Reservoir and sluice operation hazard source identification and risk evaluation method

Abstract

The invention relates to the technical field of hydraulic engineering, and discloses a reservoir and sluice operation hazard source identification and risk evaluation method, which comprises the following steps: data acquisition and transmission: arranging sensors for acquiring operation state data of a reservoir and a sluice in real time, including water level, flow, meteorological conditions and equipment state, the data are transmitted to a computing platform through a wireless network; and constructing and applying a multi-level Bayesian network: constructing a multi-level Bayesian network model according to the collected data, modeling various risk factors hierarchically, calculating the probability of each risk factor, and performing joint evaluation on the risk factors through conditional probabilities. Through the multilevel Bayesian network, the fuzzy mathematical modeling, the dynamic feedback mechanism and the multi-target optimization decision, the risk assessment precision, the real-time adjustment capability and the multi-target optimization level of the reservoir and the sluice are improved, and safe and efficient operation management is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy projects, and specifically provides a method for identifying hazards and risk assessment in the operation of reservoirs and sluice gates. Background Art

[0002] In the management of reservoirs and sluice gates, ensuring their safe and efficient operation is of utmost importance. With the increasing complexity of climate change and water resource management, the operation of reservoirs and sluice gates faces more uncertainties and risks. Therefore, a management technology for the operation of reservoirs and sluice gates is needed.

[0003] Most of the existing reservoir and sluice gate management technologies rely on single risk factor assessment or traditional statistical methods for decision-making support. These technologies can help monitor basic data such as water level and flow rate, and provide decision-making references for daily operations. In some cases, these technologies can effectively prevent some simple operation errors and ensure the basic stability of the reservoir or sluice gate.

[0004] However, there are still some deficiencies in the existing technologies. First, the existing risk assessment methods are usually based on static analysis of single factors, and it is difficult to handle the interdependent relationships between complex and dynamic risk factors. For example, factors such as water level and flow rate are often interrelated, and traditional methods are difficult to comprehensively capture the dynamic changes of these factors. In addition, many existing systems only rely on preset operation parameters and lack the ability of real-time adjustment. Especially when emergencies occur, they cannot quickly respond and take effective emergency measures. Moreover, most of the existing multi-objective optimization decisions focus on the optimization of single objectives, while ignoring the balance and coordination between multiple risk objectives. These deficiencies limit the effectiveness of the existing technologies in the face of complex and changing actual operating environments. Summary of the Invention

[0005] In view of the deficiencies of the existing technologies, the present invention provides a method for identifying hazards and risk assessment in the operation of reservoirs and sluice gates, which solves the problems in the existing technologies that multiple risk factors cannot be comprehensively evaluated, the lack of real-time dynamic adjustment ability, and the deficiency of multi-objective optimization decision-making.

[0006] To achieve the above objectives, the present invention is realized through the following technical solutions: A method for identifying hazards and risk assessment in the operation of reservoirs and sluice gates, including the following steps: Data collection and transmission: Deploy sensors to collect real-time operation status data of reservoirs and sluice gates, including water level, flow rate, meteorological conditions, and equipment status, and transmit the data to the computing platform through a wireless network; Multi-level Bayesian network construction and application: Construct a multi-level Bayesian network model based on the collected data, model various risk factors hierarchically, calculate the probability of each risk factor, and conduct a joint assessment of each risk factor through conditional probability; Fuzzy mathematics modeling and reasoning: Conduct fuzzy mathematics modeling for each potential risk factor, define fuzzy sets and membership functions, and use a fuzzy inference system to reason about uncertain risks and obtain an evaluation result; Multi-objective optimization decision-making: Comprehensively optimize multiple risk objectives through a multi-objective optimization algorithm to obtain a globally optimal decision-making scheme; Dynamic feedback and closed-loop control: Based on real-time data and risk assessment results, implement a dynamic feedback mechanism, adjust control measures, and conduct closed-loop control through a feedback system.

[0007] Preferably, the data collection and transmission include: Deploy sensors to collect data on the water levels, flows, meteorology, and temperature and humidity of reservoirs and sluice gates; Transmit the collected data to the data processing platform in real time through a wireless network, and perform data cleaning and preprocessing to ensure data validity.

[0008] Preferably, the multi-level Bayesian network construction and application include: Divide the risk factors of reservoirs and sluice gates into multiple levels, including the structure layer, equipment layer, environment layer, and operation layer; For the risk factor nodes in each level, establish a Bayesian network through conditional probability relationships, and perform joint probability calculations based on the real-time collected data to obtain the risk assessment of each level; Use the Bayesian network to dynamically update the probabilities of each node, and adjust the evaluation values of the risk factors at each level according to real-time data.

