Reliability analysis method and system for new energy electric vehicle charging pile

Abstract

The invention discloses a reliability analysis method and system for a new energy electric vehicle charging pile, and relates to the technical field of new energy electric vehicles, the reliability coefficient of the charging pile is generated according to a working state set, and if the reliability coefficient is higher than a reliability threshold value, the reliability coefficient is marked as a to-be-overhauled point and marked on an electronic map; predicting the use state of each charging pile in the maintenance area, obtaining a reliability coefficient corresponding to prediction data, generating a prediction coefficient set after summarizing, obtaining a stability coefficient from the prediction coefficient set, and determining the corresponding charging pile as a to-be-overhauled point if the stability coefficient exceeds a stability threshold value; and obtaining a corresponding maintenance priority, planning a corresponding maintenance path by using the trained path planning model in combination with the position of each to-be-maintained point and the maintenance priority, and displaying the corresponding maintenance path on an electronic map. And when reliability analysis is carried out, the coverage range is wider, and part of charging piles with poor expectation can be processed in advance.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy electric vehicles, and in particular to a reliability analysis method and system for a charging pile of a new energy electric vehicle. Background Art

[0002] New energy electric vehicle charging piles are equipment that provide charging services for plug-in new energy vehicles. The input end of the charging pile is directly connected to the AC power grid, and the output end is equipped with a charging plug for charging electric vehicles. Charging piles generally provide two charging methods: conventional charging and fast charging. People can use a specific charging card to swipe the card on the human-computer interaction interface provided by the charging pile to perform corresponding operations such as charging method, charging time, and cost data printing. The charging pile display can display data such as charging amount, cost, and charging time.

[0003] In the application publication number CN110782126A, a method for evaluating the operating reliability of a DC charging pile for electric vehicles with integrated multiple failure models is disclosed, including: studying the potential failure modes of the DC charging pile for electric vehicles, establishing corresponding candidate failure models, constructing an operating reliability evaluation model for the DC charging pile for electric vehicles with integrated multiple failure models based on Bayesian model averaging, using an improved finite memory quasi-Newton method to determine the weight and variance of the conditional probability function of each failure model, and calculating the operating reliability of the DC charging pile for electric vehicles.

[0004] The above application targets the potential failure modes of DC charging piles for electric vehicles, incorporates candidate failure models into the same framework, uses multi-model integration technology to perform operational reliability analysis, eliminates the limitations of single model failure research, and effectively improves the accuracy of operational reliability analysis of DC charging piles for electric vehicles.

[0005] With reference to the above application and in combination with the prior art, in the existing charging pile reliability evaluation method, when analyzing and evaluating the reliability of the charging pile, the normal use status is usually used as the starting point for evaluation. However, considering that the distribution density of the charging piles is difficult to adapt to their usage density when they are installed, some charging piles are used more frequently and there will be queues every time. Some charging piles are used relatively less frequently due to the small number of users nearby. This leads to a large difference in the reliability between different charging piles. If only a small number of charging piles are evaluated for reliability, the overall real situation cannot be reflected. At the same time, when the charging piles are inspected and repaired based on the results of the reliability evaluation, there will inevitably be certain omissions.

[0006] To this end, the present invention provides a reliability analysis method and system for a new energy electric vehicle charging pile. Summary of the invention

[0007] 1. Technical issues to be resolved

[0008] In view of the deficiencies of the prior art, the present invention provides a reliability analysis method and system for a new energy electric vehicle charging pile. The reliability coefficient of the charging pile is generated from a working state set. If it is higher than the reliability threshold, it is marked as a point to be repaired and marked on an electronic map. The use status of each charging pile in the maintenance area is predicted to obtain the reliability coefficient corresponding to the predicted data, and a prediction coefficient set is generated after summarization. The stability coefficient is obtained from the prediction coefficient set. If it exceeds the stability threshold, the corresponding charging pile is determined as a point to be repaired. The corresponding maintenance priority is obtained, and the trained path planning model is used. Combined with the location and maintenance priority of each point to be repaired, the corresponding maintenance path is planned and displayed on the electronic map. When performing reliability analysis, the coverage is wider, and it is also conducive to processing some charging piles with poor expectations in advance, thereby solving the problems in the background technology.

