Health detection method, device and equipment for charging pile cluster and storage medium

Abstract

The invention relates to the technical field of charging pile detection, in particular to a health detection method, device and equipment of a charging pile cluster and a storage medium, and the health detection method of the charging pile cluster comprises the following steps: firstly, mutually acquiring visual data of each charging pile of the charging pile cluster, and monitoring cable butt joint operation before charging to obtain visual monitoring information; obtaining equipment loss characteristics in combination with the attitude sensing data; power supply performance characteristics of equipment during charging are acquired, and finally the two types of characteristics are accumulatively processed to generate health detection information, so that multi-dimensional and full-process detection is realized, the health condition of the charging pile is comprehensively and accurately evaluated, faults are early warned in advance, and the maintenance cost is reduced; reasonable arrangement of operation and maintenance resources is assisted, the charging reliability and safety are improved, and the health condition of the charging pile can be comprehensively detected.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of charging pile detection, in particular to a health detection method and device for a charging pile cluster, equipment and a storage medium. BACKGROUND

[0002] With the emphasis on environmental protection and sustainable development worldwide, the market demand for electric vehicles as a clean energy transportation tool is growing, and the number of electric vehicles is rapidly increasing, which has led to a large-scale increase in the number of charging piles, forming numerous charging pile clusters. As charging infrastructure for electric vehicles, charging piles are used frequently, and cable connection operations are performed frequently during daily use, which can cause wear and tear and aging of the cables, interfaces and other components of the charging pile. At the same time, the long charging process can also put pressure on the electrical system and cooling system of the charging pile, which can easily cause faults. Electric piles are usually installed in outdoor environments and are affected by various natural environmental factors, such as high temperature, humidity, dust, etc. These environmental factors can accelerate the damage and aging of the charging pile equipment and reduce its service life. In addition, improper human operation can also damage the charging pile. Traditional charging pile detection methods often only focus on electrical parameters such as voltage, current and power, while ignoring the wear and tear of the charging pile in the physical structure and operation process. This single detection method cannot comprehensively and accurately assess the health of the charging pile, and can easily miss some potential fault risks.

[0003] Application Content Therefore, it is necessary to provide a health detection method, device, equipment and storage medium for a charging pile cluster to comprehensively detect the health of the charging pile.

[0004] In a first aspect, the present application provides a health detection method for a charging pile cluster, the method comprising: Collecting visual data between each charging pile in the charging pile cluster to monitor the cable connection operation of each charging pile before vehicle charging and obtain visual monitoring information of each charging pile; Obtaining attitude sensing data of each charging pile during cable connection operation, and combining the monitoring information to analyze and obtain device wear characteristics of each charging pile; Collecting data on the device performance of each charging pile during vehicle charging to obtain power supply performance characteristics of each charging pile; Cumulatively processing the device wear characteristics and power supply performance characteristics of each charging pile to generate health detection information of each charging pile.

[0005] In a second aspect, the present application also provides a health detection system of a charging pile cluster, which is used to implement the health detection method of the charging pile cluster according to any one of the first aspect, and comprises: a visual monitoring module, configured to collect visual data between the charging piles in the charging pile cluster, so as to monitor cable docking operations before vehicle charging and obtain visual monitoring information of the charging piles; a posture sensing module, configured to acquire posture sensing data of the charging piles during the cable docking operations, and analyze device wear characteristics of the charging piles in combination with the monitoring information; a performance analysis module, configured to collect data of device performance of the charging piles during vehicle charging, and obtain power supply performance characteristics of the charging piles; a health detection module, configured to cumulatively process the device wear characteristics and the power supply performance characteristics of the charging piles, so as to generate health detection information of the charging piles.

[0006] In a third aspect, the present application provides a health detection device of a charging pile cluster, comprising a memory and a processor, wherein the memory stores a program capable of running on the processor, and the processor implements the health detection method of the charging pile cluster according to any one of the first aspect when executing the program.

[0007] In a fourth aspect, the present application provides a computer readable storage medium, which stores a program, and the program causes the processor to execute the health detection method of the charging pile cluster according to any one of the first aspect when running on the processor.

[0008] The health detection method of the charging pile cluster collects visual data between the charging piles in the charging pile cluster, monitors cable docking operations before charging to obtain visual monitoring information, combines posture sensing data to obtain device wear characteristics, collects device performance during charging to obtain power supply performance characteristics, and finally cumulatively processes the two types of characteristics to generate health detection information, so as to realize multi-dimensional and full-process detection, comprehensively and accurately evaluate the health status of the charging piles, early warn faults, reduce maintenance costs, reasonably arrange operation and maintenance resources, improve charging reliability and safety, and comprehensively detect the health status of the charging piles. BRIEF DESCRIPTION OF DRAWINGS

[0009] Figure 1 FIG. 1 is a schematic diagram of steps of the health detection method of the charging pile cluster in an embodiment; Figure 2 FIG. 2 is a structural schematic diagram of the health detection system of the charging pile cluster in an embodiment. DETAILED DESCRIPTION

[0010] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not intended to limit the present application.

[0011] The health detection method of the charging pile cluster provided by the embodiments of the present application can be applied to the application environment as shown in the figure: Figure 1 S1: visual data collection between each charging pile of the charging pile cluster to monitor the cable docking operation of each charging pile before vehicle charging, and obtain visual monitoring information of each charging pile; S2: obtaining posture sensing data of each charging pile during cable docking operation, and combining with the monitoring information to analyze and obtain device wear characteristics of each charging pile; S3: collecting data of the device performance of each charging pile during vehicle charging to obtain power supply performance characteristics of each charging pile; S4: cumulative processing of the device wear characteristics and power supply performance characteristics of each charging pile to generate health detection information of each charging pile.

[0012] Specifically, in step S1 of the embodiments provided by the present application, a visual sensing module pre-installed on each charging pile is used to collect visual data of the entire area where the charging pile cluster is located. The visual sensing module is composed of a plurality of visual sensors arranged in an array, and each visual sensor is arranged on a visual angle control structure. When performing the visual data collection task, each visual angle control structure adjusts the orientation angle of the corresponding visual sensor, thereby ensuring that the visual information of the entire area can be collected in all directions without dead angles. The area where the charging pile cluster is located is relatively large, and a single sensor cannot achieve comprehensive coverage. By arranging a plurality of visual sensors in an array and adjusting the visual angle through the visual angle control structure, it can be ensured that the visual information of the entire area can be collected, providing basic data for subsequent accurate monitoring of the cable docking operation. Different time periods, different vehicle parking conditions and other factors can cause the visual range to be collected to change. The visual sensor with adjustable visual angle can flexibly adjust the collection range according to the actual situation, improving the pertinence and effectiveness of data collection.

