A fault identification method and system of a charging cabinet

Abstract

The application provides a fault identification method and system of a charging cabinet. It relates to the technical field of data intelligent processing. The fault event set of the charging cabinet is obtained and frequent sequence mining is performed to generate the fault event trait sequence. By mining frequent items, a plurality of fault event gene order tables are generated. The first fault event input fault event trait sequence is obtained, the first fault trait sequence is matched, and the plurality of fault event gene order tables are further input to generate the first fault event gene order table. The fault gene identification result is generated and sent to the charging cabinet management terminal. The technical problem that the fault identification method of the charging cabinet in the prior art is mainly based on experience and the method lacks intelligence, resulting in low fault identification efficiency and insufficient accuracy of the fault identification result, and the identification deviation easily causes subsequent operation and maintenance risks is solved. By mining the fault gene order table, the fault event is matched and investigated layer by layer, and the intelligent, efficient and accurate identification of the charging cabinet fault is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data intelligent processing, and particularly relates to a fault identification method and system of a charging cabinet. BACKGROUND

[0002] In response to sustainable energy development and realizing energy saving and environmental protection of transportation, electric vehicles emerge as the times require. Meanwhile, electric vehicle charging is limited to a certain extent, and a charging cabinet becomes an energy supply device for electric vehicles, which can charge and store the batteries of electric vehicles. The charging cabinet is arranged in many places such as shopping malls, communities and enterprises. Therefore, troubleshooting of the charging cabinet has become a key problem. At present, the charging cabinet fault identification is mainly based on the experience of the staff to troubleshoot the equipment, so that the troubleshooting direction is wide, and the final troubleshooting result has a certain subjectivity, which causes certain limitations for subsequent charging cabinet operation and maintenance.

[0003] In the prior art, the fault identification method of the charging cabinet is mainly based on experience troubleshooting, which has insufficient method intelligence, low fault identification efficiency and insufficient fault identification result accuracy, and the existing identification deviation easily causes subsequent operation and maintenance risks. SUMMARY

[0004] The present application provides a fault identification method and system of a charging cabinet, which is used to solve the technical problem that the fault identification method of the charging cabinet in the prior art is mainly based on experience troubleshooting, which has insufficient method intelligence, low fault identification efficiency and insufficient fault identification result accuracy, and the existing identification deviation easily causes subsequent operation and maintenance risks.

[0005] In view of the above problems, the present application provides a fault identification method and system of a charging cabinet.

[0006] In the first aspect, the present application provides a fault identification method of a charging cabinet, which comprises:

[0007] obtaining a charging cabinet fault event set;

[0008] frequent sequence mining is performed on the charging cabinet fault event set to generate a fault event character sequence;

[0009] frequent item mining is performed on the fault event character sequence to generate a plurality of fault event gene order tables;

[0010] a first fault event is input into the fault event character sequence through a charging cabinet monitoring device, and a first fault character sequence is matched;

[0011] the first fault character sequence is input into the plurality of fault event gene order tables to generate a first fault event gene order table;

[0012] According to the first fault event gene sequence table, troubleshooting is performed, and a fault gene identification result is generated;

[0013] The fault gene identification result is sent to a charging cabinet management terminal.

[0014] In a second aspect, the application provides a fault identification system of a charging cabinet, and the system comprises:

[0015] An event set acquisition module is configured to acquire a charging cabinet fault event set;

[0016] A sequence generation module is configured to perform frequent sequence mining on the charging cabinet fault event set, and generate a fault event trait sequence;

[0017] A sequence table generation module is configured to perform frequent item mining on the fault event trait sequence by traversing the fault event trait sequence, and generate a plurality of fault event gene sequence tables;

[0018] A sequence matching module is configured to acquire a first fault event by a charging cabinet monitoring device, input the fault event trait sequence, and match a first fault trait sequence;

[0019] A first sequence table generation module is configured to input the first fault trait sequence into the plurality of fault event gene sequence tables, and generate a first fault event gene sequence table;

[0020] A fault identification module is configured to perform troubleshooting according to the first fault event gene sequence table, and generate a fault gene identification result;

[0021] A result sending module is configured to send the fault gene identification result to a charging cabinet management terminal.

