Charging safety detection method and device, computer equipment and storage medium

Abstract

The invention relates to a charging safety detection method and device, computer equipment and a storage medium. The method comprises the steps of obtaining charging data of a charging pile; inputting the charging data to the multi-task prediction model to obtain a safety detection result of the charging pile; wherein the multi-task prediction model comprises an expert network and a target prediction network; the expert network comprises at least one public expert network and a plurality of private expert networks; the public expert network is used for performing feature extraction and fusion processing on the charging data related to the physical fault and the charging data related to the virtual fault of the charging pile to obtain a public feature vector; the private expert network is used for performing feature extraction on the charging data of the charging pile in different scenes to obtain private feature vectors; and the target prediction network is used for performing multi-task prediction on a target feature vector obtained by fusing the public feature vector and the private feature vector to obtain a security detection result. Therefore, the relevance of different charging characteristics is considered, and the accuracy of a safety detection result is improved.

Description

Technical Field

[0001] The present application relates to the field of charging detection technology, and in particular to a charging safety detection method, device, computer equipment and storage medium. Background Art

[0002] With the popularity of new energy vehicles, charging piles have been used more and more widely to meet the charging needs of new energy vehicles. Charging piles are electrical equipment and are usually placed outdoors with a high failure rate. When a failure occurs and the charging function cannot be completed, it not only affects the user experience of new energy vehicle users, but also endangers the safety of users' lives and property in serious cases.

[0003] At present, the safety diagnosis of charging pile equipment usually describes various fault conditions through a fault list so that maintenance personnel can quickly and accurately locate and diagnose faults. However, as a system integration device, the charging pile integrates multiple components such as the billing module, control module, and power regulation module. There are many kinds of faults that may occur during the charging process, and the faults may occur on different components at different time periods, that is, in different scenarios; relying on the fault list for maintenance affects the timeliness and accuracy of fault diagnosis; and different faults affect each other, and the correlation between different fault factors cannot be considered. Summary of the invention

[0004] Based on this, it is necessary to provide a charging safety detection method, device, computer equipment and storage medium to address the above technical problems.

[0005] In a first aspect, the present application provides a charging safety detection method, the method comprising: Acquiring charging data of a charging pile; the charging data including charging features of different feature types; Inputting the charging data into a multi-task prediction model to obtain a safety detection result of the charging pile; Among them, the multi-task prediction model includes an expert network and a target prediction network; the expert network includes at least one public expert network and multiple private expert networks; the public expert network is used to extract features and fuse the charging data related to the physical fault of the charging pile and the charging data related to the virtual fault to obtain a public feature vector; the private expert network is used to extract features from the charging data of the charging pile in a specific scenario and / or a specific task, respectively, to obtain a private feature vector; the target prediction network is used to perform multi-task prediction on the target feature vector obtained by fusing the public feature vector and the private feature vector to obtain the safety detection result.

[0006] In one embodiment, the multi-task detection model also includes a gating network; each of the expert networks is connected to multiple gating networks; the gating networks are connected to the target prediction networks one-to-one; the gating networks are used to obtain weighting coefficients based on the public feature vectors and / or the private feature vectors; the weighting coefficients correspond to the weights of each feature vector.

[0007] In one embodiment, the method further comprises: Calculating according to the first input feature and the first output feature of the public expert network to obtain first relevant information; wherein the first input feature includes charging data related to the physical fault of the charging pile and charging data related to the virtual fault; the first output feature is the public feature vector; Maximizing the first relevant information to obtain a first loss function based on the public expert network; Calculating according to the first output feature and the second output feature of each of the private expert networks to obtain second related information; wherein the second output feature is the private feature vector; Minimizing the second relevant information to obtain a second loss function based on the private expert network; A target loss function of the multi-task prediction model is determined according to the first loss function and the second loss function.

[0008] In one embodiment, the method further comprises: In the case where the safety detection result is a charging pile fault, obtaining a fault characteristic parameter corresponding to the safety detection result; the fault characteristic parameter includes at least one of a fault type, a fault location, and a fault range; Generate a reminder task according to the fault characteristic parameters and determine the priority corresponding to the reminder task; According to the priority of the reminder task, the alarm information corresponding to the reminder task is output in sequence.

