Charging interface fault diagnosis method, device and system and electronic equipment

Abstract

The invention discloses a fault diagnosis method, device and system for a charging interface and electronic equipment. The method comprises the steps that a charging monitoring data set corresponding to a first charging interface is obtained in the process that the first charging interface is connected with a different second charging interface for charging, and under the condition that the first charging interface is a charging interface of a target charging gun, the second charging interface is a vehicle charging socket; under the condition that the first charging interface is a target vehicle charging socket, the second charging interface is a charging interface of the charging gun; based on the charging monitoring data set, fault diagnosis is carried out on the first charging interface to obtain a fault diagnosis result, and the fault diagnosis result is used for representing whether the first charging interface has a fault risk or not. According to the invention, the technical problem of low accuracy and timeliness of fault diagnosis of the charging interface in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of vehicle charging technology, and more specifically, to a method, apparatus, system, and electronic device for diagnosing faults in a charging interface. Background Technology

[0002] With the rapid expansion of the new energy vehicle market, the efficient operation of charging infrastructure has become a focus of industry attention. The overheating fault diagnosis mechanism for charging interfaces in related technologies mainly relies on temperature sensors at the vehicle's charging socket to monitor temperature rise during charging. When the detected temperature exceeds a preset threshold, it automatically reduces the charging current or interrupts charging to prevent safety risks caused by overheating. However, this passive response mechanism has significant drawbacks: it is difficult to accurately determine whether the overheating is caused by wear on the charging gun terminals, wear on the vehicle's charging socket, or a combination of both, resulting in low fault diagnosis efficiency and high costs; it typically requires on-site inspection and multiple comparative tests by professional technicians, a time-consuming and labor-intensive process; users often only realize the problem when the charging power suddenly drops or charging is interrupted, affecting their charging experience and the continuity of their travel plans. Therefore, the accuracy and timeliness of fault diagnosis for charging interfaces in related technologies are relatively low.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This invention provides a method, apparatus, system, and electronic device for diagnosing faults in charging interfaces, thereby addressing at least the technical problems of low accuracy and timeliness in diagnosing faults in charging interfaces in related technologies.

[0005] According to one aspect of the present invention, a fault diagnosis method for a charging interface is provided, comprising: acquiring a charging monitoring dataset corresponding to the first charging interface during the charging process of the first charging interface being connected to different second charging interfaces, wherein, when the first charging interface is the charging interface of a target charging gun, the second charging interface is a vehicle charging socket, and when the first charging interface is a target vehicle charging socket, the second charging interface is the charging interface of the charging gun; and performing fault diagnosis on the first charging interface based on the charging monitoring dataset to obtain a fault diagnosis result, wherein the fault diagnosis result is used to characterize whether the first charging interface has a fault risk.

[0006] In this embodiment of the invention, when the first charging interface is the charging interface of the target charging gun, the charging monitoring dataset is obtained, including: obtaining multiple charging temperature data of the target charging gun corresponding to the target charging gun during the process of the target charging gun being connected to different vehicle charging sockets for charging, and obtaining the charging monitoring dataset.

[0007] In this embodiment of the invention, based on the charging monitoring dataset, fault diagnosis is performed on the first charging interface to obtain fault diagnosis results, including: determining multiple first deviations between multiple charging gun charging temperature data and preset charging gun charging temperature data, wherein the preset charging gun charging temperature data is used to represent the pre-set standard charging temperature data of the target charging gun; and based on the multiple first deviations, fault diagnosis is performed on the target charging gun to obtain fault diagnosis results.

[0008] In this embodiment of the invention, based on multiple first deviations, fault diagnosis is performed on the target charging gun to obtain a fault diagnosis result, including: when the average of the multiple first deviations is greater than a preset threshold, the fault diagnosis result is determined to be that the target charging gun has a fault risk; when the average is less than or equal to the preset threshold, the fault diagnosis result is determined to be that the target charging gun does not have a fault risk.

[0009] In this embodiment of the invention, when the first charging interface is the charging socket of the target vehicle, the charging monitoring dataset is obtained, including: obtaining the charging temperature data of multiple charging sockets corresponding to the charging socket of the target vehicle during the charging process when the charging socket of the target vehicle is connected to different charging guns, and obtaining the charging monitoring dataset.

[0010] In this embodiment of the invention, based on the charging monitoring dataset, fault diagnosis is performed on the first charging interface to obtain fault diagnosis results, including: determining multiple second deviations between multiple charging socket charging temperature data and preset charging socket charging temperature data, wherein the preset charging socket charging temperature data is used to represent the pre-set standard charging temperature data of the target charging socket; and based on the multiple second deviations, fault diagnosis is performed on the target charging socket to obtain fault diagnosis results.

[0011] In this embodiment of the invention, the method further includes: when the target vehicle charging socket is in a non-charging state, acquiring first temperature data collected by the socket temperature sensor of the target vehicle charging socket and second temperature data collected by the vehicle exterior temperature sensor of the vehicle corresponding to the target vehicle charging socket; determining whether the socket temperature sensor is in a fault state based on the first temperature data and the second temperature data; and when the socket temperature sensor is not in a fault state, acquiring multiple charging socket charging temperature data based on the socket temperature sensor.

[0012] According to another aspect of the present invention, a fault diagnosis device for a charging interface is also provided, comprising: an acquisition module, configured to acquire a charging monitoring dataset corresponding to the first charging interface during the charging process of the first charging interface being connected to different second charging interfaces, wherein, when the first charging interface is a target charging gun, the second charging interface is a vehicle charging socket, and when the first charging interface is a target vehicle charging socket, the second charging interface is a charging gun; and a fault diagnosis module, configured to perform fault diagnosis on the first charging interface based on the charging monitoring dataset, and obtain a fault diagnosis result, wherein the fault diagnosis result is used to characterize whether the first charging interface has a fault risk.

[0013] According to another aspect of the present invention, a fault diagnosis system for a charging interface is also provided, comprising: a first charging interface for connecting and charging with different second charging interfaces, wherein, when the first charging interface is the charging interface of a target charging gun, the second charging interface is a vehicle charging socket, and when the first charging interface is the charging socket of a target vehicle, the second charging interface is the charging interface of a charging gun; and a cloud, communicatively connected to the first charging interface, wherein the cloud is used to acquire a charging monitoring dataset corresponding to the first charging interface during the charging process of the first charging interface connecting and charging with different second charging interfaces, and to perform fault diagnosis on the first charging interface based on the charging monitoring dataset to obtain a fault diagnosis result, wherein the fault diagnosis result is used to characterize whether the first charging interface has a fault risk.

[0014] According to another aspect of the present invention, an electronic device is also provided, comprising: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods of various embodiments of the present invention during runtime.

[0015] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is executed, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of the present invention.

[0016] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the methods of various embodiments of the present invention.

[0017] According to another aspect of the present invention, a computer program product is also provided, including a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods of various embodiments of the present invention.

[0018] According to another aspect of the present invention, a computer program is also provided, which, when executed by a processor, implements the methods of the various embodiments of the present invention.

