Power grid operation risk assessment system and method based on multi-dimensional data

Abstract

The invention discloses a power grid operation risk assessment system based on multi-dimensional data. The power grid operation risk assessment system comprises a data analysis module used for setting primary influence factors and secondary influence factors of power grid operation risks; a first-level influence factor weight distribution module performs weight distribution on corresponding power grid operation risk first-level influence factors according to the power grid fault record data; a second-level influence factor weight distribution module performs weight distribution on corresponding power grid operation risk second-level influence factors according to the power grid fault record data in combination with the first-level influence factor weight; and the scoring module quantifies the power grid operation risk secondary influence factors according to the formulated influence factor scoring detailed rules and obtains scores, then multiplies the scores by the corresponding secondary influence factor weights to obtain comprehensive scores of the power grid operation risk secondary influence factors, and performs risk grading according to the comprehensive scores. According to the method, various risk factors of power grid operation can be determined, the power grid operation condition is evaluated, and specific countermeasures corresponding to the risk levels are given.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid operation risk assessment, and specifically refers to a power grid operation risk assessment system and method based on multi-dimensional data. Background Technique

[0002] With the continuous growth of power demand and the wide access of new energy, the complexity and uncertainty of the power grid have increased significantly. Conducting effective risk assessment can help identify potential fault points and vulnerable links, so as to take preventive measures in advance, reduce the occurrence frequency and impact scope of power outages, ensure the continuity and security of power supply, and meet the basic needs of social and economic development and people's lives.

[0003] There are many deficiencies in the existing technologies for power grid operation risk assessment. In terms of risk factor identification, it is neither comprehensive nor in-depth enough, unable to fully consider various emerging risk factors, and the analysis and classification of risk factors are relatively rough, making it difficult to accurately determine the risk sources; in terms of data scoring, subjective factors have a greater impact, resulting in lower consistency and reliability of the assessment results and being difficult to adapt to complex power grid operation conditions; in terms of risk response, there is a lack of systematic integration measures, the coordination and cooperation mechanism between departments is imperfect, and there is no specific tracking assessment system, making the risk control lack pertinence and effectiveness and unable to effectively guarantee the safe and stable operation of the power grid.

[0004] Therefore, it is an urgent technical problem to invent a power grid operation risk assessment system based on multi-dimensional data, which can clarify various risk factors of power grid operation, accurately assess the power grid operation status, and give specific response measures corresponding to the risk levels. Summary of the Invention

[0005] The purpose of the present invention is to provide a power grid operation risk assessment system and method based on multi-dimensional data. The present invention can clarify various risk factors of power grid operation, accurately assess the power grid operation status, and give specific response measures corresponding to the risk levels.

[0006] To achieve this purpose, a power grid operation risk assessment system based on multi-dimensional data designed by the present invention includes:

[0007] The data analysis module is used to set various primary influencing factors of power grid operation risks and corresponding secondary influencing factors of power grid operation risks under various primary influencing factors of power grid operation risks according to historical power grid fault records;

[0008] The primary influencing factor weight distribution module is used to collect corresponding power grid fault record data according to various primary influencing factors of power grid operation risks, and perform weight distribution on the corresponding primary influencing factors of power grid operation risks according to the collected power grid fault record data to obtain the weights of each primary influencing factor;

[0009] The secondary influencing factor weight distribution module is used to distribute weights to the corresponding secondary influencing factors of grid operation risks under various primary influencing factors of grid operation risks according to the collected grid fault record data combined with the corresponding primary influencing factor weights, so as to obtain the weights of each secondary influencing factor;

[0010] The scoring module is used to quantify various secondary influencing factors of grid operation risks according to the formulated influencing factor scoring rules, obtain the scores of various secondary influencing factors of grid operation risks, multiply the scores of each secondary influencing factor of grid operation risks by the corresponding secondary influencing factor weights, obtain the comprehensive scores of each secondary influencing factor of grid operation risks, and conduct risk grading according to the comprehensive scores.

