Risk assessment method and system for pumped storage power station construction

Abstract

The invention discloses a risk assessment method and system for pumped storage power station construction, which is applied to the technical field of pumped storage power station engineering project management, and comprises the following steps: constructing a multi-dimensional risk assessment system, and analyzing an interaction relationship among risk influence factors to identify key influence factors and risk conduction paths thereof; obtaining project construction data of a target power station; extracting quantitative risk index data based on the system; determining key parameters based on the key influence factors; comparing the key parameter with a preset risk threshold to determine a risk level; and finally generating a risk assessment result based on the risk conduction path, the key parameters and the risk levels thereof. According to the method provided by the invention, the systematic risk source and the conduction path thereof can be accurately identified from massive project construction data, and systematic, accurate and quantifiable dynamic evaluation of the construction risk of the pumped storage power station is realized.

Description

Technical Field

[0001] This invention relates to the field of pumped storage power station project management technology, and in particular to a risk assessment method and system for the construction of pumped storage power stations. Background Technology

[0002] As pumped storage power station construction enters a phase of rapid development, the scale of projects is becoming increasingly large and the technical complexity is constantly increasing. The risks they face during construction, including geographical environment, technical engineering, construction resources, and external environment, are intertwined, posing unprecedented challenges to risk management in project construction.

[0003] Currently, risk assessment methods for pumped storage power station construction are mostly static assessments. These methods typically rely on fixed assessment checklists or historical experience data, treating risk factors as isolated entities and assigning risk scores only at specific project stages. This directly leads to a severe disconnect between risk assessment conclusions and the actual dynamic risk status of the project, causing project management to remain in a passive "post-event response" mode, unable to achieve proactive "pre-event warning" prevention and control. Summary of the Invention

[0004] This invention provides a risk assessment method and system for the construction of pumped storage power stations, which solves the technical problem of how to accurately identify the sources of systemic risks and their transmission paths from massive amounts of multi-dimensional project construction data, so as to achieve a systematic, accurate and quantifiable dynamic assessment of the risks of pumped storage power station construction.

[0005] To address the aforementioned technical problems, embodiments of the present invention provide a risk assessment method for the construction of pumped storage power stations, comprising: Construct a multi-dimensional risk assessment system for pumped storage power stations, with at least one risk influencing factor under each dimension; Analyze the interaction relationships among the various risk influencing factors, identify key influencing factors based on the analysis results, and determine the risk transmission paths among the key influencing factors; Obtain project construction data for the target pumped storage power station; Based on the multi-dimensional risk assessment system, quantitative risk indicator data corresponding to each risk influencing factor are extracted from the project construction data. Based on the aforementioned key influencing factors, the corresponding key parameters are determined from the quantitative risk indicator data; Determine the risk level corresponding to the key parameters; Based on the risk transmission path, the key parameters, and the corresponding risk levels, a risk assessment result for the construction of the target pumped storage power station project is generated.

[0006] As one preferred embodiment, the analysis of the interactions between the various risk influencing factors, and the identification of key influencing factors based on the analysis results, includes: Construct an initial relationship matrix representing the intensity of the direct influence between each of the aforementioned risk factors; The initial relationship matrix is ​​optimized to obtain the comprehensive influence matrix; Based on the comprehensive impact matrix, calculate the centrality index of each of the risk influencing factors; The key influencing factors are identified based on the centrality index.

[0007] As one preferred embodiment, the extraction of quantitative risk indicator data corresponding to each risk influencing factor from the project construction data is performed on heterogeneous tables within the project construction data, including: The logical structure of the heterogeneous tables is parsed, and each heterogeneous table is transformed into a structured data list based on the logical structure. The quantitative risk indicator data is obtained by extracting data corresponding to the risk influencing factors from the structured data list.

[0008] As one preferred embodiment, determining the corresponding key parameters from the quantitative risk indicator data based on the key influencing factors includes: Construct an initial mapping link from the key influencing factors to the quantitative risk indicator data; Based on the constructed probability mapping model, the strength of the mapping relationship of each group in the initial mapping link is quantified; Based on the strength of the mapping relationship, quantitative risk indicator data with a correlation strength higher than a preset threshold are selected for each key influencing factor, and the quantitative risk indicator data is determined as the key parameter corresponding to the key influencing factor.

