Method, system and equipment for evaluating potential risk of cavern repository and medium

Abstract

The invention provides a method, a device, equipment and a medium for assessing potential risks of a cavern repository, and relates to the technical field of nuclear radiation environment influence assessment, and the assessment method comprises the steps: obtaining a basic list and project feature information of cavern disposal safety assessment; screening and evaluating the basic list according to the project feature information to generate a corresponding key list; and carrying out network analysis processing on the key list through a complex network analysis algorithm to generate a corresponding relation network, and carrying out scene analysis processing on the relation network based on preset key scene parameters to generate a potential risk scene of the cavern repository. According to the method, efficient identification, scientific classification, accurate assessment screening and deep processing analysis of feature-event-process data are realized, powerful support is provided for potential risk assessment of the cavern repository, and scientificity and reliability of decision making are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of nuclear radiation environmental impact assessment, and in particular to a method, system, equipment and medium for assessing the potential risks of a cave disposal repository. Background Art

[0002] The safe disposal of radioactive waste is one of the prominent issues facing the current development of nuclear energy and the use of nuclear technology, and it is also a key and difficult issue in radioactive waste management. Existing technologies offer a safe and reliable method of disposal. Radioactive waste that is no longer retrievable can be buried in a mountain above the surface or in stable rock mass below the surface. A multi-barrier isolation system combining artificial and natural barriers is used to ensure the long-term safety of the waste and avoid unnecessary harm to humans and the environment. However, due to the radioactivity and toxicity of radioactive waste, the design and operation of such repositories must be strictly regulated and controlled, and scientific safety demonstrations and evaluations must be conducted to minimize potential impacts on the environment and human health. Therefore, a cave disposal safety assessment checklist is needed to accurately assess the safety of the repository.

[0003] Currently, the feature-event-process data definitions used by countries like the United States, Sweden, and Finland, as well as international organizations like OCED / NEA, vary widely, with significant differences in inventory classification and structure, varying in scope and depth, and lacking systematicity and operability. This makes it difficult to directly reference and use these data to meet my country's waste disposal safety assessment requirements. Therefore, there is room for improvement. Summary of the Invention

[0004] In view of the shortcomings of the prior art described above, the purpose of the present invention is to provide a method, system, equipment and medium for assessing the potential risks of cave disposal repositories, so as to solve the technical problem that the data definition and inventory construction of the potential risk assessment method of radioactive waste cave disposal repositories in the prior art lack unified standards and systematicity.

[0005] To achieve the above and other related objectives, the present invention provides a method for assessing potential risks of a cavern repository, comprising:

[0006] Obtain a basic checklist and project characteristics information for potential risk assessment of cavern repositories;

[0007] Screening and evaluating the basic list based on the project characteristic information to generate a corresponding key list;

[0008] The key list is subjected to network analysis processing by a complex network analysis algorithm to generate a corresponding relationship network, and the relationship network is subjected to scenario analysis processing based on preset key scenario parameters to generate a potential risk scenario of the cave disposal repository.

[0009] In one embodiment of the present invention, the basic list is obtained according to the following steps:

[0010] Obtain field data on engineering geology, hydrogeology, geophysics, waste characteristics, and engineering design of cavern repositories;

[0011] All the field data are classified and processed to generate the basic list.

[0012] In one embodiment of the present invention, the step of screening and evaluating the basic list based on the project characteristic information to generate a corresponding key list includes:

[0013] Perform relevance screening on the basic list according to the project characteristic information to generate an intermediate list;

[0014] The intermediate list is evaluated according to its importance to generate a key list.

[0015] In one embodiment of the present invention, the step of performing relevance screening on the basic list based on the project characteristic information to generate an intermediate list includes:

[0016] Based on the repository design specifications, site geological conditions and safety assessment standards in the project characteristic information, the correlation coefficient of each feature-event-process data in the basic list is calculated, and all feature-event-process data with a correlation coefficient greater than a preset correlation threshold are selected to generate an intermediate list.

[0017] In one embodiment of the present invention, the step of performing importance assessment on the intermediate list to generate a key list includes:

[0018] Performing probability prediction and consequence severity prediction on each feature-event-process data item in the intermediate list to generate corresponding probability prediction values ​​and severity prediction values;

[0019] Based on the probability prediction value and the severity prediction value, a risk value of each feature-event-process data is calculated and generated, and all feature-event-process data with risk values ​​greater than a preset risk threshold are selected to generate a key list.

[0020] In one embodiment of the present invention, the steps of performing network analysis processing on the key list using a complex network analysis algorithm to generate a corresponding relationship network, and performing scenario analysis processing on the relationship network based on preset key scenario parameters to generate a potential risk scenario for the cave disposal repository include:

[0021] Analyzing and processing each feature-event-process data item in the key list using a complex network analysis algorithm to generate a relationship network between each feature-event-process data item and other related feature-event-process data items;

[0022] Constructing a simulation scenario based on preset key scenario parameters and all the relationship networks;

[0023] Performing simulation prediction analysis on the simulation scenario to generate corresponding risk assessment data;

[0024] The risk level of the simulation scenario is determined according to the risk assessment data, and the simulation scenario with a risk level greater than or equal to a preset level is set as a potential risk scene of the disposal repository.