[0009] In one embodiment, the obtaining of the fault characteristic parameter corresponding to the safety detection result includes: Obtain the number of faults, charging failures, charging fault temperature and charging fault current value that occur in the same charging pile within a preset time period; The generating a reminder task and determining a priority corresponding to the reminder task according to the fault characteristic parameter includes: The priority corresponding to the reminder task is determined according to at least one of the fault quantity, the charging failure number, the charging fault temperature and the charging fault current value.

[0010] In one embodiment, determining the priority corresponding to the reminder task according to at least one of the number of faults, the number of charging failures, the charging fault temperature, and the charging fault current value includes: Determine the charging pile whose number of faults is greater than or equal to a first threshold as a first candidate charging pile; Selecting a charging pile whose number of charging failures is greater than or equal to a second threshold from the first candidate charging piles as a second candidate charging pile; wherein the priority corresponding to the reminder task of the second candidate charging pile is higher than the priority corresponding to the reminder task of the first candidate charging pile; The second candidate charging piles are sorted according to at least one of the charging fault temperature and the charging fault current value, and the priorities corresponding to the reminder tasks of the second candidate charging piles are determined in sequence according to the sorting results.

[0011] In one embodiment, the sequentially outputting the alarm information corresponding to the reminder task includes at least one of the following: The first method is to output the warning information in sequence based on the voice of the voice device of the charging pile; The second method: sending the alarm information to a third-party terminal; the alarm information is used for display by the third-party terminal; The third type: sending the alarm information to the third-party terminal; the alarm information is used for motor vibration output based on the third-party terminal.

[0012] In a second aspect, the present application also provides a charging safety detection device, the device comprising: An acquisition module is used to acquire charging data of a charging pile; the charging data includes charging features of different feature types; The detection module is used to input charging data into the multi-task prediction model to obtain the safety detection results of the charging pile; Among them, the multi-task prediction model includes an expert network and a target prediction network; the expert network includes at least one public expert network and multiple private expert networks; the public expert network is used to extract features and fuse charging data related to physical faults of charging piles and charging data related to virtual faults to obtain public feature vectors; the private expert network is used to extract features of charging data of charging piles in specific scenarios and / or specific tasks respectively to obtain private feature vectors; the target prediction network is used to perform multi-task prediction on the target feature vector obtained by fusing the public feature vector and the private feature vector to obtain a safety detection result.

[0013] In a third aspect, the present application further provides a computer device, wherein the computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method described in any embodiment of the present application is implemented.

[0014] In a fourth aspect, the present application further provides a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the method described in any embodiment of the present application is implemented.

[0015] The above-mentioned charging safety detection method, device, computer equipment, storage medium and computer program product determine the safety detection result of the charging pile (for example, the probability of failure of the charging pile) by obtaining the charging data related to the physical failure of the charging pile and the charging data related to the virtual failure and inputting them into the multi-task prediction model. It can adapt to the charging characteristics corresponding to different scenarios / tasks, and there is no need to perform separate algorithm modeling for each characteristic, thereby improving processing performance; and the correlation between different types of charging characteristics can be considered to improve the accuracy of the safety detection results. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is an application environment diagram of a charging safety detection method according to an exemplary embodiment; Figure 2 is a flow chart of a charging safety detection method according to an exemplary embodiment; Figure 3 is a structural diagram of a multi-task detection model according to an exemplary embodiment; Figure 4 is a structural block diagram of a charging safety detection device according to an exemplary embodiment; Figure 5 The figure is a diagram showing the internal structure of a computer device according to an exemplary embodiment. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0018] The terms "first", "second" and "third" in the embodiments of the present application are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, the features defined as "first", "second" and "third" may explicitly or implicitly include at least one of the features. In the description of the present application, "at least one" is used to indicate one or more; "multiple" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or equipment.

[0019] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0020] The charging safety detection method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the computer device 102 communicates with the server 104 through a network; optionally, the network can be a wired network or a wireless network. The charging pile 106 can obtain the charging data that the computer device 102 needs to process in real time during the charging of the target vehicle. Among them, the computer device 102 can be any mobile terminal or a fixed terminal. The terminal can be a device that provides voice and / or data connectivity to the user. Exemplarily, the terminal can be an Internet of Things terminal, such as a sensor device, a mobile phone (or a "cellular" phone) and a computer with an Internet of Things terminal, for example, a fixed, portable, pocket-sized, handheld, computer-built-in or vehicle-mounted device. The server 104 can be an independent server or a server cluster composed of multiple servers, and can also be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.

