Power grid equipment updating and reconstruction investment risk avoidance strategy generation system

Abstract

The invention discloses a power grid equipment updating and reconstruction investment risk evasion strategy generation system. The system comprises a data input module, a data preprocessing and fusion module, a dynamic risk quantitative analysis module, an evasion strategy generation module and a result output and feedback module. The grid equipment updating and reconstruction investment risk avoidance strategy generation system is accurate and comprehensive in risk assessment, can effectively identify potential risks, reduces the blindness of investment decisions, is not liable to cause investment waste or unstable operation after equipment updating, and guarantees safe and economical operation of a grid.

Description

Technical Field

[0001] This invention relates to the field of power grid equipment upgrading and renovation technology, and specifically to a system for generating investment risk avoidance strategies for power grid equipment upgrading and renovation. Background Technology

[0002] In the process of upgrading and transforming power grid equipment, investment risk assessment and mitigation are crucial. Currently, investment risk assessment for power grid equipment upgrading and transformation largely relies on manual experience and historical data statistics, lacking scientific and systematic risk quantification analysis models. This results in inaccurate and incomplete risk assessments, difficulty in effectively identifying potential risks, and blind investment decisions, easily leading to investment waste or unstable operation after equipment upgrades, thus affecting the safe and economical operation of the power grid. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to overcome the above-mentioned defects of the prior art and provide a power grid equipment upgrading and transformation investment risk avoidance strategy generation system that provides accurate and comprehensive risk assessment, can effectively identify potential risks, reduce blind investment decisions, avoid investment waste or unstable operation after equipment upgrades, and ensure the safe and economical operation of the power grid.

[0004] The technical solution adopted by this invention to solve its technical problem is as follows: A system for generating investment risk avoidance strategies for power grid equipment upgrading and renovation, comprising: The data input module is used to receive project data for power grid equipment upgrading and renovation and multi-source risk data, including equipment operating parameters, environmental impact factors, market fluctuation data and historical investment records. The data preprocessing and fusion module is used to clean, denoise and standardize the project data and the multi-source risk data, and generate a risk feature dataset in a unified format through feature extraction algorithms. The dynamic risk quantification and analysis module is used to identify a set of key risk factors based on the risk feature dataset using the random forest algorithm, extract dynamically updated risk feature parameters from the risk feature dataset using principal component analysis, and quantify the weight coefficients of each risk factor in the set of key risk factors using a multiple linear regression model to generate investment risk assessment results. The risk avoidance strategy generation module is used to generate targeted risk avoidance strategy solutions based on the investment risk assessment results and a predefined strategy rule library, using a decision tree algorithm. The results output and feedback module is used to output the risk avoidance strategy to the user interface and collect user feedback data to optimize the strategy rule base.

[0005] Preferably, the data input module is specifically used for: According to the preset data acquisition cycle, the system sends engineering project data request messages to the power grid production management system and sends multi-source risk data call instructions to external data sources. Receives project data from the power grid production management system; receives equipment operating parameters, environmental impact factors, market fluctuation data, and historical investment records from the meteorological monitoring system, the power market trading platform, and the equipment asset database.

[0006] Preferably, the data preprocessing and fusion module is specifically used for: Missing values ​​are filled and outliers are removed from the project data, equipment operating parameters, environmental influencing factors, market fluctuation data, and historical investment records. The sliding window algorithm is used to smooth and filter the continuous equipment operating parameters to remove noise, and the Z-score standardization method is used to convert the equipment operating parameters, environmental influencing factors, market fluctuation data and historical investment records of different dimensions into standardized data of the same dimension. The feature importance assessment algorithm is used to select the feature subset with the highest correlation to power grid equipment investment risk from the standardized data, and the feature subset is dimensionally merged and integrated to generate the risk feature dataset in a unified format.

