Power grid project risk assessment system and method integrating Pareto and Borda rules

Abstract

The invention provides a power grid project risk assessment system and method integrating Pareto and Borda rules, and belongs to the technical field of project risk assessment. The system comprises a multi-dimensional risk interaction relationship identification module used for analyzing an interdependence relationship between risk factors of a target power grid project and constructing a multi-dimensional risk relationship network atlas; the risk interaction weight generation module is used for determining interaction weights among the risk factors; the risk sorting and Pareto processing module is used for sorting the risk factors based on a Pareto analysis method to obtain a preliminary risk priority sorting list and key risk factors; and the multi-party dynamic game decision-making module is used for performing game decision-making on the sorting schemes of the plurality of project participants based on a Borda rule, and obtaining a final risk priority sorting list in combination with the initial risk priority sorting list. According to the invention, the rationality of risk sorting can be improved, and the project risk assessment effect can be improved based on the sorting result.

Description

Technical Field

[0001] The present invention relates to the technical field of project risk assessment, and particularly to a power grid project risk assessment system and method integrating Pareto and Borda rules. Background Art

[0002] In modern large-scale power grid projects, risk assessment is a key link to ensure the safety and smooth implementation of the project. Power grid projects involve various complex risk factors, including technical risks (such as equipment failures, technical failures), financial risks (such as shortage of funds, cost overruns), environmental risks (such as extreme weather, geological changes), and management risks (such as project delays, decision-making mistakes). There are often complex interactions among these risk factors, and their impacts are not independent, but have a chain reaction on the overall project through various dynamic interactions.

[0003] Currently, traditional risk assessment methods usually focus on independent analysis of single risk factors or use simple statistical means to linearly combine multiple risks. Although these methods can identify the impacts of individual risks on the project, they have great limitations in dealing with the interactions among multiple risk factors and cannot accurately reveal the dynamic dependence relationships among risk factors. In addition, when facing the complexity of power grid projects, the traditional risk assessment system lacks a flexible and efficient ranking adjustment mechanism. Existing methods for risk ranking under multi-party participation often rely only on the opinions of a certain participant, lacking fair integration of the experiences and opinions of all parties. When conflicts occur among different ranking methods, there is a lack of effective integration means, resulting in the final ranking results being insufficiently comprehensive and reasonable. This leads to the ranking results may not take into account the concerns of all stakeholders, resulting in an insufficiently comprehensive risk assessment result for the project and making it difficult to achieve good risk early warning and handling. Summary of the Invention

[0004] Embodiments of the present invention provide a power grid project risk assessment system and method integrating Pareto and Borda rules to solve the problem of poor results in project risk assessment based on existing risk ranking methods.

[0005] In a first aspect, embodiments of the present invention provide a power grid project risk assessment system integrating Pareto and Borda rules, including:

[0006] A multi-dimensional risk interaction relationship identification module, configured to analyze the interdependence relationships among the risk factors of a target power grid project and construct a multi-dimensional risk relationship network graph; wherein, the risk factors include power grid equipment status monitoring data;

[0007] A risk interaction weight generation module, configured to determine the interaction weights among the risk factors based on the multi-dimensional risk relationship network graph;

[0008] A risk ranking and Pareto processing module, which is used to rank each risk factor based on the Pareto analysis method and each interaction weight to obtain a preliminary risk priority ranking list and key risk factors;

[0009] A multi-party dynamic game decision-making module, which is used to make game decisions on the ranking schemes of multiple project participants based on the Borda rule, and combine with the preliminary risk priority ranking list to obtain a final risk priority ranking list, so as to conduct risk assessment on the target power grid project based on the final risk priority ranking list.

[0010] In a possible implementation manner, the multi-dimensional risk interaction relationship recognition module includes:

[0011] A data acquisition unit, which is used to acquire multi-source data during the process of the target power grid project; among them, the multi-source data includes power grid equipment status monitoring data, financial statements, external environment data and management reports;

[0012] A data analysis engine, which is used to regard each type of multi-source data as a risk factor and analyze the mutual dependence relationship between each risk factor based on spatio-temporal correlation;

[0013] An interaction impact calculation unit, which is used to quantify the interaction impact degree between each risk factor based on the mutual dependence relationship between each risk factor;

[0014] A risk relationship network construction unit, which is used to generate a multi-dimensional risk relationship network map based on the interaction impact degree between each risk factor.

[0015] In a possible implementation manner, the formula for the data analysis engine to analyze the mutual dependence relationship between each risk factor based on spatio-temporal correlation is:

[0016]

[0017] Among them, C ij (t) represents the correlation coefficient between risk factors i and j at time t, D i (t) represents the dynamic change amount of risk factor i at time t, D j (t) represents the dynamic change amount of risk factor j at time t, cov(D i (t), D j (t)) represents the covariance between risk factors i and j, represents the standard deviation of risk factor i, represents the standard deviation of risk factor j.

[0018] In a possible implementation manner, the formula for the interaction impact calculation unit to quantify the interaction impact degree between each risk factor is:

[0019] E ij E(t) = C ij (t)·D j (t)

[0020] Among them, E ij (t) represents the degree of interaction influence of risk factor i on risk factor j at time t, C ij (t) is the correlation coefficient between risk factors i and j, D j (t) represents the dynamic change amount of risk factor j at time t.