[0019] In this embodiment of the invention, firstly, during the process of connecting and charging the first charging interface with different second charging interfaces, the charging monitoring dataset corresponding to the first charging interface is obtained. When the first charging interface is the charging interface of the target charging gun, the second charging interface is the vehicle charging socket. When the first charging interface is the target vehicle charging socket, the second charging interface is the charging interface of the charging gun. Next, based on the charging monitoring dataset, the first charging interface is diagnosed to obtain the fault diagnosis result. The fault diagnosis result is used to characterize whether the first charging interface has a fault risk. By collecting and analyzing the charging monitoring dataset corresponding to the first charging interface and multiple different second charging interfaces during the charging process, and by statistically analyzing the charging data of the first charging interface during normal daily use, fault diagnosis is based on the stable analysis of the long-term data trend of the first charging interface. This enables accurate location and early warning of overheating faults of the first charging interface, allowing for predictive maintenance before faults occur. This improves the timeliness of fault diagnosis, charging safety, and user experience. The use of cloud-based big data analysis makes the diagnostic results more accurate and comprehensive, reduces the false alarm rate, eliminates the need for on-site inspections by technicians, and improves operation and maintenance efficiency. This solves the technical problem of low accuracy and timeliness in fault diagnosis of charging interfaces in related technologies. Attached Figure Description

[0020] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0021] Figure 1 This is a flowchart of a fault diagnosis method for a charging interface according to an embodiment of the present invention;

[0022] Figure 2 This is a schematic diagram of an optional charging interface fault diagnosis system framework according to an embodiment of the present invention;

[0023] Figure 3 This is a schematic diagram of an optional charging interface fault diagnosis process according to an embodiment of the present invention;

[0024] Figure 4 This is a schematic diagram of an optional socket temperature sensor self-test process according to an embodiment of the present invention;

[0025] Figure 5 This is a schematic diagram of a fault diagnosis device for a charging interface according to an embodiment of the present invention. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] According to one aspect of the present invention, a method for diagnosing a charging interface fault is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0029] Figure 1 This is a flowchart of a fault diagnosis method for a charging interface according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0030] Step S102: Obtain the charging monitoring dataset corresponding to the first charging interface during the process of connecting and charging the first charging interface with different second charging interfaces.

[0031] Wherein, if the first charging interface is the charging interface of the target charging gun, the second charging interface is the vehicle charging socket; if the first charging interface is the target vehicle charging socket, the second charging interface is the charging interface of the charging gun.

[0032] The target charging gun mentioned above can refer to a specific charging gun in the fault diagnosis process, that is, a charging gun whose health status needs to be assessed.

[0033] The aforementioned target vehicle charging socket may refer to the charging socket of a specific vehicle during the fault diagnosis process, i.e., the charging socket of a vehicle whose health status needs to be assessed.

[0034] The aforementioned different second charging interfaces can refer to multiple charging sockets of different vehicles in the diagnostic process of the target charging gun, or multiple different charging guns in the diagnostic process of the target vehicle's charging socket.

[0035] The aforementioned charging monitoring dataset refers to a series of data collected during the charging process of the first charging interface connected to different second charging interfaces, including but not limited to charging current, charging voltage, charging gun terminal temperature, vehicle socket temperature rise, and ambient temperature. This data can be used to calculate contact resistance, temperature rise trends, and other indicators to assess the health status of the first charging interface. The charging monitoring dataset can contain information needed to assess charging interface performance degradation and health risks, serving as a data foundation for fault diagnosis.

[0036] In one optional embodiment, electrical and temperature parameters of the first charging interface during charging with multiple different second charging interfaces can be collected. When the first charging interface is the charging interface of the target charging gun, charging temperature data of the target charging gun when charging different vehicle charging sockets can be collected. When the first charging interface is the target vehicle charging socket, temperature data of the target vehicle charging socket when charging the target vehicle charging socket with different charging guns can be collected. Each charging event can generate a set of charging monitoring data, which may include charging current, charging voltage, charging interface temperature rise, etc. This charging monitoring data can be uploaded to the cloud to form a charging monitoring dataset, providing a detailed data foundation for subsequent fault diagnosis. To ensure the accuracy and reliability of the diagnosis, the construction of the charging monitoring dataset can also consider the following technical details: the charging monitoring dataset may include the electrical and temperature behavior of the first charging interface in the initial stage of charging, such as the first 60 seconds of charging. The temperature rise in this stage can better reflect the contact resistance state of the charging interface. The charging monitoring dataset can contain data from multiple charging events under different charging conditions, including charging with different charging guns, different vehicles, and different ambient temperatures. This eliminates the influence of single factors and ensures the objectivity and comprehensiveness of the diagnostic results. Data collection and uploading take privacy and data security into account, employing anonymization and encrypted transmission to ensure the confidentiality of user information.

[0037] In the above process, by constructing a charging monitoring dataset, a quantitative assessment of the health status of the first charging interface can be achieved. The changing trend of the charging interface contact resistance can be analyzed from the time and space dimensions. Early identification of signs of performance degradation of the charging interface can be achieved, avoiding the risk of overheating. Furthermore, by accurately locating the source of the fault, the operation and maintenance efficiency can be improved, and the misjudgment rate in the fault diagnosis process can be reduced.

[0038] Step S104: Based on the charging monitoring dataset, perform fault diagnosis on the first charging interface to obtain the fault diagnosis result.

[0039] Among them, the fault diagnosis results are used to characterize whether there is a fault risk in the first charging interface.

[0040] The aforementioned fault diagnosis results refer to the diagnostic results obtained by analyzing charging monitoring datasets, which can characterize whether there is a fault risk in the first charging interface. The fault diagnosis results can include whether the first charging interface has a fault risk or not. The presence of a fault risk could mean that the contact resistance or temperature rise parameter of the first charging interface exceeds a preset threshold, or that there are other obvious abnormalities, in which case corresponding early warning or maintenance measures can be taken; the absence of a fault risk indicates that the performance of the first charging interface is within an acceptable range and can continue to be used safely.

[0041] In one optional embodiment, the health status of the first charging interface can be assessed using data analysis algorithms based on a constructed charging monitoring dataset. This process may include mining electrical parameters and temperature data in the charging monitoring dataset to identify signs of performance degradation. When diagnosing the target charging gun, the relationship between charging current and gun head temperature rise when charging different vehicles can be analyzed, and the contact resistance heating can be calculated using Joule's law and the heat formula. When diagnosing the target vehicle charging socket, the focus can be on analyzing the temperature rise trend of the target vehicle charging socket when charging at different charging piles. The health status of the charging socket can be assessed by calculating contact resistance heating. By comparing actual data with preset health indicators, such as contact resistance thresholds, it can be determined whether the first charging interface has performance degradation or overheating risk. In fault diagnosis, the contact resistance value of the first charging interface in the initial stage of charging can be calculated. For the target charging gun, the average of the contact resistance values ​​calculated in multiple recent charging events can be statistically analyzed. If the average value is significantly higher than the corresponding threshold, it can be determined that the target charging gun is severely worn and has an overheating fault risk. For the target vehicle's charging socket, while ensuring the charging gun is in good condition, check the change in contact resistance value of the target vehicle's charging socket on different charging guns. If the contact resistance value exceeds the corresponding threshold for multiple consecutive charging cycles, it can be diagnosed that the target vehicle's charging socket is severely worn and there is a risk of overheating.

[0042] In the above process, fault diagnosis based on charging monitoring datasets enables precise location and early warning of overheating faults at the first charging interface, allowing for predictive maintenance before the fault occurs, thus improving charging safety and user experience. Utilizing cloud-based big data analysis makes the diagnostic results more accurate and comprehensive, reducing false alarm rates and improving operational efficiency.

[0043] In this embodiment of the invention, firstly, during the process of connecting and charging the first charging interface with different second charging interfaces, the charging monitoring dataset corresponding to the first charging interface is obtained. When the first charging interface is the charging interface of the target charging gun, the second charging interface is the vehicle charging socket. When the first charging interface is the target vehicle charging socket, the second charging interface is the charging interface of the charging gun. Next, based on the charging monitoring dataset, the first charging interface is diagnosed to obtain the fault diagnosis result. The fault diagnosis result is used to characterize whether the first charging interface has a fault risk. By collecting and analyzing the charging monitoring dataset corresponding to the first charging interface and multiple different second charging interfaces during the charging process, and by statistically analyzing the charging data of the first charging interface during normal daily use, fault diagnosis is based on the stable analysis of the long-term data trend of the first charging interface. This enables accurate location and early warning of overheating faults of the first charging interface, allowing for predictive maintenance before faults occur. This improves the timeliness of fault diagnosis, charging safety, and user experience. The use of cloud-based big data analysis makes the diagnostic results more accurate and comprehensive, reduces the false alarm rate, eliminates the need for on-site inspections by technicians, and improves operation and maintenance efficiency. This solves the technical problem of low accuracy and timeliness in fault diagnosis of charging interfaces in related technologies.