[0007] Preferably, the dynamic risk quantification analysis module is specifically used for: The risk feature dataset is input into a preset random forest model to sort the features by importance, and key risk factors with importance scores higher than a preset threshold are selected to form the key risk factor set. Principal component analysis is performed on the standardized data contained in the set of key risk factors, and the top N principal components whose cumulative contribution rate reaches a preset percentage are extracted as the risk feature parameters. Using the risk characteristic parameters as independent variables and historical investment returns as dependent variables, a multiple linear regression model is constructed and the weight coefficients of each risk factor in the set of key risk factors are obtained by solving the model. A comprehensive risk score is calculated based on the weighting coefficients and a preset risk assessment rule base. Based on the score interval mapping relationship, an investment risk assessment result containing the risk level and the main sources of risk is generated.

[0008] Preferably, the dynamic risk quantification analysis module is specifically used for: Using the risk characteristic parameters as independent variables and the corresponding historical investment return rate as the dependent variable, the matrix expression of the multiple linear regression model is constructed. The ridge regression algorithm is used to solve the coefficient matrix of the multiple linear regression model to obtain the initial weight coefficients of each risk feature parameter; the initial weight coefficients are then normalized so that the sum of all weight coefficients is 1. Based on the mapping relationship between each risk factor in the set of key risk factors and the risk characteristic parameters, the normalized weight coefficients are assigned to the corresponding risk factors to obtain the weight coefficients of each risk factor in the set of key risk factors.

[0009] Preferably, the expression for the multiple linear regression model is: ; Where Y represents the historical rate of return on investment, β0 represents the model intercept term, and β i X represents the i-th risk feature parameter. i The linear coefficients, γ j X represents the j-th risk characteristic parameter. j The logarithmic adjustment factor, W j Let represent the time decay factor of the j-th risk feature parameter, ε represent the random error term, and n and m represent the number of linear and nonlinear risk feature parameters, respectively; λ represents the attenuation coefficient, t j This represents the time span of the data corresponding to the j-th risk feature parameter.

[0010] Preferably, the dynamic risk quantification analysis module is specifically used for: The comprehensive risk score is obtained by weighting and summing the quantitative scores of each risk factor in the set of key risk factors based on the weighting coefficients. The comprehensive risk score is matched and mapped with a preset risk level threshold range to determine the corresponding risk level, and the key risk factors with a concentration weight coefficient greater than the preset threshold are identified as the main sources of risk. The investment risk assessment results are generated in a structured format by combining the risk level and the main sources of risk.

[0011] Preferably, the avoidance strategy generation module is specifically used for: Based on the risk level, select the corresponding decision tree model branch from the strategy rule base; The main sources of risk are used as input parameters to traverse the branch nodes of the decision tree model; Based on the node judgment rules of the decision tree model, the corresponding risk avoidance measures are matched from the strategy rule base; The successfully matched risk avoidance measures are sorted and combined according to priority to generate the risk avoidance strategy scheme.

[0012] Preferably, the result output and feedback module is specifically used for: The risk avoidance strategy is visualized on the user interface according to a preset display template; The system receives user evaluation data and modification suggestions for the risk avoidance strategy through the user interface, and performs correlation analysis between the evaluation data and modification suggestions and the corresponding risk avoidance strategy execution effect data. Based on the correlation analysis results, the effectiveness score of each rule in the strategy rule base is calculated, and the rules in the strategy rule base are dynamically updated and optimized based on the effectiveness score.

[0013] Preferably, the result output and feedback module is further used for: Real-time monitoring of the changing trends of risk levels in the investment risk assessment results; When the risk level is detected to have escalated continuously beyond a predetermined threshold within a preset time, an early warning signal is automatically triggered. The warning signal is associated with the currently generated risk avoidance strategy and pushed to the corresponding user through the highlighted display method of the user interface.

[0014] The beneficial effects of the strategy generation system of this invention are as follows: By integrating multi-source data, performing dynamic quantitative evaluation, and generating intelligent avoidance strategies, the strategy generation system of this invention solves the problem of inaccuracy in evaluation methods based on human experience and historical data, improves the scientificity and comprehensiveness of risk assessment, optimizes investment decisions for power grid equipment upgrades and renovations, reduces blind investment, and ensures the safety and stability of power grid operation. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of an embodiment of the power grid equipment upgrading and renovation investment risk avoidance strategy generation system of the present invention. Detailed Implementation

[0016] The present invention will be further described below with reference to the embodiments and accompanying drawings.