[0009] As one preferred embodiment, the step of generating a risk assessment result for the construction of the target pumped storage power station project based on the risk transmission path, the key parameters, and the corresponding risk level includes: Based on the risk transmission path, the key parameters, and the risk level, a risk network diagram is constructed; Based on the topology of the risk network graph, the comprehensive risk value of the risk network graph is calculated. Based on the comprehensive risk value and the risk transmission path, the risk assessment results for the construction of the target pumped storage power station project are generated.

[0010] Another embodiment of the present invention provides a risk assessment system for the construction of pumped storage power stations, comprising: The module is used to build a multi-dimensional risk assessment system for pumped storage power stations, where each dimension has at least one risk influencing factor. The coupling module is used to analyze the interaction between the various risk influencing factors, identify key influencing factors based on the analysis results, and determine the risk transmission path between the key influencing factors. The acquisition module is used to acquire project construction data for the target pumped storage power station. The extraction module is used to extract quantitative risk indicator data corresponding to each risk influencing factor from the project construction data based on the multi-dimensional risk assessment system. The mapping module is used to determine the corresponding key parameters from the quantitative risk indicator data based on the key influencing factors. The determination module is used to determine the risk level corresponding to the key parameters; The assessment module is used to generate a risk assessment result for the construction of the target pumped storage power station project based on the risk transmission path, the key parameters, and the corresponding risk level.

[0011] As one preferred embodiment, the coupling module is further configured to: Construct an initial relationship matrix representing the intensity of the direct influence between each of the aforementioned risk factors; The initial relationship matrix is ​​optimized to obtain the comprehensive influence matrix; Based on the comprehensive impact matrix, calculate the centrality index of each of the risk influencing factors; The key influencing factors are identified based on the centrality index.

[0012] As one preferred embodiment, the extraction module is further configured to: The logical structure of the heterogeneous tables is parsed, and each heterogeneous table is transformed into a structured data list based on the logical structure. The quantitative risk indicator data is obtained by extracting data corresponding to the risk influencing factors from the structured data list.

[0013] As one preferred embodiment, the mapping module is further configured to: Construct an initial mapping link from the key influencing factors to the quantitative risk indicator data; Based on the constructed probability mapping model, the strength of the mapping relationship of each group in the initial mapping link is quantified; Based on the strength of the mapping relationship, quantitative risk indicator data with a correlation strength higher than a preset threshold are selected for each key influencing factor, and the quantitative risk indicator data is determined as the key parameter corresponding to the key influencing factor.

[0014] As one preferred embodiment, the evaluation module is further configured to: Based on the risk transmission path, the key parameters, and the risk level, a risk network diagram is constructed; Based on the topology of the risk network graph, the comprehensive risk value of the risk network graph is calculated. Based on the comprehensive risk value and the risk transmission path, the risk assessment results for the construction of the target pumped storage power station project are generated.

[0015] Compared with the prior art, the beneficial effects of the embodiments of the present invention are at least one of the following: (1) This invention establishes an assessment framework by constructing a multi-dimensional risk assessment system, then analyzes the interactions between factors, identifies key influencing factors, and determines their risk transmission paths; subsequently, it extracts and locks specific parameters corresponding to key factors from the project construction data of the target pumped storage power station, realizing the transformation from qualitative to quantitative; finally, it dynamically deduces the overall risk situation of the project by quantifying the risk level of the parameters and combining them with the transmission path, thereby generating accurate risk assessment conclusions. This invention overcomes the limitation of treating risk factors as isolated individuals in traditional assessments, and can dynamically simulate the risk propagation process, thereby achieving accurate early warning of systemic risks.