[0025] In one embodiment of the present invention, after the steps of performing network analysis processing on the critical list using a complex network analysis algorithm to generate a corresponding relationship network, and performing scenario analysis processing on the relationship network based on preset key scenario parameters to generate a potential risk scenario for the cave disposal repository, the following steps are further included:

[0026] Based on the risk level of each potential risk scenario, a corresponding handling strategy is generated.

[0027] The present invention also provides a system for assessing potential risks of a cave disposal repository, comprising:

[0028] Information acquisition module, used to obtain the basic list and project characteristic information for potential risk assessment of cavern disposal repositories;

[0029] A key list generation module is used to screen and evaluate the basic list according to the project characteristic information to generate a corresponding key list;

[0030] The risk scenario generation module is used to perform network analysis processing on the key list through a complex network analysis algorithm to generate a corresponding relationship network, and to perform scenario analysis processing on the relationship network based on preset key scenario parameters to generate a potential risk scenario for the cave disposal repository.

[0031] The present invention further provides an electronic device, comprising:

[0032] one or more processors;

[0033] A storage device for storing one or more programs, which, when executed by the one or more processors, enables the electronic device to implement an assessment method for assessing potential risks of a cave disposal repository as described in any one of the above items.

[0034] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor of a computer, the computer is enabled to execute any of the above-mentioned methods for assessing the potential risks of a cave disposal repository.

[0035] As described above, the method, system, equipment and medium for assessing the potential risks of a cave disposal repository of the present invention have the following beneficial effects: the present invention realizes the efficient identification, scientific classification, precise assessment and screening, and in-depth processing and analysis of feature-event-process data, providing strong support for the potential risk assessment of cave disposal repositories and improving the scientific nature and reliability of decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A schematic flow chart of a method for assessing potential risks of a cave disposal repository provided by an embodiment of the present invention;

[0037] Figure 2 Shown is a structural block diagram of a system for assessing potential risks of a cave disposal repository provided by one embodiment of the present invention;

[0038] Figure 3 Shown is a structural schematic diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0039] The following describes the embodiments of the present invention through specific examples. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments. The details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other unless they conflict.

[0040] It should be noted that the illustrations provided in the following embodiments are merely schematic illustrations of the basic concept of the present invention. Therefore, the illustrations only show components related to the present invention and are not drawn according to the number, shape, and size of components in actual implementation. In actual implementation, the type, quantity, and proportion of each component may be changed arbitrarily, and the component layout may also be more complex.

[0041] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the embodiments of the present invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring the embodiments of the present invention.

[0042] First of all, it should be noted that the Features, Events and Processes (FEPs) list is used to describe and analyze various factors that may affect the long-term safety of radioactive waste cave disposal facilities, providing a scientific basis for evaluating the safety consequences of disposal and formulating targeted measures.

[0043] When conducting safety assessments of radioactive waste cavern repositories, the identification and screening of FEPs (characteristics, events, and processes) is a critical step in ensuring the long-term safety and performance of the repository. These data are used to identify and understand site characteristics, natural events, and physical and chemical processes that may have significant impacts on the repository system. Identifying and screening FEP data helps assess the safety vulnerabilities of the disposal system and develop effective management measures and response strategies to address potential risks and challenges.

[0044] The present invention provides a method, system, device, and medium for assessing the potential risks of rock cave repositories. These methods relate to the field of nuclear radiation environmental impact assessment and can be used to accurately assess the potential risks of rock cave repositories. These methods are described in detail below using specific examples.

[0045] See also Figure 1 The present invention provides a method for assessing potential risks of a cave disposal repository, which may include the following steps:

[0046] Step S100: Obtain a basic list and project characteristic information for potential risk assessment of a cave disposal repository.

[0047] In one embodiment of the present invention, when executing step S100, specifically, a basic list of potential risks for cave disposal sites and project characteristic information is obtained, thereby establishing a preliminary safety assessment framework, clarifying the general safety requirements and specific project characteristics of cave disposal projects, and providing basic data for subsequent screening and analysis. The basic list can be based on industry standards, national regulations, and historical cases to extract general safety requirements for cave disposal. In this embodiment, the basic list can be obtained by following the steps below:

[0048] Step S110: Acquire field data on engineering geology, hydrogeology, geophysics, waste characteristics, and engineering design of the cave disposal repository.