[0017] Example: like Figure 1 As shown, this invention provides a system for generating investment risk avoidance strategies for power grid equipment upgrades and renovations, comprising: The data input module 110 is used to receive engineering project data and multi-source risk data for power grid equipment upgrading and renovation. The multi-source risk data includes equipment operating parameters, environmental impact factors, market fluctuation data and historical investment records. Among them, power grid equipment upgrading and renovation refers to technical activities that replace, upgrade, or repair existing power grid equipment to improve equipment performance and ensure the safe and stable operation of the power grid; for example, a project to replace and upgrade a 110kV transformer in a certain area. Project data refers to a collection of information describing the basic attributes of a power grid equipment upgrading and renovation project, including project name, budget amount, construction period, and equipment specifications; for example, the budget for the aforementioned transformer replacement project is 5 million yuan, the construction period is 90 days, and the equipment specifications are 110kV / 50MVA. Multi-source risk data refers to a collection of data related to investment risks in power grid equipment upgrading and renovation from different channels, including equipment operating parameters, environmental impact factors, market fluctuation data, and historical investment records; for example, in a transformer replacement project, data on transformer load rate, local temperature, electricity price fluctuations, and historical investment records of similar projects are collected. Equipment operating parameters refer to technical indicators reflecting the operating status of power grid equipment, including load rate, number of failures, and insulation aging degree; for example, in a transformer replacement project, the current load rate of the transformer is 85%, and the number of failures in the past year is 3. Environmental impact factors refer to external environmental factors that affect power grid equipment upgrade and renovation projects, including temperature, humidity, wind speed, and geological disaster risks; for example, the average summer temperature in the transformer replacement project area is 35℃, and the annual rainfall is 1200mm. Market fluctuation data refers to economic indicators that change in the power market environment, including fluctuations in electricity prices, raw material prices, and labor costs; for example, in the transformer replacement project, the price of copper increased by 10% during the project cycle, and the average fluctuation rate of the electricity market price was 5%. Historical investment records refer to investment-related data for similar power grid equipment upgrade and renovation projects in the past, including investment amount, rate of return, and cost overruns; for example, in the transformer replacement project, referring to the investment records of similar transformer projects in the same region last year, the investment amount was 4.5 million yuan, and the rate of return was 8%. The data preprocessing and fusion module 120 is used to clean, denoise and standardize the project data and the multi-source risk data, and generate a risk feature dataset in a unified format through a feature extraction algorithm. Among them, the risk feature dataset refers to a unified format data set formed after preprocessing and fusion, which is used for risk quantification analysis; for example, in a transformer replacement project, a feature data table is generated after standardizing equipment operating parameters, environmental impact factors, etc. The dynamic risk quantification analysis module 130 is used to identify a set of key risk factors based on the risk feature dataset using the random forest algorithm, extract dynamically updated risk feature parameters from the risk feature dataset using the principal component analysis method, and quantify the weight coefficients of each risk factor in the set of key risk factors using a multiple linear regression model to generate investment risk assessment results. The key risk factor set refers to the set of factors that significantly impact investment risk, identified from the risk characteristic dataset. For example, in a transformer replacement project, load factor, temperature, and copper price are identified as key risk factors. Risk characteristic parameters refer to representative parameters extracted from the key risk factor set through principal component analysis. For example, in a transformer replacement project, principal component scores extracted from load factor, temperature, and copper price are used as risk characteristic parameters. Risk factors refer to uncertain events or conditions that may cause investment in power grid equipment upgrades to deviate from expectations. For example, in a transformer replacement project, excessively high load factor, abnormal temperature, and rising copper price are all risk factors. Weighting coefficients refer to the relative importance of each risk factor to investment risk, quantified by a multiple linear regression model. For example, in a transformer replacement project, the weighting coefficient for load factor is 0.5, for temperature is 0.3, and for copper price is 0.2. The investment risk assessment result refers to the conclusions regarding the risk level and main sources of risk generated based on the weighting coefficients and the risk assessment rule base. For example, in a transformer replacement project, the assessment result is a high-risk level, with excessively high load factor and rising copper price as the main sources of risk. The risk avoidance strategy generation module 140 is used to generate targeted risk avoidance strategy solutions based on the investment risk assessment results and in combination with a predefined strategy rule library, using a decision tree algorithm. The predefined strategy rule base refers to a pre-stored set of logical rules for risk avoidance strategies; for example, in a transformer replacement project, the rule base includes rules such as "if the load rate is higher than 80%, it is recommended to expand capacity." Risk avoidance strategy solutions refer to a combination of targeted measures generated based on the investment risk assessment results; for example, in a transformer replacement project, strategy solutions such as "procure copper materials in advance" and "add a spare transformer" are generated. The result output and feedback module 150 is used to output the risk avoidance strategy to the user interface and collect user feedback data to optimize the strategy rule base. The user interface refers to a visual platform for information interaction between the system and the user; for example, in a transformer replacement project, a web-based interface displays risk assessment results and strategy solutions.

