Intelligent water conservancy project management method based on dynamic data analysis

Abstract

The invention discloses an intelligent water conservancy project management method based on dynamic data analysis, and belongs to the technical field of water conservancy projects. The method comprises the following steps: step 1, collecting operation data of a water conservancy project in real time; 2, constructing a water regimen prediction model to predict future water regimen changes in a rolling manner; 3, calculating priority weights of flood control, water supply and power generation targets in real time; 4, selecting an optimal scheduling scheme; 5, analyzing the selected scheduling scheme into a hierarchical control instruction sequence and executing the hierarchical control instruction sequence; and step 6, intelligent switching of scheduling modes and automatic triggering of emergency plans are realized.

Description

Technical Field

[0001] The present application relates to the technical field of water conservancy projects, and more specifically, to a smart water conservancy project management method based on dynamic data analysis. Background Art

[0002] With the continuous increase in the scale and complexity of water conservancy projects, traditional water conservancy management methods have been difficult to meet the requirements of modern water conservancy projects for intelligent, real-time, and precise management. Traditional water conservancy management methods rely on manual experience and simple rule scheduling, lacking real-time perception and accurate prediction of dynamic water regime changes, resulting in problems such as low management efficiency, unscientific scheduling, and lagging emergency response. This management method not only fails to respond promptly to sudden water regime changes but may also cause conflicts among multiple objectives such as flood control, water supply, and power generation, affecting the reasonable scheduling and use of water resources.

[0003] In recent years, with the rapid development of sensor technology, the Internet of Things, big data analysis, and artificial intelligence technology, the management methods of water conservancy projects are gradually developing towards the direction of intelligence and automation. Through real-time data collection and analysis, combined with advanced algorithms such as deep learning for water regime prediction and target priority analysis, precise scheduling and intelligent control of water conservancy projects can be achieved. However, the current smart water conservancy project management method based on dynamic data analysis still faces many technical challenges, especially in aspects such as the accuracy of the water regime prediction model, the optimization of the scheduling scheme, the automatic triggering of the emergency plan, and the intelligent switching of the scheduling mode, and a perfect technical system with practical application value has not yet been formed.

[0004] In summary, how to dynamically optimize the scheduling scheme of water conservancy projects based on real-time data and prediction results, improve the accuracy and intelligence level of scheduling, ensure the reasonable allocation of target priorities under different water regime conditions, and be able to automatically trigger the emergency plan has become a technical problem that urgently needs to be solved. Summary of the Invention

[0005] In order to overcome a series of defects existing in the prior art, the purpose of the present application is to provide a smart water conservancy project management method based on dynamic data analysis for the above problems, including the following steps:

[0006] Step 1, collect the operation data of water conservancy projects in real time;

[0007] Step 2, construct a water regime prediction model to roll and predict future water regime changes;

[0008] Step 3, calculate the priority weights of flood control, water supply, and power generation targets in real time;

[0009] Step 4, select the optimal scheduling scheme;

[0010] Step 5: Parse the selected scheduling plan into a hierarchical control instruction sequence and execute it;

[0011] Step 6: Realize intelligent switching of the scheduling mode and automatic triggering of the emergency plan.

[0012] Furthermore, Step 1 includes the following steps:

[0013] According to the specific type, scale and terrain conditions of the water conservancy project, plan the layout positions of various sensors;

[0014] Select appropriate sensor devices based on the monitoring requirements, including submersible level gauges, ultrasonic flow meters, multi-parameter water quality analyzers and automatic weather stations, to ensure that the measurement accuracy and stability meet the actual needs;

[0015] Adopt a combination of wired communication and wireless communication to build a stable and reliable data transmission system;

[0016] Design and deploy a data acquisition mechanism to achieve: unified acquisition and storage of various sensor data, unified management of historical data of hydrological stations, water conservancy project facilities and environmental monitoring points, and standardized processing of data from different sources and in different formats;

[0017] Through setting reasonable data validity judgment rules, conduct outlier detection, data repair and supplementation processing on the collected original data; at the same time, establish a data quality evaluation index system to regularly evaluate the integrity, accuracy and timeliness of the data.