[0013] ​More specifically, real-time monitoring of vehicle location information allows for more targeted detection. When a vehicle enters the charging station cluster and accurately stops in front of a designated charging station, other charging stations adjacent to that station are marked as detection units. This is because the proximity of charging stations allows for clearer and more accurate acquisition of visual data regarding the cable connection between the designated charging station and the vehicle, avoiding unnecessary interference and data redundancy. Monitoring every cable connection operation across all charging stations would be wasteful of resources. By marking detection units, resources can be allocated more efficiently, improving the overall monitoring efficiency of the charging station cluster. More specifically, the visual sensing module on the detection unit is activated to collect visual data specifically for the cable connection process between the vehicle and the designated charging pile. This collected data serves as the visual monitoring information for that designated charging pile. The cable connection operation is a crucial step before vehicle charging, and its standardization and accuracy directly affect the safety and lifespan of the charging pile. Collecting visual data during this process provides a detailed understanding of the operation, offering important evidence for subsequent analysis of charging pile equipment wear and tear. Using the collected visual data on the cable connection operation of the designated charging pile as its visual monitoring information clarifies the ownership of monitoring data for each charging pile, facilitating accurate health checks and assessments of individual charging piles in the future.

[0014] Specifically, in step S2 of the embodiment provided in this application, a multi-dimensional detection sensor group is pre-installed inside the charging pile. When the charging pile and the vehicle are connected via cable, the sensor group collects the charging pile's own attitude data in real time, such as tilt angle, sway amplitude, and torsion degree, thereby obtaining attitude sensing data. During the cable connection operation, the attitude change of the charging pile can reflect the external force it bears during the operation. The multi-dimensional detection sensor group can accurately collect this attitude data from multiple dimensions, providing a direct physical basis for subsequent analysis of equipment loss. Different attitude changes correspond to different degrees and types of equipment loss. For example, excessive tilting or swaying may cause loose internal wiring connections, affecting the normal operation of the charging pile. Real-time collection of attitude sensing data can promptly capture the dynamic changes during the cable connection operation, ensuring the accuracy and timeliness of the data. This helps to promptly detect the potential impact of abnormal operating behavior on the charging pile, so as to take corresponding measures for prevention and repair. More specifically, the previously obtained charging pile visual monitoring information is analyzed in detail to identify various specific actions of the charging pile during the cable docking operation, such as the plugging force, plugging direction, and whether there is excessive pulling, and the operation behavior information at each continuous time node is recorded in chronological order to form the operation behavior information. Although the visual monitoring information is intuitive, it lacks detailed quantification and chronological recording of operation behavior. By analyzing the monitoring information, the operation behavior information at the continuous time node is obtained, which can more carefully understand each link of the cable docking operation and determine the time and manner of each action, which is very important for analyzing the causes and processes of equipment wear and tear. Because different operation behaviors at different time points may have different effects on the equipment, the operation behavior information provides a clear operation behavior model for subsequent analysis, which facilitates comparison and correlation with the posture sensing data. Through this model, it can be more accurately determined whether the operation behavior is standardized and which operation behavior will cause equipment wear and tear.

[0015] More specifically, the posture sensing data and the operation behavior information are associated and matched to check the consistency between them. If it is found that the operation behavior information deviates from the posture sensing data, the operation behavior information is corrected. Then, according to the corrected operation behavior information and the posture sensing data, the wear and tear, stress changes, etc. of the charging pile caused by the cable docking operation are analyzed to determine the equipment wear and tear characteristics of the charging pile, such as the wear degree of the cable interface and the loosening possibility of the internal structure. The posture sensing data and the operation behavior information reflect the cable docking operation from different angles, but there may be some errors or incompleteness between them. Through information matching and correction, these two kinds of data can be integrated to eliminate errors and improve the accuracy of analysis. For example, visual monitoring cannot accurately determine the stress inside the charging pile during the operation, while posture sensing data can make up for this deficiency. At the same time, posture sensing data cannot accurately identify the specific actions of the operation, while operation behavior information can provide detailed action descriptions. Combined with the corrected operation behavior information and the posture sensing data, the wear and tear of the charging pile caused by the cable docking operation can be more accurately evaluated. Different combinations of operation behavior and posture changes will cause different types and degrees of equipment wear and tear. Through comprehensive analysis, the specific equipment wear and tear characteristics can be determined to provide a scientific basis for the maintenance and management of the charging pile.

[0016] Specifically, in step S3 of the embodiments provided in the present application, electrical parameter sensors (such as current sensors, voltage sensors, power sensors, etc.) and temperature sensors are installed inside the charging pile. During the vehicle charging process, the electrical parameters (such as current, voltage, power, etc.) and temperature parameters (such as the temperature of key components inside the charging pile, cable temperature, etc.) of the charging pile are continuously collected. The collected electrical parameters and temperature parameters are arranged in chronological order to form electrical information flow and temperature information flow, which comprehensively reflects the charging state. Electrical parameters and temperature parameters are key indicators reflecting the running state of the charging pile during the charging process. Electrical parameters are directly related to the power supply capacity and power quality of the charging pile, while temperature parameters reflect the heat loss inside the charging pile. Continuous collection of these two types of parameters and generation of information flow can comprehensively and dynamically record the running state of the charging pile during the entire charging process, providing a rich data basis for subsequent analysis. During the charging process, electrical parameters and temperature parameters may change abnormally, and these changes are often early signals of potential faults in the charging pile. Continuous data collection can capture these abnormal changes in time to take timely measures for processing and avoid further expansion of faults.

[0017] More specifically, the electrical information flow is processed and analyzed, the fluctuation range of electrical parameters is calculated, such as the fluctuation amplitude of current, the fluctuation frequency of voltage, etc. At the same time, the electrical parameters are compared with the pre-set safety threshold to determine whether there is a situation beyond the safety range. According to the analysis results, the performance characteristics of the charging pile in the electrical aspect are summarized, such as the stability of electrical parameters, whether there is overcurrent, overvoltage, etc. Abnormal conditions. Fluctuation of electrical parameters and exceeding of safety threshold may cause damage to the electrical system of the charging pile and the vehicle, and even cause safety accidents. By analyzing the data fluctuation and safety threshold of the electrical information flow, abnormal conditions in the electrical system can be found in time, such as overcurrent, overvoltage, etc., so that appropriate protection measures can be taken to ensure the electrical safety of the charging process. The stability of electrical parameters is an important indicator for evaluating the power supply quality of the charging pile. By analyzing the fluctuation of electrical parameters, the stability of the charging pile during power supply can be understood, and whether it can provide stable and reliable power supply for the vehicle can be judged More specifically, the temperature information flow is analyzed to extract the temperature rising trend characteristics. The temperature curve can be fitted, the temperature rising rate can be calculated, and other methods can be used to judge the temperature change of the charging pile during the charging process. For example, whether the temperature shows an abnormal rapid rising trend, or whether it exceeds the reasonable temperature range within the normal charging time. According to these analysis results, the temperature performance characteristics of the charging pile are obtained. The temperature is too high, which is one of the common causes of charging pile failure. By extracting the temperature rising trend characteristics, the abnormal temperature rise of the charging pile during the charging process can be found in time, and heat dissipation measures or fault troubleshooting can be taken in advance to prevent equipment damage and safety accidents caused by overheating. The temperature rising trend can also reflect the heat dissipation performance of the charging pile. If the temperature rises too fast or too high, it means that the heat dissipation system of the charging pile has a problem and needs to be checked and maintained. By analyzing the temperature performance characteristics, the heat dissipation performance of the charging pile can be evaluated to provide a basis for optimizing the heat dissipation design More specifically, the obtained electrical performance characteristics and temperature performance characteristics are compared and analyzed with the data in the historical database. The historical database stores normal operation data of the charging pile under different working conditions and related data when the charging pile fails. By comparing the current performance characteristics with the historical data, it is determined whether the charging pile has performance degradation and whether there is a potential risk of failure. According to the analysis results, the power supply performance characteristics of the charging pile are comprehensively evaluated. The historical database stores a large amount of charging pile operation data and failure cases. These data are valuable experience resources. By comparing and analyzing the current electrical performance characteristics and temperature performance characteristics with the historical data, previous experience can be learned to more accurately determine whether the charging pile has performance degradation and potential failure. A single electrical performance characteristic or temperature performance characteristic may not be able to comprehensively and accurately evaluate the power supply performance of the charging pile. Combined with the historical database for comprehensive analysis, more factors and working conditions can be considered to improve the accuracy and reliability of the evaluation of the power supply performance characteristics of the charging pile More specifically, the relevant data of the vehicle connected with the charging pile during the charging process is collected, such as charging time, charging power, battery temperature, etc. The vehicle charging performance characteristics such as charging efficiency and charging speed stability are generated by analyzing these data. The vehicle charging performance characteristics are compared and analyzed with the power supply performance characteristics of the charging pile to verify the accuracy of the power supply performance characteristics. If differences are found between the two, the power supply performance characteristics of the charging pile are corrected according to the vehicle charging performance characteristics to improve the reliability of the power supply performance characteristics. The charging performance of the vehicle directly reflects the power supply effect of the charging pile. By collecting the charging data of the vehicle and generating the charging performance characteristics, the power supply performance characteristics of the charging pile can be verified from the user's perspective. If there are differences between the two, it may mean that the power supply of the charging pile has problems that need to be further checked and corrected. Considering the vehicle charging performance characteristics can make up for the limitations of evaluating only from the charging pile data, improve the reliability of the entire detection process, and through mutual verification and correction, the power supply performance of the charging pile can be more accurately evaluated to provide a more reliable basis for the maintenance and management of the charging pile.