[0021] In one embodiment, Figure 2 As shown, a charging safety detection method is provided, which is applied to Figure 1The terminal in is used as an example to illustrate, including the following steps: Step S202, acquiring charging data of the charging pile; the charging data includes charging features of different feature types.

[0022] In the embodiment of the present application, the charging data indicates a set of information used by the charging pile to reflect various states during the charging process.

[0023] Optionally, the charging data may include but is not limited to at least one of historical charging data, real-time charging data and charging restriction data.

[0024] Exemplarily, the charging restriction data may include but is not limited to at least one of charging power restriction data, charging mode restriction data, charging temperature restriction data and single charging time restriction data. For example, a charging pile may support multiple charging modes, and the charging modes may include fast charging mode, slow charging mode, constant current charging mode or constant voltage charging mode, etc. The charging pile may match the appropriate charging mode for charging according to the model of the target vehicle.

[0025] In some embodiments, the charging data may include charging features of different feature types. Optionally, the charging data may include, but are not limited to, time series charging data, space series charging data, and user behavior charging data; Optionally, the charging data may include charging data related to physical faults and charging data related to virtual faults. Among them, the charging data related to physical faults may include, but are not limited to, at least one of voltage detection data, meter communication data, operation data of internal components of charging piles (such as control panels, power modules, etc.), operation data of external components of charging piles (such as plugs, sockets, charging cables, etc.), and external environment data; the charging data related to virtual faults may include, but are not limited to, at least one of internal software operation data of charging piles, charging pile system upgrade or update data, and network communication / configuration data. Based on the charging data related to physical faults, the computer device can determine whether the voltage at the detection point of the predicted charging pile is normal, whether the internal components of the charging pile are damaged, whether the charging cable or connector is faulty, whether there is a device failure caused by an external extreme environment, etc.; based on the charging data related to virtual faults, the computer device can determine whether there is a fault in the internal software of the charging pile, whether the system update or upgrade fails, whether the communication between the charging pile and the cloud server is normal, etc.

[0026] Step S204, inputting the charging data into a multi-task prediction model to obtain a safety detection result of the charging pile; Among them, the multi-task prediction model includes an expert network and a target prediction network; the expert network includes at least one public expert network and multiple private expert networks; the public expert network is used to extract features and fuse the charging data related to the physical fault of the charging pile and the charging data related to the virtual fault to obtain a public feature vector; the private expert network is used to extract features from the charging data of the charging pile in a specific scenario and / or a specific task, respectively, to obtain a private feature vector; the target prediction network is used to perform multi-task prediction on the target feature vector obtained by fusing the public feature vector and the private feature vector to obtain the safety detection result.

[0027] In an embodiment of the present application, the multi-task detection model can complete multiple related or unrelated tasks at the same time, share the task feature extraction layer, and thus improve computing efficiency and resource utilization.

[0028] In some embodiments, the multi-task detection model may include but is not limited to at least one of a multi-gate mixture-of-experts (MMoE), a customized gate control (CGC) model, or a progressive layered extraction (PLE) model.

[0029] In the embodiment of the present application, the expert network may be a feature extraction network, which may extract features of part or all of the features of the charging data input into the expert network.

[0030] In some embodiments, the charging habits of different users vary greatly. For example, users have different charging time preferences. Some users tend to charge at night, while some users tend to charge during the day, and the corresponding charging scenarios are different. Or, due to the wide variety of new energy vehicles, the battery capacity, chemical composition and health status vary, and the corresponding charging scenarios are also different. Or, the grid load is different in different time periods. For example, the grid load is larger during peak hours, and the output power of the charging pile may correspond to different charging scenarios in different time periods.

[0031] In some embodiments, the specific task may include, but is not limited to, one of a task of paying attention to voltage anomalies, a task of paying attention to current fluctuations, and a task of paying attention to charging temperature fluctuations.