[0009] (II) Technical solution

[0010] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0011] A reliability analysis method for a new energy electric vehicle charging pile, comprising:

[0012] After establishing an electronic map that at least covers the maintenance area, monitor the operating conditions of several charging piles in the maintenance area, obtain the corresponding operating condition set, and then generate the operating condition coefficient Qc(t, p) from the operating condition set. If the obtained operating condition coefficient Qc(t, p) exceeds the condition threshold, issue an early warning instruction to the outside;

[0013] After receiving the early warning instruction, the working state set is constructed from the current working state data of the charging pile, and the reliability coefficient Kk(w, v, t) of the charging pile is generated from the working state set. If the reliability coefficient Kk(w, v, t) is higher than the reliability threshold, it is marked as a point to be repaired, and each point to be repaired is marked on the electronic map;

[0014] Use the trained operation prediction model to predict the usage status of each charging pile in the maintenance area, obtain the reliability coefficient Kk(w, v, t) corresponding to the prediction data, and generate a prediction coefficient set after summarization; obtain the stability coefficient Bo(t, t) from the prediction coefficient set. If the obtained stability coefficient Bo(t, t) exceeds the stability threshold, the corresponding charging pile is determined as a point to be repaired and marked on the electronic map;

[0015] After converting and obtaining the risk level Fs of each maintenance point, the corresponding maintenance priority Tx(n, s) is obtained by combining its proximity to the center Cn. The trained path planning model is used to combine the location of each maintenance point and the maintenance priority Tx(n, s) to plan the corresponding maintenance path and display it on the electronic map.

[0016] Furthermore, an electronic map covering at least the maintenance area is established, the location of each charging device is electronically marked, its operating conditions are monitored, and several groups of temperature differences ΔTt and frequency differences ΔTp obtained from the monitoring results are summarized to construct an operating condition set for the charging pile; an operating condition coefficient Qc(t, p) is generated from the operating condition set, and if the obtained operating condition coefficient Qc(t, p) exceeds the condition threshold, a warning instruction is issued to the outside.

[0017] Furthermore, the operating condition coefficient Qc(t, p) is generated from the operating condition set in the following manner: after linear normalization of the temperature difference ΔTt and the frequency difference ΔTp, the corresponding data values ​​are mapped to the interval [0, 1], and then according to the following formula:

[0018]

[0019] The meaning of the coefficient is: n is a positive integer greater than 1, i = 1, 2, ..., n, which is the number of charging piles in the maintenance area, and the weight coefficient is: 0≤Q1≤1, 0≤Q2≤1, and Q2+Q1=1; is the rated value of the temperature difference, is the rated value of the frequency difference.

[0020] Furthermore, after receiving the early warning instruction, the current working status of each charging pile in the maintenance area is monitored and obtained, and summarized into a working status set, which includes the comprehensive failure rate Zv, charging stability Zw, and loss time Zt of the charging pile; the reliability coefficient Kk(w, v, t) of the charging pile is generated by the working status set. If the reliability coefficient Kk(w, v, t) is higher than the reliability threshold, the corresponding charging pile is marked as a maintenance point and marked on the electronic map.

[0021] Furthermore, the working data and environmental condition data of the charging piles are collected in the maintenance area, and the corresponding modeling feature set is generated after feature recognition. After the initial model is established using the neural convolutional network, the initial model is trained and tested, and the trained initial model is used as the operation prediction model of the charging pile;

[0022] Set prediction conditions, use the trained operation prediction model to predict the usage status of each charging pile in the maintenance area, continuously obtain several groups of prediction data, obtain the reliability coefficients Kk(w, v, t) corresponding to the several groups of prediction data, and generate a prediction coefficient set after aggregation; if the obtained stability coefficient Bo(t, t) exceeds the stability threshold, it will be determined as a point to be inspected and marked on the electronic map.

[0023] Furthermore, the stability Wt and dispersion St of the reliability coefficient Kk(w, v, t) belonging to each charging station are obtained from the prediction coefficient set, and the stability coefficient Bo(t, t) is generated by associating the two. The specific method is as follows:

[0024] After linear normalization of the stability Wt and the discreteness St, the corresponding data values ​​are mapped to the interval [0, 1], and then according to the following formula:

[0025]

[0026] Among them, the meanings of the parameters are: 0≤ρ≤1, 0≤ζ≤1, and 0.6≤ρ+ζ≤1.2.