[0018] Furthermore, Step 2 includes the following steps:

[0019] Based on the historical data after standardized processing, build a structured database containing water level, flow, precipitation and water quality indicators;

[0020] Select a suitable deep learning algorithm for model design, divide the structured data according to a suitable time window, extract key features, and continuously optimize the model parameters through multiple rounds of training and verification;

[0021] Obtain the future weather forecast data provided by the meteorological department in real time, and at the same time collect the real-time flow data of the upstream hydrological station as the input variables of the model;

[0022] Connect the trained deep learning model with the real-time data stream, establish an automated data update and model prediction mechanism, realize the rolling prediction of hydrological elements in the future for a period of time, and visually display the prediction results through a visualization interface;

[0023] Regularly evaluate the accuracy of the model prediction results, and continuously improve the model performance by comparing the errors between the predicted values and the actual observed values.

[0024] Furthermore, step 3 includes the following steps:

[0025] Based on the real-time water regime monitoring data and water regime prediction results, establish an evaluation index system covering three major objectives: flood control safety, water supply guarantee, and power generation benefit;

[0026] According to the real-time water regime and prediction results, compare the objectives of flood control safety, water supply guarantee, and power generation benefit pairwise, use the 1-9 scale method to determine the relative importance, and construct a judgment matrix to reflect the dynamic priority relationship of each objective under different water regime conditions;

[0027] Calculate the maximum eigenvalue and corresponding eigenvector of the judgment matrix and perform normalization processing to obtain the preliminary weight values of the three objectives. At the same time, calculate the consistency ratio of the judgment matrix to ensure the rationality of the evaluation results;

[0028] According to the characteristics of different time periods and the actual operation status of the reservoir, design a dynamic weight adjustment rule, and based on the calculation results of the eigenvector, establish a weight adaptive adjustment mechanism to make the finally determined objective priority more in line with the actual scheduling requirements.

[0029] Furthermore, step 4 includes the following steps:

[0030] According to the real-time monitoring data and prediction results, combined with the weight values of each objective, establish a comprehensive optimization model, and at the same time consider the boundary conditions including the reservoir water level constraint and the downstream ecological flow demand to ensure that the optimization results meet the actual operation requirements;

[0031] Generate a series of non-dominated solutions through population iterative evolution to form a Pareto optimal solution set, and each solution corresponds to a feasible reservoir scheduling scheme;

[0032] For the generated set of scheduling schemes, establish a fuzzy evaluation index system including safety, economy, and reliability. Determine the membership function through a combination of expert scoring and data analysis, and establish a fuzzy relation matrix to provide a quantitative basis for scheme evaluation;

[0033] Use the fuzzy comprehensive evaluation method, combined with the importance degree of each evaluation index, to calculate the comprehensive score of each scheduling scheme;

[0034] Take the scheduling scheme with the highest score as the optimal scheme, clarify the specific scheduling parameters and formulate corresponding emergency plans.

[0035] Furthermore, the comprehensive score of each scheduling scheme is expressed by the formula: where B i represents the comprehensive score of the i-th scheduling scheme, which is the total score after weighting each evaluation index and is used to reflect the overall quality of the scheme; w jrepresents the relative importance of the j-th index; r ij represents the membership value of the i-th scheduling plan on the j-th index, r ij ∈R, where R is the membership matrix, and its matrix form is: m represents the number of scheduling plans; n is the number of evaluation indicators.

[0036] Furthermore, step 5 includes the following steps:

[0037] Decompose the preferred scheduling plan according to the time series, formulate specific control instructions including reservoir water level control targets, flood discharge flow and power generation flow parameters, and divide the instruction sequence into a gate control layer and a unit control layer according to the characteristics of different control objects;

[0038] Send the control instructions to the corresponding execution devices, and at the same time establish a safety verification mechanism for the control instructions to ensure that the issued control instructions are within the allowable range of the devices, and avoid equipment damage caused by incorrect instructions;

[0039] Collect the equipment operation status data in real time, compare and analyze the actual execution effect with the control instructions, calculate the execution deviation, and classify it according to the deviation size;

[0040] For the detected execution deviation, adopt the fuzzy PID control algorithm, calculate the correction amount of the control parameters in real time, and dynamically adjust the control instructions through closed-loop feedback to achieve rapid compensation for the execution deviation.