[0021] In a possible implementation, the multi-dimensional risk relationship network graph is represented as: G = (N, E, W);

[0022] Among them, G represents the multi-dimensional risk relationship network graph, N represents the node set of risk factors, E represents the edge set between risk factors, represents the interaction relationship of risk factors, and W represents the weight set of edges, reflecting the interaction influence intensity of each edge.

[0023] In a possible implementation, the risk interaction weight generation module is specifically used for:

[0024] Taking the weights of each edge in the multi-dimensional risk relationship network graph as the interaction weights between each risk factor.

[0025] In a possible implementation, the risk ranking and Pareto processing module includes:

[0026] The weight integration unit is used to integrate the total interaction influence of each risk factor based on the interaction weights between each risk factor, and obtain the comprehensive influence of each risk factor on other risk factors;

[0027] The sorting unit is used to sort each risk factor based on each comprehensive influence, and obtain a preliminary risk priority ranking list;

[0028] The Pareto priority marking unit is used to identify the risk factor with the greatest impact on the whole in the preliminary risk priority ranking list based on the Pareto analysis method as the key risk factor.

[0029] In a possible implementation, the multi-party dynamic game decision-making module includes:

[0030] The opinion collection unit is used to collect the ranking schemes of each risk factor by different project participants;

[0031] The Borda counting unit is used to assign ranking scores to each risk factor based on the Borda counting rule and the ranking of each risk factor in each ranking scheme;

[0032] A game analysis unit for weighting the ranking scores of each risk factor and generating a weighted ranking list of risk factors based on the weighted ranking scores;

[0033] A risk priority adjustment unit for adjusting the preliminary risk priority ranking list based on the weighted ranking list to obtain the final risk priority ranking list.

[0034] In a possible implementation, the formula for the game analysis unit to weight the ranking scores of each risk factor is:

[0035]

[0036] where S i represents the weighted ranking score of risk factor R i W p represents the weight of participant p, R ip represents the ranking score of participant p for risk factor R i m is the total number of participants, and i includes technical risks, financial risks, environmental risks, and management risks.

[0037] In a second aspect, an embodiment of the present invention provides a power grid project risk assessment method integrating the Pareto and Borda rules, including:

[0038] A multi-dimensional risk interaction relationship identification module analyzes the interdependent relationships between the risk factors of the target power grid project and constructs a multi-dimensional risk relationship network graph; where the risk factors include power grid equipment status monitoring data;

[0039] A risk interaction weight generation module determines the interaction weights between the risk factors based on the multi-dimensional risk relationship network graph;

[0040] A risk ranking and Pareto processing module ranks the risk factors based on the Pareto analysis method and each interaction weight to obtain a preliminary risk priority ranking list and key risk factors;

[0041] A multi-party dynamic game decision-making module makes game decisions on the ranking schemes of multiple project participants based on the Borda rule and combines the preliminary risk priority ranking list to obtain the final risk priority ranking list, so as to conduct risk assessment on the target power grid project based on the final risk priority ranking list.

[0042] An embodiment of the present invention provides a power grid project risk assessment system and method integrating Pareto and Borda rules. First, through real-time data, the dynamic interaction relationships between different risk factors are identified, and a multi-dimensional risk relationship network map reflecting the complex dependencies between these risks is constructed to accurately reveal potential linkage effects. Then, through the Pareto analysis method, the key risk factors with the greatest impact on the project are preferentially identified, and the risk ranking opinions of multiple parties are integrated in combination with the Borda rule to ensure the full participation of multiple opinions and improve the rationality of the ranking. Based on this ranking result, the effect of risk assessment on the project can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0044] Figure 1 is a schematic structural diagram of a power grid project risk assessment system integrating Pareto and Borda rules provided by an embodiment of the present invention;

[0045] Figure 2 is a schematic structural diagram of a risk ranking and Pareto processing module provided by an embodiment of the present invention;

[0046] Figure 3 is a schematic structural diagram of a multi-party dynamic game decision-making module provided by an embodiment of the present invention;

[0047] Figure 4 is a flowchart of the implementation of a power grid project risk assessment method integrating Pareto and Borda rules provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present invention. However, those skilled in the art should clearly understand that the present invention can also be implemented in other embodiments without these specific details. In other cases, the detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present invention.

[0049] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will be described through specific embodiments with reference to the drawings.

[0050] Figure 1The following is a schematic structural diagram of a power grid project risk assessment system integrating the Pareto and Borda rules provided by the embodiments of the present invention:

[0051] A multi-dimensional risk interaction relationship identification module, which is used to analyze the interdependent relationships among the risk factors of the target power grid project and construct a multi-dimensional risk relationship network map; among them, the risk factors include power grid equipment status monitoring data;

[0052] A risk interaction weight generation module, which is used to determine the interaction weights among the risk factors based on the multi-dimensional risk relationship network map;

[0053] A risk ranking and Pareto processing module, which is used to rank the risk factors based on the Pareto analysis method and each interaction weight to obtain a preliminary risk priority ranking list and key risk factors;

[0054] A multi-party dynamic game decision-making module, which is used to make game decisions on the ranking schemes of multiple project participants based on the Borda rule, and combine the preliminary risk priority ranking list to obtain a final risk priority ranking list, so as to conduct risk assessment on the target power grid project based on the final risk priority ranking list.

