Method and system for measuring importance degree of charging equipment based on multiple evaluation factors

Abstract

The invention discloses a method and a system for measuring importance of charging equipment based on multiple evaluation factors, which are used for determining the importance of each system or component of the charging equipment, and comprise the following steps of: firstly, determining each key subsystem and component, secondly, determining importance evaluation factors of each subsystem or component of the charging equipment and a calibration range thereof, and establishing a factor evaluation model; the method comprises the steps of obtaining an evaluation index, establishing a statistical analysis process, calculating the importance degree of a charging equipment subsystem or part, analyzing the importance degree data of the charging equipment subsystem or part, and finally sorting the charging equipment subsystem or part according to the importance degree value and carrying out an operation and maintenance decision. An importance degree judgment matrix is established for evaluation factors, an analytic hierarchy process and a random forest are adopted to realize reasonable sorting of the importance degree evaluation factors, and a series of importance degree values are obtained through a Monte Carlo analysis method so as to reduce random factors.

Description

Technical Field

[0001] The present invention relates to the technical field of charging equipment importance measurement, and in particular to a charging equipment importance measurement method based on multiple evaluation factors. Background Art

[0002] With the popularization of electric vehicles and the large-scale operation of charging equipment, the operation and maintenance of charging equipment has received more and more attention from operators. The operation and maintenance of charging equipment is one of the important processes to ensure the charging experience and benefits. At present, charging equipment is mainly based on a combination of regular maintenance and post-maintenance, and regular maintenance work is carried out in accordance with the maintenance instructions provided by the charging equipment manufacturer, and simple equipment maintenance decisions are made based on personal experience. Charging equipment is mainly based on planned maintenance, supplemented by post-maintenance, and carried out according to the requirements of the operating work quota. Due to the wide distribution and scattered characteristics of charging stations, it is difficult for maintenance personnel to deal with on-site failures. This passive maintenance and indiscriminate maintenance work lacks a scientific and systematic analysis of the operating status of the equipment. The maintenance of charging equipment often has the phenomenon of over-repair and under-repair.

[0003] Charging equipment is a highly integrated and automated power electronic device that integrates multiple disciplines such as electrical, automation, software and mechanics. The systems are closely connected to each other. Each subsystem and corresponding component has different technical characteristics and operating conditions, and the influencing factors involved are also different.

[0004] There are few existing technologies that combine the systems and key components in the charging equipment with evaluation factors to evaluate the importance. For example, the invention patent with publication number CN109544006A discloses a method for evaluating the economic benefits of electric vehicle charging stations. It develops a reasonable evaluation model and method to obtain an evaluation method for economic benefits, but still does not consider how to evaluate the importance of charging equipment.

[0005] Therefore, given the complexity of the charging equipment system, the "importance" of each component to the realization of the charging equipment function is different. The importance of the charging equipment is analyzed to quantify and grade the importance of each component. The maintenance strategy of the charging equipment is based on the importance assessment of each subsystem and component, which can effectively reduce the blindness of the charging equipment maintenance, thereby improving the reliability of the charging equipment and reducing the operating cost and maintenance cost. Summary of the invention

[0006] Purpose of the invention: In order to overcome the shortcomings of the prior art, the present invention provides a charging equipment importance measurement method based on multiple evaluation factors. The method is used to solve the problem of low reliability of charging equipment caused by the blindness of charging equipment maintenance. The present invention also provides a charging equipment importance measurement system based on multiple evaluation factors.

[0007] Technical solution: According to a first aspect of the present invention, a charging equipment importance measurement method based on multiple evaluation factors is provided, and the method comprises the following steps:

[0008] S1 selects the key subsystems and key components of the charging equipment based on the historical fault data of the charging equipment, including: the main control system and the corresponding key components: the main control board and the communication board; the metering system and the corresponding key components: the DC meter box shunt; the insulation monitoring system and the corresponding key components: insulation monitoring; the heat dissipation system and the corresponding key components: the fan; the DC main circuit system and the corresponding key components: the DC contactor and the fuse; the AC main circuit system and the corresponding key components: the AC contactor; the power change system and the corresponding key components: the power module; the control power supply system and the corresponding key components: the auxiliary power supply and the air switch; the human-computer interaction system and the corresponding key components: the card reader and the screen; the charging connection system and the corresponding key components: the charging gun and the gun lock drive; the environmental monitoring system and the corresponding key components: the temperature sensor and the smoke sensor;

[0009] S2 In order to analyze the importance of each subsystem or equipment of the charging equipment, 9 specific evaluation factors and their evaluation criteria are selected, and the evaluation factors include: the impact of failure on the safety of personnel, equipment and environment; the impact of failure on system function; the impact of failure on maintenance costs; the impact of failure on downtime losses; the impact of monitorability; the impact of downtime; the impact of maintenance difficulty; the impact of failure frequency; the timeliness of spare parts supply, and the corresponding classification and calibration range are carried out according to the 9 evaluation factors;

[0010] S3 uses linear weighting to establish a mathematical model for the nine evaluation factors. The mathematical model multiplies the score of each evaluation factor by the corresponding weight, and then sums the nine evaluation factors to obtain the evaluation index of the corresponding importance.

[0011] S4 calculates the weights in step S3. First, the weight values ​​of the evaluation factors corresponding to the key subsystems or key components of each charging device are obtained by using the hierarchical analysis method. Secondly, the constructed random forest model is used for parameter tuning to dynamically adjust and optimize the initial weights to achieve feature importance sorting and relationship. Finally, the importance values ​​of the series are obtained by using the Monte Carlo analysis method to reduce random factors.

[0012] S5 performs data analysis on sample data of each key subsystem or key component of the charging equipment and determines the final importance value;

[0013] S6 ranks the key subsystems or key components of the charging equipment according to their importance values ​​and makes operation and maintenance decisions.

[0014] Further, including:

[0015] The impact of failure on the safety of personnel, equipment and environment in the evaluation factors is defined as: This factor is the impact of charging equipment failure on personal safety threats, harm to charging vehicles, and environmental pollution;

[0016] The impact of failure on system function in the evaluation factor is defined as: This factor is the impact of equipment failure on system function and performance during equipment operation, and considers whether the equipment has backup, and comprehensively considers the impact evaluation;

[0017] The impact of failure on maintenance costs in the evaluation factors is defined as: This factor comprehensively considers the complexity of the equipment and the cost of spare parts;

[0018] The impact of failure on downtime loss in the evaluation factors is defined as: This factor takes into account the impact of the station system caused by equipment downtime, as well as the corresponding spare equipment costs and the corresponding economic losses caused by equipment downtime;

[0019] The monitorability impact in the evaluation factor is defined as: This factor considers the applicable technology and means for monitoring the status of charging equipment, and fully considers the technical level required for the equipment status and the cost required for monitoring;

[0020] The impact of downtime in the evaluation factor is defined as: this factor takes into account the type and capacity of the charging equipment, the type of medium and voltage level inside the equipment, and the maintenance content. The downtime includes all the time from the shutdown of the charging equipment to maintenance, commissioning, and re-commissioning;

[0021] The influence of the difficulty of maintenance in the evaluation factor is defined as: This factor takes into account the difficulty of maintenance of the charging equipment. It should visit and investigate the operation and maintenance personnel according to the actual situation, and adjust and confirm the evaluation of this factor based on experience;

[0022] The influence of failure frequency in the evaluation factor is defined as: This factor considers the frequency of failure of the charging equipment, and combines the historical data of equipment operation to evaluate the equipment management based on the feelings of the on-site operation and maintenance personnel;

[0023] The timeliness of spare parts supply in the evaluation factor is defined as: This factor considers the timeliness evaluation of the supply of relevant forced spare parts of the charging equipment, and is evaluated based on the feelings of the on-site operation and maintenance personnel on the equipment management.

