Remote auxiliary maintenance system and method for charging station equipment

Abstract

The invention belongs to the field of charging station maintenance, relates to a data analysis technology, aims to solve the problem that an electrical monitoring result of charging equipment in a charging station cannot be verified and analyzed in the prior art, and particularly relates to a remote auxiliary maintenance system and method for charging station equipment. Comprising an autonomous monitoring module, an inspection and verification module, a fault analysis module and a health management module which are connected in sequence, and the autonomous monitoring module, the inspection and verification module and the fault analysis module are all in communication connection with a database; marking a charging pile in the charging station as a monitoring object, and marking the monitoring object as an electrical normal object or an electrical abnormal object at the end moment of the monitoring time period; according to the invention, the dynamic quantitative evaluation of the electrical state of the charging pile is realized, the problem of abnormal leak detection caused by lack of fluctuation characteristic analysis in the prior art is solved, and through the two-dimensional cooperative judgment of the current and the voltage, the false alarm caused by the accidental error of the sensor is avoided, and the detection sensitivity of the gradual change type fault is improved.

Description

Technical Field

[0001] This invention belongs to the field of charging station maintenance and involves data analysis technology. Specifically, it is a remote auxiliary maintenance system and method for charging station equipment. Background Technology

[0002] The remote assisted maintenance system for charging station equipment is an intelligent platform that integrates technologies such as the Internet of Things, augmented reality, artificial intelligence, and 5G communication to achieve remote diagnosis of charging pile faults, real-time guidance for maintenance, and predictive maintenance.

[0003] The invention patent with publication number CN104518563B discloses a control method for an electric vehicle charging system based on new energy applications. This control method can balance the electrical load pressure brought by the newly added electrical load capacity of electric vehicle charging stations, and establishes a safe, reliable, noise-free, and pollution-free intelligent charging system for electric vehicles. However, this control method cannot verify and analyze the electrical monitoring results of the charging equipment in the charging station. When the components at the acquisition end malfunction, it cannot provide timely feedback and replacement, resulting in low operational safety of the charging station. Furthermore, when both the charging pile and the charging environment are abnormal, the existing technology cannot generate multiple independent and targeted processing decisions through disassembly and analysis, resulting in low efficiency in handling anomalies.

[0004] To address the aforementioned technical problems, this application proposes a solution. Summary of the Invention

[0005] The purpose of this invention is to provide a remote assisted maintenance system and method for charging station equipment, which solves the problem that the existing technology cannot verify and analyze the electrical monitoring results of charging equipment in charging stations; The technical problem to be solved by the present invention is: how to provide a remote auxiliary maintenance system and method for charging station equipment that can verify and analyze the electrical monitoring results of charging equipment in the charging station.

[0006] The objective of this invention can be achieved through the following technical solutions: A remote auxiliary maintenance system for charging station equipment includes an autonomous monitoring module, an inspection and verification module, a fault analysis module, and a health management module connected in sequence. The autonomous monitoring module, the inspection and verification module, and the fault analysis module are all connected to a database. The autonomous monitoring module marks the charging piles in the charging station as monitoring objects, generates a monitoring cycle and divides the monitoring cycle into several monitoring periods. At the end of the monitoring period, the monitoring objects are marked as electrically normal objects or electrically abnormal objects. The inspection and verification module divides the charging station into several inspection areas, generates a corresponding inspection route for each inspection area, sets several monitoring points on the inspection route, and marks the monitoring points as normal or abnormal at the end of the monitoring period. The fault analysis module marks the monitoring point closest to the monitored object within the inspection area as a verification point, and the remaining monitoring points as environmental monitoring points. A verification combination is formed by the monitored object and the corresponding verification point. The ratio of the number of times an environmental monitoring point is marked as an environmental anomaly point to the total number of environmental monitoring points is marked as the environmental monitoring coefficient. The environmental monitoring coefficient is used to determine whether the working environment of the inspection area meets the requirements during the monitoring period. The module also analyzes the autonomous monitoring results and inspection verification results of the verification combination. The health management module performs equipment health management analysis for charging stations.

