Dam system natural disaster damage emergency disposal decision-making method and system

Abstract

The invention belongs to the technical field of emergency processing, and discloses a reservoir dam system natural disaster damage emergency disposal decision-making method and system, and the method comprises the steps: carrying out the classification of preprocessed multi-source data through a clustering algorithm, and constructing a disaster information database based on the classification result; constructing a risk assessment model to assess the damage risk of natural disasters to the dam system, generating a plurality of emergency disposal strategies in combination with a multi-objective optimization algorithm, and screening out an optimal emergency disposal strategy by using a scenario simulation technology; a dynamic resource allocation algorithm is utilized to allocate resources, and a digital twinborn technology is utilized to simulate the dam system in real time to predict damage risks; and executing corresponding emergency disposal measures and recording emergency disposal effect data, evaluating the emergency disposal effect data by using a data envelope analysis algorithm, and optimizing the risk evaluation model based on an evaluation result. According to the invention, the data integration and processing capability is enhanced, and the accuracy and stability of the prediction result are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of emergency treatment, and in particular to a natural disaster damage emergency treatment decision-making method and system for a reservoir-dam system. Background Art

[0002] As an important water conservancy infrastructure, the reservoir-dam system plays a key role in water resource management, power generation, flood prevention and disaster reduction, etc. However, the reservoir-dam system is vulnerable to a variety of natural disasters, such as earthquakes, floods, mudslides and extreme climate events, which may cause damage to the reservoir-dam structure, thus affecting its safety and operational efficiency.

[0003] When dealing with natural disaster damage, traditional reservoir-dam systems often rely on expert experience and on-site investigations for emergency response decisions. Although this approach can guide emergency response actions to a certain extent, it has problems with low decision-making efficiency and limited accuracy, and is difficult to adapt to complex and rapidly changing disaster situations. In the face of these challenges, the development of big data analysis and machine learning technologies has provided new opportunities for improving emergency response methods. By extensively collecting and analyzing historical emergency response cases of reservoir-dam hydropower stations after natural disasters, a detailed case database is constructed, which covers key information such as the types, quantities, and technical solutions of emergency materials used in the emergency response process.

[0004] However, in the process of building the database, there are significant differences in data formats and description methods between different cases. This diversity not only increases the difficulty of data integration, but also makes the integration and processing process extremely complicated and time-consuming. In addition, when faced with complex and changing disaster scenarios, existing methods may lead to inaccurate or unstable prediction results due to insufficient robustness, bringing greater uncertainty to emergency response work.

[0005] Therefore, how to provide a decision-making method and system for emergency response to natural disaster damage in reservoir-dam systems is an issue that needs to be solved urgently. Summary of the invention

[0006] The embodiment of the present invention provides a natural disaster damage emergency response decision-making method and system for a reservoir-dam system to solve the above-mentioned technical problems existing in the prior art.

[0007] In order to have a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended to be a general review, nor is it intended to identify key / important components or to delineate the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a preface to the detailed description that follows.

[0008] According to a first aspect of an embodiment of the present invention, a method for making decisions on emergency response to natural disaster damage in a reservoir-dam system is provided.

[0009] In one embodiment, a natural disaster damage emergency response decision-making method for a reservoir-dam system includes:

[0010] Collect and preprocess multi-source natural disaster data, classify the preprocessed multi-source data using clustering algorithms, and build a disaster information database based on the classification results;

[0011] Analyze the natural disaster data in the disaster information database, build a risk assessment model to evaluate the damage risk of natural disasters to the reservoir and dam system, and generate several emergency response strategies by combining multi-objective optimization algorithms, and use scenario simulation technology to screen out the optimal emergency response strategy;

[0012] Based on the selected optimal emergency response strategy, resources are allocated using a dynamic resource allocation algorithm, and digital twin technology is used to simulate the reservoir-dam system in real time to predict damage risks;

[0013] Based on the predicted damage risk, corresponding emergency response measures are implemented and the emergency response effect data are recorded. The emergency response effect data are evaluated using the data envelopment analysis algorithm, and the risk assessment model is optimized based on the evaluation results.