[0044] In this embodiment of the invention, when the first charging interface is the charging interface of the target charging gun, the charging monitoring dataset is obtained, including: obtaining multiple charging temperature data of the target charging gun corresponding to the target charging gun during the process of the target charging gun being connected to different vehicle charging sockets for charging, and obtaining the charging monitoring dataset.

[0045] The aforementioned multiple charging gun temperature data can refer to the temperature data of the target charging gun terminals collected during the initial charging phase when the target charging gun is connected to the charging sockets of different vehicles. It can also be a record of the charging gun terminal temperature when the target charging gun is serving multiple different vehicles, and can be used to evaluate the health status and performance of the target charging gun.

[0046] In an optional embodiment, when the first charging interface is the charging interface of the target charging gun, the construction of the charging monitoring dataset can focus on collecting electrical parameters and temperature rise data of the target charging gun during the charging process when connected to different vehicle charging sockets. Specifically, at the initial stage of each charging session, the charging pile terminal can record the temperature changes and charging current of the target charging gun terminals. This data can be collected by the charging pile's built-in temperature control system and current monitoring module and uploaded to the cloud via a secure network channel. Each piece of data in the charging monitoring dataset can be associated with the charging gun number, the charging vehicle number, the charging time, the ambient temperature, and the temperature and current information of the charging gun terminals. To ensure the accuracy and reliability of fault diagnosis, data filtering and preprocessing techniques can also be used when constructing the charging monitoring dataset. For example, temperature fluctuations caused by non-contact resistance due to sudden changes in the external environment during charging, such as direct sunlight or wind speed changes, can be excluded, retaining stable data points reflecting contact resistance heating. Consistency checks can also be performed on the uploaded data to ensure its integrity and authenticity, avoiding data quality issues that could affect the accuracy of fault diagnosis.

[0047] In the above process, by collecting and analyzing the charging temperature data of the target charging gun when charging multiple different vehicles, the resulting charging monitoring dataset can contain real-time status information of the target charging gun under specific environments, reflecting the long-term performance trend of the target charging gun's contact end. Based on the charging monitoring dataset, the cloud can accurately determine whether the target charging gun has a fault risk, whether wear, oxidation, or foreign matter has caused an abnormal increase in contact resistance, resulting in a temperature rise exceeding the safety threshold.

[0048] In this embodiment of the invention, based on the charging monitoring dataset, fault diagnosis is performed on the first charging interface to obtain fault diagnosis results, including: determining multiple first deviations between multiple charging gun charging temperature data and preset charging gun charging temperature data, wherein the preset charging gun charging temperature data is used to represent the pre-set standard charging temperature data of the target charging gun; and based on the multiple first deviations, fault diagnosis is performed on the target charging gun to obtain fault diagnosis results.

[0049] The aforementioned preset charging gun temperature data refers to the terminal temperature data of the target charging gun during the initial charging stage under standard conditions, when charging with a constant reference current. This temperature data can be determined based on test results of the target charging gun's specifications and design parameters, reflecting the standard charging temperature behavior of the target charging gun under ideal, wear-free conditions. The preset charging gun temperature data can serve as benchmark data, used to compare with the charging gun temperature data collected during actual charging to assess the health status of the target charging gun. A significant deviation between the actual charging gun temperature data and the preset charging gun temperature data indicates a high internal contact resistance in the target charging gun, which may be due to terminal wear, oxidation, or other faults.

[0050] The aforementioned multiple first deviations refer to the multiple deviation values ​​between the charging temperature data recorded by the target charging gun during multiple charging processes and the preset charging temperature data. The first deviation can be calculated by subtraction, subtracting the preset charging temperature data from the actual charging temperature data measured during charging to obtain the first difference. Further analysis can be performed based on these multiple first differences, such as calculating the average deviation or observing the trend of the deviation values, to determine whether the target charging gun has experienced performance degradation.

[0051] In one optional embodiment, after receiving multiple charging temperature data of the target charging gun being charged on different vehicle charging sockets, the cloud can perform the following steps for fault diagnosis: The charging temperature data of multiple charging guns in each charging event can be compared with preset charging temperature data to calculate the first deviation for each charging event. These multiple first deviations can reflect the difference between the terminal temperature of the target charging gun during actual charging and the ideal state, thereby determining whether the target charging gun has a fault risk and obtaining a fault diagnosis result. By comparing with the charging temperature data of multiple charging guns during actual charging, the temperature rise trend of the charging gun contact terminals can be identified, and it can be determined whether the contact resistance of the target charging gun has increased abnormally due to wear, oxidation, or foreign object blockage. To improve the accuracy of the diagnosis, the influence of factors such as charging current and ambient temperature on the temperature rise can also be comprehensively considered to ensure accurate fault diagnosis under different charging conditions.

[0052] In the aforementioned process, fault diagnosis is performed based on multiple first deviations between the charging temperature data of various charging guns and the preset charging temperature data. This enables a quantitative assessment of the health status of the target charging gun, ensuring the objectivity and accuracy of the diagnostic results. Through big data analysis, the performance degradation trend of the target charging gun during long-term use can be identified, providing early warning of potential overheating faults. This diagnostic logic can predict and locate faults in advance, avoiding sudden derating or interruption during charging, improving user experience and the operational efficiency of the charging network. Remote data processing and fault analysis in the cloud reduces reliance on on-site manual inspections, significantly lowering maintenance costs, improving charging facility maintenance strategies, and providing technical support for the long-term reliable operation of charging facilities.

[0053] In this embodiment of the invention, based on multiple first deviations, fault diagnosis is performed on the target charging gun to obtain a fault diagnosis result, including: when the average of the multiple first deviations is greater than a preset threshold, the fault diagnosis result is determined to be that the target charging gun has a fault risk; when the average is less than or equal to the preset threshold, the fault diagnosis result is determined to be that the target charging gun does not have a fault risk.

[0054] The aforementioned mean can refer to the average first deviation calculated based on multiple first deviations collected during multiple charging processes. This calculation process may include summarizing and analyzing multiple temperature rise data deviations generated by the target charging gun in different charging events.

[0055] The aforementioned preset threshold can refer to a standard value pre-set in the fault diagnosis system, which can be used to determine the health status of the first charging interface. The preset threshold can be derived from the analysis and statistics of the charging gun's contact resistance, and can characterize the temperature rise limit at which the target charging gun transitions from a relatively new state to a state of significant performance degradation, i.e., a risk of failure. The determination of the preset threshold can be based on theoretical analysis and actual testing of the charging interface. For example, accelerated aging experiments can be used to observe the temperature rise changes of the charging gun under various conditions until it reaches a state where it is no longer suitable for safe charging; the corresponding temperature rise change can then be set as the preset threshold. The preset threshold can be set to consider both normal temperature rise during the charging process of the first charging interface and abnormal temperature rise caused by wear, loosening, or foreign objects.

