water resource scheduling model of the unit to be used. The genetic algorithm automatically searches for the optimal combination of model parameters by simulating natural selection and genetic mechanisms to improve the accuracy and adaptability of the model.

[0096] The optimized model is used to process the preprocessed basic data of the unit water resource scheduling to obtain new scheduling simulation results. The new scheduling simulation results are tested for compliance to check whether they meet the unit water resource scheduling demand information and the stability indicators of the power grid.

[0097] Model replacement or reconstruction: If the compliance test result is qualified, the optimized model will be used to replace the original water resource scheduling model of the unit to be used. If the compliance test result is still unqualified, the model reconstruction process will be triggered, and the model construction unit will be responsible for rebuilding the unit water resource scheduling model.

[0098] The model optimization unit plays a key role in the unit water resource intelligent dispatching system. Through the optimization of historical data and compliance comparison results by genetic algorithms, the accuracy and adaptability of the water resource dispatching model of the unit to be used are continuously improved, and the occurrence of dispatching errors is reduced. Through the compliance detection step, the optimized model can meet the dispatching requirements and power grid stability indicators, and ensure the rationality and safety of the dispatching plan. Through continuous model optimization and replacement, the unit water resource intelligent dispatching system is always running in the best state, improving the stability and reliability of the system.

[0099] In summary, the model optimization unit is an indispensable part of the unit's water resources intelligent scheduling system. It continuously improves the accuracy and adaptability of the model through genetic algorithm optimization and compliance detection mechanism, and improves the accuracy and compliance of the scheduling plan.

[0100] The model building unit is used to determine the data type corresponding to the unit water resource scheduling demand information according to the unit water resource scheduling demand information, and collect the real-time data in the unit water resource scheduling process according to the data type corresponding to the unit water resource scheduling demand information, retrieve the historical unit water resource scheduling process data corresponding to the data type corresponding to the unit water resource scheduling demand information, and use the historical unit water resource scheduling process data, the real-time data in the unit water resource scheduling process and the unit water resource scheduling demand information to train the deep learning model to obtain a reconstructed unit water resource scheduling model, and the reconstructed unit water resource scheduling model is tested by the model detection unit. If the test result is qualified, the reconstructed unit water resource scheduling model is used to process the unit water resource scheduling basic data and the unit water resource scheduling demand information.

[0101] In the unit water resource intelligent scheduling system of the present invention, the model building unit undertakes the important task of building and optimizing the unit water resource scheduling model. The following is a detailed analysis of the model building unit:

[0102] The model building unit is mainly responsible for: determining the required data types, such as water level, flow, water quality, unit operating parameters, grid load, etc., based on the unit water resource scheduling demand information. According to the determined data type, collect real-time data in the unit water resource scheduling process, and retrieve the corresponding historical unit water resource scheduling process data. Use the collected historical data and real-time data, as well as the unit water resource scheduling demand information, to train the deep learning model to build a new unit water resource scheduling model. The newly constructed model is tested by the model detection unit to improve its compliance with scheduling requirements and grid stability indicators. If the newly constructed model passes the test, it will be applied to the unit water resource scheduling system to process the basic data of the unit water resource scheduling and scheduling demand information.

[0103] The operation process of the model building unit is as follows: Receive the unit water resource scheduling demand information, analyze and determine the required data type. According to the determined data type, obtain real-time data from the data acquisition unit, and retrieve the corresponding historical data from the scheduling database. Preprocess the collected data, including data cleaning, formatting, normalization, etc., to improve data quality. Use the preprocessed data to train the deep learning model. During the training process, the model will continuously adjust its parameters to better fit the data and predict future water resource scheduling.

[0104] During the training process, the model is evaluated regularly to check whether its performance meets the requirements. Evaluation indicators include prediction accuracy, stability, convergence speed, etc. Based on the evaluation results, the model is optimized to improve its performance. Optimization methods include adjusting model structure, parameters, training algorithms, etc.

[0105] The trained model is submitted to the model detection unit for detection. The detection content includes whether the model meets the dispatching requirements and whether it can ensure the stability of the power grid.

[0106] If the model passes the test, it will be applied to the unit water resource scheduling system to replace the original model for data processing and scheduling decision-making.

