Water conservancy project design decision support system and method based on multi-source data fusion

Abstract

The invention discloses a water conservancy project design decision support system and method based on multi-source data fusion, and relates to the technical field of water conservancy projects, and the method comprises the steps: a multi-source data receiving module is connected with and receives synchronous multi-source data of a water conservancy region; the standardization processing module obtains hydraulic engineering projects, determines and unifies evaluation indexes, and generates an evaluation matrix; the lightweight modeling module trains the decision support module based on the multi-source data and the evaluation matrix; the project decision scheme determination module performs decision and feedback analysis in combination with the decision support module, and formulates a decision scheme; and the project guidance module displays the decision scheme through a terminal interface and guides the implementation of the hydraulic engineering project. The technical problems of data dispersion and inaccurate decision in the existing hydraulic engineering design decision are solved, and the technical effect of improving the decision scientificity and accuracy of the hydraulic engineering is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of water conservancy engineering, and in particular to a water conservancy engineering design decision support system and method based on multi-source data fusion. Background Art

[0002] With global climate change and population growth, water resource management and water conservancy project construction have become particularly important in ensuring sustainable social and economic development. However, the current water conservancy project design and decision-making process faces key problems such as difficulty in integrating multi-source data, lack of diversity and standardization of evaluation indicators, and complex decision-making processes and untimely responses. These challenges have led to insufficient scientificity and accuracy in decision-making of water conservancy projects, affecting the optimal allocation of resources and the overall efficiency of engineering projects. Therefore, it is urgent to develop a system that can efficiently integrate multi-source data, unify evaluation standards, and support intelligent decision-making, so as to improve the level of water conservancy project design and management and ensure the optimal implementation and sustainable development of engineering projects.

[0003] At the current stage, relevant technologies exist in the technical problems of data dispersion and inaccurate decision-making in water conservancy project design decisions. Summary of the invention

[0004] The present application solves the technical problems of data dispersion and inaccurate decision-making in existing water conservancy project design decisions by providing a water conservancy project design decision support system and method based on multi-source data fusion.

[0005] This application provides a water conservancy project design decision support system based on multi-source data fusion, including:

[0006] A multi-source data receiving module, the multi-source data receiving module is used to connect to a multi-source data interface and receive multi-source data of a water conservancy area, wherein the multi-source data is synchronously collected data; a standardization processing module, the standardization processing module is used to obtain water conservancy engineering projects, determine engineering evaluation indicators and perform forward standardization processing of the indicators to generate an engineering evaluation matrix; a lightweight modeling module, the lightweight modeling module is used to perform lightweight modeling based on the multi-source data, and supervise the training of a decision support module in combination with the engineering evaluation matrix, wherein the decision support module includes a point hub unit and a linear channel unit; a project decision plan determination module, the project decision plan determination module is used to perform engineering decision and evaluation feedback analysis for the water conservancy engineering project in combination with the decision support module to determine the project decision plan; a project guidance module, the project guidance module is used to display the project decision plan on a terminal interface and provide guidance for the water conservancy engineering project.

[0007] This application provides a water conservancy project design decision support method based on multi-source data fusion, including:

[0008] Connect a multi-source data interface to receive multi-source data of a water conservancy area, wherein the multi-source data is synchronously collected data; obtain water conservancy engineering projects, determine engineering evaluation indicators and perform forward normalization of the indicators to generate an engineering evaluation matrix; perform lightweight modeling based on the multi-source data, and supervise the training of a decision support module in combination with the engineering evaluation matrix, wherein the decision support module includes a point hub unit and a linear channel unit; for the water conservancy engineering project, perform engineering decision and evaluation feedback analysis in combination with the decision support module to determine a project decision plan; display the project decision plan on a terminal interface to provide guidance for the water conservancy engineering project.

[0009] The water conservancy project design decision support system and method based on multi-source data fusion proposed in this application are firstly connected to and received by the multi-source data receiving module for synchronous multi-source data of the water conservancy area; the standardization processing module obtains the water conservancy project, determines and unifies the evaluation indicators, and generates an evaluation matrix; the lightweight modeling module trains the decision support module based on the multi-source data and the evaluation matrix; the project decision plan determination module combines the decision support module to perform decision and feedback analysis and formulate a decision plan; the project guidance module displays the decision plan through the terminal interface to guide the implementation of the water conservancy project, thereby achieving the technical effect of improving the scientificity and accuracy of the decision-making of the water conservancy project. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solution of the embodiment of the present disclosure, the accompanying drawings of the embodiment of the present disclosure will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0011] Figure 1 A schematic diagram of the structure of a water conservancy project design decision support system based on multi-source data fusion provided in an embodiment of the present application;

[0012] Figure 2 A flow chart of a water conservancy project design decision support method based on multi-source data fusion provided in an embodiment of the present application.

[0013] Explanation of the reference numerals: multi-source data receiving module 10 , standardization processing module 20 , lightweight modeling module 30 , project decision-making scheme determination module 40 , project guidance module 50 . DETAILED DESCRIPTION

[0014] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0015] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0016] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0017] The embodiment of the present application provides a water conservancy project design decision support system based on multi-source data fusion, such as Figure 1 As shown, the system comprises:

[0018] The multi-source data receiving module 10 is used to connect the multi-source data interface and receive the multi-source data of the water conservancy area, wherein the multi-source data is synchronously collected data. Specifically, the multi-source data receiving module 10 connects the multi-source data interface to receive the synchronously collected data of the water conservancy area. First, the adaptability analysis and connection preparation of various data source interfaces are required. For the data source interfaces such as satellite remote sensing images, image and video monitoring equipment, radar equipment, etc., the communication protocol, data format, transmission rate and other parameters are studied, and the corresponding connection cables, communication modules and drivers are prepared to ensure that the physical connection is stable and reliable, laying the foundation for receiving data. Then, the data acquisition equipment is synchronously calibrated, the remote sensing satellite is sent a time synchronization signal and orbit calibration instructions through the ground station, the monitoring equipment is synchronized with the NTP or GPS timing technology, and the radar equipment is calibrated for frequency, phase and parameter settings to ensure that the data is accurately synchronized in time and space and improve the data quality. Finally, the receiving process is started according to the predetermined time interval and transmission protocol, data is received using a high-speed data channel, integrity checks and error checks are performed on the received data, problematic data are marked or repaired, and then preliminarily classified and sorted according to type, time, location and other information, and stored in a cache area or local database to prepare for subsequent processing and analysis, thereby providing a high-quality data foundation for the water conservancy project design decision support system, promoting water conservancy project construction, ensuring the rational use of water resources, and promoting the coordinated development of the economy, society and the ecological environment.