[0009] Preferably, the joint probability calculation formula of the Bayesian network is: Wherein, P(X 1 ,X 2 ,…,X n ) represents the joint probability distribution of all risk factors in the reservoir and sluice gate system; X i , is the i-th risk factor, Pa(X i ) is the set of parent nodes of the i-th node, indicating the calculation of the probability of each node under the condition of the parent node; P(X i |Pa(X i )) represents the conditional probability of the i-th risk factor under the condition that its parent node Pa(X i ) is given.

[0010] Preferably, the fuzzy mathematics modeling and reasoning include: Define the fuzzy sets for each risk factor, and use membership functions to describe the fuzziness of the risk factors; Use fuzzy inference rules to infer the membership degrees of each fuzzy set and obtain a comprehensive risk assessment; Update the fuzzy inference system based on the real-time collected risk data to obtain the latest risk assessment results.

[0011] Preferably, the definition of the membership function includes: For the water level risk factor, define the membership functions for normal water level, warning water level, and dangerous water level; For the equipment status risk factor, define the membership functions for normal status, minor fault, and serious fault; For the weather condition risk factor, define the membership functions for good weather, bad weather, and extreme weather.

[0012] Preferably, the multi-objective optimization decision includes: Define multiple optimization objectives, namely minimizing flood risk, minimizing equipment damage risk, and minimizing energy consumption; Through the multi-objective optimization algorithm, comprehensively solve multiple risk objectives to obtain a balanced optimal solution; Use the Pareto optimal solution to select the final optimization decision and balance the priorities among various risk objectives.

[0013] Preferably, the constraint conditions for the multi-objective optimization include: Set upper and lower limits for the water level to ensure that the reservoir and sluice operate within the safe water level range; Set upper and lower limits for the flow rate to ensure that the flow rate does not exceed the system design capacity; Constrain the operating status of the equipment to ensure that the equipment is within the safe operating range and prevent failures.

[0014] Preferably, the dynamic feedback and closed-loop control include: Monitor the operating status of the reservoir and sluice in real time, and obtain real-time data through sensors; Adjust the operating parameters of the reservoir and sluice according to the real-time data and risk assessment results; According to the feedback of the execution effect, dynamically adjust the risk assessment results and optimization decisions to ensure continuous improvement.

[0015] Preferably, the feedback mechanism includes: Collect data in real time through sensors and input the data into the risk assessment model; Adjust the operating decisions of the reservoir and sluice in real time according to the feedback data; Verify the effect of the adjusted operation and further optimize the management strategy through the feedback mechanism.

[0016] The present invention provides a method for identifying and risk - assessing the operation hazards of reservoirs and sluice gates, with the following beneficial effects: 1. Through a multi - objective optimization algorithm, the present invention comprehensively considers multiple risk objectives, enabling the operation decisions of reservoirs and sluice gates to balance multiple factors such as flood risk, equipment damage, and energy consumption. This method avoids the limitation of traditional technologies that only focus on a single objective, ensuring the high efficiency and safety of the system in a complex environment.

[0017] 2. By introducing Bayesian networks and fuzzy mathematics methods, the risk assessment accuracy of reservoirs and sluice gates is improved, especially in dealing with uncertainties and fuzzy factors. This combined technology is more flexible and adaptable than existing models when dealing with the dependency relationships of complex risk factors, avoiding overly simplistic risk judgments.

[0018] 3. The present invention can not only collect data in real - time but also adjust operation parameters according to the latest risk assessment results, forming a dynamic feedback closed - loop. Different from traditional static control systems, the present invention can make timely adjustments when dealing with emergencies, maximizing the safe operation of reservoirs and sluice gates. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is the flowchart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the specification of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0021] Please refer to the attached Figure 1 , the embodiments of the present invention provide a method for identifying and risk - assessing the operation hazards of reservoirs and sluice gates, including the following steps: S1. Data collection and transmission: Sensors are arranged to collect the operation status data of reservoirs and sluice gates in real - time, including water level, flow rate, meteorological conditions, and equipment status, and the data is transmitted to the computing platform through a wireless network; Accurate and real - time data input can be obtained through data collection and transmission, thus providing an effective basis for the identification and risk assessment of the hazards of reservoirs and sluice gates. Specifically, sensors are arranged to collect the operation status data of reservoirs and sluice gates in real - time, and these data are transmitted to the central computing platform for processing through a stable wireless network. In this process, not only reasonable selection of sensors and data collection devices is required, but also the real - time nature of data, the stability of transmission, as well as data processing and cleaning need to be considered.

[0022] First, in order to effectively monitor the operation status of reservoirs and sluice gates, sensors need to be deployed at key positions. These sensors include: Water level sensors: Used to monitor the water level changes of reservoirs and sluice gates in real time. Abnormal changes in water level usually directly affect the safety of reservoirs. Therefore, high-precision water level sensors are required to ensure the accuracy of data.