[0055] In this embodiment, the target power grid project may be an ongoing power grid infrastructure project, such as a construction project of a power generation system, a transmission system, a distribution system, a energy storage system, or a combination of the above.

[0056] The multi-dimensional risk interaction relationship identification module: Based on the real-time data of the project, automatically identify the dynamic interaction relationships among different risk factors, construct a multi-dimensional risk dependence map, identify their interactions and dependencies, so as to reveal the potential linkage effects of risk factors. The risk factors include technical risks, financial risks, environmental risks, and management risks;

[0057] The risk interaction weight generation module: Obtain the interaction weights among the risk factors according to the multi-dimensional risk dependence map;

[0058] The risk ranking and Pareto processing module: Combine the interaction weights provided by the risk interaction weight generation module to rank each risk factor, generate a preliminary risk priority ranking list, and select key factors using the Pareto analysis method;

[0059] Multi-party dynamic game decision-making module: It conducts game analysis on the experience risk ranking opinions of each project participant through the Borda rule, and performs weighted processing on different experience risk ranking opinions to ensure that the opinions of each participant are fairly considered. Based on the game result of the Borda rule, it adjusts the preliminary risk priority ranking list generated by Pareto to obtain the final risk priority ranking list. This ranking not only considers the objective importance of risk factors (obtained by Pareto analysis), but also takes into account the different concerns of all parties regarding risk ranking, ensuring the fairness and comprehensiveness of the final decision. For example:

[0060] The project management party may be more concerned about financial risks and project duration risks.

[0061] The construction party may attach more importance to construction safety risks and equipment risks.

[0062] The regulatory agency may give priority to environmental risks and legal risks.

[0063] After risk assessment of the target power grid project based on the final risk priority ranking list determined by the present invention, if the risk is relatively high, then based on the order in the final risk priority ranking list, risk factors can be controlled to reduce the risk level of the target power grid project. For example, if the top 3 risk factors in the final risk priority ranking list are the status monitoring data of transmission equipment, distribution equipment, and wind turbines, then the operating states of transmission equipment, distribution equipment, and wind turbines are adjusted first, and their status monitoring data are optimized, so as to effectively control the risk level of the power grid project.

[0064] The embodiment of the present invention first identifies the dynamic interaction relationships between different risk factors through real-time data, and constructs a multi-dimensional risk relationship network graph reflecting the complex dependencies between these risks, accurately revealing potential linkage effects. Then, through the Pareto analysis method, it first identifies the key risk factors with the greatest impact on the project, and combines the Borda rule to integrate the risk ranking opinions of multiple participants, ensuring the full participation of multiple opinions and improving the rationality of the ranking. Based on this ranking result, the effect of risk assessment on the project can be improved.

[0065] In a possible implementation manner, the multi-dimensional risk interaction relationship identification module includes:

[0066] A data acquisition unit for acquiring multi-source data during the process of the target power grid project; wherein, the multi-source data includes power grid equipment status monitoring data, financial statements, external environment data, and management reports;

[0067] A data analysis engine for taking each type of multi-source data as a risk factor and analyzing the interdependent relationships between risk factors based on spatio-temporal correlation;

[0068] An interaction impact calculation unit, configured to quantify the interaction impact degree among risk factors based on the interdependent relationship among the risk factors;

[0069] A risk relationship network construction unit, configured to generate a multi-dimensional risk relationship network graph based on the interaction impact degree among the risk factors.

[0070] In this embodiment, the multi-source data during the project includes power grid equipment status monitoring data, financial statements, external environment data, and management reports, and the data is used to evaluate technical risks, financial risks, environmental risks, and management risks.

[0071] A data analysis engine: Based on the collected real-time data, automatically identify the interactions among risk factors, analyze the time dynamic changes of each risk factor and their potential interdependent relationships, and reveal the correlations among different risk factors and their impacts on the overall project risk.

[0072] An interaction impact calculation module: Quantify the interaction impact degree among different risk factors through the analysis results of the data analysis engine;

[0073] A risk relationship network construction module: Generate a multi-dimensional risk relationship network graph based on the interaction impact calculation results. Each node in the graph represents a risk factor, and the edges between nodes represent the dependencies among different risk factors, revealing the potential linkage effects.

[0074] In a possible implementation manner, the formula for the data analysis engine to analyze the interdependent relationship among risk factors based on spatio-temporal correlation is:

[0075]

[0076] where C ij (t) represents the correlation coefficient between risk factors i and j at time t, D i (t) represents the dynamic change amount of risk factor i at time t, D j (t) represents the dynamic change amount of risk factor j at time t, cov(D i (t), D j (t)) represents the covariance between risk factors i and j, represents the standard deviation of risk factor i, represents the standard deviation of risk factor j.

[0077] In this embodiment, the data analysis engine uses correlation analysis to analyze the relationships among risk factors.