[0024] Further, including:

[0025] In step S3, a mathematical model is established by using linear weighting, which is expressed as:

[0026]

[0027] Among them, m i is the evaluation range of the i-th evaluation factor, a i is the weight of the i-th evaluation factor, and satisfies i is the serial number of the evaluation factor.

[0028] Further, including:

[0029] The step S4 calculates the weight in the step S3, including the following steps:

[0030] S41 takes the key subsystems and key components of the charging equipment as evaluation objects, and establishes the calibration range of each evaluation factor and the simulation sample size N for each evaluation object. The larger the value of the simulation sample size, the more accurate the simulation;

[0031] S42 uses the hierarchical analysis method to establish the relative importance judgment matrix D and determine the priority order of each evaluation factor;

[0032] S43 generates a random weight number between 0 and 1 as the weight of the corresponding evaluation factor;

[0033] S44: When the current sample k=1, the random forest model is used to dynamically adjust and optimize the initial weights obtained; according to the priority order generated in step S42, the optimized weights are sorted in this order and assigned a i ;

[0034] S45 calculates the importance evaluation index Index of each key subsystem or key component, and determines whether the statistical sample k reaches the sample size N, outputs the sample size, and completes the simulation calculation.

[0035] Further, including:

[0036] In step S5, data analysis is performed on the sample data of each key subsystem or key component of the charging device, and a final importance value is determined, which specifically includes:

[0037] Data analysis is carried out on N sample data of each evaluation object to extract the statistical characteristic values ​​of the mean, median, kurtosis, skewness and standard deviation of the sample data. It is defined that sample data with a kurtosis less than 10, a skewness not greater than 2, and a standard deviation less than 3 are more consistent with or approximately normal distribution and are ideal sample data. The mean of the importance evaluation index corresponding to this type of sample data is taken as the final importance value.

[0038] Further, including:

[0039] In step S6, the key subsystems or key components of the charging equipment are sorted according to the importance values, and operation and maintenance decisions are made, including:

[0040] For key subsystems or key components whose final importance values ​​are in the top 30% of the ranking, their importance is relatively high, and corresponding resources need to be allocated preferentially in operation and maintenance. The charging station operator should arrange advance maintenance plans based on production needs; for key subsystems or key components ranked between 30% and 70%, their importance is moderate, and a regular inspection strategy is adopted; for key subsystems or key components ranked between 70% and 100%, their importance is relatively low, and maintenance is carried out according to the company's operational needs when there are abnormal phenomena but it does not affect operational needs.

[0041] Further, including:

[0042] In step S2, the corresponding classification and calibration range are performed according to the 9 evaluation factors, specifically including:

[0043] The corresponding classifications of the impact of the failure on the safety of personnel, equipment and environment include: extremely serious threat, corresponding calibration range: 90-100; serious threat, corresponding calibration range: 75-90; general threat, corresponding calibration range: 45-75; slight threat, corresponding calibration range: 20-45; almost no threat, corresponding calibration range: 0-10;

[0044] The corresponding classifications of the impact of the failure on the system function include: complete loss of function, corresponding calibration range: 90-100; basic loss of function, corresponding calibration range: 75-90; significant loss of function, corresponding calibration range: 45-75; slight threat to personnel, equipment and environment, corresponding calibration range: 20-45; almost no threat to personnel, equipment and environment, corresponding calibration range: 0-10;

[0045] The categories corresponding to the impact of the failure on the maintenance cost include: extremely high, with a corresponding calibration range of 90-100; high, with a corresponding calibration range of 75-90; medium, with a corresponding calibration range of 50-75; low, with a corresponding calibration range of 30-50;

[0046] The classifications corresponding to the failure impact on downtime loss include: extremely high, with a corresponding calibration range of 90-100; high, with a corresponding calibration range of 75-90; average, with a corresponding calibration range of 45-75; low, with a corresponding calibration range of 25-45;

[0047] The categories corresponding to the monitorability impact include: extremely high technology and cost, with a corresponding calibration range of 85-100; high technology and cost, with a corresponding calibration range of 65-85; medium technology and cost, with a corresponding calibration range of 45-65; low technology and cost, with a corresponding calibration range of 25-45;

[0048] The corresponding classifications of downtime impact include: downtime of more than 10 days, the corresponding calibration range is: 90-100; downtime of 5 to 10 days, the corresponding calibration range is: 65-90; downtime of 2 to 5 days, the corresponding calibration range is: 35-65; downtime of 1 to 2 days, the corresponding calibration range is: 10-35; downtime of no more than 1 day, the corresponding calibration range is: 0-10;

[0049] The corresponding classifications of the maintenance difficulty include: extremely complex equipment and environment, with a corresponding calibration range of 90-100; relatively complex equipment and environment, with a corresponding calibration range of 75-90; medium complexity of equipment and environment, with a corresponding calibration range of 45-75; low complexity of equipment and environment, with a corresponding calibration range of 25-45; very low complexity of equipment and environment, with a corresponding calibration range of 10-25;

[0050] The classifications corresponding to the impact of the failure frequency include: extremely low occurrence frequency, the corresponding calibration range is: 90-100; low occurrence frequency, the corresponding calibration range is: 65-90; medium occurrence frequency, the corresponding calibration range is: 40-65; high occurrence frequency, the corresponding calibration range is: 10-40; very high occurrence frequency, the corresponding calibration range is: 0-10;

[0051] The categories corresponding to the timeliness of the supply of spare parts include: very timely, with a corresponding calibration range of 80-100; relatively timely, with a corresponding calibration range of 60-80; basically meeting the requirements, with a corresponding calibration range of 35-60; occasionally lagging, with a corresponding calibration range of 10-35; often lagging, with a corresponding calibration range of 0-10.

[0052] On the other hand, the present invention also provides a charging equipment importance measurement system based on multiple evaluation factors, the system comprising:

[0053] The key subsystem and key component screening module is used to screen out the key subsystems and key components of the charging equipment according to the historical fault data of the charging equipment, including: the main control system and the corresponding key components: the main control board and the communication board; the metering system and the corresponding key components: the DC meter box shunt; the insulation monitoring system and the corresponding key components: insulation monitoring; the heat dissipation system and the corresponding key components: the fan; the DC main circuit system and the corresponding key components: the DC contactor and the fuse; the AC main circuit system and the corresponding key components: the AC contactor; the power change system and the corresponding key components: the power supply module; the control power supply system and the corresponding key components: the auxiliary power supply and the air switch; the human-computer interaction system and the corresponding key components: the card reader and the screen; the charging connection system and the corresponding key components: the charging gun and the gun lock drive; the environmental monitoring system and the corresponding key components: the temperature sensor and the smoke sensor;

[0054] The evaluation factor construction module is used to screen out 9 specific evaluation factors and their evaluation criteria, including: the impact of failure on the safety of personnel, equipment and environment; the impact of failure on system function; the impact of failure on maintenance costs; the impact of failure on downtime losses; the impact of monitorability; the impact of downtime; the impact of maintenance difficulty; the impact of failure frequency; the timeliness of spare parts supply, and the corresponding classification and calibration range are carried out according to the 9 evaluation factors;

[0055] The evaluation index calculation module is used to establish a mathematical model for the nine evaluation factors using linear weighting. The mathematical model multiplies the score of each evaluation factor by the corresponding weight, and then sums the nine evaluation factors to obtain the evaluation index of the corresponding importance.