[0011] Preferably, the grid fault record data includes records of grid faults caused by quality problems, design and manufacturing defects, and equipment aging due to equipment own factors; records of grid faults caused by overvoltage, overload, short-circuit current, and system oscillation due to grid operation factors; records of grid faults caused by lightning strikes, heavy rain, strong winds, and ice and snow due to natural factors; records of grid faults caused by operation errors, improper maintenance, and external force damage due to human factors; records of grid faults caused by pollution influence, animal influence, and electromagnetic interference influence due to operation environment factors.

[0012] Preferably, the primary influencing factors include equipment own factors, grid operation factors, natural factors, human factors, and operation environment factors.

[0013] Preferably, the secondary influencing factors include equipment aging factors, design and manufacturing defect factors, and quality problem factors based on equipment own factors; overvoltage factors, overload factors, short-circuit current factors, and system oscillation factors based on grid operation factors; lightning strike factors, heavy rain factors, strong wind factors, and ice and snow factors based on natural factors; operation error factors, improper maintenance factors, and external force damage factors based on human factors; pollution factors, animal influence factors, and electromagnetic interference factors based on operation environment factors.

[0014] Preferably, the specific grading criteria for risk grading according to the comprehensive score are: 80 - 100 points are for high-risk level I; 60 - 79 points are for medium-high-risk level II; 40 - 59 points are for medium-risk level III; 20 - 39 points are for medium-low-risk level IV; 0 - 19 points are for low-risk level V.

[0015] Preferably, the value range of the influence weight is proportional to that of the influence factor.

[0016] The beneficial effects of the present invention:

[0017] The present invention provides a power grid operation risk assessment system based on multi-dimensional data. Through historical data analysis, various risk factors such as nature, equipment itself, human, operation environment, and power grid operation are identified. A data collection interface is set up to collect power grid data, and the Apriori association algorithm is used to allocate weights to risk factors to determine the recommended weights of different factors. According to various conditions of equipment and faults, corresponding scores are set to formulate detailed scoring rules. Finally, through quantitative assessment of operation risks, a comprehensive score is obtained and risk grading is carried out. Different levels correspond to different risk degrees and countermeasures. This set of solutions effectively solves the problems of the comprehensiveness of power grid operation risk factors, the objectivity of power grid operation condition scoring, and the lack of countermeasures in the existing technology, provides a more scientific, comprehensive and practical solution for power grid operation risk assessment, and helps to ensure the safe and stable operation of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a schematic structural diagram of the present invention;

[0019] Figure 2 is a schematic flow diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Therefore, the detailed description of the embodiments of the present invention provided below in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0021] The following further elaborates the present invention in detail in conjunction with the accompanying drawings and specific embodiments:

[0022] Embodiment 1

[0023] A power grid operation risk assessment system based on multi-dimensional data, as Figure 1 shown, it includes:

[0024] The data analysis module is used to set various primary influencing factors of power grid operation risks and corresponding secondary influencing factors of power grid operation risks under various primary influencing factors of power grid operation risks according to historical power grid fault records;

[0025] The first-level influencing factor weight distribution module is used to collect the corresponding power grid fault record data according to various first-level influencing factors of power grid operation risks, and distribute weights to the corresponding first-level influencing factors of power grid operation risks according to the collected power grid fault record data to obtain the weights of each first-level influencing factor;

[0026] The second-level influencing factor weight distribution module is used to distribute weights to the corresponding second-level influencing factors of power grid operation risks under various first-level influencing factors of power grid operation risks according to the collected power grid fault record data combined with the corresponding first-level influencing factor weights to obtain the weights of each second-level influencing factor;

[0027] The scoring module is used to quantify various second-level influencing factors of power grid operation risks according to the formulated influencing factor scoring rules to obtain the scores of various second-level influencing factors of power grid operation risks, multiply the scores of each second-level influencing factor of power grid operation risks by the corresponding second-level influencing factor weights to obtain the comprehensive scores of each second-level influencing factor of power grid operation risks, and conduct risk grading according to the comprehensive scores.