[0018] This invention addresses the inaccuracy of assessment methods based on human experience and historical data by integrating multi-source data, dynamic quantitative evaluation, and generating intelligent avoidance strategies. It improves the scientific rigor and comprehensiveness of risk assessment, optimizes investment decisions for power grid equipment upgrades and renovations, reduces blind investment, and ensures the safety and stability of power grid operation.

[0019] In an alternative embodiment, the data input module 110 is specifically used for: According to the preset data acquisition cycle, the system sends engineering project data request messages to the power grid production management system and sends multi-source risk data call instructions to external data sources. The power grid production management system refers to an internal information system that manages the operation of power grid equipment and engineering projects; for example, in a transformer replacement project, project progress and equipment status data are obtained from this system. Engineering project data request messages refer to data query commands sent to the power grid production management system; for example, in a transformer replacement project, a message containing the project number is sent to obtain budget data. External data sources refer to external systems or databases that provide risk data; for example, in a transformer replacement project, meteorological monitoring systems and electricity market trading platforms are external data sources. Multi-source risk data retrieval commands refer to data retrieval commands sent to external data sources; for example, in a transformer replacement project, a command is sent to the meteorological monitoring system to obtain temperature data. Receives project data from the power grid production management system; receives equipment operating parameters, environmental impact factors, market fluctuation data, and historical investment records from the meteorological monitoring system, the power market trading platform, and the equipment asset database; Among these, the meteorological monitoring system refers to an external system that provides data on environmental impact factors; for example, in a transformer replacement project, real-time temperature and humidity data are obtained from this system. The electricity market trading platform refers to an external system that provides market fluctuation data; for example, in a transformer replacement project, electricity price and copper price fluctuation data are obtained from this platform. The equipment asset database refers to a database that stores equipment-related data such as historical investment records; for example, in a transformer replacement project, investment records for similar transformer projects are obtained from this database.

[0020] Among the above-mentioned optional methods, further improvements can be made to the real-time performance and comprehensiveness of the system through automation and multi-source data collection, ensuring that the sources of risk assessment data are richer and more accurate.

[0021] In an alternative embodiment, the data preprocessing and fusion module 120 is specifically used for: Missing values ​​are filled and outliers are removed from the project data, equipment operating parameters, environmental influencing factors, market fluctuation data, and historical investment records. The sliding window algorithm is used to smooth and filter the continuous equipment operating parameters to remove noise, and the Z-score standardization method is used to convert the equipment operating parameters, environmental influencing factors, market fluctuation data and historical investment records of different dimensions into standardized data of the same dimension. Standardized data refers to data that has been standardized by Z-score; for example, in a transformer replacement project, load rate, temperature, etc. are converted into data with a mean of 0 and a standard deviation of 1. The feature importance assessment algorithm is used to select the feature subset with the highest correlation to power grid equipment investment risk from the standardized data, and the feature subset is merged and fused by dimension to generate the risk feature dataset in a unified format; Among them, the feature importance assessment algorithm refers to the algorithm used to select the features with the highest correlation to risk, such as random forest; for example, in the transformer replacement project, the random forest algorithm is used to calculate the importance score of each feature. The feature subset refers to the set of highly correlated features selected from standardized data; for example, in the transformer replacement project, load rate, temperature, and copper price are selected as feature subsets.