[0056] The weight calculation module is used to calculate the weights in the evaluation index calculation module. First, the hierarchical analysis method is used to obtain the weight values ​​of the evaluation factors corresponding to the key subsystems or key components of each charging device. Secondly, the constructed random forest model is used for parameter tuning to dynamically adjust and optimize the initial weights to achieve feature importance sorting and relationship; finally, the importance values ​​of the series are obtained through the Monte Carlo analysis method to reduce random factors;

[0057] A data analysis module is used to perform data analysis on sample data of each key subsystem or key component of the charging equipment and determine the final importance value;

[0058] The sorting module is used to sort the key subsystems or key components of the charging equipment according to their importance values ​​and make operation and maintenance decisions.

[0059] Further, including:

[0060] The impact of failure on the safety of personnel, equipment and environment in the evaluation factors is defined as: This factor is the impact of charging equipment failure on personal safety threats, harm to charging vehicles, and environmental pollution;

[0061] The impact of failure on system function in the evaluation factor is defined as: This factor is the impact of equipment failure on system function and performance during equipment operation, and considers whether the equipment has backup, and comprehensively considers the impact evaluation;

[0062] The impact of failure on maintenance costs in the evaluation factors is defined as: This factor comprehensively considers the complexity of the equipment and the cost of spare parts;

[0063] The impact of failure on downtime loss in the evaluation factors is defined as: This factor takes into account the impact of the station system caused by equipment downtime, as well as the corresponding spare equipment costs and the corresponding economic losses caused by equipment downtime;

[0064] The monitorability impact in the evaluation factor is defined as: This factor considers the applicable technology and means for monitoring the status of charging equipment, and fully considers the technical level required for the equipment status and the cost required for monitoring;

[0065] The impact of downtime in the evaluation factor is defined as: this factor takes into account the type and capacity of the charging equipment, the type of medium and voltage level inside the equipment, and the maintenance content. The downtime includes all the time from the shutdown of the charging equipment to maintenance, commissioning, and re-commissioning;

[0066] The influence of the difficulty of maintenance in the evaluation factor is defined as: This factor takes into account the difficulty of maintenance of the charging equipment. It should visit and investigate the operation and maintenance personnel according to the actual situation, and adjust and confirm the evaluation of this factor based on experience;

[0067] The influence of failure frequency in the evaluation factor is defined as: This factor considers the frequency of failure of the charging equipment, and combines the historical data of equipment operation to evaluate the equipment management based on the feelings of the on-site operation and maintenance personnel;

[0068] The timeliness of spare parts supply in the evaluation factor is defined as: This factor considers the timeliness evaluation of the supply of relevant forced spare parts of the charging equipment, and is evaluated based on the feelings of the on-site operation and maintenance personnel on the equipment management.

[0069] Further, including:

[0070] The data analysis module performs data analysis on sample data of each key subsystem or key component of the charging device and determines the final importance value, specifically including:

[0071] Data analysis is carried out on N sample data of each evaluation object to extract the statistical characteristic values ​​of the mean, median, kurtosis, skewness and standard deviation of the sample data. It is defined that sample data with a kurtosis less than 10, a skewness not greater than 2, and a standard deviation less than 3 are more consistent with or approximately normal distribution and are ideal sample data. The mean of the importance evaluation index corresponding to this type of sample data is taken as the final importance value.

[0072] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0073] (1) The present invention first screens the key subsystems and key components of the charging equipment, in order to better analyze the importance of each subsystem or device of the charging equipment, reduce the complexity of the entire system analysis, and highlight some factors with strong correlation found in the operation of the charging equipment; and classify and calibrate these factors into evaluation factors, and on this basis establish an importance judgment matrix, and use the hierarchical analysis method and random forest model to achieve a reasonable sorting of each importance evaluation factor, and use the Monte Carlo analysis method to obtain a series of importance values ​​to reduce random factors, and use statistical analysis to verify the rationality of the data, so that the importance value obtained is more accurate, thereby improving the reliability of the charging equipment.

[0074] (2) The present invention performs data analysis on the importance of the subsystems or components of the charging equipment, which replaces the data preprocessing in the prior art, selects sample data corresponding to the statistical characteristic values ​​that meet certain conditions, and takes the average of the importance evaluation indexes corresponding to such sample data as the final importance value. The importance values ​​of each subsystem obtained by this method are more accurate and meet actual needs;

[0075] (3) The present invention calculates the importance of the subsystems or components of the charging equipment, and continuously optimizes and improves the weights of the evaluation factors of each importance through the random forest model in a Monte Carlo problem-solving manner, so that the robustness of the importance ranking of each subsystem or component is enhanced, thereby reducing the impact of human interference in the importance evaluation process of the subsystems or components of the charging equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 is a flow chart of a charging equipment importance measurement method based on multiple evaluation factors according to an embodiment of the present invention;

[0077] Figure 2 is a flow chart of importance statistics simulation according to an embodiment of the present invention;

[0078] Figure 3 It is a schematic structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0079] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0080] like Figure 1 As shown, the present invention first provides a charging equipment importance measurement method based on multiple evaluation factors, specifically, including:

[0081] S01 selects the key subsystems and key components of the charging equipment based on the historical fault data of the charging equipment, including: the main control system and the corresponding key components: the main control board and the communication board; the metering system and the corresponding key components: the DC meter box shunt; the insulation monitoring system and the corresponding key components: insulation monitoring; the heat dissipation system and the corresponding key components: the fan; the DC main circuit system and the corresponding key components: the DC contactor and the fuse; the AC main circuit system and the corresponding key components: the AC contactor; the power change system and the corresponding key components: the power module; the control power supply system and the corresponding key components: the auxiliary power supply and the air switch; the human-computer interaction system and the corresponding key components: the card reader and the screen; the charging connection system and the corresponding key components: the charging gun and the gun lock drive; the environmental monitoring system and the corresponding key components: the temperature sensor and the smoke sensor.

[0082] Charging equipment involves multiple subsystems and components. To simplify the analysis process, importance assessment is performed based on the consequences of charging equipment failures, combined with experience and specific impact levels. Among the impacts of charging equipment failures, the consequences of failures have different experiences for operation and maintenance personnel and users. It is necessary to conduct a comprehensive assessment based on specific experience and experience, and select key subsystems and components of charging equipment for importance analysis. The statistics of key subsystems and components of common charging equipment are shown in Table 1. This process can reduce the early data statistics work, thereby improving the efficiency of later analysis.

[0083] Table 1 Key subsystems and components of charging equipment

[0084]

[0085] S02 In order to analyze the importance of each subsystem or equipment of the charging equipment, 9 specific evaluation factors and their evaluation criteria are screened out, and the evaluation factors include: the impact of failure on the safety of personnel, equipment and environment; the impact of failure on system function; the impact of failure on maintenance costs; the impact of failure on downtime losses; the impact on monitorability; the impact on downtime; the impact on the difficulty of inspection and maintenance; the impact on failure frequency; and the timeliness of spare parts supply, and corresponding classification and calibration range are carried out according to the 9 evaluation factors.