[0078] cov(D i (t), D jCov(Ri(t), Rj(t)) is the covariance between risk factors i and j, representing their common change trend. and σi and σj are the standard deviations of risk factors i and j, representing their fluctuation ranges.

[0079] D i (t) and D j (t) can be obtained through time series analysis, representing the change or fluctuation of risk factors over a certain period of time, and is obtained by calculating the value of a certain key indicator (such as equipment health score, budget execution deviation) at time t, as follows:

[0080] 1. Technical risk is related to the performance and failure rate of the technology or equipment used in the project, including equipment status monitoring data (such as the operating status, voltage, current, temperature, etc. of wind turbines), and these data are collected through a real-time monitoring system to record the health status and abnormal events of the equipment. D i (t) represents the data change related to technical risk at time t, such as the frequency or status of equipment failures.

[0081] 2. Financial risk comes from the project's budget, cost expenditure, and cash flow situation. Through financial statements, cash flow records, etc., data on risk factors such as insufficient funds and cost overruns are obtained, such as the situation of funds shortage and financial default risk. D j (t) represents the data change related to financial risk at time t, such as the budget execution situation or cash flow.

[0082] 3. Environmental risk can be obtained through external environmental data, mainly including weather data (such as temperature, wind speed, precipitation, etc.) and geological data (such as soil stability), and these data are collected in real time through weather stations or other environmental monitoring systems. For example, the impact of extreme weather on project construction. D i (t) represents the data change related to environmental risk at time t, such as a drastic change in weather conditions.

[0083] 4. Management risk is related to the implementation and progress of project management. Data is obtained through project management reports, progress control systems, or construction records, and such data reflects problems such as decision-making mistakes and project schedule delays in management. D j (t) represents the data change related to management risk at time t, such as a delay in the project schedule or an adjustment of management decisions.

[0084] In a possible implementation, the formula for the interaction impact calculation unit to quantify the interaction impact degree between risk factors is:

[0085] E ij (t) = C ij (t) · D j (t)

[0086] Among them, E ij (t) represents the degree of interaction between risk factor i and risk factor j at time t, and C ij (t) is the correlation coefficient between risk factors i and j, and D j (t) represents the dynamic change amount of risk factor j at time t.

[0087] In this embodiment, the degree of interaction E ij (t) in the interaction impact calculation module quantifies the mutual influence strength between risk factor i and risk factor j, and is defined as: E ij (t) = C ij (t) · D j (t), where E ij (t) represents the degree of interaction between risk factor i and risk factor j at time t, and C ij (t) is the correlation coefficient between risk factors i and j, reflecting their mutual dependence or correlation. By multiplying the correlation coefficient C ij (t) with D j (t), the actual impact degree of the change of risk factor j on risk factor i is calculated. This is based on risk transmission and propagation, that is, risk factors with high correlation will affect each other, and the magnitude of the influence depends on their current risk status. The change of risk factor j will be transmitted to i through its association with i. Therefore, the magnitude of D j (t) affects this transmission effect.

[0088] In a possible implementation, the multi-dimensional risk relationship network graph is represented as: G = (N, E, W);

[0089] Among them, G represents the multi-dimensional risk relationship network graph, N represents the set of nodes of risk factors, E represents the set of edges between risk factors, representing the interaction relationship of risk factors, and W represents the set of weights of edges, reflecting the interaction impact strength of each edge.

[0090] In this embodiment, the multi-dimensional risk relationship network graph is represented as: G = (N, E, W), where G is the multi-dimensional risk relationship network graph, N is the set of nodes of risk factors, E is the set of edges between risk factors, representing the interaction relationship of risk factors, and W is the set of weights of edges, reflecting the interaction impact strength of each edge.

[0091] Set of nodes: Represent the set of all risk factors with N = {R 1 , R 2 ,..., R n}, where R i is the i-th risk factor and n is the total number of risk factors;

[0092] Edge set: Represent the interaction relationships between all risk factors with E = {(R i , R j ) | i, j ∈ N, i ≠ j}. Each edge connects two different risk factors R i and R j ;

[0093] Edge weight: The weight W ij of each edge represents the interaction intensity between risk factors R i and R j .

[0094] In a possible implementation, the risk interaction weight generation module is specifically used for:

[0095] Taking the weights of each edge in the multi-dimensional risk relationship network graph as the interaction weights between each risk factor.

[0096] In this embodiment, the interaction weights between each risk factor are the edge weights W ij , W ij in the multi-dimensional risk relationship network graph. The edge weight between nodes is expressed as: where W ij is the edge weight between risk factors i and j, represents the sum of all interaction weights related to risk factor i, ensuring the normalization of the edge weight.

[0097] In a possible implementation, the risk ranking and Pareto processing module includes:

[0098] A weight integration unit, which is used to integrate the total interaction impact of each risk factor based on the interaction weights between each risk factor to obtain the comprehensive influence of each risk factor on other risk factors;

[0099] A sorting unit, which is used to sort each risk factor based on each comprehensive influence to obtain a preliminary risk priority ranking list;

[0100] A Pareto priority marking unit, which is used to identify the risk factor with the greatest impact on the whole in the preliminary risk priority ranking list as the key risk factor based on the Pareto analysis method.

