Water Conservancy Project Management Intelligent System

Abstract

The present invention relates to the technical field of water conservancy project management, specifically to an intelligent water conservancy project management system, which includes a basin integrated management module, a water resources allocation module, a water quality and environment monitoring module, an engineering maintenance and fault response module, an engineering project evaluation and optimization module, a risk management and emergency response module, an unconventional water source development module, and a project decision-making and strategy formulation module. In the present invention, by simulating the impact of different control schemes on water resources supply and demand, the operation strategy of reservoirs and sluices can be optimized, and by analyzing sensor data and historical operation trends, future maintenance needs can be predicted and corresponding maintenance and repair plans can be formulated to enhance the sustainability of engineering projects. The simulation analysis of project operation performance and data uncertainty processing make engineering projects more stable and reliable, and the development of unconventional water sources further expands the available range of water resources.

Description

Technical Field

[0001] The invention relates to the technical field of water conservancy project management, and in particular to an intelligent system for water conservancy project management. Background Art

[0002] The technical field of water conservancy project management involves the planning, design, implementation, monitoring and maintenance of various engineering projects related to water resources, including but not limited to reservoirs, dams, irrigation systems, water treatment facilities and flood control measures. The core goal is to optimize the use and management of water resources, ensure water security, and enhance disaster management capabilities, while taking into account environmental protection and socio-economic development needs. With technological advances, this field has gradually incorporated information technology and intelligent tools to improve the efficiency and effectiveness of project management.

[0003] Among them, the water conservancy project management intelligent system is an advanced management tool that integrates data analysis, artificial intelligence and information technology, and is designed to improve the planning, execution, monitoring and maintenance efficiency of water conservancy projects. The purpose is to optimize resource allocation, improve decision-making quality, reduce project risks, and enhance the sustainability and environmental compatibility of projects through intelligent means. Through the realization of the goals, the system expects to achieve higher engineering efficiency, cost-effectiveness and positive impacts on the environment and society.

[0004] Traditional water conservancy project management technologies are limited in accuracy and flexibility. They rely on relatively fixed water resource management strategies and are difficult to quickly adapt to environmental changes or emergencies, limiting the efficiency and accuracy of resource allocation. For example, when encountering extreme weather events, traditional systems are unable to adjust reservoir water release strategies in a timely manner, resulting in impaired flood control capabilities and even flood disasters. In addition, due to the lack of efficient data analysis tools, existing technologies are insufficient in predicting maintenance needs and identifying potential failures, increasing the risk of unexpected failures and affecting the stable operation and long-term sustainability of engineering projects. In the development of unconventional water sources, existing technologies lack effective simulation tools and are unable to fully evaluate and utilize these resources, resulting in the failure to reasonably utilize potential water sources, which not only limits the overall availability of water resources, but also weakens the ability to adapt to environmental changes. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose an intelligent water conservancy project management system.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: The water conservancy project management intelligent system includes:

[0007] The integrated watershed management module dynamically simulates the water cycle and resource distribution in the watershed based on GIS data, and generates watershed resource management strategies by combining hydrological cycle analysis, land use change assessment and the impact of human activities;

[0008] The water resource allocation module optimizes the operation strategies of reservoirs and sluice gates based on the basin resource management strategy, determines the allocation ratio of water resources among different uses, simulates the impact of different regulation schemes on water resource supply and demand, and generates water resource allocation instructions;

[0009] The water quality and environmental monitoring module evaluates the impact of water source development on water quality based on the results of unconventional water source development, analyzes the potential impact of engineering activities on the surrounding ecosystem, and generates environmental impact assessment results;

[0010] The engineering maintenance and fault response module analyzes sensor data to identify potential faults and abnormal behaviors based on the environmental impact assessment results, predicts future maintenance needs based on historical data and operating trends, and generates maintenance and repair plans;

[0011] The project evaluation and optimization module simulates and analyzes the operational performance of the water conservancy project based on the maintenance and repair plan, processes the uncertainty and incompleteness of the project data, and generates an optimized project plan;

[0012] The risk management and emergency response module analyzes project risk factors based on the optimized project plan, classifies and prioritizes risks, conducts quantitative risk assessment, and generates a rapid response plan;

[0013] The unconventional water source development module simulates the flow and distribution of unconventional water sources under environmental conditions based on the water resource allocation instructions, optimizes the collection, treatment and utilization processes of water sources, and generates unconventional water source utilization plans;

[0014] The project decision and strategy formulation module evaluates and analyzes the project decision points based on the rapid response plan and unconventional water resource utilization plan, and generates project decisions and management strategies based on the project's economic, environmental and social impact factors.

[0015] As a further solution of the present invention, the watershed resource management strategy specifically includes a water resource rational allocation plan, ecological protection zone demarcation and pollution source control measures; the water resource allocation instructions include a reservoir release plan, an irrigation water scheduling strategy and an urban water supply priority arrangement; the environmental impact assessment results include parameters of water quality changes, affected biological species and their habitats and potential environmental change trends; the maintenance and repair plan specifically includes maintenance task arrangements and management strategies for required spare parts; the optimized project plan includes economic analysis results of the plan, resource allocation optimization measures and environmental protection measures; the rapid response plan includes risk level classification, operating steps under emergencies and resource recovery timeline; the unconventional water source utilization plan includes design parameters of rainwater collection systems, optimization steps of recycled water treatment processes and specific measures to improve utilization efficiency; the project decision-making and management strategy includes a step plan for project implementation, an allocation plan for key resources and response measures to potential risks.