[0007] Furthermore, the specific process of marking the monitored objects includes: acquiring current anomaly data and voltage anomaly data for each monitored object at the end of the monitoring period. The current anomaly data is the difference between the maximum and minimum output current of the charging module of the monitored object during the monitoring period, and the voltage anomaly data is the difference between the maximum and minimum output voltage of the charging module of the monitored object during the monitoring period. The current anomaly threshold and voltage anomaly threshold are obtained from the database. The current anomaly data and voltage anomaly data are compared with the current anomaly threshold and voltage anomaly threshold respectively. Based on the comparison results, the monitored objects are marked as electrically normal objects or electrically abnormal objects.

[0008] Furthermore, the specific process of marking the monitored object as an electrically normal object or an electrically abnormal object includes: if the current abnormality data is less than the current abnormality threshold and the pressure abnormality data is less than the pressure abnormality threshold, then it is determined that the electrical status of the monitored object meets the requirements during the monitoring period, and the corresponding monitored object is marked as an electrically normal object; otherwise, it is determined that the electrical status of the monitored object does not meet the requirements during the monitoring period, and the corresponding monitored object is marked as an electrically abnormal object.

[0009] Furthermore, the specific process of marking monitoring points includes: during the monitoring period, controlling the inspection robot to sequentially acquire air temperature data and smoke data of each monitoring point according to the inspection route. The air temperature data is the air temperature value of the monitoring point, and the smoke data is the smoke concentration value of the monitoring point. The air temperature threshold and smoke threshold are obtained through the database. The air temperature data and smoke data are compared with the air temperature threshold and smoke threshold respectively, and the monitoring point is marked as an environmental normal point or an environmental abnormal point based on the comparison result.

[0010] Furthermore, the specific process of comparing the air temperature data and smoke data with the air temperature threshold and smoke threshold respectively includes: if the air temperature data is less than the air temperature threshold and the smoke data is less than the smoke threshold, then the environmental verification status of the monitoring point is determined to meet the requirements, and the corresponding monitoring point is marked as an environmental normal point; otherwise, the environmental verification status of the monitoring point is determined to not meet the requirements, and the corresponding monitoring point is marked as an environmental abnormal point.

[0011] Furthermore, the specific process for determining whether the working environment of the inspection area meets the requirements during the monitoring period includes: obtaining the environmental monitoring threshold from the database, comparing the environmental monitoring coefficient with the environmental monitoring threshold; if the environmental monitoring coefficient is less than the environmental monitoring threshold, it is determined that the working environment of the inspection area meets the requirements during the monitoring period; if the environmental monitoring coefficient is greater than or equal to the environmental monitoring threshold, it is determined that the working environment of the inspection area does not meet the requirements during the monitoring period, generating an environmental risk signal and sending the environmental risk signal to the mobile terminal of the management personnel.

[0012] Furthermore, the specific process for analyzing the autonomous monitoring results and inspection verification results of the verification combination includes: if the monitoring object and verification point in the verification combination are marked as an electrical abnormality object and an environmental abnormality point respectively, it is determined that the monitoring object has an operational abnormality, an abnormality handling signal is generated, and the abnormality handling signal is sent to the mobile terminal of the management personnel; if the monitoring object and verification point in the verification combination are marked as an electrical normality object and an environmental normality point respectively, it is determined that the monitoring object is operating normally; otherwise, it is determined that the acquisition element has an operational abnormality, an acquisition optimization signal is generated, and the acquisition optimization signal is sent to the mobile terminal of the management personnel; after the management personnel complete the abnormality handling and acquisition optimization of the monitoring object, they send the processed monitoring object fault type to the health management module.

[0013] Furthermore, the health management module is used to perform equipment health management analysis on the charging station: when the fault type is diagnosed, the continuous running time of the corresponding monitored object is marked as a continuous value. All continuous values ​​within the monitoring period corresponding to the fault type constitute a continuous set. The continuous set is cleaned to obtain the state threshold value of the fault type. The minimum value of the state threshold values ​​of all fault types is marked as the continuous threshold value. In the next monitoring period, when the continuous running time of the monitored object reaches the continuous threshold value, the monitored object is forcibly powered off for L1 minutes after the current charging task is completed.

[0014] Furthermore, the specific process of cleaning the persistent set includes: calculating the variance of all elements in the persistent set to obtain the continuous distribution coefficient, comparing the continuous distribution coefficient with a preset continuous distribution threshold; if the continuous distribution coefficient is greater than the continuous distribution threshold, the largest and smallest elements in the persistent set are removed, and then the continuous distribution coefficient is recalculated, and so on, until the continuous distribution coefficient is less than the continuous distribution threshold; if the continuous distribution coefficient is less than the continuous distribution threshold, the smallest element in the persistent set is marked as the state critical value of the fault type.