[0023] Flow sensors: Used to detect the inflow and outflow of reservoirs. Excessive or too little flow may indicate equipment failures or system abnormalities. Timely detection of flow changes is crucial for system safety.

[0024] Meteorological sensors: Meteorological conditions (such as precipitation, wind speed, temperature, etc.) also have a significant impact on the safety of reservoirs and sluice gates. Therefore, meteorological sensors need to monitor environmental changes in real time to provide data support for subsequent risk assessments.

[0025] Equipment health status monitoring instruments: Used to monitor the operation status of sluice gates and related equipment, such as the opening and closing of sluice gates, the status of valves, etc. Equipment failures are common sources of risks in reservoir management. Therefore, real-time equipment monitoring is required.

[0026] The data collected by these sensors is transmitted to the computing platform through a wireless network. The stability and low latency of data transmission are key factors to ensure that the system can respond in real time and make optimized decisions. During the data transmission process, wireless local area networks (Wi-Fi), cellular networks (4G / 5G), etc. can be used to select the most suitable network communication method to ensure efficient data transmission.

[0027] In some embodiments, the data transmission frequency can be set to per minute or per hour, and the specific frequency is determined according to actual needs and the operation of the reservoir. After the data reaches the computing platform, the system will first preprocess the data, including removing outliers and noise, and standardizing the data for subsequent analysis.

[0028] After the data is transmitted to the computing platform, data cleaning and preprocessing should be carried out first. The specific operations include: Outlier detection: If a sensor fails or an abnormal situation occurs, the system will identify these abnormal data through algorithms and eliminate them. For example, if the data of the water level sensor fluctuates too much, it may be due to equipment failure or sensor error, and the system should automatically exclude these data points.

[0029] Data completion: When some sensor data is lost, the data can be completed by methods such as interpolation. For the data missing during some periods, it can be estimated based on historical data and the data of other sensors.

[0030] Data Standardization: To ensure the comparability of different types of data, it is necessary to standardize different data (such as water level, flow rate, meteorological data, etc.) so that it conforms to a unified standard format. This helps with subsequent data analysis and evaluation.

[0031] In the data collection and transmission process, the quality of data has a profound impact on subsequent risk assessment. Therefore, in order to extract useful information from the collected data, it is often necessary to apply certain probability models for data analysis. For example, for the water level data collected by a water level sensor, the following formula can be used to describe its probability distribution and changes: Among them, P(Water Level) represents the joint probability distribution of water level data, covering the joint change trend of water level data at various locations in the reservoir; is the water level data collected by the i-th water level sensor, representing the water level change at a specific location; represents the set of parent nodes related to the water level data, that is, other factors affecting the water level change, such as flow rate, meteorological conditions, etc.; represents under the given set of parent nodes the conditional probability of the i-th water level factor.

[0032] This formula is modeled through a Bayesian network and combines data from other sensors (such as flow sensors, meteorological monitoring devices, etc.) to provide basic probability calculations for subsequent risk assessment and decision-making. These calculations will be further applied in step S2 to help us understand and predict the relationship between reservoir water level changes and other factors.

[0033] Through the above data collection and transmission mechanism, the system can obtain the operating status of the reservoir and the sluice in real time and transmit the data to the computing platform for processing and analysis. This step provides high-quality data support for subsequent Bayesian network analysis, fuzzy reasoning, risk assessment, and optimization decision-making. The reasonable layout of sensors and the stability of data transmission can effectively ensure that the system obtains real-time and accurate data input, laying a foundation for the efficient operation of the entire system.

[0034] On this basis, by cleaning, preprocessing, and standardizing the data, the reliability of the data can be effectively improved, unnecessary noise can be eliminated, and accurate information can be provided for subsequent risk assessment and decision-making. In addition, through the joint modeling and risk assessment of water level and other data using Bayesian networks and fuzzy mathematical models, reservoir managers can be helped to timely discover potential risks and avoid accidents.

[0035] By deploying different types of sensors, real-time data transmission, data preprocessing, and cleaning, it is ensured that the system obtains high-quality input data. This provides a solid foundation for subsequent risk assessment and optimization decision-making.

[0036] S2. Multi - level Bayesian Network Construction and Application: Construct a multi - level Bayesian network model based on the collected data, model various risk factors hierarchically, calculate the probability of each risk factor, and conduct a joint assessment of each risk factor through conditional probability; Use a multi - level Bayesian network (MLBN) to model various risk factors of reservoirs and sluice gates, calculate the probability value of each risk factor through probability inference, and evaluate the risk status of the entire system. This process is the core part of the entire risk assessment, which can effectively identify potential hazard sources in the reservoir and sluice gate systems and provide data support for subsequent risk assessment and decision - making optimization.

