Intelligent water resource configuration and scheduling system and method thereof

Abstract

The invention relates to the technical field of water resource scheduling, and discloses an intelligent water resource configuration and scheduling system and method, and the system comprises a data collection module which is used for collecting the related data of water resources in real time, including but not limited to the water level of a water source, the water quality, the meteorological data, the water demand of a user, and the like; the data processing and analysis module is used for cleaning and integrating the collected data by using a big data processing technology, and carrying out demand prediction and water resource supply and demand matching through an artificial intelligence algorithm; a scheduling decision module, an execution module and a feedback mechanism module. Multi-dimensional data such as water sources and weather are collected in real time, data processing and demand prediction are carried out through the artificial intelligence technology, and it is ensured that water resource scheduling can cope with rapidly-changed demand and supply conditions; through an optimization algorithm and a scheduling decision model, the water resource utilization efficiency is maximized, and the problems of excessive water pumping, resource waste and the like are avoided.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water resources scheduling, in particular to an intelligent water resources allocation and scheduling system and method. BACKGROUND

[0002] Water resources allocation and scheduling is a necessary means for fine management and allocation of regional water resources, aiming to realize artificial allocation of water resources in space and time through scientific modeling and simulation technology, so as to improve the comprehensive utilization efficiency of water resources.

[0003] Most existing water resources scheduling technologies are based on static models or empirical rules, which are difficult to adapt to complex water resource demand fluctuations and environmental changes. For example, reservoir scheduling often only considers flow prediction, ignoring water quality, spatial and temporal differences in water demand, and failing to effectively utilize real-time data for scheduling optimization. In addition, existing technologies lack mechanisms for automatic adjustment and real-time feedback, resulting in low scheduling efficiency, serious waste, and difficulty in flexible adaptation in different scenarios.

[0004] In view of this, we propose an intelligent water resources allocation and scheduling system and method. SUMMARY

[0005] The purpose of the present application is to provide an intelligent water resources allocation and scheduling system and method to solve the problems raised in the background art.

[0006] To achieve the above purpose, the present application provides the following technical solutions:

[0007] The intelligent water resources allocation and scheduling system comprises:

[0008] A data acquisition module for real-time collection of water resources related data, including but not limited to water source water level, water quality, meteorological data, user water demand, etc.

[0009] A data processing and analysis module for cleaning and integrating the collected data using big data processing technology, and performing demand prediction and water resources supply-demand matching through artificial intelligence algorithms.

[0010] A scheduling decision module for generating an optimized scheduling scheme of water resources based on the data processing and analysis results, and transmitting it to the execution module.

[0011] An execution module for performing corresponding water resources scheduling operations, including but not limited to reservoir water release, water pipeline regulation, user water allocation, etc.

[0012] A feedback mechanism module for collecting effect data after execution and feeding back to the data processing and analysis module for closed-loop optimization.

[0013] Preferably, the data processing and analysis module comprises:

[0014] a data cleaning submodule for denoising, missing value filling and standardization processing of the original data;

[0015] a data integration submodule for integrating data from different sources and constructing a unified database;

[0016] a demand prediction submodule for predicting future water resource demand using artificial intelligence algorithms and generating short-term and long-term water demand prediction reports;

[0017] a supply-demand matching module for reasonably matching water resources according to the prediction results and historical water consumption data to optimize the scheduling scheme.

[0018] Preferably, the calculation formula for predicting water demand based on the deep neural network model is:

[0019] ;

[0020] wherein, represents the water demand at the current prediction time point, represents the actual water consumption in the previous time period, .

[0021] Preferably, the scheduling decision module comprises:

[0022] an optimization algorithm module for optimizing water resource scheduling based on the objective function to ensure that the water demand of each region is met at the minimum cost;

[0023] a scheduling strategy generation module for generating water resource scheduling strategies based on the optimization results, including but not limited to reservoir scheduling, water pipeline regulation, water flow regulation, etc.

[0024] Preferably, the optimization algorithm module adopts a multi-objective linear programming model, and the optimization objective function is:

[0025] ;

[0026] wherein, represents the water supply of the i-th water source, represents the water shortage of the j-th region, is the corresponding unit water supply cost and water shortage penalty weight.