[0015] A method for remote assisted maintenance of charging station equipment includes the following steps: Step 1: Conduct autonomous monitoring and analysis of the electrical operating status of the charging pile; Step 2: Conduct regional inspection, verification, and analysis of the charging equipment at the charging stations; Step 3: Conduct fault analysis on the charging equipment at the charging station; Step 4: Conduct equipment health management analysis for the charging station.

[0016] The present invention has the following beneficial effects: 1. This application realizes dynamic quantitative assessment of the electrical status of charging piles, solves the problem of abnormal missed detection caused by the lack of fluctuation characteristic analysis in the existing technology, and avoids false alarms caused by occasional sensor errors through dual-dimensional collaborative judgment of current and voltage, and improves the detection sensitivity of gradual faults, providing an accurate status marking basis for subsequent fault diagnosis. 2. This application can effectively identify abnormalities in the environment surrounding charging equipment, such as local overheating or early fire hazards. By marking abnormal monitoring points in a timely manner, it provides reliable environmental status data for subsequent fault analysis. This solution solves the problem of insufficient coverage of fixed monitoring points, and at the same time reduces equipment deployment costs through mobile data collection, providing dynamic environmental monitoring assurance for the safe operation of charging stations. 3. This application can accurately identify the root cause of abnormal operation of charging piles, avoid misjudgment caused by environmental interference or failure of data acquisition equipment, and guide maintenance personnel to quickly locate the problem type through the classification signal mechanism, shorten the fault investigation time. The feedback mechanism of the processing results provides accurate fault type data for the health management module, and optimizes the process of formulating equipment maintenance strategies. 4. This application can dynamically adjust the power-off strategy of the equipment based on historical operating data, and proactively implement protective measures before the charging pile reaches the safe operating limit to avoid secondary failures caused by continuous equipment overload. At the same time, the calculation accuracy of the state critical value is improved through the cleaning and processing mechanism to ensure that the power-off decision matches the actual wear and tear of the equipment, thereby extending the service life of key components and reducing maintenance costs. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a system block diagram of Embodiment 1 of the present invention; Figure 2 This is a flowchart of the method in Embodiment 2 of the present invention. Detailed Implementation

[0019] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. 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.

[0020] In existing technologies, remote auxiliary maintenance systems for charging station equipment mostly focus on single-dimensional monitoring functions, lacking a comprehensive verification mechanism for electrical status and environmental factors. When charging pile operating data is abnormal, existing systems struggle to distinguish between equipment malfunctions and environmental interference, easily leading to misjudgments or missed detections. For example, in high-temperature and high-humidity environments, charging piles may stop working due to environmental factors triggering protection mechanisms. Traditional monitoring systems often directly diagnose this as equipment malfunction, failing to identify the true source of the fault. This limitation necessitates multiple on-site inspections by maintenance personnel, increasing maintenance costs and impacting the operational efficiency of charging stations.

[0021] To address these issues, researchers discovered that charging equipment anomalies are often accompanied by changes in environmental parameters, but existing technologies lack a data correlation analysis mechanism. By studying the spatiotemporal correlation between charging pile operation data and environmental monitoring data, a dual verification mechanism is proposed. First, a periodic electrical monitoring system is established, simultaneously implementing regional environmental inspections, forming verification combinations between the nearest monitoring points and charging piles. When electrical and environmental anomalies occur simultaneously, environmental factors are prioritized for investigation; when only electrical anomalies are present, the focus shifts to monitoring equipment status. This tiered judgment mechanism effectively distinguishes fault types, while setting environmental monitoring coefficients to assess regional environmental stability, providing auxiliary decision-making basis for anomaly diagnosis.

[0022] Example 1: As Figure 1 As shown, a remote auxiliary maintenance system for charging station equipment includes an autonomous monitoring module, an inspection and verification module, a fault analysis module, and a health management module connected in sequence. The autonomous monitoring module, the inspection and verification module, and the fault analysis module are all connected to a database.