[0027] Furthermore, the corresponding risk level Fs is determined for the points to be repaired, and the corresponding points to be repaired are marked respectively; combined with the location data of the points to be repaired, the proximity center Cn of each point to be repaired on the electronic map is calculated and obtained, and the risk level Fs of each point to be repaired is combined to obtain the corresponding maintenance priority Tx(n, s).

[0028] Furthermore, the maintenance priority Tx(n, s) is obtained as follows: after linear normalization of the proximity center Cn and the risk level Fs, the corresponding data values ​​are mapped to the interval [0, 1], and then according to the following formula:

[0029]

[0030] The meaning of the parameter is: 0≤F1≤1, 0≤F2≤1, and F1+F2=1. The specific value is adjusted and set by the user. Cn avg is the mean of the closeness center, Fs avg is the mean risk level.

[0031] Furthermore, a path planning algorithm is selected to establish an initial model, and after testing and training, a trained path planning model is obtained; the trained path planning model is used, combined with the location of each maintenance point and the maintenance priority Tx(n, s), to plan a corresponding maintenance path for the maintenance personnel, so that they can maintain each maintenance point in turn along the maintenance path, and the maintenance path is displayed on an electronic map.

[0032] A reliability analysis system for a new energy electric vehicle charging pile, comprising:

[0033] The early warning unit monitors the operating conditions of several charging piles in the maintenance area, obtains the corresponding operating condition set, and then generates an operating condition coefficient from the operating condition set. If it exceeds the condition threshold, an early warning instruction is issued to the outside;

[0034] The analysis unit constructs a working state set from the current working state data of the charging pile, generates a reliability coefficient of the charging pile from the working state set, and if it is higher than the reliability threshold, marks it as a point to be repaired and marks it on the electronic map;

[0035] The prediction unit predicts the usage status of each charging pile in the maintenance area, obtains the reliability coefficient corresponding to the prediction data, summarizes and generates a prediction coefficient set, and obtains the stability coefficient from the prediction coefficient set. If the stability coefficient exceeds the stability threshold, the corresponding charging pile is determined as a point to be repaired;

[0036] The path planning unit obtains the corresponding maintenance priority, uses the trained path planning model, combines the location and maintenance priority of each maintenance point, plans the corresponding maintenance path and displays it on the electronic map.

[0037] (III) Beneficial effects

[0038] The present invention provides a reliability analysis method and system for a new energy electric vehicle charging pile, which has the following beneficial effects:

[0039] 1. After obtaining the evaluation results, select the parts that need to be repaired from several charging piles and determine them as the first point to be repaired. In this way, after completing the reliability evaluation, all charging piles can be covered without omissions, and the coverage is wider. At the same time, according to the reliability analysis results, the corresponding parts to be repaired are determined from several charging piles. After completing the reliability analysis, the corresponding maintenance targets can be determined to facilitate corresponding processing.

[0040] 2. Predict the working status of the charging piles in the maintenance area and obtain the corresponding stability coefficient Bo(t, t) again to supplement the current reliability evaluation and cover the next working status of the charging piles. By predicting the stability coefficient Bo(t, t), select the parts whose working status may deteriorate from each charging pile and use them as the second maintenance point. In this way, when doing reliability analysis, the coverage is wider, which is also conducive to early processing of some charging piles with poor expected working status.

[0041] 3. Obtain the corresponding maintenance priority Tx( n，s), and use the trained path planning model to determine the corresponding maintenance path. Therefore, after completing the reliability evaluation, the maintenance personnel can carry out maintenance on each maintenance point in an orderly manner. When the working condition of some charging piles deteriorates or has a trend of deterioration, timely maintenance and repair can be carried out, thereby ensuring the reliability of the operation of each charging pile and extending the service life of each charging pile. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic diagram of the reliability analysis method of the charging pile of the present invention;

[0043] Figure 2 It is a schematic diagram of the reliability analysis system structure of the charging pile of the present invention. DETAILED DESCRIPTION

[0044] 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.