[0022] One or more technical solutions provided in the application have at least the following technical effects or advantages:

[0023] The embodiment of the application provides a fault identification method of a charging cabinet, acquires a charging cabinet fault event set; frequent sequence mining is performed on the charging cabinet fault event set, and a fault event character sequence is generated; frequent item mining is performed on the fault event character sequence through iteration, and a plurality of fault event gene order tables are generated; a first fault event is input into the fault event character sequence through a charging cabinet monitoring device, and a first fault character sequence is matched; the first fault character sequence is input into the plurality of fault event gene order tables, and a first fault event gene order table is generated; fault checking is performed according to the first fault event gene order table, and a fault gene identification result is generated; and the fault gene identification result is sent to a charging cabinet management terminal, thereby solving the technical problems that the fault identification method of the charging cabinet in the prior art is mainly based on experience checking, the method intelligence degree is insufficient, the fault identification efficiency is low, the fault identification result precision is insufficient, and the existing identification deviation is easy to cause subsequent operation and maintenance risks, the fault event is matched and checked layer by layer by mining the fault gene order table, and intelligent, efficient and accurate identification of the charging cabinet fault is realized. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 A fault identification method flow chart of a charging cabinet is provided for the application.

[0025] Figure 2 A fault event character sequence acquisition flow chart in the fault identification method of the charging cabinet is provided for the application.

[0026] Figure 3 A plurality of fault event gene order table acquisition flow charts in the fault identification method of the charging cabinet are provided for the application.

[0027] Figure 4 A charging cabinet fault identification system structure diagram is provided for the application.

[0028] The reference signs are explained as follows: an event set acquisition module 11, a sequence generation module 12, an order table generation module 13, a sequence matching module 14, a first order table generation module 15, a fault identification module 16, and a result sending module 17. DETAILED DESCRIPTION

[0029] The application provides a fault identification method and system of a charging cabinet, and solves the technical problems that the fault identification method of the charging cabinet in the prior art is mainly based on experience checking, the method intelligence degree is insufficient, the fault identification efficiency is low, the fault identification result precision is insufficient, and the existing identification deviation is easy to cause subsequent operation and maintenance risks.

[0030] Embodiment one

[0031] As Figure 1As shown, the present application provides a fault identification method of a charging cabinet, the method comprises:

[0032] Step S100: acquiring a charging cabinet fault event set;

[0033] Specifically, in response to sustainable energy development, electric vehicles are emerging to achieve energy saving and environmental protection of transportation. At the same time, there are certain limitations in electric vehicle charging. Charging cabinets become energy supply equipment for electric vehicles, which can charge and store electric vehicle batteries. There will inevitably be certain faults in the use process of charging cabinets. The fault identification method of the charging cabinet provided by the present application can determine the fault event gene order table through big data retrieval and information mining based on the fault event set to troubleshoot and determine the fault identification intelligence and accuracy to improve the identification efficiency. First, the charging cabinet fault event set, i.e. various fault event types of the charging cabinet, is retrieved, for example, unable to charge, slow charging speed, abnormal module start, uneven charging speed, and unable to connect. The above fault events will cause abnormal charging of electric vehicles, causing inconvenience to users. The charging fault event set is used as an information source to be analyzed, and the charging cabinet fault identification analysis is performed based on the charging fault event set.

[0034] Step S200: frequent sequence mining on the charging cabinet fault event set to generate a fault event trait sequence;

[0035] Specifically, the charging cabinet fault event set is divided based on fault types to determine various fault event types and corresponding fault event trait sequences, i.e. the performance state of the fault event, including output voltage intensity, output current intensity, charging module temperature, charging module noise, charging cabinet humidity, etc. There is a single or multiple association influence relationship with the fault event. The multiple traits matched with each fault event type are used as the fault event trait sequence. Further, the fault event trait sequence is pruned to exclude the influence of multiple fault event types on the traits, and the pruning result is subjected to frequent item mining. The trait sequence matched with the multiple fault event types, which is representative and has high accuracy, is used as the final determined fault event trait sequence.

[0036] Further, as shown, Figure 2 The step S200 of the present application further comprises:

[0037] Step S210: acquiring multiple groups of fault event types and fault event trait sequences from the charging cabinet fault event set;

[0038] Step S220: pruning certain state sequences from the plurality of groups of fault event types and the fault event trait sequence to generate a fault event non-certain trait sequence;

[0039] Step S230: mining k-item frequent items from the plurality of groups of fault event types and the fault event non-certain trait sequence to generate the fault event trait sequence.

[0040] Further, the pruning certain state sequences from the plurality of groups of fault event types and the fault event trait sequence to generate a fault event non-certain trait sequence includes:

[0041] Step S221: performing one-item certain state analysis on the fault event trait sequence based on the plurality of groups of fault event types to generate one-item state certain confidence;

[0042] Step S222: pruning one-item certain state from the fault event trait sequence when the one-item state certain confidence is greater than or equal to one-item certain confidence threshold to generate one-item certain state pruning result;

[0043] Step S223: performing m-item certain state analysis on the fault event trait sequence based on the plurality of groups of fault event types to generate m-item state certain confidence;

[0044] Step S224: pruning m-item certain state from the fault event trait sequence when the m-item state certain confidence is greater than or equal to m-item certain confidence threshold to generate m-item certain state pruning result;

[0045] Step S225: taking the union of the one-item certain state pruning result and the m-item certain state pruning result to generate the fault event non-certain trait sequence.

