A fault identification processing method, system, device and storage medium of a charging pile

Abstract

The application provides a charging pile fault identification processing method, system, device and storage medium. The fault identification processing method comprises: when detecting that a target charging pile has charging abnormalities, obtaining multiple historical charging orders associated with the target charging pile; according to multiple identification factors representing charging pile faults, statistically analyzing the multiple historical charging orders to obtain characteristic values of each identification factor; judging whether the characteristic values of the multiple identification factors hit a target fault discrimination rule corresponding to a current time period; if so, determining that the target charging pile has a fault, and performing fault processing on the target charging pile according to a fault processing strategy corresponding to the hit target fault discrimination rule. Based on this mode, the application can efficiently identify the charging pile that truly has a fault by comprehensively analyzing the multiple historical charging orders associated with the target charging pile having charging abnormalities, so as to improve the fault identification accuracy and fault processing efficiency of the charging pile.

Description

Technical Field

[0001] This application relates to the field of information processing technology, and more specifically, to a method, system, device, and storage medium for fault identification and processing of charging piles. Background Technology

[0002] Currently, existing technologies for fault identification and handling of charging piles mainly rely on passive feedback from on-site inspection personnel and users when they discover charging anomalies. However, charging anomalies are not necessarily caused by equipment failure in the charging pile. For example, when the charging vehicle and the charging pile are incompatible, or when the charging vehicle itself malfunctions, it will also lead to charging failure (i.e., charging anomaly). Based on this, the existing technologies can only detect whether a charging pile is experiencing a charging anomaly, but cannot efficiently identify the target charging pile that actually has a fault and needs to be handled. This results in low accuracy in fault identification and low efficiency in fault handling. Summary of the Invention

[0003] In view of this, the purpose of this application is to provide a method, system, device and storage medium for fault identification and processing of charging piles. By comprehensively analyzing multiple historical charging orders associated with the target charging pile with charging anomalies, the charging pile that has actually malfunctioned can be efficiently identified, thereby improving the accuracy of fault identification and the efficiency of fault processing for charging piles.

[0004] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings.

[0005] In a first aspect, embodiments of this application provide a fault identification and processing method for charging piles, the fault identification and processing method comprising:

[0006] When a charging anomaly is detected at the target charging station, multiple historical charging orders associated with the target charging station are retrieved.

[0007] Based on multiple identification factors characterizing charging pile faults, statistical analysis is performed on the multiple historical charging orders to obtain the characteristic value of each identification factor;

[0008] Determine whether the feature values ​​of the various identification factors match the target fault discrimination rule corresponding to the current time period;

[0009] If the fault is detected, the target charging pile is determined to be faulty, and the fault is handled according to the fault handling strategy corresponding to the fault detection rule.

[0010] Secondly, embodiments of this application provide a fault identification and processing system for charging piles. The fault identification and processing system includes a charging service platform, at least one charging pile, an operation and maintenance user terminal, and a charging user terminal; wherein the charging service platform is used for:

[0011] When a charging anomaly is detected at the target charging station, multiple historical charging orders associated with the target charging station are retrieved.

[0012] Based on multiple identification factors characterizing charging pile faults, statistical analysis is performed on the multiple historical charging orders to obtain the characteristic value of each identification factor;

[0013] Determine whether the feature values ​​of the various identification factors match the target fault discrimination rule corresponding to the current time period;

[0014] If the fault is detected, the target charging pile is determined to be faulty, and the fault is handled according to the fault handling strategy corresponding to the fault detection rule.

[0015] Thirdly, embodiments of this application provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the above-described fault identification and processing method for charging piles.

[0016] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the above-described fault identification and processing method for charging piles.

[0017] The technical solutions provided by the embodiments of this application may include the following beneficial effects:

[0018] This application provides a method, system, device, and storage medium for fault identification and processing of charging piles. When a charging anomaly is detected in a target charging pile, multiple historical charging orders associated with the target charging pile are acquired. Based on various identification factors characterizing charging pile faults, statistical analysis is performed on these historical charging orders to obtain feature values ​​for each identification factor. It is then determined whether the feature values ​​of these factors match the target fault discrimination rule corresponding to the current time period. If they match, the target charging pile is determined to be faulty, and fault processing is performed on the target charging pile according to the fault processing strategy corresponding to the matched target fault discrimination rule. Based on this approach, this application, by comprehensively analyzing multiple historical charging orders associated with the target charging pile experiencing a charging anomaly, can efficiently identify the charging pile that is truly faulty, thereby improving the accuracy of charging pile fault identification and the efficiency of fault processing. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A schematic diagram of the structure of a fault identification and processing system for a charging pile provided in an embodiment of this application is shown;

[0021] Figure 2 A flowchart illustrating a fault identification and processing method for a charging pile provided in an embodiment of this application is shown.

[0022] Figure 3 A flowchart illustrating a method for determining the plurality of fault discrimination rules corresponding to the current time period, provided in an embodiment of this application, is shown.

[0023] Figure 4 The illustration shows a flowchart of a method for determining whether the feature values ​​of multiple identification factors match the target fault discrimination rule corresponding to the current time period, according to an embodiment of this application.

[0024] Figure 5 The illustration shows a flowchart of a first method for determining fault identification rules associated with the same execution object under different rule effective time periods, as provided in an embodiment of this application.

[0025] Figure 6 The illustration shows a flowchart of a second method for determining fault identification rules associated with the same execution object under different rule effective time periods, as provided in an embodiment of this application.

[0026] Figure 7 The flowchart of the third method for determining fault identification rules associated with the same execution object under different rule effective time periods provided in the embodiments of this application is shown.

[0027] Figure 8 This is a schematic diagram of the structure of an electronic device 800 provided in an embodiment of this application. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the accompanying drawings in this application are for illustrative and descriptive purposes only and are not intended to limit the scope of protection of this application. Furthermore, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of this application. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0029] Furthermore, the described embodiments are merely some, not all, of the embodiments of this application. The components of the embodiments of this application described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0030] It should be noted that the term "comprising" will be used in the embodiments of this application to indicate the presence of the features declared thereafter, but does not exclude the addition of other features.

[0031] Currently, existing technologies for fault identification and handling of charging piles mainly rely on passive feedback from on-site inspection personnel and users when they discover charging anomalies. However, charging anomalies are not necessarily caused by equipment failure in the charging pile. For example, when the charging vehicle and the charging pile are incompatible, or when the charging vehicle itself malfunctions, it will also lead to charging failure (i.e., charging anomaly). Based on this, the existing technologies can only detect whether a charging pile is experiencing a charging anomaly, but cannot efficiently identify the target charging pile that actually has a fault and needs to be handled. This results in low accuracy in fault identification and low efficiency in fault handling.

[0032] Based on this, embodiments of this application provide a method, system, device, and storage medium for fault identification and processing of charging piles. By comprehensively analyzing multiple historical charging orders associated with a target charging pile with charging anomalies, the system can efficiently identify the charging pile that has actually malfunctioned, thereby improving the accuracy of fault identification and the efficiency of fault processing for charging piles.

[0033] Here, in the embodiments of this application, Figure 1This paper presents a schematic diagram of the structure of a fault identification and processing system for a charging pile according to an embodiment of this application. Figure 1 As shown, the fault identification and processing system includes: a charging service platform 100, at least one charging pile 101, at least one charging user terminal 102, and at least one maintenance user terminal 103; the complete charging process for a vehicle can be briefly described as follows:

[0034] The user of the vehicle to be charged can send a charging request to the charging service platform 100 through the charging user terminal 102. Based on the received charging request, the charging service platform 100 creates a charging order for this charging service and sends a start charging command to the charging pile 101 specified in the charging request. After receiving the start charging command from the charging service platform 100, the charging pile 101 begins charging the vehicle to be charged. During the charging process, the charging pile 101 continuously reports various charging interaction data generated during the charging process to the charging service platform 100. When it is determined that the charging is over (e.g., upon receiving a stop charging request from the charging user terminal 102, or confirming that the vehicle to be charged is fully charged based on the charging interaction data reported by the charging pile 101), the charging service platform 100 sends a stop charging command and the reason for stopping the charging to the charging pile 101. At this time, the charging pile 101 stops charging the vehicle to be charged and reports the billing information corresponding to this charging to the charging service platform 100.