[0045] See also Figure 1 The present invention provides a reliability analysis method for a new energy electric vehicle charging pile, comprising the following steps:

[0046] Step 1: After establishing an electronic map that at least covers the maintenance area, monitor the operating conditions of several charging piles in the maintenance area, obtain a corresponding operating condition set, and then generate an operating condition coefficient Qc(t, p) from the operating condition set. If the obtained operating condition coefficient Qc(t, p) exceeds the condition threshold, issue an early warning instruction to the outside;

[0047] The step 1 includes the following contents:

[0048] Step 101: After obtaining the distribution range of the charging piles, determine them as the maintenance area, establish an electronic map covering at least the maintenance area, and after obtaining the location data of each charging pile, electronically mark the location of each charging device. After the charging piles enter the working state, monitor their operating conditions and obtain the corresponding working temperature and working frequency;

[0049] After setting the corresponding rated values, the deviations of the two from the rated values ​​are obtained respectively and marked as temperature difference ΔTt and frequency difference ΔTp; after summarizing the temperature difference ΔTt and frequency difference ΔTp of several groups, a set of operating conditions for the charging pile is constructed;

[0050] Step 102: Generate an operating condition coefficient Qc(t, p) from the operating condition set. The specific method is as follows: After linear normalization of the temperature difference ΔTt and the frequency difference ΔTp, map the corresponding data values ​​to the interval [0, 1] according to the following formula:

[0051]

[0052] The meaning of the coefficient is: n is a positive integer greater than 1, i = 1, 2, ..., n, which is the number of charging piles in the maintenance area, and the weight coefficient is: 0≤Q1≤1, 0≤Q2≤1, and Q2+Q1=1; is the rated value of the temperature difference, is the rated value of the frequency difference; as a supplement, ΔTt i is the temperature difference at position i, ΔTp i is the frequency difference at position i;

[0053] Based on historical data and management expectations for charging piles, condition thresholds are set in advance. If the obtained operating condition coefficient Qc(t, p) exceeds the condition threshold, it means that when the charging frequency and operating temperature are high, the current working status of the charging piles in the maintenance area is relatively poor as a whole and requires maintenance. At this time, an early warning instruction is issued to the outside; otherwise, it means that the operating conditions are relatively good, there is no operating risk for the time being, and the current operating conditions can continue to be maintained.

[0054] When using, combine the contents in steps 101 and 102:

[0055] When the charging piles in the maintenance area are in working condition, the operating condition coefficient Qc(t, p) is generated by the operating condition set to evaluate the overall state of the maintenance area. When the operating temperature and charging frequency of most charging piles in the maintenance area are high, it means that the overall working intensity of the charging piles in the maintenance area is large and the working conditions are relatively poor. At this time, an early warning is issued to the outside to increase the sample for large analysis when doing reliability analysis, thereby increasing the reliability of the analysis results.

[0056] In the prior art, when analyzing and evaluating the reliability of charging piles, the normal use status is usually used as the starting point for evaluation. However, considering that the distribution density of charging piles is difficult to adapt to their usage density when they are installed, some charging piles are used more frequently and there will be queues every time. Some charging piles are used relatively less frequently due to the small number of users nearby. This leads to a large difference in the reliability between different charging piles. If the reliability of only a small number of charging piles is evaluated, the overall real situation cannot be reflected. At the same time, when the charging piles are inspected and repaired based on the results of the reliability evaluation, certain omissions are inevitable.

[0057] Step 2: After receiving the warning instruction, a working state set is constructed from the current working state data of the charging pile, and the reliability coefficient Kk(w, v, t) of the charging pile is generated from the working state set. If the reliability coefficient Kk(w, v, t) is higher than the reliability threshold, it is marked as a point to be repaired, and each point to be repaired is marked on the electronic map;

[0058] The step 2 includes the following contents:

[0059] Step 201: After receiving the early warning instruction, monitor and obtain the current working status of each charging pile in the maintenance area, and summarize it to form a working status set, which specifically includes the following contents:

[0060] Step 2011, obtaining the failure rate of each component of the charging pile, for example: rectifier cabinet component, charging pile component and gun line component, and combining the maintenance time required for each failure, taking the ratio of maintenance time to total maintenance time as the weight, and obtaining the comprehensive failure rate Zv by weighted average of multiple failure rates;

[0061] Step 212: Count the charging power Cp of the charging pile each time it enters the charging state, and calculate its charging stability Zw. The specific method is as follows:

[0062]

[0063] Where, m is the number of charging times, Cp avg is the mean value of charging power;

[0064] Step 213: The maintenance time required for each charging pile failure is taken as the loss time Zt;

[0065] Step 2014: setting an evaluation cycle, for example, taking 1 day or 3 days as one evaluation cycle, and summarizing the above data after continuously acquiring several evaluation cycles to establish a working status set of the charging pile;

[0066] Step 202: Generate the reliability coefficient Kk(w, v, t) of the charging pile from the working state set. The specific method is as follows: After linear normalization of the comprehensive failure rate Zv, charging stability Zw and loss time Zt, map the corresponding data values ​​to the interval [0, 1], and then use the following formula:

[0067]

[0068] Among them, Zw avg is the historical average of charging stability, is the qualified standard value of charging stability; Zv avg is the historical mean of the comprehensive failure rate, is the qualified standard value of the comprehensive failure rate; Zt avg is the historical mean of loss time, is the qualified standard value for lost time;

[0069] n is a positive integer greater than 1, which is the number of evaluation cycles; weight coefficient: 0≤β≤1, 0≤α≤1, 0≤γ≤1, and α+β+γ=1, and its specific value is adjusted and set by the user; it should be noted that Zw i is the charging stability at position i, zv i is the comprehensive failure rate at position i, Zt i is the loss time at position i;

[0070] A reliability threshold is set in advance. If the reliability coefficient Kk(w, v, t) is higher than the reliability threshold, it means that the corresponding charging pile is not reliable enough, and a fault has occurred or the risk of a fault is high. It may be necessary to prepare for inspection and maintenance. At this time, it is marked as a point to be inspected and repaired, specifically the first point to be inspected and repaired, and each first point to be inspected and repaired is marked on the electronic map. If the reliability coefficient Kk(w, v, t) is not higher than the threshold, it means that the current use status of the charging pile is temporarily within an acceptable range and can continue to maintain its current working status.

[0071] When using, combine the contents in steps 201 and 202:

[0072] After receiving the early warning instruction, the working status of each charging pile in the maintenance area is monitored, and then the reliability coefficient Kk (w, v, t) of the charging pile is generated, so as to complete the reliability evaluation of each charging pile. After obtaining the evaluation result, the part that needs to be repaired is screened out from several charging piles, and it is determined as the first point to be repaired. After the reliability evaluation is completed, all charging piles can be covered without omissions, and the coverage is wider. At the same time, according to the reliability analysis results, the corresponding part to be repaired is determined from several charging piles. After completing the reliability analysis, the corresponding maintenance target can be determined to facilitate corresponding processing.

[0073] Step 3: Use the trained operation prediction model to predict the usage status of each charging pile in the maintenance area, obtain the reliability coefficient Kk(w, v, t) corresponding to the prediction data, and generate a prediction coefficient set after summarizing; obtain the stability coefficient Bo(t, t) from the prediction coefficient set. If the obtained stability coefficient Bo(t, t) exceeds the stability threshold, the corresponding charging pile is determined as a maintenance point and marked on the electronic map;

[0074] The step three includes the following contents:

[0075] Step 301: Collect working data of charging piles in the maintenance area, including usage frequency of charging piles, specification data of charging piles and their components, current usage status data, and environmental condition data, such as ambient temperature and humidity. After feature recognition of the above data, obtain corresponding data features and generate corresponding modeling feature sets after aggregation.

[0076] After the initial model is established using the neural convolutional network, some data are extracted from the modeling feature set as training and test sets, respectively, the initial model is trained and tested, and the trained initial model is used as the operation prediction model of the charging pile and output;

[0077] Step 302: After adjusting the charging frequency and operating temperature of the charging piles in the maintenance area by 5%, the two are used as prediction conditions. Under the prediction conditions, the trained operation prediction model is used to predict the usage status of each charging pile in the maintenance area. After setting a prediction cycle, for example, one day is used as a prediction cycle, several groups of prediction data are continuously obtained, and the reliability coefficients Kk(w, v, t) corresponding to the several groups of prediction data are obtained, and a prediction coefficient set is generated after aggregation;