[0016] As a further solution of the present invention, the water resource allocation module includes:

[0017] The water diversion operation submodule uses a genetic algorithm to simulate the inheritance and variation of individuals in the natural selection process based on the watershed resource management strategy to generate a water diversion strategy plan;

[0018] The water balance control submodule calculates the crowding degree to maintain the diversity of the population based on the water transfer strategy, updates the population through cyclic iteration, captures the optimal solution that meets the multi-objective conditions, and generates the water allocation ratio;

[0019] The allocation efficiency optimization submodule adopts a simulated annealing algorithm based on the water allocation ratio. The initial solution is the water allocation ratio. The new solution space is captured by randomly fine-tuning the water allocation ratio, and the global optimal solution is analyzed to generate water resource allocation instructions.

[0020] As a further solution of the present invention, the engineering maintenance and fault response module includes:

[0021] The fault diagnosis submodule identifies faults and abnormal behaviors based on the environmental impact assessment results, performs feature extraction, identifies abnormal states and potential fault points of the equipment, and generates fault diagnosis results;

[0022] The predictive maintenance submodule uses a long short-term memory network to predict future maintenance needs based on the fault diagnosis results, analyzes historical data and current operating trends, predicts potential equipment failure types and maintenance time points, and generates maintenance demand prediction results;

[0023] The repair strategy submodule plans the time schedule, required resources and personnel allocation of maintenance operations based on the maintenance demand prediction results, combined with the fault type, predicted maintenance time point, and availability of maintenance resources, and generates a maintenance and repair plan.

[0024] As a further solution of the present invention, the long short-term memory network is according to the formula:

[0025] ;

[0026] Calculate the forget gate output and generate the equipment maintenance demand prediction result, where: is the sigmoid activation function, is the weight matrix of the forget gate, is the hidden state of the previous time step, is the input of the current time step, is the bias term of the forget gate, To adjust the parameters, is the weight matrix, respectively for past behavior data , maintenance requirements of the previous cycle , and current environmental parameters .

[0027] As a further solution of the present invention, the engineering project evaluation and optimization module includes:

[0028] The scheme simulation submodule performs operation performance simulation analysis of the water conservancy project based on the maintenance and repair plan, simulates the water resource allocation efficiency and water quality changes under different conditions, and generates scheme simulation analysis results;

[0029] Based on the simulation analysis results of the scheme, the cost-benefit analysis submodule conducts grey correlation analysis on the project cost, potential economic benefits and operation and maintenance costs through the grey prediction model, reveals the cost-benefit ratio of the different schemes, identifies the optimal cost-benefit scheme, and generates cost-benefit optimization results;

[0030] The sustainability evaluation submodule evaluates the comprehensive sustainability of the difference plans based on the cost-benefit optimization results, combined with resource utilization efficiency, environmental impact and socio-economic benefits, reveals the plan with the most efficient resource utilization and the least environmental impact, and generates an optimized project plan.

[0031] As a further solution of the present invention, the grey relational analysis method is according to the formula:

[0032] ;

[0033] Calculate the cost-effectiveness ratio and generate the cost-effectiveness optimization result, where: For sequence with sequence The correlation between is the absolute value of the difference between the two series at the same point in time, and Represent all possible minimum and maximum differences, respectively. is the resolution coefficient, and is the weighting coefficient.

[0034] As a further solution of the present invention, the unconventional water source development module includes:

[0035] The resource simulation submodule simulates the flow and distribution of unconventional water sources under environmental conditions based on the water resource allocation instructions, simulates the behavior of unconventional water sources under different environmental conditions, evaluates the efficiency of the collection and transportation process, and generates resource flow simulation results;

[0036] The utilization efficiency optimization submodule optimizes the collection efficiency and processing capacity to maximize the water collection, processing and utilization processes based on the resource flow simulation results, and generates a utilization efficiency optimization plan;

[0037] The solution generation submodule comprehensively evaluates the collection method, processing technology and utilization path based on the utilization efficiency optimization plan, optimizes the management and use strategy of unconventional water sources, and generates an unconventional water source utilization plan.

[0038] Compared with the prior art, the advantages and positive effects of the present invention are:

[0039] In the present invention, by simulating the impact of different control schemes on water resource supply and demand, the operation strategy of reservoirs and sluices can be optimized, the reasonable allocation of resources among various uses can be realized, and the utilization efficiency of water resources can be improved. By analyzing sensor data and historical operation trends, future maintenance needs can be predicted and corresponding maintenance and repair plans can be formulated, which not only enhances the sustainability of engineering projects, but also improves the response efficiency and accuracy of engineering maintenance. The simulation analysis of project operation performance and data uncertainty processing make the engineering projects more stable and reliable. The optimized project plan can effectively improve engineering efficiency and cost-effectiveness. The development of unconventional water sources optimizes the collection, processing and utilization processes of water sources through flow and distribution simulation, further expands the available range of water resources, and improves environmental adaptability and comprehensive utilization of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is a system flow chart of the present invention;

[0041] Figure 2 It is a schematic diagram of the system framework of the present invention;

[0042] Figure 3It is a flow chart of the watershed integrated management module of the present invention;

[0043] Figure 4 This is a flow chart of the water resource allocation module of the present invention;

[0044] Figure 5 It is a flow chart of the water quality and environment monitoring module of the present invention;

[0045] Figure 6 It is a flow chart of the engineering maintenance and fault response module of the present invention;

[0046] Figure 7 It is a flow chart of the engineering project evaluation and optimization module of the present invention;

[0047] Figure 8 It is a flow chart of the risk management and emergency response module of the present invention;

[0048] Fig. 9 Develop a modular flow chart for the unconventional water source of the present invention;

[0049] Fig.10 This is a flow chart of the project decision-making and strategy formulation module of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0051] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0052] Embodiment 1

[0053] See also Figure 1 and Figure 2 , the intelligent system for water conservancy project management includes:

[0054] The integrated watershed management module dynamically simulates the water cycle and resource distribution in the watershed based on GIS data, and generates watershed resource management strategies by combining hydrological cycle analysis, land use change assessment and the impact of human activities;