[0049] In one embodiment of the present invention, when executing step S110, specifically, a multi-source data acquisition system is first constructed to integrate and collect data from multiple fields such as engineering geology, hydrogeology, geophysics, waste characteristics, and engineering design. Data is acquired through multidisciplinary collaboration, and the systematicness and accuracy of the data are ensured. Among them, engineering geological data may include rock type (e.g., granite, limestone), rock structure (e.g., joint and fault distribution), rock strength (e.g., compressive strength, shear strength), and geostress field distribution. Project address data may be collected using methods such as field surveys, remote sensing technology, and geophysical exploration. Hydrogeological data may include groundwater level, permeability, groundwater flow direction, and aquifer type (e.g., pores, fractures, and karst). Hydrogeological data may be collected using methods such as borehole hydrological logging, tracer testing, and numerical simulation. Geophysical data may include seismic activity (e.g., historical earthquake records), geomagnetic anomalies, and geothermal gradients. Geophysical data may be collected using methods such as seismic monitoring networks, gravity and magnetic exploration, and geothermal measurements. Waste characteristic data may include waste type (e.g., low / medium level radioactive waste, industrial solid waste), type of nuclide (e.g., cesium-137, strontium-90), radioactivity intensity (Bq / g), and chemical stability (pH value, redox potential). Waste characteristic data may be obtained by methods such as laboratory analysis and long-term monitoring. Engineering design data may include cave structure design (e.g., excavation section dimensions, support form), barrier system (e.g., engineered barrier + natural barrier), and construction technology (e.g., TBM excavation parameters). Engineering design data may be obtained by methods such as extraction from design drawings and construction logs. In this embodiment, the above-mentioned multi-source data acquisition system can share information in real time with the international authoritative feature-event-process data database to ensure the comprehensiveness and timeliness of the data.

[0050] Furthermore, an interdisciplinary expert team can be organized to conduct discussions using an online collaborative platform, with experts providing opinions based on a unified data template to ensure that the information is standardized and accurate.

[0051] Step S120: Classify all domain data and generate a basic list.

[0052] In one embodiment of the present invention, when executing step S120, specifically, after collecting a large amount of domain data, to facilitate subsequent screening, evaluation, and management, it is necessary to systematically organize and classify this data using a preset classification model. First, a scientific and reasonable classification system is pre-established based on the needs and characteristics of cave disposal safety assessments. Classification criteria can be based on the data's source domain (engineering geology, hydrogeology, etc.), the data's properties (physical properties, chemical properties, mechanical properties, etc.), the data's impacted objects (rock mass, groundwater, waste, etc.), and the data's importance (key parameters, general parameters, etc.). In this embodiment, a multi-level classification structure can be adopted. For example, the first-level classification may include: external factors, waste bodies and packaging, disposal repositories, lithosphere, and biosphere. Taking the lithosphere as an example, the second-level classification may include: lithosphere characteristics and properties, lithosphere processes, and contaminant migration. Taking the lithosphere characteristics and properties as an example, the third-level classification may include: structural characteristics, joints and fissures, faults and folds, and rock mass integrity. Then, define clear names, codes, units, data types, accuracy requirements, data sources and other information for each classification level and specific data entry to ensure the standardization and comparability of the data.

[0053] Next, a classification model is constructed for classification processing. In this embodiment, a classification model can be constructed in a variety of ways, for example: a tree structure model, which organizes data into a tree structure according to a hierarchical relationship for easy browsing and retrieval. A labeling model, which adds multiple labels to each data entry, to facilitate multi-dimensional data query and analysis. A relational database model, which stores data in a relational database, and implements data classification and association through table structure and field definition. A knowledge graph model, which constructs data from different fields and their mutual relationships into a knowledge graph, to achieve more intelligent data retrieval and reasoning. The various forms of collected field data (such as text reports, drawings, tables, test data, etc.) are then sorted and entered according to the preset classification model. Various methods can be used, such as manual entry, automated data collection, and data conversion. Strict quality checks are performed during the data entry process to ensure the accuracy, completeness, and consistency of the data.

[0054] Finally, all the classified data in all fields are summarized to form a basic list. The basic list can be a structured document, a spreadsheet, a database or a knowledge graph, which contains all the initial information related to the safety assessment of cave disposal. The basic list should have a clear structure, clear classification and detailed description to facilitate subsequent screening and evaluation. The basic list contains a very comprehensive list of feature-event-process data, covering all potentially relevant factors. Through the above detailed implementation method, data from various fields of cave disposal can be systematically acquired and organized, and finally a comprehensive and structured basic list can be generated, laying a solid foundation for subsequent safety assessment work. The rationality of the classification model and the quality of the data directly affect the accuracy and reliability of the subsequent evaluation.

[0055] Project characteristic information, specific to a specific cavern disposal project, will be used for screening and evaluation within the basic inventory. This information may include repository design specifications, site geological conditions, and safety assessment criteria. This information can be obtained through a variety of means, including detailed site surveys, geological investigations, hydrogeological surveys, geophysical exploration, laboratory testing, literature review, and expert consultation. The quality and completeness of this information directly determine the reliability of the subsequent assessment.

[0056] Step S200: Screen and evaluate the basic list based on the project characteristic information to generate a corresponding key list.

[0057] In one embodiment of the present invention, when executing step S200, specifically, after obtaining a comprehensive basic list and specific project characteristic information, the key to this step is to identify the factors that have the most significant impact on the current project security, thereby forming a more streamlined and targeted key list. In this embodiment, step S200 may include the following steps:

[0058] Step S210: Perform relevance screening on the basic list based on the project feature information to generate an intermediate list.