[0028] In the above technical solution, the various first-level influencing factors of power grid operation risks and the corresponding second-level influencing factors of power grid operation risks under various first-level influencing factors of power grid operation risks are set according to historical power grid fault records, and are obtained by comprehensively analyzing various causes of power system faults, the characteristics of equipment itself, operating conditions, and various internal and external factors with reference to "Power System Fault Analysis" (written by P.M. Anderson, translated by Wang Jiqiang, etc. Power System Fault Analysis [M]. China Electric Power Press) and "Power System Fault Analysis" (Wu Linwei. Power System Fault Analysis [J]. Value Engineering, 2020, 39(16): 203-204.).

[0029] In the above technical solution, by systematically analyzing historical data, the main causes of power grid faults can be identified and understood more accurately, providing basic data for risk assessment.

[0030] In the above technical solution, the various first-level influencing factors of power grid operation risks are shown in Table 1 below:

[0031]

[0032] Table 1 First-level influencing factor table of power grid operation risks

[0033] In the above technical solution, the various second-level influencing factors of power grid operation risks are shown in Table 2 below:

[0034]

[0035] Table 2 Second-level influencing factor table of power grid operation risks

[0036] In the above technical solution, the power grid fault record data includes records of power grid faults caused by quality problems, design and manufacturing defects, and equipment aging due to equipment - related factors; records of power grid faults caused by over - voltage, over - load, short - circuit current, and system oscillation due to power grid operation factors; records of power grid faults caused by lightning strikes, heavy rain, strong winds, and ice and snow due to natural factors; records of power grid faults caused by operation errors, improper maintenance, and external force damage due to human factors; records of power grid faults caused by pollution effects, animal effects, and electromagnetic interference effects due to operating environment factors.

[0037] In the above technical solution, the system oscillation refers to the phenomenon that generators in the power system lose synchronization and there are periodic changes in the relative angles between rotors. It is an electromagnetic transient process that can cause large - amplitude fluctuations in electrical quantities such as voltage, current, and power in the system.

[0038] In the above technical solution, the primary influencing factors include equipment - related factors, power grid operation factors, natural factors, human factors, and operating environment factors.

[0039] In the above technical solution, the secondary influencing factors include equipment aging factors, design and manufacturing defect factors, and quality problem factors based on equipment - related factors; over - voltage factors, over - load factors, short - circuit current factors, and system oscillation factors based on power grid operation factors; lightning strike factors, heavy rain factors, strong wind factors, and ice and snow factors based on natural factors; operation error factors, improper maintenance factors, and external force damage factors based on human factors; pollution factors, animal effect factors, and electromagnetic interference factors based on operating environment factors.

[0040] In the above technical solution, the distribution of weights helps to distinguish the importance of different primary influencing factors, ensuring that the risk assessment is more accurate and targeted; by refining the weight distribution to secondary influencing factors, the risk can be evaluated more meticulously, improving the accuracy and practicality of the risk assessment.

[0041] In the above technical solution, the specific method for distributing weights to the primary influencing factors of the corresponding power grid operation risks based on the collected power grid fault record data is as follows:

[0042] Based on the collected power grid fault record data and combined with the Apriori association algorithm, the following relational expression is used to distribute weights to the primary influencing factors of the corresponding power grid operation risks;

[0043] S(X n1 →Y)=count(X n1 →Y) / count(T)

[0044] Among them, S represents support, which is used to determine the value range of the influencing factor. (X n1 →Y) represents Xn1 The first-level influencing factors and Y faults occur in combination. T represents the entire fault record set, and count represents the number of occurrences.

[0045] The confidence level refers to X n1 The number of occurrences of the combination of factors and Y faults divided by the total number of occurrences of Y faults. The confidence level C of the association rule in all transactions is denoted as:

[0046] C(X n1 →Y) = count(X n1 →Y) / count(Y)

[0047] Among them, C represents the confidence level, which is the fault probability when factor x occurs and is also the influence weight. count(Y) represents the number of occurrences of Y faults.

[0048] In the above technical solution, the association rule between the first-level influencing factors and the faults of power transmission and transformation equipment can be expressed by the association rule implication X 1 →Y, where:

[0049] X 1 = {first-level influencing factor X 11 , first-level influencing factor X 21 , …, first-level influencing factor X n1}

[0050] Y = {fault 1, fault 2, …, fault n}

[0051] Among them, X 1 represents the first-level influencing factor set, and Y represents the fault set selected from the entire fault record set with a support greater than the set support.