[0046] Specifically, the set of charging cabinet fault events is analyzed and classified to determine a plurality of groups of fault event types, such as inability to charge, slow charging speed, etc. Different fault event types have different fault performance traits. Fault event recursion can be performed based on fault performance traits, including voltage strength, output current strength, charging module temperature, charging module noise, charging cabinet humidity, etc. A fault event trait sequence is generated based on the fault performance state corresponding to a group of fault event types, and the fault event trait sequence is obtained.

[0047] Specifically, based on the plurality of groups of fault event types, the fault event trait sequence is traversed to determine the frequency of occurrence of the fault event trait sequence in the plurality of groups of fault event types for certain analysis. The fault event trait sequence is subjected to single-state analysis to determine the frequency of occurrence in the plurality of groups of fault event types and to determine the single-state certainty confidence. A single-state certainty confidence threshold is set, i.e., a frequency threshold for single fault event trait analysis. When the single-state certainty confidence is greater than or equal to the single-state certainty confidence threshold, the corresponding single fault event trait is taken as a single certain state, which is pruned from the fault event trait sequence as a single certain state pruning result.

[0048] The frequency of occurrence of two fault event traits in the plurality of groups of fault event types is counted as a two-state certainty confidence. A two-state certainty confidence threshold is set. Two fault event traits greater than or equal to the two-state certainty confidence are taken as a two-state certain state analysis result. The two-state certain state pruning result is obtained by trait pruning. The above certain term analysis pruning is repeated. The m-state certainty confidence is generated by m-state certain state analysis, where m is the maximum number of terms of the fault event trait sequence. The m-state certainty confidence threshold is set for analysis pruning to generate the m-state certain state pruning result. The one-state certain state pruning result is taken together with the m-state certain state pruning result. After the fault event trait sequence is pruned from the multi-state certain state pruning result, the remaining sequence is taken as the fault event non-certain trait sequence. Since the state characteristic value sequence appearing multiple times in different events does not have representativeness and does not have discriminability for event recognition, it is cleaned to improve the subsequent recognition efficiency and accuracy.

[0049] The plurality of groups of fault event types and the fault event certain trait sequence are further mapped and corresponded. Sequence frequent item mining is performed on the mapping result. Non-frequent items are pruned to obtain the fault event trait sequence, which is a fault analysis matching trait determined by screening.

[0050] Further, the k-item frequent item mining of the plurality of groups of fault event types and the fault event non-certain trait sequence is performed to generate the fault event trait sequence. The step S230 of the present application further includes:

[0051] Step S231: According to the plurality of groups of fault event types, the Nth fault event type and the Nth group of fault event non-certain trait sequence are obtained.

[0052] Step S232: based on the Nth fault event type, a non-frequent item is calculated for the Nth group of fault event non-frequent item sequence, generating a non-frequent item;

[0053] Step S233: the non-frequent item is pruned from the Nth group of fault event non-frequent item sequence, generating a frequent set;

[0054] Step S234: based on the Nth fault event type, a k non-frequent item is calculated for the Nth group of fault event non-frequent item sequence, generating a k non-frequent item;

[0055] Step S235: the k non-frequent item is pruned from the Nth group of fault event non-frequent item sequence, generating a k frequent set;

[0056] Step S236: the one frequent set to the k frequent set is serialized after taking the union, generating the Nth group of fault event trait sequence, which is added to the fault event trait sequence.

[0057] Specifically, based on the multiple groups of fault event types, a group of data is randomly extracted as the Nth fault event type and the corresponding Nth group of fault event non-frequent trait sequence. For the Nth group of fault event certain trait sequence, the single item frequency in the Nth fault event type is calculated, a frequent degree threshold is set, and a single trait with a single frequent degree less than the single frequent degree threshold is set as the non-frequent item. Then the non-frequent item in the Nth group of fault event non-frequent trait sequence is pruned, and the remaining trait sequence is set as a prime frequent set.

[0058] Similarly, the number of items is increased successively, and the corresponding item frequent degree threshold is set for trait frequent degree judgment. Until the k non-frequent item frequent degree calculation is completed, those less than the k frequent degree threshold are set as the k frequent item, and the k frequent item is pruned from the Nth group of fault event non-frequent trait sequence, and the rest is set as the k frequent set. The one frequent set to the k frequent set is taken and union, and the multiple trait sequences existing are merged. At different times, they are arranged in time sequence, and at the same time sequence, they are differentiated into multiple sequences, generating the Nth group of fault event trait sequence, which is the representative non-frequent trait sequence of the Nth fault event type. Wherein, the N matches the multiple groups of fault event types, and the frequent item mining is performed for the multiple groups of fault event types respectively, and is added to the fault event trait sequence to eliminate accidental items, so that the fault event trait sequence has representativeness.