[0041] Furthermore, the correction amount of the control parameters is expressed as: where, Δ adjust (T) represents the execution deviation correction amount at time T; Δ total (T) represents the total deviation from the starting time to the current time T; Δ(t) represents the difference between the actual operation status and the control instructions at time t; dt is the small increment of time; dΔ(t) is the change amount of the deviation, indicating the change amount of the deviation value within a time interval dt; K p is the proportional constant, representing the immediate response to the current deviation; K r is the integral constant, representing the correction of the long-term deviation; K d is the differential constant, representing the correction of the deviation change rate.

[0042] Furthermore, step 6 includes the following steps:

[0043] Based on historical scheduling experience and expert knowledge, establish a rule base including regular scheduling, flood control scheduling and emergency scheduling modes, and sort out the judgment conditions, disposal processes and control strategies in various scheduling scenarios to form a structured rule system;

[0044] Establish a threshold system for water level, flow rate, and equipment status indicators. By continuously monitoring the changing trends of the indicators, the current scheduling status is evaluated in real time, and corresponding warning signals are automatically triggered when the preset thresholds are reached.

[0045] Based on the fuzzy inference algorithm, automatically determine the currently most suitable scheduling mode and achieve smooth switching between different scheduling modes when needed.

[0046] Formulate corresponding emergency response procedures for emergencies of different levels. When abnormal situations are detected, the corresponding emergency plans can be automatically activated according to the event level, and relevant personnel can be notified in a timely manner.

[0047] Record and evaluate the entire process of triggering each emergency plan, including triggering conditions, response time, and disposal effect information. Through continuous summarization and optimization, continuously improve the accuracy and effectiveness of emergency response.

[0048] Furthermore, the most suitable scheduling mode is expressed by the formula: Where Y is the output value of the most suitable scheduling mode; μD q represents the membership degree value of scheduling mode D q , that is, the output value of fuzzy inference; y q represents the numerical representation of scheduling mode D q .

[0049] Smooth switching between different scheduling modes is achieved through the following formula: S(γ) = αY current +(1 - α)Y new ), where S(γ) is the smooth transition value at time γ; α is the smoothing factor, with a value range of 0 to 1; Y current is the current scheduling mode value; Y new is the target scheduling mode value.

[0050] Compared with the prior art, the present application has the following beneficial effects:

[0051] The present application realizes the dynamic optimization management of intelligent water conservancy projects by collecting operation data of water conservancy projects in real time, constructing a water regime prediction model, calculating the weights of scheduling objectives, selecting the optimal scheduling plan, parsing and executing hierarchical control instructions, and intelligently switching scheduling modes and emergency plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 is a schematic flowchart of a method for managing an intelligent water conservancy project based on dynamic data analysis disclosed in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0053] To make the objectives, technical solutions, and advantages of the present invention more clear, the following will describe the technical solutions in the embodiments of the present invention in more detail with reference to the accompanying drawings in the embodiments of the present invention. In the drawings, the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions from beginning to end. The described embodiments are some, but not all, of the embodiments of the present invention.

[0054] All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0055] The embodiments described below with reference to the accompanying drawings and directional terms are exemplary and are intended to explain the present invention and should not be construed as a limitation of the present invention.

[0056] As Figure 1 shown, a smart water conservancy project management method based on dynamic data analysis includes the following steps:

[0057] Step 1, collect the operation data of the water conservancy project in real time;

[0058] Step 2, construct a water regime prediction model to roll-predict the future water regime changes;

[0059] Step 3, calculate the priority weights of flood control, water supply, and power generation objectives in real time;

[0060] Step 4, select the optimal scheduling plan;

[0061] Step 5, parse the selected scheduling plan into a hierarchical control instruction sequence and execute it;

[0062] Step 6, realize the intelligent switching of the scheduling mode and the automatic triggering of the emergency plan.

[0063] The smart water conservancy project management method based on dynamic data analysis disclosed in this embodiment realizes the efficient management and optimal scheduling of water resources through integrating real-time data collection, accurate water regime prediction, intelligent target priority calculation, optimized scheduling plan selection, hierarchical control execution, and emergency plan triggering mechanism. It not only improves the operation efficiency and emergency response ability of water conservancy projects, but also greatly reduces resource waste and environmental impact. Through the application of deep learning and data analysis technologies, this method can intelligently handle complex multi-objective decision-making problems, ensure the coordinated development of flood control, water supply, and power generation, and provide strong support for the modern management of water conservancy projects.