[0018] Specifically, in step S4 of the embodiments provided in the present application, the equipment loss characteristics and power supply performance characteristics of each charging pile obtained by previous analysis are recorded in detail, and a corresponding time stamp is added to each set of recorded characteristic data. The time stamp is used to mark the specific time when the characteristic data is collected or generated, ensuring that the data has a clear time sequence. The time stamp provides a clear time identifier for the equipment loss characteristic and power supply performance characteristic data, facilitating subsequent data tracing and management. When it is necessary to view the health status of the charging pile in a certain time period, the relevant data record can be quickly located through the time stamp. Subsequent time sequence correlation analysis and cumulative processing depend on accurate time sequence. The time stamp ensures that the time sequence of the data will not be chaotic, providing a basis for accurate data analysis and trend prediction.

[0019] More specifically, based on the timestamp, the power supply performance characteristics of each vehicle power supply are analyzed, and the change law of the power supply performance characteristics over time is observed, such as the numerical change of the power supply efficiency, output stability and other indicators at different time points. By fitting and analyzing these data, a curve reflecting the change of the charging pile performance health over time, i.e. the performance health change curve, is generated. The performance health change curve can intuitively show the change trend of the performance of the charging pile over time. By observing the curve, it can be found whether the performance of the charging pile has declined, and the speed and amplitude of the decline, which helps to take measures in advance to maintain and adjust, avoid the reduction of charging efficiency or failure caused by performance decline, and the performance health change curve can be used as an important basis for fault warning. When the curve shows abnormal fluctuations or a significant downward trend, it indicates that the charging pile has potential fault risks. Through real-time monitoring and analysis of the curve, an early warning signal can be sent to provide a time window for timely repair and maintenance.

[0020] More specifically, according to the timestamp, the equipment wear characteristics recorded each time are accumulated, such as the wear degree of the cable interface, the loosening condition of the internal structure and other equipment wear characteristics. The cumulative calculation is performed in chronological order. Through analysis and prediction of the accumulated equipment wear data, a curve reflecting the change of the charging pile physical structure health condition over time, i.e. the structure health prediction curve, is obtained. The cumulative processing of the equipment wear characteristics can comprehensively reflect the wear of the charging pile physical structure during long-term use. The structure health prediction curve can understand the health condition of the charging pile physical structure and predict possible future structural problems. When the structure health prediction curve shows that the wear of a certain component is close to the critical value, it needs to be replaced or repaired in time to avoid serious failure caused by physical structure damage. According to the structure health prediction curve, a reasonable maintenance plan can be made. For charging piles with faster structural wear, the maintenance frequency can be appropriately increased; and for charging piles with better structural health condition, the maintenance cycle can be extended, thereby improving the maintenance efficiency and reducing the maintenance cost.

[0021] More specifically, based on the timestamp, the structural health prediction curve and the performance health change curve are aligned on the time axis, so that the data of the two curves at the same time point can be corresponded, and then the relationship between the structural health and the performance health at the same time point is analyzed, for example, whether the performance of the charging pile decreases when the physical structure has a certain degree of loss. There is often a close correlation between structural health and performance health. By aligning the structural health prediction curve and the performance health change curve and performing correlation analysis, the correlation can be revealed, the effect of structural loss on the performance of the charging pile can be understood, and whether the performance change will accelerate the loss of the structure. It helps to deeply understand the failure mechanism of the charging pile. The correlation analysis result provides an important basis for comprehensively evaluating the health of the charging pile. When evaluating the health of the charging pile, both performance and structure should be considered, and the mutual influence between the two should be considered. Through correlation analysis, the overall health of the charging pile can be more comprehensively and accurately evaluated More specifically, according to the correlation analysis result of structural health and performance health, combined with historical failure data and failure mode knowledge base, risk detection is performed on various failure modes that may occur in the charging pile. For example, if it is found that there is a strong correlation between the decline of structural health and the decline of performance health, and a certain specific failure mode is prone to occur when the structural loss reaches a certain degree, then the risk of this failure of the charging pile can be predicted. Considering various failure risk factors, a comprehensive charging pile health detection information report is generated. Based on the correlation analysis result, the risk of failure mode is detected, which can predict various failures that may occur in the charging pile in advance, take preventive measures, reduce the probability of failure, and improve the reliability and availability of the charging pile. Through risk detection of various failure modes, the generated health detection information can provide comprehensive and detailed reference for operation and maintenance of the charging pile. According to the health detection information, targeted maintenance strategies can be developed to ensure that the charging pile is always in good operating condition.

[0022] The present application provides a health detection method for a charging pile cluster, which has the following beneficial effects: The health detection method for the charging pile cluster first collects visual data of each charging pile in the charging pile cluster, monitors the visual monitoring information of the charging cable docking operation before charging, and then combines the attitude sensing data to obtain the equipment loss characteristics. The equipment performance characteristics are collected during charging, and finally the two types of characteristics are accumulated and processed to generate health detection information, realizing multi-dimensional and full-process detection, comprehensively and accurately evaluating the health of the charging pile, early warning of failure, and reducing maintenance cost; helping to reasonably arrange operation and maintenance resources, improving charging reliability and safety, and comprehensively detecting the health of the charging pile.