[0035] Regarding the above charging process, it should be noted that: the charging service platform 100 can be a charging cloud platform created based on a cloud interaction system; wherein, the cloud interaction system includes a server and client devices (i.e., terminal devices); the charging pile 101 refers to a charging device capable of charging vehicles; the charging user terminal 102 represents the electronic device (e.g., a mobile phone, tablet, or other terminal device) held by the user requesting charging; and the maintenance user terminal 103 represents the electronic device held by the manufacturer of the charging pile 101 or the maintenance personnel responsible for maintaining the charging pile 101. This application embodiment does not limit the specific device type or quantity of the charging user terminal 102, charging pile 101, and maintenance user terminal 103.

[0036] In addition, regarding the various charging interaction data continuously reported by the charging pile 101 to the charging service platform 100 during the charging process, it should be noted that the above-mentioned charging interaction data includes, but is not limited to: the charging order number of this charging, the user identity information uid (UserID, user identifier) ​​of this charging request, the type of exception code (if a charging exception occurs during the charging process, the charging pile can report the corresponding exception code type according to the specific circumstances of the charging exception. Generally, the charging pile can report more than 300 exception code types), the vehicle vin (Vehicle Identification Number) code information, and the charging order creation time of this charging; the specific data content of the above-mentioned charging interaction data is not limited in any way in this embodiment.

[0037] In this embodiment, the fault identification and processing method for charging piles provided in this application is applied to the charging service platform side of the aforementioned fault identification and processing system. The following is a detailed description of the fault identification and processing method for charging piles provided in this embodiment:

[0038] Reference Figure 2 As shown, Figure 2 This paper presents a flowchart illustrating a fault identification and processing method for a charging pile according to an embodiment of this application. The fault identification and processing method includes steps S201-S204; specifically:

[0039] S201, when a charging abnormality is detected at the target charging pile, obtain multiple historical charging orders associated with the target charging pile.

[0040] Here, as Figure 1 As shown, the fault identification and processing system includes multiple charging piles 101. As can be seen from the description of the complete charging process above, during the charging process, each charging pile 101 can continuously report various charging interaction data to the charging service platform 100. At this time, the charging service platform 100 can detect whether each charging pile 101 has a charging abnormality based on the charging interaction data reported by each charging pile 101. The charging pile 101 that is determined to have a charging abnormality is used as the target charging pile in step S201. That is, the target charging pile only represents the charging pile with a charging abnormality, rather than the charging pile that has a fault.

[0041] It should be noted that the aforementioned target charging piles represent charging piles that the charging service platform has actually detected as having charging anomalies. The specific number of the aforementioned target charging piles is determined based on the actual detection results. This application embodiment does not impose any limitation on the specific number of the aforementioned target charging piles.

[0042] In this embodiment of the application, taking a target charging pile as an example, and in conjunction with the relevant description of the complete charging process described above, as an optional implementation, the charging service platform can determine that a charging abnormality has been detected in the target charging pile when any of the following abnormal situations are detected during the charging process:

[0043] Abnormal Situation 1: Based on the charging request sent by the charging user, the charging service platform sends a start charging command to the target charging pile specified in the charging request. If no confirmation feedback information for the start charging command is received from the target charging pile within the preset response time period, it is determined that an abnormal situation of charging start failure has occurred.

[0044] Abnormal Situation 2: Based on the charging interaction data continuously reported by the target charging pile during the charging process, if it is detected that the battery level of the vehicle to be charged does not increase significantly in adjacent time periods, then an abnormal situation of charging failure is determined to have occurred.

[0045] Abnormal situation 3: An abnormal situation of the target charging station tripping is detected; where tripping indicates that the vehicle battery is not fully charged when the user does not actively stop the charging.

[0046] Abnormal situation 4: When the charging speed of the target charging pile for the vehicle to be charged is significantly lower than the preset speed threshold, that is, the charging speed is too slow because the charging power provided by the target charging pile is insufficient to meet the charging power requirements of the vehicle to be charged per unit time.

[0047] Abnormal situation 5: After charging is completed and the charging order for this charging is suspended, no response information is received from the target charging pile for the charging order for a long time (i.e., exceeding the preset waiting time threshold).

[0048] Here, based on the above optional implementation methods, and considering the need to identify charging anomalies caused by charging pile malfunctions (i.e., to identify that the target charging pile with the charging anomaly has indeed experienced a equipment malfunction), and taking into account that the charging interaction data reported by the charging pile during the charging process contains anomaly code types, and that some anomaly code types include specific anomaly codes that can clearly indicate that the charging anomaly is caused by non-equipment malfunctions (e.g., anomaly code 5009 indicates that the output capability of a BHM type charging pile does not match the vehicle model), as another optional implementation method, the charging service platform can also, upon detecting any of the following anomalies in the target charging pile, determine that the target charging pile has a charging anomaly only after confirming that the anomaly code reported by the target charging pile does not belong to the above-mentioned specific anomaly codes (i.e., anomaly codes that can clearly indicate that the charging anomaly is caused by non-equipment malfunctions), based on the specific anomaly code reported by the target charging pile. This is to filter out some target charging piles whose charging anomalies are clearly caused by non-equipment malfunctions before subsequent actual fault identification, thereby improving the accuracy and efficiency of subsequent fault identification of the target charging pile.

[0049] Specifically, in step S201, considering the real-time nature of charging pile fault identification, as a preferred embodiment, the historical charging orders generated (i.e. managed) by the target charging pile in the most recent detection time period can be preferred as the above-mentioned multiple historical charging orders actually obtained, so as to improve the accuracy of subsequent fault identification of the target charging pile.

[0050] It should be noted that the specific time period of the aforementioned most recent detection period can be set according to the actual fault identification needs (e.g., it can be the most recent month). This application embodiment does not impose any limitations on the specific time period of the aforementioned most recent detection period or the specific number of the aforementioned historical charging orders that can be obtained.

[0051] S202, based on multiple identification factors characterizing charging pile faults, perform statistical analysis on the multiple historical charging orders to obtain the feature value of each identification factor.

[0052] Here, before the charging service platform performs fault identification processing on the target charging pile, it can abstract multiple identification factors that can characterize the charging pile fault (i.e., the charging abnormality is likely caused by the charging pile fault) based on the charging orders associated with each of the multiple charging piles that have experienced charging abnormalities. Each identification factor corresponds to a comprehensive statistical feature derived from the statistical analysis of multiple charging orders associated with the same charging pile. In other words, each identification factor defines a specific calculation method for statistical analysis of multiple charging orders associated with the same charging pile. Thus, in the actual fault identification process, the charging service platform can perform statistical analysis on multiple historical charging orders associated with the target charging pile that has experienced charging abnormalities based on the multiple identification factors that characterize the charging pile fault, and obtain the specific value of each identification factor corresponding to the target charging pile (i.e., the aforementioned feature value).

[0053] In the embodiments of this application, the aforementioned multiple identification factors characterizing charging pile faults include both common features that can characterize charging pile faults from a general perspective (i.e., common features of charging orders that result in charging abnormalities due to charging pile faults) and individual features that can characterize charging pile faults from different specific scenarios (i.e., charging scenarios where there is a high degree of correlation between charging abnormalities and charging pile faults) (which are also equivalent to scenario-based features abstracted in each specific scenario). Based on the existence of the aforementioned common features, the efficiency of identifying faulty charging piles can be accelerated. By adding the aforementioned individual features during the fault identification process, the accuracy of the charging pile fault identification results can be improved.

[0054] Specifically, before performing step S202 above, by combining charging order data associated with multiple charging piles that have historically experienced charging anomalies, the charging service platform can pre-determine various identification factors that can characterize charging pile faults through the methods described in steps a1-a2:

[0055] Step a1: From the charging orders associated with the first type of charging piles that have experienced charging anomalies, extract the common features that can characterize the charging pile faults from a general perspective as the identification factors.

[0056] Here, the common features essentially represent the common characteristics of charging orders that are abnormal due to charging pile failure. In other words, the general dimension corresponding to the common features is essentially equivalent to a general charging scenario where the causal relationship between charging pile failure (cause) and charging abnormality (result) is low (below a preset correlation threshold). Therefore, when executing step a1, the charging service platform can obtain the charging orders generated under the above general charging scenario (i.e., the first type of charging pile where charging abnormality occurs) (i.e., the charging orders associated with the above first type of charging pile) as the first raw data for abstracting the above common features.

[0057] In this application embodiment, as an optional embodiment, the above-mentioned common features that can serve as identification factors may include at least one of the following:

[0058] Common feature 1: Abnormality rate; whereby the abnormality rate represents the ratio between the number of abnormal orders (i.e., charging orders with abnormal charging) generated by the same charging pile (i.e., the same first-class charging pile with charging abnormality) within a detection time period (e.g., one month; the specific time period length is not limited in this application embodiment) and the total number of charging orders generated.