[0014] In one embodiment, the collecting and preprocessing of multi-source natural disaster data, classifying the pre-processed multi-source data using a clustering algorithm, and constructing a disaster information database based on the classification results includes:

[0015] Collect natural disaster data from multi-source databases and pre-process the collected natural disaster data;

[0016] Clustering algorithms are used to classify the pre-processed natural disaster data to identify different types of risk areas;

[0017] Based on the classification results of the clustering algorithm, the disaster information database structure is designed, and the preprocessed natural disaster data and clustering classification results are imported into the disaster information database structure to construct the disaster information database.

[0018] In one embodiment, the use of a clustering algorithm to classify the pre-processed natural disaster data to identify different types of risk areas includes:

[0019] According to the pre-processed natural disaster data, a data set is established, and the initial cluster center and the preset number of clusters are determined by analyzing the natural disaster attributes in the data set;

[0020] Combine the feature differences between the data points in the data set and the initial cluster centers, optimize the objective function of the clustering algorithm, perform the clustering process to obtain preliminary clustering results, and identify the fuzzy data points in the preliminary clustering results;

[0021] Based on the Euclidean distance between the fuzzy data points in the preliminary clustering results and the centers of each cluster and the eigenvalues ​​of the data points, the fuzzy membership of the fuzzy data points is corrected to obtain the clustering results to identify different types of risk areas.

[0022] In one embodiment, the formula of the objective function of the optimized clustering algorithm is:

[0023]

[0024] In the formula, F′(H,c) represents the optimized objective function; H represents the optimized membership matrix; c represents the initial cluster center; l represents the preset number of clusters; i represents the index value of the number of clusters; h represents the number of data points in the data set; k represents the index value of the number of data points; g represents the initial cluster center; ik represents the fuzzy membership of the kth data point to the ith initial cluster center; n represents the preset fuzzy parameter; d ik represents the Euclidean distance from the kth data point to the ith initial cluster center; v ik Represents the difference in eigenvalues ​​between the kth data point and the i-th initial cluster center.

[0025] In one embodiment, the natural disaster data in the disaster information database is analyzed, a risk assessment model is constructed to assess the damage risk of natural disasters to the reservoir and dam system, and several emergency response strategies are generated in combination with a multi-objective optimization algorithm, and the optimal emergency response strategy is screened out using scenario simulation technology, including:

[0026] Extract historical natural disaster data from the disaster information database and select characteristic parameters related to the state of the reservoir-dam system;

[0027] Combining grey system theory and fuzzy comprehensive evaluation model, the damage risk of reservoir-dam system under natural disasters is evaluated by using selected characteristic parameters to obtain the risk level.

[0028] Based on the obtained risk level, the optimization objectives and constraints of the emergency response strategy are defined, and a multi-objective optimization algorithm is used to generate several emergency response strategies;

[0029] Use scenario simulation technology to simulate the application process of various emergency response strategies under different natural disaster scenarios and record the simulation results;

[0030] According to the optimization objectives and constraints of the emergency response strategy, all simulation results are analyzed to screen and determine the optimal emergency response strategy.

[0031] In one embodiment, the grey system theory and the fuzzy comprehensive evaluation model are combined to evaluate the damage risk of the reservoir-dam system under natural disasters using the selected characteristic parameters, and the risk levels obtained include:

[0032] Apply the time series analysis of grey system theory to predict the trend of the selected characteristic parameters, calculate the correlation between the characteristic parameters and the damage risk through grey correlation analysis, and determine the weight of the characteristic parameters;

[0033] Define the risk assessment level and the corresponding membership function, and build a fuzzy comprehensive evaluation model by combining the weights of the determined characteristic parameters;

[0034] The constructed fuzzy comprehensive evaluation model is evaluated using fuzzy operators to calculate the comprehensive risk level score of the reservoir-dam system under natural disasters, and the risk level is determined according to the defined risk evaluation level.

[0035] In one embodiment, the time series analysis using grey system theory is used to predict the trend of the selected characteristic parameters, and the correlation between the characteristic parameters and the damage risk is calculated by grey correlation analysis, and the weight of the characteristic parameters is determined, including:

[0036] Establish a time series according to the selected characteristic parameters, and build a grey prediction model for each characteristic parameter based on the time series;

[0037] Using the constructed grey prediction model, the trend of each characteristic parameter is predicted to obtain the predicted value sequence of each characteristic parameter, and the reference series of damage risk is selected from the predicted value sequence according to actual needs;

[0038] Calculate the grey correlation coefficient between the predicted value sequence of each characteristic parameter and the selected reference sequence, and analyze the correlation coefficient at all moments to quantify the relationship between the characteristic parameter and the damage risk;

[0039] Based on the calculated correlation coefficients at all times, the average correlation between each characteristic parameter and the reference series is calculated, and the characteristic parameters are sorted according to the average correlation to determine the weight of each characteristic parameter in the damage risk assessment.