[0023] The autonomous monitoring module is used to autonomously monitor and analyze the electrical operating status of charging piles. It marks charging piles within the charging station as monitoring objects, generates a monitoring cycle, and divides the monitoring cycle into several monitoring periods. At the end of each monitoring period, it acquires current and voltage anomaly data for each monitoring object. Current anomaly data is the difference between the maximum and minimum output current of the charging module of the monitoring object during the monitoring period, and voltage anomaly data is the difference between the maximum and minimum output voltage of the charging module of the monitoring object during the monitoring period. It obtains current and voltage anomaly thresholds from a database and compares the current and voltage anomaly data with these thresholds respectively. If both the current and voltage anomaly data are less than the current and voltage anomaly thresholds, the electrical status of the monitoring object during the monitoring period is deemed to meet the requirements, and the corresponding monitoring object is marked as an electrically normal object. Otherwise, the electrical status of the monitoring object during the monitoring period is deemed to not meet the requirements, and the corresponding monitoring object is marked as an electrically abnormal object.

[0024] Among them, current fluctuation data refers to the fluctuation range of the charging module's output current. This is specifically achieved by using a current sensor to collect and calculate the difference between extreme current values ​​during the monitoring period, reflecting the current stability during charging. Voltage fluctuation data refers to the fluctuation range of the charging module's output voltage. This is specifically achieved by using a voltage sensor to collect and calculate the difference between extreme voltage values ​​during the monitoring period, reflecting the voltage stability during charging. Current and voltage fluctuation thresholds are pre-set upper limits for allowable current and voltage fluctuations. These thresholds can be determined through statistical analysis of historical operating data or equipment specifications, and are used to establish electrical condition judgment benchmarks.

[0025] Specifically, at the end of the monitoring period, the current sensor and voltage sensor respectively collect the output current and voltage data of the charging module, and extract the difference between the maximum and minimum values ​​during the monitoring period to obtain current fluctuation data and voltage fluctuation data. After the current fluctuation threshold and voltage fluctuation threshold stored in the database are retrieved, the measured fluctuation data are compared with the thresholds. For example, when both the current fluctuation data and the voltage fluctuation data are less than the current fluctuation threshold, it indicates that the current and voltage fluctuations are within the safe range and the device is marked as electrically normal; otherwise, if either data exceeds the threshold, an anomaly is triggered. This dual-parameter collaborative judgment mechanism can avoid misjudgment based on a single parameter and simultaneously cover the risks of overcurrent and overvoltage during the charging and discharging process.

[0026] The inspection and verification module is used to perform regional inspection and verification analysis of the charging equipment of the charging piles. The charging station is divided into several inspection areas, and a corresponding inspection route is generated for each inspection area. Several monitoring points are set on the inspection route. During the monitoring period, the inspection robot is controlled to sequentially acquire air temperature data and smoke data of each monitoring point according to the inspection route. The air temperature data is the air temperature value of the monitoring point, and the smoke data is the smoke concentration value of the monitoring point. The air temperature threshold and smoke threshold are obtained from the database. The air temperature data and smoke data are compared with the air temperature threshold and smoke threshold respectively. If the air temperature data is less than the air temperature threshold and the smoke data is less than the smoke threshold, the environmental verification status of the monitoring point is determined to meet the requirements, and the corresponding monitoring point is marked as an environmental normal point; otherwise, the environmental verification status of the monitoring point is determined to not meet the requirements, and the corresponding monitoring point is marked as an environmental abnormal point.

[0027] The inspection robot refers to an automated device that can move along a preset path and is equipped with environmental monitoring equipment. Specifically, it can be implemented using a mobile robot with navigation and sensor modules, used to dynamically collect environmental parameters at different monitoring points. Air temperature data refers to the real-time air temperature value measured by a temperature sensor, such as an infrared temperature measurement module or a thermocouple sensor, used to reflect the thermal environment around the charging equipment. Smoke data refers to the concentration of smoke particles detected by a gas sensor, such as a photoelectric smoke detector or an ionization smoke sensor, used to identify potential fire hazards. Air temperature threshold and smoke threshold are pre-set environmental safety thresholds; for example, the air temperature threshold could be 40 degrees Celsius, and the smoke threshold could be 2.5 milligrams per cubic meter, dynamically retrieved from a database to adapt to different environmental conditions.