[0032] In some embodiments, there may be multiple expert networks, and the expert networks are used to learn higher-order cross features from the input features for fitting of subsequent tasks. There are generally at least two expert networks, and different expert networks can learn different types of cross features. Among them, the expert network may include a public expert network and a private expert network. The public expert network may include one or more fully connected networks arranged in parallel, and perform feature extraction and fusion processing on the charging data to obtain a public feature vector; each private expert network processes the charging data under a specific scenario and / or a specific task to obtain a corresponding private feature vector; illustratively, the first private expert network receives the charging data corresponding to the task of focusing on voltage anomalies, and obtains the corresponding first private feature vector; the second private expert network receives the charging data corresponding to the task of focusing on current fluctuations, and obtains the corresponding second private feature vector; the third private expert network receives the charging data corresponding to the task of focusing on charging temperature fluctuations, and obtains the corresponding third private feature vector; the target prediction network fuses the public feature vector and the private feature vector, and performs multi-task prediction to obtain the corresponding safety detection result of the charging pile. For example, the PLE model can be used as a multi-task prediction model, which helps to utilize shared features in the early stage, and focus on feature specificity in the later stage, reducing interference between tasks. For example, Figure 3 As shown, Figure 3 This is a structural diagram of a multi-task prediction model. The multi-task prediction model may include multiple layers of expert networks. Each layer of expert networks may include a public expert network and private expert networks corresponding to two tasks / scenarios. The bottom-level expert network processes the original input (i.e., input features) or the output of the previous layer to extract low-level features. The high-level expert network further extracts higher-level and more abstract features based on the bottom-level output. Expert networks at different layers can capture information at different levels. The input features are charging data after data preprocessing. The first output features and the second output features are safety detection results.

[0033] In the above-mentioned charging safety detection method, by obtaining the charging data related to the physical fault of the charging pile and the charging data related to the virtual fault and inputting them into the multi-task prediction model, the safety detection result of the charging pile (for example, the probability of the charging pile failing) is determined. This method can adapt to the charging characteristics corresponding to different scenarios / tasks, and there is no need to perform separate algorithm modeling for each characteristic, thereby improving processing performance; and the correlation between different types of charging characteristics can be considered to improve the accuracy of the safety detection results.

[0034] In one embodiment, the multi-task detection model also includes a gating network; each of the expert networks is connected to multiple gating networks; the gating networks are connected to the target prediction networks one-to-one; the gating networks are used to obtain weighting coefficients based on the public feature vectors and / or the private feature vectors; the weighting coefficients correspond to the weights of each feature vector.

[0035] In the embodiment of the present application, the gated network screens the feature vectors extracted by the expert network according to their importance and task relevance, classifies or normalizes some or all of the feature vectors, and outputs the weighted coefficients of each feature vector.

[0036] In some embodiments, after extracting the public feature vector and the private feature vector, the expert network can transmit them to the corresponding gating network to determine the corresponding attention weights; the gating network can dynamically adjust the weight distribution between different scenarios / tasks to better adapt to the differences between different scenarios / tasks.

[0037] In some embodiments, the gating network can classify or normalize part or all of the public feature vectors and private feature vectors output by the expert network, and output the weighted coefficients corresponding to each feature vector; the target prediction network can perform weighted processing on the corresponding feature vectors through the weighted coefficients, for example, multiplying the first private feature vector by the weighted coefficient corresponding to the first private feature vector to obtain the weighted first private feature vector; multiplying the second private feature vector by the weighted coefficient corresponding to the second private feature vector to obtain the weighted second private feature vector; and so on, until the weighted feature vectors are obtained; and the target prediction network fuses the weighted feature vectors to obtain the target feature vector, and further performs classification / regression processing to obtain the security detection result.

[0038] In this embodiment, the gating network allows the model to focus on the most useful information at an early stage, reduces redundant calculations, and helps reduce interference between tasks; thus, it is suitable for charging safety prediction with fine control at the input level.

[0039] In some embodiments, the method further comprises: Calculating according to the first input feature and the first output feature of the public expert network to obtain first relevant information; wherein the first input feature includes charging data related to the physical fault of the charging pile and charging data related to the virtual fault; the first output feature is the public feature vector; Maximizing the first relevant information to obtain a first loss function based on the public expert network; Calculating according to the first output feature and the second output feature of each of the private expert networks to obtain second related information; wherein the second output feature is the private feature vector; Minimizing the second relevant information to obtain a second loss function based on the private expert network; A target loss function of the multi-task prediction model is determined according to the first loss function and the second loss function.