[0023] In one embodiment, the visual data of each charging pile in the charging pile cluster is collected to monitor the docking operation of each charging pile before the vehicle is charged, and the step of obtaining the visual monitoring information of each charging pile includes: S11: collecting visual data of the area where the charging pile cluster is located by the visual sensing module pre-installed on each charging pile in the charging pile cluster to obtain raw visual data; S12: when the raw visual data shows that the vehicle enters the area where the charging pile cluster is located and stops at a designated parking position in front of the charging pile, the charging pile at the adjacent position is marked as a detection unit; S13: collecting visual data of the cable docking operation of the vehicle and the designated charging pile by the visual sensing module on the detection unit as visual monitoring information of the designated charging pile.

[0024] Specifically, on each charging pile in the charging pile cluster, a visual sensing module is installed in advance, which is composed of a plurality of visual sensors arranged in an array, and each visual sensor is installed on a visual angle control structure. Turning on the visual sensing module enables it to collect visual data of the entire area where the charging pile cluster is located. During the collection process, the visual angle control structure adjusts the orientation angle of each visual sensor according to the preset rules or real-time conditions to ensure comprehensive and dead-angle-free coverage of the entire area, thereby obtaining the raw visual data of the area. The area where the charging pile cluster is located is usually large, and a single visual sensor cannot cover the entire area. By deploying multiple visual sensors in an array and adjusting the visual angle through the visual angle control structure, comprehensive visual data collection of the entire area can be ensured, which can capture the entire process of the vehicle entering the area and provide complete basic data for subsequent monitoring and analysis. Different time periods, weather conditions, and vehicle parking situations may cause the visual range to be collected to change. The visual angle-adjustable visual sensor can flexibly adjust the collection range according to the actual situation, improve the effectiveness and pertinence of data collection, and ensure accurate raw visual data in various scenarios.

[0025] More specifically, the position information of the vehicle is monitored in real time by using the positioning devices (such as cameras, ground coils, etc.) installed in the charging pile cluster area or the monitoring function of the visual sensing module itself. When it is detected that a vehicle enters the area where the charging pile cluster is located and accurately parks at a parking position in front of a specified charging pile, the next operation is triggered. According to the position of the specified charging pile where the vehicle is parked, the charging piles in the adjacent positions are marked as detection units. The "adjacent positions" can be defined according to the actual situation, for example, they can be the charging piles closest to the specified charging pile. Marking the charging piles adjacent to the specified charging pile as detection units can make the monitoring more focused on the cable connection operation between the vehicle and the specified charging pile. Due to the advantage of position, the visual sensing module of the adjacent charging pile can capture the details in the cable connection process more clearly and accurately, avoiding image blur or information loss caused by too far distance, thereby improving the monitoring accuracy. If all the visual sensing modules of the charging piles are required to comprehensively monitor every cable connection operation, it will cause waste of resources. By marking the detection units, only the adjacent charging piles are allowed to participate in the monitoring, which can reasonably allocate resources, improve the monitoring efficiency of the entire charging pile cluster, and reduce the operating cost of the system.

[0026] More specifically, once the marking of the detection units is completed, the visual sensing modules on the detection units are immediately started. These visual sensing modules begin to focus on collecting visual data of the cable connection operation between the vehicle and the specified charging pile. The collected visual data about the cable connection operation between the vehicle and the specified charging pile are sorted, the noise and useless information are removed, and they are stored as visual monitoring information of the specified charging pile. The cable connection operation is a key link before charging of the vehicle, and its standardization and accuracy directly affect the normal use of the charging pile and the charging safety of the vehicle. Collecting the visual data of the operation process can help understand the operation in detail and timely find possible problems, such as loose cable connection and improper operation, which provides an important basis for subsequent equipment wear analysis and fault troubleshooting. Storing and managing the collected visual data as the visual monitoring information of the specified charging pile facilitates subsequent targeted analysis and evaluation of individual charging piles. The visual monitoring information of each charging pile can be associated with other data (such as equipment wear characteristics and power supply performance characteristics) of the charging pile, so as to more comprehensively understand the health status of the charging pile.

[0027] In one embodiment, the visual sensing module includes a plurality of visual sensors arranged in an array, and each visual sensor is arranged on a visual angle control structure. When the visual sensing module performs the collection task of visual data, each visual angle control structure adjusts the orientation visual angle of each visual sensor.

[0028] Specifically, the field of view of a single visual sensor is limited, and deploying multiple visual sensors in an array form can significantly expand the overall monitoring coverage area. The sensors at different positions cooperate with each other to reduce the monitoring blind area and ensure that the entire area where the charging pile cluster is located can be effectively monitored. For example, in a large charging pile parking lot, vehicles may enter and exit and charge in every corner and gap. By reasonably arranging the sensor array, the visual information of these scenes can be captured comprehensively. The angle control structure can adjust the orientation angle of each visual sensor, so that data collection can be performed from multiple different angles. The multi-angle data can provide more abundant information, which helps to more accurately identify and analyze objects and events. For example, when monitoring the cable connection operation of the vehicle and the charging pile, shooting from different angles can clearly see the plugging process of the cable, the state of the connection part, and other details, avoiding information loss due to single-angle shielding or limitations.

[0029] More specifically, the use scenarios of the charging pile cluster are complex and changeable. Different time periods, different weather conditions, and different vehicle parking situations may affect the demand for visual data collection. The angle-adjustable visual sensor can flexibly adjust the collection range and angle according to the actual situation. For example, when vehicles are densely parked, the angle of the sensor can be adjusted to focus more on the connection area of the vehicle and the charging pile. When there are fewer vehicles, the angle can be expanded to monitor a wider area. In actual application, some sudden situations or dynamic changes may occur, such as abnormal movement of vehicles, illegal operation of personnel, etc. The angle control structure can quickly respond to these changes and timely adjust the orientation angle of the visual sensor to track the dynamic development of the target object or event. This can capture key information in real time and provide timely and accurate data support for subsequent analysis and processing.

[0030] More specifically, by adjusting the orientation angle of the visual sensor, the sensor can be more accurately aligned with the target object, avoiding image blurring, distortion, and other problems caused by poor shooting angles. Clear image data helps to more accurately identify the characteristics and details of the object, improving the identification accuracy of cable connection operations, equipment appearance conditions, etc. For example, in identifying the identification on the surface of the charging pile, the wear condition of the cable, etc., clear images can provide more reliable basis. The angle control structure can adjust the angle of the sensor according to the actual situation to avoid unnecessary interference factors such as strong light reflection, obstructions, etc., reducing the influence of these interferences and noises. This can improve the quality of the collected visual data, making subsequent data analysis more accurate and reliable. For example, in strong sunlight, the angle of the sensor can be adjusted to avoid direct sunlight, thereby obtaining clearer and more stable image data.

[0031] More specifically, by flexibly adjusting the visual angle of the visual sensor, the function of each sensor can be more effectively utilized, and the use of excessive sensors to cover the entire area can be avoided, which means that the number of sensors can be reduced under the condition of meeting the same monitoring requirements, thereby reducing the hardware cost and installation and maintenance cost of the system. Since the collected data is more accurate and targeted, it reduces unnecessary redundant information, so in the subsequent data processing and analysis process, the complexity and workload of processing can be reduced, and the efficiency of data processing can be improved, which helps to save the computing resources and time cost of the system, and makes the health detection system of the entire charging pile cluster run more efficiently.