[0028] Specifically, within the designated inspection area, the inspection robot moves to each monitoring point according to the generated inspection route, collecting real-time air temperature and smoke concentration data through its onboard sensors. After collection, the system compares the air temperature data with air temperature thresholds in the database, and simultaneously compares the smoke data with smoke thresholds. If both do not exceed the thresholds, the monitoring point is considered to have a normal environmental condition and is marked as a normal environmental point; if either data exceeds the threshold, an environmental anomaly risk is identified and it is marked as an abnormal environmental point. Through dynamic inspection and threshold comparison, the system can accurately identify localized areas of environmental anomaly within the charging station.

[0029] The environmental monitoring coefficient refers to the ratio of the number of times an environmental monitoring point is marked as an environmental anomaly to the total number of environmental monitoring points. Specifically, it can be calculated by statistically analyzing the number of anomaly markings for each environmental monitoring point during the monitoring period and calculating its proportion. It is used to quantitatively assess the frequency of environmental anomalies in the inspected area. The environmental monitoring threshold refers to a preset reference value for the frequency of environmental anomalies. Specifically, it can be set as a fixed value or dynamically adjusted value based on historical data or industry standards. It is used to determine whether an environmental anomaly has reached a critical state requiring intervention. The environmental risk signal refers to the instruction information that triggers environmental anomaly handling measures. Specifically, it can be pushed to the management personnel's terminal through mobile communication networks or IoT platforms to prompt the initiation of the environmental anomaly investigation process.

[0030] Specifically, after the monitoring period ends, the system first counts the number of environmental anomalies marked among all environmental monitoring points within the inspection area and calculates the proportion of these anomalies to the total number of monitoring points, obtaining the environmental monitoring coefficient. The environmental monitoring coefficient is then compared with a preset environmental monitoring threshold. If the environmental monitoring coefficient is below the threshold, it indicates that the current environmental condition of the area is within a controllable range; if the environmental monitoring coefficient reaches or exceeds the threshold, it is determined that there is an environmental risk in the area, and an early warning signal containing location information is automatically generated and sent to the management personnel's terminal. For example, when the environmental monitoring threshold is set to 0.3, if an area has 10 environmental monitoring points and 4 of them are marked as anomalies, the environmental monitoring coefficient is 0.4. In this case, the system will trigger an early warning and notify management personnel to conduct on-site verification.

[0031] The fault analysis module is used to perform fault analysis on the charging equipment of the charging station. It marks the monitoring point closest to the monitored object within the inspection area as a verification point, and the remaining monitoring points as environmental monitoring points. A verification combination is formed by the monitored object and its corresponding verification point. The ratio of the number of times an environmental monitoring point is marked as an environmental anomaly point to the total number of environmental monitoring points is marked as the environmental monitoring coefficient. An environmental monitoring threshold is obtained from the database, and the environmental monitoring coefficient is compared with the environmental monitoring threshold. If the environmental monitoring coefficient is less than the environmental monitoring threshold, the working environment of the inspection area during the monitoring period is deemed to meet the requirements; if the environmental monitoring coefficient is greater than or equal to the environmental monitoring threshold, the working environment of the inspection area during the monitoring period is deemed to not meet the requirements, an environmental risk signal is generated, and the environmental risk signal is sent to the management personnel. The mobile terminal analyzes the autonomous monitoring and inspection results of the verification combination: if the monitoring object and verification point in the verification combination are marked as an electrical abnormality object and an environmental abnormality point respectively, it is determined that the monitoring object has an operational abnormality, an abnormality handling signal is generated and sent to the mobile terminal of the management personnel; if the monitoring object and verification point in the verification combination are marked as an electrical normality object and an environmental normality point respectively, it is determined that the monitoring object is operating normally; otherwise, it is determined that the acquisition element has an operational abnormality, an acquisition optimization signal is generated and sent to the mobile terminal of the management personnel; after the management personnel complete the abnormality handling and acquisition optimization of the monitoring object, they send the processed fault type of the monitoring object to the health management module.

[0032] The verification set refers to a group of associated data consisting of the charging pile monitoring object and the nearest monitoring point in its area. Specifically, the spatial distance relationship between the two can be determined using a coordinate positioning algorithm, used to cross-verify the correlation between charging pile anomalies and environmental factors. The anomaly handling signal is an instruction that triggers maintenance personnel to troubleshoot the charging pile. Specifically, it can generate text information containing the location and type of the anomaly through a preset communication protocol, used to guide on-site operations. The acquisition optimization signal is an alarm message indicating anomalies in sensors or data transmission components. Specifically, it can be determined by comparing the consistency of data from multiple monitoring points in the same area, used to locate faults in the acquisition equipment.