[0037] In the aforementioned step S1, we have collected various operation data of reservoirs and sluice gates through sensors. Next, we apply this data to the Bayesian network and construct a multi - level structural model. As a directed graph structure, the Bayesian network can model various risk factors of reservoirs and sluice gates and their dependencies through conditional probability, thus realizing a comprehensive assessment of risks. The construction and application of the Bayesian network ensure that the system can handle complex causal relationships in a dynamic environment and provide a basis for subsequent decision - making.

[0038] In the process of constructing the Bayesian network, it is first necessary to divide various risk factors in reservoirs and sluice gates into multiple levels. Generally speaking, there are multiple risk levels in the system, and the common levels include: Structure layer: including structural factors such as the stability of the dam body and the condition of the dam foundation; Equipment layer: including the equipment condition of the sluice gate, the mechanical operation status, valve operation, etc.; Environment layer: involving the impact of meteorological conditions (such as precipitation, wind speed, temperature, etc.) on the operation of the reservoir; Operation layer: mainly considering human factors, such as the management ability and response speed of operators.

[0039] Each factor in these levels is correlated with each other through conditional probability. Each node represents a risk factor, and each edge represents the dependency relationship between factors. The Bayesian network helps us deduce the overall risk status of the reservoir and sluice gate systems by calculating the conditional probability of each factor node.

[0040] In the Bayesian network, the state of each node depends on the state of its parent node. Therefore, for each risk factor its probability value is determined by the state of its parent node The parent node represents other factors that affect this factor.

[0041] Under the dependency relationships among multiple nodes, the Bayesian network calculates the conditional probabilities of each node to obtain the joint probability distribution of all factors in the system. The specific joint probability formula is as follows: where P(X 1 , X 2 , …, X n ) represents the joint probability distribution of all risk factors in the reservoir and sluice system; is the i-th risk factor, is the set of parent nodes of the i-th node, indicating that the probability of each node is calculated under the conditions of its parent nodes; represents the conditional probability of the i-th risk factor given its parent nodes . By calculating the conditional probabilities between each factor and its parent nodes, the Bayesian network can infer the risk states of all factors in the system and conduct a joint assessment of the overall system risk.

[0042] The Bayesian network can model the complex dependency relationships between factors. For example, the change in water level is not only affected by water flow but also by meteorological conditions (such as precipitation, wind speed, etc.). Equipment failures may exacerbate the risks of the reservoir system. Therefore, reasonable dependency relationships need to be established between the equipment status and other factors such as water level and flow rate.

[0043] Through the Bayesian network, these interrelationships can be clearly described and comprehensive information can be provided for risk assessment. In some embodiments, flow rate and meteorological conditions are usually regarded as the parent nodes affecting the change in water level, and the change in water level may further affect the working status of the equipment. Therefore, when constructing the Bayesian network, it is necessary to ensure that the set of parent nodes of each node can accurately reflect all the influencing factors of that factor.

[0044] The Bayesian network is not only a static model but also a dynamic model that can be updated in real time. Whenever new data is input, the Bayesian network updates the probability of each node according to the real-time data, thereby realizing the dynamic inference of the system.

[0045] For example, in the reservoir system, when the water level changes, the flow rate and meteorological conditions may also change accordingly. By combining conditional probabilities with real-time data, the Bayesian network can dynamically adjust the state of each factor, thereby obtaining an updated risk assessment result.

[0046] In some embodiments, when a certain sensor data (such as water level) changes, the Bayesian network updates the risk assessment result of the entire system by calculating the impact of this change on other factors. This dynamic update mechanism ensures that the system can provide accurate risk assessment in a complex and ever-changing environment.

[0047] Through the inference of the Bayesian network, we can obtain the probability distribution of each risk factor and comprehensively evaluate the risk status of the entire reservoir system. These evaluation results can be used for subsequent risk management and decision-making. For example, the system can adjust the operation parameters of the reservoir, such as the opening of the sluice gate, equipment maintenance, etc., according to the real-time risk assessment results to reduce the overall risk of the system.

[0048] Through the multi-level Bayesian network model, accurate modeling and joint risk assessment of each risk factor in the reservoir and sluice system are achieved. The Bayesian network can calculate the conditional probability, infer the dependence relationship between factors, and update the risk assessment result based on real-time data.

[0049] S3. Fuzzy mathematical modeling and reasoning: Conduct fuzzy mathematical modeling for each potential risk factor, define fuzzy sets and membership functions, and use a fuzzy inference system to reason about uncertain risks and obtain evaluation results; Fuzzy mathematical modeling and reasoning can effectively handle and express the uncertainty and ambiguity between factors. In this step, we use fuzzy sets, membership functions, and fuzzy inference rules to model various risk factors of the reservoir and sluice, and obtain the overall risk assessment through comprehensive reasoning. These methods are particularly suitable for situations where there are no clear boundaries and multiple factors need to be considered comprehensively.