[0064] Further, Step 1 includes the following steps:

[0065] According to the specific type, scale, and terrain conditions of the water conservancy project, plan the layout positions of various sensors;

[0066] Select appropriate sensor devices based on monitoring requirements, including submersible water level gauges, ultrasonic flow meters, multi-parameter water quality analyzers, and automatic weather stations, to ensure that the measurement accuracy and stability meet the actual needs;

[0067] Adopt a combination of wired and wireless communication methods to build a stable and reliable data transmission system;

[0068] Design and deploy a data acquisition mechanism to achieve: unified acquisition and storage of various sensor data, unified management of historical data of hydrological stations, water conservancy project facilities, and environmental monitoring points, and standardized processing of data from different sources and in different formats;

[0069] By setting reasonable data validity judgment rules, perform outlier detection, data repair, and supplementation on the collected raw data; at the same time, establish a data quality evaluation index system to regularly evaluate the integrity, accuracy, and timeliness of the data.

[0070] In the management of intelligent water conservancy projects, every link of real-time data acquisition is crucial. From sensor layout to the design of the data acquisition mechanism, every step directly affects the operation efficiency and decision-making accuracy of the system. By reasonably planning the sensor positions, selecting appropriate devices, building a stable communication system, and designing an effective data acquisition mechanism, the data acquisition of water conservancy projects is ensured to be stable, accurate, and efficient. By strictly detecting and repairing data validity, the quality and credibility of the data are improved, providing a solid data foundation for subsequent water regime prediction and dispatching decisions. Ultimately, this series of technical measures ensures the intelligent management of water conservancy projects, improves the utilization efficiency of resources and the emergency response ability, and provides a strong guarantee for the safe and sustainable operation of water conservancy projects.

[0071] Furthermore, step 2 includes the following steps:

[0072] Based on the historical data after standardized processing, construct a structured database containing water level, flow, precipitation, and water quality indicators;

[0073] Select a suitable deep learning algorithm for model design, divide the structured data according to a suitable time window, extract key features, and continuously optimize the model parameters through multiple rounds of training and verification;

[0074] Obtain the future weather forecast data provided by the meteorological department in real time, and at the same time collect the real-time flow data of the upstream hydrological station as the input variables of the model;

[0075] Connect the trained deep learning model with the real-time data stream, establish an automated data update and model prediction mechanism, achieve rolling prediction of hydrological elements in the next period of time, and visually display the prediction results through a visualization interface;

[0076] Regularly evaluate the accuracy of the model prediction results, and continuously improve the model performance by comparing the errors between the predicted values and the actual observed values.

[0077] The core of step 2 lies in achieving accurate rolling prediction of hydrological elements through the construction and optimization of the deep learning model. Through the standardized processing of historical data, the selection of appropriate algorithms, the integration of real-time data, and the automated update of the model, the entire hydrological prediction process can operate continuously and provide real-time and effective prediction results. The optimization of each link improves the prediction accuracy, ensuring that the management decisions of water conservancy projects can respond to hydrological changes in a timely manner. In this process, the model is continuously trained and evaluated, thus possessing strong adaptability and long-term stability, providing a reliable technical guarantee for the scientific management and emergency response of water conservancy projects.

[0078] Furthermore, step 3 includes the following steps:

[0079] Based on the real-time water regime monitoring data and the water regime prediction results, establish an evaluation index system covering three major goals: flood control safety, water supply guarantee, and power generation benefit;

[0080] According to the real-time water regime and prediction results, compare the flood control safety, water supply guarantee, and power generation benefit goals pairwise, use the 1-9 scale method to determine the relative importance, and construct a judgment matrix to reflect the dynamic priority relationship of each goal under different water regime conditions;

[0081] Calculate the maximum eigenvalue and the corresponding eigenvector of the judgment matrix and perform normalization processing to obtain the preliminary weight values of the three goals. At the same time, calculate the consistency ratio of the judgment matrix to ensure the rationality of the evaluation results;

[0082] According to the characteristics of different time periods and the actual operation status of the reservoir, design a dynamic weight adjustment rule, and establish a weight adaptive adjustment mechanism based on the calculation results of the eigenvector, so that the finally determined goal priority is more in line with the actual scheduling requirements.