[0059] Step S300: traverse the fault event trait sequence to mine frequent items and generate multiple fault event gene order tables;

[0060] Further, as shown in the step S300, the step of traversing the fault event trait sequence to perform frequent item mining to generate a plurality of fault event gene sequence tables comprises: Figure 3

[0061] Step S310: Taking the i-th fault event trait sequence of the Nth group of fault event trait sequences as a screening condition, the fault factor investigation result is collected.

[0062] Step S320: Traversing the fault factor investigation result to perform sorting and scoring to generate a plurality of sorting and scoring values.

[0063] Step S330: According to the plurality of sorting and scoring values, the fault factor investigation result is sorted to generate the i-th fault event gene sequence table, which is added to the Nth group of fault event gene sequence tables.

[0064] Step S340: The Nth group of fault event gene sequence tables is added to the plurality of fault event gene sequence tables.

[0065] Further, the step of traversing the fault factor investigation result to perform sorting and scoring to generate a plurality of sorting and scoring values comprises:

[0066] Step S321: Traversing the fault factor investigation result to perform frequency evaluation to generate a plurality of frequency evaluation values, wherein the plurality of frequency evaluation values have a first weight.

[0067] Step S322: Traversing the fault factor investigation result to perform timeliness evaluation to generate a plurality of timeliness evaluation values, wherein the plurality of timeliness evaluation values have a second weight.

[0068] Step S323: According to the first weight, the second weight, the plurality of frequency evaluation values and the plurality of timeliness evaluation values, the fault factor investigation result is sorted and scored to generate the plurality of sorting and scoring values.

[0069] Specifically, the Nth group of fault event trait sequences is extracted, and the i-th fault event trait sequence corresponding to the Nth fault event type is taken as a screening condition to perform fault factor investigation, i.e., state quantities such as voltage intensity, current intensity, charging module temperature, etc. that cause fault trait representation, including environmental temperature, humidity, etc., to obtain the fault factor investigation result, such as overvoltage, overtemperature, etc.

[0070] ​Specifically, the fault factor investigation results are traversed, and the frequency of each fault factor is evaluated. The higher the frequency, the higher the evaluation value. The frequency evaluation value of each fault factor in the plurality of fault factor investigation results is determined. Further, the fault factor investigation results are further traversed and time effectiveness evaluation is performed. The closer the time effectiveness value, the more advanced the detection means and the higher the accuracy. Generally, the time effectiveness evaluation value within a certain time period is the same, for example, the technical innovation time is taken as the division node of the time period. The time effectiveness of each fault factor is evaluated, and the plurality of time effectiveness evaluation values are generated. The plurality of frequency evaluation values have a first weight, and the plurality of time effectiveness evaluation values have a second weight. Based on the first weight, the second weight, the plurality of frequency evaluation values, and the plurality of time effectiveness evaluation values, the fault factor investigation results are evaluated and weighted. The calculation result is taken as the plurality of ranking score values, which can effectively improve the fitting degree of the plurality of ranking score values and the corresponding fault factors.

[0071] Further, the plurality of ranking score values are taken as the fault factor ranking standard, and the fault factor investigation results are sequentially arranged. The higher the score value, the higher the ranking. The i th fault event gene sequence table is generated and added to the N th group of fault event gene sequence tables. The N th group of fault event gene sequence tables includes a plurality of sequence tables adapted to the N th group of fault event types. Further, the N th group of fault event gene sequence tables is added to the plurality of fault event gene sequence tables, including a plurality of adaptive sequence tables of fault event types. The integration process of the fault gene sequence table is the same. By ranking the fault factors, the possibility of the fault factors is analyzed to determine the fault factor investigation.

[0072] Further, the i th fault event trait sequence of the N th group of fault event trait sequences is taken as a screening condition, and the fault factor investigation results are collected. The step S310 of the present application further includes:

[0073] Step S311: The i th fault event trait sequence is added to the first screening identification information to generate a first screening task.

[0074] Step S312: Based on the blockchain, the first screening task is published, and a plurality of blockchain node feedback information is obtained. Any one of the plurality of blockchain node feedback information includes agreement information or rejection information.

[0075] Step S313: The blockchain node that feeds back the agreement information is scored for information accuracy, and a credit score is generated.

[0076] Step S314: When the credit score meets the credit score threshold, the blockchain node is added to the information collection node set.

[0077] Step S315: uploading the fault factor investigation result when the number of the information collection node set meets the preset number.

