A water conservancy project scheduling optimization method and system based on cloud computing

Abstract

The present invention discloses a method and system for optimizing water conservancy project scheduling based on cloud computing, including real-time water conservancy data collection and transmission to the cloud through Internet of Things technology; pre-processing, management, and storage of the collected water conservancy data; constructing a multi-objective scheduling model based on the actual needs of the water conservancy project to solve the optimal control strategy of the water conservancy project; performing real-time scheduling calculations through a cloud platform, updating the current optimal solution for water conservancy project scheduling based on data analysis and machine learning models; and presenting the real-time status and scheduling results of the water conservancy project through visualization tools on the cloud platform. The water conservancy data integration method based on the clustering algorithm of the present invention can effectively process multi-source, heterogeneous data, update scheduling through clustering, and provide scientific decision support for water conservancy project scheduling. By combining clustering results with cloud computing, it can provide efficient and accurate solutions in multi-objective optimization, water resources management, and dynamic scheduling.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering scheduling, and in particular to a water conservancy engineering scheduling optimization method and system based on cloud computing. Background Art

[0002] Water conservancy projects refer to infrastructure systems used to regulate, protect, and utilize water resources, typically including reservoirs, dams, channels, and sluice gates. The scheduling of water conservancy projects is crucial for ensuring the rational allocation of water resources, improving water resource utilization efficiency, ensuring reservoir safety, promoting ecological protection, and optimizing economic benefits. With the rapid development of cloud computing and big data technologies, cloud-based optimization methods for water conservancy project scheduling have become an important means of addressing these issues. Cloud computing provides powerful computing power and flexible resource scheduling capabilities, enabling intelligent, automated, and optimized water conservancy project scheduling. Therefore, developing water conservancy project scheduling algorithms based on cloud computing is a promising research direction.

[0003] Currently, a Chinese invention patent application numbered CN202410842738.6 discloses a method for optimizing ecological scheduling plans for water conservancy projects. The method obtains revenue and expenditure data for the region where the water conservancy project is located and real-time status data for the water conservancy project. An optimization model consisting of constraints and objective functions is established to obtain three types of optimization plans based on a balance between economic and environmental benefits. The key technical points are: based on the revenue and expenditure data of the region where the water conservancy project is located, the local construction direction for the water conservancy project is evaluated, and an appropriate corresponding plan is automatically selected based on the evaluation results. The corresponding scheduling process is implemented while ensuring a certain environmental foundation. After the selected plan is selected, the effectiveness of the plan is secondary determined to ensure that the obtained evaluation value provides a basis for staff to adjust or schedule. However, existing water conservancy data integration methods based on clustering algorithms cannot effectively process multi-source, heterogeneous data, cannot update scheduling through clustering, and thus cannot provide scientific decision support for water conservancy project scheduling. Furthermore, the clustering results cannot be combined with cloud computing to provide efficient and accurate solutions for multi-objective optimization, water resources management, and dynamic scheduling. Summary of the Invention

[0004] The technical problem solved by the present invention is that the existing technology cannot achieve the water conservancy data integration method based on clustering algorithm to effectively process multi-source and heterogeneous data, cannot update scheduling through clustering, and thus cannot provide scientific decision support for water conservancy project scheduling, and cannot combine clustering results with cloud computing to provide efficient and accurate solutions in multi-objective optimization, water resources management and dynamic scheduling.

[0005] To solve the above technical problems, the present invention provides the following technical solution: a water conservancy project scheduling optimization method based on cloud computing, comprising the following steps:

[0006] Step S1: Collect water conservancy data in real time through IoT technology and transmit it to the cloud;

[0007] Step S2: pre-processing, managing and storing the collected water conservancy data;

[0008] Step S3: Based on the actual needs of the water conservancy project, a multi-objective scheduling model is constructed to solve the optimal control strategy of the water conservancy project;

[0009] Step S4: Perform real-time scheduling calculations through the cloud platform and update the current optimal solution for water conservancy project scheduling based on data analysis and machine learning models;

[0010] Step S5: Present the real-time status and scheduling results of the water conservancy project through the visualization tools of the cloud platform.

[0011] Preferably, the step S1 includes:

[0012] Water conservancy data is collected in real time and transmitted to the cloud through Internet of Things technology. The water conservancy data includes water level, flow, precipitation, meteorological data and historical scheduling data. The equipment for collecting the water conservancy data includes water level sensors, flow meters, weather stations and computer big data networks.