[0055] The water resource allocation module optimizes the operation strategies of reservoirs and sluices based on the basin resource management strategy, determines the allocation ratio of water resources among different uses, simulates the impact of different regulation schemes on water resource supply and demand, and generates water resource allocation instructions;

[0056] The water quality and environmental monitoring module evaluates the impact of water source development on water quality based on the results of unconventional water source development, analyzes the potential impact of engineering activities on the surrounding ecosystem, and generates environmental impact assessment results;

[0057] The engineering maintenance and fault response module analyzes sensor data to identify potential faults and abnormal behaviors based on environmental impact assessment results, predicts future maintenance needs based on historical data and operating trends, and generates maintenance and repair plans;

[0058] The project evaluation and optimization module simulates and analyzes the operating performance of water conservancy projects based on maintenance and repair plans, processes the uncertainty and incompleteness of project data, and generates optimized project plans;

[0059] The risk management and emergency response module analyzes project risk factors based on the optimized project plan, classifies and prioritizes risks, conducts quantitative risk assessment, and generates a rapid response plan;

[0060] The unconventional water source development module simulates the flow and distribution of unconventional water sources under environmental conditions based on water resource allocation instructions, optimizes the collection, treatment and utilization processes of water sources, and generates unconventional water source utilization plans;

[0061] The project decision-making and strategy formulation module evaluates and analyzes project decision points based on rapid response plans and unconventional water resource utilization plans, and generates project decisions and management strategies based on project economic, environmental and social impact factors.

[0062] The specific strategies for river basin resource management include the rational allocation of water resources, demarcation of ecological protection zones and pollution source control measures; the water resource allocation instructions include reservoir release plans, irrigation water scheduling strategies and urban water supply priority arrangements; the environmental impact assessment results include parameters of water quality changes, affected biological species and their habitats and potential environmental change trends; the maintenance and repair plan includes maintenance task arrangements and management strategies for required spare parts; the optimized project plan includes the results of economic analysis of the plan, resource allocation optimization measures and environmental protection measures; the rapid response plan includes risk level classification, operational procedures under emergencies and resource recovery timelines; the unconventional water source utilization plan includes the design parameters of the rainwater collection system, the optimization steps of the recycled water treatment process and specific measures to improve utilization efficiency; the project decision-making and management strategies include the step plan for project implementation, the allocation plan for key resources and response measures to potential risks.

[0063] See also Figure 2 and Figure 3 , the integrated watershed management module includes:

[0064] The watershed simulation submodule calculates water balance based on GIS data, simulates surface runoff and evapotranspiration, and generates a basic model of watershed hydrological dynamics;

[0065] The watershed simulation submodule is based on GIS data. First, a watershed spatial database is established. The database records the topography, soil type, and vegetation coverage of the watershed in detail. Each data point is equipped with coordinate information and corresponding geographic characteristic values. Through GIS analysis tools, surface characteristics are classified and quantified, such as slope analysis and soil permeability assessment. Next, the water balance formula is introduced: in, Represents precipitation, represents evapotranspiration, represents the groundwater infiltration, Represents surface runoff. The system calculates monthly precipitation and evapotranspiration values ​​based on real-time meteorological data and historical climate patterns, and then uses soil and terrain data to estimate groundwater infiltration and surface runoff to ensure accurate calculation of water balance, simulate surface runoff and evapotranspiration processes, and generate a basic model of basin hydrological dynamics.

[0066] The ecological protection measures submodule analyzes river hydrological conditions based on the basic model of watershed hydrological dynamics, conducts ecosystem service assessment for different management scenarios, quantifies ecosystem service value, and generates ecological protection and restoration strategies;

[0067] The submodule of ecological protection measures is based on the basic model of basin hydrological dynamics. It constructs a river hydrological condition simulation model, which takes into account the topographic characteristics, river network and climate conditions of the basin. First, key hydrological parameters such as peak flow, low water flow and average flow in the basin hydrological dynamic model are extracted, and then the river hydrological conditions are analyzed. The ecosystem service assessment model is used to evaluate the health status and service functions of the ecosystem under different management scenarios. The model is based on the quantification method of ecological service value: in, Represents the total service value, Representative The value per unit area of ​​ecological services, Represents the area covered by the service. Through assessment, the value of ecosystem services is quantified, and specific ecological protection and restoration strategies are generated based on the assessment results.

[0068] The resource allocation strategy submodule optimizes the strategy based on ecological protection and restoration strategies, analyzes the optimal allocation ratio of water resources, and generates a watershed resource management strategy;

[0069] The resource allocation strategy submodule is based on the ecological protection and restoration strategy. First, the resource optimization objective function is established, which is guided by the sustainable use of water resources and the needs of ecological protection. Using the linear programming model, the objective function is set to maximize the ecological service value and water resource utilization efficiency in the basin: in, and The weight parameters representing ecological services and water resource efficiency, Represents the value of ecological services, Represents water resource efficiency. By adjusting and The value of water resources is analyzed, the resource allocation effects under different strategies are analyzed, and finally the optimal water resources allocation ratio and river basin resource management strategy are formed.

[0070] See also Figure 2 and Figure 4 , water resource allocation module includes:

[0071] The water diversion operation submodule is based on the watershed resource management strategy and uses genetic algorithms to simulate the inheritance and variation of individuals in the natural selection process to generate water diversion strategy plans;

[0072] The water diversion operation submodule is based on the basin resource management strategy. First, an initial population based on basin-specific parameters is established. Each population member represents a potential water diversion strategy, including operation parameters such as water source switching and water volume regulation. Genetic operations such as crossover and mutation are introduced by simulating the natural selection process. The crossover operation randomly selects some parameters of two strategy plans to exchange to generate a new strategy plan, while the mutation operation randomly changes some parameters in the strategy plan with a small probability to simulate genetic variation in nature. The efficiency and practicality of each strategy plan are evaluated by setting a fitness function, which is calculated based on the utilization rate of water resources and the ability to meet downstream needs. Strategies with high fitness are more likely to be selected as the next generation. This process is iterated until the optimal water diversion strategy plan is found.