[0022] Among the above-mentioned optional methods, further improvements can be made to the quality of the analyzed data through data cleaning, denoising, and feature extraction, ensuring that the logic of subsequent risk assessment and strategy generation is more reliable.

[0023] In an alternative embodiment, the dynamic risk quantification analysis module 130 is specifically used for: The risk feature dataset is input into a preset random forest model to sort the features by importance, and key risk factors with importance scores higher than a preset threshold are selected to form the key risk factor set. Principal component analysis is performed on the standardized data contained in the set of key risk factors, and the top N principal components whose cumulative contribution rate reaches a preset percentage are extracted as the risk feature parameters. The contribution rate refers to the proportion of the variance of the original data explained by each principal component in the principal component analysis. For example, in the transformer replacement project, the contribution rate of the first principal component is 60%, which means that it contains 60% of the original information. Using the risk characteristic parameters as independent variables and historical investment returns as dependent variables, a multiple linear regression model is constructed and the weight coefficients of each risk factor in the set of key risk factors are obtained by solving the model. A comprehensive risk score is calculated based on the weighting coefficients and a preset risk assessment rule base. Based on the score interval mapping relationship, an investment risk assessment result containing the risk level and the main sources of risk is generated. The preset risk assessment rule base refers to a knowledge base that stores risk score mapping rules; for example, in a transformer replacement project, the rule base defines a score of 0-30 as low risk, 31-70 as medium risk, and 71-100 as high risk. The comprehensive risk score refers to the overall risk value calculated based on weighted coefficients and quantified scores of risk factors; for example, in a transformer replacement project, a score of 75 is obtained by weighting scores based on load rate, temperature, etc. The score range refers to the threshold range that maps the comprehensive risk score to a risk level; for example, in a transformer replacement project, a score of 0-30 corresponds to low risk, 31-70 to medium risk, and 71-100 to high risk. The main risk source refers to risk factors whose weighted coefficients exceed the preset threshold; for example, in a transformer replacement project, a load rate weight of 0.5 (threshold 0.3) is identified as a main risk source.

[0024] Among the above optional methods, the dynamic risk quantification analysis module is further utilized to identify key risk factors and quantify their weights through the random forest algorithm, thereby improving the dynamism and accuracy of risk assessment.

[0025] In an alternative embodiment, the dynamic risk quantification analysis module 130 is specifically used for: Using the risk characteristic parameters as independent variables and the corresponding historical investment return rate as the dependent variable, the matrix expression of the multiple linear regression model is constructed. The ridge regression algorithm is used to solve the coefficient matrix of the multiple linear regression model to obtain the initial weight coefficients of each risk feature parameter; the initial weight coefficients are then normalized so that the sum of all weight coefficients is 1. Ridge regression algorithm refers to a regularization algorithm used to solve the coefficients of a multiple linear regression model; for example, in a transformer replacement project, ridge regression is used to solve the weight coefficients of risk characteristic parameters. Based on the mapping relationship between each risk factor in the set of key risk factors and the risk characteristic parameters, the normalized weight coefficients are assigned to the corresponding risk factors to obtain the weight coefficients of each risk factor in the set of key risk factors.

[0026] The normalized weight coefficients refer to the weight coefficients that have been normalized to a sum of 1. For example, in a transformer replacement project, the load rate weight is 0.5, the temperature weight is 0.3, and the copper price weight is 0.2, and their sum after normalization is 1.

[0027] Among the above-mentioned optional methods, the weight allocation can be further optimized through the ridge regression algorithm to improve the scientific nature and analytical accuracy of the model, and provide a more reliable basis for risk assessment.