[0101] In this embodiment, the structure of the risk ranking and Pareto processing module is as Figure 2 shown. Among them, the weight integration unit: Based on the edge weight W ij provided by the risk interaction weight generation module, integrate the total interaction impact of each risk factor R i and calculate its comprehensive influence I i, the calculation formula for the comprehensive influence is as follows: Among them, I i represents the comprehensive influence of the risk factor R i , n is the total number of all risk factors;

[0102] Sorting unit: Based on the comprehensive influence I i of each risk factor R i , sort the comprehensive influences of all risk factors from high to low. The sorting result is the priority list of risk factors, and a preliminary risk priority sorted list is obtained;

[0103] Pareto priority marking unit: Based on the 80 / 20 rule of the Pareto analysis method, identify the key factors that have the greatest impact on the whole. The key factors may include the top 20% of the risk factors sorted by comprehensive influence, and mark the top 20% of the key risk factors as the objects to be processed first.

[0104] In a possible implementation, the multi-party dynamic game decision-making module includes:

[0105] Opinion collection unit, which is used to collect the sorting schemes of each risk factor by different project participants;

[0106] Borda counting unit, which is used to assign ranking scores to each risk factor based on the Borda counting rule and the rankings of each risk factor in each sorting scheme;

[0107] Game analysis unit, which is used to perform weighted processing on the ranking scores of each risk factor and generate a weighted sorted list of risk factors based on the weighted ranking scores;

[0108] Risk priority adjustment unit, which is used to adjust the preliminary risk priority sorted list based on the weighted sorted list to obtain the final risk priority sorted list.

[0109] In this embodiment, the structure of the multi-party dynamic game decision-making module is as Figure 3 shown. Opinion collection unit: Collect the empirical sorting opinions of each risk factor from different project participants. The project participants independently sort the risk factors according to their own priorities of concern, and generate different risk sorted lists. The project participants include the project management party, the construction party, and the supervision agency;

[0110] Borda counting unit: According to the Borda rule, perform weighted processing on the risk sorting opinions provided by each participant, and assign a ranking score to each risk factor in the sorted list provided by each project participant. The ranking scores are assigned according to the ranking positions of the risk factors. The risk factors ranked higher get higher scores, and the risk factors ranked lower get lower scores, decreasing in turn;

[0111] Game analysis unit: Conduct game analysis through the Borda rule, and weight the ranking scores of each risk factor R i to obtain the total score S i of the risk factor R i , summarize the weighted ranking opinions of each party, comprehensively consider the opinions of different parties, and generate a weighted ranking list of risk factors;

[0112] Risk priority adjustment unit: Based on the Borda game analysis results, adjust the preliminary risk priority ranking list generated by Pareto analysis.

[0113] The specific steps of the Borda rule game analysis are as follows:

[0114] Collect the ranking opinions of each party: the project management party, the construction party, and the supervision agency. Each party independently ranks the risk factors R1, R2, …, Rn according to its concerned priorities, and each ranking list arranges the risk factors from the most important to the least important. For example, the project management party may rank the financial risk R2 first, while the construction party ranks the technical risk R1 first.

[0115] Borda score allocation: For each party's ranking list, use the Borda count method to allocate scores to each risk factor. The score allocation rule is based on the ranking position, and the risk factors ranked higher get higher scores. The score allocation method is as follows:

[0116] If a certain party has a total of n risk factors, the risk factor ranked 1st by it gets n points, the 2nd gets n - 1 points, and so on, and the last one gets 1 point.

[0117] Example: Suppose there are 5 risk factors R1, R2, R3, R4, R5 and the ranking lists of 3 parties:

[0118] Project management party: R2, R3, R1, R5, R4;

[0119] Construction party: R1, R4, R2, R3, R5;

[0120] Supervision agency: R4, R1, R3, R2, R5;

[0121] Borda score allocation of the project management party: R2 = 5 points, R3 = 4 points, R1 = 3 points, R5 = 2 points, R4 = 1 point.

[0122] Weight the opinions of each party: The opinions of each party have different importance in the project, so different weights need to be assigned to each party:

[0123] The weight of the project management party is W1 = 0.5;

[0124] The weight of the construction party is W2 = 0.3;

[0125] The weight of the regulatory agency is W3 = 0.2;

[0126] Then, the Borda scores of each participating party are weighted. The weighted score calculation formula for each risk factor is: Si = W1·Ri1 + W2·Ri2 + W3·Ri3, where Si represents the total score of the risk factor Ri; Ri1, Ri2, and Ri3 are the Borda scores of the project management party, the construction party, and the regulatory agency for the risk factor Ri respectively, and W1, W2, and W3 are the weights of the project management party, the construction party, and the regulatory agency respectively.

[0127] Example (weighted calculation): Suppose the Borda scores of the risk factor R1 for the project management party, the construction party, and the regulatory agency are 3, 5, and 4 respectively. Then the total score is:

[0128] S1 = 0.5·3 + 0.3·5 + 0.2·4 = 1.5 + 1.5 + 0.8 = 3.8;

[0129] After calculating the weighted scores for all risk factors, a weighted ranking list of risk factors is generated. The list is sorted in descending order of the total score Si, reflecting the priority of different risk factors in the comprehensive opinions of all participating parties. According to the magnitude of the weighted scores, all risk factors are sorted in descending order of priority to generate the final weighted ranking list.

