A charging station charging equipment health prediction and operation and maintenance work order automatic generation system

Abstract

The application belongs to the technical field of charging equipment operation and maintenance, and discloses a charging station charging equipment health prediction and operation and maintenance work order automatic generation system. The system comprises a data access unit, a session segmentation unit, a reference section establishment unit, an offset calculation unit, an abnormality screening unit, a degradation determination unit, a health evaluation unit and a work order data generation unit; by obtaining the operation data of multiple charging sessions, dividing the continuous operation sections according to the charging sessions, establishing the corresponding reference section data, comparing the characteristic offset results, screening out the pseudo abnormalities in combination with the multi-source record corresponding relationship and the retry recovery situation, further determining the maintenance object, the degradation state, the risk level and the remaining allowed operation interval according to the degradation rule library, and generating the work order data when the trigger condition is met, the scheme can be used for charging equipment degradation identification and operation and maintenance disposal.

Description

Technical Field

[0001] This invention belongs to the field of charging equipment operation and maintenance technology, specifically a system for predicting the health of charging equipment in charging stations and automatically generating operation and maintenance work orders. Background Technology

[0002] During operation, charging equipment in a charging station typically needs to sequentially complete stages such as connection establishment, output establishment, stable output, and shutdown. This type of equipment generally includes power conversion components, charging connection components, insulation detection components, switch execution components, heat dissipation components, and communication components. These components work together within the same charging session, and their operating status is simultaneously affected by factors such as load changes, ambient temperature, humidity, ventilation conditions, and communication quality. As the number of operations increases, some components may gradually exhibit phenomena such as slower response, increased temperature rise, prolonged action delay, or increased communication round-trip delay.

[0003] In existing technologies, the management of charging equipment status typically revolves around data acquisition, anomaly identification, fault diagnosis, and work order processing. This involves collecting equipment parameters, control records, communication logs, and alarm information, and then determining the equipment status based on threshold exceedances, fault codes, alarm records, or parameter results during a single charging process. In the event of a shutdown, a clear fault alarm, or a user complaint, maintenance personnel determine the repair target and generate a work order by combining operation logs, maintenance records, and on-site inspection results. This approach can complete the detection and handling of equipment anomalies, but its judgment is mostly based on the overall result of the entire charging process, anomaly records at a single moment, or a single alarm message.

[0004] However, the parameter performance of charging equipment varies in different charging sessions and operating phases. Early degradation of the same component often manifests as slight deviations in a specific phase rather than as continuous failure and shutdown. In this case, if there is a lack of corresponding analysis of multi-source data by charging session and operating phase, anomalies caused by communication retries, control retries, instantaneous fluctuations, or inconsistent recordings can easily be confused with anomalies caused by continuous component degradation. At the same time, if there is a lack of continuous judgment on the segment where anomalies occur, the order of characteristic changes, and the repetition of adjacent charging sessions, it is difficult to accurately locate the maintenance object corresponding to the anomaly, and it is also difficult to determine whether the anomaly is an occasional disturbance or a continuous evolution trend. This affects the accuracy of health status assessment results and work order handling content, resulting in insufficient timeliness and pertinence of operation and maintenance processing. Summary of the Invention

[0005] The purpose of this invention is to provide a system for predicting the health of charging equipment in charging stations and automatically generating maintenance work orders, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a system for predicting the health of charging equipment in charging stations and automatically generating maintenance work orders. This system analyzes and processes the operation of charging equipment in multiple charging sessions. First, it identifies each charging session and divides it into continuous operating segments. Then, it establishes reference segment data corresponding to each continuous operating segment based on historical normal charging sessions. On this basis, it identifies the continuous offset results in the current charging session and then determines the maintenance object, degradation status, risk level, and corresponding work order data.

[0007] Specifically, the system first acquires operational data of the target charging device across multiple charging sessions. This operational data includes device status data, control execution data, session interaction data, and environmental data. Based on event records in the session interaction data and combined with changes in device status data during the charging process, each charging session is identified, and each charging session is divided into at least three consecutive operating segments arranged chronologically. By dividing the charging sessions, the operating states of the same device at different charging stages can be identified separately, thus enabling subsequent analysis to be based on data corresponding to the consecutive operating segments and avoiding confusion caused by parameter changes at different stages.

[0008] After completing the charging session segmentation, the system establishes reference segment data for each continuous operating segment based on data corresponding to each continuous operating segment in historical normal charging sessions. Subsequently, it extracts the operating characteristics of the current charging session within each continuous operating segment and compares these characteristics with the corresponding reference segment data to obtain the feature offset results for each continuous operating segment. By comparing the operating characteristics of the current charging session with the reference segment data of the corresponding continuous operating segment, abnormal changes can be located to specific continuous operating segments, providing segment-level basis for subsequent continuous offset identification and degradation analysis.

[0009] After obtaining the feature offset results, the system further combines the correspondence between device-side event records, control execution records, and communication logs under the same charging session identifier, as well as the recovery status of the feature offset results after communication retries or control retries, to filter out anomalies. Data inconsistency anomalies and recoverable control anomalies are removed from the feature offset results, while persistent offset results are retained. By first filtering out data inconsistency anomalies and recoverable control anomalies, non-persistent offsets caused by record mismatches, communication fluctuations, or short-term control anomalies can be excluded, allowing subsequent degradation judgments to focus on data changes related to the continuous operating state of the device.

[0010] After obtaining the continuous offset result, the system calls the pre-stored degradation rule base to determine the degradation. The degradation rule base records the correspondence between the maintenance object and the operating characteristics, the corresponding continuous operating segments, and the order of occurrence. When the same operating characteristic continuously offsets in at least two adjacent continuous operating segments, or when multiple operating characteristics corresponding to the same maintenance object sequentially offset in at least two adjacent continuous operating segments according to the order of occurrence recorded in the degradation rule base, the degradation determination result of the current charging session is formed. When the same degradation determination result is repeated in at least two consecutive charging sessions, the corresponding maintenance object and degradation state are further determined. By combining the continuous operating segment offset relationship in a single charging session with the repeated results in consecutive charging sessions, one-time fluctuations and continuous degradation can be distinguished, providing continuous data support for the determination of maintenance objects and degradation states.

[0011] After determining the degradation state, the system determines the risk level and the remaining allowable operating range based on the number of times the degradation state occurs in continuous charging sessions, the cumulative offset of the feature offset results, and the difference in feature offset results between adjacent charging sessions. The remaining allowable operating range is either the range of the number of remaining allowable operating sessions or the range of the remaining allowable operating time. Finally, when the risk level reaches a preset risk threshold, or the remaining allowable operating range is less than a preset operating range threshold, the system generates work order data based on the maintenance object, degradation state, and continuous offset results. In this way, the results of operation status analysis, anomaly screening, degradation judgment, and risk assessment can be linked with the operation and maintenance data, so that the generation of work order data is based on the continuous degradation identification results in continuous charging sessions, improving the correspondence between work order data and maintenance requirements.

[0012] This invention provides a system for predicting the health of charging equipment in charging stations and automatically generating maintenance work orders. The system processes the operation of the target charging equipment in multiple charging sessions. First, it identifies each charging session and divides it into continuous operating segments. Then, it establishes reference segment data corresponding to each continuous operating segment based on historical normal charging sessions. On this basis, it forms the feature offset result of the current charging session, then filters out non-persistent anomalies, determines the maintenance objects and degradation status, and outputs work order data corresponding to the maintenance handling.