[0059] In one embodiment of the present invention, when executing step S210, specifically, the relevance of each item in the basic list to the current project is evaluated one by one based on the project characteristic information. For example, based on the repository design specifications, site geological conditions, and safety assessment standards in the project characteristic information, the correlation coefficient of each feature-event-process data item in the basic list is calculated, and all feature-event-process data items with a correlation coefficient greater than a preset correlation threshold are selected to generate an intermediate list. This step is used to identify which information in the basic list is closely related to the specific cave disposal project, and to exclude or mark information with weaker relevance or inapplicability, thereby obtaining a more targeted intermediate list.

[0060] In this embodiment, first, the project characteristic information is interpreted and understood in detail. For the disposal repository design specifications, the design specifications followed by the project can be studied in depth, such as the disposal repository design requirements for specific waste types, the standards for engineering barriers, the regulations for monitoring systems, etc. Understand which characteristic-event-process data are explicitly required to be considered or controlled in the design specifications. For the site geological conditions, it is necessary to analyze the geological, hydrogeological, geophysical and other condition reports of the project location in detail. Understand which characteristic-event-process data are site-specific, such as specific lithology, fault zone distribution, groundwater flow characteristics, ground stress state, etc. For safety evaluation standards, it is necessary to master the safety evaluation standards and guidelines adopted by the project, such as performance objectives, risk limits, potential failure modes that need to be evaluated, etc. Understand which characteristic-event-process data are key factors that must be considered in the safety evaluation process.

[0061] Then, establish a correlation evaluation system between feature-event-process data and project feature information. Specifically, give a clear and specific description of each feature-event-process data in the basic list to ensure that its meaning is clear and avoid ambiguity. Then evaluate the correlation between feature-event-process data and project feature information from multiple dimensions. In this embodiment, the evaluation dimensions may include five aspects: direct impact, site specificity, regulatory requirements, safety criticality, and waste sensitivity. Set scoring rules for each correlation dimension. For example, a graded score (such as: highly correlated = 3, moderately correlated = 2, low correlated = 1, irrelevant = 0) or a more refined numerical score can be used. The scoring rules should be as objective and quantifiable as possible.

[0062] Next, the correlation coefficient of each feature-event-process data is calculated, and a preset correlation threshold is set. Specifically, an interdisciplinary expert team (geology, hydrology, engineering, safety, etc.) is organized to evaluate and score each feature-event-process data in the basic list according to the project characteristic information and the set correlation dimensions and scoring rules. If different correlation dimensions have different importance, a weight can be assigned to each dimension, and then the weighted average score is calculated as the correlation coefficient of the feature-event-process data. The determination of weights can be based on expert consensus or a specific method (such as AHP). A suitable correlation threshold is set according to the specific circumstances of the project, the rigor of the safety assessment, and expert judgment. The correlation threshold can take into account factors such as the scale of the basic list, the focus of the safety assessment, and expert opinions.

[0063] Finally, all feature-event-process data from the basic list with correlation coefficients greater than a preset threshold are selected to form an intermediate list. This list contains those features-event-process data deemed to have a certain degree of relevance to the current project. This preliminary screening of feature-event-process data is more targeted than the basic list, excluding factors with low relevance to the current project and laying the foundation for subsequent materiality assessment and scenario analysis.

[0064] Step S220: Perform importance assessment on the intermediate list to generate a key list.

[0065] In one embodiment of the present invention, when executing step S220, specifically, after obtaining the intermediate list related to the project, factors that have a critical impact on the safety of the repository are further identified and their importance is evaluated, ultimately forming a critical list focusing on core safety issues. In this embodiment, step S220 may include the following steps:

[0066] Step S221: perform occurrence probability prediction and consequence severity prediction on each feature-event-process data in the intermediate list, and generate corresponding probability prediction value and severity prediction value.

[0067] In one embodiment of the present invention, when executing step S220, specifically, first, for each feature-event-process data item in the intermediate list, the probability of its occurrence during the entire lifecycle of the cavern disposal facility (including the construction, operation, closure, and long-term management phases) is predicted. This probability can be obtained, for example, by analyzing historical data. By reviewing historical event data from similar cavern projects or related industries, the frequency of occurrence of specific feature-event-process data is statistically analyzed, and a probability prediction is made based on statistical analysis. For example, regional earthquake historical data can be analyzed to predict the probability of a future earthquake of a specific intensity. Alternatively, expert judgment can be used. For example, experts in relevant fields (geology, hydrology, engineering, safety, etc.) are organized to conduct discussions and, based on their professional knowledge and experience, assess the probability of occurrence of each feature-event-process data item. This is typically achieved using a qualitative or semi-quantitative scale (e.g., very low, low, medium, high, very high), or converted into a numerical probability range. Alternatively, a statistical model can be used to obtain the probability. Based on monitoring data or model prediction results, a statistical model is established to predict the probability of occurrence of the feature-event-process data. For example, the probability of future instability can be predicted based on rock mass deformation monitoring data.

[0068] For each feature-event-process data, a clear probability prediction value is output. The probability prediction value can be a numerical probability, for example, the annual probability of occurrence is 10 -4; or use a probability range, for example, the probability of occurrence is between 0.01 and 0.1; or use a qualitative level, for example, very low, low, medium, high, very high, which needs to be converted into a numerical value for subsequent calculations.