[0040] In some embodiments, the computer device can measure the information correlation between two random variables in the form of mutual information (MI), which can well measure the degree of mutual dependence between the two random variables. MI maximization can maximize the correlation between the two random variables, and MI minimization can minimize the correlation between the two variables.

[0041] Exemplarily, one way to determine mutual information is: ;in, Indicates the first variable; Indicates the second variable; Indicates the joint distribution of the first and second variables; and Indicates the marginal distributions of the first and second variables.

[0042] For example, one way to estimate a lower bound between two variables is: ;in, Indicates the variational lower bound; Indicates auxiliary distribution; Indicates the joint distribution of the first and second variables; and Indicates the marginal distributions of the first and second variables; Indicates the expectation of the joint distribution; Indicates the expectation of marginal distributions; .

[0043] For example, in order to maximize the mutual information between variables to obtain the first loss function, the first variable can be replaced by the first input feature, and the second variable can be replaced by the first output feature; one way to determine the first loss function is: .

[0044] Exemplarily, in order to minimize the mutual information between variables to obtain the second loss function, the first variable may be replaced by the first output feature, and the second variable may be replaced by the second output feature; a method for determining the second loss function is: .

[0045] In one embodiment, the computer device may use the weighted sum of the first loss function, the second loss function and the third loss function of the target prediction network as the target loss function; wherein the third loss function may be used to indicate the error between the safety training results obtained by the multi-task prediction model based on the charging training sample data (with labeled safety detection results) during the training process and the actual safety detection results of each charging training sample data; for example, the third loss function may be a cross entropy function.

[0046] In an embodiment of the present application, the first relevant information and the second relevant information related to the public expert network in the target loss function can well guide the training of the multi-task prediction model, reduce information redundancy, and improve the model performance and the accuracy of the security detection results.

[0047] In some embodiments, the method further comprises: In the case where the safety detection result is a charging pile fault, obtaining a fault characteristic parameter corresponding to the safety detection result; the fault characteristic parameter includes at least one of a fault type, a fault location, and a fault range; Generate a reminder task according to the fault characteristic parameters and determine the priority corresponding to the reminder task; According to the priority of the reminder task, the alarm information corresponding to the reminder task is output in sequence.

[0048] In the embodiment of the present application, the fault type may include but is not limited to at least one of a communication fault, an electrical fault, a mechanical fault, a control logic fault, a sensor fault and a display interface fault.

[0049] In the embodiment of the present application, the fault location may include but is not limited to at least one of the internal components, external interfaces, internal software and remote services (non-local systems such as cloud services) of the charging pile.

[0050] In the embodiment of the present application, the fault scope may include but is not limited to at least one of a local fault, a systemic fault, a temporary fault and a permanent fault.

[0051] In some embodiments, the fault characteristic parameters may also include but are not limited to at least one of the number of faults, the number of charging failures, the charging fault temperature, the charging fault current value, and the number of communication failures.

[0052] In some embodiments, the computer device may determine the priority of the reminder task corresponding to each fault characteristic parameter according to at least one of the importance, the order of occurrence time, and the urgency of the fault characteristic parameter. Exemplarily, the fault characteristic parameter of the first charging pile indicates that the number of faults is 1 and the fault type is a display interface fault; the fault characteristic parameter of the second charging pile indicates that the number of abnormalities is 1 and the fault type is an electrical fault; the urgency of the electrical fault is higher than the urgency of the display interface fault, and the computer device may determine that the priority of the reminder task of the second charging pile is higher than the priority of the reminder task of the first charging pile; the computer device may preferentially output the alarm information corresponding to the reminder task of the second charging pile.

[0053] In the embodiment of the present application, by outputting the alarm information corresponding to the reminder task, the safety detection result of the charging pile can be determined intuitively, which is convenient for the operator to directly determine the location, urgency and type of abnormal fault in the charging pile, thereby facilitating the operator to make corresponding processing according to the alarm information in a timely manner, reducing the inconvenience caused to users by charging pile failures and improving the user experience; and it can also reduce the safety risks caused by charging pile failures and ensure charging safety.