[0013] Specifically, the system first acquires the operational data of the target charging device in multiple charging sessions. This operational data is provided by the target charging device, the controller connected to the target charging device, and the corresponding communication module, and includes device status data, control execution data, session interaction data, and environmental data. The device status data includes input-side electrical parameters, output-side electrical parameters, temperature parameters, insulation status parameters, and switching action parameters. The control execution data includes power adjustment parameters, heat dissipation execution parameters, and start / stop control parameters. The session interaction data includes plug-in events, handshake events, charging start events, charging end events, and communication message records. The environmental data includes at least one of the following: ambient temperature, cabinet temperature, humidity parameters, and ventilation status parameters. By uniformly incorporating the device's status, control execution process, session interaction process, and environmental condition-related data into the processing scope, a complete data foundation can be provided for subsequent continuous operation segment identification and degradation analysis.

[0014] After acquiring the operational data, the system identifies and segments each charging session. The continuous operation segment includes at least three of the following: session preparation segment, connection establishment segment, output establishment segment, stable output segment, and exit segment.

[0015] The connection establishment segment is determined based on the plug-in event and the handshake event. The output establishment segment is determined based on the target output range corresponding to the power adjustment parameters and the process of the output side electrical parameters changing from the initial state to the target output range. The stable output segment is determined based on the process of the output side electrical parameters entering the target output range and maintaining it.

[0016] By dividing a single charging session into continuous operating segments, the operating status at different stages can be identified separately, thereby establishing reference segment data for each continuous operating segment and providing a unified comparison basis for subsequent segment-level anomaly analysis.

[0017] After completing the division of continuous operation segments, the system establishes reference segment data for each continuous operation segment. The reference segment data uses the data of the corresponding operation segment in a charging session with no historical fault alarms, no emergency stop records, and charging end records as the basic data, and includes the normal value range and allowable fluctuation range of each operation feature within the corresponding operation segment.

[0018] To improve the comparability between reference segment data and the current charging session, data from the corresponding operating segment of the target charging device in historical charging sessions with the same rated power level and the same ambient temperature range as the current charging session are selected first. When the number of historical charging sessions that meet the above conditions is less than the preset minimum sample size, data from the corresponding operating segment of historical charging sessions of the same model charging device with the same rated power level and the same ambient temperature range are used as supplementary reference data. Through the above processing, the reference segment data can be made to correspond with the current device and the current operating condition, while avoiding the impact on the integrity of the reference basis due to insufficient historical data of a single device.

[0019] After establishing reference segment data, the system extracts the operating characteristics of each continuous operating segment in the current charging session. The operating characteristics include at least two of the following: input voltage fluctuation, output voltage settling time, output current following deviation, temperature rise rate, contactor action delay, insulation recovery time, and communication round-trip delay. Subsequently, the extracted operating characteristics are compared with the corresponding reference segment data, and feature offset results are formed based on the direction and amount of deviation of the operating characteristics relative to the reference segment data. By comparing the operating characteristics in the current charging session with the reference segment data of the corresponding continuous operating segments, abnormal changes can be located to specific continuous operating segments, forming the segment-level offset basis required for subsequent anomaly screening and degradation determination.

[0020] After generating the feature offset results, the system performs anomaly screening. Within the same charging session, if after communication retry or control retry, the operating feature corresponding to the anomaly recovers to the normal value range defined by the reference segment data of the current continuous operating segment and remains so until the end of the current continuous operating segment, then the corresponding feature offset result is determined as a recoverable control anomaly.

[0021] If the device-side event records, control execution records, and communication logs are under the same charging session identifier and within the preset time tolerance range, and no corresponding record is formed for the same running event, and the corresponding running characteristics do not continue to deviate in the next adjacent continuous running segment, then the corresponding feature offset result is determined as a data inconsistency anomaly; the feature offset results that are not screened out are retained as continuous offset results; by first removing data inconsistency anomalies and recoverable control anomalies, interference caused by communication fluctuations, short-term control anomalies, or record mismatches can be reduced, so that subsequent degradation judgment is based on continuous offset results.

[0022] After obtaining the continuous offset results, the system uses the degradation rule base to make degradation judgments. The degradation rule base is established based on the component configuration relationship of the target charging equipment, the correspondence between the corresponding detection points of the components and the operating characteristics, and the historical maintenance confirmation records. The historical maintenance confirmation records are records of the disappearance of the corresponding abnormality or the restoration to normal after inspection after maintenance, and at least include the maintenance object, the section where the abnormality occurred, the actual replaced or repaired components, and the inspection results after maintenance.

[0023] The maintenance objects include one of the following: power conversion components, charging connection components, insulation detection components, switching execution components, heat dissipation components, and communication components. Based on the degradation rule base, when the same operating characteristic continuously deviates in at least two adjacent consecutive operating segments, or when multiple operating characteristics corresponding to the same maintenance object deviate sequentially in at least two adjacent consecutive operating segments according to a pre-recorded order of occurrence, a degradation judgment result is formed. By mapping the continuous deviation result to the degradation rule base, abnormal changes at the parameter level can be transformed into degradation judgment results related to the specific maintenance object.

[0024] After the degradation judgment result is formed, the system further conducts a health assessment. The health assessment is based on the cumulative result of the absolute value of the deviation of the same operating characteristics corresponding to the same maintenance object in the continuous charging session, as well as the change of the difference of the corresponding deviation between adjacent charging sessions, to determine the risk level and the remaining allowable operating range.

[0025] The remaining allowable operating range is either the range of the remaining allowable number of operating sessions or the range of the remaining allowable operating time. The relevant risk thresholds and operating range thresholds are preset based on the rated parameters of the target charging equipment, the safety requirements for component operation, and the maintenance response time. By incorporating the cumulative offset in continuous charging sessions and the changes between adjacent charging sessions into the assessment, the health status judgment can be based on a continuous evolution process.

[0026] After completing the health assessment, the system generates work order data corresponding to the maintenance procedures. Work order data includes equipment identification, the section where the anomaly occurred, the object to be maintained, the maintenance items, and the time limit for handling. It also includes verification items, disassembly / repair items, replacement items, and re-inspection items corresponding to the degradation rule base. When there are already unclosed work order data for the same target charging equipment, the same object to be maintained, the same degradation state, and the same section where the anomaly occurred, the current work order data is merged and updated with the unclosed work order data. Through the above work order data generation and updating methods, a direct correspondence can be established between the degradation judgment result and the maintenance content, and duplicate work order data for the same degradation state can be avoided.

[0027] This invention also provides a method for predicting the health of charging equipment in charging stations and automatically generating maintenance work orders. Based on the operational data of the target charging equipment in multiple charging sessions, this method divides the charging process into continuous operating segments, filters out anomalies, determines degradation, and conducts health assessments. Work order data is generated when the risk level reaches a preset risk threshold or the remaining allowable operating range is less than a preset operating range threshold.

[0028] Specifically, the first step is to acquire the operational data of the target charging device in multiple charging sessions. This operational data includes device status data, control execution data, session interaction data, and environmental data. Based on the event records in the session interaction data and the changes in device status data during the charging process, each charging session is identified, and each charging session is divided into at least three consecutive operating segments arranged in chronological order. By identifying the charging sessions and dividing the continuous operating segments, the operational states at different stages of a single charging process can be mapped to specific continuous operating segments, providing a unified basis for subsequent reference segment data establishment and feature offset analysis.

[0029] After completing the division of continuous operating segments, reference segment data is established for each continuous operating segment based on the data of the corresponding operating segments in historical normal charging sessions. Subsequently, the operating characteristics of the current charging session in each continuous operating segment are extracted, and the operating characteristics in each continuous operating segment are compared with the corresponding reference segment data to obtain the feature offset results corresponding to each continuous operating segment. By comparing the operating characteristics in the current charging session with the reference segment data of the corresponding continuous operating segment, abnormal changes can be mapped to specific continuous operating segments, and the feature offset results required for subsequent anomaly screening and degradation determination can be formed.