[0059] For example, taking charging pile a that has an abnormal charging situation as an example, if the detection period is 1 month, and the total number of charging orders for charging pile a in the most recent month is 200, and the abnormal orders marked with charging abnormality are 120, then the abnormality rate of charging pile a can be calculated to be 60%.

[0060] Here, a higher anomaly rate indicates a higher probability of charging failure (equivalent to the above-mentioned charging anomaly). A higher probability of charging failure for a charging pile indicates that the charging pile is likely to have a device malfunction. However, since there is no strong causal relationship between charging pile malfunction and charging failure (i.e., it is impossible to directly rule out the influence of non-charging pile reasons such as vehicle reasons or improper user operation on the result of charging failure), the above-mentioned anomaly rate is a common feature that can be used as an identification factor rather than an individual feature.

[0061] Common feature 2: Number of failed users; where the number of failed users represents the number of users corresponding to abnormal orders generated by the same charging pile (i.e., the same first-class charging pile where charging abnormality occurred) within a detection time period.

[0062] It should be noted that when counting the number of failed users, for multiple abnormal orders belonging to the same user, the user count is only counted once to exclude the interference of some novice users (i.e. users who are prone to operational errors that cause charging abnormalities) on the final count of failed users.

[0063] Here, a larger number of failed users means that there are more different users who fail to charge at the same charging station. In this case, based on the above-mentioned anomaly rate, by counting the above-mentioned failed users, we can effectively exclude the above-mentioned novice users and the situation where users continuously retry due to vehicle malfunctions, resulting in an excessively high anomaly rate for a single charging station.

[0064] It should be noted that the common features that can be used as identification factors include, but are not limited to, the two common features mentioned above. This application does not impose any limitations on the specific number or meaning of the common features that can be used as identification factors.

[0065] Step a2: From the charging orders associated with the second type of charging piles that have charging abnormalities in specific scenarios, abstract the scenario-based features that can characterize the charging pile faults from the perspective of individual characteristics as the identification factors.

[0066] Here, unlike the general charging scenario represented in step a1 above, in step a2, the specific scenario described above represents a charging scenario where the causal correlation between charging pile malfunction and charging anomaly is higher than a preset relevant threshold. That is, the specific scenario described above represents a concrete charging scenario in which charging ultimately fails due to charging pile malfunction (which is equivalent to charging anomaly). At this time, compared with the common features mentioned above, based on the existence of the scenario-specific features (which are equivalent to individual features) corresponding to each specific scenario, it is possible to more accurately filter out the charging piles that actually have equipment malfunctions from the charging piles that have charging anomalies, so as to improve the accuracy of the charging pile malfunction identification results.

[0067] In this embodiment of the application, as an optional embodiment, the above-mentioned contextual features that can serve as identification factors may include at least one of the following:

[0068] Contextual Feature 1: User Historical Success Rate; where, the user historical success rate represents the charging success rate of users who failed to charge (i.e., users corresponding to abnormal orders) within the recent detection period.

[0069] Here, for charging piles experiencing charging anomalies, if a user fails to charge at that pile, a single charging failure (i.e., an abnormal charging event) cannot accurately pinpoint whether the cause of the failure is a malfunction in the charging pile itself. Therefore, in this specific scenario, by statistically analyzing the charging success rate (i.e., the user's historical success rate) of the user who failed to charge at the same charging pile over a recent detection period, a high historical success rate (e.g., exceeding a preset success rate threshold) indicates that the user has a high success rate at other charging piles. In this case, the failure at the current charging pile suggests that the failure is highly likely due to a malfunction in the charging pile itself. Based on these common characteristics, this approach effectively filters out charging failures caused by user error or other user-related reasons, improving the accuracy of charging pile fault identification.

[0070] It should be noted that when calculating the above-mentioned historical success rate of users on a per-charging-pile basis (e.g., calculating the feature value of the above-mentioned historical success rate of users from multiple historical charging orders associated with a target charging pile), considering that the same charging pile may be associated with multiple users who have failed to charge within the same time detection period, the above-mentioned historical success rate of users can be the maximum value or the average value of the historical success rates of these users respectively. In this case, the embodiments of this application do not impose any limitations.

[0071] For example, if user x1 fails to charge this time, but has a total of 20 associated charging orders in the recent detection period (e.g., 1 month), and 18 of those charging orders are successfully charged (e.g., charging orders that have been confirmed and paid), then user x1's historical success rate is 90% (i.e., the number of successfully charged orders divided by the total number of charging orders).

[0072] Scenario-based feature 2: Specific exception codes; Each abnormal order corresponds to a specific exception code, and the specific exception code type here indicates that the charging failure is clearly the responsibility of the charging pile (which is equivalent to the charging pile malfunctioning).

[0073] Specifically, the specific exception codes that can serve as the above-mentioned scenario-based feature 2 include, but are not limited to: exception code 3049 (indicating module power-on timeout in the charging pile), exception code 3008 (indicating module communication failure in the charging pile), exception code 3010 (indicating charging module failure in the charging pile), and exception code 3016 (indicating DC bus output contactor failure to operate / maloperate in the charging pile). This application embodiment does not limit the specific exception codes that can serve as the above-mentioned scenario-based feature 2.

[0074] Scenario-based feature 3: Target anomaly codes disclosed by specific charging pile manufacturers (i.e., companies that produce charging piles); Among them, some specific charging pile manufacturers disclose target anomaly codes that can indicate that the charging piles they produce have malfunctioned (different from the general anomaly codes applicable to all charging piles). In this specific scenario, when the charging pile that has malfunctioned belongs to the above-mentioned specific charging pile manufacturer, the target anomaly code can be used as an identification factor belonging to the scenario-based feature.

[0075] It should be noted that the target anomaly codes disclosed by different charging pile companies are different. For example, the target anomaly code disclosed by charging pile company q1 is 3023 (indicating that the charging interface of the charging pile produced by charging pile company q1 has an over-temperature fault), while the target anomaly code disclosed by charging pile company q2 is 4016 (indicating that the module in the charging pile produced by charging pile company q2 has a three-phase imbalance fault). This application embodiment does not limit the specific category of the above target anomaly codes.

[0076] Scenario-based feature 4: Temperature / humidity features related to abnormal weather; Specifically, in the specific scenario of charging in high-temperature weather, since high-temperature weather elements are prone to inducing overheating anomalies in charging piles, the anomaly code indicating overheating in each abnormal order can be obtained as the aforementioned temperature feature; while in the specific scenario of charging in thunderstorm weather, since thunderstorm weather elements are prone to inducing insulation anomalies in charging piles, the anomaly code indicating insulation in each abnormal order can be obtained as the aforementioned humidity feature.

[0077] It should be noted that there are multiple types of over-temperature anomaly codes that can serve as the above-mentioned temperature characteristics and insulation material anomaly codes that can serve as the above-mentioned humidity characteristics. Therefore, the embodiments of this application do not impose any limitations on these types of anomaly codes.

[0078] For example, over-temperature exception code 5051 indicates that the components inside the charging pile are overheating, and over-temperature exception code 5053 indicates that the battery pack inside the charging pile is overheating, etc.; insulation exception code 9031 indicates that the insulation detection voltage of the charging pile is abnormal, and insulation exception code 3055 indicates that the communication of the insulation sampling box of the charging pile is faulty, etc.

[0079] It should be noted that the contextual features that can be used as identification factors include, but are not limited to, the four contextual features given in the above examples. This application does not impose any limitations on the specific number of contextual features that can be used as identification factors or on the specific meaning of the features.

[0080] It should be noted that, in order to ensure the accuracy of fault identification of the target charging pile in practical applications, each of the common features / scenario-based features that can be used as identification factors can be added to the actual fault identification process corresponding to step S202 after a feature observation period. If the fault identification results obtained based on the common features / scenario-based features can continuously meet the verification conditions during this feature observation period (e.g., the accuracy can continuously meet the verification condition of being higher than a certain accuracy threshold), then the common features / scenario-based features that can continuously meet the verification conditions can be added to the actual fault identification process corresponding to step S202 as identification factors that have passed the time test and verification.

[0081] Specifically, in step S202, when performing statistical analysis on multiple historical charging orders associated with the target charging pile, the specific method for obtaining the feature value of each identification factor can be found in the relevant descriptions of each common feature / scenario-specific feature mentioned above. Repeated details will not be repeated here.

[0082] S203, determine whether the feature values ​​of the various identification factors match the target fault discrimination rule corresponding to the current time period.