[0130] The risk priority adjustment unit adopts the maximum ranking method. The risk factor ranked first in the preliminary risk priority ranking list is used as the risk factor ranked first in the final list. For other risk factors, the highest (optimal) ranking among their Borda rankings and the preliminary risk priority ranking list is taken as the final ranking;

[0131] Compare the rankings of each risk factor in the Borda and Pareto rankings, and select the better position (i.e., the smaller ranking number) in the two rankings as the final ranking. If the risk factor Ri ranks 3rd in the Borda and 5th in the Pareto, then select 3 as the final ranking.

[0132] In a possible implementation, the formula for the game analysis unit to weight the ranking scores of each risk factor is:

[0133]

[0134] where, S i represents the weighted ranking score of the risk factor R i and Wp Denotes the weight of participant p, R ip Denotes the ranking score of participant p for risk factor R i , where m is the total number of participants, and i includes technical risk, financial risk, environmental risk, and management risk.

[0135] In this embodiment, the weights of different project participants are set based on their role importance and expertise factors in the project. The total score S i Is expressed as where S i Denotes the total score of risk factor R i , W p Denotes the weight of participant p, R ip Denotes the ranking score of participant p for risk factor R i , where m is the total number of participants, and i includes technical risk, financial risk, environmental risk, and management risk.

[0136] As can be seen from the above, the beneficial effects of the present invention include:

[0137] In the present invention, through the multi-dimensional risk interaction relationship identification module, it is possible to automatically identify the dynamic interaction relationships between different risk factors based on real-time data, and construct a multi-dimensional risk relationship network graph reflecting the complex dependencies between these risks. The generation of this graph is based on the interaction weights between risk factors, which can accurately reveal potential linkage effects. Through quantitative interaction weight analysis, the system can not only identify the influence of individual risks, but also reveal the complexity of the mutual influence of various risk factors, thus providing a more comprehensive perspective for accurate risk assessment and management, avoiding the limitations of traditional risk assessment methods that only focus on single factors or simple combinations, and significantly improving the forward-looking and dynamic adjustment capabilities of the system.

[0138] In the present invention, by using the Pareto analysis method to first identify the key risk factors that have the greatest impact on the project, and combining the Borda rule to integrate the risk ranking opinions of multiple parties, a two-level risk ranking optimization mechanism is formed. First, the Pareto analysis generates a preliminary risk priority ranking list based on the individual influence and mutual interaction weights of each risk factor, so as to identify a few risk factors that contribute the most to the overall project risk. Then, through the Borda rule, through multi-party opinion game analysis, the different ranking opinions are weighted and integrated to ensure that the experience and attention of each project participant are fairly considered. The two-level ranking method can not only accurately identify the key risks in the project, but also ensure the full participation of multi-party opinions, greatly improving the rationality and fairness of the ranking.

[0139] In the present invention, a flexible decision rule is introduced during the sorting adjustment process to address potential conflicts between the Borda and Pareto sorting results. Especially when resolving high-priority risk sorting conflicts, after setting the first-place priority rule, the maximum ranking method is used to ensure the optimal positions of various risk factors are retained in subsequent sorting, thereby achieving efficient simplification of the sorting process.

[0140] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not imply the order of execution. The execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0141] The following is an apparatus embodiment of the present invention. For details not described in detail herein, reference may be made to the corresponding method embodiments above.

[0142] Figure 4 The implementation flowchart of the power grid project risk assessment method integrating Pareto and Borda rules provided by the embodiments of the present invention is shown. For the sake of convenience of description, only the parts related to the embodiments of the present invention are shown and are described in detail as follows:

[0143] Step 401, the multi-dimensional risk interaction relationship recognition module analyzes the interdependent relationships among the risk factors of the target power grid project and constructs a multi-dimensional risk relationship network graph; among them, the risk factors include power grid equipment status monitoring data.

[0144] Step 402, the risk interaction weight generation module determines the interaction weights among the risk factors based on the multi-dimensional risk relationship network graph.

[0145] Step 403, the risk ranking and Pareto processing module ranks the risk factors based on the Pareto analysis method and each interaction weight to obtain a preliminary risk priority ranking list and key risk factors.

[0146] Step 404, the multi-party dynamic game decision-making module makes game decisions on the sorting schemes of multiple project participants based on the Borda rule, and combines the preliminary risk priority ranking list to obtain a final risk priority ranking list, so as to conduct risk assessment on the target power grid project based on the final risk priority ranking list.

[0147] In a possible implementation manner, the multi-dimensional risk interaction relationship recognition module includes a data acquisition unit, a data analysis engine, an interaction impact calculation unit, and a risk relationship network construction unit.

[0148] Step 401 includes:

[0149] The data acquisition unit acquires multi-source data during the process of a target power grid project; among them, the multi-source data includes power grid equipment status monitoring data, financial statements, external environment data, and management reports;

[0150] The data analysis engine takes each type of multi-source data as a risk factor and analyzes the interdependent relationship between each risk factor based on spatio-temporal correlation;

[0151] The interaction impact calculation unit quantifies the interaction impact degree between each risk factor based on the interdependent relationship between each risk factor;

[0152] The risk relationship network construction unit generates a multi-dimensional risk relationship network graph based on the interaction impact degree between each risk factor.