[0016] (2) This invention significantly improves the scientific nature and effectiveness of risk management in the construction of pumped storage power stations through an innovative risk assessment mechanism. It can accurately identify potential major risks and hidden dangers, enabling project managers to take effective preventive measures before risks occur. By accurately locating key risk parameters, the efficiency of risk management resource allocation is optimized, concentrating limited resources on the most critical control points. At the same time, a systematic risk assessment system is established to provide continuous and reliable risk decision support for each stage of project construction, comprehensively enhancing the project's full-process control capabilities, effectively ensuring the smooth implementation of the project and improving investment efficiency. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating a risk assessment method for the construction of a pumped storage power station in one embodiment of the present invention. Figure 2 This is a diagram illustrating the effect of extracting quantitative risk indicator data from a risk assessment method for the construction of a pumped storage power station in one embodiment of the present invention. Figure 3 This is a schematic diagram of a risk assessment system for the construction of a pumped storage power station in one embodiment of the present invention; Figure label: The module includes a construction module 11, a coupling module 12, an acquisition module 13, an extraction module 14, a mapping module 15, a judgment module 16, and an evaluation module 17. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0019] In the description of this application, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first," "second," "third," etc., may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0020] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal communication between two components. The terms "vertical," "horizontal," "left," "right," "upper," "lower," and similar expressions used herein are for illustrative purposes only and do not indicate or imply that the device or component referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as limiting the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0021] In the description of this application, it should be noted that, unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this specification is for the purpose of describing specific embodiments only and is not intended to limit the invention. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0022] One embodiment of the present invention provides a risk assessment method for the construction of pumped storage power stations. For details, please refer to [link / reference]. Figure 1 , Figure 1 The diagram shown illustrates a risk assessment method for the construction of a pumped storage power station according to one embodiment of the present invention, including steps S1 to S7: S1: Construct a multi-dimensional risk assessment system for pumped storage power stations, with at least one risk influencing factor under each dimension; The multi-dimensional risk assessment system refers to classifying various risk factors affecting the construction of pumped storage power stations into several interrelated assessment dimensions according to their attributes and characteristics, including at least four dimensions: geographical environment, technical engineering, construction resources, and external environment. Each dimension includes several specific risk influencing factors.

[0023] In this embodiment, the risk influencing factors included in each dimension are: The geographical environment dimension includes geographical location, topographical conditions, hydrological and water resource conditions, etc. The technical engineering dimension includes construction scale and installed capacity, survey and design, construction difficulty and equipment reliability, etc. Construction resources include material supply and human resource allocation; External environmental dimensions include policies, regulations, and climate conditions.

[0024] Because the construction of pumped storage power stations involves complex systems engineering across multiple disciplines and stages, a single-dimensional risk assessment cannot fully reflect the various risks faced by the project. By establishing a multi-dimensional assessment system, key risk areas throughout the entire project construction process can be systematically covered, providing a foundation for subsequent systematic risk assessment work.

[0025] S2: Analyze the interaction between various risk factors, identify key influencing factors based on the analysis results, and determine the risk transmission path between key influencing factors; Key influencing factors refer to a small number of core factors in the complex system of pumped storage power station construction that have significant influence and can significantly drive or change the risk status of the entire system. The risk transmission path can clearly describe the complete chain of events in which a problem with one key influencing factor logically affects and triggers other key influencing factors, ultimately leading to what consequences.

[0026] Specifically, in one embodiment of the present invention, experts in the field are required to score the direct relationship between every two risk influencing factors in a pre-constructed assessment system, typically using scores from 0 to 4 to represent no impact, weak impact, moderate impact, strong impact, and extremely strong impact. The scoring results are averaged to obtain an initial relationship matrix representing the strength of the direct impact between each key influencing factor.

[0027] Preferably, in one embodiment of the present invention, the interaction relationships between various risk influencing factors are analyzed, and key influencing factors are identified based on the analysis results, including: Construct an initial relationship matrix representing the intensity of the direct influence between various risk factors; The initial relationship matrix is ​​optimized to obtain the comprehensive influence matrix; Based on the comprehensive impact matrix, calculate the centrality index of each risk influencing factor; Key influencing factors are identified based on centrality indicators.

[0028] The initial key matrix refers to a data structure that records the intensity of direct influence between various risk factors in matrix form; the comprehensive influence matrix is ​​a matrix that contains both direct and indirect influences after normalization and matrix operations; the centrality index is a quantitative parameter that measures the importance of a risk factor in the system, and is obtained by adding the influence degree and the degree of being influenced by the factor.