[0078] Further, the blockchain nodes feeding back the consent information are scored for information accuracy, and a credit score is generated. Step S313 of the application further includes:

[0079] Step S3131: obtaining an information accuracy scoring formula:

[0080]

[0081] wherein Accuracy represents the credit score result, s1 represents the number of fault cause dimension accuracy, s0 represents the number of fault cause dimension error, d represents the fault cause dimension accurate reason deviation parameter, J represents the total number of fault cause dimension accurate data analyzed, and j belongs to J. j wherein Accuracy represents the credit score result, s1 represents the number of fault cause dimension accuracy, s0 represents the number of fault cause dimension error, d represents the fault cause dimension accurate reason deviation parameter, J represents the total number of fault cause dimension accurate data analyzed, and j belongs to J.

[0082] Step S3132: scoring the blockchain nodes feeding back the consent information for information accuracy according to the information accuracy scoring formula, and generating the credit score.

[0083] Specifically, based on the Nth group of fault event trait sequences, the ith fault event trait sequence is extracted as a screening condition, added to the first screening identification information, and a plurality of sequence items are taken as a screening direction to generate the first screening task. The first screening task is published on a blockchain, and it is determined whether the plurality of blockchain nodes accept the task. The consent information or the rejection information is taken as the blockchain node feedback information, and the plurality of blockchain node feedback information is generated, wherein the plurality of blockchain node feedback information has a node identifier.

[0084] Specifically, when the feedback information is the consent information, the information accuracy of the corresponding blockchain node is scored, and a plurality of nodes to be evaluated are determined. The information accuracy scoring formula is obtained as follows: wherein Accuracy represents the credit score result, s1 represents the number of fault cause dimension accuracy, s0 represents the number of fault cause dimension error, d represents the fault cause dimension accurate reason deviation parameter, J represents the total number of fault cause dimension accurate data analyzed, and j belongs to J. jThe parameter of the fault cause dimension accuracy is represented by J, the total number of data of the fault cause dimension accuracy of the J analysis is represented by J, and j belongs to J. The information of the blockchain node is collected and counted to obtain the above parameter data, and the information statistical result is input into the information accuracy rate scoring formula, and the calculation result is taken as the credit score of the node. The information accuracy rate scoring formula is used to evaluate the plurality of nodes to be evaluated respectively to determine the credit score result corresponding to the node. The information accuracy rate scoring formula is used to evaluate the blockchain node, which can effectively improve the objectivity and accuracy of the evaluation result.

[0085] Further, the credit score threshold is set, that is, the accuracy threshold of the blockchain node information judgment, the blockchain node whose credit score meets the credit score threshold is taken as a demand node and added to the information collection node set, the fault factor is investigated based on the information collection node set, and when the number of collected information meets the preset number, the collected result is integrated to generate the fault factor investigation result and upload. Through the evaluation and screening of the blockchain node, the collection accuracy of the node information is improved, and the subsequent fault analysis accuracy is improved.

[0086] Step S400: obtaining the first fault event and inputting the fault event trait sequence through the charging cabinet monitoring device, and matching the first fault trait sequence;

[0087] Step S500: inputting the first fault trait sequence into the plurality of fault event gene order tables to generate a first fault event gene order table;

[0088] Step S600: performing fault investigation according to the first fault event gene order table to generate a fault gene identification result;

[0089] Step S700: sending the fault gene identification result to the charging cabinet management terminal.

[0090] Specifically, the first fault event is obtained based on the charging cabinet monitoring device, the charging cabinet monitoring device is a device for real-time operation monitoring of the charging cabinet, the fault event is determined to be reported and processed, the first fault event is input into the fault event trait sequence, and the matching result is taken as the first fault trait sequence through multi-level matching of the fault event type and the fault event trait sequence. Further, the first fault trait sequence is input into the plurality of fault event gene order tables, the plurality of fault event gene order tables are traversed, gene matching and integration are performed for each fault trait, and the first fault event gene order table is generated. The first fault event gene order table is the fault source of the analyzed first fault event, and the acquisition of the first fault event gene order table provides a troubleshooting direction for subsequent fault investigation.

[0091] Further, based on the first fault event gene sequence table, the fault source is checked one by one, for example, the temperature of the charging module is too high, the charging cabinet is detected based on the charging cabinet, the fault gene is determined, the first fault event gene sequence table is marked based on the detection result, the fault gene identification result is generated, the fault gene identification result is sent to the charging cabinet management terminal, the fault gene identification result is visually displayed, the operation and maintenance management of the charging cabinet is carried out for the receiving information, so as to improve the fault identification efficiency and realize the intelligent and accurate identification of the charging cabinet operation fault.