[0013] Preferably, step S2 includes the following sub-steps:

[0014] Step S21: cleaning, filling missing values ​​and normalizing the collected water conservancy data;

[0015] Step S22: The water conservancy data is stored and managed according to the large-scale distributed storage system HDFS provided by cloud computing, and all real-time data and historical data are uploaded to the cloud database of the large-scale distributed storage system HDFS.

[0016] Preferably, step S3 includes the following sub-steps:

[0017] Step S31: constructing a multi-objective scheduling model based on the actual needs of the water conservancy project, wherein the actual needs include water resource allocation, reservoir water storage and discharge management, maximization of water resource utilization, environmental protection, and equipment maintenance;

[0018] Step S32: performing cloud computing and distributed processing according to the multi-objective scheduling model, and using a distributed computing framework to solve the multi-objective scheduling model;

[0019] Step S33: solving the optimal control strategy of the water conservancy project through a dynamic programming method.

[0020] Preferably, the step S31 includes the following sub-steps:

[0021] Step S311: using an optimized clustering algorithm to integrate and manage the actual demand and pre-processed water conservancy data;

[0022] Step S312: Calculate the degree of imbalance in minimizing water resource distribution, the mathematical expression of which is:

[0023] ;

[0024] in, represents the degree of minimization of imbalance in water resource distribution, Indicates the The actual water supply for class demand, Indicates the The ideal water supply for each type of demand, is the number of demand types;

[0025] The mathematical expression for calculating the maximum water resource utilization is:

[0026] ;

[0027] in, Maximize water resource utilization. represents the total output of water conservancy projects, including agricultural irrigation and power generation, Indicates the water consumption of the corresponding water conservancy project;

[0028] The objective function is set after weighted average of minimizing the imbalance of water resource distribution and maximizing water resource utilization;

[0029] Step S313: Set the total amount of water resources as a constraint condition, and its mathematical expression is:

[0030] ;

[0031] in, Indicates the The water supply in the region, Indicates the total amount of water resources;

[0032] Step S314: Using a multi-objective optimization method, a set of Pareto optimal solutions is found, i.e., solutions that cannot improve a certain objective without compromising other objectives. Once the optimal solution is obtained, the multi-objective scheduling model is completed.

[0033] Preferably, step S311 includes the following sub-steps:

[0034] Step S3111: define the actual demand and pre-processed water conservancy data as data points respectively, and group the actual demand data points into clusters , the water conservancy data points are grouped into clusters ;

[0035] Step S3112: Randomly select an initial cluster center, calculate the distance between the data point and the cluster center, and assign the data point to the nearest cluster;

[0036] Step S3113: recalculate the center of each cluster and update the cluster center position;

[0037] Step S3114: Repeat steps S3112 and S3113 until the cluster center no longer changes or the maximum number of iterations is reached;

[0038] Step S3115: Calculate the similarity between clusters using the Bayesian fusion method.

[0039] Preferably, step S4 includes the following sub-steps:

[0040] Step S41: Perform real-time scheduling calculations through the cloud platform;

[0041] Step S42: Evaluate the effectiveness of the scheduling plan through the decision support system provided by the cloud computing platform to help engineering managers make the best choice;

[0042] Step S43: Update the current optimal solution for water conservancy project scheduling based on data analysis and machine learning models.

[0043] Preferably, the step S43 includes:

[0044] The data mining technology is used to obtain the potential laws of historical water conservancy scheduling, and the multi-objective scheduling model is simulated using matleb simulation software to obtain the execution status of the scheduling process. The potential laws of historical water conservancy scheduling and the execution status of the scheduling process are extracted through neural networks to obtain historical water conservancy scheduling features and current water conservancy scheduling features. The historical water conservancy scheduling features and current water conservancy scheduling features are respectively used as data point sets to form clusters. and clusters , the cluster and clusters Through integrated management by optimized clustering algorithm, the optimal control strategy of the updated water conservancy project is obtained after step S3, and iteratively updated to the preset iterative update threshold to obtain the final current optimal solution for water conservancy project scheduling.

[0045] Preferably, step S5 includes:

[0046] Visualizations include water levels, flows, weather forecasts, and scheduling plans.

[0047] A water conservancy project scheduling optimization system based on cloud computing includes a collection module, a management module, a control model module, and a visualization module:

[0048] The collection module is used to collect water conservancy data in real time through Internet of Things technology and transmit it to the cloud;

[0049] The management module is used to pre-process, manage and store the collected water conservancy data;

[0050] The control model module is used to build a multi-objective scheduling model based on the actual needs of the water conservancy project, solve the optimal control strategy of the water conservancy project, perform real-time scheduling calculations through the cloud platform, and update the current optimal solution for water conservancy project scheduling based on data analysis and machine learning models;

[0051] The visualization module is used to present the real-time status and scheduling results of the water conservancy project through the visualization tool of the cloud platform.