[0056] In one optional embodiment, after acquiring multiple first deviations when the target charging gun is connected to charging sockets of multiple different vehicles, the cloud can perform in-depth analysis of these first deviations to assess the health status of the target charging gun. Specifically, the multiple first deviations can be statistically analyzed and their average value calculated. If the calculated average value is greater than a preset threshold, it can be determined that the charging temperature of the target charging gun is abnormally high, indicating wear or performance degradation. Based on this, a fault diagnosis result can be generated, determining that the target charging gun has a fault risk. If the average value of the first deviations is less than or equal to the preset threshold, it can be determined that the temperature performance of the target charging gun meets the standard, its health status is good, and there is no risk of overheating. The preset threshold value can be set based on a large amount of experimental data and analysis of actual operating scenarios, taking into account multiple factors such as the charging gun material, design, usage conditions, and safety standards. By comparing with the average value of the first deviations, the trend of charging gun performance changes can be quickly identified, and early warnings can be given before overheating occurs, achieving early fault diagnosis. This process can also perform data quality control, including checking the reasonableness of deviation values ​​and removing abnormal data, to ensure the accuracy and reliability of the diagnostic results.

[0057] In the above process, by setting a reasonable preset threshold and comparing it with the average of multiple first deviations, the overheating fault of the target charging gun can be accurately located and early warning can be provided. This diagnostic logic ensures charging safety while avoiding unnecessary charging interruptions and improving the user charging experience. This application, by statistically analyzing the charging data of each charging session during normal use of the target charging gun, can identify subtle changes in the contact resistance of the target charging gun, and promptly detect and prevent potential fault hazards.

[0058] In this embodiment of the invention, when the first charging interface is the charging socket of the target vehicle, the charging monitoring dataset is obtained, including: obtaining the charging temperature data of multiple charging sockets corresponding to the charging socket of the target vehicle during the charging process when the charging socket of the target vehicle is connected to different charging guns, and obtaining the charging monitoring dataset.

[0059] The aforementioned multiple charging socket temperature data may refer to multiple temperature data of the target vehicle's charging socket during the initial charging stage when the target vehicle's charging socket is connected to multiple different charging guns for charging.

[0060] In one optional embodiment, when the first charging interface is the target vehicle's charging socket, and when the target vehicle's charging socket is connected to different charging piles for charging, the charging temperature change of the target vehicle's charging socket during the initial charging stage can be collected. This series of charging socket charging temperature data, along with information such as charging current and ambient temperature, can be used to construct a charging monitoring dataset. The resulting charging monitoring dataset can reflect the temperature status during the charging process and the long-term performance trend of the target vehicle's charging socket. The encrypted charging monitoring dataset can be uploaded to the cloud via the vehicle's communication module, providing detailed reference information for subsequent fault diagnosis. During the construction of the charging monitoring dataset, a high-precision temperature sensor can be used to monitor the temperature of the target vehicle's charging socket to ensure the accuracy and reliability of the data. To eliminate the influence of environmental factors, the ambient temperature during charging can also be recorded simultaneously for subsequent analysis of relative temperature rise changes. Before uploading the data to the cloud, preliminary data preprocessing can be performed, such as eliminating outliers and temperature rise correction, to reduce the burden of data transmission and improve the efficiency of cloud analysis.

[0061] In the aforementioned process, by collecting and analyzing the charging temperature data of the target vehicle's charging socket during charging with multiple different charging guns, the health status of the charging socket can be assessed from the vehicle's perspective, accurately locating the source of overheating faults. By longitudinally comparing the charging data of the target vehicle's charging socket across different charging sessions, the trend of contact resistance changes over time can be identified, thereby predicting and diagnosing performance degradation or fault risks in advance. This cross-validation of vehicle-side and charging pile-side data allows for a more comprehensive assessment of the charging interface's condition. The collection and analysis of vehicle-side data enhances the flexibility and operability of fault diagnosis, enabling customized warnings based on the vehicle's charging history, improving user experience and charging safety.

[0062] In this embodiment of the invention, based on the charging monitoring dataset, fault diagnosis is performed on the first charging interface to obtain fault diagnosis results, including: determining multiple second deviations between multiple charging socket charging temperature data and preset charging socket charging temperature data, wherein the preset charging socket charging temperature data is used to represent the pre-set standard charging temperature data of the target charging socket; and based on the multiple second deviations, fault diagnosis is performed on the target charging socket to obtain fault diagnosis results.

[0063] The aforementioned preset charging socket charging temperature data refers to the terminal temperature data of the charging socket under standard conditions, when the target vehicle's charging socket is charging at a constant reference current under ideal conditions. This preset charging socket charging temperature data can serve as a reference benchmark for comparison and analysis with charging socket charging temperature data collected during actual charging to assess the health status of the target charging socket. The preset charging socket charging temperature data can be obtained through standard testing procedures during vehicle production or charging pile testing. The determination of the preset charging socket charging temperature data can consider socket design, material characteristics, and environmental factors to ensure the target charging socket's temperature performance under ideal conditions, thus serving as a comparison benchmark.

[0064] The aforementioned multiple second deviations refer to multiple deviation values ​​between the collected charging socket temperature data and the preset charging socket temperature data when the target vehicle's charging socket is charged with different charging guns. Calculating multiple second deviations helps determine whether the temperature rise of the target vehicle's charging socket deviates from the preset standard when charging with different charging guns, and the degree of such deviation. The second deviations can be obtained through subtraction, that is, by subtracting the preset charging socket temperature data from the actual charging socket temperature data measured during each charging process, and the difference obtained can be used as the second deviation.

[0065] In one optional embodiment, during the charging process of the target vehicle's charging socket connected to different charging guns, the cloud can collect and analyze the charging socket's charging temperature data in each charging event, comparing it with preset charging socket charging temperature data to identify the health status of the target charging socket. The preset charging socket charging temperature data can be charging temperature data obtained from charging tests under standard laboratory conditions during vehicle factory testing or charging socket replacement. This data reflects the temperature rise of the charging socket under ideal conditions and can serve as an important benchmark for the charging socket's health status. The cloud can compare the charging socket's charging temperature data during each charging process with the preset charging socket charging temperature data to calculate multiple second deviations. By statistically analyzing these second deviations, it can determine whether the target vehicle's charging socket is at risk of wear or other performance degradation, obtaining a fault diagnosis result. To make the fault diagnosis more accurate, the cloud can also comprehensively analyze the impact of parameters such as charging current and ambient temperature on the second deviations, ensuring that the diagnostic results are not affected by fluctuations in charging conditions. The preset charging socket charging temperature data can also be updated periodically to adapt to changes in the temperature rise benchmark caused by factors such as the increasing age of the vehicle and advancements in charging technology, maintaining the timeliness and accuracy of the fault diagnosis.

[0066] The introduction of this diagnostic logic in the above process enables a quantitative assessment of the health status of the target vehicle's charging socket and allows for the timely detection of early signs of charging socket performance degradation during charging, providing early warning of faults. The charging monitoring dataset, constructed from historical charging data, reveals the trend of charging socket contact resistance evolution over time, providing more comprehensive information for diagnosis and maintenance. This significantly improves the accuracy and efficiency of fault diagnosis, reduces the number of charging derating incidents due to socket overheating, and enhances the user's charging experience.

[0067] In this embodiment of the invention, the method further includes: when the target vehicle charging socket is in a non-charging state, acquiring first temperature data collected by the socket temperature sensor of the target vehicle charging socket and second temperature data collected by the vehicle exterior temperature sensor of the vehicle corresponding to the target vehicle charging socket; determining whether the socket temperature sensor is in a fault state based on the first temperature data and the second temperature data; and when the socket temperature sensor is not in a fault state, acquiring multiple charging socket charging temperature data based on the socket temperature sensor.

[0068] The aforementioned first temperature data can refer to the temperature data collected by the target vehicle's charging socket temperature sensor when the vehicle is in a stationary state and not charging. Here, "not charging" means that the vehicle's charging socket is not connected to the charging gun, and the vehicle's charging system is not in operation.