[0030] After obtaining the feature offset results, the correspondence between the device-side event records, control execution records, and communication logs under the same charging session identifier is further combined with the recovery status of the feature offset results after communication retries or control retries. Data inconsistency anomalies and recoverable control anomalies are removed from the feature offset results to obtain the continuous offset results. Screening out data inconsistency anomalies and recoverable control anomalies first can reduce interference caused by record mismatch, communication fluctuations, or short-term control anomalies, so that subsequent degradation judgment is based on the continuous offset results.

[0031] After obtaining the continuous offset results, a pre-stored degradation rule base is used for determination. The degradation rule base records the correspondence between the maintenance object and the operating characteristics, the corresponding continuous operating segments, and the order of occurrence. When the same operating characteristic continuously offsets in at least two adjacent continuous operating segments, or when multiple operating characteristics corresponding to the same maintenance object sequentially offset in at least two adjacent continuous operating segments according to the order of occurrence recorded in the degradation rule base, a degradation determination result for the current charging session is formed. When the same degradation determination result is repeated in at least two consecutive charging sessions, the corresponding maintenance object and degradation state are further determined. By combining the continuous offset results in a single charging session with the repeated results in consecutive charging sessions, one-time fluctuations and continuous degradation can be distinguished, so that the determination of the maintenance object and degradation state is based on the data changes in the continuous charging session.

[0032] After determining the degradation state, the risk level and remaining allowable operating range are determined based on the number of times the degradation state occurs in continuous charging sessions, the cumulative offset of the feature offset results, and the difference in feature offset results between adjacent charging sessions. The remaining allowable operating range is either the range of the number of remaining allowable operating sessions or the range of the remaining allowable operating time. By incorporating the cumulative offset in continuous charging sessions and the difference in the difference between adjacent charging sessions into the evaluation, the health status judgment can be based on a continuous evolution process.

[0033] After the health assessment is completed, when the risk level reaches the preset risk threshold, or when the remaining allowable operating range is less than the preset operating range threshold, work order data is generated based on the maintenance object, degradation status, and continuous offset results. Through the above processing, the generation of work order data is based on the analysis of continuous operating sections, anomaly screening, degradation judgment, and health assessment results, so that a corresponding relationship is formed between work order data and maintenance needs.

[0034] The beneficial effects of this invention are as follows: 1. This invention acquires operational data from multiple charging sessions of a target charging device and divides a single charging process into at least three consecutive operational segments arranged in chronological order according to the charging session identification results. This allows device status data, control execution data, session interaction data, and environmental data to be analyzed separately within each corresponding operational stage, rather than being judged as a whole based on the overall result of the entire charging process. Secondly, by establishing reference segment data for each consecutive operational segment and comparing the operational characteristics of the corresponding segment in the current charging session with the reference segment data, subtle deviations in different operational stages can be identified at the segment level, thereby avoiding the masking of local abnormal signs by the overall statistics of the entire charging process. Furthermore, by establishing a correspondence between charging sessions and consecutive operational segments, abnormal behaviors in the connection establishment stage, output establishment stage, stable output stage, and exit stage can be judged at specific stages, thereby improving the accuracy of early degradation identification.

[0035] 2. This invention introduces the corresponding analysis of equipment-side event records, control execution records, and communication logs under the same charging session identifier. Combined with the retention of operational characteristics after communication or control retries, feature offset results are filtered out, preventing anomalies such as one-time communication jitter, short-term control fluctuations, isolated record loss, or record inconsistencies from directly entering the degradation judgment process. Secondly, by retaining unrecovered feature offset results that are not judged as data inconsistency anomalies as continuous offset results, subsequent judgments focus more on anomaly information that reflects the true changes in the equipment's state. Furthermore, by establishing a correspondence between the maintenance object and operational characteristics, its corresponding continuous operating segment, and the order of occurrence, and combining continuous offsets in adjacent continuous operating segments and recurrence in adjacent charging sessions for degradation judgment, occasional disturbances can be distinguished from continuous degradation trends, thereby improving the accuracy of maintenance object location and the stability of degradation state identification.

[0036] 3. This invention determines the risk level and remaining allowable operating range based on the number of times the degradation state occurs in continuous charging sessions, the cumulative offset of feature offset results, and the difference change between adjacent charging sessions. This allows equipment health status assessment to move beyond static judgments after a single anomaly trigger and reflect the cumulative changes and evolution trends of the degradation degree. Secondly, by generating work order data when the risk level reaches a preset risk threshold or the remaining allowable operating range is less than a preset operating range threshold, and writing the anomaly occurrence section, maintenance object, maintenance item, and handling time limit into the work order, the work order content can directly correspond to the current abnormal state of the equipment and the maintenance direction. In addition, by merging and updating unclosed work orders for the same target charging equipment, the same maintenance object, the same degradation state, and the same anomaly occurrence section, duplicate dispatching and fragmented handling can be reduced, thereby improving the continuity, timeliness, and pertinence of operation and maintenance. Attached Figure Description

[0037] Figure 1 This invention provides a system diagram for predicting the health of charging equipment in charging stations and automatically generating maintenance work orders. Figure 2 This is a flowchart of the degradation determination, health assessment, and work order generation processes of this invention. Detailed Implementation

[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0039] like Figures 1 to 2 As shown, this embodiment of the invention provides a system for predicting the health of charging equipment in charging stations and automatically generating maintenance work orders, including: It includes a data access unit, a session segmentation unit, a reference segment establishment unit, an offset calculation unit, an anomaly screening unit, a degradation judgment unit, a health assessment unit, and a work order data generation unit. The above units operate collaboratively in the processing sequence of charging session identification, operating segment division, reference segment establishment, feature offset calculation, anomaly screening, degradation judgment, health assessment, and work order data generation.

[0040] The data access unit is used to acquire the operation data of multiple charging sessions of the target charging device. The operation data includes device status data, control execution data, session interaction data and environmental data. The session segmentation unit identifies each charging session based on the event records in the session interaction data and the change process of the device status data. After identifying a single charging session, the charging session is divided into at least three consecutive operation segments arranged in chronological order.

[0041] The continuous operation segments are connected one after another within the same charging session and are used to characterize the operating status of the charging session at different stages. The historical normal charging sessions used to establish reference segment data are also divided into at least three continuous operation segments arranged in chronological order according to the above segmentation rules, so that the corresponding segment data in the historical normal charging sessions corresponds to each continuous operation segment in the current charging session.

[0042] The reference segment establishment unit establishes reference segment data for each continuous operating segment based on the segment data corresponding to each continuous operating segment in historical normal charging sessions. The offset calculation unit extracts the operating features of the current charging session in each continuous operating segment and compares the operating features in each continuous operating segment with the corresponding reference segment data to obtain the feature offset results corresponding to each continuous operating segment. The operating features are feature data extracted from device status data, control execution data, session interaction data, and environmental data to characterize the device operating status, control response status, or session response status. The feature offset results are the deviation of the operating features from the corresponding reference segment data. The deviation can be characterized by at least one of feature value difference, feature duration difference, feature change rate difference, and event occurrence timing deviation.

[0043] After obtaining the feature offset results, the anomaly screening unit filters out anomalies caused by data inconsistency or recoverable control fluctuations. Specifically, the anomaly screening unit associates relevant records in the device-side event records, control execution records, and communication logs based on the same charging session identifier, and determines whether the time of the associated records falls within a preset time tolerance range. The preset time tolerance range is preset based on the record acquisition period, transmission delay, and clock alignment error.