[0029] Specifically, the initial relation matrix A is obtained based on the expert scoring results. n×n (where n is the number of elements in the qualitative analysis index set, a) ij After representing the magnitude of the influence of indicator i on indicator j, the normalized influence matrix B is obtained by dividing each element of matrix A by the maximum value of the row sums. n×n Subsequently, the comprehensive influence matrix was calculated using the matrix operation formula of the Decision Laboratory Analysis (DEMATEL) method. The calculation formula is as follows: Next, the sum of all elements in each row of the comprehensive influence matrix is ​​calculated as the influence degree of the corresponding risk factor; simultaneously, the sum of all elements in each column is calculated as the affected degree of the corresponding risk factor. Finally, the influence degree and affected degree of each factor are added together to obtain its centrality; the affected degree is then subtracted from the influence degree to obtain its causality. Based on the centrality, the risk factors are ranked, and factors with a centrality value greater than the average centrality of all risk factors are identified as key influencing factors.

[0030] To determine the risk transmission path among key influencing factors, the first step is to calculate the causality degree (a core indicator in the DEMATEL method, used to determine whether a factor plays an active "cause" or a passive "effect" in a complex system) based on the comprehensive influence matrix. Then, based on the calculated causality degree, key influencing factors are divided into causal factors and result factors: those with a causality degree greater than zero are causal factors, and those with a causality degree less than zero are result factors. Causal factors drive system changes and are the initiating end of risk, while result factors reflect the system state and are the acting end of risk. Next, the influence relationships between causal factors and result factors are extracted from the comprehensive influence matrix and sorted according to influence intensity from high to low, constructing a clear multi-level hierarchical structure. This structure clearly demonstrates the complete path of risk transmission from the root cause, through intermediate links, to the result factor. This embodiment, based on the analysis of the interaction relationships between various risk influencing factors, selects key influencing factors including: land acquisition area, reservoir regulation capacity, total installed capacity, excavation volume, fly ash cost, rated head, water conveyance system length, construction water price, normal reservoir water level, and steel reinforcement cost.

[0031] S3: Obtain project construction data for the target pumped storage power station; Among them, project construction data refers to technical documents such as the feasibility study report, feasibility study technical report, and comprehensive description report of the target pumped storage power station. These documents were chosen because they are authoritative documents that must be completed before project decision-making, as stipulated by energy industry standards. They systematically contain all key parameters of the project, from geographical environment, technical engineering, construction resources to the external environment, and are the most complete data source for conducting systematic risk assessments.

[0032] S4: Based on a multi-dimensional risk assessment system, extract quantitative risk indicator data corresponding to each risk influencing factor from project construction data; Quantitative risk indicator data refers to machine-readable data with clear numerical values ​​and physical dimensions, extracted from the original project construction data and uniquely linked to specific risk influencing factors in a multi-dimensional risk assessment system. In practice, data processing personnel need to rely on a risk factor-data mapping table pre-established by domain experts. This mapping table precisely indicates the data source, data name, and unit of measurement for each risk influencing factor. For example, the quantitative risk indicator data for the influencing factor of construction scale and installed capacity under the technology dimension is mapped to the total installed capacity value in the engineering characteristics table of the feasibility study report, typically in megawatts.

[0033] Preferably, in one embodiment of the present invention, the extraction of quantitative risk indicator data corresponding to each risk influencing factor from the project construction data is performed using heterogeneous tables within the project construction data, including: Parse the logical structure of heterogeneous tables and transform each heterogeneous table into a structured data list based on the logical structure; Data corresponding to risk influencing factors are extracted from the structured data list to obtain quantitative risk indicator data.

[0034] Because feasibility study reports for pumped storage power stations come from diverse sources and have widely varying table formats, manual processing is inefficient and prone to errors. Therefore, when extracting quantitative risk indicator data from project construction data, especially given the large number of heterogeneous tables in the original reports, an intelligent table parsing technology is needed to ensure the accuracy and efficiency of data extraction.