[0069] Then, the severity of the consequences is predicted for each feature-event-process data in the intermediate list. Specifically, for each feature-event-process data in the intermediate list, the extent of the potential negative impact that it may have on the safety of the cave disposal facility and the surrounding environment once it occurs is predicted. Aspects of the assessment may include: waste containment damage, environmental pollution, engineering structure failure, radiation impact, and socio-economic impact. In this embodiment, the severity of the consequences can be predicted using methods such as numerical simulation, scenario analysis, expert judgment, and analogy analysis. For example, when using numerical simulation methods, seepage models, solute transport models, rock mechanics models, etc. can be used to simulate the scope and extent of the physical and chemical impacts that may be caused by the occurrence of feature-event-process data.

[0070] For each feature-event-process data item, a clear severity prediction value is output. This severity prediction value can be expressed as a numerical probability, such as a potential economic loss of $100,000; an environmental impact indicator, such as the area of ​​contamination or the multiple of the concentration exceeding the standard; or a qualitative rating, such as minor, moderate, or severe. This needs to be converted into a numerical value for subsequent calculations.

[0071] Step S222: Based on the probability prediction value and the severity prediction value, calculate and generate the risk value of each feature-event-process data, and select all feature-event-process data with risk values ​​greater than the preset risk threshold to generate a key list.

[0072] In one embodiment of the present invention, when executing step S220, specifically, a risk value is first calculated for each feature-event-process data item based on the predicted probability value and the predicted severity value. In this embodiment, risk value = predicted probability value * predicted severity value. For each feature-event-process data item, a clear risk value or risk level is calculated.

[0073] Then, all feature-event-process data with risk values ​​greater than a preset risk threshold are selected to generate a critical list. Specifically, one or more risk thresholds can be set based on the project's safety objectives, regulatory requirements, risk tolerance, and expert judgment. The calculated risk value for each feature-event-process data item is compared with the preset risk threshold. All feature-event-process data with risk values ​​greater than the preset risk threshold are selected and included in the critical list. These feature-event-process data items are considered potential high-risk factors that require focused attention and management in the project.

[0074] Finally, a critical list is generated. This list is a list of risk-selected features, events, and processes that highlight factors that pose a high risk to cavern disposal safety. This list serves as the core input for subsequent scenario analysis, allowing for a deeper analysis of potential risk scenarios arising from these critical risk factors and the development of appropriate risk control and management measures.

[0075] Step S300: Perform network analysis on the key list using a complex network analysis algorithm to generate a corresponding relationship network, and perform scenario analysis on the relationship network based on preset key scenario parameters to generate a potential risk scenario for the cave disposal repository.

[0076] In one embodiment of the present invention, when executing step S300, specifically, the complex network analysis algorithm is an important tool for studying network structure, dynamic behavior and its functions, and may include: degree distribution calculation, clustering coefficient calculation, centrality measurement and community detection algorithm, etc., which can explore the structural characteristics, dynamic behavior and functional significance of complex networks. Scenario analysis refers to a method of evaluating the performance and potential consequences of a system under different scenarios that may occur in the future by constructing and analyzing these scenarios. By constructing different possible scenarios, analyze how key risk factors evolve under these scenarios and the possible impact on the safety of the repository and the surrounding environment. In this embodiment, step S300 may include the following steps:

[0077] Step S310: Analyze and process each feature-event-process data item in the key list using a complex network analysis algorithm to generate a relationship network between each feature-event-process data item and other related feature-event-process data items.

[0078] In one embodiment of the present invention, when executing step S310, specifically, first, the interactions between feature-event-process data are identified. Each feature-event-process data item in the key list is analyzed to identify various possible relationships between them. In this embodiment, the relationships between feature-event-process data may include causal relationships, influence relationships, trigger relationships, shared factors, and inhibition relationships. A causal relationship refers to the possibility that the occurrence or change of one feature-event-process data item may directly lead to the occurrence or change of another feature-event-process data item. For example, an earthquake may cause a rockfall. An influence relationship refers to the possibility that the state of one feature-event-process data item may affect the state or rate of another feature-event-process data item. For example, groundwater flow rate affects the migration rate of pollutants. A trigger relationship refers to the possibility that the occurrence of one feature-event-process data item may trigger the occurrence of another feature-event-process data item. For example, corrosion reaching a certain level may trigger the failure of an engineering barrier. A shared factor refers to the possibility that multiple feature-event-process data items may be affected by the same factor. For example, climate change may simultaneously affect rainfall and groundwater levels. Inhibition relationship means that the occurrence of one feature-event-process data may inhibit the occurrence of another feature-event-process data. For example, an effective drainage system can inhibit the increase of pore water pressure.

[0079] Next, a network of relationships is constructed between the feature-event-process data and its related feature-event-process data. Specifically, each feature-event-process data item in the key list is considered a node in the network, and the interactions between them are considered edges in the network. The direction of the edge can indicate the direction of influence, for example, from cause to effect. The weight of the edge can indicate the strength or likelihood of the interaction.