[0027] Preferably, the execution module comprises:

[0028] a reservoir scheduling module for controlling the water level and water volume of the reservoir according to the scheduling strategy and implementing water release operation;

[0029] A diversion conduit adjustment module is configured to adjust the flow of the water delivery conduit according to a scheduling scheme to ensure that the water demand of each region is met.

[0030] A user water allocation module is configured to real-time allocate the water demand of each user according to the scheduling strategy.

[0031] Preferably, the feedback mechanism module comprises:

[0032] An implementation effect monitoring submodule is configured to monitor the running effect of the execution module, including the water resource supply amount, the user water usage, and the water resource waste, and to perform effect evaluation on the running effect;

[0033] A closed-loop optimization submodule is configured to feed back the implementation effect data to the data processing and analysis module according to the effect evaluation to perform dynamic optimization and improve the scheduling decision.

[0034] Preferably, the effect evaluation function in the feedback mechanism module is:

[0035] ;

[0036] wherein, represents a scheduling error index, and and represent the planned and actual water supply amount of the kth scheduling period, is the total number of periods.

[0037] Preferably, the system determines the scheduling priority order through a water source priority scoring model, and the scoring function is as follows:

[0038] ;

[0039] wherein, represents the priority score of the ith water source, represents the water source replenishment capacity, represents the current pollution concentration, represents the maximum allowable pollution threshold, represents the unit scheduling cost, is a weighting coefficient.

[0040] The intelligent water resource allocation and scheduling method is characterized in that the method comprises:

[0041] Real-time collection of water resource related data, including but not limited to water source water level, water quality, weather data, user water demand, etc.

[0042] Cleaning and integration of the collected data using big data processing technology, and demand prediction and water resource supply-demand matching through artificial intelligence algorithm;

[0043] According to the data processing and analysis results, an optimized scheduling scheme of water resources is generated and transmitted to an execution module;

[0044] According to the scheduling decision, corresponding water resource scheduling operations are performed, including but not limited to reservoir water release, water pipeline regulation, user water allocation, etc.

[0045] The effect data after execution is collected and fed back to the data processing and analysis module for closed-loop optimization.

[0046] By the above technical solution, the present application provides an intelligent water resource allocation and scheduling system and method. At least the following beneficial effects are achieved:

[0047] The present application collects multi-dimensional data such as water sources and weather in real time, processes the data through artificial intelligence technology, and predicts the demand to ensure that water resource scheduling can respond to rapidly changing demand and supply conditions; through optimization algorithm and scheduling decision model, the water resource utilization efficiency is maximized, and problems such as excessive water pumping and resource waste are avoided; through historical data and real-time feedback information, deep learning or reinforcement learning algorithm is used for prediction and decision optimization to provide accurate water resource scheduling scheme; through collecting feedback data after scheduling and effect evaluation, the scheduling scheme can be continuously optimized to achieve continuous improvement.

[0048] The intelligent water resource allocation and scheduling system and method provided by the present application has high flexibility and accuracy through the cooperation of data acquisition, processing and analysis, scheduling decision, execution feedback and other modules, and through continuous collection and analysis of multi-dimensional information such as water resources and water demand, the scheduling scheme is optimized through intelligent algorithm to ensure reasonable allocation and efficient use of water resources. BRIEF DESCRIPTION OF DRAWINGS

[0049] The accompanying drawings described herein are used to provide a further understanding of the present application and form a part of the present application:

[0050] Figure 1 The accompanying drawings described herein are used to provide a further understanding of the present application and form a part of the present application: DETAILED DESCRIPTION

[0051] The technical solutions in the embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0052] Please refer to Figure 1 as shown, Figure 1The application provides an intelligent water resource allocation and scheduling system, and a structure diagram of the intelligent water resource allocation and scheduling system is shown.

[0053] 101. A data acquisition module for collecting water resource related data in real time, including but not limited to water level, water quality, weather data, user water demand, etc.

[0054] 102. A data processing and analysis module for cleaning and integrating the collected data by using big data processing technology, and predicting demand and matching water resource supply and demand by using artificial intelligence algorithm.