[0069] The aforementioned vehicle exterior temperature sensor can refer to a sensor installed on the outside of the vehicle. The vehicle exterior temperature sensor can be used to obtain the temperature data of the external environment of the vehicle when the vehicle is stationary and not charging, as a reference benchmark.

[0070] The aforementioned second temperature data may refer to the temperature data collected by the vehicle's external temperature sensor corresponding to the target vehicle's charging socket. This data can reflect the temperature of the vehicle's environment and can serve as a reference for the first temperature data.

[0071] In one optional embodiment, a fault self-test program can be automatically initiated when the target vehicle's charging socket is not in use. The socket temperature sensor can be used to collect temperature data from the charging socket, which is marked as the first temperature data. The temperature reading from the external temperature sensor can also be read and marked as the second temperature data. When the vehicle is stationary for an extended period, the first temperature data should be close to the second temperature data. If there is a significant deviation between the first and second temperature data, exceeding a preset reasonable deviation threshold, it can be determined that the socket temperature sensor has malfunctioned or is experiencing signal drift. The self-test program for the socket temperature sensor can further analyze parameters such as response time and signal stability to rule out temporary errors caused by external interference. To improve the accuracy of fault diagnosis, the self-test program can be repeatedly executed during multiple stationary cycles of the vehicle to ensure consistent diagnostic results. Cross-validation can also be performed using temperature sensors from other parts of the vehicle, such as cabin temperature sensors and engine compartment temperature sensors, to further confirm the fault status of the socket temperature sensor.

[0072] In the above process, by performing a self-test on the socket temperature sensor when not charging, faults or signal drift in the sensor itself can be proactively identified and eliminated, ensuring the accuracy and reliability of data during the fault diagnosis phase. This avoids false alarms or missed alarms due to overheating caused by sensor failure, ensuring the stability of the charging control system. If the socket temperature sensor is confirmed to be fault-free, subsequent fault diagnosis is performed based on the charging socket temperature data collected by the sensor, making the process more efficient and accurate. The self-test mechanism can detect and address sensor problems early, avoiding charging efficiency losses and user experience degradation caused by sensor failure, thus improving the reliability and safety of the entire charging system. By performing a self-test on the socket temperature sensor while the vehicle is stationary, faults in the socket temperature sensor can be accurately identified, ensuring the accuracy of subsequent fault diagnosis data.

[0073] The technical solution proposed in this application is described below with reference to an optional embodiment. This application proposes a charging interface over-temperature fault diagnosis system and method based on vehicle-charging-pile-cloud collaboration, relating to the field of electric vehicle technology. The proposed vehicle-charging-pile-cloud collaborative charging interface over-temperature fault diagnosis system can be composed of a vehicle terminal, a charging pile terminal, and a cloud service platform. This application collects current and temperature rise data in real time at both the vehicle and charging pile ends during the initial charging stage and uploads it to the cloud. The cloud service platform uses cross-validation logic for collaborative analysis: that is, by analyzing the historical trend of charging data from the same charging gun to different vehicles, the health status of the charging gun is diagnosed; and by analyzing the historical trend of charging data from the same vehicle to different charging guns, the health status of the vehicle socket is diagnosed, thereby accurately isolating the over-temperature fault source. At the same time, the vehicle end compares the charging interface temperature with the ambient temperature in a static state to achieve self-testing of the temperature sensor. This application achieves accurate fault location, predictive maintenance, and a comprehensive improvement in system reliability.

[0074] This application specifically addresses the following technical issues: it resolves the problem of inaccurate fault location and low troubleshooting efficiency caused by the difficulty in defining fault responsibility in the vehicle-charging pile coupling system; it addresses the problem of a passive and lagging early warning mechanism, making it difficult to achieve predictive maintenance in the early stages of performance degradation, thus affecting the user's charging experience; it resolves the problem of a single diagnostic dimension and high misjudgment rate caused by the isolation of vehicle and charging pile data; and it resolves the problem of the difficulty in effectively detecting latent faults in the vehicle charging socket temperature sensor itself, thus affecting the overall reliability of the system.

[0075] The technical solution of this application offers the following benefits: It achieves precise isolation and intelligent diagnosis of fault sources, significantly improving operation and maintenance efficiency. Through the "vehicle-charging station-cloud" collaborative analysis architecture and cross-validation logic designed in this application—that is, using data from the same charging station on different vehicles to determine the health of the charging station, and using data from the same vehicle on different charging stations to determine the health of the vehicle's charging socket—it can automatically, quickly, and accurately pinpoint whether the overheating fault originates at the vehicle or the charging station end. This transforms fault diagnosis from on-site operations relying on manual experience to data analysis completed in seconds on the cloud, greatly improving operation and maintenance efficiency and reducing manpower and time costs. It also enables predictive maintenance, improving user experience and safety. By establishing an initial contact resistance baseline (R1) and a contact resistance lifespan threshold (R2), and continuously monitoring the long-term trend of contact resistance changes, this application can issue early maintenance warnings to users before the performance of the vehicle's charging socket terminals deteriorates to the point of causing overheating. This allows users to schedule maintenance in advance, avoiding power drops or interruptions during emergency charging, significantly improving the reliability and satisfaction of the charging experience. Simultaneously, it helps operators proactively maintain assets, preventing problems before they occur and improving the security and availability of the charging network. Breaking down data silos and constructing a multi-source data fusion diagnostic approach, this application fully utilizes vehicle-to-everything (V2X) technology to fuse and intelligently analyze vehicle and charging pile data in the cloud, generating significant synergistic effects. This big data-based diagnostic method, compared to single-dimensional analysis, provides more comprehensive and reliable diagnostic conclusions, significantly reducing the false positive rate and offering an innovative technical solution for the industry. It provides comprehensive system status monitoring, improving overall system reliability: the newly added temperature sensor self-test function in non-charging states, through cross-verification with ambient temperature, can effectively identify sensor drift or malfunctions, ensuring the accuracy of temperature data uploaded to the vehicle controller, avoiding secondary problems caused by sensor false alarms, and enhancing the reliability of the entire charging system. The implementation of this application will reduce user complaints and after-sales costs due to charging failures, and by improving the availability and reliability of public charging facilities, it will help promote the further popularization and development of the electric vehicle industry.

[0076] The data baseline established in this application, i.e., in the cloud, is the initial contact resistance baseline (R1). After vehicle off-line testing or each replacement of the charging socket, under standard experimental conditions, such as 25°C, a standard charging gun is used to charge with a constant current I_initial, and the initial stage T1 is collected, such as the current I(t) and the vehicle socket temperature rise ΔT(t) at multiple time points within 60 seconds. This can be represented as follows:

[0077] ΔT(t)=T_socket(t)-T_ambient(t);

[0078] Among them, T_socket(t) can represent the parameter of the vehicle socket temperature changing over time, and T_ambient(t) can represent the parameter of the ambient temperature changing over time.

[0079] The initial contact resistance baseline (R1) is calculated using a formula and encrypted along with information such as the vehicle identification number, socket model, and test environment temperature, then uploaded to a cloud database as the health fingerprint of the vehicle's socket. The contact resistance lifespan threshold (R2) is determined through bench testing, where accelerated insertion and removal aging tests are performed on the same model of charging socket, recording the growth curve of the initial contact resistance baseline (R1) throughout its entire lifespan. The contact resistance value at which the socket performance degrades to a point where continued safe use is no longer permitted, such as when the temperature rise exceeds safety standards or irreversible mechanical damage occurs, is defined as the contact resistance lifespan threshold (R2). The contact resistance lifespan threshold (R2) can be a threshold derived from a large number of experimental statistics, rather than data from a single vehicle, and is stored in the cloud.