[0083] In this embodiment, the electronic device (or the person holding the electronic device) that needs to handle the faulty charging pile can be abstracted as the execution object of the fault handling strategy; at this time, as an optional embodiment, refer to Figure 1 The fault identification and processing system shown above includes, but is not limited to: the charging service platform itself, the operation and maintenance user terminal (representing the electronic device held by the manufacturer of the charging pile or the operation and maintenance personnel responsible for maintaining the charging pile), the charging pile itself that has malfunctioned, and the charging user terminal that requests to use the malfunctioning charging pile for charging.

[0084] Here, for each of the above-mentioned execution objects, based on the specific object type and activity characteristics of each execution object, the time for each execution object to execute the fault handling strategy (e.g., usually limited to the whole day, but can also be limited to a specific time interval within the whole day based on specific needs, without mandatory restrictions) can be divided into multiple rule effective time periods. Each rule effective time period corresponds to a fault judgment rule. That is, each execution object is associated with multiple rule effective time periods, and each rule effective time period associated with each execution object corresponds to a fault judgment rule. The division methods of the above multiple rule effective time periods associated with different execution objects can be different, and the fault judgment rules corresponding to the same execution object in different rule effective time periods are also different.

[0085] Based on this, according to the current time period when executing step S203, the effective time period of the specific rule associated with each of the above execution objects can be determined. Thus, multiple fault discrimination rules corresponding to the current time period (i.e., fault discrimination rules corresponding to the effective time period of the above specific rule associated with each execution object) can be obtained. At this time, it is only necessary to determine whether there is a target fault rule that can be hit by the feature values ​​of multiple identification factors among the multiple fault discrimination rules corresponding to the current time period obtained in step S202.

[0086] For example, taking the execution objects as a charging service platform, an operation and maintenance user terminal, and a charging user terminal, if the current time period is within the effective period t1 of the rule associated with the charging service platform, within the effective period t2 of the rule associated with the operation and maintenance user terminal, and within the effective period t3 of the rule associated with the charging user terminal, then the fault identification rule z1 corresponding to the effective period t1, the fault identification rule z2 corresponding to the effective period t2, and the fault identification rule z3 corresponding to the effective period t3 can be obtained as the fault identification rule corresponding to the current time period. That is, the multiple fault identification rules corresponding to the current time period are: fault identification rule z1, fault identification rule z2, and fault identification rule z3. When the feature values ​​of the multiple identification factors obtained in step S202 meet the fault identification rule z1 but do not meet the fault identification rules z2 and z3, then it is determined that fault identification rule z1 belongs to the hit target fault identification rule.

[0087] It should be noted that, based on the above examples, there are multiple fault identification rules corresponding to the current time period, and the feature values ​​of the various identification factors obtained in step S202 may hit one of these fault identification rules, or they may hit multiple of these fault identification rules (for example, the feature values ​​of the various identification factors in the above example may also simultaneously meet fault identification rules z1 and z2); based on this, the specific number of target fault rules that are actually hit is not limited in this application embodiment.

[0088] S204, if the target charging pile is found to be faulty, the target charging pile is then processed according to the fault handling strategy corresponding to the fault judgment rule.

[0089] Here, in conjunction with step S203 above, it can be seen that the multiple fault discrimination rules corresponding to the current time period belong to different execution objects. When it is determined that a charging pile has a device fault, each execution object will have its own fault handling strategy to be executed (which can be understood as each execution object having a corresponding fault handling strategy). At this time, after determining the target fault discrimination rule that is actually hit, the charging service platform can instruct the target execution object associated with the target fault discrimination rule to execute the corresponding fault handling strategy (which is also equivalent to the fault handling strategy corresponding to the target fault discrimination rule). That is, the hit of the target fault discrimination rule can simultaneously indicate that the target charging pile has a fault and that the target execution object needs to perform fault handling on the target charging pile that has a fault according to the corresponding fault handling strategy (i.e., the fault handling strategy takes effect).

[0090] Specifically, taking the execution objects as including the charging service platform, the operation and maintenance user terminal, and the charging user terminal as an example, after obtaining the target execution objects associated with the above-mentioned target fault judgment rules, the specific fault handling strategies that can be executed for the target charging pile that has been determined to have a fault in step S204 are as follows, depending on the different target execution objects:

[0091] Fault handling strategy 1: When the target execution object is a charging service platform, the charging service platform performs a suspension operation on the target charging pile and sets the device status of the target charging pile to a suspended state with service paused.

[0092] Here, when the target fault identification rule is a fault identification rule associated with the charging service platform, the charging service platform can determine that the target charging pile has a device fault based on the hit of the target fault identification rule. At this time, the charging service platform can perform a suspension operation on the target charging pile, setting the device status of the target charging pile to a suspended state of suspended service, so as to prevent users from using the faulty target charging pile for charging.

[0093] Fault handling strategy 2: When the target execution object is the operation and maintenance user terminal, send a warning message to the operation and maintenance user terminal to indicate that the target charging pile has a device fault, so as to prompt the operation and maintenance user terminal to arrange operation and maintenance personnel to repair the target charging pile according to the received warning message.

[0094] Here, when the target fault identification rule is a fault identification rule associated with the charging service platform, the charging service platform can determine that the target charging pile has a device fault based on the hit of the target fault identification rule. At this time, since the target execution object associated with the target fault identification rule is the operation and maintenance user terminal, the charging service platform can send a warning message to the operation and maintenance user terminal to indicate that the target charging pile has a device fault, so that after seeing the warning message displayed on the operation and maintenance user terminal, the operation and maintenance personnel can promptly repair the target charging pile that has a fault.

[0095] It should be noted that, considering that different charging piles may correspond to different manufacturers, when implementing fault handling strategy 2, the operation and maintenance user terminal that sends the warning information to the charging service platform can be the operation and maintenance user terminal corresponding to the manufacturer of the target charging pile.

[0096] Fault handling strategy 3: When the target execution object is a charging user terminal, in response to receiving the charging request from the charging user terminal for the target charging pile, display a prompt message on the target charging pile indicating that the target charging pile has malfunctioned, and send a guidance prompt message to the charging user terminal to guide the user to change the charging pile for charging.

[0097] Here, when the target fault identification rule is a fault identification rule associated with the charging service platform, the charging service platform can determine that the target charging pile has a device fault based on the hit of the target fault identification rule. At this time, since the target execution object associated with the target fault identification rule is the charging user terminal, the charging service platform can display a prompt message on the target charging pile after actually receiving the charging user terminal's charging request for the target charging pile (e.g., the user can scan the charging QR code on the target charging pile to send the above charging request to the charging service platform). The prompt message will indicate that the target charging pile has a device fault and will promptly provide the user with guidance prompts to guide the user to switch to another charging pile for charging.

[0098] It should be noted that, based on the example content at step S203, the feature values ​​of multiple identification factors may simultaneously hit different fault discrimination rules associated with different execution objects in the current time period (that is, there are multiple target fault discrimination rules that are hit). At this time, since the execution objects corresponding to different target fault discrimination rules are different, the above fault handling strategies 1-3 will not cause interaction conflicts even if they are executed simultaneously. The specific execution of the above fault handling strategies 1-3 depends on the actual target fault discrimination rules that are hit. This application embodiment does not impose any limitation on whether the above fault handling strategies 1-3 are executed simultaneously.

[0099] The specific implementation process of each of the above steps in the embodiments of this application will be described in detail below:

[0100] Regarding the specific implementation process of step S203 above, combined with the analysis of steps S203-S204 above, it can be seen that before actually judging whether the feature values ​​of multiple identification factors hit the target fault discrimination rule, not all fault discrimination rules (i.e., the fault discrimination rules corresponding to each rule effective time period of each execution object) are used as the rule range for judging whether it hits, but based on the current time period, multiple fault discrimination rules that can be effective in the current time period (i.e., multiple fault discrimination rules corresponding to the current time period) are obtained as the rule range for judging whether it hits. That is, if the current time period is not within the rule effective time period corresponding to a fault discrimination rule, even if the feature values ​​of multiple identification factors can hit the fault discrimination rule, it will not be effective because the current time period is not within the rule effective time period of the fault discrimination rule (i.e., the execution object will not execute the subsequent corresponding fault handling strategy). Therefore, it is necessary to judge whether the feature values ​​of multiple identification factors can hit the fault discrimination rule in order to improve the judgment efficiency of the target fault discrimination rule (which is equivalent to narrowing the range of fault identification rules that can be judged).

[0101] Based on this, in one alternative implementation, such as Figure 3 As shown, Figure 3 This illustration shows a flowchart of a method for determining multiple fault discrimination rules corresponding to a current time period, as provided in an embodiment of this application. Before executing step S203, the method includes steps S301-S302, specifically:

[0102] S301, based on the effective time periods of multiple rules associated with each execution object, determine the target rule effective time period in which the current time period falls under the effective time periods of multiple rules associated with each execution object.