[0044] If relevant records are missing, or if there are conflicts in the status descriptions of the same execution node from different record sources, or if the time correspondence between relevant records does not meet the preset time tolerance range requirements, the corresponding anomaly will be identified as a data inconsistency anomaly.

[0045] In cases where communication retries or control retries exist, the anomaly screening unit further determines whether the corresponding feature offset result still exists after the retry is completed. If the corresponding operating feature recovers to the allowable fluctuation range corresponding to the reference segment data after the retry is completed, the anomaly is identified as a recoverable control anomaly and removed. If the corresponding operating feature still does not recover to the allowable fluctuation range after the retry is completed, the corresponding result is retained as a continuous offset result.

[0046] The degradation determination unit determines the continuous offset result based on the pre-stored degradation rule base; the degradation rule base records the correspondence between the maintenance object and the operating characteristics, the continuous operating section to which it belongs and the order of occurrence. The degradation rule base can be pre-established based on historical maintenance records, historical degradation samples and preset operation and maintenance rules.

[0047] When the same operating feature shifts consecutively in at least two adjacent operating segments, or when multiple operating features corresponding to the same maintenance object shift sequentially in at least two adjacent operating segments according to the order of occurrence recorded in the degradation rule base, the degradation determination unit forms the degradation determination result of the current charging session; when degradation determination results pointing to the same maintenance object and representing the same degradation state appear repeatedly in at least two adjacent charging sessions, the corresponding maintenance object and degradation state are further determined.

[0048] After the maintenance target and degradation state are determined, the health assessment unit assesses the health risk of the target charging equipment based on the number of times the degradation state occurs in adjacent charging sessions, the cumulative offset of the feature offset results, and the change range of the feature offset results between adjacent charging sessions, and obtains the risk level and the remaining allowable operating range.

[0049] Wherein, the cumulative offset is the sum of the offset values ​​of the corresponding operating characteristics of the same maintenance object in a continuous charging session, the change amplitude is the absolute value of the difference between the corresponding offset values ​​of adjacent charging sessions, the risk level is determined according to a pre-set grading standard, and the remaining allowable operating range is the range of the number of remaining allowable operating sessions or the range of the remaining allowable operating time.

[0050] When the risk level reaches a preset risk threshold, or the remaining allowable operating range is less than a preset operating range threshold, the work order data generation unit generates work order data based on the determined maintenance object, degradation status, and continuous offset results. The work order data includes at least the equipment identifier, maintenance object, degradation status, risk level, recommended maintenance time limit, and corresponding continuous offset result summary for subsequent operation and maintenance processing.

[0051] Based on the above embodiments, the operational data acquired by the data access unit comes from the target charging device body, the controller connected to the target charging device, and the communication module corresponding to the target charging device. The target charging device body mainly provides data reflecting the power input and output status and component working status, the controller mainly provides data reflecting the control execution process, and the communication module mainly provides data reflecting the session interaction process and communication round-trip status.

[0052] Specifically, the equipment status data includes input-side electrical parameters, output-side electrical parameters, temperature parameters, insulation status parameters, and switching action parameters; the control execution data includes power regulation parameters, heat dissipation execution parameters, and start / stop control parameters; the session interaction data includes plug-in events, handshake events, charging start events, charging end events, and communication message records; and the environmental data includes one or more of the following: ambient temperature, cabinet temperature, humidity parameters, and ventilation status parameters.

[0053] Input-side electrical parameters characterize the state of the charging equipment when receiving electrical energy from the grid or distribution side; output-side electrical parameters characterize the state when outputting electrical energy to the vehicle side; temperature parameters characterize the thermal state of power components, connection points, or the interior of the cabinet; insulation status parameters characterize insulation detection results or insulation changes; switch action parameters characterize the action state of contactors, relays, or other switch actuators; power regulation parameters characterize the adjustment process of output power, output voltage, or output current during charging control; heat dissipation execution parameters characterize the start-up, shutdown, and adjustment process of fans, radiators, or other heat dissipation components; and start-up / stop control parameters characterize the control execution during session start-up, charging start-up, charging stop-up, and exit processes.

[0054] After being accessed, the aforementioned multi-source operational data are associated according to a unified charging session identifier and time base, so as to provide a mutually corroborating data basis for subsequent event correspondence, segment comparison and anomaly screening within the same charging session.

[0055] In this embodiment, after the session segmentation unit identifies a single charging session, it divides the charging session into continuous operation segments. The identification of the charging session is based on the plug-in event, handshake event, charging start event, charging end event and related communication message records in the session interaction data, and is verified in combination with the change process of output side electrical parameters, switch action parameters and insulation status parameters in the device status data.

[0056] When the event records in the session interaction data and the state changes in the device status data correspond within a preset time tolerance range, they are considered to belong to the same charging session. For each identified charging session, it can be divided into at least three of the following: session preparation segment, connection establishment segment, output establishment segment, stable output segment, and exit segment. The session preparation segment can be identified based on the device being in standby mode before the plugging event, insulation detection start-up records, pre-charge preparation control records, or preset state records before connection establishment, and is used to characterize the charging device's preparation process from standby to a connection-establishable state. The connection establishment segment is based on the plugging event and the... The hand event is determined to characterize the process of establishing the charging connection relationship; the output establishment segment is determined based on the target output voltage range, target output current range, or target output power range corresponding to the power adjustment parameters in the control execution data, and the process of the output-side electrical parameters in the device status data changing from the initial state to the target output range, wherein the initial state is the state when a stable charging output has not yet been formed; the stable output segment is determined based on the process of the output-side electrical parameters entering the target output range and maintaining it; the exit segment is determined based on the charging end event, the execution of the shutdown control, and the process of the output-side electrical parameters falling back and the switch execution component resetting.

[0057] For charging sessions that involve mid-term exit, communication interruption, or user-initiated termination, as long as at least three main operational phases arranged in chronological order can be identified, they can serve as the basis for subsequent comparisons and evaluations.

[0058] When establishing reference segment data, the reference segment establishment unit first filters out comparable historical normal charging sessions. The historical normal charging sessions are charging sessions with no historical fault alarms, no emergency stop records, and charging end records. The corresponding operating segments are also divided according to the aforementioned segmentation rules.

[0059] The reference segment establishment unit uses the data corresponding to the same continuous operating segments in these historical normal charging sessions as the basic data to establish reference segment data for each continuous operating segment; the reference segment data includes the normal value range and allowable fluctuation range of each operating characteristic within the corresponding operating segment.

[0060] In some implementations, the normal value range can be determined based on the mean and dispersion of historical samples in the corresponding operating segment, or based on the quantile interval of historical samples in the corresponding operating segment; the allowable fluctuation range can be determined based on the normal value range, combined with equipment measurement error, control adjustment error and environmental fluctuation, and then with an added preset margin; to improve the relevance of the reference segment data to the current charging session, data from the corresponding operating segment of the historical charging session in which the target charging device is at the same rated power level and in the same ambient temperature range as the current charging session is preferentially selected.

[0061] The ambient temperature range can be divided according to a pre-set temperature classification. The ambient temperature range to which the current charging session belongs can be determined based on the temperature classification with the highest proportion of ambient temperature samples during the corresponding time period of the charging session. When the number of historical charging sessions that meet the rated power level and ambient temperature range conditions is less than the preset minimum sample size, data from the corresponding operating segment of historical charging sessions of the same model charging equipment under the same rated power level and the same ambient temperature range are used as supplementary reference data to balance sample sufficiency and data comparability. The preset minimum sample size is preset based on statistical stability requirements, the number of historical sessions of the target charging equipment, and the availability of samples of the same model equipment.