[0052] In the above technical solution, the support refers to the probability of the event combination occurring in all samples. Here, the result of the support S is used to judge the value range of the influencing factor, and the representative of the value range reflects the influence degree of the influencing factor; the confidence level C refers to the fault probability when a certain factor occurs, and the result of the confidence level C is used to calculate the weight.

[0053] In the above technical solution, through the Apriori algorithm for modeling and analyzing factors and faults, the maximum frequent item set of the first-level influencing factors and faults of power grid operation risk can be finally obtained, and the confidence level between meteorology and equipment faults can be obtained. By setting the minimum confidence level threshold, strong association information can be obtained. The association results are shown in Table 3 below:

[0054] Influencing factors Range of influencing factor values Suggested weight Factors of the equipment itself 0.7-0.9 0.35 Factors of power grid operation 0.6-0.8 0.3 Natural factors 0.5-0.7 0.2 Human factors 0.4-0.6 0.1 Factors of operating environment 0.3-0.5 0.05

[0055] Table 3 Association Results of First-Level Influencing Factors of Power Grid Operation Risk

[0056] In the above technical solution, the specific method for allocating weights to the secondary influencing factors of the corresponding power grid operation risk based on the collected power grid fault record data is as follows:

[0057] Based on the collected power grid fault record data and combined with the Apriori association algorithm, the following relational expression is used to allocate weights to the secondary influencing factors of the corresponding power grid operation risk;

[0058] S(X n2 →Y) = count(X n2 →Y) / count(T)

[0059] Among them, S represents the support degree, which is used to determine the value range of the influencing factor. (X n2 →Y) represents the occurrence of the combination of the secondary influencing factor X n2 and the Y fault. T represents the entire fault record set, and count represents the number of occurrences;

[0060] The confidence degree refers to the number of occurrences of the event combination of the X n2 factor and the Y fault divided by the total number of occurrences of the Y fault. The confidence degree C of the association rule in all transactions is denoted as:

[0061] C(X n2 →Y) = count(X n2 →Y) / count(Y)

[0062] Among them, C represents the confidence degree, that is, the fault probability when the x factor occurs, and it is also the influencing weight. count(Y) represents the number of occurrences of the Y fault;

[0063] The association rule between the secondary influencing factor and the power transmission and transformation equipment fault can be expressed by the association rule implication X 2 →Y, where:

[0064] X 2 = {secondary influencing factor X 12 , secondary influencing factor X 22 , …, secondary influencing factor X n2}

[0065] Y = {fault 1, fault 2, …, fault n}

[0066] Among them, X 2 represents the set of secondary influencing factors, and Y represents the set of faults selected from the entire fault record set that is greater than the set support degree.

[0067] In the above technical solution, through the modeling and analysis of factors and faults by the Apriori algorithm, the maximum frequent item sets of the secondary influencing factors of power grid operation risk and faults can be finally obtained, and the confidence level between meteorology and equipment faults can be obtained. By setting the minimum confidence threshold, strong association information can be obtained. The association results are shown in Table 4 below:

[0068]

[0069]

[0070] Table 4 Association Results of Secondary Influencing Factors of Power Grid Operation Risk

[0071] In the above technical solution, the value range of the influence weight is proportional to that of the influence factor.

[0072] In the above technical solution, the value range of the influence factor represents the degree range in which the factor may affect faults under different specific circumstances, while the weight represents the relative importance ratio of the factor when considering all factors comprehensively. For example, the influence factor of the equipment's own factors has a relatively high value, indicating that it has a relatively key influence on the occurrence of faults in most cases, and its weight is 0.35, which means that when comprehensively evaluating the fault risk of power grid equipment, the importance of this factor is about 35%.

[0073] In the above technical solution, the Apriori algorithm used is the first association rule mining algorithm and also the most classical algorithm. It uses an iterative method of layer-by-layer search to find the relationships of item sets in the database to form rules. Its process consists of connection (similar to matrix operation) and pruning (removing those unnecessary intermediate results). In this algorithm, the concept of item set is the set of items. The set containing K items is called a k-item set. The frequency of the item set is the number of transactions containing the item set, which is called the frequency of the item set. If an item set meets the minimum support, it is called a frequent item set. This algorithm has relatively strong applicability and relatively flexible requirements for the existing input data. It can also be well adapted to the data represented in binary form. Therefore, this algorithm is used for data analysis.