[0052] The beneficial effects of the present invention are as follows: the water conservancy data integration method based on clustering algorithm can effectively process multi-source and heterogeneous data, provide scientific decision support for water conservancy project scheduling through clustering update scheduling, and by combining clustering results with cloud computing, provide efficient and accurate solutions in multi-objective optimization, water resources management and dynamic scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 A basic flow chart of a water conservancy project scheduling optimization method based on cloud computing is provided as an embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0055] Reference Figure 1 , as an embodiment of the present invention, provides a water conservancy project scheduling optimization method based on cloud computing, comprising the following steps:

[0056] Step S1: Collect water conservancy data in real time through IoT technology and transmit it to the cloud;

[0057] Step S2: pre-processing, managing and storing the collected water conservancy data;

[0058] Step S3: Based on the actual needs of the water conservancy project, a multi-objective scheduling model is constructed to solve the optimal control strategy of the water conservancy project;

[0059] Step S4: Perform real-time scheduling calculations through the cloud platform and update the current optimal solution for water conservancy project scheduling based on data analysis and machine learning models;

[0060] Step S5: Present the real-time status and scheduling results of the water conservancy project through the visualization tool of the cloud platform.

[0061] The step S1 comprises:

[0062] Water conservancy data is collected in real time and transmitted to the cloud through Internet of Things technology. The water conservancy data includes water level, flow, precipitation, meteorological data and historical scheduling data. The equipment for collecting the water conservancy data includes water level sensors, flow meters, weather stations and computer big data networks.

[0063] The step S2 includes the following sub-steps:

[0064] Step S21: Cleaning, filling missing values ​​and normalizing the collected water conservancy data to ensure the accuracy and consistency of the data;

[0065] Step S22: The water conservancy data is stored and managed according to the large-scale distributed storage system HDFS provided by cloud computing, and all real-time data and historical data are uploaded to the cloud database of the large-scale distributed storage system HDFS to ensure the security, reliability and accessibility of the data.

[0066] The step S3 includes the following sub-steps:

[0067] Step S31: constructing a multi-objective scheduling model based on the actual needs of the water conservancy project, wherein the actual needs include water resource allocation, reservoir water storage and discharge management, maximization of water resource utilization, environmental protection, and equipment maintenance;

[0068] Step S32: performing cloud computing and distributed processing according to the multi-objective scheduling model, and using a distributed computing framework to solve the multi-objective scheduling model;

[0069] Step S33: solving the optimal control strategy of the water conservancy project through a dynamic programming method.

[0070] The cloud computing, distributed processing and dynamic programming methods are all existing technologies.

[0071] The step S31 includes the following sub-steps:

[0072] Step S311: using an optimized clustering algorithm to integrate and manage the actual demand and pre-processed water conservancy data;

[0073] Step S312: Calculate the degree of imbalance in minimizing water resource distribution, the mathematical expression of which is:

[0074] ;

[0075] in, represents the degree of minimization of imbalance in water resource distribution, Indicates the The actual water supply for class demand, Indicates the The ideal water supply for each type of demand, is the number of demand types;

[0076] The mathematical expression for calculating the maximum water resource utilization is:

[0077] ;

[0078] in, Maximize water resource utilization. represents the total output of water conservancy projects, including agricultural irrigation and power generation, Indicates the water consumption of the corresponding water conservancy project;

[0079] Optimizing water use efficiency in areas such as irrigation and industrial water use can be achieved by maximizing water use benefits or reducing water waste;

[0080] The objective function is set after weighted average of minimizing the imbalance of water resource distribution and maximizing water resource utilization;

[0081] Step S313: Set the total amount of water resources as a constraint condition, and its mathematical expression is:

[0082] ;

[0083] in, Indicates the The water supply in the region, Indicates the total amount of water resources;

[0084] The total amount of water resources is limited, so the total water supply should be less than or equal to the available water resources;

[0085] Step S314: Using a multi-objective optimization method, a set of Pareto optimal solutions is found, i.e., solutions that cannot improve a certain objective without compromising other objectives. Once the optimal solution is obtained, the multi-objective scheduling model is completed.