[0019] Standardization processing module 20, the standardization processing module 20 is used to obtain water conservancy projects, determine engineering evaluation indicators and perform positive standardization of indicators, and generate an engineering evaluation matrix. Specifically, the standardization processing module 20 comprehensively collects and analyzes relevant information for water conservancy projects, and sorts out engineering evaluation indicator systems such as engineering cost, construction period, water resource utilization efficiency, and environmental impact. Then, the indicators are classified, and the reciprocal of extremely small indicators such as engineering cost, which are as small as possible, is converted into the same form as extremely large indicators such as water resource utilization efficiency. Special indicators such as construction period are positively converted through specific functional relationships. Then, methods such as Min-Max standardization are used, and according to the maximum value of each indicator in all samples, it is mapped to the [0,1] interval through a formula to eliminate dimensional differences. Finally, these indicators are arranged in a certain order to form an engineering evaluation matrix in which each row represents the standardized score of the water conservancy project plan on each indicator and each column corresponds to a specific indicator, providing a data basis and evaluation means for water conservancy project design decisions, promoting high-quality development of water conservancy projects, ensuring the rational development and utilization of water resources, and promoting the coordinated development of the economy, society and ecological environment.

[0020] In a possible implementation, the standardization processing module 20 further includes: an evaluation index determination unit, which is used to traverse the engineering evaluation index, classify and determine multiple groups of evaluation indicators, wherein the evaluation indicators are classified into extremely large, extremely small and interval types. Specifically, the evaluation index determination unit collects evaluation indicators in various stages of engineering planning, construction, operation, and environmental, socio-economic impacts, such as dam design parameters, construction resource input, water resource utilization efficiency, environmental impact data, etc. for water conservancy projects, and organizes them to form an initial indicator set. Then, the above indicators are analyzed in detail and classified into extremely large, extremely small and interval types. Among them, water resource utilization efficiency, flood control capacity, etc. are extremely large indicators; engineering construction costs, environmental pollution emissions, etc. are extremely small indicators; construction period, reservoir water level, etc. are interval indicators. Through accurate classification, the direction of subsequent indicator conversion and standardization processing is clarified, ensuring that water conservancy project design decisions are carried out under a scientific indicator system, promoting the high-quality development of water conservancy projects, meeting the social demand for water resources and water conservancy facilities, and promoting the coordinated development of the economy, society and ecological environment.

[0021] The conversion index determination unit is used to traverse the multiple groups of evaluation indicators, perform positive conversion on the extremely small and interval types, and determine the conversion index. Specifically, the conversion index determination unit first obtains the classified multiple groups of evaluation indicators, traverses and screens out the extremely small and interval indicators, and checks and preprocesses the indicator data to ensure its completeness and accuracy. For extremely small indicators, such as engineering construction costs, they are usually converted into the same form as the extremely large indicators (such as cost-effectiveness ratio 1 / cost) by taking the inverse method, and pay attention to the denominator not being zero and the rationality of the data during processing; for interval indicators, such as construction period, according to its reasonable interval (assuming [100,120] days), by defining the early completion benefit index (120-construction period) / 20 and the delayed completion loss index (construction period-100) / 20, or by using the function mapping method, it is converted into an evaluation form similar to the extremely large indicator. When converting, the actual weight given to the project is considered to reflect its degree of excellence. After the conversion is completed, it is determined as the conversion indicator, and its numerical range and the logic of correlation with other indicators are fully verified. It is also adjusted through actual cases or simulation data testing to ensure its reliable quality, provide a basis for subsequent indicator standardization and engineering evaluation, promote the scientific development of water conservancy projects, meet social needs, and promote the coordinated development of the economy, society and the ecological environment.

[0022] The indicator standardization unit is used to integrate the extremely large indicators and the conversion indicators, and perform indicator standardization to generate the engineering evaluation matrix. Specifically, the indicator standardization unit first receives the extremely large indicators and the conversion indicators, carefully combs and checks to ensure that the data is complete and accurate, and uses methods such as linear interpolation, mean filling, correction or deletion to deal with possible missing or abnormal values, and establishes a unified data dictionary. Then, the two types of indicators are integrated according to the established framework, classified and sorted according to the various stages, functional modules and influencing factors of the water conservancy project, and the indicators of economy, water resources management, environment and other aspects are grouped separately. Then, the Min-Max standardization method is used to determine the maximum value of each indicator in all data through analysis, and then the indicator value is mapped to the [0,1] interval according to the formula (x-min) / (max-min) to eliminate the dimension difference. Finally, the standardized indicators are arranged in a predetermined order, and an engineering evaluation matrix is ​​generated in which each row represents the score of the water conservancy project plan on each indicator and each column corresponds to a specific indicator, providing a data basis and evaluation means for water conservancy project design decision-making, promoting the high-quality development of water conservancy projects, ensuring the rational development and utilization of water resources, and promoting the coordinated development of the economy, society and ecological environment.

[0023] A lightweight modeling module 30 is used to perform lightweight modeling based on the multi-source data, and supervise the training of a decision support module in combination with the engineering evaluation matrix, wherein the decision support module includes a point hub unit and a linear channel unit. Specifically, the lightweight modeling module 30 first obtains water conservancy project-related data from multiple sources such as satellite remote sensing images, GIS databases, and on-site monitoring equipment, and after pre-processing such as cleaning, interpolation, and format unification, lightweight modeling is performed using technologies such as polygon simplification, texture compression, and LOD model construction. It is then combined with an engineering evaluation matrix covering indicators such as engineering costs and water resource utilization efficiency, and the evaluation data is mapped to the model elements. Then, using the combined model as a benchmark, a large number of water conservancy decision samples are input into the decision support module containing point hub units and linear channel units, so that the decision support module can make decision predictions, such as predicting reservoir scheduling plans by point hub units and analyzing water flow distribution by linear channel units. The output is compared with the optimal decision result, and the module parameters are adjusted through back propagation algorithms. After multiple iterative training, the decision support module can provide accurate decision support for water conservancy projects, promote the development of water conservancy projects, and ensure the rational use of water resources and the coordinated development of the economy, society and the ecological environment.