[0086] Specifically: It is very complicated to evaluate the impact of each subsystem or component on the failure of the charging equipment. During the evaluation process, it is necessary to select factors that have a greater impact on the importance of the charging equipment. The main evaluation factors include: the impact of failure on personnel and environmental safety, the impact of failure on system functions, whether there are backup equipment, the impact of failure on related equipment, failure frequency, operating conditions, maintenance costs, production losses, monitorability, downtime, difficulty of maintenance, etc.

[0087] In order to better analyze the importance of each subsystem or equipment of the charging equipment, reduce the complexity of the whole system analysis, and highlight some highly correlated factors found in the operation of the charging equipment, 9 specific evaluation factors and their evaluation criteria are summarized. The specific influencing factors are defined as follows, and the calibration range of the evaluation factors is shown in Table 2.

[0088] 1) Impact of failure on the safety of personnel, equipment and environment This factor mainly considers the impact of equipment failure on personal safety threats (such as electric shock and mechanical collision), hazards to charging vehicles (such as damage to batteries and vehicle burns), and environmental pollution (such as noise and radiation).

[0089] 2) Impact of failure on system function This factor mainly considers the impact of equipment failure on system function and performance during equipment operation, and considers whether the equipment has backup, and comprehensively considers the impact evaluation.

[0090] 3) Impact of failure on maintenance costs This factor takes into account the complexity of the equipment and the cost of spare parts.

[0091] 4) Impact of failure on downtime losses This factor mainly considers the impact of equipment downtime on the station system (such as the overall experience of the charging station), as well as the corresponding backup equipment costs and the corresponding economic losses caused by equipment downtime (such as charging fee revenue).

[0092] 5) Monitorability This factor mainly considers the applicability of charging equipment status monitoring technologies and means (such as remote operation and maintenance and the use of artificial intelligence), while fully considering the technical level required for the equipment status and the cost of monitoring.

[0093] 6) Downtime impact This factor mainly considers the type and capacity of the charging equipment, the type of medium and voltage level inside the equipment, maintenance content, etc. The downtime includes all the time from equipment shutdown to maintenance, debugging, and re-commissioning.

[0094] 7) The influence of the difficulty of maintenance This factor mainly considers the importance analysis of charging equipment, which serves the maintenance of equipment. The difficulty of maintenance is an important factor in evaluating the importance of equipment. Because this factor varies greatly due to the influence of personnel, equipment and environment, there are large subjective factors. In order to ensure the objectivity and compliance of the evaluation of this factor, the operation and maintenance personnel should be visited and investigated according to the actual situation, and the evaluation of this factor should be adjusted and confirmed based on experience.

[0095] 8) Impact of failure frequency This factor mainly considers the frequency of equipment failures, mainly combined with the historical data of equipment operation, and evaluated based on the on-site operation and maintenance personnel's feelings about equipment management.

[0096] 9) Timely supply of spare parts: This factor mainly considers the timeliness of the supply of relevant forced spare parts of charging equipment, and is evaluated based on the on-site operation and maintenance personnel's feelings about equipment management.

[0097] Table 2 Importance evaluation factors and their calibration ranges

[0098]

[0099]

[0100] S03 uses linear weighting to establish a mathematical model for the nine evaluation factors. The mathematical model multiplies the score of each evaluation factor by the corresponding weight, and then sums the nine evaluation factors to obtain an evaluation index of corresponding importance.

[0101] Specifically: The 9 defined evaluation factors are linearly weighted to establish a mathematical model. The importance of the subsystems or components of the charging equipment adopts this mathematical model. The mathematical model is established by linear weighting, which is expressed as:

[0102]

[0103] Among them, m i is the evaluation range of the i-th evaluation factor, a i is the weight of the i-th evaluation factor, and satisfies i is the serial number of the evaluation factor.

[0104] S04 calculates the weights in step S03. First, the analytic hierarchy process is used to obtain the weight values ​​of the evaluation factors corresponding to the key subsystems or key components of each charging device. Secondly, the constructed random forest model is used to tune the parameters to dynamically adjust and optimize the initial weights to achieve feature importance sorting and relationship; finally, the importance values ​​of the series are obtained through the Monte Carlo analysis method to reduce random factors.

[0105] Specific examples include:

[0106] In order to comprehensively evaluate the sensitivity of weights and reduce human factors, the analytic hierarchy process is used to obtain the weight values.

[0107] 1) Construct a judgment matrix

[0108] Assume that the analyzed object (subsystem or component of the charging equipment) has n (n=9) evaluation factors, and the relative importance judgment matrix between the factors is D.

[0109]

[0110] In the formula, u ij represents the relative importance of the i-th evaluation factor to the j-th evaluation factor, u ji represents the relative importance of the jth evaluation factor to the ith evaluation factor, u ji for u ij The relative importance values ​​are shown in Table 3.

[0111] Table 3 Relative importance value table

[0112]

[0113] 2) Obtain the weight of each influencing factor

[0114] Calculate the maximum eigenvalue λ of the judgment matrix D max , and substitute it into the linear equation system to solve it.

[0115]

[0116] Solve for w1, w2, ... w n , get the maximum characteristic root λ max The corresponding eigenvector is the weight of each influencing factor. Through the size of these weights, the priority order of each evaluation factor can be obtained, which is the same as a i Sort by order of the same size and assign evaluation factors to calculate the importance evaluation index.

[0117] 3) Conduct consistency testing

[0118] Since the relative importance value has a certain degree of randomness, in order to obtain a more reasonable weight coefficient, the following formula is used to carry out consistency test.

[0119] CR=CI / RI

[0120] Where CR is the random consistency ratio of the judgment matrix; CI is the general consistency index of the judgment matrix, CI = λ max -n / n-1; RI is the average random consistency index of the judgment matrix. For judgment matrices of order 1 to 9, the specific values ​​are shown in Table 4.

[0121] Table 4 RI value table

[0122] N 1 2 3 4 5 6 7 8 9 RI 0 0 0.58 0.9 1.12 1.24 1.32 1.41 1.45

[0123] When CR is less than 0.1, it is considered that the calculation result of the current judgment matrix has satisfactory consistency, indicating that the weight distribution is reasonable; otherwise, the judgment matrix needs to be adjusted until satisfactory consistency is obtained.

[0124] 4) Random Forest Parameter Tuning

[0125] The 9 evaluation factors are set as input features, and the actual importance results are used as output labels to construct a random forest model. The core principle of random forest is that it achieves fine-tuning prediction by constructing a large number of decision trees and integrating their respective prediction results. In the process of training the random forest model, cross-validation is used to ensure the performance and generalization ability of the model, and to select the most appropriate hyperparameters, such as the number and depth of decision trees.

[0126] Random forests are composed of numerous decision trees, each of which is constructed based on randomly sampled samples and randomly selected features during training. The trained random forest model is used to predict the importance of evaluation factors for the subsystems or components of the charging equipment, and the prediction results generated by the random forest are integrated with the importance obtained by the hierarchical analysis method, such as by calculating the weighted average of the two to achieve integration, where the weight distribution can be set based on experience. The hierarchical analysis method is based on the knowledge and experience of experts and provides an initial framework for the importance of evaluation factors. The random forest automatically learns and discovers complex patterns from the data to supplement and optimize the results of the hierarchical analysis method. Specifically, the weights determined by the hierarchical analysis method can be used as the initial weight setting of the features in the random forest model, providing an initial start for the training of the model. Through the learning and training of the random forest, these initial weights are dynamically adjusted and optimized to achieve the ranking and relationship of feature importance, and also provide reference and verification for the judgment matrix of the hierarchical analysis method. Through the above process, the potential complex relationship between the evaluation factors is excavated, the deviation and limitations that may be brought about by a single method are reduced, thereby improving the accuracy and reliability of the importance evaluation, and making the ranking of the importance evaluation factors of the charging equipment more reasonable.