[0073] The water balance control submodule calculates the crowding degree to maintain population diversity based on the water transfer strategy, updates the population through cyclic iteration, captures the optimal solution that meets multi-objective conditions, and generates the water allocation ratio;

[0074] The water balance control submodule is based on the water transfer strategy scheme and uses the concept of crowding in the genetic algorithm to maintain the diversity of the population. The calculation of crowding is based on the difference between each strategy scheme and its nearest neighbor strategy scheme. A larger difference means a higher crowding, which helps prevent the algorithm from converging to the local optimal solution too early. The population is continuously updated through selection, crossover and mutation in the cyclic iteration process. After each generation of iteration, the performance of each strategy is evaluated based on crowding and fitness, and the optimal solution that meets the multi-objective conditions is gradually captured. The conditions include maximizing the utilization efficiency of water resources and meeting specific ecological protection needs, thereby generating a more refined and reasonable water allocation ratio.

[0075] The allocation efficiency optimization submodule is based on the water allocation ratio and adopts the simulated annealing algorithm. The initial solution is the water allocation ratio. The new solution space is captured by randomly fine-tuning the water allocation ratio, and the global optimal solution is analyzed to generate water resource allocation instructions.

[0076] The allocation efficiency optimization submodule is based on the water allocation ratio and adopts the simulated annealing algorithm. The initial solution is the current optimal water allocation ratio. The simulated annealing process is started at high temperature, and the temperature parameters are gradually reduced. The water allocation ratio is randomly fine-tuned at each temperature stage to explore new solution space. Random fine-tuning includes adjusting the release volume of a specific reservoir or adjusting the flow distribution of a river. After each fine-tuning, the fitness of the new solution is calculated and compared with the current solution. If the new solution shows a higher fitness or accepts a new solution with a lower fitness with a certain probability at the current temperature, the algorithm is prevented from falling into a local optimum. As the temperature gradually decreases, the probability of accepting a worse solution gradually decreases, and it tends to stabilize more at the global optimal solution. The final generated water resource allocation instruction aims to ensure the balance and efficient use of water in the entire basin.

[0077] See also Figure 2 and Figure 5 , water quality and environmental monitoring modules include:

[0078] The water quality simulation submodule conducts water quality assessment based on the results of unconventional water source development, constructs water quality simulation scenarios, simulates the changes of indicators in water bodies over time and space, evaluates the impact of development activities on water quality, and generates water quality simulation analysis results;

[0079] The water quality simulation submodule is based on the results of unconventional water source development. It starts with collecting baseline data on basic water quality parameters in the development area, such as pH, dissolved oxygen, turbidity, etc., to create an initial water quality database. Based on the baseline data, a water quality simulation scenario is constructed using a specific water quality model to simulate the changes of various indicators in the water body over time and space, and using differential equations to simulate the transmission and attenuation of pollutants: in, Indicates the distance from the source point In the concentration of time, is the initial concentration, is the natural decay rate, is the water flow velocity. The simulation process includes dynamic tracking of foreign substances introduced by unconventional water source development activities (such as recycled water utilization, rainwater collection, etc.) and their potential impact on existing water quality. The simulation results will provide quantitative data for further analysis to evaluate the specific impact of development activities on water quality and generate water quality simulation analysis results.

[0080] The protection measures submodule conducts quantitative analysis of the potential ecological risks of different protection measures based on the results of water quality simulation analysis, including the selection of sensitive species, exposure concentration thresholds and ecological effect thresholds, and generates ecological risk assessment results;

[0081] The protection measures submodule evaluates differential protection measures based on the results of water quality simulation analysis. First, sensitive species are selected. Species may be more sensitive to specific pollutants. For this purpose, a sensitive species database is established based on their ecological characteristics and known sensitivity. Then, exposure concentration thresholds and ecological effect thresholds are set and calculated based on the ecological model. The model is as follows: in, represents the risk factor, It is a lethal concentration causing 50% mortality. is the concentration in the environment. The model can help assess the potential risk level of sensitive species under current and predicted water quality conditions. Through analysis, the possible impact of different protection measures on sensitive ecosystems can be quantitatively evaluated to generate ecological risk assessment results.

[0082] The environmental impact assessment submodule conducts difference analysis based on the ecological risk assessment results, combined with land cover types and normalized vegetation index, evaluates the impact of engineering activities on land use, and generates environmental impact assessment results;

[0083] The environmental impact assessment submodule continues to conduct a deeper analysis based on the results of the ecological risk assessment. First, the land cover type data and the Normalized Difference Vegetation Index (NDVI) are integrated. The data is obtained through remote sensing technology to reflect the growth status of vegetation and changes in land cover in different regions. Using the difference analysis model: in, Indicates the change of NDVI before and after the engineering activities, and Represent the NDVI values ​​before and after the activity, respectively. Through this model, the specific impact of engineering activities on land use, including vegetation destruction and land cover type transformation, is evaluated. The analysis results provide a quantitative basis for environmental impact assessment, help formulate land use and environmental protection strategies, and generate environmental impact assessment results.

[0084] See also Figure 2 and Figure 6 , the engineering maintenance and fault response module includes:

[0085] The fault diagnosis submodule identifies faults and abnormal behaviors based on the environmental impact assessment results, performs feature extraction, identifies abnormal states and potential fault points of equipment, and generates fault diagnosis results;

[0086] Based on the results of the environmental impact assessment, the fault diagnosis submodule first analyzes the data collected from the environmental monitoring equipment, including but not limited to sensor readings such as temperature, humidity, and vibration. The data is preprocessed, including noise filtering and normalization, for subsequent analysis. Through feature extraction methods such as principal component analysis (PCA), key signal features are extracted, and the features can represent the current working status of the equipment. These features are further classified using clustering algorithms to identify abnormal states and potential fault points of the equipment. Specifically, the K-means clustering algorithm is used to calculate the distance between the feature vector and each cluster center, determine the nearest cluster center, and classify it into the corresponding state. Finally, based on the abnormal characteristics of the equipment and historical fault data, the fault diagnosis results are generated, which describe in detail the possible fault types and their severity under each abnormal state.