[0024] In a possible implementation, the lightweight modeling module 30 further includes: a water conservancy three-dimensional model building unit, which is used to receive the multi-source data, perform data preprocessing calibration and fusion, and build a water conservancy three-dimensional model. Specifically, the water conservancy three-dimensional model building unit first receives water conservancy project-related data from multiple sources such as satellite remote sensing, GIS database, and field measurement equipment, and stores and organizes them according to attributes such as data types. Then, data preprocessing is performed to remove erroneous or abnormal data by cleaning, supplement missing data by interpolation, and remove high-frequency noise by smoothing. Then, calibration is performed for problems such as coordinate system, measurement accuracy, and time base inconsistency, and the data is unified to the same coordinate system, accuracy standard, and time base. After that, a fusion method based on features or pixels (data points) is used to analyze data complementarity and redundancy, and to fuse multi-source data, such as combining large-area information from satellite remote sensing with local accurate data from field measurements. Finally, professional software is used to construct a terrain surface model based on the fused data, water conservancy elements such as rivers, lakes, and reservoirs and material textures are added, optimization checks are performed to eliminate geometric defects, and a three-dimensional model that can accurately reflect the actual situation of the water conservancy project is built, providing a visualization platform for water conservancy project decision-making, promoting the development of water conservancy, and ensuring the rational use of water resources and the coordinated development of the economy, society and the ecological environment.

[0025] The decision support module training unit is used to call the water conservancy decision sample, and supervise the training of the initial decision support module based on the water conservancy three-dimensional model. Specifically, the decision support module training unit first collects water conservancy decision samples from multiple sources such as historical water conservancy project archives, professional simulation experiments, and water conservancy expert experience cases, and organizes them into a sample library according to dimensions such as project type, decision problem category, and decision condition characteristics. Then, the topography, water system distribution, and facility layout information in the water conservancy three-dimensional model are converted into a digital format, and the input data is updated according to the dynamic information of the model to build a simulation environment for the decision support module. Then, the input data of the decision sample is sent to the initial decision support module to generate a decision output, which is compared with the optimal decision result in the sample, and the error indicators such as the degree of achievement of the decision goal, resource utilization efficiency, and environmental and social impact are calculated. Then, the internal parameters of the module are adjusted according to the error using algorithms such as back propagation. After multiple iterative training, the initial decision support module learns the decision-making rules and becomes an intelligent tool that can provide optimized decision-making solutions, providing decision support for all stages of water conservancy projects, promoting the development of water conservancy, and ensuring the rational use of water resources and the coordinated development of the economy, society, and ecological environment.

[0026] A decision-making method determination unit is used to determine a decision-making method, wherein the decision-making method is based on collaborative decision-making between point hub decision-making and linear channel decision-making. Specifically, the decision-making method determination unit first analyzes the characteristics, functions and operation rules of point hubs (such as reservoirs, sluices, etc.) and linear channels (such as water diversion channels, rivers, etc.) in water conservancy projects, including their structural parameters, the regulation of water flow and the mutual influence with the surrounding areas, and analyzes their status and role in the water conservancy network. Then, according to the overall goal of the water conservancy project, collaborative decision-making goals covering water resource utilization efficiency, operation costs, flood control and drought relief, ecological environment and social and economic benefits are set to clarify the decision-making direction. Then, various factors affecting collaborative decision-making such as nature (meteorology, hydrology, topography, etc.), engineering (design parameters, facility conditions, etc.), economy (investment, cost, benefit, etc.), society (water demand, population distribution, etc.) and environment (ecological impact, etc.) are sorted out to grasp the complex situation faced by decision-making. Finally, we use multi-objective optimization algorithms, system dynamics models, intelligent decision support systems and other methods to develop specific collaborative decision-making methods to achieve efficient collaborative decision-making between the two, improve the comprehensive benefits of water conservancy projects, promote the sustainable development of water conservancy, meet social needs, ensure water resources security and the coordinated development of the economy, society and the ecological environment.

[0027] A lightweight loss function determination unit is used to determine a lightweight loss function based on the decision-making method, perform distillation training on the initial decision support module, and determine the point hub unit and the linear channel unit. Specifically, the lightweight loss function determination unit first deeply analyzes the coordination mode of the point hub decision and the linear channel decision, clarifies the functions and parameters of the hub (such as reservoir water level, storage capacity, flood discharge, etc.) and the characteristics and factors of the channel (such as roughness, slope, water diversion flow distribution, etc.) and the mutual influence relationship between the two, and lays the foundation for constructing the function. Then, combined with mathematical methods, a weighted combination of lightweight loss functions is designed based on the sum of squares of flow deviations and other performance indicators (flood control, water resource utilization efficiency, economic cost, etc.), and simplified operations are used to ensure that it can reflect decision errors and is easy to calculate. Then, this function is used to perform distillation training on the initial decision support module, guided by the soft labels output by the teacher model, and the module parameters are adjusted according to the loss value (considering the result difference and lightweight constraints) through back propagation. After multiple iterations, it is close to the teacher model capability and meets the lightweight requirements, thereby improving the operation efficiency and speed. Finally, after the training converges, the unit responsible for point hub decision-making (which can formulate plans such as reservoir flood discharge based on a variety of information) and the unit responsible for linear channel decision-making (which can plan channel water delivery and maintenance plans based on a variety of factors) are determined from the module to enhance the modularity and interpretability of the module, provide strong support for water conservancy project decision-making, promote the development of water conservancy, and ensure the rational use of water resources and the coordinated development of the economy, society and ecological environment.