[0062] The offset calculation unit extracts operating features in each continuous operating segment and compares them with the corresponding reference segment data. The extracted operating features include at least two of the following: input voltage fluctuation, output voltage settling time, output current following deviation, temperature rise rate, contactor operation delay, insulation recovery time, and communication round-trip delay.

[0063] Among them, input voltage fluctuation is defined as the degree of fluctuation of the input side electrical parameters relative to the average value of the segment or the target input state within the current continuous operating segment; output voltage settling time is defined as the time taken for the output side electrical parameters to reach the target output range from the initial state; output current following deviation is defined as the difference or absolute value of the difference between the actual output current and the target output current; temperature rise rate is defined as the change of temperature parameters per unit time; contactor action delay is defined as the time difference between the issuance of the switch action command and the confirmation of the action feedback; insulation recovery time is defined as the time taken for the insulation status parameters to recover from an abnormal state or a low value state to the normal range; and communication round-trip delay is defined as the time difference between the sending of the communication request message and the return of the corresponding response message.

[0064] The offset calculation unit generates feature offset results based on the deviation direction and deviation amount of each operating feature relative to the corresponding reference segment data. The deviation direction is used to characterize whether the operating feature is higher or lower than the corresponding reference segment data, and the deviation amount is used to characterize the degree of deviation.

[0065] By comparing the operating characteristics within a continuous operating segment with the corresponding reference segment data, the characteristic changes in different operating stages can be identified separately, thereby reducing the masking of local degradation symptoms by the overall statistics of the entire charging session.

[0066] The anomaly screening unit further identifies the feature offset results to eliminate false anomalies caused by short-term retry recovery or inconsistent recording; within the same charging session, if there is a communication retry or control retry, the operating characteristics after the retry is completed are used as the basis for further judgment.

[0067] If the operational characteristics corresponding to the anomaly recover to the normal value range defined by the reference segment data of the current continuous operating segment after retry, and remain so until the end of the current continuous operating segment, then the corresponding characteristic offset result is determined as a recoverable control anomaly.

[0068] This type of anomaly indicates that the abnormal offset is mainly caused by instantaneous communication jitter, short-term control execution fluctuations, or temporary interference, and does not form a persistent abnormal trend within the current continuous operating segment. On the other hand, the anomaly screening unit also performs time correlation on the equipment-side event records, control execution records, and communication logs under the same charging session identifier. If the above records do not form a corresponding record for the same operating event within the preset time tolerance range, and the corresponding operating feature does not continue to deviate in the next adjacent continuous operating segment, then the feature offset result is determined as a data inconsistency anomaly.

[0069] The same operational event here can refer to the same plug-in confirmation, the same handshake completion, the same power adjustment execution, the same switching action, or the same stop control, etc. Since this type of anomaly lacks multi-source record support and does not continue to appear in subsequent adjacent continuous operation segments, it is identified as a data inconsistency anomaly. The preset time tolerance range is preset based on the data acquisition cycle, communication transmission delay, and clock alignment error. Feature offset results that are not identified as recoverable control anomalies and data inconsistency anomalies are retained as continuous offset results. Through the above screening process, it is possible to avoid misjudging one-time communication anomalies, isolated record missingness, or recovery status after short-term retries as equipment degradation.

[0070] The degradation rule base is established based on the component configuration relationship of the target charging device, the correspondence between the corresponding detection points of the components and the operating characteristics, and the historical maintenance confirmation records. The component configuration relationship is used to clarify the connection and coordination relationship of each component in the whole machine. The correspondence between the corresponding detection points of the components and the operating characteristics is used to establish the mapping basis between the maintenance object and the abnormal characteristics. The historical maintenance confirmation records are used to verify and solidify the mapping relationship.

[0071] The historical maintenance confirmation record is a record of the disappearance of the corresponding abnormality or the restoration to normal after inspection after maintenance. It includes at least the maintenance object, the section where the abnormality occurred, the actual replaced or repaired component, and the inspection results after maintenance. The maintenance object includes one of the following: power conversion component, charging connection component, insulation detection component, switch execution component, heat dissipation component, and communication component.

[0072] Based on this, the degradation rule base records the correspondence between the maintenance object and its operating characteristics, the continuous operating section to which it belongs, and the order of occurrence; the degradation judgment unit judges the continuous offset result based on the degradation rule base.

[0073] When the same operating feature shifts continuously in at least two adjacent consecutive operating segments, or when multiple operating features corresponding to the same maintenance object shift sequentially in at least two adjacent consecutive operating segments according to the order of occurrence recorded in the degradation rule base, a degradation judgment result for the current charging session is formed. When degradation judgment results pointing to the same maintenance object and representing the same degradation state are repeated in at least two adjacent charging sessions, the corresponding maintenance object and degradation state are further determined. By incorporating segment location, feature order, and cross-session repetition into the judgment criteria, degradation judgment can be made closer to the evolution process of component degradation from weak to strong.

[0074] After identifying the target device and its degradation state, the health assessment unit evaluates the risk level and remaining permissible operating range of the target charging equipment. The cumulative offset is the sum of the absolute values ​​of the deviations of the same operating characteristics corresponding to the same target device in continuous charging sessions, used to characterize the cumulative degree of similar degradation characteristics across multiple charging sessions. The difference change is the difference in the corresponding deviations between adjacent charging sessions, used to characterize whether the degradation trend is intensifying, slowing down, or remaining basically stable. The risk threshold and operating range threshold are preset based on the rated parameters of the target charging equipment, component operation safety requirements, and maintenance response time.

[0075] Among them, the rated parameters are used to limit the normal operating boundaries of the equipment under the design conditions, the component operation safety requirements are used to limit the degree of deviation that different maintenance objects can withstand, and the maintenance response time is used to limit the time window required from the generation of the work order to the completion of the maintenance under the existing operation and maintenance resources.

[0076] In some implementations, the correspondence between risk level and remaining allowable operating range is pre-stored in the evaluation rule table. The health assessment unit completes the risk level classification according to the evaluation rule table based on the number of occurrences of the degradation state in the continuous charging session, the cumulative offset, and the difference change, and further determines the range of remaining allowable operating sessions or the range of remaining allowable operating time.

[0077] By using the above method, we can avoid judging the health status of the equipment based solely on the static over-limit results of a single measurement value. Instead, we can combine the offset within the continuous operating segment, the continuation between adjacent continuous operating segments, and the evolution between adjacent charging sessions to conduct a comprehensive assessment of the equipment risk.

[0078] When the risk level reaches the preset risk threshold, or the remaining allowable operating range is less than the preset operating range threshold, the work order data generation unit generates work order data. The work order data includes at least the equipment identifier, the section where the anomaly occurred, the object to be repaired, the repair items, and the handling time limit. In order to enable the work order content to directly support the operation and maintenance execution, the work order data also includes verification items, disassembly and inspection items, replacement items, and re-inspection items corresponding to the degradation rule base.

[0079] Among them, the verification item is used to prompt maintenance personnel to prioritize checking the measurement points, connection status or log records corresponding to the current degradation state; the disassembly and inspection item is used to prompt the components or connection parts that need to be disassembled and inspected; the replacement item is used to prompt the components that should be prioritized for replacement when the corresponding risk level is reached; and the re-inspection item is used to prompt the operating characteristics and continuous operating section performance that need to be verified again after maintenance is completed.