[0087] The predictive maintenance submodule uses a long short-term memory network to predict future maintenance needs based on fault diagnosis results, analyzes historical data and current operating trends, predicts potential equipment failure types and maintenance time points, and generates maintenance demand prediction results;

[0088] Long short-term memory network, according to the formula:

[0089] ;

[0090] Calculate the forget gate output and generate the equipment maintenance demand prediction result, where: is the sigmoid activation function, which is used to compress the value between 0 and 1. is the weight matrix of the forget gate, is the hidden state of the previous time step, is the input of the current time step, is the bias term of the forget gate, is the newly introduced adjustment parameter, are the newly introduced weight matrices, respectively, for past behavioral data , maintenance requirements of the previous cycle , and current environmental parameters.

[0091] The execution process is as follows:

[0092] First, calculate the original forget gate output, including the weight matrix The hidden state at the previous time step and the current input A combination of , and secondly, introduce additional behavioral data , Maintenance requirements of the previous cycle And the current environment parameters , each through its own weight matrix Weighting and adjustment parameters It is used to balance the influence of these new data. Finally, all these weighted information are combined with the bias and processed by the sigmoid function to obtain the final forget gate output.

[0093] The repair strategy submodule plans the maintenance schedule, required resources and personnel allocation based on the maintenance demand prediction results, combined with the fault type, predicted maintenance time point, and availability of maintenance resources, and generates a maintenance and repair plan;

[0094] The repair strategy submodule develops a detailed maintenance plan based on the maintenance demand prediction results, the predicted fault type and maintenance time point. This includes arranging a schedule for maintenance operations, the required resources and staffing. First, prioritize the maintenance of key equipment based on the urgency of the equipment failure and the predicted time point. Next, evaluate the required maintenance resources, such as replacement parts, tools, and the availability of professional technicians. Combined with the scheduling of resources and personnel, develop a final maintenance operation plan. The plan lists in detail the specific date, time, required personnel and materials for maintenance activities to ensure efficient and timely maintenance execution, thereby minimizing equipment downtime and operational interruptions, and generating a maintenance and repair plan.

[0095] See also Figure 2 and Figure 7 , the engineering project evaluation and optimization module includes:

[0096] The scheme simulation submodule conducts simulation analysis of the operation performance of water conservancy projects based on the maintenance and repair plan, simulates the water resource allocation efficiency and water quality changes under different conditions, and generates scheme simulation analysis results;

[0097] Based on the maintenance and repair plan, the scenario simulation submodule starts by building an operational performance simulation model of the water conservancy project, which simulates in detail the distribution and flow of water resources under different operating conditions and their impact on water quality. The model uses the defined hydraulic and water quality parameters, such as flow rate, water level, dissolved oxygen, chemical oxygen demand, etc., to simulate the behavior of the water body under the influence of the project through differential equations: in, represents the water quality parameter at time, is the parameter value at the previous time step, and Represent the water resource input and output of this time step respectively. By simulating the changes of these parameters under different engineering operations, the water resource allocation efficiency and water quality changes under specific environmental conditions are evaluated, which helps to understand the performance of different engineering schemes in actual operation, predict possible efficiency changes and environmental impacts, and generate scheme simulation analysis results.

[0098] Based on the results of the scheme simulation analysis, the cost-benefit analysis submodule conducts grey correlation analysis on the project cost, potential economic benefits and operation and maintenance costs through the grey prediction model, reveals the cost-benefit ratio of different schemes, identifies the optimal cost-benefit scheme, and generates cost-benefit optimization results;

[0099] Grey correlation analysis method, according to the formula:

[0100] ;

[0101] Calculate the cost-effectiveness ratio and generate the cost-effectiveness optimization result, where: For sequence with sequence The correlation between is the absolute value of the difference between two series at the same point in time, and Represent all possible minimum and maximum differences, respectively. is the resolution coefficient, and are newly introduced weighting coefficients, which are used to weight the sum of differences and their sum of squares respectively to increase the sensitivity of the model to differences.

[0102] The execution process is as follows:

[0103] First, the absolute value of the difference between the two sequences at each time point is calculated, and the sum of these differences and the sum of their squares are found. Then all possible minimum and maximum differences are calculated. The newly introduced weighting coefficients and are used to emphasize the importance of the total difference and the square of the difference. In this way, the cost-effectiveness ratio of different plans can be evaluated and compared more accurately. Finally, by adjusting the resolution coefficient, the impact of subtle differences in the association calculation can be balanced, thereby more effectively identifying the cost-effective plan.

[0104] The sustainability evaluation submodule evaluates the comprehensive sustainability of the different plans based on the cost-effectiveness optimization results, combined with resource utilization efficiency, environmental impact and socio-economic benefits, reveals the plan with the most efficient resource utilization and the least environmental impact, and generates the optimized project plan;

[0105] The sustainability evaluation submodule conducts a comprehensive sustainability evaluation of different plans based on the cost-effectiveness optimization results, taking into account resource utilization efficiency, environmental impact and social and economic benefits. The multi-index comprehensive evaluation model is used to quantify and integrate resource utilization efficiency, environmental protection indicators and economic benefit indicators: ,in, represents the overall sustainability score, and Represent environmental impact, resource utilization efficiency and economic benefit scores respectively. is the weight of each indicator. By adjusting the weight parameters, the importance of these factors can be balanced according to the focus of the project strategy. The evaluation process helps to reveal options that can maximize resource efficiency and minimize environmental impact while maintaining economic benefits, generating optimized project options.