[0032] In one embodiment, the step of obtaining the attitude sensing data of each charging pile when performing the cable docking operation and combining the monitoring information to analyze the equipment wear characteristics of each charging pile includes: S21: acquiring the data of the attitude of the charging pile when the cable docking operation is performed with the vehicle by the multi-dimensional detection sensor group pre-deployed in the charging pile, to obtain attitude sensing data; S22: analyzing the specific operation behavior of the charging pile when performing the cable docking operation according to the monitoring information, to obtain operation behavior information occurring at consecutive time nodes; S23: information matching and correction of the operation behavior information based on the attitude sensing data, to analyze the equipment wear caused by the cable docking operation behavior of the charging pile, and obtain the equipment wear characteristics.

[0033] Specifically, multi-dimensional detection sensor groups are pre-installed at key positions of the charging pile. These sensor groups usually include acceleration sensors, gyroscopes, tilt sensors and other types of sensors, which can detect the attitude changes of the charging pile from multiple dimensions. When it is detected that the charging pile starts the cable docking operation with the vehicle, the multi-dimensional detection sensor group starts to collect the attitude data of the charging pile itself in real time, such as tilt angle, shaking amplitude, rotation condition, etc. After processing and conversion, the collected data forms attitude sensing data. During the cable docking operation, the attitude change of the charging pile can directly reflect the external force condition it bears. The multi-dimensional detection sensor group can accurately collect these attitude data from multiple dimensions, providing a direct physical basis for subsequent analysis of equipment wear. Different attitude changes may correspond to different degrees and types of equipment wear. For example, excessive tilt or shaking may cause internal wiring connection to loosen, affecting the normal operation of the charging pile. Real-time collection of attitude sensing data can timely capture the dynamic changes in the cable docking operation process, ensuring the accuracy and timeliness of the data, which helps to timely discover the potential impact of abnormal operation behavior on the charging pile, so as to take corresponding measures for prevention and repair.

[0034] It should be noted that the posture sensing sensor needs to be arranged at the cable interface position of the charging pile in particular, to sense the impact force and cable interface stability of the charging pile when the cable is docked with high precision sensing accuracy, to estimate the loss of the cable docking operation through the sensed impact force data, and to judge the loss condition of the cable interface through the sensing data of the cable interface stability.

[0035] More specifically, from the previously acquired charging pile visual monitoring information, video clips or image sequences related to the cable docking operation are extracted, computer vision technology and pattern recognition algorithms are used to analyze the extracted monitoring information, and the specific actions of the charging pile when performing the cable docking operation are identified, such as the insertion and removal direction, force, speed of the cable, and the hand actions of the operator, etc. At the same time, the time points of each action are recorded to form continuous time node operation behavior information. Although the visual monitoring information is intuitive, it lacks detailed quantification and time sequence recording of the operation behavior. By analyzing the monitoring information, the continuous time node operation behavior information is obtained, which can more carefully understand each link of the cable docking operation and determine the time and manner of each action. This is very important for analyzing the causes and processes of equipment loss, because different operation behaviors at different time points may have different effects on the equipment. The operation behavior information provides a clear operation behavior model for subsequent analysis, which is convenient for comparison and correlation with the posture sensing data. Through this model, it can be more accurately judged whether the operation behavior is standardized and which operation behavior may cause equipment loss.

[0036] More specifically, the attitude sensing data and the operation behavior information are aligned and matched according to the time nodes, the corresponding relationship between the attitude change and the operation behavior at the same time point is found out, for example, when the cable plugging and unplugging action is detected, it is checked whether the attitude of the charging pile at this time has a corresponding shaking or tilting, if it is found that there is inconsistency or contradiction between the operation behavior information and the attitude sensing data, the operation behavior information is corrected according to the attitude sensing data, for example, the visual monitoring information may not accurately judge the plugging and unplugging force of the cable due to the problem of visual angle, and the attitude sensing data can more accurately reflect the external force borne by the charging pile during the operation, so as to correct the operation behavior information, combined with the corrected operation behavior information and the attitude sensing data, the wear and tear, stress change and other conditions caused by the cable docking operation behavior to each component of the charging pile are analyzed, for example, excessive plugging and unplugging force may cause the wear and tear of the cable interface to be intensified, and frequent shaking may cause the internal line connection of the charging pile to be loose, through the analysis of these conditions, the equipment wear and tear characteristics of the charging pile are determined, such as the wear and tear degree of the cable interface and the looseness possibility of the internal structure, the attitude sensing data and the operation behavior information reflect the cable docking operation from different angles, but there may be certain errors or incompleteness between the two, through information matching and correction, the two kinds of data can be integrated, the errors can be eliminated, and the accuracy of the analysis can be improved, for example, the visual monitoring may not accurately judge the stress condition inside the charging pile during the operation, and the attitude sensing data can make up for this deficiency; at the same time, the attitude sensing data may not accurately identify the specific action of the operation, and the operation behavior information can provide detailed action description, combined with the corrected operation behavior information and the attitude sensing data, the wear and tear caused by the cable docking operation to the equipment of the charging pile can be more accurately evaluated, different operation behaviors and attitude change combinations may cause different types and degrees of equipment wear and tear, through comprehensive analysis, the specific equipment wear and tear characteristics can be determined, and scientific basis can be provided for the maintenance and management of the charging pile.

[0037] In one embodiment, the step of obtaining the power supply performance characteristics of each charging pile by collecting the equipment performance data of each charging pile during vehicle charging includes: S31: continuously collecting the electrical parameters and temperature parameters of the charging pile during vehicle charging to generate an electrical information flow and a temperature information flow; S32: analyzing the electrical information flow for data fluctuation and safety threshold to obtain electrical performance characteristics; S33: extracting the temperature rising trend characteristics of the temperature information flow to obtain temperature performance characteristics; S34: performing reference analysis on the electrical performance characteristics and the temperature performance characteristics through a historical database to evaluate the performance decline and potential failure of the charging pile to obtain the power supply performance characteristics.

[0038] Specifically, sensors for collecting electrical parameters and temperature parameters are installed at key internal positions of the charging pile. Electrical parameter sensors such as current sensors, voltage sensors, power sensors, etc. are used to measure the current, voltage, power, etc. output by the charging pile. Temperature sensors are installed near the heat-generating components of the charging pile (such as transformers, power modules, etc.) to monitor their temperature changes in real time. During the vehicle charging process, the sensors continuously collect electrical parameters and temperature parameters and transmit the collected data to the data processing unit at certain time intervals (such as every second, every minute, etc.). The data processing unit organizes these data into continuous data streams, generating electrical information streams and temperature information streams respectively. Electrical parameters and temperature parameters are key indicators reflecting the operating status of the charging pile during the charging process. Electrical parameters are directly related to the power supply capacity and power quality of the charging pile, while temperature parameters reflect the heat loss inside the charging pile. Continuous collection of these two types of parameters and generation of information streams can comprehensively and dynamically record the operating status of the charging pile during the entire charging process, providing a rich data foundation for subsequent analysis. During the charging process, electrical parameters and temperature parameters may experience abnormal changes, which are often early signals of potential faults in the charging pile. Continuous data collection can capture these abnormal changes in a timely manner to take appropriate measures for processing and prevent further expansion of faults.