[0127] The statistical analysis process uses computer-aided calculations and Monte Carlo algorithm to perform statistical simulations on the random variable weight. The basic process of Monte Carlo problem solving includes: constructing a probability model, defining random variables, simulating and calculating evaluation indicators, and obtaining statistical samples. See the flowchart of the simulation calculation process for details. Figure 2 The specific process is as follows:

[0128] 1) According to S01 to S02, establish the evaluation object (subsystem or component), the score of each factor of each evaluation object, and the simulation sample size N (the larger the value, the more accurate the simulation, but the greater the amount of calculation);

[0129] 2) According to S03, the relative importance judgment matrix D is established by using the hierarchical analysis method to determine the priority order of each evaluation factor;

[0130] 3) Generate a random weight number between 0 and 1 as the weight of the corresponding factor, and assign the random weight number to the a according to the priority order generated in S03. i .

[0131] 4) When K=1, the random forest optimization hierarchical analysis method is used to dynamically adjust and optimize the initial weights;

[0132] 5) Calculate the index of each key subsystem or key component, and determine whether the statistical sample k reaches the sample size N.

[0133] 6) Output the sample size and complete the simulation calculation.

[0134] S5 performs data analysis on sample data of each key subsystem or key component of the charging equipment and determines the final importance value.

[0135] Data analysis is performed on the N sample data of each evaluation object, and statistical characteristic values ​​such as the mean, median, kurtosis, skewness and standard deviation of the evaluation indicators corresponding to the sample data are extracted. It is defined that the sample data corresponding to the evaluation indicators with a kurtosis of less than 10, a skewness of no more than 2, and a standard deviation of less than 3 are more in line with or approximately normal distribution, and are relatively ideal sample data. The mean of the importance evaluation indicators corresponding to such sample data is taken as the final importance value. Specifically, the evaluation index values ​​corresponding to the sample data of each evaluation object are traversed, and the corresponding mean, median, kurtosis, skewness and standard deviation are calculated in turn, and the sample data with a kurtosis of less than 10, a skewness of no more than 2, and a standard deviation of less than 3 are screened out; and the mean of the evaluation index values ​​corresponding to the sample data that meet the requirements is taken as the final importance value of the current evaluation object.

[0136] This design selects out the deviated sample data and retains the sample data that conforms to the actual situation. Therefore, the calculated importance value is more accurate. Combined with the relevant methods mentioned above, the weights of the evaluation factors of each importance are continuously optimized and improved, so that the robustness of the importance ranking of each subsystem or component is enhanced, thereby reducing the impact of human interference in the importance evaluation process of the charging equipment subsystem or component, thereby effectively reducing the blindness of charging equipment maintenance, thereby improving the reliability of charging equipment, and reducing operating costs and maintenance costs.

[0137] S06 ranks the key subsystems or key components of the charging equipment according to their importance values ​​and makes operation and maintenance decisions.

[0138] Obtain the numerical order of importance of the subsystems or components of the charging equipment, and make operation and maintenance decisions based on this order. Combined with the practical experience of the operation and maintenance personnel and operators, formulate an operation and maintenance plan: For the top 30% of the key subsystems or key components, the importance is relatively high, and the corresponding resources need to be allocated in operation and maintenance first. The charging station operator should arrange a pre-maintenance plan according to production needs; for the systems or components ranked 30% to 70%, the importance is moderate, and a regular inspection strategy can be adopted; for the systems or components ranked 70% to 100%, the importance is relatively low, and maintenance can be carried out according to the needs of the enterprise operation when there are abnormal phenomena but it does not affect the operation needs.

[0139] Therefore, an embodiment of the present invention provides a charging equipment importance measurement method and auxiliary operation and maintenance decision-making thereof. By evaluating the charging equipment system or components, an importance determination matrix is ​​established for the evaluation factors, and the hierarchical analysis method and random forest are used to achieve reasonable sorting of the importance evaluation factors. A series of importance values ​​are obtained through the Monte Carlo analysis method to reduce random factors, and statistical analysis is used to verify the rationality of the data. Then, the sorting method is used to guide the formulation of operation and maintenance decisions, thereby improving the full life cycle service capability of the charging equipment, improving the charging experience, and reducing the difficulty and cost of operation and maintenance uncertainty based on experience.

[0140] In order to verify the above method, this embodiment performs the following analysis, specifically:

[0141] 1) According to Tables 1 and 2, the importance evaluation factors of the key subsystems of the charging equipment are calibrated, and the average value is taken according to the joint scores of the operation and maintenance personnel and the operator personnel. The calibrated values ​​of the importance evaluation factors are shown in Table 5.

[0142] Table 5 Importance of charging equipment subsystem and calibration values ​​of evaluation factors

[0143]

[0144]

[0145] 2) Establish the weight distribution of influencing factors and the priority order of each evaluation factor. Establish the relative importance according to Table 3 and obtain the judgment matrix D.

[0146]

[0147] The maximum characteristic root λ of the judgment matrix D is obtained by calculation max =9.188, the general consistency index of the judgment matrix CI = 0.0235, the average random consistency index RI = 1.45, and then the random consistency ratio of the judgment matrix CR = 0.0162 < 0.1, the judgment matrix has satisfactory consistency, and the established relative importance matrix is ​​reasonable. Then the weight distribution priority ranking of the above evaluation factors is obtained, as shown in Table 6.

[0148] Table 6 Priority order of evaluation factors

[0149]

[0150] 3) Use the S04 analysis process to obtain simulated statistical samples and conduct data analysis

[0151] Through calculation and simulation, the number of simulations is N = 5000, and N Index values ​​are obtained for each system. The data stability is evaluated according to S05, and the final data is shown in Table 7.

[0152] Table 7 Simulation data results of charging equipment subsystem importance

[0153]

[0154] 4) The subsystems of the charging equipment are ranked in order of importance according to Table 8. The results are shown in Table 8.

[0155] Table 8 System importance index and ranking

[0156]

[0157]

[0158] According to the sorting in the table, it can be seen that the main control system, power change system and charging connection system are at the forefront of the key subsystems of the charging equipment, and the heat dissipation system, metering system and environmental monitoring system are ranked relatively late. The overall smoothness is in line with the experience and cognition of general operation and maintenance personnel and operators, which shows that this method is practical and the content described in S06 can be used to carry out operation and maintenance decisions.

[0159] Example 2: Analysis of the importance of charging equipment components

[0160] 1) Establish the importance of charging equipment components and evaluation factor calibration values ​​based on Table 1 and Table 2, see Table 9.

[0161] Table 9 Importance of charging equipment components and calibration values ​​of evaluation factors

[0162]

[0163]

[0164] 2) To simplify the calculation, the judgment matrix of component importance evaluation factors adopts the system judgment matrix D, and the same evaluation factor priority order as Table 6 is obtained.

[0165] 3) Take the simulation number N = 8000 times, obtain N Index values ​​for each component, evaluate the data stability according to S05, and the final data is shown in Table 10.

[0166] Table 10 Simulation data results of importance of charging equipment components

[0167]

[0168] 4) Rank the importance of the charging equipment components according to Table 10. The results are shown in Table 11.