[0106] See also Figure 2 and Figure 8 , the risk management and emergency response modules include:

[0107] The risk assessment submodule analyzes project risk factors based on the optimized project plan, classifies and prioritizes risks, assesses the impact and probability of risks, and generates risk assessment results;

[0108] The risk assessment submodule is based on the optimized project plan and first uses a systematic risk management framework that includes risk identification, analysis and evaluation. During the risk identification process, all potential risk factors related to the project are collected and analyzed, such as technical errors, financial issues, legal constraints and environmental changes. Through risk analysis methods such as fault tree analysis (FTA) and event tree analysis (ETA), risks are classified and prioritized based on the degree of impact and probability of occurrence. Risk Assessment Model in, Represents the risk priority, is the probability of the risk occurring, It is the degree of risk impact, which can quantitatively describe the severity and urgency of each risk, thereby assisting decision makers to clarify focus and resource allocation and generate risk assessment results.

[0109] The plan-making submodule formulates response measures and plans for different risk levels based on the risk assessment results, through team discussions and expert consultations, including risk avoidance, mitigation, transfer and acceptance strategies, and generates emergency plans;

[0110] Based on the risk assessment results, the plan formulation submodule organizes the project management team and experts in related fields to conduct multiple rounds of discussions and consultations. In this process, corresponding response strategies are formulated according to the levels and characteristics of different risks, including risk avoidance, risk reduction, risk transfer and risk acceptance. For example, for high-probability and high-impact risks, avoidance strategies are adopted, such as changing the design or choosing a different construction method; while for low-probability but high-impact risks, transfer strategies are adopted, such as transferring risks through insurance or contract terms. Each strategy will specify specific operating steps and responsible persons in detail to ensure that a quick and effective response can be made when a risk occurs, and a detailed emergency plan can be generated.

[0111] The rapid response submodule sets up early warning and notification mechanisms based on emergency plans, allocates resources and initiates actions, optimizes response processes and communication efficiency through simulation exercises and real-time monitoring, and generates rapid response plans;

[0112] The rapid response submodule sets up an efficient early warning and notification mechanism based on the emergency plan. This includes establishing an early warning system, using the latest information technology, such as the Internet of Things and big data analysis, to monitor key project parameters and environmental indicators in real time, and immediately notifying relevant personnel and departments once potential risks are detected. At the same time, resources are deployed, such as pre-configuring necessary technical equipment and rescue supplies, as well as rapid mobilization of personnel. Through regular simulation exercises, emergency processes and communication efficiency are tested and optimized to ensure that all team members can respond quickly and effectively implement various measures in the plan when a real crisis occurs, thereby reducing losses and quickly resuming normal operations, and generating a rapid response plan.

[0113] See also Figure 2 and Fig. 9 , the unconventional water source development module includes:

[0114] The resource simulation submodule simulates the flow and distribution of unconventional water sources under environmental conditions based on water resource allocation instructions, simulates the behavior of unconventional water sources under different environmental conditions, evaluates the efficiency of the collection and transportation process, and generates resource flow simulation results;

[0115] The resource simulation submodule is based on the water resource allocation directive. It first defines the physical and chemical properties of unconventional water sources such as recycled water, rainwater collection and desalinated water. A hydrogeological model is used to simulate the flow and distribution of these unconventional water sources under different geographical and environmental conditions. The model considers soil type, permeability, terrain slope and local climate factors such as rainfall and evaporation. The simulation process uses the hydrodynamic equation in, Represents water head, Represents the permeability of the soil. Represents source or sink terms, describing the increase or decrease of water volume. The model can show the flow path and speed of unconventional water sources underground or on the surface, as well as the impact of environmental factors on the flow, so as to evaluate the efficiency of the collection and transportation process of unconventional water sources in the actual geographical environment and generate resource flow simulation results.

[0116] The utilization efficiency optimization submodule optimizes the collection efficiency and processing capacity to maximize the water collection, processing and utilization processes based on the resource flow simulation results, and generates a utilization efficiency optimization plan;

[0117] The utilization efficiency optimization submodule analyzes the bottlenecks and shortcomings of current water collection, treatment and utilization based on the results of resource flow simulation. Based on the simulation results, key inefficient links are identified, such as unreasonable settings of water collection points or insufficient processing capacity of treatment facilities. Links are optimized by introducing advanced water treatment technologies and adjusting the water collection structure. For example, the location of the collection point is adjusted to capture more precipitation, or the treatment facility is upgraded to improve its processing speed and quality. This submodule will also apply mathematical optimization models, such as linear programming, to determine the best resource allocation and operation strategy. in, Indicates The efficiency of each collection or processing node, Indicates the capacity of the node, which can maximize the overall collection efficiency and processing capacity and generate a utilization efficiency optimization plan.

[0118] The solution generation submodule comprehensively evaluates the collection method, treatment technology and utilization path based on the utilization efficiency optimization plan, optimizes the management and use strategy of unconventional water sources, and generates unconventional water source utilization plans;

[0119] The scheme generation submodule comprehensively evaluates the collection methods, treatment technologies and utilization paths of unconventional water sources based on the utilization efficiency optimization scheme. Multi-criteria decision analysis methods, such as utility theory, are used to evaluate the economic feasibility, feasibility and environmental impact of various possible schemes. Considering the complexity and diversity of unconventional water source utilization, this evaluation includes life cycle cost analysis of different technologies, potential environmental impacts and social acceptance considerations. Through this systematic evaluation, optimized management and use strategies are proposed, such as adjusting the end use of water sources or improving treatment processes to reduce energy consumption and pollution. Finally, all key factors are comprehensively considered to generate unconventional water source utilization schemes that are suitable for different regions and needs to ensure the sustainable use and efficient management of water resources.