[0103] Here, the aforementioned execution object represents the execution device that handles the faulty charging pile when it is determined that a fault has occurred; that is, it can be, for example, [the following is an example of an execution device]. Figure 1 The charging service platform 100, operation and maintenance user terminal 103, and charging user terminal 102 are shown; any repetitions will not be repeated here.

[0104] In this embodiment of the application, based on the analysis of the above steps, it can be seen that the effective time periods of the rules associated with the same execution object can be different, and the fault judgment rules corresponding to the same execution object in different effective time periods are also different. Therefore, since the peak usage period of the charging pile needs to be considered when handling the faulty charging pile, as an optional embodiment, multiple different effective time periods of the rules associated with the same execution object can be divided according to the charging frequency of the charging pile (that is, according to the peak usage nodes of the charging pile throughout the day), and the specific fault judgment rules corresponding to the same execution object in different effective time periods can be configured accordingly.

[0105] Specifically, taking a charging service platform as an example, when dividing the charging frequency of charging piles into multiple different rule failure time periods associated with the charging service platform, we can first obtain the time when the charging service platform executes the fault handling strategy (e.g., 0-24 hours a day) and the charging peak nodes included in the time of the fault handling strategy (e.g., there are 2 charging peak nodes in the whole day, namely 2 o'clock and 22 o'clock). At this time, based on each charging peak node, the time of the above fault handling strategy is divided according to a preset fixed step size (e.g., the preset fixed step size is 2 hours), so as to obtain multiple different rule failure time periods associated with the charging service platform (e.g., obtaining 3 rule effective time periods: 0 o'clock-2 o'clock, 2 o'clock-22 o'clock, 22 o'clock-24 o'clock).

[0106] For example, taking the above example where the effective time periods of multiple rules associated with the charging service platform are 0:00-2:00, 2:00-22:00, and 22:00-24:00, if the current time period is 21:00, then the effective time period of the target rule corresponding to the current time period on the charging service platform is determined to be 2:00-22:00.

[0107] It should be noted that this application embodiment does not limit the specific division of the effective time period of the rules associated with different execution objects; for example, the effective time period of multiple rules associated with the charging service platform can be: 0:00-2:00, 2:00-22:00, 22:00-24:00, and the effective time period associated with the operation and maintenance user terminal can also be directly 0:00-24:00 (equivalent to being able to inspect and repair the charging piles that have malfunctioned throughout the day), or it can be: 8:00-12:00, 12:00-18:00, 18:00-21:00 (equivalent to providing charging pile inspection and repair services only during a part of the working time interval throughout the day).

[0108] S302, from the multiple fault discrimination rules associated with each execution object, obtain the fault discrimination rule associated with each execution object during the effective time period of the target rule as the multiple fault discrimination rules corresponding to the current time period.

[0109] Here, taking the execution targets as the charging service platform, the operation and maintenance user terminal, and the charging user terminal as an example, if the target rule effective time period corresponding to 21:00 on the charging service platform side is 2:00-22:00, the target rule effective time period corresponding to the operation and maintenance user terminal side is 18:00-21:00, and the target rule effective time period corresponding to the charging user terminal side is 0:00-24:00; then, from the multiple fault discrimination rules associated with each execution target, we can obtain the fault discrimination rule z2 associated with the charging service platform side during the rule effective time period of 2:00-22:00, the fault discrimination rule z3 associated with the operation and maintenance user terminal side during the rule effective time period of 18:00-21:00, and the fault discrimination rule z0 associated with the charging user terminal side during the rule effective time period of 0:00-24:00 as the multiple fault discrimination rules corresponding to the current time period of 21:00 (i.e., the above-mentioned fault discrimination rules z2, z3, and z0).

[0110] In this embodiment of the application, after obtaining multiple fault discrimination rules corresponding to the current time period by following the methods described in steps S301-S302 above, such as Figure 4 As shown, Figure 4 This illustration shows a flowchart of a method for determining whether the feature values ​​of multiple identification factors match the target fault discrimination rule corresponding to the current time period, according to an embodiment of this application. When executing step S203, the method includes steps S401-S403, specifically:

[0111] S401, obtain multiple fault identification rules corresponding to the current time period.

[0112] Here, the specific execution method of step S401 can be referred to the specific execution method of steps S301-S302 above, and the repeated parts will not be repeated here.

[0113] S402, for each of the fault discrimination rules corresponding to the current time period, select multiple identification factors from the multiple identification factors that fall within the specific combination specified in the fault discrimination rule.

[0114] Here, from the perspective of the specific rule content of the fault identification rule, each fault identification rule specifies a specific combination of identification factors that need to be included (e.g., the specific combination of identification factors that need to be included is the abnormality rate, the number of failed users, and the historical success rate of users at step S202 above), and the value range conditions that the feature value of each identification factor needs to meet under the specific combination (e.g., the value range condition that the feature value of "abnormality rate" needs to meet is greater than or equal to 50%, the value range condition that the feature value of "number of failed users" needs to meet is greater than or equal to 4, and the value range condition that the feature value of "historical success rate of users" needs to meet is greater than or equal to 80%).

[0115] It should be noted that this application embodiment does not impose any limitations on the specific combination method of the above-mentioned specific combination specified in each fault discrimination rule, or on the value range conditions that the feature value of each identification factor under the specific combination needs to satisfy.

[0116] For example, for a fault discrimination rule z1, the specific combination specified in fault discrimination rule z1 may include all identification factors belonging to common features and at least one identification factor belonging to scenario-specific features, or it may only include all identification factors belonging to common features. As for the value range conditions that each identification factor needs to meet, taking the value range condition that the feature value of "abnormality rate" needs to meet as greater than or equal to 50% as an example, it may also be specified that the value range condition that the feature value of "abnormality rate" needs to meet is greater than or equal to 60%.

[0117] S403, when the feature values ​​of multiple identification factors selected within the specific combination meet the value range conditions specified in the fault discrimination rule, it is determined that the fault discrimination rule belongs to the target fault discrimination rule that has been hit.

[0118] For example, taking the fault identification rule z1 associated with the charging service platform as an example, if fault identification rule z1 is a specific combination of three identification factors: "abnormal rate" + "number of failed users" + "user historical success rate", wherein the characteristic value of "abnormal rate" must be greater than or equal to 50%, the characteristic value of "number of failed users" must be greater than or equal to 4, and the characteristic value of "user historical success rate" must be greater than or equal to 80%; then, based on the characteristic value of each identification factor obtained in step S202, the characteristic values ​​corresponding to the three identification factors "abnormal rate", "number of failed users" and "user historical success rate" that are located in the above specific combination are selected. When the selected characteristic values ​​of "abnormal rate", "number of failed users" and "user historical success rate" all meet the above value range conditions, then it is determined that fault identification rule z1 belongs to the target fault identification rule that has been hit.

[0119] The following describes the specific feature meanings represented by the various identification factors shown in step S202 above. For the specific fault identification rules corresponding to the same execution object under different rule effective time periods, this application embodiment provides at least the following three optional configuration methods:

[0120] In the first optional configuration method, such as Figure 5 As shown, Figure 5 This illustration shows a flowchart of a first method for determining fault identification rules associated with the same execution object under different rule effective time periods, as provided in an embodiment of this application. Before executing steps S301-S302, the method includes steps S501-S502, specifically:

[0121] S501, the first combination of multiple identification factors that meet the preset discrimination conditions, and the first value range condition that the feature value of each identification factor under the first combination needs to meet, are used as the first fault discrimination rule associated with the same execution object under the first rule effective time period.

[0122] It should be noted that the specific content of the above-mentioned preset discrimination conditions can be set according to the actual rule setting requirements, and this application embodiment does not impose any limitations on this; for example, the preset discrimination conditions may include all identification factors belonging to common features and at least one identification factor belonging to scenario features; or they may include at least one identification factor belonging to common features and at least two identification factors belonging to scenario features, etc.

[0123] Here, since the first combination mentioned above is a combination of multiple identification factors that meet the preset discrimination conditions selected from all identification factors, the types of identification factors included in the first combination are fewer than the types of multiple identification factors that characterize charging pile faults (i.e., all types of identification factors that can characterize charging pile faults).

[0124] For example, taking the first combination as "abnormality rate" + "number of failed users" + "user historical success rate" as an example, the first fault judgment rule can be: the value range of the abnormality rate meets the condition of being greater than or equal to 50% + the value range of the number of failed users meets the condition of being greater than or equal to 4 + the value range of the user historical success rate meets the condition of being greater than or equal to 80%.