[0035] Specifically, firstly, the logical structure of the heterogeneous tables needs to be parsed. This involves identifying the text content of each cell and its precise coordinates on the page, and then analyzing the spatial alignment and containment relationships between cells. This process aims to understand how the table expresses the hierarchy and subordinate logic of data through its layout, thereby transforming visual layout information into a computer-understandable logical structure. Subsequently, based on the parsed logical structure, the system transforms these heterogeneous tables into a standard structured data list. This embodiment uses a tree structure model to accomplish this transformation. The system recursively analyzes the spatial relationships between cells, constructing a data tree. In this tree, the root node represents the entire table, its multi-level child nodes represent different levels of table header categories, and the final leaf nodes store specific numerical data with dimensions. In this way, the complex hierarchical relationships expressed by cell merging and indentation in the original table are completely and clearly preserved in a structure that can be traversed and queried by the program. Figure 2 As shown, Figure 2 This is a diagram illustrating the effect of extracting quantitative risk indicator data from the risk assessment method for the construction of pumped storage power stations in this embodiment of the invention.

[0036] S5: Based on key influencing factors, determine the corresponding key parameters from quantitative risk indicator data; Key parameters refer to the few core parameters selected from massive amounts of quantitative risk indicator data that have the most decisive impact on key influencing factors. These key parameters are the core control points on the risk transmission path, and precise management of them can achieve twice the result with half the effort. In specific implementation, domain experts first need to construct an initial mapping link from key influencing factors to quantitative risk indicator data based on their extensive engineering experience. This mapping link is a knowledge framework that specifies which aspects of quantitative data should theoretically be considered to assess a particular key influencing factor.

[0037] Preferably, in one embodiment of the present invention, determining corresponding key parameters from quantitative risk indicator data based on key influencing factors includes: Construct an initial mapping link from key influencing factors to quantitative risk indicator data; Based on the constructed probability mapping model, the strength of the mapping relationship of each group in the initial mapping link is quantified; Based on the strength of the mapping relationship, quantitative risk indicator data with a correlation strength higher than a preset threshold are selected for each key influencing factor, and the quantitative risk indicator data are determined as the key parameters corresponding to the key influencing factors.

[0038] The probabilistic mapping model is a mathematical tool used to reveal and quantify the strength of correlations between variables in uncertain environments. In practice, firstly, based on an initial mapping link constructed by domain experts, it is determined which quantitative risk indicator data have a theoretical correlation with each key influencing factor. For example, the key influencing factor "material cost" will establish an initial mapping with the price data of multiple materials such as cement, steel bars, and diesel. Subsequently, the strength of the above mapping relationship is quantified using a pre-constructed probabilistic mapping model. This model is trained based on historical project data (historical quantitative indicators and corresponding expert risk level assessments), for example, using an ordered Logit model. Its function is to objectively measure the statistical representativeness and explanatory power of each quantitative risk indicator data on its associated key influencing factors. The higher the relationship strength, the more accurately the quantitative data reflects the state changes of the corresponding key influencing factor. Finally, the calculated mapping relationship strength is used for screening. For each key influencing factor, those quantitative risk indicator data with relationship strengths higher than a preset threshold are retained and formally identified as the key parameters corresponding to that key influencing factor. The preset threshold is set based on retaining the top K percent of quantitative risk indicator data with the strongest mapping relationship for each key influencing factor. The K value can be set according to management needs, such as 20% or 30%.

[0039] The construction process of the probabilistic mapping model is as follows: First, based on the historical pumped storage power station project database, actual data of each quantitative indicator and retrospective level assessments of the corresponding key influencing factors by domain experts are collected to form a training sample set. Next, considering the characteristic that key influencing factors are ordered discrete variables, an ordered Logit model is selected as the modeling framework. Using the training sample set, the model parameters are calibrated using the maximum likelihood estimation method and introducing L2 regularization. The purpose of this step is to enable the model to accurately learn the mapping pattern from quantitative indicators to the levels of influencing factors, laying the foundation for subsequent intensity quantification.

[0040] S6: Determine the risk level corresponding to the key parameters; Risk level is a qualitative or semi-quantitative label used to characterize the degree of risk, typically an ordered discrete sequence. In practice, it's necessary to first set risk thresholds for each key parameter; these thresholds constitute the benchmark for risk level classification. The thresholds are set based on several sources: First, mandatory standards or guidelines issued by the industry or enterprise; for example, the threshold for "construction land acquisition area" might refer to relevant national regulations on land acquisition for large-scale projects. Second, empirical ranges derived from statistical analysis of a large amount of historical pumped-storage power station project data; for example, determining a reasonable fluctuation range by analyzing the "unit indicator" data of hundreds of successful projects. Third, the judgment experience accumulated by domain experts based on long-term engineering practice.