[0120] See also Figure 2 and Fig.10 , the project decision-making and strategy formulation module includes:

[0121] The decision analysis submodule builds an evaluation index system based on the rapid response plan and unconventional water source utilization plan, compares the indicators and calculates the weights, evaluates the consistency ratio, and generates a decision path optimization plan;

[0122] The decision analysis submodule begins to build a comprehensive evaluation index system based on the rapid response plan and the unconventional water source utilization plan, which includes but is not limited to indicators such as project efficiency, cost-effectiveness, environmental impact and social acceptance. The analytic hierarchy process (AHP) is used to compare and calculate the weights of indicators. The process includes establishing a hierarchical model, performing pairwise comparisons, and calculating the relative weights of each indicator. The formula is as follows: in, is the judgment matrix, , is the consistency indicator vector, is a weight vector. By calculating the consistency ratio (CR), the consistency of the indicator system is evaluated to see whether it is acceptable. If the CR value is less than 0.1, the consistency of the comparison is considered acceptable. Finally, by combining the weights of each indicator and the actual data, each alternative is evaluated to determine the optimal decision path and generate a decision path optimization plan.

[0123] The execution monitoring submodule processes and analyzes project data based on the decision path optimization scheme, adjusts the data collection frequency, performs data visualization, reveals the project execution status, and generates real-time monitoring records of project execution;

[0124] The execution monitoring submodule is responsible for data processing and analysis of the project based on the decision path optimization solution. First, the frequency of data collection is adjusted to ensure that the collected data can reflect the latest status of project execution. Then, data visualization tools such as dashboards and charts are used to display key project indicators such as time progress, cost usage, and resource consumption. Visualization helps project teams and stakeholders quickly identify key issues and trends in the project. In addition, through real-time data streaming and analysis, the project management team can more accurately grasp the status of project execution, adjust and optimize execution strategies in a timely manner, and generate real-time monitoring records of project execution.

[0125] The strategy formulation submodule adjusts and optimizes strategies based on real-time monitoring records of project execution, adjusts project resource and time allocation strategies, and generates project decision-making and management strategies;

[0126] The strategy formulation submodule adjusts and optimizes the project strategy based on the real-time monitoring records of project execution. In this process, the effectiveness of the existing resource and time allocation strategy will be evaluated based on the data and trends revealed in the monitoring records. If resource waste or time delays are found, reallocation of resources or adjustment of the project milestone schedule will be considered. In addition, according to the actual situation of project implementation and changes in the external environment, such as policy adjustments or market dynamics, the adaptability and efficiency of the project will be improved. Adjustments will be proposed in the form of detailed strategy documents, discussed and approved in team meetings to ensure that all project members are clear about the future direction of action and generate project decision-making and management strategies.

[0127] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. Intelligent water conservancy project management system, characterized by: The system comprises: The integrated watershed management module dynamically simulates the water cycle and resource distribution in the watershed based on GIS data, and generates watershed resource management strategies by combining hydrological cycle analysis, land use change assessment and the impact of human activities; The water resource allocation module optimizes the operation strategies of reservoirs and sluice gates based on the basin resource management strategy, determines the allocation ratio of water resources among different uses, simulates the impact of different regulation schemes on water resource supply and demand, and generates water resource allocation instructions; The water quality and environmental monitoring module evaluates the impact of water source development on water quality based on the results of unconventional water source development, analyzes the potential impact of engineering activities on the surrounding ecosystem, and generates environmental impact assessment results; The engineering maintenance and fault response module analyzes sensor data to identify potential faults and abnormal behaviors based on the environmental impact assessment results, predicts future maintenance needs based on historical data and operating trends, and generates maintenance and repair plans; The project evaluation and optimization module simulates and analyzes the operational performance of the water conservancy project based on the maintenance and repair plan, processes the uncertainty and incompleteness of the project data, and generates an optimized project plan; The risk management and emergency response module analyzes project risk factors based on the optimized project plan, classifies and prioritizes risks, conducts quantitative risk assessment, and generates a rapid response plan; The unconventional water source development module simulates the flow and distribution of unconventional water sources under environmental conditions based on the water resource allocation instructions, optimizes the collection, treatment and utilization processes of water sources, and generates unconventional water source utilization plans; The project decision and strategy formulation module evaluates and analyzes the project decision points based on the rapid response plan and unconventional water source utilization plan, and generates project decisions and management strategies based on the project's economic, environmental and social impact factors; The water resource allocation module includes a water allocation operation submodule, a water balance control submodule and an allocation efficiency optimization submodule; The water diversion operation submodule establishes an initial population based on basin-specific parameters based on the basin resource management strategy. Each population member represents a potential water diversion strategy. By simulating the natural selection process, crossover and mutation genetic operations are introduced. The crossover operation randomly selects some parameters of two strategy plans and exchanges them to generate a new strategy plan. The mutation operation simulates genetic variation in nature by randomly changing the parameters in the strategy plan. The efficiency and practicality of each strategy plan are evaluated by setting the fitness function. The process is iterated repeatedly until the optimal water diversion strategy plan is found. The water balance control submodule is based on the water transfer strategy and the concept of crowding in the genetic algorithm to maintain population diversity. Based on the difference between each strategy and its nearest neighbor strategy, the population is updated cyclically through selection, crossover and mutation in the cyclic iteration process. After each generation of iteration, the strategy performance is evaluated according to crowding and fitness, and the optimization solution that meets multi-objective conditions is gradually captured, including maximizing the utilization efficiency of water resources and meeting specific ecological protection needs, and generating a water allocation ratio. The allocation efficiency optimization submodule adopts the simulated annealing algorithm based on the water allocation ratio. The initial solution is the current optimal water allocation ratio. The simulated annealing process is started, and the temperature parameter is gradually reduced. The water allocation ratio is randomly adjusted at each temperature stage to explore new solution space. Random fine-tuning includes adjusting the release amount of specific reservoirs and adjusting the flow distribution of rivers. The fitness of the new solution is calculated and compared with the current solution, gradually tending to the global optimal solution, and generating water resource allocation instructions. The water quality and environmental monitoring module includes a water quality simulation submodule, a protection measures submodule, and an environmental impact assessment submodule; The water quality simulation submodule collects baseline data on basic water quality parameters in the development area based on the unconventional water source development results, creates an initial water quality database, builds water quality simulation scenarios based on baseline data, simulates the changes of indicators in water bodies over time and space, and uses differential equations to simulate the transmission and attenuation of pollutants. ,in, represents the concentration at time t at a distance from the source point x, is the initial concentration, k is the natural attenuation rate, and v is the water velocity, which is used to evaluate the impact of development activities on water quality and generate water quality simulation analysis results; The protection measures submodule evaluates differential protection measures based on the water quality simulation analysis results, selects sensitive species, establishes a sensitive species database based on ecological characteristics and known sensitivities, sets exposure concentration thresholds and ecological effect thresholds, and performs calculations based on ecological models. The model is as follows: , where R represents the risk factor, is the lethal concentration that causes 50% mortality, EC is the concentration in the environment, assesses the potential risk level of sensitive species under current and predicted water quality conditions, and generates ecological risk assessment results; Based on the ecological risk assessment results, the environmental impact assessment submodule integrates land cover type data and normalized vegetation index to reveal the growth status of vegetation and land cover changes in different regions, and uses the difference analysis model to: ,in, Indicates the change of NDVI before and after the engineering activities, and They represent the NDVI values ​​before and after the activity, respectively, and evaluate the impact of engineering activities on land use and generate environmental impact assessment results.