[0040] In one embodiment, the optimal emergency response strategy selected is used to allocate resources using a dynamic resource allocation algorithm, and the digital twin technology is used to simulate the reservoir-dam system in real time to predict the damage risk, including:

[0041] Based on the selected optimal emergency response strategy, evaluate the resource requirements under different natural disaster scenarios, and set the initial allocation strategy and benefit function for each resource;

[0042] Using dynamic resource allocation algorithms, the allocation evolution process is iteratively simulated to identify and optimize resource allocation strategies;

[0043] Based on the optimized resource allocation strategy, digital twin technology is used to simulate the reservoir-dam system in real time, obtain simulation results, and use reinforcement learning algorithms to predict damage risks.

[0044] In one embodiment, executing corresponding emergency response measures based on the predicted damage risk and recording emergency response effect data, evaluating the emergency response effect data using a data envelopment analysis algorithm, and optimizing the risk assessment model based on the evaluation results include:

[0045] Based on the predicted damage risk, implement corresponding emergency response measures and record the emergency response effect data of each measure during the emergency response process;

[0046] According to the recorded emergency response effect data of various measures, a data envelopment analysis evaluation model is constructed, the decision-making units and evaluation indicators are defined, and corresponding weights are set for each evaluation indicator;

[0047] The data envelopment analysis algorithm is used to evaluate the relative efficiency of each decision-making unit, and the evaluation results are analyzed and a feedback mechanism from emergency response to risk assessment model is established to optimize the risk assessment model.

[0048] According to a second aspect of an embodiment of the present invention, a natural disaster damage emergency response decision-making system for a reservoir-dam system is provided.

[0049] In one embodiment, the natural disaster damage emergency response decision-making system for a reservoir-dam system comprises:

[0050] The data processing and classification module is used to collect and preprocess multi-source natural disaster data, classify the pre-processed multi-source data using a clustering algorithm, and build a disaster information database based on the classification results;

[0051] The risk assessment and strategy generation module is used to analyze the natural disaster data in the disaster information database, build a risk assessment model to assess the damage risk of natural disasters to the reservoir and dam system, and generate several emergency response strategies in combination with a multi-objective optimization algorithm, and use scenario simulation technology to select the optimal emergency response strategy;

[0052] The resource allocation and real-time simulation module is used to allocate resources based on the selected optimal emergency response strategy using a dynamic resource allocation algorithm, and to use digital twin technology to simulate the reservoir-dam system in real time to predict damage risks;

[0053] The emergency response and model optimization module is used to execute corresponding emergency response measures and record emergency response effect data based on the predicted damage risk, evaluate the emergency response effect data using the data envelopment analysis algorithm, and optimize the risk assessment model based on the evaluation results.

[0054] According to a third aspect of an embodiment of the present invention, a computer device is provided.

[0055] In some embodiments, the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0056] According to a fourth aspect of embodiments of the present invention, a computer-readable storage medium is provided.

[0057] In one embodiment, the computer-readable storage medium stores a computer program, and the computer program implements the steps of the above method when executed by a processor.

[0058] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0059] 1. The present invention constructs a disaster information database by integrating multi-source natural disaster data and performing preprocessing, which significantly enhances the data integration and processing capabilities, thereby improving the accuracy and stability of the prediction results; in addition, combined with multi-objective optimization algorithms and scenario simulation technology, it can not only generate a variety of emergency response strategies, but also scientifically screen out the optimal strategy to ensure the high scientificity and pertinence of strategy formulation.

[0060] 2. The present invention realizes the optimal configuration of resources and real-time monitoring and prediction functions by introducing dynamic resource allocation algorithm and digital twin technology, making emergency response more rapid and effective, minimizing potential losses, and not only improving resource utilization efficiency, but also enhancing the real-time response capability and flexibility of the reservoir and dam system in the face of natural disasters.