[0039] More specifically, statistical methods are used to process the data in the electrical information stream to calculate indicators such as the fluctuation amplitude and frequency of electrical parameters (such as current, voltage, power). For example, the maximum and minimum values of the current over a period of time and the fluctuation range are calculated to analyze whether there are abnormal fluctuations. The electrical parameters are compared with pre-set safety thresholds, such as the maximum allowed current and the minimum allowed voltage, to determine whether the electrical parameters exceed the safety threshold range. If they do, there may be safety hazards. Based on the results of data fluctuation analysis and safety threshold comparison, the electrical performance characteristics of the charging pile are summarized, such as the stability of electrical parameters, whether there are overcurrent, overvoltage, etc. abnormal situations. Fluctuations in electrical parameters and exceeding safety thresholds may cause damage to the electrical systems of the charging pile and the vehicle, and even cause safety accidents. By analyzing the electrical information stream for data fluctuations and safety thresholds, abnormal conditions in the electrical system, such as overcurrent and overvoltage, can be detected in a timely manner, allowing appropriate protective measures to be taken to ensure electrical safety during the charging process. The stability of electrical parameters is an important indicator for evaluating the power quality of the charging pile. Analyzing the fluctuations of electrical parameters can help understand the stability of the charging pile during power supply and determine whether it can provide stable and reliable power supply to vehicles.

[0040] More specifically, the data in the temperature information stream is smoothed to remove noise and outliers, improving the quality of the data. Curve fitting, slope calculation, and other methods are used to analyze the temperature rise trend over time. For example, by fitting the temperature curve, the rate of temperature rise is calculated to determine whether the temperature rise is too fast or too slow. Based on the results of temperature trend analysis, the temperature performance characteristics of the charging pile are determined, such as whether the temperature rise is normal, whether there is a risk of overheating, etc. Excessive temperature is one of the common causes of charging pile failure. By extracting the characteristics of the temperature rise trend, temperature abnormalities during charging can be detected in a timely manner, and heat dissipation measures or troubleshooting can be taken in advance to prevent equipment damage and safety accidents caused by overheating. The temperature rise trend can also reflect the heat dissipation performance of the charging pile. If the temperature rises too fast or too high, it may indicate that the charging pile's heat dissipation system has a problem that needs to be checked and maintained. By analyzing the temperature performance characteristics, the heat dissipation performance of the charging pile can be evaluated to provide a basis for optimizing heat dissipation design.

[0041] More specifically, the data in the temperature information stream is smoothed to remove noise and outliers, improving the quality of the data. Curve fitting, slope calculation, and other methods are used to analyze the temperature rise trend over time. For example, by fitting the temperature curve, the rate of temperature rise is calculated to determine whether the temperature rise is too fast or too slow. Based on the results of temperature trend analysis, the temperature performance characteristics of the charging pile are determined, such as whether the temperature rise is normal, whether there is a risk of overheating, etc. Excessive temperature is one of the common causes of charging pile failure. By extracting the characteristics of the temperature rise trend, temperature abnormalities during charging can be detected in a timely manner, and heat dissipation measures or troubleshooting can be taken in advance to prevent equipment damage and safety accidents caused by overheating. The temperature rise trend can also reflect the heat dissipation performance of the charging pile. If the temperature rises too fast or too high, it may indicate that the charging pile's heat dissipation system has a problem that needs to be checked and maintained. By analyzing the temperature performance characteristics, the heat dissipation performance of the charging pile can be evaluated to provide a basis for optimizing heat dissipation design.

[0042] In one embodiment, further comprising: collecting historical data of charging performance of the vehicle in the cable-connection state with the charging pile, and analyzing and generating vehicle charging performance characteristics, verifying the power supply performance characteristics of the charging pile based on the vehicle charging performance characteristics, and correcting the power supply performance characteristics according to the analysis result.

[0043] Specifically, by establishing a data communication interface with the vehicle's charging management system, such as using data interaction functions in standard charging protocols (such as CCS, CHAdeMO, etc.), or developing a special data collection module to connect with the vehicle's OBD (On-Board Diagnostics) interface, the vehicle's charging-related data is obtained, and the historical data of the vehicle's charging performance in the cable-connection state with the charging pile is collected, including charging time, charging power, battery temperature, charging power change, battery SOC (State of Charge) change, etc. These data can reflect the actual performance of the vehicle during charging. The collected historical data is stored in a database and classified according to vehicle identification, charging time, etc. for subsequent query and analysis. The vehicle charging performance characteristics are introduced to verify the power supply performance characteristics of the charging pile, and the actual charging effect of the vehicle is used to evaluate the power supply of the charging pile, which makes up for the limitations of evaluating only from the charging pile's own data. The vehicle as a load of the charging pile, its charging performance can more directly reflect the power supply quality of the charging pile, thereby improving the accuracy of evaluating the power supply performance characteristics of the charging pile. Errors in the power supply performance characteristics of the charging pile can be found in time and corrected, which helps to more accurately understand the actual operating status of the charging pile, and provides a more reliable basis for the maintenance and management of the charging pile. For example, accurate power supply power and efficiency data can help determine whether the charging pile has problems such as excessive power loss or inaccurate metering.

[0044] More specifically, data analysis and machine learning algorithms are used to extract vehicle charging performance features from the data, such as calculating average charging power, charging efficiency (the ratio of charging power to total power consumed), battery temperature rise rate, slope of SOC change curve, etc. These features can quantify the charging performance of the vehicle. By analyzing the vehicle charging performance features and charging pile power supply performance features, a more comprehensive judgment can be made on whether the charging pile has potential faults. When there is inconsistency or abnormality between the two, the fault cause can be more accurately located, improving the reliability of fault diagnosis. For example, if the battery overheats during vehicle charging but the charging pile temperature performance is normal, it may indicate that the temperature monitoring system of the charging pile has a fault, and abnormal changes in vehicle charging performance may be an early signal of charging pile failure. Through continuous monitoring and analysis of vehicle charging performance features, potential faults can be detected in advance, and timely maintenance and repair measures can be taken to avoid further deterioration of the fault and reduce downtime and maintenance costs of the charging pile.

[0045] More specifically, the vehicle charging performance features are matched with the corresponding charging pile power supply performance features to ensure that both are data generated in the same charging process. By comparing the vehicle charging performance features and the charging pile power supply performance features, the consistency and differences between the two are analyzed, such as checking whether the power supply power displayed by the charging pile is consistent with the actual charging power received by the vehicle, whether the change in the vehicle battery temperature is related to the temperature performance features of the charging pile, etc. Statistical methods are used to evaluate the correlation between the vehicle charging performance features and the charging pile power supply performance features. If there is a strong positive or negative correlation between the two, it indicates that the power supply performance of the charging pile has a significant impact on the vehicle charging performance. If the correlation is weak, it may be that other factors are affecting the vehicle charging performance, or there is an error in the power supply performance features of the charging pile.