[0028] In a possible implementation, the lightweight loss function determination unit further includes: a first lightweight loss function determination subunit, the first lightweight loss function determination subunit is used to obtain a first water conservancy decision sample, and determine a first lightweight loss function based on the decision demand of the point hub. Specifically, the first lightweight loss function determination subunit first collects various first water conservancy decision samples related to water conservancy projects, and the samples cover actual decision cases and simulation data under different geographical conditions, climate conditions, project scales, and water resource demand scenarios. The sources include historical water conservancy project archives, simulation experiment results of professional water conservancy research institutions, and experience cases of senior water conservancy experts. For each sample, the initial state parameters (such as water level, storage capacity, equipment operation parameters, etc.) of the point hub (such as reservoirs, sluices, pumping stations, etc.), external environmental conditions (such as rainfall, river water flow, water demand distribution, etc.) and corresponding decision measures (such as the flood discharge of the reservoir, the opening and closing time and degree of the sluice, the pumping power of the pumping station, etc.) and the final engineering effect (such as flood control effect, irrigation area, power generation efficiency, etc.) are recorded in detail. Based on the above sample data, the key decision-making needs of point hubs in the water conservancy system are analyzed. For example, in flood control, it is necessary to accurately decide the timing and flow of flood discharge from the reservoir based on the current water level of the reservoir, the inflow flow and the carrying capacity of the downstream river to ensure the safety of the surrounding areas; in water resource allocation, it is necessary to reasonably arrange the pumping and water delivery plans of the pumping stations based on the water demand of different regions and the temporal and spatial distribution of water resources. Through an in-depth understanding of decision-making needs, mathematical modeling and statistical analysis methods are used to determine the first lightweight loss function. The first lightweight loss function aims to quantify the degree of difference between the output of the initial decision support module when processing point hub decisions and the ideal decision, and analyze the impact of the decision on water resource utilization efficiency, project operation stability, economic cost and environmental impact. The deviation of factors, for example, uses the sum of squares based on flow deviation to measure the accuracy of reservoir flood discharge decision-making, incorporates the square of the difference between the actual flood discharge and the theoretical optimal flood discharge into the loss function, and combines the quantitative indicators of other factors to construct a comprehensive and targeted first lightweight loss function through weighted combination, which provides a clear optimization goal for subsequent distillation training, ensures that distillation training can focus on improving the quality and efficiency of point hub decision-making, promotes the scientific and precise development of water conservancy projects in point hub decision-making, meets the society's needs for efficient operation of water conservancy projects and rational use of water resources, improves the country's water resources security level and water conservancy infrastructure construction level, and promotes the stable development of the economy and society and the protection and improvement of the ecological environment.

[0029] The first output sample determination subunit is used to input the first water conservancy decision sample into the initial decision support module to determine the first output sample. Specifically, the first output sample determination subunit first collects and organizes the first water conservancy decision sample from multiple sources such as historical water conservancy project records, simulation experiment data of professional institutions, and expert experience cases, and ensures data quality through screening, classification and preprocessing. At the same time, the initial decision support module based on rule reasoning, machine learning model and other architectures is initialized, parameters are set, input and output format ranges are determined, and basic data are loaded. The samples are input into the module one by one. The model is a neural network architecture. The sample data is processed by neuron activation, weight matrix multiplication and nonlinear activation function operation in the hidden layer and then the decision result is generated in the output layer. If it is a rule reasoning system, the decision result is drawn by matching the preset rules. Finally, the decision result generated by the collection and sorting module is used as the first output sample, and the data is checked and verified to ensure accuracy and completeness. The sample identifier and timestamp are added to provide a key data basis for subsequent comparison with the teacher model and distillation training, so as to improve the module performance, provide scientific and accurate support for water conservancy project decision-making, promote the development of water conservancy, and ensure the rational use of water resources and the coordinated development of economic society and ecological environment.

[0030] The first probability distribution determination subunit is used to determine the first probability distribution based on the lightweight loss function, wherein the first probability distribution represents the probability of logical transmission of elements in the initial decision support module. Specifically, the first probability distribution determination subunit first obtains the lightweight loss function constructed based on the first water conservancy decision sample and the point hub decision demand, deeply analyzes its composition and parameters, and collects a large number of decision samples and the output data of the corresponding initial decision support module and preprocesses them. Then, according to the decision deviation reflected by the lightweight loss function, the logical relationship of the input parameters, intermediate variables, decision output and other elements in the initial decision support module is sorted out, and the logical relationship model is established by using statistical methods and machine learning algorithms combined with the physical principles and operation laws of water conservancy projects. Finally, based on the model and data, the first probability distribution is calculated using a probability statistics method, and the probability of occurrence is determined according to the frequency of occurrence of the decision path in the sample and the degree of correlation with the lightweight loss function, and the probability distribution reflecting the possibility of logical transmission of elements and related to the quality of the decision is obtained, which provides guidance for distillation training, improves the performance of the decision support module, promotes the development of water conservancy, and ensures the rational use of water resources and the coordinated development of the economy, society and ecological environment.

[0031] The distillation training subunit is used to combine the first water conservancy decision sample, the first probability distribution and the first output sample to perform a distillation training on the initial decision support module. Specifically, the distillation training subunit first summarizes and organizes the first water conservancy decision sample, the first probability distribution and the first output sample, builds the hardware and software environment for distillation training, determines the algorithm framework such as TensorFlow or PyTorch, and prepares computing resources. Then, a teacher model built based on a neural network architecture (such as a multi-layer perceptron, a convolutional neural network, etc.) or other machine learning methods (such as a decision tree integration model, etc.) and fully trained is introduced, and the decision sample is input into it to generate a soft label containing decision logic and empirical knowledge. Then, the decision process of the initial decision support module and the teacher model is compared according to the first probability distribution to locate the weak links and paths to be optimized of the former. Then, the loss value is calculated by combining the first output sample, the soft label and a lightweight loss function (which can be in the form of mean square error, cross entropy, etc.) that comprehensively considers factors such as decision accuracy and model complexity. If the decision is different from the soft label and the decision path is unreasonable, the loss value increases. Finally, an optimization algorithm based on gradient descent (such as Adam) is used to back-propagate the update module internal parameters according to the loss value, and the update step size and amplitude are controlled according to the learning rate. After multiple iterations, its decision-making ability is improved to complete a distillation training, which provides strong support for scientific decision-making in water conservancy projects, promotes the development of water conservancy, ensures the rational use of water resources and the coordinated development of the economy, society and ecological environment. The algorithm selection in training is comprehensively determined based on the characteristics of the water conservancy project decision-making problem and the data situation to ensure efficient and accurate training.

[0032] The project decision scheme determination module 40 is used to carry out engineering decision and evaluation feedback analysis for the water conservancy project in combination with the decision support module to determine the project decision scheme. Specifically, the project decision scheme determination module 40 collects multi-source data from field sensors, geographic information systems, historical engineering archives, meteorological departments, etc. for water conservancy projects, and after pre-processing such as cleaning, standardization, and integration and association, it is converted to the format required by the decision support module (rule-based expert system, machine learning model, etc.) to determine its initial parameters and operation mode. The data is then input into the decision support module, which analyzes factors such as engineering safety, economy, ecology, and social needs based on the internal algorithm logic, and simulates and analyzes to generate a series of plans for key decision points such as reservoir water storage and release, and sluice scheduling. Then, a multi-dimensional evaluation indicator system is established, and each plan is quantitatively evaluated using methods such as model calculation, simulation analysis, and expert experience judgment. At the same time, actual operation data, expert and stakeholder feedback are collected, and comparative analysis is performed to identify problems. Based on this, the plan is optimized or regenerated and evaluated again. After repeated cycles, the optimal project decision plan is determined to provide decision-making support for water conservancy projects, promote their development, ensure the rational use of water resources, and the coordinated development of the economy, society and the ecological environment.