[0080] Next, select an appropriate complex network analysis algorithm. Specifically, you can choose an appropriate complex network analysis algorithm based on the type of relationship and the analysis goal. For example, use an adjacency matrix to construct a matrix that represents the connection relationship and weight between nodes. Or use centrality analysis to calculate the degree centrality (number of connected nodes), betweenness centrality (the number of shortest paths between other pairs of nodes), closeness centrality (the average distance to other nodes), and eigenvector centrality (the degree of connection to important nodes) of the node to identify key features - events - process data in the network.

[0081] Finally, by applying the selected complex network analysis algorithm, a visual or data-structured feature-event-process data relationship network is generated to clearly display the interdependence and influence relationships between key feature-event-process data.

[0082] Step S320: construct a simulation scenario based on preset key scenario parameters and all relationship networks.

[0083] In one embodiment of the present invention, when executing step S320, specifically, first, a set of key scenario parameters are preset, which represent internal and external conditions or triggering events that may have a significant impact on the safety of the repository. These parameters may include, for example, external disturbances, internal state changes, and values ​​of uncertainty parameters. Among them, external disturbances are, for example, earthquakes of different intensities and focal locations, rainfall events of different magnitudes, extreme temperature changes, and human activities. Internal state changes are, for example, changes in waste degradation rates, degradation rates of engineering barrier performance, and evolution of groundwater chemical composition. The values ​​of uncertainty parameters are, for example, setting different value ranges or discrete values ​​for certain key feature-event-process data parameters with uncertainty as inputs for different scenarios.

[0084] Next, select or combine key scenario parameters. Specifically, based on the analysis objectives and risk assessment requirements, select one or more key scenario parameters and set their specific values ​​or states. Scenarios can be constructed based on a single parameter change, or complex scenarios can be constructed with multiple parameter combinations.

[0085] Finally, scenario parameters are introduced into the relationship network to construct simulation scenarios. Specifically, selected key scenario parameters serve as initial perturbations or conditional changes in the network, influencing the state or behavior of relevant feature-event-process data within the network. For example, setting an earthquake event of a specific intensity will directly affect the initial state of feature-event-process data related to rock mass stability within the network. By introducing key scenario parameters into the feature-event-process data relationship network, a specific simulation scenario is defined, describing the possible evolution of the system under specific conditions.

[0086] Step S330: Perform simulation prediction analysis on the simulation scenario to generate corresponding risk assessment data.

[0087] In one embodiment of the present invention, when executing step S330, specifically, first, based on the properties and interaction relationships of the feature-event-process data, an appropriate simulation prediction method is selected, such as a rule-based reasoning method, a numerical simulation method, a system dynamics model, an agent model, etc. Then, based on the constructed simulation scenario and the selected simulation prediction method, a simulation is run to predict the evolution of key feature-event-process data and possible consequences under a specific scenario. Finally, relevant risk assessment data is extracted from the simulation prediction results. In this embodiment, the risk assessment data may include failure probability, the amount and range of pollutant migration, the degree of structural deformation and damage, potential economic losses or environmental impact indicators, and risk indicators.

[0088] Step S340: Determine the risk level of the simulation scenario based on the risk assessment data, and set the simulation scenario with a risk level greater than or equal to a preset level as a potential risk scenario of the repository.

[0089] In one embodiment of the present invention, when executing step S340, specifically, first, different risk levels and their corresponding risk assessment data thresholds are set based on the project's safety goals, regulatory requirements and risk tolerance. The risk level standards should be clear and operational. Then, the risk assessment data generated by each simulation scenario is compared with the preset risk level standards to determine the risk level of the scenario. Simulation scenarios with risk levels greater than or equal to the preset level are set as potential risk scenarios for the repository. These scenarios represent high-risk future states that require special attention and further analysis. Furthermore, each simulation scenario identified as a potential risk scenario is described in detail, including triggering conditions, the evolution of key feature-event-process data, the final risk assessment data and potential consequences. These descriptions should be clear, easy to understand, and provide a basis for risk management decisions.

[0090] As can be seen above, the final output of this step is a set of "potential risk scenarios" that describe high-risk scenarios that the repository may face in the future. These scenarios not only include the likelihood of risk occurrence and potential consequences, but also reveal the key characteristics—events—process data that lead to these risks and their interactions, providing important information for developing targeted risk mitigation and management strategies.

[0091] In one embodiment of the present invention, after identifying potential risk scenarios and assessing their risk levels, the next crucial step is to develop corresponding response strategies aimed at reducing the likelihood of the risk occurring, mitigating potential negative consequences, or preparing for risk events. The response strategy should be tailored to the risk level; higher risk levels generally require more proactive and rigorous response measures.