[0107] The model building unit plays a vital role in the unit water resources intelligent scheduling system:

[0108] By continuously collecting new real-time and historical data, new models are trained to adapt to the ever-changing water resource scheduling environment and needs. Using advanced algorithms such as deep learning to train models can more accurately predict future water resource scheduling and improve scheduling accuracy and efficiency. Through model detection and optimization mechanisms, the newly constructed model is improved to meet scheduling needs and power grid stability indicators, ensuring the stable operation of the system.

[0109] The model building unit is one of the key links in realizing the intelligent upgrade of the unit’s water resources intelligent scheduling system. By continuously optimizing the model, it promotes the system to a higher level of intelligent development.

[0110] In summary, the model building unit plays a vital role in the intelligent water resource scheduling system of the unit. It continuously improves the scheduling accuracy and stability of the system by building and optimizing the deep learning model, and promotes the system to a higher level of intelligence.

[0111] Specifically, in the unit water resource intelligent scheduling system provided by the present invention, the model detection unit is also used for:

[0112] Acquire the unit water resource scheduling simulation results from the data processing unit, and simultaneously acquire the unit water resource scheduling demand information from the data acquisition unit;

[0113] Compare the unit water resource scheduling simulation results with the unit water resource scheduling demand information to check whether the unit water resource scheduling simulation results meet the requirements in the demand information;

[0114] Based on the results of comparative analysis, determine whether the unit water resource scheduling simulation results meet the unit water resource scheduling demand information. If yes, proceed to the next step to compare with the stability index of the power grid. If not, return to the data processing unit or model optimization unit for model adjustment.

[0115] If the simulation results in the comparison result meet the requirement information, the compliance check will continue. If not, the model optimization or reconstruction process will be triggered.

[0116] Specifically, in the unit water resource intelligent scheduling system provided by the present invention, the model optimization unit is also used for:

[0117] The result of data processing on the preprocessed basic data of water resources scheduling of the unit using the optimized water resources scheduling model of the unit to be used is obtained from the model optimization unit.

[0118] According to the actual needs of the unit's water resource scheduling, set compliance testing standards, including water balance, water quality requirements and scheduling efficiency indicators;

[0119] Compare the data processing results with the set compliance standards in detail to check whether various indicators meet the compliance requirements, including analyzing whether the water volume is balanced, whether the water quality meets the standards, and whether the scheduling efficiency meets the standards;

[0120] According to the results of the comparative test, determine whether the data processing results are compliant. If compliant, output the compliance test results and use the optimized water resource scheduling model of the unit to be used to replace the original model;

[0121] If it is not compliant, the non-compliant detection result is output and the reconstruction process of the water resource scheduling model of the unit to be used is triggered.

[0122] Specifically, in the unit water resource intelligent scheduling system provided by the present invention, the model building unit is also used for:

[0123] The model building unit transmits the reconstructed unit water resource scheduling model to the model detection unit, and the model detection unit sets the detection standard according to the actual demand of the unit water resource scheduling;

[0124] The model detection unit uses the set detection standards to detect the reconstructed unit water resource scheduling model. The detection process includes simulating the actual water resource scheduling scenario and comparing the output results with the detection standards;

[0125] The model detection unit evaluates whether the reconstructed unit water resource scheduling model is qualified. If the detection result is qualified, the reconstructed unit water resource scheduling model can be used in the actual unit water resource scheduling; if the detection result is unqualified, the model construction unit needs to make corresponding adjustments and optimizations based on the feedback information, and then retransmit the model for detection.

[0126] In a second aspect, the present invention provides a method for intelligent dispatching of water resources of a unit, which is applied to the intelligent dispatching system for water resources of a unit as described above, comprising:

[0127] Step S101, obtaining basic data of water resource scheduling of the unit and water resource scheduling demand information of the unit, the basic data of water resource scheduling of the unit includes basic water resource data, unit operation data, power grid load data, stability index of the power grid and water resource scheduling data;

[0128] Step S102, matching the unit water resource scheduling demand information in a preset unit water resource scheduling model knowledge base to obtain the unit water resource scheduling model to be used, preprocessing the unit water resource scheduling basic data to obtain the preprocessed unit water resource scheduling basic data, substituting the preprocessed unit water resource scheduling basic data into the unit water resource scheduling model to be used to obtain the unit water resource scheduling simulation result;