[0033] Specifically, at the end of the monitoring period, the system matches and analyzes the electrical status marking results of the charging pile with the environmental status marking results of its corresponding verification point. When both the charging pile and the verification point are marked as abnormal, the system determines that the charging pile has an operational fault and generates an abnormality handling signal to notify the management personnel for maintenance; when both are normal, the system confirms that the charging pile is in a stable operating state; when there is a discrepancy between the electrical status and environmental status markings, the system identifies an abnormality in the data acquisition equipment and triggers a data acquisition optimization signal to prompt maintenance personnel to check the sensors or communication lines. After the management personnel have completed the handling, they upload the confirmed fault type to the health management module to provide data support for subsequent equipment maintenance.

[0034] The health management module is used to perform equipment health management analysis on charging stations: When a fault type is diagnosed, the continuous running time of the monitored object is marked as a persistent value. A persistent set is formed from all persistent values ​​within the monitoring period corresponding to the fault type. The persistent set is then cleaned: the variance of all elements in the persistent set is calculated to obtain the continuous distribution coefficient. This coefficient is compared with a preset continuous distribution threshold. If the continuous distribution coefficient is greater than the threshold, the largest and smallest elements in the persistent set are removed, and the continuous distribution coefficient is recalculated. This process continues until the continuous distribution coefficient is less than the threshold. If the continuous distribution coefficient is less than the threshold, the smallest element in the persistent set is marked as the state threshold for the fault type. The minimum value of all state thresholds for all fault types is marked as the persistent threshold. In the next monitoring period, when the continuous running time of the monitored object reaches the persistent threshold, the monitored object is forcibly powered off for L1 minutes after the current charging task is completed.

[0035] Among them, the duration value refers to the continuous fault-free operation time of the corresponding charging pile when the fault type is diagnosed and confirmed. Specifically, it can be implemented by using a timer module combined with a fault trigger signal to quantify the stable operation capability of the equipment before a specific fault occurs. A persistent set refers to a dataset of persistent values ​​generated over multiple monitoring periods for the same fault type. Specifically, it can be archived by fault category through a data storage unit to reflect the historical operational characteristics of that fault type. Data cleaning refers to the process of removing outlier data by calculating variance and comparing it with a threshold. Specifically, iterative algorithms can be used to dynamically filter data. For example, the initial variance can be calculated and compared with a preset threshold. If it exceeds the threshold, the extreme values ​​can be removed and the calculation can be repeated until the variance meets the requirements, thereby eliminating the impact of data fluctuations on the analysis results. The state critical value refers to the minimum duration value retained in the duration set after cleaning treatment. Specifically, the minimum value in the set can be extracted by a sorting algorithm, which is used to characterize the shortest time threshold for safe operation of the equipment under this fault type. The persistent critical value refers to the minimum value among all fault type state critical values. Specifically, the minimum value can be selected by traversing all fault type state critical values ​​through the global comparison module to determine the unified trigger condition for forced power-off of the equipment.

[0036] Specifically, the health management module first collects persistent values ​​corresponding to different fault types to form a persistent set, and then uses variance calculation to determine the dispersion of the data distribution. If the variance exceeds a preset threshold, the maximum and minimum values ​​are continuously removed until the variance reaches the target. Finally, the minimum value among the remaining data is taken as the state critical value for that fault type. Subsequently, the system compares the state critical values ​​of all fault types and selects the minimum value as the persistent critical value. When the charging pile reaches this critical value during subsequent operation, the system automatically triggers a power-off procedure after completing the current charging task, forcing the equipment to stop operating for L1 minutes for cooling or maintenance.