[0092] Specifically, first, review each potential risk scenario identified in the previous step in detail, including its triggering conditions, the evolution of key feature-event-process data, the final risk assessment data, and the determined risk level (e.g., low, medium, high, or very high). Next, develop a set of general response principles and strategy frameworks corresponding to different risk levels. For example, for very high risks, the most aggressive risk avoidance or elimination strategies should be adopted. If avoidance is unavoidable, the most rigorous risk mitigation and emergency response plans should be developed. For high risks, priority should be given to risk reduction measures, including design changes, operational optimization, and enhanced monitoring, along with a detailed emergency response plan. For medium risks, risk control measures should be considered, such as improved operating procedures, enhanced training, regular inspections and maintenance, and a basic emergency response plan. For low risks, a risk acceptance or monitoring strategy can be adopted, with regular assessments to determine if the risk is escalating, and general response measures developed. Next, develop a specific response strategy for each specific potential risk scenario based on its risk level and characteristics. Finally, organize multidisciplinary experts to review the developed response strategies and assess their effectiveness, feasibility, and cost-effectiveness. Optimize and refine the plan based on the review feedback. Document the response strategy for each potential risk scenario in a detailed document. This document should include a description of the risk scenario, risk level, response objectives, specific response measures, implementation plan, and responsible departments. The response strategy document should be a key component of the project safety management plan.

[0093] See also Figure 2 The present invention also provides a system for assessing the potential risks of a cavern repository. This system 1 corresponds one-to-one with the assessment method in the above embodiment. The system can include an information acquisition module 11, a key list generation module 12, and a risk scenario generation module 13. The functional modules are described in detail below:

[0094] The information acquisition module 11 can be used to obtain the basic list and project characteristic information for the potential risk assessment of cave disposal repositories. Furthermore, the information acquisition module 11 can be specifically used to obtain the basic list and project characteristic information for the safety assessment of cave disposal, so as to establish a preliminary safety assessment framework, clarify the general safety requirements and specific project characteristics of cave disposal projects, and provide basic data for subsequent screening and analysis. Among them, the basic list can extract the general safety requirements for cave disposal based on industry specifications, national regulations and historical cases. The project characteristic information is for a specific cave disposal project, and it will be used for screening and evaluation in the basic list. The project characteristic information may include repository design specifications, site geological conditions and safety evaluation standards.

[0095] The key list generation module 12 can be used to screen and evaluate the basic list based on the project characteristic information to generate a corresponding key list. Furthermore, the key list generation module 12 can be specifically used to screen the basic list for relevance based on the project characteristic information to generate an intermediate list; and to perform importance evaluation on the intermediate list to generate a key list.

[0096] The risk scenario generation module 13 can be used to perform network analysis processing on the critical list using a complex network analysis algorithm to generate a corresponding relationship network, and based on preset key scenario parameters, perform scenario analysis processing on the relationship network to generate a potential risk scenario for the cave disposal repository. Furthermore, the risk scenario generation module 13 can be specifically used to analyze and process each feature-event-process data in the critical list using a complex network analysis algorithm to generate a relationship network between each feature-event-process data and other related feature-event-process data; construct a simulation scenario based on the preset key scenario parameters and all relationship networks; perform simulation prediction analysis on the simulation scenario to generate corresponding risk assessment data; determine the risk level of the simulation scenario based on the risk assessment data, and set the simulation scenario with a risk level greater than or equal to the preset level as a potential risk scenario for the disposal repository.

[0097] The specific definitions of the potential risk assessment system for cavern repositories can be found in the definitions of the assessment method above and will not be repeated here. Each module in the aforementioned processing system can be implemented in whole or in part through software, hardware, or a combination thereof. Each of these modules can be embedded in or independent of a processor in a computer device in hardware form, or stored in a computer device memory in software form, allowing the processor to call and execute the corresponding operations of each module.

[0098] An embodiment of the present invention also provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by one or more processors, the electronic device implements the method for assessing the potential risks of a cave disposal repository provided in the above-mentioned embodiments.

[0099] See also Figure 3 The electronic device 2 may include a memory 21, a processor 22 and a bus, and may also include a computer program stored in the memory 21 and executable on the processor 22, such as a program for assessing potential risks of a cave disposal repository.

[0100] Among them, the memory 21 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (for example: SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory 21 can be an internal storage unit of the electronic device 2, such as a mobile hard disk of the electronic device 2. In other embodiments, the memory 21 can also be an external storage device of the electronic device 2, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 2. Furthermore, the memory 21 can also include both an internal storage unit of the electronic device 2 and an external storage device. The memory 21 can not only be used to store application software and various types of data installed in the electronic device 2, such as the code for the assessment of potential risks of the cave disposal repository, but can also be used to temporarily store data that has been output or is to be output.

[0101] In some embodiments, the processor 22 may be composed of an integrated circuit, such as a single packaged integrated circuit or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and a combination of various control chips. The processor 22 is the control core (Control Unit) of the electronic device 2. It utilizes various interfaces and circuits to connect the various components of the entire electronic device 2. It executes or runs programs or modules stored in the memory 21 (such as a fatigue prediction model training program) and calls data stored in the memory 21 to perform various functions of the electronic device 2 and process data.

[0102] The processor 22 executes the operating system and various installed applications of the electronic device 2. The processor 22 executes the applications to implement the steps in the above-mentioned method for assessing potential risks of a cave disposal repository.

[0103] Exemplarily, the computer program may be divided into one or more modules, which are stored in the memory 21 and executed by the processor 22 to implement the present application. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the electronic device 2. For example, the computer program may be divided into an information acquisition module 11, a key list generation module 12, and a risk scenario generation module 13.