[0050] First, the risk factors of the reservoir and sluice need to be represented by fuzzy sets, and the state of each factor can be described by a fuzzy value. Risk factors such as water level, flow rate, and equipment health status often cannot clearly define the boundaries between normal and abnormal. Therefore, using fuzzy sets can better adapt to this uncertainty.

[0051] For example, for the risk factor of water level, it can be classified into three fuzzy sets: normal water level, warning water level, and dangerous water level. Each fuzzy set is defined by a membership function to indicate the degree to which the water level value belongs to the set. The following is a further definition of the membership function for the water level risk factor: Normal water level: The water level is within the expected safe range, usually defined as the water level being lower than the warning water level: where x is the actual water level, x 1 is the lower limit of the normal water level range, and x 2 is the upper limit of the normal water level range.

[0052] Warning water level: The water level is close to but does not exceed the danger value, usually defined as the water level close to the maximum safety level: Among them, x 3 and x 4 respectively represent the lower and upper limits of the warning water level range.

[0053] Danger water level: When the water level reaches or exceeds the warning water level, it indicates that the system is in a high-risk state: Among them, x 5 and x 6 respectively represent the lower and upper limits of the danger water level range; μ normal (x), μ warning (x) and μ dangerous (x) respectively represent the membership degrees of the water level x belonging to the normal water level, warning water level, and danger water level.

[0054] These membership functions enable the water level data to be represented in a fuzzy manner, thereby enabling precise classification of the water level. Each water level value will be mapped to different membership degrees, and the possibility of its belonging to different states will be weighted according to the membership degrees.

[0055] In the fuzzy reasoning part, in this embodiment, fuzzy rules are used to comprehensively evaluate various risk factors. These rules process fuzzy values through logical operations and output an overall risk assessment result.

[0056] For example, assume we have the following fuzzy rules: If the water level is high and the equipment status is normal, the risk is high; If the water level is normal and the equipment status is good, the risk is low.

[0057] These rules combine factors such as the water level and equipment status to infer the overall risk state. Specifically, the system will calculate their membership degrees in their respective membership sets according to the input water level data and equipment status, and then perform reasoning according to the fuzzy rules. For example, if the water level value belongs to the warning water level set and the equipment status belongs to the normal state, the system may obtain a fuzzy assessment result of medium risk.

[0058] During the implementation process, the calculation of fuzzy reasoning depends on two main steps: fuzzification and defuzzification.

[0059] Fuzzification: Convert the input actual values (such as water level, flow rate, etc.) into membership degrees to determine the degree to which the value belongs to each fuzzy set.

[0060] Defuzzification: Convert the fuzzy results generated during the reasoning process into specific numerical values for subsequent decision support.

[0061] The defuzzification process generally adopts the centroid method, that is, by calculating the central position of the fuzzy evaluation results, it is converted into a clear numerical value. The formula is as follows: Where, represents the membership degree of the fuzzy set; represents the value of the corresponding fuzzy set; μ total is the final evaluation value after defuzzification, representing the comprehensive risk assessment.

[0062] In this embodiment, the evaluation results of multiple risk factors can be integrated through the fuzzy comprehensive evaluation method. The membership degrees of these factors will be assigned different weights according to their importance or influence degree, and finally a comprehensive risk value is obtained.

[0063] For example, the risk assessments of multiple factors such as water level, flow rate, and equipment status can be integrated through the following weighted summation formula: Where: is the weight of the i-th risk factor, representing the influence degree of this factor on the overall risk; is the membership degree of the i-th factor.

[0064] Through this weighted method, the influences of different risk factors can be integrated into a final risk assessment result. The comprehensive evaluation value μ total can be used to guide subsequent optimization decisions. For example, when the risk assessment value is high, the system may automatically adjust measures such as the opening degree of the sluice gate and start standby equipment to reduce risks.

[0065] By defining fuzzy sets, membership functions, and inference rules, the system can handle uncertainties and fuzziness, and flexibly evaluate the risk status. The fuzzy comprehensive evaluation method not only improves the accuracy of risk assessment, but also can adapt to the dynamic changes of the reservoir and sluice gate in real time, ensuring the safe operation of the system in a complex environment.

[0066] S4. Multi-objective optimization decision-making: Through the multi-objective optimization algorithm, comprehensively optimize multiple risk objectives to obtain the global optimal decision-making plan; Multi-objective optimization decision-making is used to consider multiple risk objectives and find the best balance point. The core task of this step is to optimize the risk objectives of the reservoir and sluice gate (such as flood risk, equipment damage risk, energy consumption minimization, etc.) to ensure that the reservoir and sluice gate can achieve the optimal operation efficiency while maintaining safety.

[0067] In this embodiment, we adopt two common multi-objective optimization algorithms, namely Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Through these algorithms, the system can optimize multiple objective functions simultaneously and find a set of Pareto optimal solutions, from which the most suitable solution is selected.