[0033] In a possible implementation, the project decision-making scheme determination module 40 further includes: an engineering project traversal unit, which is used to traverse the water conservancy project and determine the key hub nodes and linear channel sections, wherein the linear channel sections are determined by adjacent key hub nodes. Specifically, the engineering project traversal unit first collects water conservancy project information, including consulting design drawings to obtain facility layout structures, etc., conducting field investigations to record actual conditions, surrounding environment and influencing factors, collecting historical operation data, and using satellite remote sensing and GIS technology to master regional geographic information, laying a data foundation for subsequent work. Then, professional methods and algorithms are used to deeply analyze each component, and large reservoirs, important sluices, pumping stations and other facilities that play a key role in water resources regulation and have functional integrity and independence are determined as key hub nodes. Based on the layout and operation logic of water conservancy projects, nodes that affect system stability and the realization of major functions are prioritized to focus on decision-making priorities. Then, based on the natural flow direction of water and the actual connection relationship, the linear channel sections are divided with adjacent key hub nodes as endpoints. Taking into account the physical characteristics of the channel, the distribution of ancillary facilities and their role in the system, parts with similar conditions and needs are grouped into one section, thereby decomposing the complex channel system so that precise strategies can be formulated for each section, improving the project operation efficiency and the rationality of water resources allocation, promoting the scientific development of water conservancy projects, and ensuring the coordinated development of water resources utilization with the economy, society and ecological environment.

[0034] The water conservancy project decomposition unit is used to decompose the water conservancy project based on the key hub nodes and the linear channel sections, and determine the first hub project and the second channel project. Specifically, the water conservancy project decomposition unit first obtains the key hub node and linear channel section information, organizes the structure and operation logic of the water conservancy project, clarifies the water resource flow path, facility interaction and regional functional requirements, and builds a functional module division framework. Then, for each key hub node, the facilities and equipment directly related to it and the ancillary buildings are integrated into an independent first hub project, such as the dam and spillway of the reservoir, fully considering the relevance and functional integrity of the physical facilities. Each project has independent decision-making and operation management requirements, such as the need to decide the water storage level of the reservoir, so as to realize the refinement and specialization of hub decision-making management and ensure the stable operation of the hub. Then, based on the linear channel sections between adjacent key hub nodes, determine the second channel project including the channel body, ancillary buildings and maintenance and management facilities along the line, such as the construction, operation management and maintenance guarantee of the water transfer channel. According to its water transfer function and upstream and downstream conditions, decide on the water diversion plan and flow, optimize water transfer and water resources allocation, and thus accurately decompose water conservancy engineering projects, provide objects for subsequent decision-making analysis, promote the scientific development of water conservancy projects, and ensure the coordinated development of water resources utilization and economic, social and ecological environment.

[0035] A water conservancy decision analysis unit, which is used to transmit the first hub project to the point hub unit, and transmit the second channel project to the linear channel unit, to perform water conservancy decision analysis, and determine the project decision plan. Specifically, the water conservancy decision analysis unit first receives the detailed data of the first hub project and the second channel project from the water conservancy project decomposition unit, and ensures that the point hub unit and the linear channel unit are ready, optimizes the data transmission interface, and ensures accurate data transmission. Then, the data of the first hub project enters the point hub unit, which analyzes factors such as flood control, water resource utilization, and economic costs based on the decision model constructed in combination with the principles of water conservancy engineering, such as flood evolution simulation of the reservoir to determine the flood discharge strategy, use planning algorithms to optimize the water supply plan, and evaluate the plan through cost-benefit analysis to generate and evaluate multiple feasible decision plans. At the same time, the data of the second channel project is sent to the linear channel unit, which uses the flow distribution algorithm to determine the water diversion flow based on the hydraulic principles, water demand and synergy, and relies on reliability analysis to formulate maintenance plans, and also generates multiple decision plans and comprehensively evaluates them. Finally, the two units will feed back their respective plans to the water conservancy decision-making analysis unit, which will take into account the mutual influence between hubs and channels, simulate the operation under different combinations through an overall model, and use a multi-objective decision-making method to evaluate the performance of each combination in terms of water resource utilization, engineering safety, economy and ecology, and select the project decision-making plan with the best comprehensive performance, provide a scientific decision-making basis for water conservancy projects, promote their development, and ensure the coordinated development of water resource utilization and economic, social and ecological environment.

[0036] In a possible implementation, the water conservancy decision analysis unit further includes: a first decision scheme determination subunit, which is used to output the first hub scheme and the first channel scheme, and combine them to determine the first decision scheme. Specifically, the first decision scheme determination subunit first integrates the decision analysis results on the first hub project received from the point hub unit. The point hub unit considers factors such as water resource allocation, flood control, equipment operation and surrounding environment, and uses water conservancy engineering models and algorithms to generate solutions covering various working conditions and goals. After multi-dimensional evaluation, the relatively optimal first hub solution is selected. At the same time, the decision analysis results of the linear channel unit on the second channel project are obtained. The linear channel unit uses relevant models and algorithms based on hydraulic characteristics, water demand and synergy to formulate solutions including water distribution, maintenance and emergency plans, and determines the best first channel solution after comprehensive evaluation. Then the two are organically combined into the first decision-making plan, which fully considers the interaction between hubs and channels. By establishing a joint operation model to simulate the implementation effects under different working conditions, the plan is fine-tuned and optimized to ensure its adaptability and overall optimality, provide guidance for water conservancy project decision-making, promote the development of water conservancy, and ensure the coordinated development of water resources utilization and economic, social and ecological environment.