[0061] 3. The present invention establishes a rapid feedback mechanism from emergency response to risk assessment model, ensuring that the model and strategy are always in the best state, improving the safety of the reservoir and dam system and its ability to respond to emergencies in the long term, and promoting the scientificity and rationality of decision-making.

[0062] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0064] Figure 1 is a flow chart showing a decision-making method for emergency response to natural disaster damage in a reservoir-dam system according to an exemplary embodiment;

[0065] Figure 2 It is a principle block diagram of a natural disaster damage emergency response decision-making system for a reservoir-dam system according to an exemplary embodiment;

[0066] Figure 3 The figure is a schematic diagram showing the structure of a computer device according to an exemplary embodiment. DETAILED DESCRIPTION

[0067] The following description and accompanying drawings fully illustrate the specific embodiments of this article so that those skilled in the art can practice them. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. The scope of the embodiments of this article includes the entire scope of the claims, as well as all available equivalents of the claims. Herein, the terms "first", "second", etc. are only used to distinguish one element from another, without requiring or implying any actual relationship or order between these elements. In fact, the first element can also be called the second element, and vice versa. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the structure, device or equipment including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also include elements inherent to such structure, device or equipment. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the structure, device or equipment including the elements. Each embodiment is described in a progressive manner herein, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the embodiments can be referred to each other.

[0068] The terms "longitudinal", "lateral", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. in this document indicate the orientation or position relationship based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing this document and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In the description of this document, unless otherwise specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a mechanical connection or an electrical connection, it can also be the internal communication of two elements, it can be a direct connection, or it can be an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0069] As used herein, the term "plurality" means two or more than two, unless otherwise specified.

[0070] In this document, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0071] In this article, the term "and / or" is a description of the association relationship between objects, indicating that three relationships may exist. For example, A and / or B means: A or B, or, A and B.

[0072] It should be understood that, although the various steps in the flow chart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0073] Each module in the device or system of the present application can be implemented in whole or in part by software, hardware, or a combination thereof. The above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above modules.

[0074] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0075] Figure 1 An embodiment of a natural disaster damage emergency response decision-making method for a reservoir-dam system of the present invention is shown.

[0076] In this optional embodiment, the method for emergency response decision-making for natural disaster damage to a reservoir-dam system comprises:

[0077] Step S101, collect and pre-process multi-source natural disaster data, classify the pre-processed multi-source data using a clustering algorithm, and build a disaster information database based on the classification results.

[0078] In this optional embodiment, the collecting and preprocessing of multi-source natural disaster data, classifying the pre-processed multi-source data using a clustering algorithm, and constructing a disaster information database based on the classification results include:

[0079] Collect natural disaster data from multi-source databases and preprocess the collected natural disaster data.

[0080] It should be noted that the pre-processing of the collected natural disaster data includes: data cleaning, data format conversion and standardization.

[0081] Clustering algorithms are used to classify the preprocessed natural disaster data to identify different types of risk areas.

[0082] In this optional embodiment, the use of a clustering algorithm to classify the pre-processed natural disaster data to identify different types of risk areas includes:

[0083] According to the preprocessed natural disaster data, a data set is established, and the initial cluster center and the preset number of clusters are determined by analyzing the natural disaster attributes in the data set.

[0084] Combined with the feature differences between the data points in the data set and the initial cluster centers, the objective function of the clustering algorithm is optimized, the clustering process is performed to obtain preliminary clustering results, and the fuzzy data points in the preliminary clustering results are identified.

[0085] In this optional embodiment, the formula of the objective function of the optimized clustering algorithm is:

[0086]

[0087] In the formula, F′(H,c) represents the optimized objective function; H represents the optimized membership matrix; c represents the initial cluster center; l represents the preset number of clusters; i represents the index value of the number of clusters; h represents the number of data points in the data set; k represents the index value of the number of data points; g represents the initial cluster center; ik represents the fuzzy membership of the kth data point to the ith initial cluster center; n represents the preset fuzzy parameter; d ik represents the Euclidean distance from the kth data point to the ith initial cluster center; v ik Represents the difference in eigenvalues ​​between the kth data point and the i-th initial cluster center.

[0088] Based on the Euclidean distance between the fuzzy data points in the preliminary clustering results and the centers of each cluster and the eigenvalues ​​of the data points, the fuzzy membership of the fuzzy data points is corrected to obtain the clustering results to identify different types of risk areas.