[0169] Table 11 System importance index and ranking

[0170] Sorting Main control board Importance Sorting Main control board Importance 1 Power Module 50.856 10 Shunt 45.916 2 communication board 49.815 11 Fan 45.531 3 DC contactor 49.436 12 Gun lock drive 44.542 4 Charging gun 49.014 13 Air switch 44.356 5 Insulation monitoring 48.890 14 Card reader 43.265 6 Screen 48.839 15 DC Meter 42.437 7 AC contactor 48.705 16 Smoke sensor 42.114 8 Auxiliary power supply 47.448 17 Temperature Sensor 41.156 9 Fuse 46.564

[0171] According to the sorting in the table, it can be seen that the power module, communication board and DC contactor are at the forefront of the key subsystems of the charging equipment, the screen, AC contactor and auxiliary power supply are in the middle, and the DC meter, smoke sensor and temperature sensor are at the end. The overall smoothness is in line with the experience and cognition of general operation and maintenance personnel and operators, and the content described in S06 can be used to carry out operation and maintenance decisions.

[0172] On the other hand, the present invention also provides a charging equipment importance measurement system based on multiple evaluation factors, the system comprising:

[0173] The key subsystem and key component screening module is used to screen out the key subsystems and key components of the charging equipment according to the historical fault data of the charging equipment, including: the main control system and the corresponding key components: the main control board and the communication board; the metering system and the corresponding key components: the DC meter box shunt; the insulation monitoring system and the corresponding key components: insulation monitoring; the heat dissipation system and the corresponding key components: the fan; the DC main circuit system and the corresponding key components: the DC contactor and the fuse; the AC main circuit system and the corresponding key components: the AC contactor; the power change system and the corresponding key components: the power supply module; the control power supply system and the corresponding key components: the auxiliary power supply and the air switch; the human-computer interaction system and the corresponding key components: the card reader and the screen; the charging connection system and the corresponding key components: the charging gun and the gun lock drive; the environmental monitoring system and the corresponding key components: the temperature sensor and the smoke sensor;

[0174] The evaluation factor construction module is used to screen out 9 specific evaluation factors and their evaluation criteria, including: the impact of failure on the safety of personnel, equipment and environment; the impact of failure on system function; the impact of failure on maintenance costs; the impact of failure on downtime losses; the impact of monitorability; the impact of downtime; the impact of maintenance difficulty; the impact of failure frequency; the timeliness of spare parts supply, and the corresponding classification and calibration range are carried out according to the 9 evaluation factors;

[0175] The evaluation index calculation module is used to establish a mathematical model for the nine evaluation factors using linear weighting. The mathematical model multiplies the score of each evaluation factor by the corresponding weight, and then sums the nine evaluation factors to obtain the evaluation index of the corresponding importance.

[0176] The weight calculation module is used to calculate the weights in the evaluation index calculation module. First, the hierarchical analysis method is used to obtain the weight values ​​of the evaluation factors corresponding to the key subsystems or key components of each charging device. Secondly, the constructed random forest model is used for parameter tuning to dynamically adjust and optimize the initial weights to achieve feature importance sorting and relationship; finally, the importance values ​​of the series are obtained through the Monte Carlo analysis method to reduce random factors;

[0177] A data analysis module is used to perform data analysis on sample data of each key subsystem or key component of the charging equipment and determine the final importance value;

[0178] The sorting module is used to sort the key subsystems or key components of the charging equipment according to their importance values ​​and make operation and maintenance decisions.

[0179] Further, including:

[0180] The impact of failure on the safety of personnel, equipment and environment in the evaluation factors is defined as: This factor is the impact of charging equipment failure on personal safety threats, harm to charging vehicles, and environmental pollution;

[0181] The impact of failure on system function in the evaluation factor is defined as: This factor is the impact of equipment failure on system function and performance during equipment operation, and considers whether the equipment has backup, and comprehensively considers the impact evaluation;

[0182] The impact of failure on maintenance costs in the evaluation factors is defined as: This factor comprehensively considers the complexity of the equipment and the cost of spare parts;

[0183] The impact of failure on downtime loss in the evaluation factors is defined as: This factor takes into account the impact of the station system caused by equipment downtime, as well as the corresponding spare equipment costs and the corresponding economic losses caused by equipment downtime;

[0184] The monitorability impact in the evaluation factor is defined as: This factor considers the applicable technology and means for monitoring the status of charging equipment, and fully considers the technical level required for the equipment status and the cost required for monitoring;

[0185] The impact of downtime in the evaluation factor is defined as: this factor takes into account the type and capacity of the charging equipment, the type of medium and voltage level inside the equipment, and the maintenance content. The downtime includes all the time from the shutdown of the charging equipment to maintenance, commissioning, and re-commissioning;

[0186] The influence of the difficulty of maintenance in the evaluation factor is defined as: This factor takes into account the difficulty of maintenance of the charging equipment. It should visit and investigate the operation and maintenance personnel according to the actual situation, and adjust and confirm the evaluation of this factor based on experience;

[0187] The influence of failure frequency in the evaluation factor is defined as: This factor considers the frequency of failure of the charging equipment, and combines the historical data of equipment operation to evaluate the equipment management based on the feelings of the on-site operation and maintenance personnel;

[0188] The timeliness of spare parts supply in the evaluation factor is defined as: This factor considers the timeliness evaluation of the supply of relevant forced spare parts of the charging equipment, and is evaluated based on the feelings of the on-site operation and maintenance personnel on the equipment management.

[0189] Further, including:

[0190] The data analysis module performs data analysis on sample data of each key subsystem or key component of the charging device and determines the final importance value, specifically including:

[0191] Data analysis is carried out on N sample data of each evaluation object to extract the statistical characteristic values ​​of the mean, median, kurtosis, skewness and standard deviation of the sample data. It is defined that sample data with a kurtosis less than 10, a skewness not greater than 2, and a standard deviation less than 3 are more consistent with or approximately normal distribution and are ideal sample data. The mean of the importance evaluation index corresponding to this type of sample data is taken as the final importance value.

[0192] Other technical features of the charging equipment importance measurement system based on multiple evaluation factors described in the present invention are similar to the corresponding charging equipment importance measurement method based on multiple evaluation factors, and will not be repeated here.

[0193] Figure 3 A schematic diagram of the structure of an electronic device 40 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0194] like Figure 3As shown, the electronic device 40 includes at least one processor 41, and a memory connected to the at least one processor 41, such as a read-only memory (ROM) 42, a random access memory (RAM) 43, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 41 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 42 or the computer program loaded from the storage unit 48 to the random access memory (RAM) 43. In the RAM 43, various programs and data required for the operation of the electronic device 40 can also be stored. The processor 41, the ROM 42, and the RAM 43 are connected to each other through a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0195] A number of components in the electronic device 40 are connected to the I / O interface 45, including: an input unit 46, such as a keyboard, a mouse, etc.; an output unit 47, such as various types of displays, speakers, etc.; a storage unit 48, such as a disk, an optical disk, etc.; and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0196] The processor 41 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 41 executes the various methods and processes described above, such as a method for processing the harmfulness of a charging device.

[0197] In some embodiments, the hazardousness handling method of the charging device may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 40 via the ROM 42 and / or the communication unit 49. When the computer program is loaded into the RAM 43 and executed by the processor 41, one or more steps of the hazardousness handling method of the charging device described above may be performed. Alternatively, in other embodiments, the processor 41 may be configured to execute the hazardousness handling method of the charging device in any other appropriate manner (e.g., by means of firmware).