[0028] In one alternative approach, the expression for the multiple linear regression model is: The expression for the multiple linear regression model is: ; Where Y represents the historical rate of return on investment, β0 represents the model intercept term, and β i X represents the i-th risk feature parameter. i The linear coefficients, γ j X represents the j-th risk characteristic parameter. j The logarithmic adjustment factor, W j Let represent the time decay factor of the j-th risk feature parameter, ε represent the random error term, and n and m represent the number of linear and nonlinear risk feature parameters, respectively; λ represents the attenuation coefficient, t j This represents the time span of the data corresponding to the j-th risk feature parameter.

[0029] Among the above-mentioned optional approaches, the dynamism and adaptability of risk assessment can be further enhanced by using flexible mathematical models and time decay factors.

[0030] In an alternative embodiment, the dynamic risk quantification analysis module 130 is specifically used for: The comprehensive risk score is obtained by weighting and summing the quantitative scores of each risk factor in the set of key risk factors based on the weighting coefficients. Among them, the quantitative score refers to the numerical representation of risk factors after standardization and quantification; for example, in a transformer replacement project, a load rate of 85% is quantified as a score of 80 (out of 100). The comprehensive risk score is matched and mapped with a preset risk level threshold range to determine the corresponding risk level, and the key risk factors with a concentration weight coefficient greater than the preset threshold are identified as the main sources of risk. The investment risk assessment results are generated in a structured format by combining the risk level and the main sources of risk.

[0031] Among the above-mentioned optional methods, a comprehensive risk score can be calculated by further combining weight and feature data to improve the comprehensiveness and accuracy of the assessment, while identifying the main sources of risk.

[0032] In an alternative embodiment, the evasion strategy generation module 140 is specifically used for: Based on the risk level, select the corresponding decision tree model branch from the strategy rule base; Among them, the decision tree model branch refers to the decision path based on risk level in the predefined strategy rule base; for example, in the transformer replacement project, the high risk level corresponds to the "cost control" branch; The main sources of risk are used as input parameters to traverse the branch nodes of the decision tree model; In this context, a branch node refers to a point in the decision tree model where a judgment condition is located; for example, in a transformer replacement project, a node might determine whether the load rate is higher than 80%. Based on the node judgment rules of the decision tree model, the corresponding risk avoidance measures are matched from the strategy rule base; Among them, node judgment rules refer to the conditional logic used to select paths at branch nodes; for example, in a transformer replacement project, the rule is "if the load rate is >80%, take the left branch; otherwise, take the right branch." Risk avoidance measures refer to the specific coping methods stored in the strategy rule base; for example, in a transformer replacement project, "procure copper materials in advance" is a risk avoidance measure. The successfully matched risk avoidance measures are sorted and combined according to priority to generate the risk avoidance strategy scheme.

[0033] Among the above-mentioned optional methods, risk avoidance measures can be further matched by using a decision tree algorithm, and targeted strategy solutions can be generated by combining risk level and main source.

[0034] In an alternative embodiment, the result output and feedback module 150 is specifically used for: The risk avoidance strategy is visualized on the user interface according to a preset display template; The preset display template refers to the format template used in the user interface to visualize the strategy plan; for example, in the transformer replacement project, the template includes areas such as risk level, strategy list, and charts. The system receives user evaluation data and modification suggestions for the risk avoidance strategy through the user interface, and performs correlation analysis between the evaluation data and modification suggestions and the corresponding risk avoidance strategy execution effect data. Among them, modification suggestions refer to user feedback on adjustments to the strategy plan through the interface; for example, in the transformer replacement project, users suggested changing "add a backup transformer" to "lease backup equipment"; Based on the correlation analysis results, the effectiveness score of each rule in the strategy rule base is calculated, and the rules in the strategy rule base are dynamically updated and optimized based on the effectiveness score; The correlation analysis results refer to the conclusions drawn from the correlation analysis between user feedback data and strategy execution effect data. For example, in the transformer replacement project, the analysis showed that the "procure copper materials in advance" strategy effectively reduced the risk of cost overruns. The effectiveness score refers to the numerical value of the effectiveness of rules in the strategy rule base calculated based on the correlation analysis. For example, in the transformer replacement project, the effectiveness score of the "procure copper materials in advance" rule was 90 points (out of 100).