[0089] Based on the classification results of the clustering algorithm, the disaster information database structure is designed, and the preprocessed natural disaster data and clustering classification results are imported into the disaster information database structure to construct the disaster information database.

[0090] Step S102, analyze the natural disaster data in the disaster information database, build a risk assessment model to assess the damage risk of natural disasters to the reservoir and dam system, and generate several emergency response strategies in combination with a multi-objective optimization algorithm, and use scenario simulation technology to screen out the optimal emergency response strategy.

[0091] In this optional embodiment, the natural disaster data in the disaster information database is analyzed, a risk assessment model is constructed to assess the damage risk of natural disasters to the reservoir and dam system, and several emergency response strategies are generated in combination with a multi-objective optimization algorithm. The optimal emergency response strategy is selected using scenario simulation technology, including:

[0092] Historical natural disaster data are extracted from the disaster information database, and characteristic parameters related to the state of the reservoir-dam system are selected.

[0093] It should be noted that the characteristic parameters related to the reservoir-dam system status include:

[0094] (1) Disaster type: Different types of natural disasters (such as earthquakes, floods, storms, etc.) have different degrees and ways of impact on the reservoir-dam system.

[0095] (2) Disaster intensity: The intensity of a disaster (such as the magnitude of an earthquake, the flow of a flood, etc.) is directly related to its impact and degree of damage to the reservoir-dam system. A disaster with a greater intensity will often cause more serious damage to the reservoir-dam system.

[0096] (3) Disaster frequency: The frequency of disasters reflects the frequency with which the reservoir-dam system is impacted by disasters. High frequency of disasters may cause the reservoir-dam system to be in a state of stress for a long time, increasing its risk of failure.

[0097] (4) Reservoir-dam system response: The response of the reservoir-dam system when a disaster occurs, such as deformation, displacement, cracks, etc., is a key indicator for evaluating its status.

[0098] (5) Disaster impact scope: The impact scope of the disaster on the reservoir-dam system and its surrounding environment is also one of the important characteristic parameters. Understanding the impact scope of the disaster helps to assess the degree of damage to the reservoir-dam system and the difficulty of recovery.

[0099] (6) Indicators for evaluating the status of the reservoir-dam system: such as the deformation of the dam body and foundation, seepage, uplift pressure, etc. These indicators can directly reflect the physical and mechanical properties and stability of the reservoir-dam system.

[0100] Combining grey system theory and fuzzy comprehensive evaluation model, the damage risk of reservoir-dam system under natural disasters is evaluated using selected characteristic parameters to obtain the risk level.

[0101] In this optional embodiment, the grey system theory and the fuzzy comprehensive evaluation model are combined to evaluate the damage risk of the reservoir-dam system under natural disasters using the selected characteristic parameters, and the risk levels obtained include:

[0102] The time series analysis based on grey system theory is applied to predict the trend of the selected characteristic parameters, and the correlation between the characteristic parameters and the damage risk is calculated through grey correlation analysis to determine the weight of the characteristic parameters.

[0103] In this optional embodiment, the time series analysis using grey system theory is used to predict the trend of the selected characteristic parameters, and the correlation between the characteristic parameters and the damage risk is calculated by grey correlation analysis, and the weight of the characteristic parameters is determined, including:

[0104] Establish a time series according to the selected characteristic parameters, and build a grey prediction model for each characteristic parameter based on the time series;

[0105] Using the constructed grey prediction model, the trend of each characteristic parameter is predicted to obtain the predicted value sequence of each characteristic parameter, and the reference series of damage risk is selected from the predicted value sequence according to actual needs;

[0106] Calculate the grey correlation coefficient between the predicted value sequence of each characteristic parameter and the selected reference sequence, and analyze the correlation coefficient at all moments to quantify the relationship between the characteristic parameter and the damage risk;

[0107] Based on the calculated correlation coefficients at all times, the average correlation between each characteristic parameter and the reference series is calculated, and the characteristic parameters are sorted according to the average correlation to determine the weight of each characteristic parameter in the damage risk assessment.

[0108] Define the risk assessment level and the corresponding membership function, and build a fuzzy comprehensive evaluation model by combining the weights of the determined characteristic parameters.