[0078] Step 303: Obtain the standard deviation and relative range of the reliability coefficients Kk(w, v, t) belonging to each charging station from the prediction coefficient set, use the standard deviation as the stability Wt, and the relative range as the dispersion St, and associate the two to generate the stability coefficient Bo(t, t). The specific method is as follows: After linear normalization of the stability Wt and the dispersion St, map the corresponding data values ​​to the interval [0, 1], and then follow the following formula:

[0079]

[0080] Among them, the meaning of the parameters is: 0≤ρ≤1, 0≤ζ≤1, and 0.6≤ρ+ζ≤1.2;

[0081] According to historical data and management expectations for charging piles, a stability threshold is pre-set. If the obtained stability coefficient Bo(t, t) exceeds the stability threshold, it means that the working state of the corresponding charging pile may be relatively unstable in the future and face certain operating risks, which need to be processed in advance. At this time, it is determined as a maintenance point, specifically the second maintenance point, and each second maintenance point is marked on the electronic map; on the contrary, it can be judged from the predicted data that the working state of the charging pile will remain relatively stable;

[0082] When using, combine the contents in steps 301 to 302:

[0083] After completing the reliability analysis, in order to reduce the omissions that may occur in the reliability analysis, a charging pile operation prediction model in the maintenance area is established to predict the working status of the charging piles in the maintenance area, and the corresponding stability coefficient Bo(t, t) is obtained again, so as to supplement the current reliability evaluation. Therefore, when performing reliability analysis on the charging piles, the next working status of the charging piles can also be covered, and the stability coefficient Bo(t, t) obtained by prediction can be used to screen out the parts whose working status may deteriorate from each charging pile, and use them as the second maintenance point. Therefore, when performing reliability analysis, the coverage is wider, and it is also beneficial to deal with some charging piles with poor expectations in advance.

[0084] Step 4: After converting and obtaining the risk level Fs of each maintenance point, the corresponding maintenance priority Tx(n, s) is obtained by combining its proximity to the center Cn. The trained path planning model is used to combine the location of each maintenance point and the maintenance priority Tx(n, s) to plan the corresponding maintenance path and display it on the electronic map.

[0085] The step 4 includes the following contents:

[0086] Step 401: It should be noted that the points to be inspected include the first and second points to be inspected, and the corresponding risk levels Fs are determined for the first and second points to be inspected, and the specific method is as follows:

[0087] According to the reliability coefficient Kk(w, v, t), the first maintenance points are ranked. The first 30% are determined as the first level with a value of 3, the middle 30% are the second level with a value of 2, and the last 40% are the third level with a value of 1. According to the stability coefficient Bo(t, t), the second maintenance points are ranked. The first 30% are determined as the first level with a value of 3, the middle 30% are the second level with a value of 2, and the last 40% are the third level with a value of 1.

[0088] After obtaining the risk level Fs of each maintenance point, mark the corresponding maintenance point respectively;

[0089] Step 402: Combine the location data of the maintenance points to obtain the proximity center Cn of each maintenance point on the electronic map, and combine the risk level Fs of each maintenance point to obtain the corresponding maintenance priority Tx(n, s) in association. The specific method is as follows:

[0090] After linear normalization of the closeness center Cn and the risk level Fs, the corresponding data values ​​are mapped to the interval [0, 1], and then according to the following formula:

[0091]

[0092] The meaning of the parameter is: 0≤F1≤1, 0≤F2≤1, and F1+F2=1. The specific value is adjusted and set by the user. Cn avg is the mean of the proximity center, Fs avg is the mean value of risk level;

[0093] Step 403: Select a path planning algorithm to establish an initial model, and after testing and training, obtain a trained path planning model; use the trained path planning model, combined with the location of each maintenance point and the maintenance priority Tx(n, s), to plan a corresponding maintenance path for the maintenance personnel, so that they can maintain each maintenance point in turn along the maintenance path, and display the maintenance path on the electronic map;

[0094] When using, combine the contents in steps 401 to 403:

[0095] After completing the reliability analysis of several charging piles in the maintenance area, the analysis results are used to screen out the points to be repaired in the maintenance area, namely the first and second points to be repaired. After determining the proximity center Cn and risk level Fs of each point to be repaired, the corresponding maintenance priority Tx(n, s) is obtained, and the trained path planning model is used to determine the corresponding maintenance path. Therefore, after completing the reliability evaluation, the maintenance personnel can repair each point to be repaired in an orderly manner. When the working condition of some charging piles deteriorates or there is a trend of deterioration, timely inspection and maintenance are carried out, thereby ensuring the reliability of the operation of each charging pile and extending the service life of each charging pile.