[0074] In the above technical solution, the formulated scoring rules are shown in Table 5 below:

[0075]

[0076]

[0077]

[0078]

[0079] Table 5 Scoring Rules and Corresponding Scores

[0080] In the above technical solution, the specific classification criteria for risk classification based on the comprehensive score are as follows: 80 - 100 points is the first-level high risk; 60 - 79 points is the second-level medium-high risk; 40 - 59 points is the third-level medium risk; 20 - 39 points is the fourth-level medium-low risk; 0 - 19 points is the fifth-level low risk.

[0081] In the above technical solution, the calculation of the quantitative score and the comprehensive score makes the risk assessment more objective and operable, facilitating decision-makers to take corresponding preventive measures according to the risk level.

[0082] In the above technical solution, the meanings and corresponding measures represented by different classification criteria in risk classification are as follows:

[0083] The meaning represented by the first-level high risk is that the power grid equipment is in an extremely high fault risk state, the possibility of a fault occurring is extremely high, and it may cause a large-scale power outage or a serious power grid operation accident at any time, posing a serious threat to power supply reliability;

[0084] Measures for the first-level high risk: It is necessary to immediately take emergency measures, such as emergency shutdown for maintenance, replacement of key equipment, strengthening power grid operation monitoring and protection, etc. At the same time, comprehensively check relevant factors and formulate long-term improvement plans;

[0085] The meaning represented by the second-level medium-high risk is that the equipment fault risk is relatively high, there are many factors that may cause faults, and some factors have had a relatively obvious impact on the equipment operation, and there is a high probability of local power outage or affecting the stable operation of the power grid;

[0086] Measures for the second-level medium-high risk: It is necessary to arrange a detailed equipment inspection and maintenance plan as soon as possible, specifically handle the high-risk factors, strengthen personnel training and management, optimize the power grid operation mode, and at the same time closely monitor the equipment operation status;

[0087] The meaning represented by the third-level medium risk is that there is a certain fault risk in the power grid equipment, some factors may cause faults under certain conditions, but the current impact on the power grid operation is relatively controllable, and a fault may cause local small-scale power outages or a decline in equipment performance;

[0088] Measures for the third-level medium risk: Conduct equipment maintenance and repair according to the normal plan, strengthen the monitoring of key equipment and links, analyze the development trend of potential risk factors, and take appropriate preventive measures, such as strengthening protection and optimizing the environment;

[0089] The meaning represented by the fourth-level medium-low risk is that the fault risk is at a relatively low level. Although there are some factors that may affect the equipment, these factors have a small impact on the power grid operation, and the possibility of a fault occurring in the short term is small;

[0090] Measures for coping with risks at level four (medium to low risks): Continue to maintain normal equipment operation and maintenance work, conduct regular equipment inspections and simple maintenance, pay attention to changes in low-risk factors, and if necessary, appropriately advance some maintenance plans;

[0091] The meaning represented by level five (low risks) is that the risk of power grid equipment failure is very low, the impact of various factors on the equipment is extremely small, the equipment operation status is good, and there are basically no obvious potential hazards that may cause failures in the short term;

[0092] Measures for coping with risks at level five (low risks): Maintain the existing equipment management and operation and maintenance strategies, ensure the quality of various operations and maintenance work, and continuously pay attention to the long-term changes in the power grid operation environment and equipment performance.

[0093] In the above technical solution, in terms of risk factor identification, the system overcomes the deficiencies of previous methods in insufficient consideration of emerging risk factors and insufficient in-depth subdivision of some factors, and can more accurately grasp the sources of various risks; for the scoring rules, it solves the problems of strong subjectivity, lack of unified objective standards, and difficulty in coping with complex and changeable situations, and enhances the reliability and consistency of the evaluation results; in terms of dynamic evaluation ability, it makes up for the deficiency of being unable to track the changes in the power grid operation status in real time and predict the risk trend, so as to adjust the coping strategies in a timely manner; for the coping measures, it solves the problems of lack of systematic integration, poor coordination and cooperation among departments, and lack of effect tracking and evaluation, realizes more efficient and targeted risk control, and ensures the safe and stable operation of the power grid in a complex and changeable operation environment.