[0080] Furthermore, when there are unclosed work order data with the same target charging equipment, the same maintenance object, the same degradation state, and the same abnormal occurrence section, the current work order data and the unclosed work order data are merged and updated.

[0081] When merging and updating, the original work order identifier is retained, and the latest occurrence time, cumulative occurrence count, latest continuous offset result summary, risk level changes, and recommended handling time limit are updated to avoid the same degradation issue from repeatedly generating multiple work orders with fragmented content in a short period of time.

[0082] In this embodiment, each unit operates collaboratively in the following processing order: charging session identification, continuous operation segment division, reference segment establishment, operation feature extraction and offset formation, anomaly screening, degradation judgment, health assessment, and work order generation and merging update.

[0083] Based on the above system embodiments, this embodiment also provides a method for predicting the health of charging equipment in charging stations and automatically generating maintenance work orders. This method is executed by the aforementioned system for predicting the health of charging equipment in charging stations and automatically generating maintenance work orders. It is used to identify the degradation trend of the target charging equipment based on the operation data of the entire charging session and automatically generate corresponding work order data when preset trigger conditions are met.

[0084] The method is executed in the following order: acquiring operational data, identifying charging sessions, dividing continuous operating segments, establishing reference segments, comparing operational features, screening out anomalies, determining degradation, assessing health, and generating and updating work orders.

[0085] Specifically, the operation data of multiple charging sessions of the target charging device is first acquired. The operation data originates from the target charging device itself, the controller connected to the target charging device, and the communication module corresponding to the target charging device.

[0086] Equipment status data includes input-side electrical parameters, output-side electrical parameters, temperature parameters, insulation status parameters, and switch action parameters; control execution data includes power regulation parameters, heat dissipation execution parameters, and start / stop control parameters; session interaction data includes plug-in events, handshake events, charging start events, charging end events, and communication message records; environmental data includes one or more of the following: ambient temperature, cabinet temperature, humidity parameters, and ventilation status parameters.

[0087] After the above-mentioned operational data is accessed, it is stored in association according to a unified charging session identifier and based on the timestamps corresponding to each record, so that the device-side data, control-side data and communication-side data can form a corresponding multi-source data set within the same charging session range, providing a data foundation for subsequent charging session identification, segment comparison, record verification and anomaly screening.

[0088] After acquiring the operational data, each charging session is identified and divided into at least three consecutive operational segments arranged in chronological order. The identification of the charging session is based on the plug-in event, handshake event, charging start event, charging end event and related communication message records in the session interaction data, and is verified in combination with the change process of output side electrical parameters, switch action parameters and insulation status parameters in the equipment status data.

[0089] When the event records in the session interaction data and the state changes in the device status data match each other in terms of time sequence and event meaning within a preset time tolerance range, the group of event records and state changes is considered to belong to the same charging session. The preset time tolerance range is pre-set based on the data acquisition cycle, communication transmission delay, and clock alignment error. For each identified charging session, it can be divided into at least three of the following: session preparation segment, connection establishment segment, output establishment segment, stable output segment, and exit segment. The session preparation segment can be identified based on the device being in standby mode before the plugging event, insulation detection start-up records, pre-charge preparation control records, or preset state records before connection establishment, and ends when connection establishment-related events begin to occur. The connection establishment segment is based on the plugging event and handshake event... The output establishment segment is determined based on the target output voltage range, target output current range, or target output power range corresponding to the power adjustment parameters in the control execution data, and the process of the output-side electrical parameters in the device status data changing from the initial state to the target output range. The initial state is the state before a stable output is formed. The stable output segment is determined based on the process of the output-side electrical parameters entering the target output range and maintaining it for a preset duration or continuously meeting the stable conditions defined by the corresponding reference segment data. It ends when a charging end event occurs, a shutdown control is executed, or the output-side electrical parameters continuously leave the target output range. The exit segment is determined based on the process of a charging end event, a shutdown control execution, a drop in output-side electrical parameters, and a reset of the switch execution component. For charging sessions that experience mid-process exits, communication interruptions, or user-initiated termination, as long as at least three main operating stages arranged chronologically can be identified, the session can continue to participate in subsequent processing.

[0090] After completing the identification of charging sessions and the division of continuous operating segments, corresponding reference segment data is established for each continuous operating segment of the current charging session. The reference segment data is not obtained by directly summarizing all historical sessions, but by first filtering comparable historical normal charging sessions. The historical normal charging sessions are charging sessions with no historical fault alarms, no emergency stop records, and charging end records, and are also divided into corresponding operating segments according to the aforementioned segmentation rules.

[0091] Based on this, for each continuous operating segment in the current charging session, data corresponding to the same continuous operating segment in the historical normal charging session is extracted as the basic data for establishing reference segment data.

[0092] The reference segment data includes the normal value range and allowable fluctuation range of each operating characteristic within the corresponding continuous operating segment.

[0093] In some implementations, the normal value range can be determined based on the mean and sample fluctuation of historical samples in the corresponding continuous operating section, or based on the quantile interval of historical samples in the corresponding continuous operating section; the allowable fluctuation range can be determined based on the normal value range, combined with equipment measurement error, control adjustment error and environmental fluctuation, and then with an added preset margin.

[0094] To improve the matching degree of reference segment data to the current charging session, data from the continuous operating segments of historical charging sessions with the same rated power level and the same ambient temperature range as the current charging session are preferentially used. The ambient temperature range can be divided according to a preset temperature class, and the ambient temperature range of the current charging session can be determined according to the temperature class with the highest proportion of ambient temperature sampling values ​​within the corresponding time period of the charging session. When the number of historical charging sessions that meet the conditions of rated power level and ambient temperature range is less than the preset minimum sample size, data from the continuous operating segments of historical charging sessions with the same model of charging equipment under the same rated power level and the same ambient temperature range are used as supplementary reference data. The preset minimum sample size is preset based on statistical stability requirements, the number of historical sessions of the target charging equipment, and the availability of samples of the same model of equipment.

[0095] After establishing reference segment data, the operating characteristics of the current charging session in each continuous operating segment are extracted, and the operating characteristics in each continuous operating segment are compared with the corresponding reference segment data to obtain the characteristic offset results corresponding to each continuous operating segment. The extracted operating characteristics include at least two of the following: input voltage fluctuation, output voltage settling time, output current following deviation, temperature rise rate, contactor action delay, insulation recovery time, and communication round-trip delay.

[0096] Among them, the input voltage fluctuation is the degree of fluctuation of the input side electrical parameters relative to the average value of the segment or the target input state within the current continuous operating segment; the output voltage settling time is the time it takes for the output side electrical parameters to reach the target output range from the initial state; the output current following deviation is the absolute value of the difference between the actual output current and the target output current; the temperature rise rate is the amount of change of the temperature parameter per unit time; the contactor action delay is the time difference between the time when the switch action command is issued and the time when the action feedback is confirmed; the insulation recovery time is the time it takes for the insulation status parameter to recover from an abnormal state or a low value state to the normal range; and the communication round-trip delay is the time difference between the time when the communication request message is sent and the time when the corresponding response message is returned.

[0097] During the comparison, it is not only determined whether the operating characteristics exceed the normal value range and allowable fluctuation range of the corresponding reference segment data, but also the direction and amount of deviation of the operating characteristics relative to the corresponding reference segment data.

[0098] Among them, the deviation direction is used to characterize whether the running feature is higher or lower than the corresponding reference segment data, and the deviation amount is used to characterize the degree of deviation; the resulting feature offset result contains both deviation judgment information and deviation direction and degree of deviation information.