[0125] It should be noted that this application embodiment does not impose any limitations on the specific combination of the identification factors included in the first combination, or on the specific condition settings of the first value range conditions that the feature values ​​of each identification factor need to satisfy (such as 50%, 4, 80% etc. in the above example representing specific limiting thresholds for the value range, etc.).

[0126] S502, the second value range condition that the feature value of each identification factor under the first combination needs to satisfy is used as the second fault discrimination rule associated with the same execution object under the second rule effective time period.

[0127] Here, the effective time period of the second rule represents the effective time period of the rule when the charging busyness is lower than that of the first rule. The value range of the feature value defined by the first value range condition is wider than that of the second value range condition. That is, the value range condition (such as the first value range condition) corresponding to the busier charging time period (i.e. the time period that is closer to the charging peak node) is more lenient, and the feature value of the identification factor is more likely to meet the value range condition.

[0128] Based on this, by implementing the above steps S501-S502, it is equivalent to making it easier for the feature values ​​of these identification factors to hit the first fault discrimination rule associated with the charging peak period (i.e., the first rule effective time period when charging is busy) when the feature values ​​of the actual identification factors remain unchanged. Thus, by configuring fault discrimination rules of different difficulty levels for different types of rule effective time periods, the recall rate of faulty charging piles (i.e., determining the number of faulty charging piles) can be improved during the charging peak period, and the fault identification accuracy of charging piles can be improved during the charging off-peak period (i.e., increasing the difficulty of the fault discrimination rule that can be hit) can be improved.

[0129] For example, taking the first combination as "abnormality rate" + "number of failed users" + "user historical success rate" as an example, if the first rule's effective period falls near the peak charging time of 2 AM to 10 PM, the first fault judgment rule is: the abnormality rate's characteristic value range meets the condition of being greater than or equal to 50% + the number of failed users' characteristic value range meets the condition of being greater than or equal to 4 + the user historical success rate's characteristic value range meets the condition of being greater than or equal to 80%; then the second rule's effective period can be... It is close to the charging off-peak period from 0:00 to 2:00. The second fault judgment rule is: the value range of the abnormality rate meets the condition of being greater than or equal to 70% (equivalent to the value range of 50%-100% limited by the first fault judgment rule, which is wider than the value range of 70%-100% limited by the second fault judgment rule at this time) + the value range of the number of failed users meets the condition of being greater than or equal to 5 + the value range of the user's historical success rate meets the condition of being greater than or equal to 80%.

[0130] Based on this, for the same target charging pile (i.e., the actual feature value of the identification factor remains unchanged), during the first rule's effective period when charging is busier, the target charging pile is more likely to be judged as faulty. Taking the anomalous rate as an example, this means that before the anomalous rate reaches a higher 70%, the target charging pile that is faulty can be identified during the first rule's effective period when charging is busy. This is beneficial to improving the recall rate (i.e., it is easier to be judged as faulty) and timeliness rate (i.e., it is identified as faulty charging pile before the anomalous rate reaches a higher value) of faulty charging piles during the busy charging period. During the second rule's effective period when charging is idle, by increasing the difficulty of the fault identification rule that can be hit (i.e., the second fault identification rule is more difficult to be hit by the feature value of the identification factor compared to the first fault identification rule), although the recall rate of faulty charging piles may decrease (i.e., the charging pile is more difficult to be judged as faulty), it is beneficial to improve the accuracy of the fault identification results of the recalled charging piles (i.e., although the actual number of recalled charging piles is less, the proportion of the recalled charging piles that actually have equipment faults has increased).

[0131] In the second optional configuration method, such as Figure 6 As shown, Figure 6 This illustration shows a flowchart of a second method for determining fault identification rules associated with the same execution object under different rule effective time periods, as provided in an embodiment of this application. Before executing steps S301-S302, the method includes steps S601-S602, specifically:

[0132] S601, based on the first combination, obtain at least one other identification factor besides the first combination that characterizes a charging pile fault.

[0133] Here, the specific method for determining the first combination is the same as step S501 above, and the repetitive parts will not be repeated here.

[0134] It should be noted that the other identification factors mentioned above only need to ensure that they do not overlap with the original multiple identification factors in the first combination. This application embodiment does not impose any limitations on the specific type and quantity of the other identification factors mentioned above.

[0135] For example, taking the first combination as "abnormality rate" + "number of failed users" + "user historical success rate" as an example, we can additionally obtain specific abnormal codes belonging to the contextual features (see Part 2 of the contextual features above for details) as other identification factors.

[0136] S602, the second value range condition that the feature value of each identification factor under the first combination needs to satisfy, and the third value range condition that the feature value of the other identification factors needs to satisfy, are used together as the second fault discrimination rule associated with the same execution object under the second rule effective time period.

[0137] Here, the first configuration method in steps S501-S502 above is essentially equivalent to: without changing the types of identification factors included in the first combination, but only changing the value range conditions that the feature values ​​of each identification factor under the first combination need to meet according to different rule effective time periods (that is, within the effective time period of the second rule, the first value range conditions corresponding to the original effective time period of the first rule are all replaced with the second value range conditions).

[0138] Specifically, unlike the first configuration method described above, the second configuration method in steps S601-S602 is equivalent to both increasing the types of identification factors included in the first combination and changing the value range conditions that the feature values ​​of each identification factor in the first combination need to satisfy (i.e., the value range conditions that the feature values ​​of each identification factor in the original first combination need to satisfy are also changed from the first value range condition to the second value range condition). However, the second configuration method in steps S601-S602 essentially achieves the same technical effect as the first configuration method described above (i.e., both increase the difficulty of the fault identification rules that can be hit during the second rule effective period of charging idle time, which may reduce the recall rate of faulty charging piles, but is conducive to improving the accuracy of fault identification results for recalled charging piles).

[0139] For example, taking the first and second fault identification rules given in steps S501-S502 above as examples, the second fault identification rule obtained in step S602 through the second configuration method is as follows: the value range of the abnormality rate feature value meets the condition of being greater than or equal to 70% + the value range of the number of failed users feature value meets the condition of being greater than or equal to 5 + the value range of the user's historical success rate feature value meets the condition of being greater than or equal to 80% (the same as the second fault identification rule obtained in steps S501-S502 according to the first configuration method above) + the abnormal code of the charging order also needs to belong to the specific abnormal code shown in scenario feature 2 (which is equivalent to further increasing the difficulty of the fault identification rule that can be hit on the basis of the second fault identification rule obtained according to the first configuration method).

[0140] In the third optional configuration method, such as Figure 7 As shown, Figure 7 This illustration shows a flowchart of a third method for determining fault identification rules associated with the same execution object under different rule effective time periods, as provided in an embodiment of this application. Before executing steps S301-S302, the method includes steps S701-S702, specifically:

[0141] S701, based on the first combination, obtain at least one other identification factor besides the first combination that characterizes a charging pile fault.

[0142] Here, the specific execution method of step S701 is the same as that of step S601 above, and the repeated parts will not be described again here.

[0143] S702, the first value range condition that the feature value of each identification factor under the first combination needs to satisfy, and the third value range condition that the feature value of the other identification factors needs to satisfy, are used together as the second fault discrimination rule associated with the same execution object under the second rule effective time period.

[0144] Here, compared to the second configuration method in steps S601-S602 above, the third configuration method shown in steps S701-S702 is equivalent to only increasing the types of identification factors included in the first combination, without changing the value range conditions that the feature values ​​of each identification factor under the first combination need to satisfy (i.e., not changing the first value range condition to the second value range condition). At this time, by comparing the first fault discrimination rule associated with the first rule effective time period with the second fault discrimination rule associated with the second rule effective time period obtained in step S702, the third configuration method in steps S701-S702 is essentially the same as the technical effect that the above two configuration methods want to achieve (i.e., during the second rule effective time period when charging is idle, by increasing the difficulty of the fault discrimination rule that can be hit, although the recall rate of faulty charging piles may decrease, it is beneficial to improve the accuracy of the fault identification results of the recalled charging piles).

[0145] For example, taking the first and second fault discrimination rules given in steps S501-S502 above as examples, the second fault discrimination rule obtained in step S702 through the third configuration method is as follows: the value range of the abnormality rate feature value meets the condition of being greater than or equal to 50% + the value range of the number of failed users feature value meets the condition of being greater than or equal to 4 + the value range of the user's historical success rate feature value meets the condition of being greater than or equal to 80% (the same as the first fault discrimination rule obtained in steps S501-S502 according to the first configuration method above) + the abnormal code of the charging order also needs to belong to the specific abnormal code shown in scenario feature 2 (which is equivalent to the second rule taking effect during the charging idle time period, and also increases the difficulty of the fault discrimination rule that can be hit).