[0035] Among the above-mentioned optional methods, risk avoidance strategies can be further displayed intuitively through a user interface, and the strategy rule base can be optimized through user feedback to improve the scientific nature and practicality of the strategies.

[0036] In an alternative embodiment, the result output and feedback module 150 is further configured to: Real-time monitoring of the changing trends of risk levels in the investment risk assessment results; The trend of change refers to the direction of change of the risk level in the investment risk assessment results over time; for example, in a transformer replacement project, the risk level rises from risk in the middle of the month to high risk at the end of the month. When the risk level is detected to have escalated continuously beyond a predetermined threshold within a preset time, an early warning signal is automatically triggered. The predetermined threshold refers to the critical value for risk level changes that trigger a warning signal; for example, in a transformer replacement project, a risk level increase exceeding 10% for three consecutive days is set as the threshold. The warning signal refers to a risk alert automatically triggered by the system; for example, in a transformer replacement project, a "high-risk warning" signal is triggered when the risk level change exceeds the threshold. The warning signal is associated with the currently generated risk avoidance strategy and pushed to the corresponding user through the highlighted display method of the user interface.

[0037] Among the above-mentioned optional methods, further real-time monitoring of changes in risk levels can trigger early warning signals and associate them with labeled strategy solutions, thereby enhancing users' awareness and ability to respond to risks.

Claims

1. A power grid equipment renovation investment risk avoidance strategy generation system, characterized by, The method comprises the following steps: a data input module for receiving power grid equipment renovation project data and multi-source risk data, including equipment operating parameters, environmental factors, market fluctuation data and historical investment records; a data preprocessing and fusion module for cleaning, denoising and standardizing the project data and multi-source risk data, and generating a unified format risk feature data set through feature extraction algorithm; a dynamic risk quantification analysis module for identifying a key risk factor set based on the risk feature data set using a random forest algorithm, extracting dynamically updated risk feature parameters from the risk feature data set using principal component analysis, and quantifying the weight coefficients of each risk factor in the key risk factor set using a multiple linear regression model to generate an investment risk assessment result; an avoidance strategy generation module for generating a targeted risk avoidance strategy based on the investment risk assessment result, combining a predefined strategy rule library, and generating a targeted risk avoidance strategy through a decision tree algorithm; a result output and feedback module for outputting the risk avoidance strategy to a user interface and collecting user feedback data to optimize the strategy rule library.

2. The grid equipment upgrade investment risk avoidance strategy generation system of claim 1, wherein, The data input module is specifically used for: sending project data request messages to a power grid production management system according to a preset data collection period, and sending multi-source risk data calling instructions to external data sources; receiving project data from the power grid production management system, and receiving equipment operating parameters, environmental factors, market fluctuation data and historical investment records from a meteorological monitoring system, a power market trading platform and a device asset database.

3. The grid equipment upgrade investment risk avoidance strategy generation system of claim 1 or 2, wherein, The data preprocessing and fusion module is specifically used for: performing missing value filling and outlier removal processing on the project data, equipment operating parameters, environmental factors, market fluctuation data and historical investment records; using a sliding window algorithm to perform smoothing filter denoising on continuous equipment operating parameters, and converting equipment operating parameters, environmental factors, market fluctuation data and historical investment records of different dimensions into standardized data of the same dimension through a Z-score standardization method; using a feature importance evaluation algorithm to select a feature subset with the highest correlation with power grid equipment investment risk from the standardized data, and merging and fusing the feature subset to generate a unified format risk feature data set.