[0153] In a possible implementation manner, the formula for the data analysis engine to analyze the interdependent relationship between each risk factor based on spatio-temporal correlation is:

[0154]

[0155] Among them, C ij (t) represents the correlation coefficient between risk factors i and j at time t, D i (t) represents the dynamic change amount of risk factor i at time t, D j (t) represents the dynamic change amount of risk factor j at time t, cov(D i (t), D j (t)) represents the covariance between risk factors i and j, represents the standard deviation of risk factor i, represents the standard deviation of risk factor j.

[0156] In a possible implementation manner, the formula for the interaction impact calculation unit to quantify the interaction impact degree between each risk factor is:

[0157] E ij (t) = C ij (t) · D j (t)

[0158] Among them, E ij (t) represents the interaction impact degree of risk factor i on risk factor j at time t, C ij (t) is the correlation coefficient between risk factors i and j, D j (t) represents the dynamic change amount of risk factor j at time t.

[0159] In a possible implementation manner, the multi-dimensional risk relationship network graph is expressed as: G = (N, E, W);

[0160] Among them, G represents the multi-dimensional risk relationship network graph, N represents the set of nodes of risk factors, E represents the set of edges between risk factors, represents the interaction relationship of risk factors, and W represents the set of weights of edges, reflecting the interaction influence intensity of each edge.

[0161] In a possible implementation, step 402 includes:

[0162] The risk interaction weight generation module takes the weights of each edge in the multi-dimensional risk relationship network graph as the interaction weights between each risk factor.

[0163] In a possible implementation, the risk ranking and Pareto processing module includes a weight integration unit, a ranking unit, and a Pareto priority marking unit;

[0164] Step 403 includes:

[0165] The weight integration unit integrates the total interaction influence of each risk factor based on the interaction weights between each risk factor to obtain the comprehensive influence of each risk factor on other risk factors;

[0166] The ranking unit ranks each risk factor based on each comprehensive influence to obtain a preliminary risk priority ranking list;

[0167] The Pareto priority marking unit identifies the risk factors with the greatest impact on the whole in the preliminary risk priority ranking list as key risk factors based on the Pareto analysis method.

[0168] In a possible implementation, the multi-party dynamic game decision-making module includes an opinion collection unit, a Borda counting unit, a game analysis unit, and a risk priority adjustment unit;

[0169] Step 404 includes:

[0170] The opinion collection unit collects the ranking schemes of each risk factor by different project participants;

[0171] The Borda counting unit assigns ranking scores to each risk factor based on the Borda counting rule and the rankings of each risk factor in each ranking scheme;

[0172] The game analysis unit performs weighted processing on the ranking scores of each risk factor and generates a weighted ranking list of risk factors based on the weighted ranking scores;

[0173] The risk priority adjustment unit adjusts the preliminary risk priority ranking list based on the weighted ranking list to obtain the final risk priority ranking list.

[0174] In a possible implementation, the formula for the game analysis unit to weight the ranking scores of each risk factor is as follows:

[0175]

[0176] Among them, S i represents the weighted ranking score of the risk factor R i , W p represents the weight of the participating party p, R ip represents the ranking score of the participating party p for the risk factor R i , m is the total number of participating parties, and i includes technical risks, financial risks, environmental risks, and management risks.

[0177] In the embodiments of the present invention, first, through real-time data, the dynamic interaction relationships between different risk factors are identified, and a multi-dimensional risk relationship network graph reflecting the complex dependencies between these risks is constructed to accurately reveal potential linkage effects. Then, through the Pareto analysis method, the key risk factors that have the greatest impact on the project are preferentially identified, and the risk ranking opinions of multiple participating parties are integrated in combination with the Borda rule to ensure the full participation of multiple opinions and improve the rationality of the ranking. Based on this ranking result, the effect of risk assessment for the project can be improved.

[0178] In the above embodiments, the descriptions of each embodiment have their own focuses. For parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0179] Those of ordinary skill in the art can realize that, in combination with the templates, units, and algorithm steps of the examples described in the embodiments disclosed herein, they can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0180] If the above-mentioned module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such understanding, to implement all or part of the processes in the above-mentioned embodiment methods of the present invention, it can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the above-mentioned embodiments of the power grid project risk assessment method that comprehensively combines the Pareto and Borda rules. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0181] The above-mentioned embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A power grid project risk assessment system integrating Pareto and Borda rules, characterized in that: include: The multi-dimensional risk interaction relationship identification module is used to analyze the interdependence between the risk factors of the target power grid project and construct a multi-dimensional risk relationship network map; among which, the risk factors include power grid equipment status monitoring data; A risk interaction weight generation module is used to determine the interaction weights between various risk factors based on the multi-dimensional risk relationship network map; a risk sorting and Pareto processing module is used to sort various risk factors based on the Pareto analysis method and various interaction weights to obtain a preliminary risk priority sorting list and key risk factors; a multi-party dynamic game decision-making module is used to make game decisions on the sorting schemes of multiple project participants based on the Borda rule, and obtain a final risk priority sorting list in combination with the preliminary risk priority sorting list, so as to conduct risk assessment on the target power grid project based on the final risk priority sorting list.