[0054] In some embodiments, the sequentially outputting the alarm information corresponding to the reminder task includes at least one of the following: The first method is to output the warning information in sequence based on the voice of the voice device of the charging pile; The second method: sending the alarm information to a third-party terminal; the alarm information is used for display by the third-party terminal; The third type: sending the alarm information to the third-party terminal; the alarm information is used for motor vibration output based on the third-party terminal.

[0055] In some embodiments, the warning information may be output in a table form, a list form, a graphic form, a voice form, or a vibration form.

[0056] In some embodiments, the warning information may be outputted in any form on the display component of the charging pile; or, the warning information may be sent to a third-party terminal / platform and outputted on the third-party terminal / platform.

[0057] In the embodiment of the present application, by outputting alarm information, the operator can be reminded in a timely and intuitive manner that a charging pile failure or abnormality occurs, thereby reducing the safety risks caused by charging pile failure and reducing the failure rate of the charging pile.

[0058] In some embodiments, obtaining the fault characteristic parameter corresponding to the safety detection result includes: Obtain the number of faults, charging failures, charging fault temperature and charging fault current value that occur in the same charging pile within a preset time period; The generating a reminder task and determining a priority corresponding to the reminder task according to the fault characteristic parameter includes: The priority corresponding to the reminder task is determined according to at least one of the fault quantity, the charging failure number, the charging fault temperature and the charging fault current value.

[0059] In the embodiment of the present application, the number of faults may indicate the number of faults that occur in a charging pile. For example, if the first charging pile has both a communication fault and a display fault, the number of faults may be determined to be 2.

[0060] In the embodiment of the present application, the number of charging failures may indicate the number of charging failures that occur at the charging pile within a predetermined time period.

[0061] In the embodiment of the present application, the charging fault temperature may indicate that the temperature of the charging pile during charging exceeds the reference temperature range. For example, if the reference temperature range is -20°C (degrees Celsius) to 50°C and the current charging temperature of the charging pile is 60°C, it can be determined that the current charging temperature is the charging fault temperature.

[0062] In the embodiment of the present application, the charging fault current value may indicate that the charging current of the charging pile exceeds the normal current range. For example, the charging pile is a household charging pile, the maximum charging power is 7KW, and the normal current range is 10A (ampere) to 20A; the current charging current of the charging pile is 5A, then it can be determined that the current charging current is the charging fault current value.

[0063] In some embodiments, since there can be multiple faulty charging piles and the fault characteristic parameters of the faulty charging piles can be multiple, in order to better display alarm information with higher urgency or higher importance, and facilitate operators to handle the corresponding faulty charging piles in time according to the alarm information, the priority corresponding to the reminder task can be determined according to at least one of the number of faults, the number of charging failures, the charging fault temperature and the charging fault current value; the alarm information corresponding to the reminder task can be output in sequence according to the priority.

[0064] Exemplarily, the computer device may sort the faulty charging piles from most to least according to the number of charging failures, and determine the priority of the reminder task corresponding to each faulty charging pile; and display the alarm information corresponding to each reminder task in sequence according to the priority.

[0065] In some embodiments, the computer device may assign weights to the number of faults, the number of charging failures, the charging fault temperature and the charging fault current value according to their importance, respectively; the computer device scores each faulty charging pile according to the number of faults, the number of charging failures, the charging fault temperature and the charging fault current value in turn, to obtain a first score, a second score, a third score and a fourth score; determine the target score corresponding to the reminder task of the faulty charging pile according to the first score and the corresponding weighted weight, the second score and the corresponding weighted weight, the third score and the corresponding weighted weight, and the fourth score and the corresponding weighted weight; the computer device determines the target score corresponding to the reminder task of each faulty charging pile in turn, and sorts them from large to small or from small to large according to the target scores to determine the priority.

[0066] In an embodiment of the present application, the computer device can prioritize the reminder tasks of each faulty charging pile and can preferentially display the reminder tasks and corresponding alarm information of the faulty charging piles with higher fault severity, urgency and / or importance, so that the operator can perform related processing such as maintenance in a timely manner to reduce the safety risks caused by the fault.