4. The grid equipment upgrade investment risk avoidance strategy generation system of claim 1 or 2, wherein, The dynamic risk quantification analysis module is specifically used for: inputting the risk feature data set into a preset random forest model to sort the feature importance, and selecting key risk factors with importance scores higher than a preset threshold to form the key risk factor set; performing principal component analysis on the standardized data included in the key risk factor set, and extracting the first N principal components with a contribution rate cumulative reaching a preset percentage as the risk feature parameters; using the risk feature parameters as independent variables and historical investment returns as dependent variables, constructing a multiple linear regression model and solving to obtain the weight coefficients of each risk factor in the key risk factor set; According to the weight coefficient and a preset risk assessment rule base, a comprehensive risk score is calculated, and the investment risk assessment result including a risk level and a main risk source is generated according to a score interval mapping relationship.

5. The grid equipment upgrade investment risk avoidance strategy generation system of claim 1 or 2, wherein, The dynamic risk quantitative analysis module is specifically configured to: The risk characteristic parameters are taken as independent variables, and corresponding historical investment returns are taken as dependent variables, so as to construct a matrix expression of the multiple linear regression model; The ridge regression algorithm is used to solve a coefficient matrix of the multiple linear regression model, so as to obtain initial weight coefficients of the risk characteristic parameters; and the initial weight coefficients are normalized, so that the sum of all weight coefficients is 1; According to a mapping relationship between each risk factor in the key risk factor set and the risk characteristic parameters, the normalized weight coefficients are distributed to corresponding risk factors, so as to obtain weight coefficients of each risk factor in the key risk factor set.

6. The grid equipment upgrade investment risk avoidance strategy generation system of claim 5, wherein, The expression of the multiple linear regression model is: ； where Y represents historical investment returns, β0represents a model intercept term, β i represents a linear coefficient of the i-th risk characteristic parameter X i , γ j represents a log adjustment coefficient of the j-th risk characteristic parameter X j , W j represents a time decay factor of the j-th risk characteristic parameter, and ε represents a random error term. n and m represent the number of linear and non-linear risk characteristic parameters, respectively; ; λ represents a decay coefficient, and t j represents a time span of data corresponding to the j-th risk characteristic parameter.

7. The grid equipment upgrade investment risk avoidance strategy generation system of claim 1 or 2, wherein, The dynamic risk quantitative analysis module is specifically configured to: Based on the weight coefficients, a quantitative score of each risk factor in the key risk factor set is weighted and summed, so as to obtain a comprehensive risk score; The comprehensive risk score is matched and mapped with a preset risk level threshold interval, so as to determine a corresponding risk level, and a key risk factor with a weight coefficient greater than a preset threshold in the key risk factor set is identified as a main risk source; The investment risk assessment result in a structured format is generated in combination with the risk level and the main risk source.

8. The grid equipment upgrade investment risk avoidance strategy generation system of claim 1 or 2, wherein, The avoidance strategy generation module is specifically configured to: According to the risk level, a corresponding decision tree model branch is selected from the strategy rule base; The main risk source is taken as an input parameter to traverse branch nodes of the decision tree model; According to a node judgment rule of the decision tree model, a corresponding risk avoidance measure is matched from the strategy rule base; The risk avoidance measures that are successfully matched are sorted and combined according to priorities, so as to generate the risk avoidance strategy scheme.

9. The grid equipment upgrade investment risk avoidance strategy generation system of claim 1 or 2, wherein, The result output and feedback module is specifically configured to: The risk avoidance strategy scheme is visually displayed on the user interface according to a preset display template; Evaluation data and modification suggestions of the risk avoidance strategy scheme are received through the user interface, and the evaluation data and the modification suggestions are associated with corresponding risk avoidance strategy scheme execution effect data for analysis; According to an association analysis result, an effectiveness score of each rule in the strategy rule base is calculated, and the rules in the strategy rule base are dynamically updated and optimized based on the effectiveness score.

10. The grid equipment upgrade investment risk avoidance strategy generation system of claim 1 or 2, wherein, The result output and feedback module is further configured to: A change trend of the risk level in the investment risk assessment result is monitored in real time; When it is identified that the risk level continuously upgrades by more than a predetermined threshold within a preset time, a warning signal is automatically triggered; The warning signal is associated with a currently generated risk avoidance strategy scheme and is pushed to a corresponding user in a highlighted display mode of the user interface.

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