[0129] Step S103, comparing the unit water resource scheduling simulation result with the unit water resource scheduling demand information. If the unit water resource scheduling simulation result meets the unit water resource scheduling demand information, then comparing the unit water resource scheduling simulation result with the stability index of the power grid to obtain the compliance comparison result of the unit water resource scheduling simulation result;

[0130] Step S104, when the compliance comparison result of the unit water resource scheduling simulation result is unqualified, the water resource scheduling model of the unit to be used is optimized using a genetic algorithm based on the unit water resource scheduling historical data stored in the scheduling database and the compliance comparison result of the unit water resource scheduling simulation result to obtain an optimized water resource scheduling model of the unit to be used, and the pre-processed unit water resource scheduling basic data is processed using the optimized water resource scheduling model of the unit to be used, and the data processing result is subjected to compliance detection. If the detection result is compliant, the optimized water resource scheduling model of the unit to be used is used to replace the water resource scheduling model of the unit to be used. If the detection result is uncompliant, the water resource scheduling model of the unit to be used is reconstructed;

[0131] Step S105, according to the unit water resource scheduling demand information, determine the data type corresponding to the unit water resource scheduling demand information, and collect the real-time data in the unit water resource scheduling process according to the data type corresponding to the unit water resource scheduling demand information, retrieve the historical unit water resource scheduling process data corresponding to the data type corresponding to the unit water resource scheduling demand information, and use the historical unit water resource scheduling process data, the real-time data in the unit water resource scheduling process and the unit water resource scheduling demand information to train the deep learning model to obtain a reconstructed unit water resource scheduling model, and the reconstructed unit water resource scheduling model is tested through step S103. If the test result is qualified, the reconstructed unit water resource scheduling model is used to process the unit water resource scheduling basic data and the unit water resource scheduling demand information.

[0132] Specifically, the method for intelligently scheduling water resources of a unit provided by the present invention, step S103 includes:

[0133] Acquire the unit water resource scheduling simulation result from step S101, and simultaneously acquire the unit water resource scheduling demand information from step S101;

[0134] Compare the unit water resource scheduling simulation results with the unit water resource scheduling demand information to check whether the unit water resource scheduling simulation results meet the requirements in the demand information;

[0135] Based on the results of the comparative analysis, it is determined whether the unit water resource scheduling simulation results meet the unit water resource scheduling demand information. If so, the next step is to compare with the stability index of the power grid. If not, the process returns to step S102 or step S104 to adjust the model.

[0136] If the simulation results in the comparison result meet the requirement information, the compliance check will continue. If not, the model optimization or reconstruction process will be triggered.

[0137] Specifically, the method for intelligently scheduling water resources of a unit provided by the present invention, step S104 includes:

[0138] From step S104, the result of data processing the pre-processed unit water resource scheduling basic data using the optimized unit water resource scheduling model to be used is obtained.

[0139] According to the actual needs of the unit's water resource scheduling, set compliance testing standards, including water balance, water quality requirements and scheduling efficiency indicators;

[0140] Compare the data processing results with the set compliance standards in detail to check whether various indicators meet the compliance requirements, including analyzing whether the water volume is balanced, whether the water quality meets the standards, and whether the scheduling efficiency meets the standards;

[0141] According to the results of the comparative test, determine whether the data processing results are compliant. If compliant, output the compliance test results and use the optimized water resource scheduling model of the unit to be used to replace the original model;

[0142] If it is not compliant, the non-compliant detection result is output and the reconstruction process of the water resource scheduling model of the unit to be used is triggered.

[0143] Specifically, the method for intelligently scheduling water resources of a unit provided by the present invention, step S105, comprises:

[0144] Step S105 transmits the reconstructed unit water resource scheduling model to step S103, and step S103 sets the detection standard according to the actual demand of the unit water resource scheduling;

[0145] Step S103 uses the set detection standard to detect the reconstructed unit water resource scheduling model, and the detection process includes simulating the actual water resource scheduling scenario and comparing the output result with the detection standard;

[0146] Step S103 evaluates whether the reconstructed unit water resource scheduling model is qualified. If the test result is qualified, the reconstructed unit water resource scheduling model can be used in the actual unit water resource scheduling; if the test result is unqualified, step S105 needs to make corresponding adjustments and optimizations based on the feedback information, and then retransmit the model for testing.