[0099] After obtaining the feature offset results, anomaly screening is performed on the feature offset results to remove false anomalies that do not reflect the true degradation of the device, and a continuous offset result is obtained. Anomaly screening includes two types of processing: recoverable control anomaly screening and data inconsistency anomaly screening. If there are communication retries or control retries within the same charging session, the operating characteristics after the retry is completed are used as the basis for further judgment.

[0100] If the operational characteristics corresponding to the anomaly recover to the normal value range defined by the reference segment data of the current continuous operating segment after retry, and remain so until the end of the current continuous operating segment, then the corresponding characteristic offset result is determined as a recoverable control anomaly.

[0101] This type of anomaly indicates that the offset is mainly caused by transient communication jitter, short-term control execution fluctuations, or temporary interference, and does not form a persistent abnormal trend within the current continuous operating segment.

[0102] On the other hand, the device-side event records, control execution records, and communication logs are time-correlated under the same charging session identifier.

[0103] If the above records do not form a corresponding record for the same running event within the preset time tolerance range, and the corresponding running feature does not continue to deviate in the next adjacent consecutive running segment, then the feature offset result is determined as a data inconsistency anomaly.

[0104] The same running event here can refer to the same plug-in confirmation, the same handshake completion, the same power adjustment execution, the same switching action, or the same stop control, etc. Since this type of anomaly lacks multi-source record support and does not continue to appear in subsequent adjacent continuous running segments, it is identified as a data inconsistency anomaly. The feature offset results that are not identified as recoverable control anomalies and data inconsistency anomalies are retained as continuous offset results.

[0105] After obtaining the continuous offset result, the continuous offset result is judged according to the pre-stored degradation rule library; the degradation rule library is established based on the component configuration relationship of the target charging equipment, the correspondence between the corresponding detection points of the components and the operating characteristics, and the historical maintenance confirmation records; the historical maintenance confirmation records are records of the disappearance of the corresponding abnormality or the restoration to normal after inspection after maintenance, and at least include the maintenance object, the section where the abnormality occurred, the actual replaced or repaired component, and the inspection results after maintenance.

[0106] The objects to be inspected include one of the following: power conversion components, charging connection components, insulation detection components, switching execution components, heat dissipation components, and communication components.

[0107] Based on this, the degradation rule base records the correspondence between the maintenance object and its operating characteristics, the continuous operating section to which it belongs, and the order of occurrence.

[0108] Based on this degradation rule base, when the same operating feature continuously shifts in at least two adjacent consecutive operating segments, a degradation judgment result corresponding to the same operating feature is formed; or, when multiple operating features corresponding to the same maintenance object shift sequentially in at least two adjacent consecutive operating segments according to the order of occurrence recorded in the degradation rule base, a degradation judgment result corresponding to the maintenance object is formed.

[0109] Furthermore, when degradation judgment results pointing to the same maintenance object and representing the same degradation state appear repeatedly in at least two adjacent charging sessions, the corresponding maintenance object and degradation state are determined. By incorporating segment continuation, sequence evolution, and the recurrence of adjacent charging sessions into the judgment criteria, the stability of degradation state identification can be improved.

[0110] After identifying the maintenance target and degradation state, the risk level and remaining allowable operating range are determined based on the number of times the degradation state occurs in a continuous charging session, the cumulative offset of the feature offset results, and the difference in feature offset results between adjacent charging sessions.

[0111] Among them, the cumulative offset is the sum of the absolute values ​​of the deviations of the same operating characteristics corresponding to the same maintenance object in continuous charging sessions, which is used to characterize the cumulative degree of the same type of degradation characteristics in multiple charging sessions; the difference change is the difference between the corresponding deviations between adjacent charging sessions, which is used to characterize whether the degradation trend is aggravated, slowed down or basically stable.

[0112] Risk thresholds and operating range thresholds are preset based on the rated parameters of the target charging equipment, component operation safety requirements, and maintenance response time. Rated parameters are used to limit the normal operating boundaries of the equipment under design conditions, component operation safety requirements are used to limit the degree of deviation that different maintenance objects can withstand, and maintenance response time is used to limit the time window required from work order generation to maintenance completion under existing operation and maintenance resources.

[0113] In some implementations, the evaluation rule table pre-records the correspondence between the intervals of the number of occurrences of degradation states, the intervals of cumulative offsets, the intervals of difference changes, the risk level, the intervals of the remaining allowed number of running sessions, and the intervals of the remaining allowed running time. Based on the number of occurrences of degradation states in continuous charging sessions, the cumulative offsets, and the differences, the system completes the risk level classification according to the evaluation rule table, and further determines the intervals of the remaining allowed number of running sessions or the intervals of the remaining allowed running time.

[0114] When the risk level reaches a preset risk threshold, or the remaining allowable operating range is less than a preset operating range threshold, work order data is generated based on the maintenance object, the degradation state, and the continuous offset result; the work order data includes at least the equipment identifier, the abnormal occurrence section, the maintenance object, the maintenance item, and the handling time limit.

[0115] Furthermore, the work order data also includes verification items, disassembly / inspection items, replacement items, and re-inspection items corresponding to the degradation rule base.

[0116] Among them, the verification item is used to prompt maintenance personnel to prioritize checking the measurement points, connection status or log records corresponding to the current degradation state; the disassembly and inspection item is used to prompt the components or connection parts that need to be disassembled and inspected; the replacement item is used to prompt the components that should be prioritized for replacement when the corresponding risk level is reached; and the re-inspection item is used to prompt the operating characteristics and continuous operating section performance that need to be verified again after maintenance is completed.

[0117] If there are unclosed work order data for the same target charging equipment, the same maintenance object, the same degradation state, and the same abnormal occurrence segment, a new work order with fragmented content will not be generated. Instead, the current work order data and the unclosed work order data will be merged and updated. During the merge update, the original work order identifier will be retained, and the latest occurrence time, cumulative occurrence count, latest continuous offset result summary, risk level change, and recommended handling time limit will be updated to maintain the continuity of the same degradation problem in the operation and maintenance closed loop.

[0118] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0119] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A system for predicting the health of charging equipment in charging stations and automatically generating maintenance work orders, characterized in that, include: The data access unit is used to acquire the operation data of multiple charging sessions of the target charging device. The operation data includes device status data, control execution data, session interaction data, and environmental data. The session segmentation unit is used to identify each charging session based on the event records in the session interaction data and the change process of the device status data, and to divide each charging session into at least three consecutive operating segments arranged in chronological order. The reference segment establishment unit is used to establish reference segment data corresponding to each continuous operating segment based on the data of the corresponding operating segment in the historical normal charging session. The offset calculation unit is used to extract the operating characteristics of the current charging session in each continuous operating segment, and compare the operating characteristics in each continuous operating segment with the corresponding reference segment data to obtain the feature offset results corresponding to each continuous operating segment. An anomaly filtering unit is used to remove data inconsistency anomalies and recoverable control anomalies from the feature offset results based on the corresponding records of device-side event records, control execution records, and communication logs under the same charging session identifier and within a preset time tolerance range, as well as the retention status of the feature offset results after communication retries or control retries, to obtain continuous offset results. The degradation determination unit is used to determine the continuous offset result according to a pre-stored degradation rule base. The degradation rule base records the correspondence between the maintenance object and the operating feature, the continuous operating segment to which it belongs, and the order of occurrence. When the same operating feature continuously offsets in at least two adjacent continuous operating segments, or when multiple operating features corresponding to the same maintenance object offset sequentially in at least two adjacent continuous operating segments according to the order of occurrence recorded in the degradation rule base, the degradation determination result of the current charging session is formed. When the same degradation determination result occurs repeatedly in at least two consecutive charging sessions, the corresponding maintenance object and degradation state are determined; The health assessment unit is used to determine the risk level and the remaining allowable operating range based on the number of times the degradation state occurs in a continuous charging session, the cumulative offset of the feature offset results, and the difference in the feature offset results between adjacent charging sessions. The remaining allowable operating range is either the range of the number of remaining allowable operating sessions or the range of the remaining allowable operating time. The work order data generation unit is used to generate work order data based on the maintenance object, the degradation status, and the continuous offset result when the risk level reaches a preset risk threshold or the remaining allowable operating range is less than a preset operating range threshold.