[0041] After obtaining the actual values ​​of key parameters for the target pumped storage power station, the system compares them with preset threshold ranges. For example, for the key parameter "excavation volume," the following thresholds can be set: when the actual value is lower than the 30th percentile of historical data, it is judged as "low risk"; when it is between the 30th and 70th percentiles, it is judged as "medium risk"; and when it is higher than the 70th percentile, it is judged as "high risk." This allows key parameters with different dimensions and vastly different numerical ranges to be uniformly mapped to a limited risk level system, making risks of different natures comparable and laying a solid foundation for the next step of integrating all information to generate an overall risk assessment result.

[0042] S7: Based on the risk transmission path, key parameters and corresponding risk levels, generate the risk assessment results for the construction of the target pumped storage power station project.

[0043] The final output of the risk assessment results, processed by the method in this embodiment, is a systematic conclusion encompassing the overall risk level of the project, analysis of key risk chains, and early warning of risk evolution, providing a direct basis for project decision-making. In practice, it does not simply list the risk levels of each key parameter, but rather weaves these discrete risk points into an interconnected risk network based on the causal logic revealed by the risk transmission path. The assessment results include at least two levels: first, the overall risk level of the project, expressed as a quantified comprehensive risk value; and second, an in-depth analysis of the key risk chains, indicating the root causes of the risks and their possible evolution paths.

[0044] Preferably, in one embodiment of the present invention, a risk assessment result for the construction of the target pumped storage power station project is generated based on the risk transmission path, key parameters, and corresponding risk levels, including: Construct a risk network diagram based on risk transmission paths, key parameters, and risk levels; Based on the topology of the risk network graph, the comprehensive risk value of the risk network graph is calculated. Based on the comprehensive risk value and risk transmission path, a risk assessment result for the construction of the target pumped storage power station project is generated.

[0045] A risk network diagram is a graphical model used to visually represent the interrelationships between key influencing factors. Nodes in the diagram represent key influencing factors, and edges represent the direction of risk transmission, thus transforming the abstract risk system into a computable topological structure. The comprehensive risk value is a single quantitative indicator calculated by integrating the risk levels and topological importance (such as centrality) of each node in the risk network, used to characterize the overall risk level of the project construction.

[0046] Specifically, firstly, a risk network diagram is constructed based on the risk transmission path, key parameters, and risk level. Then, based on the topology of the risk network diagram, the overall risk value of the graph is calculated. Specifically, this step utilizes the centrality index of each influencing factor calculated during the construction of the risk transmission path (i.e., step S2). This index characterizes the inherent influence and core position of the factor within the entire risk network. Then, the risk level of each node is weighted and fused with its centrality, ultimately aggregating to obtain an overall risk value that reflects the overall risk situation. This ensures that a high-risk node located at the core of the network contributes more to the overall risk value than an equally risky node located at the periphery.

[0047] Finally, based on the calculated comprehensive risk value and risk transmission path, the final risk assessment result is generated. The output includes a quantified comprehensive risk value and its corresponding level range. Simultaneously, combined with a risk network diagram, the core risk chains requiring priority attention are identified, and the transmission and evolution of risks after changes in key parameters are simulated, thus forming an in-depth assessment report that includes overall rating, root cause diagnosis, and development warning.

[0048] Another embodiment of the present invention provides a risk assessment system for the construction of pumped storage power stations. For details, please refer to [link to relevant documentation]. Figure 3 , Figure 3 The diagram shown illustrates a risk assessment system for the construction of a pumped storage power station according to one embodiment of the present invention, which includes: Module 11 is used to construct a multi-dimensional risk assessment system for pumped storage power stations, where each dimension has at least one risk influencing factor. The coupling module 12 is used to analyze the interaction between various risk influencing factors, identify key influencing factors based on the analysis results, and determine the risk transmission path between key influencing factors. Module 13 is used to acquire project construction data of the target pumped storage power station; Extraction module 14 is used to extract quantitative risk indicator data corresponding to each risk influencing factor from project construction data based on a multi-dimensional risk assessment system. Mapping module 15 is used to determine the corresponding key parameters from quantitative risk indicator data based on key influencing factors; Module 16 is used to determine the risk level corresponding to the key parameters; Assessment module 17 is used to generate risk assessment results for the construction of the target pumped storage power station project based on the risk transmission path, key parameters and corresponding risk levels.