[0046] More specifically, according to the results of the verification analysis, errors or inaccuracies that may exist in the charging pile power supply performance features are identified, such as if the actual charging efficiency of the vehicle is found to be significantly lower than the power supply efficiency displayed by the charging pile, it may be that the power measurement of the charging pile has an error. For the identified errors, appropriate correction strategies are developed, such as adjusting the measured value of the power supply power, correcting the judgment criteria for temperature rise trend, etc. The corrected power supply performance features are updated to the database for subsequent health detection and evaluation.

[0047] In one embodiment, the step of cumulatively processing the equipment wear-out features and power supply performance features of each charging pile to generate the health detection information of each charging pile includes: S41: Record the equipment wear-out features and power supply performance features of each charging pile, and generate corresponding time stamps for the recorded features; S42: Time sequence correlation analysis is performed on the power supply performance characteristics of each vehicle power supply according to the timestamps to generate a performance health change curve of the charging pile; S43: The device loss characteristics are cumulatively processed according to the timestamps to perform health status evaluation of the charging pile at the physical structure level to obtain a structure health prediction curve of the charging pile; S44: The structure health prediction curve and the performance health change curve are aligned according to the timestamps to perform correlation analysis of structure health and performance health; S45: Based on the results of the correlation analysis, risk detection of various fault modes is performed on the charging pile to generate health detection information of the charging pile.

[0048] Specifically, from the previous monitoring and analysis process of the charging pile, the device loss characteristics (such as cable wear degree, internal structure loosening condition, etc.) and power supply performance characteristics (such as electrical parameter stability, temperature change trend, etc.) of each charging pile are collected. When recording these characteristic data, a corresponding timestamp is generated for each data, which is accurate to a specific date and time, and is used to mark the time when the characteristic data is collected or generated. The device loss characteristics and power supply performance characteristics data with timestamps are stored in the database for subsequent query and analysis. The timestamp provides a clear time identification for the device loss characteristics and power supply performance characteristics data, facilitating subsequent data tracing and management. When it is necessary to view the health status of the charging pile in a certain time period, the relevant data record can be quickly located through the timestamp. The subsequent time sequence correlation analysis and cumulative processing depend on the accurate time sequence, and the timestamp ensures that the time sequence of the data will not be chaotic, providing a basis for accurate data analysis and trend prediction.

[0049] More specifically, the power supply performance characteristic data of each vehicle power supply is screened from the database and sorted by timestamp to ensure that the data is arranged in chronological order. Data analysis methods are used to analyze the variation of power supply performance characteristics over time, for example, to calculate the variation trend of power supply performance characteristics such as power supply power and charging efficiency at different time points, and to observe whether there are periodic fluctuations or abnormal changes. According to the results of time series correlation analysis, a performance health change curve of the charging pile is generated, which takes time as the horizontal axis and the quantitative indicators of power supply performance characteristics as the vertical axis, and intuitively displays the changes of the charging pile performance health over time. The performance health change curve can intuitively display the performance variation trend of the charging pile over time. By observing this curve, it can be found whether the performance of the charging pile has declined, and the speed and amplitude of the decline, which helps to take measures for maintenance and adjustment in advance to avoid reduced charging efficiency or faults caused by performance decline. The performance health change curve can be used as an important basis for fault warning. When the curve shows abnormal fluctuations or a significant downward trend, it may indicate that the charging pile has potential fault risks. Through real-time monitoring and analysis of the curve, an early warning signal can be sent to provide a time window for timely repair and maintenance.

[0050] More specifically, the device wear characteristics of each record are accumulated according to the timestamp, for example, the wear amount detected each time is accumulated for the wear degree of the cable interface; and the degree and number of looseness are comprehensively accumulated for the looseness of the internal structure. According to the accumulated device wear data, the health status of the charging pile is evaluated at the physical structure level in combination with factors such as the physical structure design and service life of the charging pile, for example, to determine whether the cable is close to the wear limit and whether there are safety hazards caused by serious looseness of the internal structure. Based on the results of the health status evaluation, a structure health prediction curve of the charging pile is generated, which reflects the variation trend of the physical structure health status of the charging pile over time and predicts possible future structural problems. The cumulative processing of device wear characteristics can comprehensively reflect the wear of the physical structure of the charging pile during long-term use. The structure health prediction curve can understand the health status of the physical structure of the charging pile and predict possible future structural problems. When the structure health prediction curve shows that the wear of a component is close to the critical value, it needs to be replaced or repaired in time to avoid serious faults caused by physical structure damage. According to the structure health prediction curve, a reasonable maintenance plan can be developed, for example, the maintenance frequency can be appropriately increased for charging piles with rapid structural wear, and the maintenance cycle can be extended for charging piles with good structural health status, thereby improving maintenance efficiency and reducing maintenance cost More specifically, based on the timestamp, the structure health prediction curve and the performance health change curve are aligned on the time axis, so that the data of the two curves at the same time point can be corresponded, and the relationship between the structure health and the performance health at the same time point is analyzed, for example, whether the performance of the charging pile also decreases correspondingly when the physical structure has a certain degree of loss, or whether there is a potential problem in the physical structure when the performance has an abnormal change. The correlation between the two can be quantified using statistical methods such as correlation coefficient calculation. There is often a close correlation between structure health and performance health. By aligning the structure health prediction curve and the performance health change curve and performing correlation analysis, the correlation can be revealed, and it can be understood how the structure loss affects the performance of the charging pile and whether the performance change will accelerate the loss of the structure. This helps to deeply understand the failure mechanism of the charging pile, and the correlation analysis result provides an important basis for comprehensively evaluating the health of the charging pile. When evaluating the health of the charging pile, only performance or structure should not be considered, but the mutual influence between the two should be considered. Through correlation analysis, the overall health of the charging pile can be more comprehensively and accurately evaluated More specifically, according to the results of the correlation analysis, combined with historical failure data and failure mode knowledge base, various possible failure modes of the charging pile are identified, for example, if it is found that there is a strong correlation between the decline of structure health and the decline of performance health, and when the structure loss reaches a certain degree, a certain specific failure mode (such as cable short circuit, internal circuit failure, etc.) is prone to occur, then this failure mode is listed as a possible risk. The identified failure modes are evaluated for risk to determine the likelihood and impact of each failure mode. Probability analysis, risk matrix and other methods can be used for evaluation. The risk assessment results of various failure modes are considered comprehensively to generate a comprehensive charging pile health detection information report, which includes the current health of the charging pile, potential failure risks, recommended maintenance measures, etc. Based on the results of correlation analysis, the risk of failure mode can be detected in advance to predict various possible failures of the charging pile, which helps to take preventive measures to reduce the probability of failure and improve the reliability and availability of the charging pile. The generated health detection information provides comprehensive and detailed reference for the operation and maintenance of the charging pile, and targeted maintenance strategies can be developed based on the health detection information to reasonably arrange maintenance resources and ensure that the charging pile is always in good operating condition.