[0068] When performing multi-objective optimization, it is first necessary to define the objective functions for each optimization goal. These objective functions reflect the key indicators that need to be optimized in our actual operation. According to the actual situation of the reservoir and the sluice, we usually consider the following several objectives: Minimization of flood risk: This objective aims to minimize the probability that the water level exceeds the warning level and avoid flood accidents. The objective function can be defined in the following form: f flood (x) = P(Flood); where P(Flood) represents the probability of a flood occurring. The objective of minimizing flood risk aims to reduce the probability that the water level exceeds the warning level, which can be calculated by evaluating the change trend of the water level.

[0069] Minimization of equipment damage risk: This objective is used to reduce the probability of equipment failure and ensure the normal operation of the equipment. The objective function can be expressed as: f device (x) = P(Failure); where P(Failure) represents the probability of equipment failure. The risk of equipment damage affects the safety and operation stability of the entire system, so it needs to be optimized.

[0070] Minimization of energy consumption: This objective takes into account the energy consumption of the reservoir and the sluice, and ensures that the energy consumption is minimized as much as possible while ensuring safe operation. The objective function can be expressed in the following form: where c i is the energy consumption coefficient related to each operating parameter , represents different operation decision variables (such as the opening of the sluice, the state of the equipment, etc.). The purpose of this objective function is to optimize the energy consumption while minimizing the risk.

[0071] Generally, when using multi-objective optimization algorithms, we need to balance multiple objectives to find a global optimal solution or a Pareto optimal solution. For this embodiment, both Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) can effectively handle this type of optimization problem.

[0072] Particle Swarm Optimization (PSO) finds the optimal solution by simulating the movement of particles in the solution space. In PSO, each particle represents a potential solution. Through swarm cooperation and information exchange, particles continuously adjust their positions and gradually approach the global optimal solution.

[0073] Genetic Algorithm (GA), on the other hand, performs multi-objective optimization by simulating natural selection and genetic principles. GA performs operations such as crossover and mutation on solutions, and selects the individuals with the best fitness generation by generation to obtain the optimal solution.

[0074] In multi-objective optimization, we combine the advantages of PSO and GA to ensure that a balance can be found among multiple risk objectives.

[0075] In multi-objective optimization, the optimization of the objective function not only needs to consider the values of each objective, but also needs to synthesize the priorities among various objectives. In this embodiment, we use the weighted sum method to combine the objective functions: where, f objective (x) is the final comprehensive objective function, representing the comprehensive evaluation value of the optimization; f i (x) is the i-th optimization objective function (such as flood risk, equipment damage risk, etc.); is the weight of the i-th objective function, reflecting the importance of this objective in the final decision-making; x is the decision variable, representing the various operation parameters of the reservoir or sluice.

[0076] The purpose of objective function weighting is to adjust the influence of each objective according to the importance of different objectives. For example, minimizing flood risk may be more important than minimizing energy consumption, so a higher weight can be set for flood risk.

[0077] In the process of multi-objective optimization, in addition to the optimization objectives, certain constraint conditions also need to be set to ensure that the optimization results are feasible. Usually, these constraint conditions can be physical limitations, operation ranges, or other safety limitations. Common constraint conditions include: g j (x) ≤ 0; where, g j (x) represents the j-th constraint condition, which is usually related to reservoir water level, flow rate, equipment status, etc. For example, the water level cannot exceed the maximum safe water level, and the flow rate must be kept within the design range, etc. These constraint conditions limit the decision-making space in the optimization process and ensure that the final solution meets the actual operation requirements.

[0078] In multi-objective optimization, since there may be conflicts between different objectives (for example, reducing flood risk may increase energy consumption), it is necessary to find a Pareto optimal solution. The Pareto optimal solution represents a solution where it is impossible to improve one objective without sacrificing other objectives.

[0079] Through multi-objective optimization algorithms such as PSO or GA, we can obtain a set of Pareto optimal solutions. Each solution represents a balance point where each objective is optimized to a certain extent. Finally, the system can select the most suitable Pareto optimal solution according to actual needs as the operation decision for the reservoir or the sluice.

[0080] By using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), we can find the optimal balance among multiple objectives, ensuring the efficient operation of the reservoir and the sluice while ensuring safety. In addition, through the setting of the weighted sum of the objective function and the constraint conditions, the system can provide flexible decision-making support during actual operation, ensuring that the final optimization plan can meet various requirements and effectively improve the safety and operation efficiency of the reservoir and the sluice.

[0081] S5, Dynamic Feedback and Closed-loop Control: Based on real-time data and the results of risk assessment, implement a dynamic feedback mechanism, adjust control measures, and perform closed-loop control through a feedback system; The core objective of the dynamic feedback mechanism is to continuously adjust the operation of the reservoir and the sluice by using real-time data and the optimization decision results mentioned above. By continuously collecting data from various sensors and comparing it with the optimization decisions in the previous steps, the dynamic feedback mechanism can timely detect problems in operation and adjust operations to avoid potential risks.