[0083] The core of Step 3 is to achieve dynamic management of the priorities of objectives such as flood control safety, water supply assurance, and power generation benefits by constructing a scientific and reasonable evaluation index system and a weight adjustment mechanism. In intelligent water conservancy management, the application of real-time water regime data and prediction results enables this process to make rapid responses and decisions in a complex hydrological environment. The relative importance of objectives is determined through the analytic hierarchy process, and the rationality of the judgment is ensured by combining consistency tests, ultimately providing a scientific and accurate basis for multi-objective scheduling. Furthermore, the dynamic weight adjustment mechanism enables flexible adjustment of objective priorities according to different time periods and the actual operating conditions of the reservoir, enhancing adaptability and practical operability. The optimization of this process will help improve the efficiency and safety of water conservancy project management, ensuring that optimal scheduling decisions can be made when dealing with various hydrological conditions.

[0084] Furthermore, Step 4 includes the following steps:

[0085] Based on the real-time monitoring data and prediction results, combined with the weight values of each objective, establish a comprehensive optimization model, and at the same time consider boundary conditions including reservoir water level constraints and downstream ecological flow requirements to ensure that the optimization results meet the actual operating requirements;

[0086] Generate a series of non-dominated solutions through population iterative evolution to form a Pareto optimal solution set, and each solution corresponds to a feasible reservoir scheduling scheme;

[0087] For the generated set of scheduling schemes, establish a fuzzy evaluation index system including safety, economy, and reliability, determine the membership function through a combination of expert scoring and data analysis, and establish a fuzzy relation matrix to provide a quantitative basis for scheme evaluation;

[0088] Use the fuzzy comprehensive evaluation method, combined with the importance degree of each evaluation index, to calculate the comprehensive score of each scheduling scheme;

[0089] Take the scheduling scheme with the highest score as the optimal scheme, clarify specific scheduling parameters and formulate corresponding emergency plans.

[0090] In Step 4, through the comprehensive optimization model and multi-dimensional evaluation system, the optimal scheduling plan is scientifically selected, fully considering the mutual relationships among reservoir water levels, downstream ecological flows, and multiple objectives such as flood control, water supply, and power generation. The Pareto optimal solution set obtained through population iterative evolution provides multiple feasible scheduling plans for decision-makers, and the fuzzy comprehensive evaluation method further refines the process of plan selection, ensuring that the selected optimal plan has comprehensive advantages in terms of safety, economy, reliability, etc. Finally, by formulating detailed scheduling parameters and emergency plans, the efficient and safe operation of the water conservancy project is guaranteed. This series of steps provides strong support for the intelligent management of water conservancy projects, enabling scheduling decisions to be more accurate and efficient, and meeting complex and changing hydrological conditions and actual needs.

[0091] Furthermore, the comprehensive score of each scheduling plan is expressed by the formula: where, B i represents the comprehensive score of the i-th scheduling plan, which is the total score after weighting each evaluation index and is used to reflect the overall quality of the plan; w j represents the relative importance of the j-th index; r ij represents the membership degree value of the i-th scheduling plan on the j-th index, r ij ∈R, where R is the membership degree matrix, and the matrix form is: m represents the number of scheduling plans; n is the number of evaluation indexes.

[0092] Furthermore, Step 5 includes the following steps:

[0093] Decompose the selected optimal scheduling plan according to the time series, formulate specific control instructions including reservoir water level control objectives, flood discharge flow and power generation flow parameters, and divide the instruction sequence into a gate control layer and a unit control layer according to the characteristics of different control objects;

[0094] Send the control instructions to the corresponding execution equipment, and at the same time establish a safety verification mechanism for the control instructions to ensure that the issued control instructions are within the allowable range of the equipment and avoid equipment damage caused by incorrect instructions;

[0095] Real-time collect the equipment operation status data, compare and analyze the actual execution effect with the control instructions, calculate the execution deviation, and classify it according to the deviation size;

[0096] For the detected execution deviation, adopt the fuzzy PID control algorithm, calculate the correction amount of the control parameter in real time, and dynamically adjust the control instructions through closed-loop feedback to achieve rapid compensation for the execution deviation.