2. The charging station charging equipment health prediction and automatic maintenance work order generation system according to claim 1, characterized in that: The operational data is provided by the target charging device, the controller connected to the target charging device, and the communication module corresponding to the target charging device; the device status data includes input-side electrical parameters, output-side electrical parameters, temperature parameters, insulation status parameters, and switch action parameters; the control execution data includes power adjustment parameters, heat dissipation execution parameters, and start / stop control parameters; the session interaction data includes plug-in events, handshake events, charging start events, charging end events, and communication message records; the environmental data includes at least one of ambient temperature, cabinet temperature, humidity parameters, and ventilation status parameters.

3. The charging station charging equipment health prediction and automatic maintenance work order generation system according to claim 2, characterized in that: The continuous operating segments obtained by the session segmentation unit include at least three of the following: session preparation segment, connection establishment segment, output establishment segment, stable output segment, and exit segment. The connection establishment segment is determined based on plug-in and handshake events. The output establishment segment is determined based on the target output range corresponding to the power adjustment parameters in the control execution data and the process of the output-side electrical parameters in the device status data changing from the initial state to the target output range. The stable output segment is determined based on the process of the output-side electrical parameters in the device status data entering and maintaining the target output range.

4. The charging station charging equipment health prediction and automatic maintenance work order generation system according to claim 3, characterized in that: When establishing reference segment data, the reference segment establishment unit uses data from the corresponding operating segment of a charging session with no historical fault alarms, no emergency stop records, and charging end records as the basic data. The reference segment data includes the normal value range and allowable fluctuation range of each operating characteristic within the corresponding operating segment. Priority is given to using data from the corresponding operating segment of a historical charging session with the same rated power level and the same ambient temperature range as the current charging session. When the number of historical charging sessions that meet the rated power level and ambient temperature range conditions is less than the preset minimum sample size, data from the corresponding operating segment of a historical charging session with the same model of charging equipment under the same rated power level and the same ambient temperature range is used as supplementary reference data.

5. The charging station charging equipment health prediction and automatic maintenance work order generation system according to claim 4, characterized in that: The operational features extracted by the offset calculation unit include at least two of the following features: input voltage fluctuation, output voltage settling time, output current following deviation, temperature rise rate, contactor operation delay, insulation recovery time, and communication round-trip delay; and the feature offset result is formed based on the deviation direction and amount of the operational features relative to the corresponding reference segment data.

6. The charging station charging equipment health prediction and automatic maintenance work order generation system according to claim 5, characterized in that: The anomaly screening unit is used to: within the same charging session, after communication retry or control retry, restore the operating characteristics corresponding to the anomaly to the normal value range limited by the reference segment data corresponding to the current continuous operating segment and maintain it until the end of the current continuous operating segment, and determine the corresponding characteristic offset result as a recoverable control anomaly. If the device-side event log, control execution log, and communication log do not form a corresponding record for the same running event under the same charging session identifier and within the preset time tolerance range, and the corresponding running characteristics do not continue to deviate in the next adjacent continuous running segment, the corresponding characteristic offset result will be determined as a data inconsistency anomaly. Feature offset results that are not identified as recoverable control anomalies or data inconsistency anomalies are retained as the persistent offset results.

7. The charging station charging equipment health prediction and automatic maintenance work order generation system according to claim 6, characterized in that: The degradation rule base is established based on the component configuration relationship of the target charging equipment, the correspondence between the corresponding detection points of the components and the operating characteristics, and the historical maintenance confirmation records. The historical maintenance confirmation records are records of the disappearance of the corresponding abnormality or the restoration to normal after re-inspection after maintenance. The historical maintenance confirmation records include at least the maintenance object, the section where the abnormality occurred, the actual replaced or repaired component, and the re-inspection results after maintenance. The maintenance object includes one of the following: power conversion component, charging connection component, insulation detection component, switch execution component, heat dissipation component, and communication component.

8. The charging station charging equipment health prediction and automatic maintenance work order generation system according to claim 7, characterized in that: In the health assessment unit, the cumulative offset is the sum of the absolute values ​​of the deviations of the same operating characteristics corresponding to the same maintenance object in a continuous charging session, and the difference change is the difference between the corresponding deviations between adjacent charging sessions; the risk threshold and the operating range threshold are preset according to the rated parameters of the target charging equipment, the component operation safety requirements and the maintenance response time.

9. The charging station charging equipment health prediction and automatic maintenance work order generation system according to claim 8, characterized in that: The work order data includes at least the equipment identifier, the section where the anomaly occurred, the object to be repaired, the repair items, and the handling time limit; the work order data also includes verification items, disassembly and inspection items, replacement items, and re-inspection items corresponding to the degradation rule base; when there are unclosed work order data with the same target charging equipment, the same object to be repaired, the same degradation state, and the same section where the anomaly occurred, the current work order data and the unclosed work order data are merged and updated.

10. A method for predicting the health of charging equipment in a charging station and automatically generating maintenance work orders, characterized in that: The system includes a charging station charging equipment health prediction and automatic maintenance work order generation system as described in any one of claims 1 to 9, and the specific steps of the method are as follows: The system acquires operational data from multiple charging sessions of the target charging device, including device status data, control execution data, session interaction data, and environmental data. Based on the event records in the session interaction data and the change process of the device status data, each charging session is identified, and each charging session is divided into at least three consecutive operating segments arranged in chronological order. For the continuous operation segment, reference segment data corresponding to each continuous operation segment is established based on the data of the corresponding operation segment in the historical normal charging session; Extract the operating features of the current charging session in each continuous operating segment, and compare the operating features in each continuous operating segment with the corresponding reference segment data to obtain the feature offset results corresponding to each continuous operating segment; Based on the corresponding records of device-side event records, control execution records, and communication logs under the same charging session identifier and within a preset time tolerance range, and the retention status of the feature offset results after communication retries or control retries, data inconsistency anomalies and recoverable control anomalies are removed from the feature offset results to obtain the continuous offset results; The continuous offset result is determined according to the pre-stored degradation rule base. The degradation rule base records the correspondence between the maintenance object and the operating feature, the continuous operating segment to which it belongs, and the order of occurrence. When the same operating feature continuously offsets in at least two adjacent continuous operating segments, or when multiple operating features corresponding to the same maintenance object offset sequentially in at least two adjacent continuous operating segments according to the order of occurrence recorded in the degradation rule base, the degradation determination result of the current charging session is formed. When the same degradation determination result occurs repeatedly in at least two consecutive charging sessions, the corresponding maintenance object and degradation state are determined; The risk level and remaining allowable operating range are determined based on the number of times the degradation state occurs in a continuous charging session, the cumulative offset of the feature offset results, and the difference in feature offset results between adjacent charging sessions. When the risk level reaches a preset risk threshold, or when the remaining allowable operating range is less than a preset operating range threshold, work order data is generated based on the maintenance object, the degradation status, and the continuous offset result.

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