[0094] Embodiment 2

[0095] A power grid operation risk assessment method based on multi-dimensional data, as Figure 2 shown, through historical data analysis, clarify the power grid operation risk factors, collect and sort out relevant power grid data according to the risk factors, and assign weights to the first-level risk factors; use the same method to assign weights to the second-level influencing factors; formulate scoring rules, quantify the operation risk assessment to obtain a comprehensive score, and conduct risk grading according to the comprehensive score.

[0096] The specific method for power grid operation risk assessment includes the following steps:

[0097] Set various first-level influencing factors of power grid operation risks and corresponding second-level influencing factors of power grid operation risks under various first-level influencing factors of power grid operation risks according to historical power grid fault records;

[0098] Collect the corresponding power grid fault record data according to various first-level influencing factors of power grid operation risks, and assign weights to the corresponding first-level influencing factors of power grid operation risks according to the collected power grid fault record data to obtain the weights of each first-level influencing factor;

[0099] Based on the collected power grid fault record data and combined with the corresponding weights of the first-level influencing factors, weight distribution is carried out for the corresponding second-level influencing factors of power grid operation risks under various first-level influencing factors of power grid operation risks, and the weights of each second-level influencing factor are obtained.

[0100] Quantify various second-level influencing factors of power grid operation risks according to the formulated scoring rules for influencing factors, obtain the scores of various second-level influencing factors of power grid operation risks, multiply the scores of each second-level influencing factor of power grid operation risks by the corresponding weights of the second-level influencing factors, obtain the comprehensive scores of each second-level influencing factor of power grid operation risks, and conduct risk grading according to the comprehensive scores.

[0101] Embodiment 3

[0102] A computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the method described in Embodiment 2 are implemented.

[0103] The content not detailed in this specification belongs to the prior art well-known to those skilled in the art.

Claims

1. A power grid operation risk assessment system based on multi-dimensional data, characterized in that: It includes: The data analysis module is used to set various primary influencing factors of power grid operation risks and corresponding secondary influencing factors of power grid operation risks under various primary influencing factors of power grid operation risks according to historical power grid fault records; The first-level influencing factor weight allocation module is used to collect corresponding power grid fault record data according to various first-level influencing factors of power grid operation risks, and to weight the corresponding first-level influencing factors of power grid operation risks according to the collected power grid fault record data to obtain the weights of each first-level influencing factor; The secondary influencing factor weight allocation module is used to allocate weights to the corresponding secondary influencing factors of power grid operation risks under various primary influencing factors of power grid operation risks according to the collected power grid fault record data combined with the corresponding primary influencing factor weights, and obtain the weights of each secondary influencing factor; The scoring module is used to quantify various secondary influencing factors of power grid operation risks according to the formulated influencing factor scoring criteria, obtain the scores of various secondary influencing factors of power grid operation risks, multiply the scores of various secondary influencing factors of power grid operation risks by the corresponding secondary influencing factor weights, obtain the comprehensive scores of various secondary influencing factors of power grid operation risks, and perform risk classification according to the comprehensive scores.

2. A power grid operation risk assessment system based on multi-dimensional data according to claim 1, characterized in that: The power grid fault record data includes records of power grid failures caused by quality problems of the equipment itself, design and manufacturing defects, and equipment aging; records of power grid failures caused by overvoltage, overload, short-circuit current, and system oscillation due to power grid operation factors; records of power grid failures caused by natural factors such as lightning strikes, heavy rains, strong winds, and ice and snow; records of power grid failures caused by human factors such as operational errors, improper maintenance, and external force damage; records of power grid failures caused by pollution, animal influences, and electromagnetic interference in the operating environment.

3. The power grid operation risk assessment system based on multi-dimensional data according to claim 1, characterized in that: The first-level influencing factors include equipment factors, power grid operation factors, natural factors, human factors and operating environment factors.