[0092] The charging cabinet fault identification method provided by the embodiments of the application has the following technical effects:

[0093] 1. The charging cabinet fault identification method provided by the application generates a fault event trait sequence by acquiring a charging cabinet fault event set and performing frequent sequence mining, generates a plurality of fault event gene sequence tables by mining frequent items, acquires a first fault event input fault event trait sequence, matches the first fault trait sequence, further inputs the plurality of fault event gene sequence tables, generates a first fault event gene sequence table, and generates a fault gene identification result and sends it to a charging cabinet management terminal. The technical problem of the prior art that the charging cabinet fault identification method is mainly based on experience and has insufficient method intelligence, resulting in low fault identification efficiency and insufficient fault identification result accuracy, and that the identification deviation exists and easily causes subsequent operation and maintenance risks is solved. By mining fault gene sequence tables and performing layer-by-layer matching and checking of fault events, intelligent, efficient and accurate identification of charging cabinet faults is realized.

[0094] 2. In the plurality of fault event gene sequence table construction processes, multi-level frequent item mining and sequence item evaluation pruning processing are performed, so that the plurality of fault event gene sequence tables established have fault event representativeness, improve the matching degree with fault events, reduce the amount of analysis data, and improve the identification accuracy of subsequent fault sources.

[0095] Embodiment Two

[0096] Based on the same inventive concept as the charging cabinet fault identification method in the foregoing embodiments, as shown in Figure 4 The application provides a charging cabinet fault identification system, which comprises:

[0097] An event set acquisition module 11 is configured to acquire a charging cabinet fault event set.

[0098] A sequence generation module 12 is configured to perform frequent sequence mining on the charging cabinet fault event set to generate a fault event trait sequence.

[0099] A sequential table generation module 13 is configured to traverse the fault event trait sequence to perform frequent item mining, and generate a plurality of fault event gene sequential tables;

[0100] A sequence matching module 14 is configured to acquire the fault event trait sequence input by the first fault event through the charging cabinet monitoring device, and match the first fault trait sequence;

[0101] A first sequential table generation module 15 is configured to input the first fault trait sequence into the plurality of fault event gene sequential tables, and generate a first fault event gene sequential table;

[0102] A fault identification module 16 is configured to perform fault troubleshooting according to the first fault event gene sequential table, and generate a fault gene identification result;

[0103] A result sending module 17 is configured to send the fault gene identification result to a charging cabinet management terminal.

[0104] Further, the system further comprises:

[0105] An event information acquisition module is configured to acquire a plurality of groups of fault event types and fault event trait sequences according to the charging cabinet fault event set;

[0106] A sequence pruning module is configured to perform certain state sequence pruning on the plurality of groups of fault event types and the fault event trait sequences, and generate fault event non-certain trait sequences;

[0107] A frequent item mining module is configured to perform k-item frequent item mining on the plurality of groups of fault event types and the fault event non-certain trait sequences, and generate the fault event trait sequence.

[0108] Further, the system further comprises:

[0109] A one-item state certain confidence generation module is configured to perform one-item certain state analysis on the fault event trait sequence based on the plurality of groups of fault event types, and generate a one-item state certain confidence;

[0110] A one-item certain state pruning result generation module is configured to prune the one-item certain state from the fault event trait sequence when the one-item state certain confidence is greater than or equal to a one-item certain confidence threshold, and generate a one-item certain state pruning result;

[0111] an m-item state certain confidence generation module configured to perform m-item certain state analysis on the fault event trait sequence based on the plurality of fault event types, and generate m-item state certain confidence;

[0112] a pruning result generation module configured to prune the m-item certain state from the fault event trait sequence when the m-item state certain confidence is greater than or equal to an m-item certain confidence threshold, and generate m-item certain state pruning result;

[0113] a fault event non-certain trait sequence generation module configured to take a union of the one-item certain state pruning result and the m-item certain state pruning result, and generate the fault event non-certain trait sequence.

[0114] Further, the system further comprises:

[0115] a trait sequence generation module configured to obtain an Nth fault event type and an Nth group of fault event non-certain trait sequence according to the plurality of fault event types;

[0116] a one-item non-frequent item generation module configured to perform one-item non-certain trait frequency calculation on the Nth group of fault event non-certain trait sequence based on the Nth fault event type, and generate one-item non-frequent item;

[0117] a one-item frequent set generation module configured to prune the one-item non-frequent item from the Nth group of fault event non-certain trait sequence, and generate one-item frequent set;

[0118] a k-item non-frequent item generation module configured to perform k-item non-certain trait frequency calculation on the Nth group of fault event non-certain trait sequence based on the Nth fault event type, and generate k-item non-frequent item;

[0119] a k-item frequent set generation module configured to prune the k-item non-frequent item from the Nth group of fault event non-certain trait sequence, and generate k-item frequent set;

[0120] a sequence addition module configured to serialize and adjust the one-item frequent set to the k-item frequent set according to a union, and generate Nth group of fault event trait sequence, and add into the fault event trait sequence.