[0082] Under normal circumstances, when real-time data such as water level, flow rate, meteorological conditions, and equipment status change, the system will update the risk assessment results through a computing platform. Subsequently, the system will compare these new risk assessment results with the optimization decision results obtained in step S4 above. If it is found that the current state is inconsistent with the optimal operation plan, adjustments will be triggered through the feedback mechanism. For example, when the water level approaches the dangerous level, the feedback mechanism will automatically adjust the sluice opening, flow rate, or activate standby equipment according to the previous optimization results.

[0083] During the process of dynamic feedback and closed-loop control, the adjustment of operation parameters is usually calculated through control formulas. In this embodiment, proportional control and PID (Proportional-Integral-Derivative) control algorithms are used to adjust operation parameters such as sluice opening and flow rate.

[0084] Suppose we need to adjust the operation parameter Δx, and this adjustment amount can be calculated by the following formula: Δx = K p ·(x target - x current ); Where: Δx represents the operation parameter to be adjusted, such as the sluice opening, flow regulation value, etc.; x target is the target value, usually the optimal value of water level, flow rate or other operation parameters, usually provided by the optimization decision; x current is the current value, that is, the data obtained from the real-time sensor (such as the current water level, flow rate, etc.); K p is the proportional gain coefficient, which is used to adjust the adjustment amplitude. This coefficient is set by the system designer according to the actual requirements and operation accuracy, and is usually optimized through the tuning process.

[0085] In some embodiments, in order to obtain more precise control, a PID controller is adopted. This controller not only depends on the current error (proportional part), but also takes into account the accumulation of past errors (integral part) and the rate of change of errors (differential part). The formula of the PID controller is: Where, e(t) is the error at the current moment, that is, e(t) = x target - x current , representing the difference between the target value and the current value; K p is the proportional gain coefficient, which is used to adjust the influence of the current error; K i is the integral gain coefficient, which is used to adjust the influence of past errors, especially to eliminate the steady-state error; K d is the differential gain coefficient, which is used to adjust the influence of the rate of change of errors; represents the rate of change of the error; is the integral term, representing the accumulated value of the error from the initial moment to the current moment. It corrects the persistent small errors by accumulating the past error values, especially helps to eliminate the system steady-state error.

[0086] Through the PID control algorithm, the system can adjust the operation parameter more precisely to achieve a fast and stable dynamic response.

[0087] In the feedback control process, the system not only adjusts the operation parameter based on the current error, but also takes into account the historical error and the trend of error change. This way enables the control system to more flexibly adapt to the external environment changes, especially when facing emergencies, it can make timely adjustments.

[0088] For example, assume that the water level exceeds the warning value. The system calculates the opening degree of the sluice gate that needs to be adjusted through a PID controller. In feedback control, the system calculates the rate of change of the error based on real-time data (such as the rate of change of the water level), thereby predicting the change trend of the water level and making an early adjustment. This early adjustment strategy can effectively prevent the reservoir water level from exceeding the standard or equipment failure.

[0089] In closed-loop control, it not only depends on the feedback data at the current moment but also combines historical data and optimization results for self-adjustment. The system regularly evaluates the control effect to ensure that the adjustment operation can be executed more precisely.

[0090] Specifically, the real-time data feedback mechanism can not only handle the current deviation but also predict future operation requirements through trend analysis of historical data. For example, if the meteorological conditions change, resulting in an increase in precipitation, the system will combine the Bayesian network analysis in step S2 and the multi-objective optimization decision in S4 mentioned above to adjust the operation mode of the reservoir in advance to reduce the future flood risk.

[0091] The core of the feedback mechanism is to continuously adjust the control parameters according to the real-time monitoring data and adjust the overall strategy according to the optimization decision. In a specific implementation, the feedback control system will be combined with an optimization system (such as the multi-objective optimization system mentioned in S4). When the system detects a change in the risk assessment result, the optimization system will provide new operation targets or adjust the target parameters.

[0092] For example, when the water level of the reservoir approaches the warning water level, the optimization decision system provides new operation targets, and the feedback mechanism calculates the corresponding control parameters to adjust the opening degree or flow rate of the sluice gate in real time, thereby preventing the water level from exceeding the dangerous threshold.

[0093] Through the proportional control and PID control algorithms, the feedback mechanism can accurately adjust the operation parameters and continuously optimize the control effect according to the actual state of the system, optimization objectives, and historical data. The combination of real-time feedback, historical data analysis, and closed-loop control ensures the safe, stable, and efficient operation of the reservoir and the sluice gate. Especially when facing sudden risks or abnormal situations, it can respond in a timely manner and make adjustments to minimize risks and losses.