[0037] The project evaluation data determination subunit is used to combine the engineering evaluation matrix to perform single evaluation and weighted total calculation of the evaluation indicators of the first decision-making plan to determine the project evaluation data. Specifically, the project evaluation data determination subunit first constructs an engineering evaluation matrix based on water conservancy engineering professional knowledge, industry standards and project-specific goals, covering aspects such as water resource utilization efficiency, engineering safety, economic cost-effectiveness, ecological and environmental impact, social service functions and their subdivided indicators, such as irrigation water utilization coefficient, dam stability coefficient, etc., to build a framework for evaluating the first decision-making plan. Then, the relevant data of the plan are collected, including on-site monitoring data, engineering design and construction records, geographical and environmental data, and socio-economic statistical data, etc., which are sorted and pre-processed to ensure that they are accurate, complete and in a unified format to match the evaluation indicator requirements. Then, for each single indicator, the corresponding method model is used for calculation according to its nature, such as calculating the irrigation water utilization coefficient based on the ratio of actual water use to water diversion, and calculating the safety factor of engineering facilities using a structural mechanics model, etc., to obtain detailed evaluation results in all aspects. Finally, the analytic hierarchy process, expert consultation method and principal component analysis method are used to determine the indicator weights. The results of individual indicators are multiplied by the corresponding weights and added together to obtain project evaluation data that reflects the overall benefit level of the plan, providing quantitative standards for plan comparison and selection, promoting the development of water conservancy project decision-making, and ensuring the coordinated development of water resources utilization and economic, social and ecological environment.

[0038] A subunit for locating a difference strategy point, the subunit for locating a difference strategy point is used to traverse the project evaluation data, make a difference judgment between a single evaluation indicator and an overall evaluation, and locate a difference strategy point, wherein an ideal evaluation matrix is ​​used as a judgment constraint, and the ideal evaluation matrix is ​​marked with tolerance. Specifically, the subunit for locating a difference strategy point first obtains the project evaluation data, and at the same time introduces an ideal evaluation matrix constructed based on the best practices of water conservancy projects, industry standards, and ideal goals of the project, which sets an ideal value and tolerance range for each evaluation indicator. Then, for the single indicator in the project evaluation data, its actual value is compared with the ideal value and tolerance of the corresponding indicator of the ideal matrix one by one, and a numerical algorithm and judgment rules are used to determine whether there is a difference, and the difference indicator and its deviation are recorded. After that, the comprehensive evaluation score of the first decision plan is compared with the comprehensive ideal score range preset by the ideal matrix to determine whether the overall evaluation is different, and to grasp the pros and cons of the plan from a macro perspective. Finally, based on the results of single and total difference judgments, comprehensive analysis and integration are carried out to determine the decision-making links corresponding to indicators with large impact and significant differences as difference strategy points, and detailed records and classifications are made to clarify the corresponding fields, problem manifestations and related relationships, so as to point out the direction for feedback and adjustment of decision-making plans, promote the development of water conservancy project decision-making, and ensure the coordinated development of water resources utilization and economic, social and ecological environment.

[0039] The decision-making scheme feedback adjustment subunit is used to feedback and adjust the first decision-making scheme for the difference strategy point. Specifically, the decision-making scheme feedback adjustment subunit first analyzes the difference strategy point determined by the difference strategy point positioning subunit, studies the various factors involved in the difference points such as the reservoir water resources dispatching strategy, collects relevant data, and uses professional knowledge and tools to find out the root causes, such as inaccurate model parameters, poor evaluation methods, or unreasonable control rules. Then, based on the analysis results, combined with the actual conditions, technical feasibility and overall goals of the project, formulate specific feedback adjustment strategies, including optimizing the parameters of the water inflow prediction model, improving the water demand assessment method, and using a multi-objective optimization algorithm to adjust the water level control strategy, and analyze the coordinated adjustment of related links. Then, the adjustment strategy is applied to the first decision-making scheme, and the relevant decision parameters, operating procedures and management measures are modified in accordance with the standard process. The adjusted scheme is fully tested and monitored, and various types of data are collected. Finally, the trial operation results are compared and evaluated with the ideal evaluation matrix target values. If expectations are not met or new problems arise, the feedback adjustment process will be initiated again. By continuously optimizing the plan, the operating efficiency and comprehensive benefits of water conservancy projects can be improved, the development of water conservancy can be promoted, and the utilization of water resources and the coordinated development of the economy, society and the ecological environment can be ensured.

[0040] In a possible implementation, the project decision scheme determination module 40 further includes: a project scheme sequence determination unit, which is used to traverse the project decision scheme, divide the project phases, and determine the project scheme sequence, wherein each project phase corresponds to a sequence node. Specifically, the project scheme sequence determination unit first collects project decision schemes covering all aspects of water conservancy projects, and pre-processes them, removes fuzzy, contradictory and incomplete parts through data cleaning, uses text analysis technology to unify the expression of key parameters, and classifies and organizes information at the same time, laying an accurate and clear data foundation for subsequent work. Then, according to the construction law of water conservancy projects and the specific characteristics of the projects, the basis for phase division is determined, and the life cycle, geological conditions, new technology application, engineering function, time schedule, resource investment and risk control are comprehensively analyzed to clarify the starting and ending points and tasks of each phase. Then, according to the traversal analysis decision scheme, project phases such as preliminary planning, engineering construction, and later operation and maintenance are divided, and each phase corresponds to a unique sequence node, which is arranged in the order of implementation to form a project scheme sequence, thereby building a clear and orderly framework for the implementation and management of water conservancy projects, promoting their scientific and meticulous development, and ensuring the coordinated development of water resources utilization and economic, social and ecological environment.

[0041] The stage engineering marking determination unit is used to traverse the project scheme sequence and determine the stage engineering standards. Specifically, the stage engineering marking determination unit first obtains the project scheme sequence from the project scheme sequence determination unit, and deeply analyzes the tasks, goals and resource requirements of each stage. Then, according to the standards, specifications and laws and regulations of the water conservancy industry, relevant information is collected and sorted, and similar engineering cases are referred to. Then, for each project stage, the detailed standards are determined in combination with the information and project requirements: in terms of quality standards, specific indicators such as dam structure strength and channel lining parameters are clarified; in terms of progress standards, the start and end time and key nodes of each stage are decomposed according to the total construction period; in terms of safety standards, construction site protection, equipment operation specifications and disaster emergency plans are covered; in terms of environmental standards, environmental protection measures and emission standards for each stage from construction to operation are formulated in accordance with environmental protection laws and regulations, such as soil erosion prevention and control, ecological flow maintenance, etc. Through this process, comprehensive stage engineering standards are determined to provide a basis for the supervision and implementation of water conservancy projects, ensure the quality, progress, safety and environmental friendliness of project construction, and promote the coordinated development of water conservancy and the economic, social and ecological environment.