[0146] Based on the above-described charging pile fault identification and processing method provided in this application embodiment, when a charging abnormality is detected in a target charging pile, multiple historical charging orders associated with the target charging pile are obtained; statistical analysis is performed on the multiple historical charging orders according to various identification factors characterizing charging pile faults to obtain feature values ​​for each identification factor; it is determined whether the feature values ​​of the multiple identification factors match the target fault discrimination rule corresponding to the current time period; if they match, it is determined that the target charging pile has a fault, and the target charging pile is processed according to the fault processing strategy corresponding to the matched target fault discrimination rule. Based on this approach, this application, by comprehensively analyzing multiple historical charging orders associated with a target charging pile experiencing a charging abnormality, can efficiently identify the charging pile that has actually malfunctioned, thereby improving the accuracy of charging pile fault identification and the efficiency of fault processing.

[0147] Based on the same inventive concept, this application also provides a fault identification and processing system corresponding to the above-mentioned fault identification and processing method. Since the principle of the fault identification and processing system in the embodiments of this application is similar to that of the above-mentioned fault identification and processing method in the embodiments of this application, the implementation of the fault identification and processing system can refer to the implementation of the above-mentioned fault identification and processing method, and the repeated parts will not be described again.

[0148] like Figure 1 As shown, the fault identification and processing system includes a charging service platform 100, at least one charging pile 110, an operation and maintenance user terminal 103, and a charging user terminal 102; wherein, the charging service platform 100 is used for:

[0149] When a charging abnormality is detected in the target charging pile 110, multiple historical charging orders associated with the target charging pile 110 are retrieved.

[0150] Based on multiple identification factors characterizing charging pile faults, statistical analysis is performed on the multiple historical charging orders to obtain the characteristic value of each identification factor;

[0151] Determine whether the feature values ​​of the various identification factors match the target fault discrimination rule corresponding to the current time period;

[0152] If the fault is detected, it is determined that the target charging pile 110 has malfunctioned, and the fault handling strategy corresponding to the fault identification rule of the detected target is used to handle the fault of the target charging pile 110.

[0153] In one optional implementation, the charging service platform 100 is used to determine multiple identification factors characterizing the charging pile faults by means of the following method:

[0154] From the charging orders associated with the first type of charging piles that experienced charging anomalies, common features that can characterize charging pile faults from a general perspective are abstracted as the identification factors.

[0155] From the charging orders associated with the second type of charging piles that experience charging anomalies in specific scenarios, the scenario-based features that can characterize charging pile faults from the perspective of individual characteristics are abstracted as the identification factors; wherein, the specific scenario characterizes the charging scenario in which the causal correlation between charging pile faults and charging anomalies is higher than a preset correlation threshold.

[0156] In an optional implementation, when determining whether the feature values ​​of the various identification factors match the target fault discrimination rule corresponding to the current time period, the charging service platform 100 is used to:

[0157] Obtain multiple fault identification rules corresponding to the current time period; wherein, each fault identification rule specifies a specific combination of identification factors that need to be included and the value range conditions that the feature values ​​of each identification factor need to satisfy under the specific combination;

[0158] For each fault discrimination rule corresponding to the current time period, multiple identification factors that fall within the specific combination specified in the fault discrimination rule are selected from a variety of identification factors;

[0159] When the feature values ​​of multiple identification factors selected within the specific combination meet the value range conditions specified in the fault discrimination rule, the fault discrimination rule is determined to belong to the target fault discrimination rule that has been hit.

[0160] In one optional implementation, the current time period corresponds to multiple fault identification rules, and the charging service platform 100 determines the multiple fault identification rules corresponding to the current time period through the following method:

[0161] Based on the effective time periods of multiple rules associated with each execution object, determine the target rule effective time period in which the current time period falls under the multiple rule effective time periods associated with each execution object; wherein, the execution object represents the execution device that performs fault handling on the faulty charging pile when it is determined that the charging pile has malfunctioned;

[0162] From the multiple fault discrimination rules associated with each execution object, obtain the fault discrimination rule associated with each execution object during the effective time period of the target rule as the multiple fault discrimination rules corresponding to the current time period.

[0163] In one optional implementation, the charging service platform 100 is used to determine the fault identification rules associated with the same execution object under different rule effective time periods by the following method:

[0164] The first combination of multiple identification factors that meet the preset discrimination conditions, and the first value range condition that the feature value of each identification factor under the first combination needs to meet, are used as the first fault discrimination rule associated with the same execution object under the first rule effective time period; wherein, the first combination includes fewer types of identification factors than the multiple identification factors that characterize the charging pile fault.

[0165] The second value range condition that the feature value of each identification factor under the first combination needs to satisfy is used as the second fault discrimination rule associated with the same execution object under the second rule effective time period; wherein, the second rule effective time period represents the rule effective time period when the charging busyness is lower than the first rule effective time period; the feature value range defined by the first value range condition is wider than the second value range condition.

[0166] In one optional implementation, the charging service platform 100 is used to determine the fault identification rules associated with the same execution object under different rule effective time periods by the following method:

[0167] Based on the first combination, at least one other identification factor characterizing the charging pile fault is obtained in addition to the first combination;

[0168] The second value range condition that the feature values ​​of each identification factor under the first combination need to satisfy, and the third value range condition that the feature values ​​of the other identification factors need to satisfy, are used together as the second fault discrimination rule associated with the same execution object during the second rule effective time period.

[0169] In one optional implementation, the charging service platform 100 is used to determine the fault identification rules associated with the same execution object under different rule effective time periods by the following method:

[0170] Based on the first combination, at least one other identification factor characterizing the charging pile fault is obtained in addition to the first combination;

[0171] The first value range condition that the feature values ​​of each identification factor under the first combination need to satisfy, and the third value range condition that the feature values ​​of the other identification factors need to satisfy, are used together as the second fault discrimination rule associated with the same execution object during the second rule effective time period.

[0172] In one optional implementation, when the target charging pile is fault-handled according to the fault handling strategy corresponding to the hit target fault discrimination rule, the charging service platform 100 is used to:

[0173] Obtain the target execution object associated with the hit target fault discrimination rule;

[0174] When the target execution object is the charging service platform 100, a suspension operation is performed on the target charging pile 110, and the device status of the target charging pile 110 is set to the suspended state of suspended service.

[0175] In one optional implementation, when the target charging pile is fault-handled according to the fault handling strategy corresponding to the hit target fault discrimination rule, the charging service platform 100 is used to:

[0176] Obtain the target execution object associated with the hit target fault discrimination rule;

[0177] When the target execution object is the operation and maintenance user terminal 103, a warning message is sent to the operation and maintenance user terminal 103 to indicate that the target charging pile 110 has a device failure, so as to prompt the operation and maintenance user terminal 103 to arrange operation and maintenance personnel to repair the target charging pile 110 according to the received warning message.

[0178] In one optional implementation, when the target charging pile is fault-handled according to the fault handling strategy corresponding to the hit target fault discrimination rule, the charging service platform 100 is used to:

[0179] Obtain the target execution object associated with the hit target fault discrimination rule;

[0180] When the target execution object is the charging user terminal 102, in response to receiving the charging request from the charging user terminal 102 for the target charging pile 110, a prompt message indicating that the target charging pile 110 has malfunctioned is displayed on the target charging pile 110, and a guidance prompt message is sent to the charging user terminal 102 to guide the user to change the charging pile for charging.

[0181] Based on the fault identification and processing system provided in this application embodiment, when a charging anomaly is detected in a target charging pile, multiple historical charging orders associated with the target charging pile are obtained; statistical analysis is performed on the multiple historical charging orders according to various identification factors characterizing charging pile faults to obtain feature values ​​for each identification factor; it is determined whether the feature values ​​of the multiple identification factors match the target fault discrimination rule corresponding to the current time period; if they match, it is determined that the target charging pile has a fault, and the target charging pile is processed according to the fault processing strategy corresponding to the matched target fault discrimination rule. Based on this approach, this application, by comprehensively analyzing multiple historical charging orders associated with a target charging pile experiencing a charging anomaly, can efficiently identify the charging pile that has actually malfunctioned, thereby improving the accuracy of fault identification and the efficiency of fault processing for charging piles.

[0182] Based on the same inventive concept, this application also provides an electronic device corresponding to the above-mentioned fault identification and processing method. Since the principle of the electronic device in the embodiments of this application is similar to that of the above-mentioned fault identification and processing method in the embodiments of this application, the implementation of the electronic device can refer to the implementation of the above-mentioned fault identification and processing method, and the repeated parts will not be described again.