4. The power grid operation risk assessment system based on multi-dimensional data according to claim 1, characterized in that: The secondary influencing factors include equipment aging factors, design and manufacturing defect factors and quality problem factors based on the equipment itself; overvoltage factors, overload factors, short-circuit current factors and system oscillation factors based on power grid operation factors; Natural factors include lightning strikes, heavy rains, strong winds, and ice and snow; human factors include operational errors, improper maintenance, and external force damage; and operating environment factors include pollution, animal influence, and electromagnetic interference.

5. The power grid operation risk assessment system based on multi-dimensional data according to claim 1, characterized in that: The specific classification standards for risk classification based on the comprehensive score are: 80-100 points for level 1 high risk; 60-79 points for level 2 medium-high risk; 40-59 points for level 3 medium risk; 20-39 points for level 4 medium-low risk; 0-19 points for level 5 low risk.

6. The power grid operation risk assessment system based on multi-dimensional data according to claim 1, characterized in that: The specific method for weighting the first-level influencing factors of the corresponding power grid operation risk based on the collected power grid fault record data is as follows: Based on the collected power grid fault record data combined with the Apriori association algorithm, the following relationship is used to assign weights to the first-level influencing factors of the corresponding power grid operation risk; S(X n1 →Y)=count(X n1 →Y) / count(T) Among them, S represents the support, which is used to determine the value range of the impact factor. n1 →Y) indicates X n1 The first-level influencing factor and Y fault combination occur, T represents the total fault record set, count represents the number of occurrences, Confidence index X n1 The number of occurrences of the combination of the factor and the event Y fault divided by the total number of occurrences of the Y fault, the confidence C of the association rule in all transactions, is recorded as: C(X n1 →Y)=count(X n1 →Y) / count(Y) Among them, C represents the confidence, that is, the impact weight, and count(Y) represents the number of occurrences of fault Y.

7. A power grid operation risk assessment system based on multi-dimensional data according to claim 6, characterized in that: The specific method for weighting the secondary influencing factors of the corresponding power grid operation risk based on the collected power grid fault record data is as follows: According to the collected power grid fault record data combined with the Apriori association algorithm, the following relationship is used to assign weights to the secondary influencing factors of the corresponding power grid operation risk; S(X n2 →Y)=count(X n2 →Y) / count(T) Among them, S represents the support, which is used to determine the value range of the impact factor. n2 →Y) indicates X n2 The secondary influencing factor and the Y fault combination occur, T represents the total fault record set, and count represents the number of occurrences; Confidence index X n2 The number of occurrences of the combination of the factor and the event Y fault is divided by the total number of occurrences of the y fault. The confidence C of the association rule in all transactions is recorded as: C(X n2 →Y)=count(X n2 →Y) / count(Y) Among them, C represents the confidence, that is, the impact weight, and count(Y) represents the number of occurrences of fault Y.

8. The power grid operation risk assessment system based on multi-dimensional data according to claim 6, characterized in that: The impact weight is proportional to the value range of the impact factor.

9. A method for evaluating power grid operation risk based on multi-dimensional data, characterized in that: According to the historical power grid fault records, various primary influencing factors of power grid operation risks and corresponding secondary influencing factors of power grid operation risks under various primary influencing factors of power grid operation risks are set; Collect corresponding power grid fault record data according to various first-level influencing factors of power grid operation risks, and assign weights to the corresponding first-level influencing factors of power grid operation risks according to the collected power grid fault record data to obtain the weights of each first-level influencing factor; According to the collected power grid fault record data and the corresponding first-level influencing factor weights, weights are allocated to the corresponding second-level influencing factors of power grid operation risks under various first-level influencing factors of power grid operation risks, and the weights of various second-level influencing factors are obtained; According to the formulated influencing factor scoring criteria, various secondary influencing factors of power grid operation risks are quantified to obtain the scores of various secondary influencing factors of power grid operation risks, and the scores of various secondary influencing factors of power grid operation risks are multiplied by the corresponding secondary influencing factor weights to obtain the comprehensive scores of various secondary influencing factors of power grid operation risks, and the risks are graded according to the comprehensive scores.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method described in claim 9 are implemented.

Images