[0086] Once the multi-objective scheduling model is constructed, actual scheduling can be carried out, including the allocation of water resources and the release and storage of reservoirs. In water conservancy project scheduling, building a multi-objective scheduling model can effectively comprehensively consider multiple objectives and find a balance between them. Through objective functions, constraints, and weighted comprehensive methods, optimal water resource allocation can be achieved.

[0087] The step S311 includes the following sub-steps:

[0088] Step S3111: define the actual demand and pre-processed water conservancy data as data points respectively, and group the actual demand data points into clusters , the water conservancy data points are grouped into clusters ;

[0089] Step S3112: Randomly select an initial cluster center, calculate the distance between the data point and the cluster center, and assign the data point to the nearest cluster;

[0090] Step S3113: recalculate the center of each cluster and update the cluster center position;

[0091] Step S3114: Repeat steps S3112 and S3113 until the cluster center no longer changes or the maximum number of iterations is reached;

[0092] Step S3115: Calculate the similarity between clusters using the Bayesian fusion method.

[0093] In this way, multiple heterogeneous data sources are clustered to form groups of similar data, which facilitates subsequent analysis and processing.

[0094] The step S4 includes the following sub-steps:

[0095] Step S41: Perform real-time scheduling calculations through the cloud platform;

[0096] Step S42: Evaluate the effectiveness of the scheduling plan through the decision support system provided by the cloud computing platform to help engineering managers make the best choice;

[0097] Step S43: Update the current optimal solution for water conservancy project scheduling based on data analysis and machine learning models.

[0098] The step S43 includes:

[0099] The data mining technology is used to obtain the potential laws of historical water conservancy scheduling, and the multi-objective scheduling model is simulated using matleb simulation software to obtain the execution status of the scheduling process. The potential laws of historical water conservancy scheduling and the execution status of the scheduling process are extracted through neural networks to obtain historical water conservancy scheduling features and current water conservancy scheduling features. The historical water conservancy scheduling features and current water conservancy scheduling features are respectively used as data point sets to form clusters. and clusters , the cluster and clusters Through integrated management by optimized clustering algorithm, the optimal control strategy of the updated water conservancy project is obtained after step S3, and iteratively updated to the preset iterative update threshold to obtain the final current optimal solution for water conservancy project scheduling.

[0100] The step S5 comprises:

[0101] The visualization content includes water level, flow, weather forecast and scheduling plan, helping decision makers to quickly understand and grasp the scheduling situation.

[0102] A water conservancy project scheduling optimization system based on cloud computing includes a collection module, a management module, a control model module, and a visualization module:

[0103] The collection module is used to collect water conservancy data in real time through Internet of Things technology and transmit it to the cloud;

[0104] The management module is used to pre-process, manage and store the collected water conservancy data;

[0105] The control model module is used to build a multi-objective scheduling model based on the actual needs of the water conservancy project, solve the optimal control strategy of the water conservancy project, perform real-time scheduling calculations through the cloud platform, and update the current optimal solution for water conservancy project scheduling based on data analysis and machine learning models;

[0106] The visualization module is used to present the real-time status and scheduling results of the water conservancy project through the visualization tool of the cloud platform.

[0107] The water conservancy data integration method based on clustering algorithm in the present invention can effectively process multi-source and heterogeneous data, provide scientific decision support for water conservancy project scheduling through clustering update scheduling, and provide efficient and accurate solutions for multi-objective optimization, water resources management and dynamic scheduling by combining clustering results with cloud computing.