[0109] The constructed fuzzy comprehensive evaluation model is evaluated using fuzzy operators to calculate the comprehensive risk level score of the reservoir-dam system under natural disasters, and the risk level is determined according to the defined risk evaluation level.

[0110] It should be noted that the risk levels include: low risk, medium risk, high risk and extremely high risk.

[0111] Based on the obtained risk level, the optimization objectives and constraints of the emergency response strategy are defined, and a multi-objective optimization algorithm is used to generate several emergency response strategies.

[0112] Use scenario simulation technology to simulate the application process of various emergency response strategies under different natural disaster scenarios, and record the simulation results.

[0113] According to the optimization objectives and constraints of the emergency response strategy, all simulation results are analyzed to screen and determine the optimal emergency response strategy.

[0114] Step S103: According to the selected optimal emergency response strategy, resources are allocated using a dynamic resource allocation algorithm, and the digital twin technology is used to simulate the reservoir-dam system in real time to predict damage risks.

[0115] In this optional embodiment, the optimal emergency response strategy selected is used to allocate resources using a dynamic resource allocation algorithm, and the digital twin technology is used to simulate the reservoir-dam system in real time to predict the damage risk, including:

[0116] Based on the selected optimal emergency response strategy, evaluate the resource requirements under different natural disaster scenarios, and set the initial allocation strategy and benefit function for each resource;

[0117] Using dynamic resource allocation algorithms, the allocation evolution process is iteratively simulated to identify and optimize resource allocation strategies;

[0118] Based on the optimized resource allocation strategy, digital twin technology is used to simulate the reservoir-dam system in real time, obtain simulation results, and use reinforcement learning algorithms to predict damage risks.

[0119] Step S104, based on the predicted damage risk, execute corresponding emergency response measures and record emergency response effect data, evaluate the emergency response effect data using a data envelopment analysis algorithm, and optimize the risk assessment model based on the evaluation results.

[0120] In this optional embodiment, executing corresponding emergency response measures based on the predicted damage risk and recording emergency response effect data, evaluating the emergency response effect data using a data envelopment analysis algorithm, and optimizing the risk assessment model based on the evaluation results include:

[0121] Based on the predicted damage risk, implement corresponding emergency response measures and record the emergency response effect data of each measure during the emergency response process;

[0122] According to the recorded emergency response effect data of various measures, a data envelopment analysis evaluation model is constructed, the decision-making units and evaluation indicators are defined, and corresponding weights are set for each evaluation indicator;

[0123] The data envelopment analysis algorithm is used to evaluate the relative efficiency of each decision-making unit, and the evaluation results are analyzed and a feedback mechanism from emergency response to risk assessment model is established to optimize the risk assessment model.

[0124] Figure 2 An embodiment of a natural disaster damage emergency response decision-making system for a reservoir-dam system of the present invention is shown.

[0125] In this optional embodiment, the natural disaster damage emergency response decision-making system for a reservoir-dam system comprises:

[0126] The data processing and classification module 201 is used to collect and pre-process multi-source natural disaster data, classify the pre-processed multi-source data using a clustering algorithm, and build a disaster information database based on the classification results;

[0127] The risk assessment and strategy generation module 202 is used to analyze the natural disaster data in the disaster information database, build a risk assessment model to assess the damage risk of natural disasters to the reservoir and dam system, and generate several emergency response strategies in combination with a multi-objective optimization algorithm, and select the optimal emergency response strategy using scenario simulation technology;

[0128] The resource allocation and real-time simulation module 203 is used to allocate resources according to the selected optimal emergency response strategy using a dynamic resource allocation algorithm, and to use digital twin technology to simulate the reservoir-dam system in real time to predict damage risks;

[0129] The emergency response and model optimization module 204 is used to execute corresponding emergency response measures and record emergency response effect data based on the predicted damage risk, evaluate the emergency response effect data using a data envelopment analysis algorithm, and optimize the risk assessment model based on the evaluation results.

[0130] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps in the above method embodiment are implemented.

[0131] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0132] In addition, the present invention also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiment when executing the computer program.