[0183] Figure 8 The present application provides a schematic diagram of the structure of an electronic device 800, including a processor 801, a memory 802, and a bus 803. The memory 802 stores machine-readable instructions executable by the processor 801. When the electronic device runs a fault identification and processing method for a charging pile as described in the embodiment, the processor 801 communicates with the memory 802 through the bus 803, and the processor 801 executes the machine-readable instructions. When the processor 801 executes the machine-readable instructions, it implements the aforementioned fault identification and processing method for the charging pile.

[0184] Specifically, the memory 802 and processor 801 mentioned above can be general-purpose memory and processor, without any specific limitations. When the processor 801 runs the computer program stored in the memory 802, it can execute the above-mentioned smoke and fire inspection method.

[0185] Corresponding to the fault identification and processing method for charging piles in this application, this application embodiment also provides a computer-readable storage medium storing a computer program, which is executed by a processor to perform the steps of the above-mentioned fault identification and processing method.

[0186] Specifically, the storage medium can be a general-purpose storage medium, such as a removable disk or hard disk. When the computer program on the storage medium is run, it can execute the above-mentioned fault identification and processing method.

[0187] In the embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. The system embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interface; the indirect coupling or communication connection between systems or units may be electrical, mechanical, or other forms.

[0188] 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 network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0189] In addition, the functional units in the embodiments provided in this application 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.

[0190] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion 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 this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0191] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0192] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application. All should be covered within the protection scope of this application. Therefore, the protection scope of this application should be determined by the protection scope of the claims.

Claims

1. A fault identification and processing method for charging piles, characterized in that, The fault identification and processing method includes: When a charging anomaly is detected at the target charging station, multiple historical charging orders associated with the target charging station are retrieved. Based on multiple identification factors characterizing charging pile faults, statistical analysis is performed on the multiple historical charging orders to obtain the feature value of each identification factor. The multiple identification factors include at least one of the following: common features characterizing charging pile faults from a general dimension, or scenario-specific features characterizing charging pile faults in a specific scenario from a unique feature dimension. The specific scenario characterizes a charging scenario where the causal correlation between charging pile faults and charging anomalies is higher than a preset relevant threshold. Determine whether the feature values ​​of the various identification factors match the target fault discrimination rule corresponding to the current time period; If the fault is detected, the target charging pile is determined to be faulty, and the fault is handled according to the fault handling strategy corresponding to the fault detection rule.

2. The fault identification and processing method according to claim 1, characterized in that, The following methods were used to determine the various identification factors characterizing the charging pile faults: The common features are abstracted from the charging orders associated with the first type of charging piles that experienced charging anomalies, and used as the identification factors. The scenario-based features are abstracted from the charging orders associated with the second type of charging piles that experience charging anomalies in specific scenarios, and used as the identification factors.

3. The fault identification and processing method according to claim 1, characterized in that, The rule for determining whether the feature values ​​of the various identification factors match the target fault discrimination rule corresponding to the current time period includes: Obtain multiple fault identification rules corresponding to the current time period; wherein, each fault identification rule specifies a specific combination of identification factors that need to be included and the value range conditions that the feature values ​​of each identification factor need to satisfy under the specific combination; For each fault discrimination rule corresponding to the current time period, multiple identification factors that fall within the specific combination specified in the fault discrimination rule are selected from a variety of identification factors; When the feature values ​​of multiple identification factors selected within the specific combination meet the value range conditions specified in the fault discrimination rule, the fault discrimination rule is determined to belong to the target fault discrimination rule that has been hit.

4. The fault identification and processing method according to claim 1, characterized in that, The current time period corresponds to multiple fault identification rules. These multiple fault identification rules are determined using the following method: Based on the effective time periods of multiple rules associated with each execution object, determine the target rule effective time period in which the current time period falls under the multiple rule effective time periods associated with each execution object; wherein, the execution object represents the execution device that performs fault handling on the faulty charging pile when it is determined that the charging pile has malfunctioned; From the multiple fault discrimination rules associated with each execution object, obtain the fault discrimination rule associated with each execution object during the effective time period of the target rule as the multiple fault discrimination rules corresponding to the current time period.

5. The fault identification and processing method according to claim 4, characterized in that, The following method is used to determine the fault identification rules associated with the same execution object under different rule effective time periods: The first combination of multiple identification factors that meet the preset discrimination conditions, and the first value range condition that the feature value of each identification factor under the first combination needs to meet, are used as the first fault discrimination rule associated with the same execution object under the first rule effective time period; wherein, the first combination includes fewer types of identification factors than the multiple identification factors that characterize the charging pile fault. The second value range condition that the feature value of each identification factor under the first combination needs to satisfy is used as the second fault discrimination rule associated with the same execution object under the second rule effective time period; wherein, the second rule effective time period represents the rule effective time period when the charging busyness is lower than the first rule effective time period; the feature value range defined by the first value range condition is wider than the second value range condition.

6. The fault identification and processing method according to claim 5, characterized in that, The method for determining the second fault discrimination rule associated with the same execution object during the effective period of the second rule further includes: Based on the first combination, at least one other identification factor characterizing the charging pile fault is obtained in addition to the first combination; The second value range condition that the feature values ​​of each identification factor under the first combination need to satisfy, and the third value range condition that the feature values ​​of the other identification factors need to satisfy, are used together as the second fault discrimination rule associated with the same execution object during the second rule effective time period.

7. The fault identification and processing method according to claim 5, characterized in that, The method for determining the second fault discrimination rule associated with the same execution object during the effective period of the second rule further includes: Based on the first combination, at least one other identification factor characterizing the charging pile fault is obtained in addition to the first combination; The first value range condition that the feature values ​​of each identification factor under the first combination need to satisfy, and the third value range condition that the feature values ​​of the other identification factors need to satisfy, are used together as the second fault discrimination rule associated with the same execution object during the second rule effective time period.

8. The fault identification and processing method according to claim 1, characterized in that, The fault handling of the target charging pile according to the fault handling strategy corresponding to the hit target fault discrimination rule includes: Obtain the target execution object associated with the hit target fault discrimination rule; When the target execution object is a charging service platform, the charging service platform performs a suspension operation on the target charging pile and sets the device status of the target charging pile to a suspended state with service paused.

9. The fault identification and processing method according to claim 1, characterized in that, The step of performing fault processing on the target charging pile according to the fault processing strategy corresponding to the hit target fault discrimination rule further includes: Obtain the target execution object associated with the hit target fault discrimination rule; When the target execution object is the operation and maintenance user terminal, a warning message is sent to the operation and maintenance user terminal to indicate that the target charging pile has a device failure, so as to prompt the operation and maintenance user terminal to arrange operation and maintenance personnel to repair the target charging pile according to the received warning message.

10. The fault identification and processing method according to claim 1, characterized in that, The step of performing fault processing on the target charging pile according to the fault processing strategy corresponding to the hit target fault discrimination rule further includes: Obtain the target execution object associated with the hit target fault discrimination rule; When the target execution object is a charging user terminal, the system receives a charging request from the charging user terminal for the target charging pile, displays a prompt message on the target charging pile indicating that the target charging pile has malfunctioned, and sends a guidance prompt message to the charging user terminal to guide the user to change the charging pile for charging.

11. A fault identification and processing system for charging piles, characterized in that, The fault identification and processing system includes a charging service platform and at least one charging pile, an operation and maintenance user terminal, and a charging user terminal; wherein, the charging service platform is used for: When a charging anomaly is detected at the target charging station, multiple historical charging orders associated with the target charging station are retrieved. Based on multiple identification factors characterizing charging pile faults, statistical analysis is performed on the multiple historical charging orders to obtain the feature value of each identification factor. The multiple identification factors include at least one of the following: common features characterizing charging pile faults from a general dimension, or scenario-specific features characterizing charging pile faults in a specific scenario from a unique feature dimension. The specific scenario characterizes a charging scenario where the causal correlation between charging pile faults and charging anomalies is higher than a preset relevant threshold. Determine whether the feature values ​​of the various identification factors match the target fault discrimination rule corresponding to the current time period; If the fault is detected, the target charging pile is determined to be faulty, and the fault is handled according to the fault handling strategy corresponding to the fault detection rule.

12. An electronic device, characterized in that, include: The device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, the steps of the fault identification and processing method for the charging pile as described in any one of claims 1 to 10 are performed.

13. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the fault identification and processing method for a charging pile as described in any one of claims 1 to 10.

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