[0147] The present invention solves the problem that when the accuracy of the model in the unit water resource intelligent scheduling system is too low, errors will occur in the unit water resource intelligent scheduling through the following technical solutions:

[0148] Obtain comprehensive basic data on unit water resource scheduling, including basic water resource data, unit operation data, power grid load data, power grid stability indicators, and water resource scheduling data. These data provide rich input information for the model, which helps to improve the accuracy and adaptability of the model. Match in the preset unit water resource scheduling model knowledge base, select the appropriate unit water resource scheduling model, and pre-process the basic data before inputting the model to obtain the unit water resource scheduling simulation results. This step improves the quality of the model input data and improves the calculation efficiency and accuracy of the model. Double comparison and detection of the unit water resource scheduling simulation results. First, compare with the unit water resource scheduling demand information to improve the model output to meet the actual needs; second, compare with the stability indicators of the power grid to improve the scheduling plan is feasible under the premise of stable operation of the power grid. This double comparison mechanism effectively avoids scheduling errors caused by insufficient accuracy of the model. When the model detection unit finds that the scheduling simulation results do not meet the requirements, the model optimization unit will intervene. It uses genetic algorithms to optimize the model based on the compliance comparison results of the historical data and simulation results stored in the scheduling database. By simulating natural selection and genetic mechanisms, genetic algorithms can automatically search and optimize model parameters to improve the accuracy and adaptability of the model.

[0149] When the model optimization still cannot meet the requirements, the model building unit will rebuild the unit water resource scheduling model. It collects real-time data and historical data based on the unit water resource scheduling demand information, and uses these data to train the deep learning model. The deep learning model has strong nonlinear mapping and generalization capabilities, and can more accurately describe the complex relationships in the unit water resource scheduling process, thereby improving the accuracy and reliability of the model.

[0150] The reconstructed unit water resource scheduling model will be tested again by the model detection unit. If the test result is qualified, the model will be used for data processing; if it is unqualified, it will continue to be optimized or rebuilt. This continuous model detection and iteration mechanism improves the model to always maintain a high level of accuracy, thereby avoiding scheduling errors caused by low model accuracy.

[0151] In summary, the present invention effectively solves the scheduling error problem caused by low model accuracy in the unit water resources intelligent scheduling system through the synergy of multiple links such as data collection, processing, detection, optimization and construction.

[0152] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations, and the above-described embodiments of the present invention do not constitute a limitation on the scope of protection of the present invention.

Claims

1. An intelligent water resource dispatching system for a unit, characterized in that: include: A data acquisition unit is used to obtain basic data of water resource scheduling of the unit and water resource scheduling demand information of the unit. The basic data of water resource scheduling of the unit includes basic water resource data, unit operation data, power grid load data, stability index of the power grid and water resource scheduling data; A data processing unit is used to match the unit water resource scheduling demand information in a preset unit water resource scheduling model knowledge base to obtain the unit water resource scheduling model to be used, preprocess the unit water resource scheduling basic data to obtain the preprocessed unit water resource scheduling basic data, substitute the preprocessed unit water resource scheduling basic data into the unit water resource scheduling model to be used, and obtain the unit water resource scheduling simulation result; The model detection unit is used to compare the unit water resource scheduling simulation result with the unit water resource scheduling demand information. If the unit water resource scheduling simulation result meets the unit water resource scheduling demand information, the unit water resource scheduling simulation result is compared with the stability index of the power grid to obtain the compliance comparison result of the unit water resource scheduling simulation result; A model optimization unit is used for optimizing the water resource scheduling model of the unit to be used by using a genetic algorithm based on the unit water resource scheduling historical data stored in the scheduling database and the compliance comparison result of the unit water resource scheduling simulation result when the compliance comparison result of the unit water resource scheduling simulation result is unqualified, so as to obtain the optimized water resource scheduling model of the unit to be used, use the optimized water resource scheduling model of the unit to be used to process the pre-processed unit water resource scheduling basic data, and perform compliance detection on the data processing result; if the detection result is compliant, use the optimized water resource scheduling model of the unit to be used to replace the water resource scheduling model of the unit to be used; if the detection result is uncompliant, reconstruct the water resource scheduling model of the unit to be used; The model building unit is used to determine the data type corresponding to the unit water resource scheduling demand information according to the unit water resource scheduling demand information, and collect the real-time data in the unit water resource scheduling process according to the data type corresponding to the unit water resource scheduling demand information, retrieve the historical unit water resource scheduling process data corresponding to the data type corresponding to the unit water resource scheduling demand information, and use the historical unit water resource scheduling process data, the real-time data in the unit water resource scheduling process and the unit water resource scheduling demand information to train the deep learning model to obtain a reconstructed unit water resource scheduling model, and the reconstructed unit water resource scheduling model is tested by the model detection unit. If the test result is qualified, the reconstructed unit water resource scheduling model is used to process the unit water resource scheduling basic data and the unit water resource scheduling demand information.