[0133] In addition, the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0134] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0135] The present invention is not limited to the structures which have been described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A decision-making method for emergency response to natural disaster damage in a reservoir-dam system, characterized in that: The method includes: Collect and preprocess multi-source natural disaster data, classify the preprocessed multi-source data using clustering algorithms, and build a disaster information database based on the classification results; Analyze the natural disaster data in the disaster information database, build a risk assessment model to evaluate the damage risk of natural disasters to the reservoir and dam system, and generate several emergency response strategies by combining multi-objective optimization algorithms, and use scenario simulation technology to screen out the optimal emergency response strategy; Based on the selected optimal emergency response strategy, resources are allocated using a dynamic resource allocation algorithm, and digital twin technology is used to simulate the reservoir-dam system in real time to predict damage risks; Based on the predicted damage risk, corresponding emergency response measures are implemented and the emergency response effect data are recorded. The emergency response effect data are evaluated using the data envelopment analysis algorithm, and the risk assessment model is optimized based on the evaluation results.

2. A decision-making method for emergency response to natural disaster damage in a reservoir-dam system according to claim 1, characterized in that: The collecting and preprocessing of multi-source natural disaster data, classifying the pre-processed multi-source data using a clustering algorithm, and constructing a disaster information database based on the classification results include: Collect natural disaster data from multi-source databases and pre-process the collected natural disaster data; Clustering algorithms are used to classify the pre-processed natural disaster data to identify different types of risk areas; Based on the classification results of the clustering algorithm, the disaster information database structure is designed, and the preprocessed natural disaster data and clustering classification results are imported into the disaster information database structure to construct the disaster information database.

3. A decision-making method for emergency response to natural disaster damage in a reservoir-dam system according to claim 2, characterized in that: The clustering algorithm is used to classify the pre-processed natural disaster data to identify different types of risk areas, including: According to the pre-processed natural disaster data, a data set is established, and the initial cluster center and the preset number of clusters are determined by analyzing the natural disaster attributes in the data set; Combine the feature differences between the data points in the data set and the initial cluster centers, optimize the objective function of the clustering algorithm, perform the clustering process to obtain preliminary clustering results, and identify the fuzzy data points in the preliminary clustering results; Based on the Euclidean distance between the fuzzy data points in the preliminary clustering results and the centers of each cluster and the eigenvalues ​​of the data points, the fuzzy membership of the fuzzy data points is corrected to obtain the clustering results to identify different types of risk areas.

4. A decision-making method for emergency response to natural disaster damage in a reservoir-dam system according to claim 3, characterized in that: The formula of the objective function of the optimized clustering algorithm is: In the formula, F′(H,c) represents the optimized objective function; H represents the optimized membership matrix; c represents the initial cluster center; l represents the preset number of clusters; i represents the index value of the number of clusters; h represents the number of data points in the data set; k represents the index value of the number of data points; g represents the initial cluster center; ik represents the fuzzy membership of the kth data point to the ith initial cluster center; n represents the preset fuzzy parameter; d ik represents the Euclidean distance from the kth data point to the ith initial cluster center; v ik Represents the difference in eigenvalues ​​between the kth data point and the i-th initial cluster center.

5. The method for emergency response decision-making for natural disaster damage in a reservoir-dam system according to claim 1 is characterized in that: The natural disaster data in the disaster information database is analyzed, and a risk assessment model is constructed to assess the damage risk of natural disasters to the reservoir and dam system. A multi-objective optimization algorithm is combined to generate several emergency response strategies, and the scenario simulation technology is used to select the optimal emergency response strategy, including: Extract historical natural disaster data from the disaster information database and select characteristic parameters related to the state of the reservoir-dam system; Combining grey system theory and fuzzy comprehensive evaluation model, the damage risk of reservoir-dam system under natural disasters is evaluated by using selected characteristic parameters to obtain the risk level. Based on the obtained risk level, the optimization objectives and constraints of the emergency response strategy are defined, and a multi-objective optimization algorithm is used to generate several emergency response strategies; Use scenario simulation technology to simulate the application process of various emergency response strategies under different natural disaster scenarios and record the simulation results; According to the optimization objectives and constraints of the emergency response strategy, all simulation results are analyzed to screen and determine the optimal emergency response strategy.