[0055] 103. A scheduling decision module for generating an optimized scheduling scheme of water resources according to the data processing and analysis results, and transmitting the scheme to an execution module.

[0056] 104. An execution module for executing corresponding water resource scheduling operations according to the scheduling decisions, including but not limited to reservoir water release, water diversion pipe adjustment, user water allocation, etc.

[0057] 105. A feedback mechanism module for collecting effect data after execution and feeding back the data to the data processing and analysis module for closed-loop optimization.

[0058] It should be noted that the hardware components of the data acquisition module include but are not limited to water level sensors, water quality monitors, weather data acquisition devices, user water consumption recorders, etc. These hardware components can collect water resource related data in real time, and transmit the data to the data processing and analysis module through a communication module. In order to adapt to the needs of different environments and scenes, the data acquisition module of the application supports flexible configuration and device replacement, ensuring stable operation under different climates, geographical conditions and demand changes.

[0059] Although water sources, weather data and water demand are used as main data inputs in the application, in order to further improve the accuracy and comprehensiveness of scheduling decisions, the application supports integration of multiple additional data sources. For example, water quality monitoring data, pollutant concentration data and watershed ecological environment data can be included in the system, so as to realize more accurate supply and demand matching and scheduling optimization. In addition, the data integration module has strong compatibility and openness, and can be seamlessly connected with existing water management systems, supporting input and output of multiple data formats.

[0060] In some embodiments, the data processing and analysis module includes:

[0061] A data cleaning submodule for denoising, missing value filling and standardization processing of original data;

[0062] a data integration submodule for integrating data from different sources and building a unified database;

[0063] a demand prediction submodule for predicting future water resource demand using artificial intelligence algorithms and generating short-term and long-term water demand prediction reports;

[0064] a supply-demand matching module for matching water resources based on prediction results and historical water data to optimize scheduling schemes.

[0065] It should be noted that the artificial intelligence algorithms in the data processing and analysis module have high flexibility and scalability. In the implementation process, different types of artificial intelligence algorithms can be selected according to specific prediction tasks. For example, deep learning algorithms are suitable for water demand prediction with large amounts of historical data and complex patterns; reinforcement learning algorithms are suitable for dynamic and large-scale supply-demand scheduling environments. The algorithm selection in this part is not only based on task complexity, but also considers the limitations of computing resources and the response speed of real-time decision-making.

[0066] In some embodiments, the calculation formula for predicting water demand based on a deep neural network model is:

[0067] ;

[0068] wherein, represents the water demand at the current prediction time point, represents the actual water consumption in the previous time period, .

[0069] In some embodiments, the scheduling decision module includes:

[0070] an optimization algorithm module for optimizing water resource scheduling based on an objective function to ensure that the water demand of each region is met at the minimum cost;

[0071] a scheduling strategy generation module for generating water resource scheduling strategies based on optimization results, including but not limited to reservoir scheduling, water pipeline regulation, and water flow regulation.

[0072] It should be noted that in the scheduling decision module, the multi-objective linear programming model used by the optimization algorithm module is one of the common optimization methods, but in the actual implementation process, more suitable algorithm models can be selected according to the complexity of the scheduling task and the characteristics of the data, such as genetic algorithms and simulated annealing. In addition, the water source priority scoring model used by the scheduling strategy generation module is also derived from multi-factor comprehensive evaluation, and the scoring weight and evaluation standard can be flexibly adjusted according to the characteristics of water resources in different regions;

[0073] In some embodiments, the optimization algorithm module adopts a multi-objective linear programming model, and the optimization objective function is:

[0074] ;

[0075] wherein, represents the water supply of the i th water source, represents the water gap of the j th region, is the corresponding unit water supply cost and water shortage penalty weight.

[0076] In some embodiments, the execution module comprises:

[0077] a reservoir scheduling module for controlling the water level and water volume of the reservoir according to the scheduling strategy, and implementing the water release operation;

[0078] a water pipeline regulation module for regulating the flow of the water pipeline according to the scheduling scheme to ensure that the water demand of each region is met;

[0079] a user water allocation module for real-time allocation of the water demand of each user according to the scheduling strategy.