2. The unit water resource intelligent dispatching system according to claim 1, characterized in that: The model detection unit is further used for: Acquire the unit water resource scheduling simulation results from the data processing unit, and simultaneously acquire the unit water resource scheduling demand information from the data acquisition unit; Compare the unit water resource scheduling simulation results with the unit water resource scheduling demand information to check whether the unit water resource scheduling simulation results meet the requirements in the demand information; Based on the results of comparative analysis, determine whether the unit water resource scheduling simulation results meet the unit water resource scheduling demand information. If yes, proceed to the next step to compare with the stability index of the power grid. If not, return to the data processing unit or model optimization unit for model adjustment. If the simulation results in the comparison result meet the requirement information, the compliance check will continue. If not, the model optimization or reconstruction process will be triggered.

3. The unit water resource intelligent dispatching system according to claim 1, characterized in that: The model optimization unit is further used for: Obtaining from the model optimization unit the result of data processing the preprocessed basic data of water resource scheduling of the unit using the optimized water resource scheduling model of the unit to be used; According to the actual needs of the unit's water resource scheduling, set compliance testing standards, including water balance, water quality requirements and scheduling efficiency indicators; Compare the data processing results with the set compliance standards in detail to check whether various indicators meet the compliance requirements, including analyzing whether the water volume is balanced, whether the water quality meets the standards, and whether the scheduling efficiency meets the standards; According to the results of the comparative test, determine whether the data processing results are compliant. If compliant, output the compliance test results and use the optimized water resource scheduling model of the unit to be used to replace the original model; If it is not compliant, the non-compliant detection result is output and the reconstruction process of the water resource scheduling model of the unit to be used is triggered.

4. The unit water resource intelligent dispatching system according to claim 1, characterized in that: The model building unit is further used for: The model building unit transmits the reconstructed unit water resource scheduling model to the model detection unit, and the model detection unit sets the detection standard according to the actual demand of the unit water resource scheduling; The model detection unit uses the set detection standards to detect the reconstructed unit water resource scheduling model. The detection process includes simulating the actual water resource scheduling scenario and comparing the output results with the detection standards; The model detection unit evaluates whether the reconstructed unit water resource scheduling model is qualified. If the detection result is qualified, the reconstructed unit water resource scheduling model can be used in the actual unit water resource scheduling; If the test result is unqualified, the model building unit needs to make corresponding adjustments and optimizations based on the feedback information, and then retransmit the model for testing.