[0104] The integrated unit implemented as a software functional module can be stored in a computer-readable storage medium, which can be either non-volatile or volatile. The software functional module, stored in a storage medium, includes instructions for causing a computer device (which can be a personal computer, computer device, or network device, etc.) or a processor to execute portions of the methods for assessing potential risks of cavernous repositories described in various embodiments of this application.

[0105] In summary, the method, system, equipment, and medium for assessing potential risks in cavernous repositories disclosed in this invention achieve efficient identification, scientific classification, precise assessment and screening, and in-depth processing and analysis of feature-event-process data. This provides strong support for potential risk assessments in cavernous repositories and enhances the scientific nature and reliability of decision-making. Therefore, this invention effectively overcomes the shortcomings of existing technologies and possesses high industrial value.

[0106] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the present invention. Anyone skilled in the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by one of ordinary skill in the art without departing from the spirit and technical principles disclosed herein are intended to be covered by the claims of the present invention.

Claims

1. A method for assessing potential risks of a cavern repository, characterized in that: include: Obtain a basic checklist and project characteristics information for potential risk assessment of cavern repositories; Screening and evaluating the basic list based on the project characteristic information to generate a corresponding key list; The key list is subjected to network analysis processing by a complex network analysis algorithm to generate a corresponding relationship network, and the relationship network is subjected to scenario analysis processing based on preset key scenario parameters to generate a potential risk scenario of the cave disposal repository.

2. The method for assessing potential risks of a cavern repository according to claim 1, characterized in that: The basic list is obtained by following the steps below: Obtain field data on engineering geology, hydrogeology, geophysics, waste characteristics, and engineering design of cavern disposal repositories; All the field data are classified and processed to generate the basic list.

3. The method for assessing potential risks of a cavern repository according to claim 1, characterized in that: The step of screening and evaluating the basic list according to the project characteristic information to generate a corresponding key list includes: Perform relevance screening on the basic list according to the project characteristic information to generate an intermediate list; The intermediate list is evaluated according to its importance to generate a key list.

4. The method for assessing potential risks of a cavern repository according to claim 3, characterized in that: The step of performing relevance screening on the basic list according to the project characteristic information to generate an intermediate list includes: Based on the repository design specifications, site geological conditions and safety assessment standards in the project characteristic information, the correlation coefficient of each feature-event-process data in the basic list is calculated, and all feature-event-process data with a correlation coefficient greater than a preset correlation threshold are selected to generate an intermediate list.

5. The method for assessing potential risks of a cavern repository according to claim 3, characterized in that: The step of performing importance assessment on the intermediate list to generate a key list includes: Performing probability prediction and consequence severity prediction on each feature-event-process data item in the intermediate list to generate corresponding probability prediction values ​​and severity prediction values; Based on the probability prediction value and the severity prediction value, a risk value of each feature-event-process data is calculated and generated, and all feature-event-process data with risk values ​​greater than a preset risk threshold are selected to generate a key list.

6. The method for assessing potential risks of a cavern repository according to claim 1, characterized in that: The steps of performing network analysis processing on the key list using a complex network analysis algorithm to generate a corresponding relationship network, and performing scenario analysis processing on the relationship network based on preset key scenario parameters to generate a potential risk scenario for the cave disposal repository include: Analyzing and processing each feature-event-process data item in the key list using a complex network analysis algorithm to generate a relationship network between each feature-event-process data item and other related feature-event-process data items; Constructing a simulation scenario based on preset key scenario parameters and all the relationship networks; Performing simulation prediction analysis on the simulation scenario to generate corresponding risk assessment data; The risk level of the simulation scenario is determined according to the risk assessment data, and the simulation scenario with a risk level greater than or equal to a preset level is set as a potential risk scene of the disposal repository.

7. The method for assessing potential risks of a cavern repository according to claim 6, characterized in that: After the step of performing network analysis processing on the key list using a complex network analysis algorithm to generate a corresponding relationship network, and performing scenario analysis processing on the relationship network based on preset key scenario parameters to generate a potential risk scenario for the cave disposal repository, the following steps are further included: Based on the risk level of each potential risk scenario, a corresponding handling strategy is generated.

8. A system for assessing potential risks of cavern disposal repositories, characterized in that: include: Information acquisition module, used to obtain the basic list and project characteristic information for potential risk assessment of cavern disposal repositories; A key list generation module is used to screen and evaluate the basic list according to the project characteristic information to generate a corresponding key list; The risk scenario generation module is used to perform network analysis processing on the key list through a complex network analysis algorithm to generate a corresponding relationship network, and to perform scenario analysis processing on the relationship network based on preset key scenario parameters to generate a potential risk scenario for the cave disposal repository.

9. An electronic device, characterized in that: The electronic device comprises: one or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, enables the electronic device to implement the evaluation method for evaluating the potential risks of a cave disposal repository as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed by a processor of a computer, the computer is caused to execute the method for assessing the potential risks of a cave disposal repository as claimed in any one of claims 1 to 7.

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