[0042] A mapping association unit is used to map and associate the project scheme sequence with the stage engineering standard. Specifically, the mapping association unit first receives the project scheme sequence data from the project scheme sequence determination unit and the stage engineering standard data from the stage engineering annotation determination unit, organizes the project scheme sequence information and classifies and organizes the stage engineering standard data. Then, mapping rules are formulated according to the management requirements and operation procedures of water conservancy projects, and the association relationship is determined according to the project stage tasks, time sequence and synergy between standards. Then, a database management system or project management software is used to map and store the project phase with the corresponding quality, progress, safety, environment and other standard information in the association table one by one. Finally, the association results are simulated, verified and checked, feedback from all parties is collected, and optimization and adjustment are made for unreasonable and unclear issues such as repeated omissions of standards and inconvenient mapping, so as to provide a standard implementation framework for the implementation and management of water conservancy projects, promote their scientific and meticulous development, and ensure the coordinated development of water resources utilization and economic, social and ecological environment.

[0043] The project guidance module 50 is used to display the project decision-making scheme on the terminal interface and guide the water conservancy project. Specifically, the project guidance module 50 first obtains the project decision-making scheme data from the water conservancy decision-making analysis unit, and converts it into a format suitable for terminal interface display after integration and preprocessing to ensure that the data is accurate, complete and consistent. Then, the terminal interface is designed according to the characteristics of the water conservancy project and the information structure, and a modular layout is adopted, such as setting a project overview module in the upper left corner, displaying the hub and channel map in the center, the data chart area on the right, and operation buttons and prompt bars at the bottom, and providing multiple display modes. Then the processed data is dynamically visualized according to this layout, and the interactive function is used to facilitate users to view details and simulate adjustments through mouse operation, and deeply understand the connotation and effect of the scheme. During the user viewing process, the module provides real-time guidance to the project based on the displayed information, such as providing progress warnings and scheduling suggestions, etc. At the same time, a feedback mechanism is established to collect user opinions and pass them to relevant parties for optimization, thereby realizing a benign interaction between the scheme and the project operation, promoting the scientific and visual development of water conservancy project decision-making, and ensuring the coordinated development of water resources utilization and economic, social and ecological environment.

[0044] The embodiment of the present application adopts a multi-source data receiving module to connect and receive synchronous multi-source data of the water conservancy area; the standardization processing module obtains water conservancy engineering projects, determines and unifies evaluation indicators, and generates an evaluation matrix; the lightweight modeling module trains the decision support module based on multi-source data and the evaluation matrix; the project decision plan determination module combines the decision support module to perform decision and feedback analysis and formulate a decision plan; the project guidance module displays the decision plan through the terminal interface to guide the implementation of the water conservancy engineering project, thereby achieving the technical effect of improving the scientificity and accuracy of decision-making in water conservancy projects.

[0045] In the above, refer to Figure 1 The water conservancy project design decision support system based on multi-source data fusion according to an embodiment of the present invention is described in detail. Figure 2 A water conservancy project design decision support method based on multi-source data fusion according to an embodiment of the present invention is described.

[0046] Water conservancy project design decision support method based on multi-source data fusion, such as Figure 2 As shown, the method includes:

[0047] Connect a multi-source data interface to receive multi-source data of a water conservancy area, wherein the multi-source data is synchronously collected data; obtain water conservancy engineering projects, determine engineering evaluation indicators and perform forward normalization of the indicators to generate an engineering evaluation matrix; perform lightweight modeling based on the multi-source data, and supervise the training of a decision support module in combination with the engineering evaluation matrix, wherein the decision support module includes a point hub unit and a linear channel unit; for the water conservancy engineering project, perform engineering decision and evaluation feedback analysis in combination with the decision support module to determine a project decision plan; display the project decision plan on a terminal interface to provide guidance for the water conservancy engineering project.

[0048] In a possible implementation, the water conservancy project design decision support method based on multi-source data fusion also includes: receiving the multi-source data, performing data preprocessing calibration and fusion, and building a water conservancy three-dimensional model; calling water conservancy decision samples, and supervising the training of an initial decision support module based on the water conservancy three-dimensional model; determining a decision-making method, wherein the decision-making method is based on collaborative decision-making between point hub decisions and linear channel decisions; determining a lightweight loss function based on the decision-making method, performing distillation training on the initial decision support module, and determining the point hub unit and the linear channel unit.

[0049] In a possible implementation, the water conservancy project design decision support method based on multi-source data fusion also includes: obtaining a first water conservancy decision sample, and determining a first lightweight loss function based on point hub decision requirements; inputting the first water conservancy decision sample into the initial decision support module to determine a first output sample; determining a first probability distribution based on the lightweight loss function, wherein the first probability distribution characterizes the logical transmission probability of elements in the initial decision support module; and performing a distillation training on the initial decision support module in combination with the first water conservancy decision sample, the first probability distribution and the first output sample.

[0050] In a possible implementation, the water conservancy project design decision support method based on multi-source data fusion also includes: traversing the water conservancy project to determine key hub nodes and linear channel sections, wherein the linear channel sections are determined by adjacent key hub nodes; based on the key hub nodes and the linear channel sections, decomposing the water conservancy project to determine a first hub project and a second channel project; transmitting the first hub project to the point hub unit, transmitting the second channel project to the linear channel unit, performing water conservancy decision analysis, and determining the project decision plan.

[0051] In a possible implementation, the water conservancy project design decision support method based on multi-source data fusion also includes: outputting a first hub plan and a first channel plan, combining them to determine a first decision plan; combining the engineering evaluation matrix, performing single evaluation and weighted overall calculation of the evaluation indicators of the first decision plan, and determining the project evaluation data; traversing the project evaluation data, performing difference judgments on single evaluation indicators and overall evaluations, and locating difference strategy points, wherein the ideal evaluation matrix is ​​used as a judgment constraint, and the ideal evaluation matrix identifier has tolerance; and feedback adjustment is performed on the first decision plan with respect to the difference strategy points.

[0052] In a possible implementation, the water conservancy project design decision support method based on multi-source data fusion also includes: traversing the project evaluation indicators, classifying and determining multiple groups of evaluation indicators, wherein the classification is performed into extremely large, extremely small and interval types; traversing the multiple groups of evaluation indicators, performing positive conversion on extremely small and interval types, and determining conversion indicators; integrating extremely large indicators with the conversion indicators, and performing indicator standardization to generate the project evaluation matrix.

[0053] In a possible implementation, the water conservancy project design decision support method based on multi-source data fusion also includes: traversing the project decision plan, dividing the project stages, and determining the project plan sequence, wherein each project stage corresponds to a sequence node; traversing the project plan sequence to determine the stage engineering standards; and mapping and associating the project plan sequence with the stage engineering standards.