[0080] The system assesses the health of charging guns at charging piles, integrates cloud-based charging pile collaboration, diagnoses charging gun wear, and collects data. At the start of each charging session, the system records charging data for the initial T1 time period when the charging pile is serving other vehicles. This data includes charging current I_pile(t), charging gun terminal temperature T_gun(t), and ambient temperature T_amb_pile. To protect privacy, only anonymized data, such as gun number, timestamp, and electrical data, can be uploaded. The calculation principle is that the temperature rise of the charging gun head is mainly composed of heat generated by the contact resistance of the gun head terminals and heat transferred from the vehicle's charging socket. By analyzing data from the initial charging stages of multiple different vehicles, it can be assumed that the average health of the vehicle's charging socket is random, thus eliminating the influence of the charging gun's own contact resistance. The calculation formula, based on Joule's law, can be expressed as follows:

[0081] Q=I_pile² R t;

[0082] Where Q represents heat, I_pile represents charging current, R represents resistance, and t represents time.

[0083] The formula for heat can be expressed as follows:

[0084] Q=c m ΔT;

[0085] Where c represents specific heat capacity, m represents mass, and ΔT represents the temperature change difference.

[0086] By combining the equations, we can obtain the following relationship between temperature rise and current and resistance within a short time T1, neglecting heat dissipation:

[0087] ΔT_gun(t)=(R_gun+R_car) (k I_pile²(t)dt);

[0088] Where ΔT_gun(t) represents the temperature change difference of the charging gun, R_gun represents the resistance of the charging gun, R_car represents the resistance of the vehicle charging socket, and k is a comprehensive coefficient related to the specific heat capacity and mass of the material.

[0089] To simplify the calculation, the integral average over time T1 can be used, and linear regression can be employed to estimate the total resistance.

[0090] (R_gun+R_car)≈Slope=ΔT_gun_avg / (I_pile_rms² k');

[0091] Where ΔT_gun_avg represents the average temperature change difference during multiple uses of the charging gun, I_pile_rms is the root mean square value of the current during the T1 time period, and k' is a calibrated constant.

[0092] The diagnostic logic involves the cloud collecting the slope values ​​from the last 10 charging attempts (the number of attempts can be calibrated) for different vehicles. Since the resistance R_car of the vehicle's charging socket is a random variable, its expected value should be within a normal range. If multiple calculated slope values ​​are consistently high, it indicates an abnormal increase in the charging gun's resistance R_gun. The implementation and benefits are as follows: when the cloud determines that the average slope value of the charging gun exceeds the contact resistance lifespan threshold (R2) / k', it indicates severe wear on the charging gun. The cloud can then send a command to the charging station, displaying a maintenance prompt on its screen and simultaneously alerting subsequent users. Users can receive this information before charging, avoiding the selection of this faulty gun and preventing charging degradation or interruption due to gun problems, thus improving the charging experience and safety.

[0093] The vehicle-side charging socket health assessment, also known as vehicle-cloud collaboration, involves vehicle socket wear diagnosis and data collection. During each charging cycle, the vehicle controller records the charging current I_car(t) and the vehicle socket temperature rise ΔT_socket(t) within the initial time period T1. This data can be obtained from the thermistor temperature sensor on the vehicle socket, along with the ambient temperature T_amb_car. The calculation formula is similar; the vehicle socket temperature rise primarily originates from its own contact resistance.

[0094] R_car≈Slope_car=ΔT_socket_avg / (I_car_rms² k'');

[0095] Where ΔT_socket_avg represents the average temperature change difference of the vehicle charging socket during multiple uses, and k'' is the vehicle-side calibration constant.

[0096] Each charging session generates an estimated contact resistance value, R3. The diagnostic logic involves the vehicle uploading this calculated R3 value and the corresponding charging gun number to the cloud. The cloud first checks the health of the charging gun used for that charging session. If the charging gun is deemed healthy, any abnormality in the vehicle's estimated contact resistance value R3 is diagnostically significant. The judgment condition is that if the cloud detects N consecutive instances (e.g., 10 times) where the estimated contact resistance value R3 calculated from charging at different charging stations all exceeds the threshold contact resistance lifespan threshold (R2), the vehicle's charging socket is considered severely worn. The cloud can then send commands to the vehicle via the in-vehicle telematics box, pushing a warning message to the user on the vehicle's screen or mobile application, indicating charging socket wear and suggesting a repair appointment. This enables predictive maintenance before socket failure, preventing users from breaking down due to charging malfunctions on the road, improving safety and user satisfaction.

[0097] Vehicle-side temperature sensor fault diagnosis: On the vehicle side, temperature sensor fault diagnosis is performed. The diagnostic logic involves the vehicle controller continuously monitoring the charging socket terminal temperature and ambient temperature. When the vehicle is not charging and has been stationary for a sufficient period of time, such as 2 hours, to ensure thermal equilibrium between the inside and outside of the vehicle, the charging socket temperature sensor value T_sensor and the average value T_amb of other ambient temperature sensors, such as the outside temperature sensor, are simultaneously read. The calculation formula can be expressed as follows:

[0098] ΔT_error = |T_sensor - T_amb|;

[0099] ΔT_error represents the absolute value of the difference between the charging socket temperature sensor value T_sensor and the average value T_amb of the outside temperature sensor. The judgment condition is that if ΔT_error persists, such as exceeding the set reasonable deviation threshold Ts (e.g., ±5°C) after five consecutive checks, the temperature sensor signal is considered distorted or faulty. A fault indicator light will then illuminate on the dashboard, indicating a charging system malfunction and requesting immediate repair. This prevents false alarms from triggering derating or stopping charging, ensuring the reliability of charging control.

[0100] For a certain car model, the cloud-defined constant k'' sets the threshold R2 = 100 μΩ, and the temperature sensor deviation threshold Ts = 5°C. In one example, user A, driving a new car with a worn charging gun, has a socket resistance R1 = 40 μΩ, far less than the contact resistance lifespan threshold (R2). They use a No. 7 charging gun at a public charging station. The vehicle and charging station upload initial 60 seconds of data, such as average current I = 200A and gun head temperature rise ΔT_gun = 15°C, to the cloud. The cloud calculates the characteristic value Slope (R4) of the No. 7 charging gun and finds it to be 120 μΩ. Searching the historical records of the No. 7 charging gun, the cloud finds the calculation results for the most recent 9 charging attempts for other vehicles: 115, 118, 122, 119, 121, 117, 123, 120, 119 μΩ. The average of the most recent 10 attempts is as high as 118.4 μΩ, exceeding the contact resistance lifespan threshold (R2, 100 μΩ). The cloud system immediately determined that the No. 7 charging gun was worn. On the one hand, it notified the charging station operator to carry out maintenance. On the other hand, it displayed a prompt to user A on the charging pile screen, warning: The performance of this charging gun has deteriorated, which may affect the charging speed. It is recommended to replace the charging gun.

[0101] In another example, a vehicle charging socket wear failure occurred. User B's vehicle had been used for many years and underwent numerous fast charging cycles. After receiving charging data from nearly 10 cycles, the cloud calculated the vehicle's charging socket resistance R3, with results of 102, 105, 101, 107, 103, 108, 104, 106, 105, and 107 μΩ. All 10 consecutive data points exceeded the contact resistance lifespan threshold (R2, 100 μΩ), and the charging guns used in these cycles were all healthy. The cloud diagnosed the problem as a wear issue with the vehicle's charging socket and sent a message to User B's vehicle infotainment system via the vehicle network, indicating that wear had been detected in the charging socket. The cloud advised User B to visit a service center for inspection and repair as soon as possible to avoid affecting subsequent fast charging.