[0097] Step 5 ensures the precise execution of the reservoir operation plan through refined control instruction decomposition, strict safety verification, real-time deviation monitoring, and dynamic adjustment of the fuzzy PID control algorithm. Each operation step is centered around multiple objectives such as ensuring reservoir safety, water supply efficiency, and power generation benefits, and adopts advanced control methods and real-time feedback mechanisms to enhance flexibility and response speed. This series of measures can ensure that the equipment is always in a safe and effective working state during the execution process, maximize the realization of the operation objectives, and improve the economy and reliability of reservoir operation.

[0098] Furthermore, the correction amount of the control parameter is expressed as: where, Δ adjust (T) represents the execution deviation correction amount up to time T; Δ total (T) represents the total deviation from the starting time to the current time T; Δ(t) represents the difference between the actual operation state and the control instruction at time t; dt is the infinitesimal increment of time; dΔ(t) is the change in deviation, representing the change in the deviation value within a time interval dt; K p is the proportional constant, representing the immediate response to the current deviation; K r is the integral constant, representing the correction of the long-term deviation; K d is the differential constant, representing the correction of the deviation change rate.

[0099] By combining the proportional, integral, and differential parts, the fuzzy PID control can flexibly handle different types of deviations, whether it is the rapidly changing instantaneous deviation, the steady-state deviation accumulated over a long time, or even the change trend of the deviation. By adaptively adjusting the magnitude of each parameter, its control strategy can be optimized according to the current operating environment and equipment state.

[0100] Generally speaking, the correction amount of the control parameter is very crucial. It adjusts the control instruction to ensure that the reservoir operation system can reflect the difference between the actual operation state and the predetermined objective in real time, thereby making the operation execution more precise and efficient. At the same time, by dynamically adjusting the control parameter, stability can be maintained under different operating conditions, and rapid response to changes in the external environment and system state can be achieved, avoiding the phenomena of overcorrection or lag correction.

[0101] Furthermore, Step 6 includes the following steps:

[0102] Based on historical operation experience and expert knowledge, establish a rule base including regular operation, flood control operation, and emergency operation modes, and organize the judgment conditions, handling procedures, and control strategies under various operation scenarios to form a structured rule system;

[0103] Establish a threshold system for water level, flow rate, and equipment status indicators. By continuously monitoring the change trends of the indicators, the current scheduling status is evaluated in real time, and corresponding warning signals are automatically triggered when the preset thresholds are reached;

[0104] Based on the fuzzy inference algorithm, automatically determine the most suitable current scheduling mode and achieve smooth switching between different scheduling modes when needed;

[0105] Formulate corresponding emergency response procedures for emergencies of different levels. When abnormal situations are detected, the corresponding emergency response plans can be automatically activated according to the event level, and relevant personnel can be notified in a timely manner;

[0106] Record and evaluate the entire process of triggering each emergency response plan, including triggering conditions, response times, and disposal effect information. Through continuous summarization and optimization, continuously improve the accuracy and effectiveness of emergency responses.

[0107] The technical implementation of step 6 combines historical experience and expert knowledge with modern intelligent algorithms. Through technical means such as rule bases, threshold systems, fuzzy inference, automated emergency responses, and effect evaluations, the reservoir scheduling has higher adaptability and automation capabilities. The reservoir scheduling can not only automatically judge the switching of scheduling modes but also respond to emergencies and continuously optimize decisions through real-time evaluations. Through these technical measures, the intelligent level has been greatly improved, not only improving the scheduling efficiency and safety but also providing efficient and reliable decision-making support when facing complex and urgent water conditions, maximizing the safe operation of the reservoir and the optimal utilization of resources.

[0108] Furthermore, the most suitable scheduling mode is expressed by the formula: where Y is the output value of the most suitable scheduling mode; μD q represents the membership degree value of scheduling mode D q , that is, the output value of fuzzy inference; y q represents the numerical representation of scheduling mode D q .

[0109] The smooth switching between different scheduling modes is achieved through the following formula: S(γ) = αY current + (1 - α)Y new ), where S(γ) is the smooth transition value at time γ; α is the smoothing factor, with a value range of 0 to 1; Y currnet is the current scheduling mode value; Y new is the target scheduling mode value.