[0198] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0199] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0200] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0201] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0202] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0203] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0204] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0205] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A charging equipment importance measurement method based on multiple evaluation factors, characterized in that: The method comprises the following steps: S1 selects the key subsystems and key components of the charging equipment based on the historical fault data of the charging equipment, including: the main control system and the corresponding key components: the main control board and the communication board; the metering system and the corresponding key components: the DC meter box shunt; the insulation monitoring system and the corresponding key components: insulation monitoring; the heat dissipation system and the corresponding key components: the fan; the DC main circuit system and the corresponding key components: the DC contactor and the fuse; the AC main circuit system and the corresponding key components: the AC contactor; the power change system and the corresponding key components: the power module; the control power supply system and the corresponding key components: the auxiliary power supply and the air switch; the human-computer interaction system and the corresponding key components: the card reader and the screen; the charging connection system and the corresponding key components: the charging gun and the gun lock drive; the environmental monitoring system and the corresponding key components: the temperature sensor and the smoke sensor; S2 In order to analyze the importance of each subsystem or equipment of the charging equipment, 9 specific evaluation factors and their evaluation criteria are selected, and the evaluation factors include: the impact of failure on the safety of personnel, equipment and environment; the impact of failure on system function; the impact of failure on maintenance costs; the impact of failure on downtime losses; the impact of monitorability; the impact of downtime; the impact of maintenance difficulty; the impact of failure frequency; the timeliness of spare parts supply, and the corresponding classification and calibration range are carried out according to the 9 evaluation factors; S3 uses linear weighting to establish a mathematical model for the nine evaluation factors. The mathematical model multiplies the score of each evaluation factor by the corresponding weight, and then sums the nine evaluation factors to obtain the evaluation index of the corresponding importance. S4 calculates the weights in step S3. First, the weight values ​​of the evaluation factors corresponding to the key subsystems or key components of each charging device are obtained by using the hierarchical analysis method. Secondly, the constructed random forest model is used for parameter tuning to dynamically adjust and optimize the initial weights to achieve feature importance sorting and relationship. Finally, the importance values ​​of the series are obtained by using the Monte Carlo analysis method to reduce random factors. S5 performs data analysis on sample data of each key subsystem or key component of the charging equipment and determines the final importance value; S6 ranks the key subsystems or key components of the charging equipment according to their importance values ​​and makes operation and maintenance decisions.

2. The charging equipment importance measurement method based on multiple evaluation factors according to claim 1 is characterized in that: The impact of failure on the safety of personnel, equipment and environment in the evaluation factors is defined as: This factor is the impact of charging equipment failure on personal safety threats, harm to charging vehicles, and environmental pollution; The impact of failure on system function in the evaluation factor is defined as: This factor is the impact of equipment failure on system function and performance during equipment operation, and considers whether the equipment has spare parts, and comprehensively considers the impact evaluation; The impact of failure on maintenance costs in the evaluation factors is defined as: This factor comprehensively considers the complexity of the equipment and the cost of spare parts; The impact of failure on downtime loss in the evaluation factors is defined as: This factor takes into account the impact of the station system caused by equipment downtime, as well as the corresponding spare equipment costs and the corresponding economic losses caused by equipment downtime; The monitorability impact in the evaluation factor is defined as: This factor considers the applicable technology and means for monitoring the status of charging equipment, and fully considers the technical level required for the equipment status and the cost required for monitoring; The impact of downtime in the evaluation factor is defined as: this factor takes into account the type and capacity of the charging equipment, the type of medium and voltage level inside the equipment, and the maintenance content. The downtime includes all the time from the shutdown of the charging equipment to maintenance, debugging, and re-commissioning; The influence of the difficulty of maintenance in the evaluation factor is defined as: This factor takes into account the difficulty of maintenance of the charging equipment. It should visit and investigate the operation and maintenance personnel according to the actual situation, and adjust and confirm the evaluation of this factor based on experience; The influence of failure frequency in the evaluation factor is defined as: This factor considers the frequency of failure of charging equipment, and combines the historical data of equipment operation to evaluate the equipment management based on the feelings of on-site operation and maintenance personnel; The timeliness of spare parts supply in the evaluation factor is defined as: This factor considers the timeliness evaluation of the supply of relevant forced spare parts of the charging equipment, and is evaluated based on the feelings of the on-site operation and maintenance personnel on the equipment management.

3. The charging equipment importance measurement method based on multiple evaluation factors according to claim 2 is characterized in that: In step S3, a mathematical model is established by using linear weighting, which is expressed as: Among them, m i is the evaluation range of the i-th evaluation factor, a i is the weight of the i-th evaluation factor, and satisfies i is the serial number of the evaluation factor.

4. The charging equipment importance measurement method based on multiple evaluation factors according to claim 3 is characterized in that: The step S4 calculates the weight in the step S3, including the following steps: S41 takes the key subsystems and key components of the charging equipment as evaluation objects, and establishes the calibration range of each evaluation factor and the simulation sample size N for each evaluation object. The larger the value of the simulation sample size, the more accurate the simulation; S42 uses the hierarchical analysis method to establish the relative importance judgment matrix D and determine the priority order of each evaluation factor; S43 generates a random weight number between 0 and 1 as the weight of the corresponding evaluation factor; S44: When the current sample k=1, the random forest model is used to dynamically adjust and optimize the initial weights obtained; according to the priority order generated in step S42, the optimized weights are sorted in this order and assigned a i ; S45 calculates the importance evaluation index Index of each key subsystem or key component, and determines whether the statistical sample k reaches the sample size N, outputs the sample size, and completes the simulation calculation.

5. The charging equipment importance measurement method based on multiple evaluation factors according to claim 4 is characterized in that: In step S5, data analysis is performed on the sample data of each key subsystem or key component of the charging device, and a final importance value is determined, which specifically includes: Data analysis is carried out on N sample data of each evaluation object, and the statistical characteristic values ​​of the mean, median, kurtosis, skewness and standard deviation of the importance evaluation index values ​​corresponding to the sample data are extracted. It is defined that sample data with a kurtosis less than 10, a skewness not greater than 2, and a standard deviation less than 3 are more consistent with or approximately normal distribution, and are ideal sample data. The mean of the importance evaluation index corresponding to this type of sample data is taken as the final importance value.

6. The charging equipment importance measurement method based on multiple evaluation factors according to claim 5 is characterized in that: In step S6, the key subsystems or key components of the charging equipment are sorted according to the importance values, and operation and maintenance decisions are made, including: For key subsystems or key components whose final importance values ​​are in the top 30% of the ranking, their importance is relatively high, and corresponding resources need to be allocated preferentially in operation and maintenance. The charging station operator should arrange advance maintenance plans based on production needs; for key subsystems or key components ranked between 30% and 70%, their importance is moderate, and a regular inspection strategy is adopted; for key subsystems or key components ranked between 70% and 100%, their importance is relatively low, and maintenance is carried out according to the company's operational needs when there are abnormal phenomena but it does not affect operational needs.