6. A decision-making method for emergency response to natural disaster damage in a reservoir-dam system according to claim 5, characterized in that: The above-mentioned combination of grey system theory and fuzzy comprehensive evaluation model uses selected characteristic parameters to evaluate the damage risk of the reservoir-dam system under natural disasters, and the risk levels obtained include: Apply the time series analysis of grey system theory to predict the trend of the selected characteristic parameters, calculate the correlation between the characteristic parameters and the damage risk through grey correlation analysis, and determine the weight of the characteristic parameters; Define the risk assessment level and the corresponding membership function, and build a fuzzy comprehensive evaluation model by combining the weights of the determined characteristic parameters; The constructed fuzzy comprehensive evaluation model is evaluated using fuzzy operators to calculate the comprehensive risk level score of the reservoir-dam system under natural disasters, and the risk level is determined according to the defined risk evaluation level.

7. A decision-making method for emergency response to natural disaster damage in a reservoir-dam system according to claim 6, characterized in that: The time series analysis using grey system theory is used to predict the trend of the selected characteristic parameters, and the correlation between the characteristic parameters and the damage risk is calculated by grey correlation analysis to determine the weight of the characteristic parameters. Establish a time series according to the selected characteristic parameters, and build a grey prediction model for each characteristic parameter based on the time series; Using the constructed grey prediction model, the trend of each characteristic parameter is predicted to obtain the predicted value sequence of each characteristic parameter, and the reference series of damage risk is selected from the predicted value sequence according to actual needs; Calculate the grey correlation coefficient between the predicted value sequence of each characteristic parameter and the selected reference sequence, and analyze the correlation coefficient at all moments to quantify the relationship between the characteristic parameter and the damage risk; Based on the calculated correlation coefficients at all times, the average correlation between each characteristic parameter and the reference series is calculated, and the characteristic parameters are sorted according to the average correlation to determine the weight of each characteristic parameter in the damage risk assessment.

8. The method for emergency response decision-making for natural disaster damage to a reservoir-dam system according to claim 1 is characterized in that: The optimal emergency response strategy selected is used to allocate resources using a dynamic resource allocation algorithm, and the digital twin technology is used to simulate the reservoir-dam system in real time to predict damage risks, including: Based on the selected optimal emergency response strategy, evaluate the resource requirements under different natural disaster scenarios, and set the initial allocation strategy and benefit function for each resource; Using dynamic resource allocation algorithms, the allocation evolution process is iteratively simulated to identify and optimize resource allocation strategies; Based on the optimized resource allocation strategy, digital twin technology is used to simulate the reservoir-dam system in real time, obtain simulation results, and use reinforcement learning algorithms to predict damage risks.

9. The method for emergency response decision-making for natural disaster damage to a reservoir-dam system according to claim 1, characterized in that: Based on the predicted damage risk, corresponding emergency response measures are executed and emergency response effect data is recorded, emergency response effect data is evaluated using a data envelopment analysis algorithm, and the risk assessment model is optimized based on the evaluation results, including: Based on the predicted damage risk, implement corresponding emergency response measures and record the emergency response effect data of each measure during the emergency response process; According to the recorded emergency response effect data of various measures, a data envelopment analysis evaluation model is constructed, the decision-making units and evaluation indicators are defined, and corresponding weights are set for each evaluation indicator; The data envelopment analysis algorithm is used to evaluate the relative efficiency of each decision-making unit, analyze the evaluation results, and establish a feedback mechanism from emergency response to risk assessment model to optimize the risk assessment model.

10. A natural disaster damage emergency response decision-making system for reservoir-dam systems, characterized in that: The system includes: The data processing and classification module is used to collect and preprocess multi-source natural disaster data, classify the pre-processed multi-source data using a clustering algorithm, and build a disaster information database based on the classification results; The risk assessment and strategy generation module is used to analyze the natural disaster data in the disaster information database, build a risk assessment model to assess the damage risk of natural disasters to the reservoir and dam system, and generate several emergency response strategies in combination with a multi-objective optimization algorithm, and use scenario simulation technology to select the optimal emergency response strategy; The resource allocation and real-time simulation module is used to allocate resources based on the selected optimal emergency response strategy using a dynamic resource allocation algorithm, and to use digital twin technology to simulate the reservoir-dam system in real time to predict damage risks; The emergency response and model optimization module is used to execute corresponding emergency response measures and record emergency response effect data based on the predicted damage risk, evaluate the emergency response effect data using the data envelopment analysis algorithm, and optimize the risk assessment model based on the evaluation results.

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