[0080] It should be noted that in the execution module, the reservoir scheduling module, the water pipeline regulation module and the user water allocation module, etc. operations, not only can be implemented for fixed water resource type or geographical location. According to different water resource configuration requirements, the corresponding scheduling strategy can be implemented in different regions. Especially in the high-frequency change of water demand scenario, the execution module can be flexibly adjusted, and real-time response to different demand changes can be ensured to realize the optimal water supply scheme.

[0081] In some embodiments, the feedback mechanism module comprises:

[0082] an implementation effect monitoring submodule for monitoring the running effect of the execution module, including the water resource supply, the user water consumption and the water resource waste, and evaluating the running effect;

[0083] a closed-loop optimization submodule for feeding back the implementation effect data to the data processing and analysis module according to the effect evaluation to perform dynamic optimization and improve the scheduling decision.

[0084] It should be noted that the effect evaluation function used in the feedback mechanism module can be selected according to the needs of different application scenarios. For example, the calculation of the scheduling error index can consider more factors, such as weather changes, sudden water source problems, etc. The closed-loop optimization process of effect feedback aims to continuously adjust the system according to the actual effect after scheduling execution, so as to ensure the long-term optimization of water resource scheduling;

[0085] The effect evaluation function in the feedback mechanism module plays a crucial role. This function can evaluate the system's performance based on scheduling errors and actual effects, and feed it back to the data processing and analysis module. In this way, the system can dynamically adjust the prediction model and scheduling strategy based on feedback data, so as to realize continuous scheduling optimization. The effect evaluation function not only includes scheduling error indicators, but also considers factors such as water resource allocation efficiency, user demand response and timeliness.

[0086] In some embodiments, the effect evaluation function in the feedback mechanism module is:

[0087] ;

[0088] wherein, represents the scheduling error indicator, and are the planned and actual water supply amounts in the kth scheduling period, is the total number of periods.

[0089] In some embodiments, the system determines the scheduling priority order through a water source priority scoring model, and the scoring function is as follows:

[0090] ;

[0091] wherein, represents the priority score of the ith water source, represents the water supply capacity, represents the current pollution concentration, represents the maximum allowable pollution threshold, represents the unit scheduling cost, is the weighting coefficient.

[0092] It should be noted that the present application supports custom scheduling priority scoring model. According to the actual situation of different water sources, such as water supply capacity, pollution concentration, cost, etc., the system can generate priority scores according to the weighting coefficient, so as to guide the formulation of scheduling strategy. This flexible scoring model ensures efficient water resource scheduling in various complex environments.

[0093] The intelligent water resource allocation and scheduling method comprises:

[0094] S1, real-time collection of water resource related data, including but not limited to water source water level, water quality, meteorological data, user water demand, etc.;

[0095] S2, using big data processing technology to clean and integrate the collected data, and using artificial intelligence algorithm to predict demand and match water resource supply and demand;

[0096] S3, generating an optimized scheduling scheme of water resources according to the data processing and analysis results, and transmitting to an execution module;

[0097] S4, performing corresponding water resource scheduling operations according to the scheduling decisions, including but not limited to reservoir water release, water diversion pipe adjustment, user water allocation, etc.

[0098] S5, collecting the effect data after execution, and feeding back to the data processing and analysis module for closed-loop optimization.

[0099] The intelligent water resource scheduling system mentioned in the application can be extended to the fields of emergency management and environmental protection in addition to the application scenarios of conventional reservoir management, urban water supply and agricultural irrigation. For example, in the case of extreme weather such as sudden floods and droughts, the system can make rapid scheduling decisions based on real-time data and prediction results to ensure the rational allocation of water resources.

[0100] It should be noted that in this paper, relationship terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between the entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment.