[0049] Preferably, in one embodiment of the present invention, the coupling module is further configured to: Construct an initial relationship matrix representing the intensity of the direct influence between various risk factors; The initial relationship matrix is ​​optimized to obtain the comprehensive influence matrix; Based on the comprehensive impact matrix, calculate the centrality index of each risk influencing factor; Key influencing factors are identified based on centrality indicators.

[0050] Preferably, in one embodiment of the present invention, the extraction module is further configured to: Parse the logical structure of heterogeneous tables and transform each heterogeneous table into a structured data list based on the logical structure; Data corresponding to risk influencing factors are extracted from the structured data list to obtain quantitative risk indicator data.

[0051] Preferably, in one embodiment of the present invention, the mapping module is further configured to: Construct an initial mapping link from key influencing factors to quantitative risk indicator data; Based on the constructed probability mapping model, the strength of the mapping relationship of each group in the initial mapping link is quantified; Based on the strength of the mapping relationship, quantitative risk indicator data with a correlation strength higher than a preset threshold are selected for each key influencing factor, and the quantitative risk indicator data are determined as the key parameters corresponding to the key influencing factors.

[0052] Preferably, in one embodiment of the present invention, the evaluation module is further configured to: Construct a risk network diagram based on risk transmission paths, key parameters, and risk levels; Based on the topology of the risk network graph, the comprehensive risk value of the risk network graph is calculated. Based on the comprehensive risk value and risk transmission path, a risk assessment result for the construction of the target pumped storage power station project is generated.

[0053] Compared with the prior art, the beneficial effects of the embodiments of the present invention are at least one of the following: (1) This invention establishes an assessment framework by constructing a multi-dimensional risk assessment system, then analyzes the interactions between factors, identifies key influencing factors, and determines their risk transmission paths; subsequently, it extracts and locks specific parameters corresponding to key factors from the project construction data of the target pumped storage power station, realizing the transformation from qualitative to quantitative; finally, it dynamically deduces the overall risk situation of the project by quantifying the risk level of the parameters and combining them with the transmission path, thereby generating accurate risk assessment conclusions. This invention overcomes the limitation of treating risk factors as isolated individuals in traditional assessments, and can dynamically simulate the risk propagation process, thereby achieving accurate early warning of systemic risks.

[0054] (2) This invention significantly improves the scientific nature and effectiveness of risk management in the construction of pumped storage power stations through an innovative risk assessment mechanism. It can accurately identify potential major risks and hidden dangers, enabling project managers to take effective preventive measures before risks occur. By accurately locating key risk parameters, the efficiency of risk management resource allocation is optimized, concentrating limited resources on the most critical control points. At the same time, a systematic risk assessment system is established to provide continuous and reliable risk decision support for each stage of project construction, comprehensively enhancing the project's full-process control capabilities, effectively ensuring the smooth implementation of the project and improving investment efficiency.

[0055] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A risk assessment method for construction of a pumped storage power plant, characterized by, The method comprises the following steps: constructing a multi-dimensional risk assessment system for pumped storage power stations, wherein there is at least one risk influencing factor under each dimension; analyzing the interaction relationship between each of the risk influencing factors, identifying key influencing factors based on the analysis results, and determining the risk transmission path between the key influencing factors; obtaining project construction data of a target pumped storage power station; based on the multi-dimensional risk assessment system, extracting quantitative risk indicator data corresponding to each of the risk influencing factors from the project construction data; based on the key influencing factors, determining corresponding key parameters from the quantitative risk indicator data; determining the risk level corresponding to the key parameters; based on the risk transmission path, the key parameters and the corresponding risk level, generating the risk assessment result of the project construction of the target pumped storage power station.