[0096] See also Figure 2 The present invention provides a reliability analysis system for a new energy electric vehicle charging pile, comprising:

[0097] The early warning unit monitors the operating conditions of several charging piles in the maintenance area, obtains the corresponding operating condition set, and then generates an operating condition coefficient from the operating condition set. If it exceeds the condition threshold, an early warning instruction is issued to the outside;

[0098] The analysis unit constructs a working state set from the current working state data of the charging pile, generates a reliability coefficient of the charging pile from the working state set, and if it is higher than the reliability threshold, marks it as a point to be repaired and marks it on the electronic map;

[0099] The prediction unit predicts the usage status of each charging pile in the maintenance area, obtains the reliability coefficient corresponding to the prediction data, summarizes and generates a prediction coefficient set, and obtains the stability coefficient from the prediction coefficient set. If the stability coefficient exceeds the stability threshold, the corresponding charging pile is determined as a point to be repaired;

[0100] The path planning unit obtains the corresponding maintenance priority, uses the trained path planning model, combines the location and maintenance priority of each maintenance point, plans the corresponding maintenance path and displays it on the electronic map.

[0101] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0102] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0103] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0104] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only some logical function divisions. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0105] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0106] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0107] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0108] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0109] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A reliability analysis method for a new energy electric vehicle charging pile, characterized in that: include, After establishing an electronic map that at least covers the maintenance area, monitor the operating conditions of several charging piles in the maintenance area, obtain the corresponding operating condition set, and then generate the operating condition coefficient Qc(t,p) from the operating condition set. If the obtained operating condition coefficient Qc(t,p) exceeds the condition threshold, issue an early warning instruction to the outside; After receiving the warning instruction, the working state set is constructed from the current working state data of the charging pile, and the reliability coefficient Kk(w,v,t) of the charging pile is generated from the working state set. If the reliability coefficient Kk(w,v,t) is higher than the reliability threshold, it is marked as a point to be repaired, and each point to be repaired is marked on the electronic map; Use the trained operation prediction model to predict the usage status of each charging pile in the maintenance area, obtain the reliability coefficient Kk(w,v,t) corresponding to the prediction data, and generate a prediction coefficient set after summarization; obtain the stability coefficient Bo(t,t) from the prediction coefficient set. If the obtained stability coefficient Bo(t,t) exceeds the stability threshold, the corresponding charging pile is determined as a maintenance point and marked on the electronic map; After converting and obtaining the risk level Fs of each maintenance point, the corresponding maintenance priority Tx(n,s) is obtained by combining it with its proximity to the center Cn. The trained path planning model is used to plan the corresponding maintenance path in combination with the location of each maintenance point and the maintenance priority Tx(n,s) and display it on the electronic map.

2. The reliability analysis method of the new energy electric vehicle charging pile according to claim 1 is characterized in that: An electronic map covering at least the maintenance area is established, the location of each charging device is electronically marked, its operating conditions are monitored, and several groups of temperature differences ΔTt and frequency differences Δx obtained from the monitoring results are summarized to construct an operating condition set for the charging pile; an operating condition coefficient Qc(t,p) is generated from the operating condition set, and if the obtained operating condition coefficient Qc(t,p) exceeds the condition threshold, a warning instruction is issued to the outside.

3. The reliability analysis method of the new energy electric vehicle charging pile according to claim 2 is characterized in that: The operating condition coefficient Qc(t,p) is generated from the operating condition set in the following manner: After linear normalization of the temperature difference ΔTt and the frequency difference ΔTp, the corresponding data values ​​are mapped to the interval [0, 1], and then according to the following formula: The meaning of the coefficient is: n is a positive integer greater than 1, i = 1, 2, ..., n, which is the number of charging piles in the maintenance area, and the weight coefficient is: 0≤Q1≤1, 0≤Q2≤1, and Q2+Q1=1; is the rated value of the temperature difference, is the rated value of the frequency difference.