[0067] In some embodiments, determining the priority of the reminder task according to at least one of the number of faults, the number of charging failures, the charging fault temperature, and the charging fault current value includes: Determine a charging pile whose number of faults is greater than or equal to a first threshold as a first candidate charging pile; A charging pile whose number of charging failures is greater than or equal to a second threshold is selected from the first candidate charging pile as a second candidate charging pile; wherein the priority corresponding to the reminder task of the second candidate charging pile is higher than the priority corresponding to the reminder task of the first candidate charging pile; The second candidate charging piles are sorted according to at least one of the charging fault temperature and the charging fault current value, and the priority corresponding to the reminder task of each second candidate charging pile is determined in turn according to the sorting result.

[0068] In some embodiments, the computer device may sort each faulty charging pile in order from most to least according to the number of faults, and determine that the faulty charging pile with a number of faults greater than or equal to the first threshold is the first candidate charging pile; for example, the number of faults of the first to third faulty charging piles is 2, 3, and 3 respectively, and the first threshold is 3, then the second faulty charging pile and the third faulty charging pile may be determined as the first candidate charging pile. The computer device may sort from high to low according to the number of charging failures within a preset time period, and select the charging pile with a number of charging failures greater than or equal to the second threshold within the preset time period from the first candidate charging pile as the second candidate charging pile; for example, the number of charging failures of the second faulty charging pile is 3 times, the number of charging failures of the third faulty charging pile is 1 time, and the second threshold is 2, then the second faulty charging pile may be determined as the second candidate charging pile. The computer device may determine that the priority corresponding to the reminder task of the second faulty charging pile is higher than the priority corresponding to the reminder task of the third faulty charging pile, and the priority corresponding to the reminder task of the third faulty charging pile is higher than the priority corresponding to the reminder task of the first faulty charging pile.

[0069] In some embodiments, the computer device can sort the second candidate charging piles from large to small according to the difference between the charging fault temperature and the reference temperature range; or, the computer device can sort the second candidate charging piles from large to small according to the difference between the charging fault current value and the normal current range, and determine that the second candidate charging pile whose difference value is greater than or equal to the third threshold is the target charging pile, and then determine that the reminder task corresponding to the target charging pile has the highest priority.

[0070] In the embodiment of the present application, the priority level of the reminder task corresponding to each faulty charging pile is determined by multiple means. Compared with considering only a single factor, charging piles with higher fault severity, urgency and / or importance can be screened out from multiple dimensions, that is, charging piles with higher reminder task priorities, which can meet the charging safety detection needs in different scenarios.

[0071] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0072] Based on the same inventive concept, the embodiment of the present application also provides a charging safety detection device for implementing the above-mentioned charging safety detection method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more charging safety detection device embodiments provided below can refer to the limitations of the charging safety detection method above, and will not be repeated here.

[0073] In one embodiment, Figure 4 As shown, a charging safety detection device is provided, comprising: The acquisition module 10 is used to acquire charging data of the charging pile; the charging data includes charging features of different feature types; The detection module 20 is used to input charging data into the multi-task prediction model to obtain the safety detection result of the charging pile; Among them, the multi-task prediction model includes an expert network and a target prediction network; the expert network includes at least one public expert network and multiple private expert networks; the public expert network is used to extract features and fuse charging data related to physical faults of charging piles and charging data related to virtual faults to obtain public feature vectors; the private expert network is used to extract features from charging data of charging piles in different scenarios to obtain private feature vectors; the target prediction network is used to perform multi-task prediction on the target feature vector obtained by fusing the public feature vector and the private feature vector to obtain safety detection results.

[0074] In some embodiments, the multi-task detection model also includes a gating network; each expert network is connected to multiple gating networks; the gating networks are connected to the target prediction network one-to-one; the gating network is used to obtain weighting coefficients based on public feature vectors and / or private feature vectors; the weighting coefficients correspond to the weights of the feature vectors of each feature type.