[0102] In another example, after user C's vehicle was parked overnight and reached thermal equilibrium, the vehicle controller automatically performed a self-check. The charging socket temperature sensor reading was 35°C, and the outside temperature was 24°C. The deviation ΔT_error was calculated as |35-24| = 11°C > Ts (5°C). This deviation persisted for several subsequent periods of inactivity. The vehicle was determined to have a faulty charging socket temperature sensor, and a yellow warning light illuminated on the dashboard, displaying a message: "Charging system malfunction, please contact service provider."

[0103] This application proposes a charging interface fault diagnosis system, including a cloud service platform, a vehicle terminal, and a charging pile terminal. The cloud service platform is configured to calculate a first characteristic value representing the connection status of a charging gun based on data from multiple different vehicles being charged by the target charging gun; and to determine the health status of the target charging gun based on the first characteristic value. The cloud service platform is also configured to calculate a second characteristic value representing the connection status of the vehicle's charging socket based on data from multiple different charging guns charging the target vehicle; and, assuming the charging gun is determined to be healthy, to determine the health status of the vehicle's charging socket based on the second characteristic value. The first and second characteristic values ​​are the equivalent contact resistance calculated based on the charging current and temperature rise data during the initial charging period, or parameters linearly related to the equivalent contact resistance. The vehicle terminal is configured to compare the charging interface temperature value with the ambient temperature value when the vehicle is in a non-charging, stationary state, and to determine a temperature sensor fault when the difference continuously exceeds a threshold.

[0104] Figure 2 This is a schematic diagram of an optional charging interface fault diagnosis system framework according to an embodiment of the present invention, as shown below. Figure 2 As shown, the system includes a cloud platform, a charging pile terminal, and a vehicle terminal. The charging pile terminal's data acquisition module collects current, gun temperature, and ambient temperature, and transmits this data to the cloud for storage and processing via the pile-end communication module. The cloud platform utilizes a fault algorithm engine for diagnostic decisions, issues commands and messages, and sends maintenance prompts to the charging pile communication module. The charging pile terminal's execution and prompting module receives these commands. Similarly, the vehicle terminal's data acquisition module, through the vehicle controller, collects data and transmits it to the cloud for storage and processing via the vehicle communication module. The cloud platform uses a fault algorithm engine for diagnostic decisions, issues commands and messages, and sends maintenance prompts to the vehicle communication module. The vehicle terminal's execution and prompting module receives these commands.

[0105] Figure 3 This is a schematic diagram of an optional charging interface fault diagnosis process according to an embodiment of the present invention, such as... Figure 3As shown, during the charging process of connecting the first charging interface with different second charging interfaces, a charging monitoring dataset corresponding to the first charging interface is obtained. When the first charging interface is the charging interface of the target charging gun, during the charging process of connecting the target charging gun with different vehicle charging sockets, multiple charging gun charging temperature data corresponding to the target charging gun are obtained, resulting in a charging monitoring dataset. Multiple first deviations between the multiple charging gun charging temperature data and preset charging gun charging temperature data are determined. Based on the multiple first deviations, fault diagnosis is performed on the target charging gun to obtain a fault diagnosis result. When the first charging interface is the target vehicle charging socket, multiple charging socket charging temperature data corresponding to the target vehicle charging socket are obtained during the charging process of connecting the target vehicle charging socket with different charging guns, resulting in a charging monitoring dataset. Multiple second deviations between the multiple charging socket charging temperature data and preset charging socket charging temperature data are determined. Based on the multiple second deviations, fault diagnosis is performed on the target charging socket to obtain a fault diagnosis result.

[0106] Figure 4 This is a schematic diagram of an optional socket temperature sensor self-test process according to an embodiment of the present invention, as shown below. Figure 4 As shown, when the target vehicle's charging socket is not charging, the system acquires first temperature data collected by the socket temperature sensor of the target vehicle's charging socket and second temperature data collected by the vehicle's external temperature sensor corresponding to the target vehicle's charging socket; based on the first and second temperature data, it determines whether the socket temperature sensor is in a faulty state; if the socket temperature sensor is not in a faulty state, it acquires multiple charging socket charging temperature data based on the socket temperature sensor.

[0107] According to another aspect of the present invention, a fault diagnosis device for a charging interface is also provided. This device can perform the fault diagnosis method for the charging interface described in the above embodiments. The specific implementation method and preferred application scenarios are the same as those described in the above embodiments, and will not be repeated here.

[0108] Figure 5 This is a schematic diagram of a fault diagnosis device for a charging interface according to an embodiment of this application, as shown below. Figure 5 As shown, the device includes the following: an acquisition module 502 and a fault diagnosis module 504.

[0109] The acquisition module 502 is used to acquire the charging monitoring dataset corresponding to the first charging interface during the charging process of the first charging interface connected to different second charging interfaces. When the first charging interface is the target charging gun, the second charging interface is the vehicle charging socket. When the first charging interface is the target vehicle charging socket, the second charging interface is the charging gun. The fault diagnosis module 504 is used to perform fault diagnosis on the first charging interface based on the charging monitoring dataset and obtain the fault diagnosis result. The fault diagnosis result is used to characterize whether there is a fault risk in the first charging interface.

[0110] In the case where the first charging interface is the charging interface of the target charging gun, the acquisition module is also used to acquire the charging temperature data of multiple charging guns corresponding to the target charging gun during the process of the target charging gun being connected to different vehicle charging sockets for charging, and to obtain a charging monitoring dataset.

[0111] The fault diagnosis module is also used to determine multiple first deviations between multiple charging gun charging temperature data and preset charging gun charging temperature data. The preset charging gun charging temperature data represents the standard charging temperature data of the target charging gun that has been set in advance. Based on the multiple first deviations, the target charging gun is fault diagnosed to obtain the fault diagnosis result.

[0112] The fault diagnosis module is also used to determine that the target charging gun has a fault risk when the average of multiple first deviations is greater than a preset threshold; and to determine that the target charging gun does not have a fault risk when the average is less than or equal to the preset threshold.

[0113] In the case where the first charging interface is the target vehicle's charging socket, the acquisition module is also used to acquire the charging temperature data of multiple charging sockets corresponding to the target vehicle's charging socket during the process of the target vehicle's charging socket being connected to different charging guns for charging, and to obtain a charging monitoring dataset.

[0114] The fault diagnosis module is also used to determine multiple second deviations between multiple charging socket charging temperature data and preset charging socket charging temperature data. The preset charging socket charging temperature data represents the standard charging temperature data of the target charging socket that has been set in advance. Based on the multiple second deviations, the target charging socket is used to perform fault diagnosis to obtain the fault diagnosis result.

[0115] The acquisition module is further configured to acquire, when the target vehicle's charging socket is not charging, first temperature data collected by the socket temperature sensor of the target vehicle's charging socket and second temperature data collected by the vehicle's external temperature sensor corresponding to the target vehicle's charging socket; determine whether the socket temperature sensor is in a fault state based on the first temperature data and the second temperature data; and collect multiple charging socket charging temperature data based on the socket temperature sensor when the socket temperature sensor is not in a fault state.

[0116] According to another aspect of the present invention, a fault diagnosis system for a charging interface is also provided. This system can execute the fault diagnosis method for the charging interface described in the above embodiments. The specific implementation method and preferred application scenarios are the same as those described in the above embodiments, and will not be repeated here.

[0117] The fault diagnosis system for the charging interface includes the following: the first charging interface and the cloud.