[0094] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for identifying and assessing the risk of reservoir and sluice operation hazards, characterized in that: The following steps are involved: Data collection and transmission: Deploy sensors to collect real-time operating status data of reservoirs and sluice gates, including water level, flow, meteorological conditions and equipment status, and transmit the data to the computing platform via wireless networks; Multi-level Bayesian network construction and application: construct a multi-level Bayesian network model based on the collected data, model various risk factors in a hierarchical manner, calculate the probability of each risk factor, and jointly evaluate each risk factor through conditional probability; fuzzy mathematical modeling and reasoning: perform fuzzy mathematical modeling on each potential risk factor, define fuzzy sets and membership functions, and use fuzzy reasoning systems to reason about uncertain risks and obtain evaluation results; Multi-objective optimization decision-making: Comprehensively optimize multiple risk objectives through multi-objective optimization algorithms to obtain the global optimal decision-making solution; Dynamic feedback and closed-loop control: Based on real-time data and risk assessment results, a dynamic feedback mechanism is implemented to adjust management and control measures, and closed-loop control is performed through the feedback system.

2. A method for identifying and assessing the hazards of reservoir and sluice operation according to claim 1, characterized in that: The data collection and transmission includes: Deploy sensors to collect data on water level, flow, weather, temperature and humidity in reservoirs and sluice gates; The collected data is transmitted to the data processing platform in real time via wireless network, and data cleaning and preprocessing are performed to ensure data validity.

3. A method for identifying and assessing the hazards of reservoir and sluice operation according to claim 1, characterized in that: The multi-level Bayesian network construction and application include: Divide the risk factors of reservoirs and sluice gates into multiple levels, including structure level, equipment level, environment level and operation level; For the risk factor nodes in each level, a Bayesian network is established through conditional probability relationships, and joint probability calculations are performed based on real-time collected data to obtain risk assessments at each level; The probability of each node is dynamically updated using the Bayesian network, and the assessment value of each level of risk factors is adjusted according to real-time data.

4. A method for identifying and assessing the hazards of reservoir and sluice operation according to claim 3, characterized in that: The joint probability calculation formula of the Bayesian network is: Among them, P(X1,X2,…,X n ) represents the joint probability distribution of all risk factors in the reservoir and sluice system; is the i-th risk factor, is the parent node set of the i-th node, indicating the probability of calculating each node under the condition of the parent node; Indicates that given its parent node The conditional probability of the i-th risk factor under the condition of .

5. A method for identifying and assessing the hazards of reservoir and sluice operation according to claim 1, characterized in that: The fuzzy mathematical modeling and reasoning include: Define the fuzzy set of each risk factor and use membership function to describe the fuzziness of risk factors; Using fuzzy inference rules, the membership of each fuzzy set is inferred to obtain a comprehensive risk assessment; Based on the risk data collected in real time, the fuzzy inference system is updated to obtain the latest risk assessment results.

6. A method for identifying and assessing the hazards of reservoir and sluice operation according to claim 5, characterized in that: The definition of the membership function includes: For water level risk factors, membership functions of normal water level, warning water level and dangerous water level are defined; For the equipment status risk factor, define the normal status, minor fault and major fault membership functions; For weather condition risk factors, membership functions of good weather, bad weather and extreme weather are defined.

7. A method for identifying and assessing the hazards of reservoir and sluice operation according to claim 1, characterized in that: The multi-objective optimization decision includes: Define multiple optimization objectives, namely, minimizing flood risk, minimizing equipment damage risk, and minimizing energy consumption; Through multi-objective optimization algorithms, multiple risk objectives are comprehensively solved to obtain the optimal balanced solution; Use the Pareto optimal solution to select the final optimization decision and balance the priorities among various risk objectives.

8. A method for identifying and assessing the hazards of reservoir and sluice operation according to claim 7, characterized in that: The constraints of the multi-objective optimization include: Set upper and lower limits on water levels to ensure that reservoirs and sluice gates operate within safe water levels; Set upper and lower limits for flow to ensure that flow does not exceed system design capacity; Constrain the operating status of the equipment to ensure that the equipment is within the safe operating range and prevent failures.

9. A method for identifying and assessing the hazards of reservoir and sluice operation according to claim 1, characterized in that: The dynamic feedback and closed-loop control include: Real-time monitoring of the operating status of reservoirs and sluice gates, and obtaining real-time data through sensors; Adjust operating parameters of reservoirs and sluice gates based on real-time data and risk assessment results; Based on feedback from execution effects, dynamically adjust risk assessment results and optimize decisions to ensure continuous improvement.

10. A method for identifying and assessing the hazards of reservoir and sluice operation according to claim 99, characterized in that: The feedback mechanism includes: Collect data in real time through sensors and input the data into risk assessment models; Adjust reservoir and sluice operation decisions in real time based on feedback data; Verify the effectiveness of the adjusted operations and further optimize the management strategy through feedback mechanisms.

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