[0054] The water conservancy project design decision support system based on multi-source data fusion provided by the embodiment of the present invention can execute the water conservancy project design decision support method based on multi-source data fusion provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0055] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0056] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A water conservancy project design decision support system based on multi-source data fusion, characterized by: The system comprises: A multi-source data receiving module, the multi-source data receiving module is used to connect to the multi-source data interface and receive multi-source data of the water conservancy area, wherein the multi-source data is synchronously collected data; A standardization processing module, which is used to obtain water conservancy engineering projects, determine engineering evaluation indicators and perform positive standardization processing on the indicators to generate an engineering evaluation matrix; A lightweight modeling module, the lightweight modeling module is used to perform lightweight modeling based on the multi-source data, combined with the engineering evaluation matrix, and supervise the training of a decision support module, wherein the decision support module includes a point hub unit and a linear channel unit; A project decision-making scheme determination module, which is used to carry out engineering decision-making and evaluation feedback analysis for the water conservancy project in combination with the decision support module to determine the project decision-making scheme; A project guidance module is used to display the project decision plan on the terminal interface and provide guidance for the water conservancy project.

2. The water conservancy project design decision support system based on multi-source data fusion according to claim 1 is characterized in that: Based on the multi-source data, lightweight modeling is performed to supervise the training of a decision support module, including: A water conservancy three-dimensional model building unit, the water conservancy three-dimensional model building unit is used to receive the multi-source data, perform data preprocessing calibration and fusion, and build a water conservancy three-dimensional model; A decision support module training unit, the decision support module training unit is used to call water conservancy decision samples, and supervise the training of an initial decision support module based on the water conservancy three-dimensional model; A decision-making method determining unit, the decision-making method determining unit is used to determine a decision-making method, wherein the decision-making method is based on collaborative decision-making of point-shaped hub decision and line-shaped channel decision; A lightweight loss function determination unit is used to determine a lightweight loss function based on the decision-making method, perform distillation training on the initial decision support module, and determine the point hub unit and the linear channel unit.

3. The water conservancy project design decision support system based on multi-source data fusion as claimed in claim 2, characterized in that: The initial decision support module is subjected to distillation training, comprising: A first lightweight loss function determination subunit, the first lightweight loss function determination subunit is used to obtain a first water conservancy decision sample and determine a first lightweight loss function based on a point hub decision requirement; A first output sample determination subunit, the first output sample determination subunit is used to input the first water conservancy decision sample into the initial decision support module to determine a first output sample; A first probability distribution determining subunit, the first probability distribution determining subunit being used to determine a first probability distribution based on the lightweight loss function, wherein the first probability distribution represents a logical transfer probability of elements in the initial decision support module; A distillation training subunit is used to combine the first water conservancy decision sample, the first probability distribution and the first output sample to perform a distillation training on the initial decision support module.

4. The water conservancy project design decision support system based on multi-source data fusion as claimed in claim 1, characterized in that: Combined with the decision support module, engineering decision and evaluation feedback analysis is performed, including: An engineering project traversal unit, the engineering project traversal unit is used to traverse the water conservancy project and determine key hub nodes and linear channel sections, wherein the linear channel sections are determined by adjacent key hub nodes; A water conservancy project decomposition unit, the water conservancy project decomposition unit is used to decompose the water conservancy project based on the key hub node and the linear channel section, and determine a first hub project and a second channel project; A water conservancy decision analysis unit, wherein the water conservancy decision analysis unit is used to transmit the first hub project to the point hub unit, and transmit the second channel project to the linear channel unit, to perform water conservancy decision analysis, and to determine the project decision plan.

5. The water conservancy project design decision support system based on multi-source data fusion as claimed in claim 4, characterized in that: Conduct water resource decision analysis, including: A first decision-making solution determining subunit, the first decision-making solution determining subunit is used to output a first hub solution and a first channel solution, and combine them to determine a first decision-making solution; A project evaluation data determination subunit, the project evaluation data determination subunit is used to combine the engineering evaluation matrix, perform single evaluation and weighted total calculation of evaluation indicators on the first decision-making scheme, and determine project evaluation data; A difference strategy point positioning subunit, the difference strategy point positioning subunit is used to traverse the project evaluation data, make a difference judgment between the single evaluation index and the overall evaluation, and locate the difference strategy point, wherein an ideal evaluation matrix is ​​used as a judgment constraint, and the ideal evaluation matrix identifier has a tolerance; A decision scheme feedback adjustment subunit is used to perform feedback adjustment on the first decision scheme with respect to the difference strategy point.

6. The water conservancy project design decision support system based on multi-source data fusion according to claim 1 is characterized in that: Perform positive standardization of indicators and generate a project evaluation matrix, including: An evaluation index determination unit, the evaluation index determination unit is used to traverse the engineering evaluation index, classify and determine a plurality of groups of evaluation indexes, wherein the evaluation indexes are classified into extremely large, extremely small and interval types; A conversion index determination unit, the conversion index determination unit is used to traverse the multiple groups of evaluation indicators, perform positive conversion on the extremely small type and the interval type, and determine the conversion index; An indicator standardization unit, wherein the indicator standardization unit is used to integrate the extremely large indicator and the conversion indicator, and perform indicator standardization to generate the engineering evaluation matrix.

7. The water conservancy project design decision support system based on multi-source data fusion according to claim 1 is characterized in that: After determining the project decision plan, including: A project solution sequence determination unit, the project solution sequence determination unit is used to traverse the project decision plan, divide the project phases, and determine the project solution sequence, wherein each project phase corresponds to a sequence node; A stage engineering mark determination unit, the stage engineering mark determination unit is used to traverse the project solution sequence and determine the stage engineering standard; A mapping association unit, wherein the mapping association unit is used to map and associate the project solution sequence with the phase engineering standard.

8. A water conservancy project design decision support method based on multi-source data fusion, characterized in that: The method is applied to the water conservancy project design decision support system based on multi-source data fusion according to any one of claims 1 to 7, and the method comprises: Connecting to a multi-source data interface to receive multi-source data of a water conservancy area, wherein the multi-source data is synchronously collected data; Obtain water conservancy project, determine the project evaluation indicators and perform positive standardization of the indicators to generate the project evaluation matrix; Based on the multi-source data, lightweight modeling is performed, combined with the engineering evaluation matrix, and a decision support module is supervised and trained, wherein the decision support module includes a point hub unit and a linear channel unit; For the water conservancy project, combined with the decision support module, engineering decision and evaluation feedback analysis are carried out to determine the project decision plan; The project decision plan is displayed on the terminal interface to provide guidance for the water conservancy project.

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