[0118] The system includes a first charging interface for connecting to different second charging interfaces for charging. When the first charging interface is the charging interface of a target charging gun, the second charging interface is the vehicle charging socket; when the first charging interface is the target vehicle charging socket, the second charging interface is the charging interface of the charging gun. The cloud interface communicates with the first charging interface and is used to acquire charging monitoring data sets corresponding to the first charging interface during the charging process involving the first charging interface and different second charging interfaces. Based on these charging monitoring data sets, the cloud interface performs fault diagnosis on the first charging interface to obtain fault diagnosis results. These fault diagnosis results characterize whether the first charging interface has a fault risk.

[0119] Embodiments of this application also provide an electronic device, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of the present invention during runtime.

[0120] The aforementioned memory can refer to devices inside a computer used to store data and programs, including RAM, hard disks, etc. RAM can be used to temporarily store running programs and data, while hard disks can be used to store programs and data long-term. Memory enables the computer to read and write data and execute programs. The aforementioned processor is responsible for executing instructions in computer programs and performing data processing. It can also be responsible for controlling and executing various operations, including arithmetic operations, logical operations, and data transmission.

[0121] Embodiments of this application also provide a computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of the present invention.

[0122] The aforementioned computer storage media can refer to the media used in computer memory to store certain discontinuous physical quantities. Computer storage media mainly include semiconductors, magnetic cores, magnetic drums, magnetic tapes, laser discs, etc. Computer-readable storage media include stored programs, which can be a set of instructions that a computer can recognize and execute, running on an electronic computer to meet certain information needs.

[0123] Embodiments of this application also provide a computer program product, including a computer program that, when executed by a processor, implements the methods of various embodiments of the present invention.

[0124] The aforementioned computer program products can refer to software programs that have been written, tested, and released, and can run on computers or other devices. Computer program products can include application programs, operating systems, utility software, etc., used to achieve specific functions or solve specific problems.

[0125] Embodiments of this application also provide a computer program product, including a non-volatile computer-readable storage medium for storing a computer program that, when executed by a processor, implements the methods in various embodiments of the present invention.

[0126] The aforementioned non-volatile computer-readable storage medium can refer to a medium for storing data. Non-volatile computer-readable storage media can retain data without loss when power is off and can be used to store long-term data, such as operating systems, applications, and user files. Non-volatile storage media can include hard disk drives, solid-state drives, optical disks, and flash memory storage devices, etc.

[0127] Embodiments of this application also provide a computer program that, when executed by a processor, implements the methods described in the various embodiments of the present invention.

[0128] The aforementioned computer program can refer to a set of instructions used to tell the computer to perform specific tasks or operations. Computer programs can be written by programmers using specific programming languages ​​and can include algorithms, data structures, logic, and control flow. Computer programs can be used for a variety of purposes, including application software, operating systems, etc.

[0129] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0130] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection can be through some interfaces; the indirect coupling or communication connection between units or modules can be electrical or other forms.

[0131] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0132] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0133] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0134] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for diagnosing faults in a charging interface, characterized in that, include: During the process of connecting and charging the first charging interface with different second charging interfaces, the charging monitoring dataset corresponding to the first charging interface is obtained. In the case that the first charging interface is the charging interface of the target charging gun, the second charging interface is the vehicle charging socket. In the case that the first charging interface is the target vehicle charging socket, the second charging interface is the charging interface of the charging gun. Based on the charging monitoring dataset, fault diagnosis is performed on the first charging interface to obtain fault diagnosis results, wherein the fault diagnosis results are used to characterize whether there is a fault risk in the first charging interface.

2. The fault diagnosis method for the charging interface according to claim 1, characterized in that, When the first charging interface is the charging interface of the target charging gun, the charging monitoring dataset is acquired, including: During the charging process of the target charging gun being connected to different vehicle charging sockets, the charging temperature data of multiple charging guns corresponding to the target charging gun are obtained to obtain the charging monitoring dataset.

3. The fault diagnosis method for the charging interface according to claim 2, characterized in that, Based on the charging monitoring dataset, fault diagnosis is performed on the first charging interface to obtain fault diagnosis results, including: Determine multiple first deviations between the plurality of charging gun charging temperature data and preset charging gun charging temperature data, wherein the preset charging gun charging temperature data is used to represent the pre-set standard charging temperature data of the target charging gun; Based on the multiple first deviations, fault diagnosis is performed on the target charging gun to obtain the fault diagnosis results.

4. The fault diagnosis method for the charging interface according to claim 3, characterized in that, Based on the aforementioned multiple first deviations, a fault diagnosis is performed on the target charging gun to obtain the fault diagnosis results, including: If the average of the plurality of first deviations is greater than a preset threshold, the fault diagnosis result is determined to be that the target charging gun has a fault risk. If the mean value is less than or equal to the preset threshold, the fault diagnosis result is determined to be that the target charging gun has no fault risk.

5. The fault diagnosis method for the charging interface according to claim 1, characterized in that, When the first charging interface is the target vehicle's charging socket, the charging monitoring dataset is acquired, including: During the charging process of the target vehicle's charging socket connected to different charging guns, the charging temperature data of multiple charging sockets corresponding to the target vehicle's charging socket are obtained to obtain the charging monitoring dataset.

6. The fault diagnosis method for the charging interface according to claim 5, characterized in that, Based on the charging monitoring dataset, fault diagnosis is performed on the first charging interface to obtain fault diagnosis results, including: Determine multiple second deviations between the plurality of charging socket charging temperature data and preset charging socket charging temperature data, wherein the preset charging socket charging temperature data is used to represent the pre-set standard charging temperature data of the target charging socket; Based on the multiple second deviations, fault diagnosis is performed on the target charging socket to obtain the fault diagnosis result.

7. The fault diagnosis method for the charging interface according to claim 5, characterized in that, The method further includes: When the target vehicle charging socket is not charging, acquire the first temperature data collected by the socket temperature sensor of the target vehicle charging socket and the second temperature data collected by the vehicle exterior temperature sensor of the vehicle corresponding to the target vehicle charging socket. Based on the first temperature data and the second temperature data, determine whether the socket temperature sensor is in a faulty state; When the socket temperature sensor is not faulty, the charging temperature data of the multiple charging sockets are collected based on the socket temperature sensor.

8. A fault diagnosis device for a charging interface, characterized in that, include: The acquisition module is used to acquire the charging monitoring dataset corresponding to the first charging interface during the charging process when the first charging interface is connected to different second charging interfaces. In the case where the first charging interface is the target charging gun, the second charging interface is the vehicle charging socket. In the case where the first charging interface is the target vehicle charging socket, the second charging interface is the charging gun. The fault diagnosis module is used to perform fault diagnosis on the first charging interface based on the charging monitoring dataset and obtain fault diagnosis results, wherein the fault diagnosis results are used to characterize whether there is a fault risk in the first charging interface.

9. A fault diagnosis system for a charging interface, characterized in that, include: The first charging interface is used to connect and charge different second charging interfaces. When the first charging interface is the charging interface of the target charging gun, the second charging interface is the vehicle charging socket. When the first charging interface is the target vehicle charging socket, the second charging interface is the charging interface of the charging gun. The cloud is connected to the first charging interface. The cloud is used to obtain the charging monitoring dataset corresponding to the first charging interface during the charging process of the first charging interface and different second charging interfaces. Based on the charging monitoring dataset, the cloud performs fault diagnosis on the first charging interface and obtains the fault diagnosis result. The fault diagnosis result is used to characterize whether there is a fault risk in the first charging interface.

10. An electronic device, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, executes the fault diagnosis method for the charging interface according to any one of claims 1 to 7.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, wherein, when the executable program is executed, it controls the device on which the storage medium is located to perform the fault diagnosis method for the charging interface according to any one of claims 1 to 7.

12. A computer program product, characterized in that, It includes computer instructions that, when executed by a processor, implement the fault diagnosis method for the charging interface according to any one of claims 1 to 7.

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