5. A method for intelligent dispatching of water resources for a unit, applied to the intelligent dispatching system for water resources for a unit as claimed in any one of claims 1 to 4, characterized in that: include: Step S101, obtaining basic data of water resource scheduling of the unit and water resource scheduling demand information of the unit, the basic data of water resource scheduling of the unit includes basic water resource data, unit operation data, power grid load data, stability index of the power grid and water resource scheduling data; Step S102, matching the unit water resource scheduling demand information in a preset unit water resource scheduling model knowledge base to obtain the unit water resource scheduling model to be used, preprocessing the unit water resource scheduling basic data to obtain the preprocessed unit water resource scheduling basic data, substituting the preprocessed unit water resource scheduling basic data into the unit water resource scheduling model to be used to obtain the unit water resource scheduling simulation result; Step S103, comparing the unit water resource scheduling simulation result with the unit water resource scheduling demand information. If the unit water resource scheduling simulation result meets the unit water resource scheduling demand information, then comparing the unit water resource scheduling simulation result with the stability index of the power grid to obtain the compliance comparison result of the unit water resource scheduling simulation result; Step S104, when the compliance comparison result of the unit water resource scheduling simulation result is unqualified, the water resource scheduling model of the unit to be used is optimized using a genetic algorithm based on the unit water resource scheduling historical data stored in the scheduling database and the compliance comparison result of the unit water resource scheduling simulation result to obtain an optimized water resource scheduling model of the unit to be used, and the pre-processed unit water resource scheduling basic data is processed using the optimized water resource scheduling model of the unit to be used, and the data processing result is subjected to compliance detection. If the detection result is compliant, the optimized water resource scheduling model of the unit to be used is used to replace the water resource scheduling model of the unit to be used. If the detection result is uncompliant, the water resource scheduling model of the unit to be used is reconstructed; Step S105, according to the unit water resource scheduling demand information, determine the data type corresponding to the unit water resource scheduling demand information, and collect the real-time data in the unit water resource scheduling process according to the data type corresponding to the unit water resource scheduling demand information, retrieve the historical unit water resource scheduling process data corresponding to the data type corresponding to the unit water resource scheduling demand information, and use the historical unit water resource scheduling process data, the real-time data in the unit water resource scheduling process and the unit water resource scheduling demand information to train the deep learning model to obtain a reconstructed unit water resource scheduling model, and the reconstructed unit water resource scheduling model is tested through step S103. If the test result is qualified, the reconstructed unit water resource scheduling model is used to process the unit water resource scheduling basic data and the unit water resource scheduling demand information.

6. The intelligent water resource dispatching method for a unit according to claim 1, characterized in that: The step S103 includes: Acquire the unit water resource scheduling simulation result from step S101, and simultaneously acquire the unit water resource scheduling demand information from step S101; Compare the unit water resource scheduling simulation results with the unit water resource scheduling demand information to check whether the unit water resource scheduling simulation results meet the requirements in the demand information; Based on the results of the comparative analysis, it is determined whether the unit water resource scheduling simulation results meet the unit water resource scheduling demand information. If so, the next step is to compare with the stability index of the power grid. If not, the process returns to step S102 or step S104 to adjust the model. If the simulation results in the comparison result meet the requirement information, the compliance check will continue. If not, the model optimization or reconstruction process will be triggered.

7. The intelligent water resource dispatching method for a unit according to claim 1, characterized in that: The step S104 includes: From step S104, the result of using the optimized water resource scheduling model of the unit to be used to process the pre-processed unit water resource scheduling basic data is obtained; According to the actual needs of the unit's water resource scheduling, set compliance testing standards, including water balance, water quality requirements and scheduling efficiency indicators; Compare the data processing results with the set compliance standards in detail to check whether various indicators meet the compliance requirements, including analyzing whether the water volume is balanced, whether the water quality meets the standards, and whether the scheduling efficiency meets the standards; According to the results of the comparative test, determine whether the data processing results are compliant. If compliant, output the compliance test results and use the optimized water resource scheduling model of the unit to be used to replace the original model; If it is not compliant, the non-compliant detection result is output and the reconstruction process of the water resource scheduling model of the unit to be used is triggered.

8. The intelligent water resource dispatching method for a unit according to claim 1, characterized in that: The step S105 includes: Step S105 transmits the reconstructed unit water resource scheduling model to step S103, and step S103 sets the detection standard according to the actual demand of the unit water resource scheduling; Step S103 uses the set detection standard to detect the reconstructed unit water resource scheduling model, and the detection process includes simulating the actual water resource scheduling scenario and comparing the output result with the detection standard; Step S103 evaluates whether the reconstructed unit water resource scheduling model is qualified. If the test result is qualified, the reconstructed unit water resource scheduling model can be used in the actual unit water resource scheduling; if the test result is unqualified, step S105 needs to make corresponding adjustments and optimizations based on the feedback information, and then retransmit the model for testing.

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