[0121] Further, the system further comprises:

[0122] The fault factor investigation module is configured to take the ith fault event trait sequence of the Nth group of fault event trait sequences as a screening condition, and collect fault factor investigation results.

[0123] The score value generation module is configured to sort and score the fault factor investigation results, and generate a plurality of sorting score values.

[0124] The sequence table addition module is configured to sort the fault factor investigation results according to the plurality of sorting score values, generate an ith fault event gene sequence table, and add the ith fault event gene sequence table into an Nth group of fault event gene sequence tables.

[0125] The gene sequence table addition module is configured to add the Nth group of fault event gene sequence tables into the plurality of fault event gene sequence tables.

[0126] Further, the system further comprises:

[0127] The frequency evaluation value generation module is configured to evaluate the frequency of the fault factor investigation results, and generate a plurality of frequency evaluation values, wherein the plurality of frequency evaluation values have a first weight.

[0128] The timeliness evaluation value generation module is configured to evaluate the timeliness of the fault factor investigation results, and generate a plurality of timeliness evaluation values, wherein the plurality of timeliness evaluation values have a second weight.

[0129] The sorting score value generation module is configured to sort and score the fault factor investigation results according to the first weight, the second weight, the plurality of frequency evaluation values, and the plurality of timeliness evaluation values, and generate the plurality of sorting score values.

[0130] Further, the system further comprises:

[0131] The screening task generation module is configured to add the ith fault event trait sequence into first screening identification information, and generate a first screening task.

[0132] The feedback information acquisition module is configured to publish the first screening task based on a blockchain, and acquire a plurality of blockchain node feedback information, wherein any one of the plurality of blockchain node feedback information includes agreement information or rejection information.

[0133] The credibility score generation module is configured to score the information accuracy of a blockchain node that feeds back the agreement information, and generate a credibility score.

[0134] a node adding module, configured to add a blockchain node into a set of information collection nodes when the reputation score meets a reputation score threshold;

[0135] a result uploading module, configured to upload the fault factor investigation result when a quantity of the set of information collection nodes meets a preset quantity.

[0136] Further, the system further comprises:

[0137] a formula obtaining module, configured to obtain an information accuracy score formula:

[0138]

[0139] wherein, Accuracy represents a reputation score result, s1 represents a quantity of fault cause dimension accurate, s0 represents a quantity of fault cause dimension error, d j represents a fault cause dimension accurate reason deviation parameter, J represents a total quantity of analyzed fault cause dimension accurate data, and j belongs to J.

[0140] a node scoring module, configured to score information accuracy of the blockchain node that feeds back the consent information according to the information accuracy score formula, and generate the reputation score.

[0141] The foregoing detailed description of the fault identification method of the charging cabinet enables those skilled in the art to clearly understand the fault identification method and system of the charging cabinet in the embodiment. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the related part is referred to the method part.

[0142] The foregoing description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for identifying a failure of a charging cabinet, characterized by, The method comprises the following steps: obtaining a set of charging cabinet fault events; performing frequent sequence mining on the set of charging cabinet fault events to generate a fault event trait sequence; traversing the fault event trait sequence to perform frequent item mining and generate a plurality of fault event gene order tables; obtaining a first fault event input into the fault event trait sequence through a charging cabinet monitoring device, and matching a first fault trait sequence; inputting the first fault trait sequence into the plurality of fault event gene order tables to generate a first fault event gene order table; performing fault troubleshooting according to the first fault event gene order table to generate a fault gene identification result; sending the fault gene identification result to a charging cabinet management terminal; wherein the step of performing frequent sequence mining on the set of charging cabinet fault events to generate a fault event trait sequence comprises the following steps: obtaining a plurality of sets of fault event types and fault event trait sequences according to the set of charging cabinet fault events; performing certain state sequence pruning on the plurality of sets of fault event types and the fault event trait sequences to generate fault event non-certain trait sequences; performing k-item frequent item mining on the plurality of sets of fault event types and the fault event non-certain trait sequences to generate the fault event trait sequence; wherein the step of performing certain state sequence pruning on the plurality of sets of fault event types and the fault event trait sequences to generate fault event non-certain trait sequences comprises the following steps: based on the plurality of sets of fault event types, traversing the fault event trait sequence to perform one-item certain state analysis to generate one-item state certain confidence; when the one-item state certain confidence is greater than or equal to a one-item certain confidence threshold, pruning the one-item certain state from the fault event trait sequence to generate a one-item certain state pruning result; based on the plurality of sets of fault event types, traversing the fault event trait sequence to perform m-item certain state analysis to generate m-item state certain confidence; when the m-item state certain confidence is greater than or equal to an m-item certain confidence threshold, pruning the m-item certain state from the fault event trait sequence to generate an m-item certain state pruning result; recursively pruning one-item certain state pruning results until the m-item certain state pruning result is taken as a union set to generate the fault event non-certain trait sequence.