2. The risk assessment method for construction of a pumped storage power plant according to claim 1, characterized by, The analysis of the interaction relationship between each of the risk influencing factors, the identification of the key influencing factors based on the analysis results, comprises: constructing an initial relationship matrix representing the direct influence strength between each of the risk influencing factors; optimizing the initial relationship matrix to obtain a comprehensive influence matrix; calculating the centrality index of each of the risk influencing factors according to the comprehensive influence matrix; identifying the key influencing factors according to the centrality index.

3. The risk assessment method for construction of a pumped storage power plant according to claim 1, characterized by, The extraction of quantitative risk indicator data corresponding to each of the risk influencing factors from the project construction data is carried out on heterogeneous tables in the project construction data, comprising: analyzing the logical structure of the heterogeneous tables, and converting each of the heterogeneous tables into a structured data list based on the logical structure; extracting data corresponding to the risk influencing factors from the structured data list to obtain the quantitative risk indicator data.

4. The risk assessment method for construction of a pumped storage power plant according to claim 1, characterized by, The determination of corresponding key parameters from the quantitative risk indicator data based on the key influencing factors comprises: constructing an initial mapping link from the key influencing factors to the quantitative risk indicator data; quantifying the mapping relationship strength of each group in the initial mapping link based on the constructed probability mapping model; according to the mapping relationship strength, screening out the quantitative risk indicator data with a correlation strength higher than a preset threshold for each of the key influencing factors, and determining that the quantitative risk indicator data is the key parameter corresponding to the key influencing factor.

5. The risk assessment method for construction of a pumped storage power plant according to claim 1, characterized by, The generation of the risk assessment result of the project construction of the target pumped storage power station based on the risk transmission path, the key parameters and the corresponding risk level comprises: based on the risk transmission path, the key parameters and the risk level, constructing a risk network graph; based on the topological structure of the risk network graph, calculating the comprehensive risk value of the risk network graph; according to the comprehensive risk value and the risk transmission path, generating the risk assessment result of the project construction of the target pumped storage power station.

6. A risk assessment system for pumped storage power plant construction, characterized by, The method comprises the following steps: constructing a multi-dimensional risk assessment system for pumped storage power stations, wherein there is at least one risk influencing factor under each dimension; The coupling module is configured to analyze interaction relationships between the risk influence factors, identify key influence factors based on an analysis result, and determine a risk transmission path between the key influence factors; The acquisition module is configured to acquire project construction data of a target pumped storage power station; The extraction module is configured to extract quantified risk indicator data corresponding to each risk influence factor from the project construction data based on the multi-dimensional risk assessment system; The mapping module is configured to determine corresponding key parameters from the quantified risk indicator data based on the key influence factors; The determination module is configured to determine a risk level corresponding to the key parameters; The evaluation module is configured to generate a risk assessment result of project construction of the target pumped storage power station based on the risk transmission path, the key parameters, and the corresponding risk levels.

7. A risk assessment system for the construction of a pumped storage power plant according to claim 6, characterized in that, The coupling module is further configured to: construct an initial relationship matrix representing direct influence strength between each risk influence factor; optimize the initial relationship matrix to obtain a comprehensive influence matrix; calculate a centrality index of each risk influence factor according to the comprehensive influence matrix; identify the key influence factors according to the centrality index.

8. The risk assessment system for construction of a pumped storage power plant according to claim 6, wherein The extraction module is further configured to: analyze a logical structure of the heterogeneous tables and convert each heterogeneous table into a structured data list based on the logical structure; extract data corresponding to the risk influence factors from the structured data list to obtain the quantified risk indicator data.

9. The risk assessment system for construction of a pumped storage power plant according to claim 6, wherein The mapping module is further configured to: construct an initial mapping link from the key influence factors to the quantified risk indicator data; quantify mapping relationship strength of each group in the initial mapping link based on the constructed probability mapping model; select, for each key influence factor, the quantified risk indicator data with a correlation strength higher than a preset threshold value according to the mapping relationship strength, and determine the quantified risk indicator data as the key parameters corresponding to the key influence factors.

10. The risk assessment system for construction of a pumped storage power plant according to claim 6, wherein The evaluation module is further configured to: construct a risk network graph based on the risk transmission path, the key parameters, and the risk levels; calculate a comprehensive risk value of the risk network graph based on a topological structure of the risk network graph; generate the risk assessment result of project construction of the target pumped storage power station according to the comprehensive risk value and the risk transmission path.

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