7. The charging equipment importance measurement method based on multiple evaluation factors according to claim 1 is characterized in that: In step S2, the corresponding classification and calibration range are performed according to the 9 evaluation factors, specifically including: The corresponding classifications of the impact of the failure on the safety of personnel, equipment and environment include: extremely serious threat, corresponding calibration range: 90-100; serious threat, corresponding calibration range: 75-90; general threat, corresponding calibration range: 45-75; slight threat, corresponding calibration range: 20-45; almost no threat, corresponding calibration range: 0-10; The corresponding classifications of the impact of the failure on the system function include: complete loss of function, corresponding calibration range: 90-100; basic loss of function, corresponding calibration range: 75-90; significant loss of function, corresponding calibration range: 45-75; slight threat to personnel, equipment and environment, corresponding calibration range: 20-45; almost no threat to personnel, equipment and environment, corresponding calibration range: 0-10; The categories corresponding to the impact of the failure on the maintenance cost include: extremely high, with a corresponding calibration range of 90-100; high, with a corresponding calibration range of 75-90; medium, with a corresponding calibration range of 50-75; low, with a corresponding calibration range of 30-50; The classifications corresponding to the failure impact on downtime loss include: extremely high, with a corresponding calibration range of 90-100; high, with a corresponding calibration range of 75-90; average, with a corresponding calibration range of 45-75; low, with a corresponding calibration range of 25-45; The categories corresponding to the monitorability impact include: extremely high technology and cost, with a corresponding calibration range of 85-100; high technology and cost, with a corresponding calibration range of 65-85; medium technology and cost, with a corresponding calibration range of 45-65; low technology and cost, with a corresponding calibration range of 25-45; The corresponding classifications of downtime impact include: downtime of more than 10 days, the corresponding calibration range is: 90-100; downtime of 5 to 10 days, the corresponding calibration range is: 65-90; downtime of 2 to 5 days, the corresponding calibration range is: 35-65; downtime of 1 to 2 days, the corresponding calibration range is: 10-35; downtime of no more than 1 day, the corresponding calibration range is: 0-10; The corresponding classifications of the maintenance difficulty include: extremely complex equipment and environment, with a corresponding calibration range of 90-100; relatively complex equipment and environment, with a corresponding calibration range of 75-90; medium complexity of equipment and environment, with a corresponding calibration range of 45-75; low complexity of equipment and environment, with a corresponding calibration range of 25-45; very low complexity of equipment and environment, with a corresponding calibration range of 10-25; The classifications corresponding to the impact of the failure frequency include: extremely low occurrence frequency, the corresponding calibration range is: 90-100; low occurrence frequency, the corresponding calibration range is: 65-90; medium occurrence frequency, the corresponding calibration range is: 40-65; high occurrence frequency, the corresponding calibration range is: 10-40; very high occurrence frequency, the corresponding calibration range is: 0-10; The categories corresponding to the timeliness of the supply of spare parts include: very timely, with a corresponding calibration range of 80-100; relatively timely, with a corresponding calibration range of 60-80; basically meeting the requirements, with a corresponding calibration range of 35-60; occasionally lagging, with a corresponding calibration range of 10-35; often lagging, with a corresponding calibration range of 0-10.

8. A charging equipment importance measurement system based on multiple evaluation factors, characterized in that: The system includes: The key subsystem and key component screening module is used to screen out the key subsystems and key components of the charging equipment according to the historical fault data of the charging equipment, including: the main control system and the corresponding key components: the main control board and the communication board; the metering system and the corresponding key components: the DC meter box shunt; the insulation monitoring system and the corresponding key components: insulation monitoring; the heat dissipation system and the corresponding key components: the fan; the DC main circuit system and the corresponding key components: the DC contactor and the fuse; the AC main circuit system and the corresponding key components: the AC contactor; the power change system and the corresponding key components: the power supply module; the control power supply system and the corresponding key components: the auxiliary power supply and the air switch; the human-computer interaction system and the corresponding key components: the card reader and the screen; the charging connection system and the corresponding key components: the charging gun and the gun lock drive; the environmental monitoring system and the corresponding key components: the temperature sensor and the smoke sensor; The evaluation factor construction module is used to screen out 9 specific evaluation factors and their evaluation criteria, including: the impact of failure on the safety of personnel, equipment and environment; the impact of failure on system function; the impact of failure on maintenance costs; the impact of failure on downtime losses; the impact of monitorability; the impact of downtime; the impact of maintenance difficulty; the impact of failure frequency; the timeliness of spare parts supply, and the corresponding classification and calibration range are carried out according to the 9 evaluation factors; The evaluation index calculation module is used to establish a mathematical model for the nine evaluation factors using linear weighting. The mathematical model multiplies the score of each evaluation factor by the corresponding weight, and then sums the nine evaluation factors to obtain the evaluation index of the corresponding importance. The weight calculation module is used to calculate the weights in the evaluation index calculation module. First, the hierarchical analysis method is used to obtain the weight values ​​of the evaluation factors corresponding to the key subsystems or key components of each charging device. Secondly, the constructed random forest model is used for parameter tuning to dynamically adjust and optimize the initial weights to achieve feature importance sorting and relationship; finally, the Monte Carlo analysis method is used to obtain the importance values ​​of the series to reduce random factors; A data analysis module is used to perform data analysis on sample data of each key subsystem or key component of the charging equipment and determine the final importance value; The sorting module is used to sort the key subsystems or key components of the charging equipment according to their importance values ​​and make operation and maintenance decisions.

9. The charging equipment importance measurement system based on multiple evaluation factors according to claim 8, characterized in that: The impact of failure on the safety of personnel, equipment and environment in the evaluation factors is defined as: This factor is the impact of charging equipment failure on personal safety threats, harm to charging vehicles, and environmental pollution; The impact of failure on system function in the evaluation factor is defined as: This factor is the impact of equipment failure on system function and performance during equipment operation, and considers whether the equipment has spare parts, and comprehensively considers the impact evaluation; The impact of failure on maintenance costs in the evaluation factors is defined as: This factor comprehensively considers the complexity of the equipment and the cost of spare parts; The impact of failure on downtime loss in the evaluation factors is defined as: This factor takes into account the impact of the station system caused by equipment downtime, as well as the corresponding spare equipment costs and the corresponding economic losses caused by equipment downtime; The monitorability impact in the evaluation factor is defined as: This factor considers the applicable technology and means for monitoring the status of charging equipment, and fully considers the technical level required for the equipment status and the cost required for monitoring; The impact of downtime in the evaluation factor is defined as: this factor takes into account the type and capacity of the charging equipment, the type of medium and voltage level inside the equipment, and the maintenance content. The downtime includes all the time from the shutdown of the charging equipment to maintenance, debugging, and re-commissioning; The influence of the difficulty of maintenance in the evaluation factor is defined as: This factor takes into account the difficulty of maintenance of the charging equipment. It should visit and investigate the operation and maintenance personnel according to the actual situation, and adjust and confirm the evaluation of this factor based on experience; The influence of failure frequency in the evaluation factor is defined as: This factor considers the frequency of failure of charging equipment, and combines the historical data of equipment operation to evaluate the equipment management based on the feelings of on-site operation and maintenance personnel; The timeliness of spare parts supply in the evaluation factor is defined as: This factor considers the timeliness evaluation of the supply of relevant forced spare parts of the charging equipment, and is evaluated based on the feelings of the on-site operation and maintenance personnel on the equipment management.

10. The charging equipment importance measurement system based on multiple evaluation factors according to claim 9, characterized in that: The data analysis module performs data analysis on sample data of each key subsystem or key component of the charging device and determines the final importance value, specifically including: Data analysis is carried out on N sample data of each evaluation object to extract the statistical characteristic values ​​of the mean, median, kurtosis, skewness and standard deviation of the sample data. It is defined that sample data with a kurtosis less than 10, a skewness not greater than 2, and a standard deviation less than 3 are more consistent with or approximately normal distribution and are ideal sample data. The mean of the importance evaluation index corresponding to this type of sample data is taken as the final importance value.

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