[0037] Example 2: Figure 2 As shown, a remote assisted maintenance method for charging station equipment includes the following steps: Step 1: Autonomous monitoring and analysis of the electrical operation status of charging piles: Mark the charging piles in the charging station as monitoring objects, and mark the monitoring objects as electrically normal or electrically abnormal at the end of the monitoring period; Step 2: Conduct regional inspection and verification analysis of the charging equipment of the charging pile: Divide the charging station into several inspection areas, generate a corresponding inspection route for each inspection area, set up several monitoring points on the inspection route, and mark the monitoring points as normal or abnormal points at the end of the monitoring period. Step 3: Perform fault analysis on the charging equipment of the charging station: Mark the monitoring point closest to the monitored object in the inspection area as the verification point, and mark the remaining monitoring points as the ring monitoring points. The monitored object and the corresponding verification point form a verification combination. Analyze the autonomous monitoring results and inspection verification results of the verification combination. Step 4: Perform equipment health management analysis on the charging station: Mark the continuous running time of the monitored object when the fault type is diagnosed as a persistent value. The persistent set is composed of all persistent values ​​within the monitoring period corresponding to the fault type. The state critical value of the fault type is obtained by cleaning the persistent set. The minimum value of the state critical value of all fault types is marked as the persistent critical value.

[0038] A remote assisted maintenance system and method for charging station equipment includes the following steps: During operation, charging piles within the charging station are marked as monitoring objects. At the end of the monitoring period, the monitoring objects are marked as electrically normal or electrically abnormal. The charging station is divided into several inspection areas, and a corresponding inspection route is generated for each inspection area. Several monitoring points are set on the inspection routes, and at the end of the monitoring period, the monitoring points are marked as environmentally normal or environmentally abnormal points. The monitoring point closest to the monitoring object within the inspection area is marked as a verification point, and the remaining monitoring points are marked as environmental monitoring points. A verification combination is formed by the monitoring object and its corresponding verification point. The autonomous monitoring results and inspection verification results of the verification combination are analyzed. The continuous running time of the corresponding monitoring object when the fault type is diagnosed is marked as a persistent value. All persistent values ​​within the monitoring period corresponding to the fault type constitute a persistent set. The persistent set is cleaned to obtain the state critical value of the fault type. The minimum value of the state critical values ​​of all fault types is marked as the persistent critical value.

[0039] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

[0040] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0041] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A remote assisted service system for a charging station device, characterized in that, The autonomous monitoring module, the inspection verification module, the fault analysis module, and the health management module are sequentially connected, and the autonomous monitoring module, the inspection verification module, the fault analysis module, and the health management module are in communication connection with the database; The autonomous monitoring module marks the charging piles in the charging station as monitoring objects, generates a monitoring period, divides the monitoring period into a plurality of monitoring time periods, and marks the monitoring objects as electrical normal objects or electrical abnormal objects at the end of the monitoring time periods; The inspection verification module divides the charging station into a plurality of inspection areas, generates a corresponding inspection route for each inspection area, sets a plurality of monitoring points on the inspection route, and marks the monitoring points as environmental normal points or environmental abnormal points at the end of the monitoring time periods; The fault analysis module marks the monitoring point closest to the monitoring object in the inspection area as a verification point, marks the remaining monitoring points as ring monitoring points, forms a verification combination with the monitoring object and the corresponding verification point, marks the ratio of the number of times that the ring monitoring point is marked as an environmental abnormal point to the total number of ring monitoring points as a ring monitoring coefficient, and determines whether the working environment of the inspection area in the monitoring time period meets the requirements through the ring monitoring coefficient; The autonomous monitoring result and the inspection verification result of the verification combination are analyzed; The health management module performs equipment health management analysis on the charging station.

2. The remote assistant maintenance system for a charging station device according to claim 1, wherein The specific process of marking the monitoring objects includes: obtaining the flow difference data and the voltage difference data of each monitoring object at the end of the monitoring time period, the flow difference data being the difference between the maximum and minimum output currents of the charging module of the monitoring object in the monitoring time period, and the voltage difference data being the difference between the maximum and minimum output voltages of the charging module of the monitoring object in the monitoring time period, comparing the flow difference data and the voltage difference data with the flow threshold value and the voltage threshold value through the database, and marking the monitoring objects as electrical normal objects or electrical abnormal objects through the comparison results.

3. The remote assistant maintenance system for a charging station device according to claim 2, wherein The specific process of marking the monitoring objects as electrical normal objects or electrical abnormal objects includes: if the flow difference data is less than the flow threshold value and the voltage difference data is less than the voltage threshold value, it is determined that the electrical state of the monitoring object in the monitoring time period meets the requirements, and the corresponding monitoring object is marked as an electrical normal object; otherwise, it is determined that the electrical state of the monitoring object in the monitoring time period does not meet the requirements, and the corresponding monitoring object is marked as an electrical abnormal object.