[0051] It can be understood that by comprehensively considering the equipment wear characteristics and power supply performance characteristics and the correlation therebetween, the health condition of the charging pile can be comprehensively and accurately evaluated, not only the performance of the charging pile is concerned, but also the health condition of the physical structure is considered, the limitations of single factor evaluation are avoided, the performance health change curve and the structure health prediction curve can timely find the trend of performance decline and structure loss of the charging pile, and early warning of potential failure risk can be made, measures can be taken for maintenance and maintenance before failure occurs, the influence of failure on normal operation of the charging pile is reduced, the maintenance cost is reduced, according to the structure health prediction curve and the failure risk evaluation result, a more reasonable maintenance plan can be made, for the charging pile with poor health condition, the maintenance frequency and intensity can be increased, for the charging pile with good health condition, the maintenance cycle can be appropriately prolonged, the maintenance efficiency is improved, the allocation of maintenance resources is optimized, by timely finding and processing potential failure risk and optimizing the maintenance plan, the reliability and usability of the charging pile can be improved, the downtime of the charging pile is reduced, the charging experience of the user is improved, and the promotion and use of electric vehicles are promoted.

[0052] In one embodiment, as shown in Figure 2 A health detection system of a charging pile cluster is provided for implementing the health detection method of the charging pile cluster according to any one of the first aspect, comprising: A visual monitoring module is configured to collect visual data between each charging pile of the charging pile cluster to monitor the cable docking operation of each charging pile before vehicle charging, and obtain visual monitoring information of each charging pile. An attitude sensing module is configured to obtain attitude sensing data of each charging pile during the cable docking operation, and analyze the equipment wear characteristics of each charging pile in combination with the monitoring information. A performance analysis module is configured to collect data of the equipment performance of each charging pile during vehicle charging, and obtain the power supply performance characteristics of each charging pile. A health detection module is configured to cumulatively process the equipment wear characteristics and the power supply performance characteristics of each charging pile to generate health detection information of each charging pile.

[0053] In the embodiment, the specific implementation of each module in the system embodiment is described above in the method embodiment, which will not be described here.

[0054] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant laws, regulations and standards of relevant countries and regions.

[0055] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by programs instructing related hardware. The programs can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, database or other medium used in the embodiments provided by the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided by the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided by the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0056] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

[0057] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of protection of the present application. Therefore, the scope of protection of the present application should be subject to the appended claims.

Claims

1. A method for health detection of a charging pile cluster, characterized in that, The method comprises the following steps: visual data of each charging pile in the charging pile cluster is collected to monitor the cable docking operation of each charging pile before vehicle charging, and visual monitoring information of each charging pile is obtained; attitude sensing data of each charging pile during cable docking operation is obtained, and the monitoring information is combined to analyze the equipment wear characteristics of each charging pile; the equipment performance of each charging pile during vehicle charging is collected to obtain the power supply performance characteristics of each charging pile; the equipment wear characteristics and power supply performance characteristics of each charging pile are cumulatively processed to generate health detection information of each charging pile. 2.The method of claim 1, wherein, The step of collecting visual data of each charging pile in the charging pile cluster to monitor the docking operation of each charging pile before vehicle charging, and obtaining visual monitoring information of each charging pile comprises the following steps: visual data of the area where the charging pile cluster is located is collected through the visual sensing module pre-installed on each charging pile in the charging pile cluster to obtain original visual data; when the original visual data shows that the vehicle enters the area where the charging pile cluster is located and stops at a designated parking position in front of a specified charging pile, the charging pile at the adjacent position is marked as a detection unit; visual data of the cable docking operation of the vehicle and the specified charging pile is collected through the visual sensing module on the detection unit as the visual monitoring information of the specified charging pile. 3.The method of claim 2, wherein, The visual sensing module comprises a plurality of visual sensors arranged in an array, each visual sensor is arranged on a visual angle control structure, and each visual angle control structure adjusts the orientation visual angle of each visual sensor when the visual sensing module performs the visual data collection task. 4.The method of claim 1, wherein, The step of collecting visual data of each charging pile in the charging pile cluster to monitor the docking operation of each charging pile before vehicle charging, and obtaining visual monitoring information of each charging pile comprises the following steps: The attitude data of the charging pile during the cable docking operation with the vehicle is collected through the detection sensor group pre-installed in the charging pile to obtain attitude sensing data; the specific operation behavior of the charging pile during the cable docking operation is analyzed according to the monitoring information to obtain operation behavior information occurring at continuous time nodes; the operation behavior information is matched and corrected based on the attitude sensing data to analyze the equipment wear caused by the cable docking operation behavior of the charging pile, and the equipment wear characteristics are obtained. 5.The method of claim 1, wherein, The step of collecting visual data of each charging pile in the charging pile cluster to monitor the docking operation of each charging pile before vehicle charging, and obtaining visual monitoring information of each charging pile comprises the following steps: electrical parameters and temperature parameters of the charging pile during vehicle charging are continuously collected to generate electrical information flow and temperature information flow; the electrical information flow is analyzed for data fluctuation and safety threshold to obtain electrical performance characteristics; the temperature information flow is analyzed for temperature rising trend to obtain temperature performance characteristics; the electrical performance characteristics and the temperature performance characteristics are analyzed by referring to the historical database to evaluate the performance decline and potential failure of the charging pile, and the power supply performance characteristics are obtained.

6. The method of claim 5, wherein, The method further comprises the following steps: The historical data of charging performance of the vehicle in the cable docking state with the charging pile is collected, and the vehicle charging performance characteristics are generated by analysis. The power supply performance characteristics of the charging pile are verified based on the vehicle charging performance characteristics, and the power supply performance characteristics are corrected according to the analysis result.

7. The method of claim 1, wherein, The step of accumulating the equipment loss characteristics and the power supply performance characteristics of each charging pile to generate the health detection information of each charging pile includes: Recording the equipment loss characteristics and the power supply performance characteristics of each charging pile, and generating corresponding time stamps for the recorded characteristics; According to the time stamp, the power supply performance characteristics of each vehicle power supply are analyzed in time sequence to generate the performance health change curve of the charging pile; According to the time stamp, the equipment loss characteristics are accumulated to evaluate the health status of the charging pile at the physical structure level, and the structure health prediction curve of the charging pile is obtained; The structure health prediction curve and the performance health change curve are aligned according to the time stamp to analyze the correlation between structure health and performance health; Based on the results of the correlation analysis, the risk detection of various fault modes of the charging pile is carried out to generate the health detection information of the charging pile.

8. A health detection device of a charging pile cluster, characterized in that, A health detection method for a charging pile cluster according to any one of claims 1-7, comprising: A visual monitoring module for collecting visual data between each charging pile in the charging pile cluster to monitor the cable docking operation of each charging pile before vehicle charging, and obtaining visual monitoring information of each charging pile; An attitude sensing module for obtaining attitude sensing data of each charging pile during cable docking operation, and combining with the monitoring information to analyze the equipment loss characteristics of each charging pile; A performance analysis module for collecting data of the equipment performance of each charging pile during vehicle charging to obtain the power supply performance characteristics of each charging pile; A health detection module for accumulating the equipment loss characteristics and the power supply performance characteristics of each charging pile to generate the health detection information of each charging pile. 9.A health detection device of a charging pile cluster, comprising a memory and a processor, the memory stores a program capable of running on the processor, characterized in that, The processor executes the program to realize the health detection method of the charging pile cluster according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, A program is stored thereon, which, when executed by a processor, causes the processor to execute the health detection method of the charging pile cluster according to any one of claims 1-7.

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