[0108] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium may be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0109] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A water conservancy project scheduling optimization method based on cloud computing, characterized in that: The following steps are involved: Step S1: Collect water conservancy data in real time through IoT technology and transmit it to the cloud; Step S2: pre-processing, managing and storing the collected water conservancy data; Step S3: Based on the actual needs of the water conservancy project, a multi-objective scheduling model is constructed to solve the optimal control strategy of the water conservancy project; The step S3 includes the following sub-steps: Step S31: constructing a multi-objective scheduling model based on the actual needs of the water conservancy project, wherein the actual needs include water resource allocation, reservoir water storage and discharge management, maximization of water resource utilization, environmental protection, and equipment maintenance; Step S32: performing cloud computing and distributed processing according to the multi-objective scheduling model, and using a distributed computing framework to solve the multi-objective scheduling model; Step S33: solving the optimal control strategy of the water conservancy project through a dynamic programming method; The step S31 includes the following sub-steps: Step S311: using an optimized clustering algorithm to integrate and manage the actual demand and pre-processed water conservancy data; The step S311 includes the following sub-steps: Step S3111: define the actual demand and pre-processed water conservancy data as data points respectively, and group the actual demand data points into clusters , the pre-processed water conservancy data points are grouped into clusters ; Step S3112: Randomly select an initial cluster center, calculate the distance between the data point and the cluster center, and assign the data point to the nearest cluster; Step S3113: recalculate the center of each cluster and update the cluster center position; Step S3114: Repeat steps S3112 and S3113 until the cluster center no longer changes or the maximum number of iterations is reached; Step S3115: Calculate the similarity between clusters using the Bayesian fusion method; Step S312: Calculate the degree of imbalance in minimizing water resource distribution, the mathematical expression of which is: ； in, represents the degree of minimization of imbalance in water resource distribution, Indicates the The actual water supply for class demand, Indicates the The ideal water supply for each type of demand, is the number of demand types; The mathematical expression for calculating the maximum water resource utilization is: ； in, Maximize water resource utilization. represents the total output of water conservancy projects, including agricultural irrigation and power generation, Indicates the water consumption of the corresponding water conservancy project; The objective function is set after weighted average of minimizing the imbalance of water resource distribution and maximizing water resource utilization; Step S313: Set the total amount of water resources as a constraint condition, and its mathematical expression is: ； in, Indicates the The water supply in the region, represents the total amount of water resources; Step S314: Using a multi-objective optimization method, a set of Pareto optimal solutions is found, i.e., solutions that cannot improve a certain objective without compromising other objectives. Once the optimal solution is obtained, the multi-objective scheduling model is completed. Step S4: Perform real-time scheduling calculations through the cloud platform and update the current optimal solution for water conservancy project scheduling based on data analysis and machine learning models; The step S4 includes the following sub-steps: Step S41: Perform real-time scheduling calculations through the cloud platform; Step S42: Evaluate the effectiveness of the scheduling plan through the decision support system provided by the cloud computing platform to help engineering managers make the best choice; Step S43: updating the current optimal solution for water conservancy project scheduling based on data analysis and machine learning models; The step S43 includes: The data mining technology is used to obtain the potential laws of historical water conservancy scheduling, and the multi-objective scheduling model is simulated using matleb simulation software to obtain the execution status of the scheduling process. The potential laws of historical water conservancy scheduling and the execution status of the scheduling process are extracted through neural networks to obtain historical water conservancy scheduling features and current water conservancy scheduling features. The historical water conservancy scheduling features and current water conservancy scheduling features are respectively used as data point sets to form clusters. and clusters , the cluster and clusters Through the optimized clustering algorithm, integrated management is performed, and after step S3, the optimal control strategy of the updated water conservancy project is obtained, and iterative updates are performed to a preset iterative update threshold to obtain the final optimal solution for the water conservancy project scheduling; Step S5: Present the real-time status and scheduling results of the water conservancy project through the visualization tools of the cloud platform.

2. The water conservancy project scheduling optimization method based on cloud computing according to claim 1, characterized in that: The step S1 comprises: Water conservancy data is collected in real time and transmitted to the cloud through Internet of Things technology. The water conservancy data includes water level, flow, precipitation, meteorological data and historical scheduling data. The equipment for collecting the water conservancy data includes water level sensors, flow meters, weather stations and computer big data networks.

3. The water conservancy project scheduling optimization method based on cloud computing according to claim 2, characterized in that: The step S2 includes the following sub-steps: Step S21: cleaning, filling missing values ​​and normalizing the collected water conservancy data; Step S22: The water conservancy data is stored and managed according to the large-scale distributed storage system HDFS provided by cloud computing, and all real-time data and historical data are uploaded to the cloud database of the large-scale distributed storage system HDFS.

4. The water conservancy project scheduling optimization method based on cloud computing according to claim 1, characterized in that: The step S5 comprises: Visualizations include water levels, flows, weather forecasts, and scheduling plans.

5. A water conservancy project scheduling optimization system based on cloud computing, used to implement the water conservancy project scheduling optimization method based on cloud computing as claimed in claim 1, characterized in that: Including collection module, management module, control model module and visualization module: The collection module is used to collect water conservancy data in real time through Internet of Things technology and transmit it to the cloud; The management module is used to pre-process, manage and store the collected water conservancy data; The control model module is used to build a multi-objective scheduling model based on the actual needs of the water conservancy project, solve the optimal control strategy of the water conservancy project, perform real-time scheduling calculations through the cloud platform, and update the current optimal solution for water conservancy project scheduling based on data analysis and machine learning models; The visualization module is used to present the real-time status and scheduling results of the water conservancy project through the visualization tool of the cloud platform.

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