4. The remote assistant maintenance system for a charging station device according to claim 3, wherein The specific process of marking the monitoring points includes: controlling the inspection robot to sequentially obtain the air temperature data and the smoke data of each monitoring point according to the inspection route in the monitoring time period, the air temperature data being the air temperature value of the monitoring point, and the smoke data being the smoke concentration value of the monitoring point, comparing the air temperature data and the smoke data with the air temperature threshold value and the smoke threshold value through the database, and marking the monitoring points as environmental normal points or environmental abnormal points through the comparison results.

5. The remote assistant maintenance system for a charging station device according to claim 4, wherein The specific process of comparing the air temperature data and the smoke data with the air temperature threshold and the smoke threshold respectively includes: if the air temperature data is less than the air temperature threshold and the smoke data is less than the smoke threshold, it is determined that the environmental verification state of the monitoring point meets the requirements, and the corresponding monitoring point is marked as an environmental normal point; otherwise, it is determined that the environmental verification state of the monitoring point does not meet the requirements, and the corresponding monitoring point is marked as an environmental abnormal point.

6. The remote assistant maintenance system for a charging station device according to claim 5, wherein The specific process of determining whether the working environment of the inspection area in the monitoring period meets the requirements includes: obtaining the environmental monitoring threshold from the database, comparing the environmental monitoring coefficient with the environmental monitoring threshold; if the environmental monitoring coefficient is less than the environmental monitoring threshold, it is determined that the working environment of the inspection area in the monitoring period meets the requirements; if the environmental monitoring coefficient is greater than or equal to the environmental monitoring threshold, it is determined that the working environment of the inspection area in the monitoring period does not meet the requirements, an environmental risk signal is generated and sent to the mobile terminal of the manager.

7. The remote assistant maintenance system for a charging station device according to claim 6, wherein The specific process of analyzing the autonomous monitoring result of the verification combination and the inspection verification result includes: if the monitoring object and the verification point in the verification combination are respectively marked as an electrical abnormal object and an environmental abnormal point, it is determined that the monitoring object has a running abnormality, an abnormality processing signal is generated and sent to the mobile terminal of the manager; if the monitoring object and the verification point in the verification combination are respectively marked as an electrical normal object and an environmental normal point, it is determined that the monitoring object is running normally; otherwise, it is determined that the acquisition element has a running abnormality, an acquisition optimization signal is generated and sent to the mobile terminal of the manager; the manager sends the fault type of the processed monitoring object to the health management module after completing the abnormality processing and the acquisition optimization processing of the monitoring object.

8. The remote assistant maintenance system for a charging station device according to claim 7, characterized in that, The health management module is used for device health management analysis of the charging station: the continuous running time of the corresponding monitoring object when the fault type is diagnosed is marked as a continuous value, all continuous values in the monitoring period corresponding to the fault type form a continuous set, the continuous set is cleaned to obtain a state critical value of the fault type, the minimum value of the state critical values of all fault types is marked as a continuous critical value, and in the next monitoring period, the continuous running time of the monitoring object reaches the continuous critical value, and the monitoring object is forced to be powered off for L1 minutes after completing the current charging task.

9. The remote assistant maintenance system for a charging station device according to claim 8, wherein, The specific process of cleaning the continuous set includes: calculating the variance of all elements in the continuous set to obtain a continuous distribution coefficient, comparing the continuous distribution coefficient with a preset continuous distribution threshold: if the continuous distribution coefficient is greater than the continuous distribution threshold, the maximum element and the minimum element in the continuous set are removed, and then the continuous distribution coefficient is recalculated, and the process is repeated until the continuous distribution coefficient is less than the continuous distribution threshold; if the continuous distribution coefficient is less than the continuous distribution threshold, the minimum element in the continuous set is marked as the state critical value of the fault type.

10. A method for remote assisted maintenance of a charging station device, characterized in that, The method comprises the following steps: Step 1: autonomously monitor and analyze the electrical running state of the charging pile; Step 2: regionally inspect and verify the charging equipment of the charging pile; Step 3: analyze the fault of the charging equipment of the charging station; Step 4: analyze the device health management of the charging station.

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