2. The power grid project risk assessment system integrating Pareto and Borda rules according to claim 1 is characterized in that: The multi-dimensional risk interaction relationship identification module includes: A data acquisition unit, used to collect multi-source data during the implementation of the target power grid project; wherein the multi-source data includes power grid equipment status monitoring data, financial statements, external environment data and management reports, the power grid equipment status monitoring data includes the operating status, voltage, current and temperature of the wind turbine, and the external environment data includes weather data and geological data; The data analysis engine is used to treat each type of multi-source data as a risk factor and analyze the interdependencies between risk factors based on spatiotemporal correlation; An interactive impact calculation unit is used to quantify the degree of interactive impact between various risk factors based on the interdependence between the various risk factors; The risk relationship network construction unit is used to generate a multi-dimensional risk relationship network map based on the degree of interaction between various risk factors.

3. The power grid project risk assessment system integrating Pareto and Borda rules according to claim 2 is characterized in that: The formula used by the data analysis engine to analyze the interdependence between risk factors based on spatiotemporal correlation is: Among them, C ij (t) represents the correlation coefficient between risk factors i and j at time t, D i (t) represents the dynamic change of risk factor i at time t, D j (t) represents the dynamic change of risk factor j at time t, cov(D i (t),D j (t)) represents the covariance between risk factors i and j, σ Di(t) represents the standard deviation of risk factor i, represents the standard deviation of risk factor j.

4. The power grid project risk assessment system integrating Pareto and Borda rules according to claim 3 is characterized in that: The formula for the interactive impact calculation unit to quantify the degree of interactive impact between risk factors is: E ij (t)=C ij (t)·D j (t) Among them, E ij (t) represents the interactive impact of risk factor i on risk factor j at time t, C ij (t) is the correlation coefficient between risk factors i and j, D j (t) represents the dynamic change of risk factor j at time t.

5. The power grid project risk assessment system integrating Pareto and Borda rules according to claim 2 is characterized in that: The multi-dimensional risk relationship network map is represented as: G = (N, E, W); Among them, G represents the multi-dimensional risk relationship network map, N represents the node set of risk factors, E represents the edge set between risk factors, indicating the interaction relationship between risk factors, and W represents the edge weight set, reflecting the intensity of the interaction influence of each edge.

6. The power grid project risk assessment system integrating Pareto and Borda rules according to claim 5 is characterized in that: The risk interaction weight generation module is specifically used for: The weight of each edge in the multi-dimensional risk relationship network map is used as the interaction weight between each risk factor.

7. The power grid project risk assessment system integrating Pareto and Borda rules according to claim 6 is characterized in that: The risk ranking and Pareto processing module includes: The weight integration unit is used to integrate the total interactive impact of each risk factor based on the interaction weights between the risk factors to obtain the comprehensive influence of each risk factor on other risk factors; A ranking unit is used to rank each risk factor based on its comprehensive influence to obtain a preliminary risk priority ranking list; The Pareto priority marking unit is used to identify the risk factors with the greatest impact on the overall situation in the preliminary risk priority ranking list based on the Pareto analysis method as key risk factors.

8. The power grid project risk assessment system integrating Pareto and Borda rules according to claim 7 is characterized in that: The multi-party dynamic game decision module includes: The opinion collection unit is used to collect the ranking plans of various risk factors from different project participants; A Borda counting unit, used to assign a ranking score to each risk factor based on the Borda counting rule and the ranking of each risk factor in each ranking scheme; A game analysis unit, used for weighting the ranking score of each risk factor, and generating a weighted ranking list of risk factors based on the weighted ranking score; A risk priority adjustment unit is used to adjust the preliminary risk priority ranking list based on the weighted ranking list to obtain a final risk priority ranking list.

9. The power grid project risk assessment system integrating Pareto and Borda rules according to claim 8, characterized in that: The formula for weighting the ranking score of each risk factor by the game analysis unit is: Among them, S i Indicates risk factor R i The weighted ranking score, W p represents the weight of participant p, R ip Represents the risk factor R of participant p i The ranking score is , m is the total number of parties involved, and i includes technical risk, financial risk, environmental risk, and management risk.

10. A power grid project risk assessment method integrating Pareto and Borda rules, characterized in that: include; The multi-dimensional risk interaction relationship identification module analyzes the interdependence between the risk factors of the target power grid project and constructs a multi-dimensional risk relationship network map; among which, the risk factors include power grid equipment status monitoring data; The risk interaction weight generation module determines the interaction weights between the risk factors based on the multi-dimensional risk relationship network map; The risk ranking and Pareto processing module ranks each risk factor based on the Pareto analysis method and each interaction weight to obtain a preliminary risk priority ranking list and key risk factors; The multi-party dynamic game decision module makes a game decision on the ranking schemes of multiple project participants based on the Borda rule, and obtains a final risk priority ranking list in combination with the preliminary risk priority ranking list, so as to conduct a risk assessment on the target power grid project based on the final risk priority ranking list.

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