[0110] Through the above formula, two key objectives can be achieved in dynamic scheduling: First, the most suitable scheduling mode is dynamically determined through the fuzzy inference algorithm to ensure that reservoir scheduling decisions are accurately made based on real-time data; Second, the smooth switching formula is used to achieve a smooth transition between different scheduling modes and avoid instability caused by mutations. Through this mechanism, the scheduling mechanism can flexibly respond in a complex and changing environment to ensure the safe, stable, and efficient operation of the reservoir.

[0111] Finally, it should be pointed out that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments or equivalently replace some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A smart water conservancy project management method based on dynamic data analysis, characterized in that: The following steps are involved: Step 1: collect the operation data of water conservancy projects in real time; Step 2: construct a water regime prediction model to make rolling predictions on future water regime changes; Step 3: Calculate the priority weights of flood control, water supply and power generation goals in real time; Step 4: Select the optimal scheduling solution; Step 5, parsing the selected scheduling scheme into a hierarchical control instruction sequence and executing it; Step 6: Realize intelligent switching of dispatching modes and automatic triggering of emergency plans.

2. According to claim 1, a smart water conservancy project management method based on dynamic data analysis is characterized in that: Step 1 includes the following steps: Plan the locations of various sensors according to the specific type, scale and terrain conditions of the water conservancy project; Select appropriate sensor equipment based on monitoring needs, including submersible water level meters, ultrasonic flow meters, multi-parameter water quality analyzers and automatic weather stations to ensure that measurement accuracy and stability meet actual needs; Use a combination of wired and wireless communications to build a stable and reliable data transmission system; Design and deploy data collection mechanisms to achieve: unified collection and storage of various sensor data, unified management of historical data of hydrological stations, water conservancy project facilities and environmental monitoring points, and standardized processing of data from different sources and formats; By setting reasonable data validity judgment rules, the collected raw data is subjected to outlier detection, data repair and supplementation processing; at the same time, a data quality evaluation index system is established to regularly evaluate the completeness, accuracy and timeliness of the data.

3. According to claim 1, a smart water conservancy project management method based on dynamic data analysis is characterized in that: Step 2 includes the following steps: Based on the standardized historical data, a structured database including water level, flow, precipitation and water quality indicators is constructed; Select appropriate deep learning algorithms for model design, divide structured data into appropriate time windows, extract key features, and continuously optimize model parameters through multiple rounds of training and verification; Obtain future weather forecast data provided by the meteorological department in real time, and collect real-time flow data from upstream hydrological stations as input variables of the model; Connect the trained deep learning model with the real-time data stream, establish an automated data update and model prediction mechanism, realize the rolling prediction of hydrological elements in the future, and intuitively display the prediction results through a visual interface; Regularly evaluate the accuracy of model prediction results, and continuously improve model performance by comparing the errors between predicted values ​​and actual observed values.

4. According to claim 1, a smart water conservancy project management method based on dynamic data analysis is characterized in that: Step 3 includes the following steps: Based on real-time water monitoring data and water forecast results, an evaluation index system covering the three major goals of flood control safety, water supply guarantee and power generation efficiency is established; According to the real-time water conditions and forecast results, the flood control safety, water supply guarantee and power generation efficiency goals are compared in pairs, and the relative importance is determined by using the 1-9 scale method. A judgment matrix is ​​constructed to reflect the dynamic priority relationship of each goal under different water conditions. The maximum eigenvalue and corresponding eigenvector of the judgment matrix are calculated and normalized to obtain the preliminary weight values ​​of the three objectives. At the same time, the consistency ratio of the judgment matrix is ​​calculated to ensure the rationality of the judgment results. According to the characteristics of different time periods and the actual operation status of the reservoir, dynamic weight adjustment rules are designed. Based on the calculation results of the characteristic vector, a weight adaptive adjustment mechanism is established to make the final target priority more in line with actual scheduling needs.

5. According to claim 1, a smart water conservancy project management method based on dynamic data analysis is characterized in that: Step 4 includes the following steps: Based on real-time monitoring data and forecast results, combined with the weight values ​​of each target, a comprehensive optimization model is established, while taking into account boundary conditions including reservoir water level constraints and downstream ecological flow requirements, to ensure that the optimization results meet actual operation requirements; Generate a series of non-dominated solutions through population iterative evolution to form a Pareto optimal solution set, each of which corresponds to a feasible reservoir operation plan; For the generated scheduling scheme set, a fuzzy evaluation index system including safety, economy and reliability is established. The membership function is determined by combining expert scoring and data analysis, and a fuzzy relationship matrix is ​​established to provide a quantitative basis for scheme evaluation. Using the fuzzy comprehensive evaluation method and combining the importance of each evaluation index, the comprehensive score of each scheduling scheme is calculated; The scheduling plan with the highest score is taken as the optimal plan, and the specific scheduling parameters are clarified and corresponding emergency plans are formulated.