[0101] Although embodiments of the application have been shown and described, it will be understood by those having ordinary skill in the art that various changes, modifications, substitutions and alterations can be made thereto without departing from the principles and spirit of the application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An intelligent water resource allocation and scheduling system, characterized in that, The system includes: The data acquisition module is used to collect water resource-related data in real time, including but not limited to water source level, water quality, meteorological data, and user water demand. The data processing and analysis module is used to clean and integrate the collected data using big data processing technology, and to perform demand forecasting and water resource supply and demand matching through artificial intelligence algorithms. The scheduling decision module is used to generate an optimized water resource scheduling plan based on the data processing and analysis results, and then transmit it to the execution module. The execution module is used to perform corresponding water resource scheduling operations based on scheduling decisions, including but not limited to reservoir water release, water diversion pipeline regulation, and user water allocation. The feedback mechanism module is used to collect the effect data after execution and feed it back to the data processing and analysis module for closed-loop optimization.

2. The intelligent water resource allocation and scheduling system according to claim 1, characterized in that, The data processing and analysis module includes: The data cleaning submodule is used to perform noise removal, missing value imputation, and standardization on the raw data. The data integration submodule is used to integrate data from different sources and build a unified database; The demand forecasting submodule uses artificial intelligence algorithms to predict future water resource demand and generate short-term and long-term water demand forecast reports. The supply and demand matching module is used to rationally match water resources supply and demand based on forecast results and historical water use data, and optimize scheduling schemes.

3. The intelligent water resource allocation and scheduling system according to claim 2, characterized in that, The calculation formula for predicting water demand based on the deep neural network model is as follows: ； in, This indicates the water demand at the current forecast point in time. This indicates the actual water consumption in the previous time period. .

4. The intelligent water resource allocation and scheduling system according to claim 1, characterized in that, The scheduling decision module includes: The optimization algorithm module is used to optimize water resource scheduling based on the objective function to ensure that the water demand of each region is met at the lowest cost. The scheduling strategy generation module is used to generate water resource scheduling strategies based on the optimization results, including but not limited to reservoir scheduling, water pipeline regulation, and water flow regulation.

5. The intelligent water resource allocation and scheduling system according to claim 4, characterized in that, The optimization algorithm module adopts a multi-objective linear programming model, and its optimization objective function is: ； in, This represents the water supply from the i-th water source. This represents the water shortage in the j-th region. The corresponding unit water supply cost and water shortage penalty weights are assigned.

6. The intelligent water resource allocation and scheduling system according to claim 1, characterized in that, The execution module includes: The reservoir scheduling module is used to control the water level and volume of the reservoir according to the scheduling strategy and to carry out water release operations. The water diversion pipeline regulation module is used to regulate the flow of water in the pipeline according to the scheduling plan to ensure that the water demand of each area is met. The user water allocation module is used to allocate the water demand of each user in real time according to the scheduling strategy.

7. The intelligent water resource allocation and scheduling system according to claim 1, characterized in that, The feedback mechanism module includes: The implementation effect monitoring submodule is used to monitor the operational effect of the execution module, including water supply, user water usage, and water waste, and to evaluate the operational effect. The closed-loop optimization submodule is used to feed back the implementation effect data to the data processing and analysis module based on the effect evaluation, so as to perform dynamic optimization and improve scheduling decisions.

8. The intelligent water resource allocation and scheduling system according to claim 7, characterized in that, The effect evaluation function in the feedback mechanism module is: ； in, Indicates scheduling error index, and These represent the planned and actual water supply for the k-th scheduling cycle. This represents the total number of cycles.

9. The intelligent water resource allocation and scheduling system according to claim 1, characterized in that, The system determines the scheduling priority order through a water source priority scoring model, the scoring function of which is as follows: ； in, This represents the priority score of the i-th water source. Indicates water supply capacity. Indicates the current pollution concentration. Indicates the maximum permissible pollution threshold. Indicates the unit scheduling cost. These are the weighting coefficients.

10. An intelligent water resource allocation and scheduling method, characterized in that, The method includes: Collect water resource-related data in real time, including but not limited to water source levels, water quality, meteorological data, and user water demand; Big data processing technology is used to clean and integrate the collected data, and artificial intelligence algorithms are used to predict demand and match water supply and demand. Based on the data processing and analysis results, an optimized water resource scheduling plan is generated and transmitted to the execution module; Based on the scheduling decisions, corresponding water resource scheduling operations are carried out, including but not limited to reservoir water release, water diversion pipeline regulation, and user water allocation. Collect post-execution performance data and feed it back to the data processing and analysis module for closed-loop optimization.

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