4. The reliability analysis method of the new energy electric vehicle charging pile according to claim 1 is characterized in that: After receiving the early warning instruction, the current working status of each charging pile in the maintenance area is monitored and obtained, and summarized into a working status set, which includes the comprehensive failure rate Zv, charging stability Zw, and loss time Zt of the charging pile; the reliability coefficient Kk(w,v,t) of the charging pile is generated by the working status set. If the reliability coefficient Kk(w,v,t) is higher than the reliability threshold, the corresponding charging pile is marked as a maintenance point and marked on the electronic map.

5. The reliability analysis method of the new energy electric vehicle charging pile according to claim 1 is characterized in that: Collect the working data and environmental condition data of the charging piles in the maintenance area, generate the corresponding modeling feature set after feature recognition, use the neural convolution network to establish the initial model, train and test the initial model, and use the trained initial model as the operation prediction model of the charging pile; Set prediction conditions, use the trained operation prediction model to predict the usage status of each charging pile in the maintenance area, continuously obtain several groups of prediction data, obtain the reliability coefficients Kk(w,v,t) corresponding to the several groups of prediction data, and generate a prediction coefficient set after aggregation; if the obtained stability coefficient Bo(t,t) exceeds the stability threshold, it will be determined as a maintenance point and marked on the electronic map.

6. The reliability analysis method of the new energy electric vehicle charging pile according to claim 5 is characterized in that: The stability Wt and dispersion St of the reliability coefficient Kk(w,v,t) belonging to each charging station are obtained from the prediction coefficient set, and the stability coefficient Bo(t,t) is generated by associating the two. The specific method is as follows: After linear normalization of the stability Wt and the discreteness St, the corresponding data values ​​are mapped to the interval [0, 1], and then according to the following formula: Among them, the meanings of the parameters are: 0≤ρ≤1, 0≤ζ≤1, and 0.6≤ρ+ζ≤1.

2.

7. The reliability analysis method of the new energy electric vehicle charging pile according to claim 1 is characterized in that: Determine the corresponding risk level Fs for the points to be repaired, and mark the corresponding points to be repaired respectively; combine the location data of the points to be repaired, calculate and obtain the proximity center Cn of each point to be repaired on the electronic map, and combine the risk level Fs of each point to be repaired to obtain the corresponding maintenance priority Tx(n,s).

8. The reliability analysis method of the new energy electric vehicle charging pile according to claim 7 is characterized in that: The maintenance priority Tx(n,s) is obtained as follows: After linear normalization of the proximity center Cn and the risk level Fs, the corresponding data values ​​are mapped to the interval [0, 1], and then according to the following formula: The meaning of the parameter is: 0≤F1≤1, 0≤F2≤1, and F1+F2=1. The specific value is adjusted and set by the user. Cn avg is the mean of the closeness center, Fs avg is the mean risk level.

9. The reliability analysis method of the new energy electric vehicle charging pile according to claim 8 is characterized in that: A path planning algorithm is selected to establish an initial model. After testing and training, a trained path planning model is obtained. The trained path planning model is used in combination with the location of each maintenance point and the maintenance priority Tx(n,s) to plan a corresponding maintenance path for the maintenance personnel, so that they can maintain each maintenance point in turn along the maintenance path, and the maintenance path is displayed on the electronic map.

10. A reliability analysis system for a new energy electric vehicle charging pile, applying the method according to any one of claims 1 to 9, characterized in that: include: The early warning unit monitors the operating conditions of several charging piles in the maintenance area, obtains the corresponding operating condition set, and then generates an operating condition coefficient from the operating condition set. If it exceeds the condition threshold, an early warning instruction is issued to the outside; The analysis unit constructs a working state set from the current working state data of the charging pile, generates a reliability coefficient of the charging pile from the working state set, and if the reliability coefficient is higher than the reliability threshold, marks it as a point to be repaired and marks it on the electronic map; The prediction unit predicts the usage status of each charging pile in the maintenance area, obtains the reliability coefficient corresponding to the prediction data, generates a prediction coefficient set after aggregation, and obtains the stability coefficient from the prediction coefficient set. If the stability coefficient exceeds the stability threshold, the corresponding charging pile is determined as a point to be repaired; The path planning unit obtains the corresponding maintenance priority, uses the trained path planning model, combines the location and maintenance priority of each maintenance point, plans the corresponding maintenance path and displays it on the electronic map.

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