6. A smart water conservancy project management method based on dynamic data analysis according to claim 5, characterized in that: The comprehensive score of each scheduling scheme is expressed as follows: Among them, B i represents the comprehensive score of the ith scheduling scheme, which is the total score after weighting each evaluation index and is used to reflect the overall quality of the scheme; w j Indicates the relative importance of the jth indicator; r ij represents the membership value of the i-th scheduling scheme on the j-th indicator, r ij ∈R, R is the membership matrix, the matrix form is: m represents the number of scheduling schemes; n represents the number of evaluation indicators.

7. The intelligent water conservancy project management method based on dynamic data analysis according to claim 1 is characterized in that: Step 5 includes the following steps: The optimal dispatching scheme is disassembled according to the time series, and specific control instructions including reservoir water level control target, flood discharge flow and power generation flow parameters are formulated. According to the characteristics of different control objects, the instruction sequence is divided into gate control layer and unit control layer; Send control instructions to the corresponding execution equipment, and establish a safety verification mechanism for control instructions to ensure that the issued control instructions are within the allowed range of the equipment to avoid equipment damage caused by instruction errors; Collect equipment operation status data in real time, compare and analyze the actual execution effect with the control instructions, calculate the execution deviation, and classify according to the deviation size; In response to the detected execution deviation, the fuzzy PID control algorithm is used to calculate the correction amount of the control parameters in real time, and the control instructions are dynamically adjusted through closed-loop feedback to achieve rapid compensation for the execution deviation.

8. The intelligent water conservancy project management method based on dynamic data analysis according to claim 7 is characterized in that: The correction amount of the control parameter is expressed as: Among them, Δ adjust (T) represents the execution deviation correction amount at time T; Δ total (T) represents the total deviation from the start time to the current time T; Δ(t) represents the difference between the actual operating state and the control instruction at time t; dt is a small increment of time; dΔ(t) is the change in deviation, which represents the change in the deviation value within a time interval dt; K p is the proportional constant, indicating the immediate response to the current deviation; K r is the integration constant, which represents the correction of long-term deviation; K d is the differential constant, which represents the correction of the deviation change rate.

9. The intelligent water conservancy project management method based on dynamic data analysis according to claim 1 is characterized in that: Step 6 includes the following steps: Based on historical dispatching experience and expert knowledge, a rule base including conventional dispatching, flood control dispatching and emergency dispatching modes is established to organize the judgment conditions, handling processes and control strategies under various dispatching scenarios to form a structured rule system; Establish a threshold system for water level, flow and equipment status indicators, continuously monitor the changing trends of indicators, evaluate the current dispatch status in real time, and automatically trigger corresponding early warning signals when the preset thresholds are reached; Based on fuzzy reasoning algorithm, it automatically determines the most suitable scheduling mode at the moment and realizes smooth switching between different scheduling modes when necessary; Formulate corresponding emergency response procedures for emergencies of different levels. When an abnormal situation is detected, the corresponding level of emergency plan can be automatically activated according to the level of the event, and relevant personnel will be notified in time; The triggering process of each emergency plan is fully recorded and evaluated, including triggering conditions, response time and disposal effect information. Through continuous summary and optimization, the accuracy and effectiveness of emergency response are continuously improved.

10. A smart water conservancy project management method based on dynamic data analysis according to claim 9, characterized in that: The most suitable scheduling mode is expressed by the formula: Among them, Y is the most suitable scheduling mode output value; μD q Indicates scheduling mode D q The membership value of y is the output value of fuzzy reasoning; q Indicates scheduling mode D q The numerical representation of The smooth switching between different scheduling modes is achieved through the following formula: S(γ) = αY current +(1-α)Y new ), where S(γ) is the smooth transition value at time γ; α is the smoothing factor, ranging from 0 to 1; Y current is the current scheduling mode value; Y new The scheduling mode value for the target.

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