2. The water conservancy project management intelligent system according to claim 1 is characterized by: The basin resource management strategy specifically includes a reasonable water resource allocation plan, ecological protection zone demarcation and pollution source control measures; the water resource allocation instructions include a reservoir release plan, irrigation water scheduling strategy and urban water supply priority arrangement; the environmental impact assessment results include parameters of water quality changes, affected biological species and their habitats and potential environmental change trends; the maintenance and repair plan specifically includes maintenance task arrangements and management strategies for required spare parts; the optimized project plan includes economic analysis results of the plan, resource allocation optimization measures and environmental protection measures; the rapid response plan includes risk level classification, operational steps under emergencies and resource recovery timeline; the unconventional water source utilization plan includes design parameters of rainwater collection systems, optimization steps of recycled water treatment processes and specific measures to improve utilization efficiency; the project decision-making and management strategies include a step plan for project implementation, a plan for the allocation of key resources and response measures to potential risks.

3. The water conservancy project management intelligent system according to claim 1 is characterized by: The engineering maintenance and fault response module includes: The fault diagnosis submodule identifies faults and abnormal behaviors based on the environmental impact assessment results, performs feature extraction, identifies abnormal states and potential fault points of the equipment, and generates fault diagnosis results; The predictive maintenance submodule uses a long short-term memory network to predict future maintenance needs based on the fault diagnosis results, analyzes historical data and current operating trends, predicts potential equipment failure types and maintenance time points, and generates maintenance demand prediction results; The repair strategy submodule plans the time schedule, required resources and personnel allocation of maintenance operations based on the maintenance demand prediction results, combined with the fault type, predicted maintenance time point, and availability of maintenance resources, and generates a maintenance and repair plan.

4. The intelligent system for water conservancy project management according to claim 3 is characterized by: The long short-term memory network is based on the formula: ； Calculate the forget gate output and generate the equipment maintenance demand prediction result, where: is the sigmoid activation function, is the weight matrix of the forget gate, is the hidden state of the previous time step, is the input of the current time step, is the bias term of the forget gate, , , To adjust the parameters, , , is the weight matrix, respectively for past behavior data , maintenance requirements of the previous cycle , and current environmental parameters .

5. The water conservancy project management intelligent system according to claim 1 is characterized by: The engineering project evaluation and optimization module includes: The scheme simulation submodule performs operation performance simulation analysis of the water conservancy project based on the maintenance and repair plan, simulates the water resource allocation efficiency and water quality changes under different conditions, and generates scheme simulation analysis results; Based on the simulation analysis results of the scheme, the cost-benefit analysis submodule conducts grey correlation analysis on the project cost, potential economic benefits and operation and maintenance costs through the grey prediction model, reveals the cost-benefit ratio of the different schemes, identifies the optimal cost-benefit scheme, and generates cost-benefit optimization results; The sustainability evaluation submodule evaluates the comprehensive sustainability of the difference plans based on the cost-benefit optimization results, combined with resource utilization efficiency, environmental impact and socio-economic benefits, reveals the plan with the most efficient resource utilization and the least environmental impact, and generates an optimized project plan.

6. The water conservancy project management intelligent system according to claim 5 is characterized by: The grey relational analysis method is based on the formula: ； Calculate the cost-effectiveness ratio and generate the cost-effectiveness optimization result, where: For sequence with sequence The correlation between is the absolute value of the difference between two series at the same point in time, and Represent all possible minimum and maximum differences, respectively. is the resolution coefficient, and is the weighting coefficient.

7. The water conservancy project management intelligent system according to claim 1 is characterized by: The unconventional water source development module includes: The resource simulation submodule simulates the flow and distribution of unconventional water sources under environmental conditions based on the water resource allocation instructions, simulates the behavior of unconventional water sources under different environmental conditions, evaluates the efficiency of the collection and transportation process, and generates resource flow simulation results; The utilization efficiency optimization submodule optimizes the collection efficiency and processing capacity to maximize the water collection, processing and utilization processes based on the resource flow simulation results, and generates a utilization efficiency optimization plan; The solution generation submodule comprehensively evaluates the collection method, processing technology and utilization path based on the utilization efficiency optimization plan, optimizes the management and use strategy of unconventional water sources, and generates an unconventional water source utilization plan.

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