2. The method of claim 1, wherein, the step of performing k-item frequent item mining on the plurality of sets of fault event types and the fault event non-certain trait sequences to generate the fault event trait sequence comprises the following steps: obtaining an Nth fault event type and an Nth set of fault event non-certain trait sequences according to the plurality of sets of fault event types; based on the Nth fault event type, performing one-item non-certain trait frequency calculation on the Nth set of fault event non-certain trait sequences to generate a one-item non-frequent item; pruning the one-item non-frequent item from the Nth set of fault event non-certain trait sequences to generate a one-item frequent set; based on the Nth fault event type, performing k-item non-certain trait frequency calculation on the Nth set of fault event non-certain trait sequences to generate k-item non-frequent items; pruning the k-item non-frequent items from the Nth set of fault event non-certain trait sequences to generate a k-item frequent set; The cycle will collect a frequent set until the k frequent sets are serialized after taking the union, generating the Nth group of fault event trait sequences, and adding them to the fault event trait sequence.

3. The method of claim 2, wherein, The traversal of the fault event trait sequence is performed for frequent item mining to generate a plurality of fault event gene order tables, including: The i th fault event trait sequence of the Nth group of fault event trait sequences is taken as a screening condition to collect fault factor investigation results; The fault factor investigation results are traversed for sorting and scoring to generate a plurality of sorting score values; The fault factor investigation results are sorted according to the plurality of sorting score values to generate the i th fault event gene order table, which is added to the Nth group of fault event gene order tables; The Nth group of fault event gene order tables is added to the plurality of fault event gene order tables.

4. The method of claim 3, wherein, The traversal of the fault factor investigation results is performed for sorting and scoring to generate a plurality of sorting score values, including: The fault factor investigation results are traversed for frequency evaluation to generate a plurality of frequency evaluation values, wherein the plurality of frequency evaluation values have a first weight; The fault factor investigation results are traversed for timeliness evaluation to generate a plurality of timeliness evaluation values, wherein the plurality of timeliness evaluation values have a second weight; The fault factor investigation results are sorted and scored according to the first weight, the second weight, the plurality of frequency evaluation values, and the plurality of timeliness evaluation values to generate the plurality of sorting score values.

5. The method of claim 3, wherein, The i th fault event trait sequence of the Nth group of fault event trait sequences is taken as a screening condition to collect fault factor investigation results, including: The i th fault event trait sequence is added to first screening identification information to generate a first screening task; Based on a blockchain, the first screening task is published to obtain a plurality of blockchain node feedback information, wherein any one of the plurality of blockchain node feedback information includes consent information or rejection information; The blockchain nodes that feedback the consent information are scored for information accuracy to generate a credit score; When the credit score of the blockchain nodes meets a credit score threshold, the blockchain nodes are added to an information collection node set; When the number of the information collection node set meets a preset number, the fault factor investigation results are uploaded.

6. The method of claim 5, wherein, The blockchain nodes that feedback the consent information are scored for information accuracy to generate a credit score, including: The information acquisition accuracy rate score formula is: Wherein, The reputation score result is represented by, The number of accurate fault cause dimensions is represented by, The number of incorrect fault cause dimensions is represented by, The fault cause dimension accurate reason deviation parameter is represented by J, and j belongs to J. The blockchain nodes that feedback the consent information are scored for information accuracy according to the information accuracy scoring formula to generate the credit score.

7. A fault recognition system of a charging cabinet, characterized by, A fault identification method for a charging cabinet for performing any one of claims 1-6, the system comprising: An event set acquisition module, the event set acquisition module is used to acquire a charging cabinet fault event set; A sequence generation module, the sequence generation module is used to perform frequent sequence mining on the charging cabinet fault event set to generate fault event trait sequences; An order table generation module, the order table generation module is used to traverse the fault event trait sequences for frequent item mining to generate a plurality of fault event gene order tables; The sequence matching module is configured to acquire a first fault event input sequence of the fault event characteristics by the charging cabinet monitoring device, and match the first fault event sequence; The first order table generating module is configured to input the first fault event sequence into a plurality of fault event gene order tables, and generate a first fault event gene order table; The fault identification module is configured to perform fault troubleshooting according to the first fault event gene order table, and generate a fault gene identification result; The result sending module is configured to send the fault gene identification result to a charging cabinet management terminal.

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