[0075] In some embodiments, the charging safety detection device further includes: A first calculation module is used to calculate according to the first input feature and the first output feature of the public expert network to obtain first relevant information; wherein the first input feature includes charging data related to the physical fault of the charging pile and charging data related to the virtual fault; the first output feature is a public feature vector; A processing module, configured to maximize the first relevant information to obtain a first loss function based on a public expert network; A second calculation module is used to calculate according to the first output feature and the second output feature of each private expert network to obtain second related information; wherein the second output feature is a private feature vector; A processing module, used for minimizing the second relevant information to obtain a second loss function based on a private expert network; The processing module is also used to determine the target loss function of the multi-task prediction model based on the first loss function and the second loss function.

[0076] In some embodiments, the charging safety detection device further includes: A first processing module is used to obtain a fault characteristic parameter corresponding to the safety detection result when the safety detection result is a charging pile fault; the fault characteristic parameter includes at least one of a fault type, a fault location, and a fault range; A determination module, used to generate a reminder task and determine the priority corresponding to the reminder task according to the fault characteristic parameters; The output module is used to output the alarm information in sequence according to the priority of the reminder task.

[0077] In some embodiments, the first processing module is used to obtain the number of faults, the number of charging failures, the charging fault temperature and the charging fault current value occurring in the same charging pile within a preset time period; The determination module is used to determine the priority corresponding to the reminder task according to at least one of the number of faults, the number of charging failures, the charging fault temperature and the charging fault current value.

[0078] In some embodiments, the determining module is configured to perform the following steps: Determine a charging pile whose number of faults is greater than or equal to a first threshold as a first candidate charging pile; A charging pile whose number of charging failures is greater than or equal to a second threshold is selected from the first candidate charging pile as a second candidate charging pile; wherein the priority corresponding to the reminder task of the second candidate charging pile is higher than the priority corresponding to the reminder task of the first candidate charging pile; The second candidate charging piles are sorted according to at least one of the charging fault temperature and the charging fault current value, and the priority corresponding to the reminder task of each second candidate charging pile is determined in turn according to the sorting result.

[0079] In some embodiments, the output module is configured to perform at least one of the following steps: The first type: the voice device based on the charging pile outputs the alarm information in sequence; The second method: sending the alarm information to a third-party terminal; the alarm information is used for display by the third-party terminal; The third method: sending the alarm information to a third-party terminal; the alarm information is used for motor vibration output based on the third-party terminal.

[0080] Each module in the above charging safety detection device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each module.

[0081] In one embodiment, a computer device is provided. The computer device may be a charging pile, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a charging safety detection method is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse.

[0082] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0083] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0084] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0085] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0086] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A charging safety detection method, characterized in that: The method comprises: Acquiring charging data of a charging pile; the charging data including charging features of different feature types; Inputting the charging data into a multi-task prediction model to obtain a safety detection result of the charging pile; Among them, the multi-task prediction model includes an expert network and a target prediction network; the expert network includes at least one public expert network and multiple private expert networks; the public expert network is used to extract features and fuse the charging data related to the physical fault of the charging pile and the charging data related to the virtual fault to obtain a public feature vector; the private expert network is used to extract features from the charging data of the charging pile in a specific scenario and / or a specific task, respectively, to obtain a private feature vector; the target prediction network is used to perform multi-task prediction on the target feature vector obtained by fusing the public feature vector and the private feature vector to obtain the safety detection result.

2. The method according to claim 1, characterized in that: The multi-task detection model also includes a gating network; each of the expert networks is connected to multiple gating networks; the gating networks are connected to the target prediction network one-to-one; the gating network is used to obtain weighting coefficients according to the public feature vectors and / or the private feature vectors; the weighting coefficients correspond to the weights of each feature vector.

3. The method according to claim 1, characterized in that The method further comprises: Calculating according to the first input feature and the first output feature of the public expert network to obtain first relevant information; wherein the first input feature includes charging data related to the physical fault of the charging pile and charging data related to the virtual fault; the first output feature is the public feature vector; Maximizing the first relevant information to obtain a first loss function based on the public expert network; Calculating according to the first output feature and the second output feature of each of the private expert networks to obtain second related information; wherein the second output feature is the private feature vector; Minimizing the second relevant information to obtain a second loss function based on the private expert network; A target loss function of the multi-task prediction model is determined according to the first loss function and the second loss function.

4. The method according to claim 1, characterized in that The method further comprises: In the case where the safety detection result is a charging pile fault, obtaining a fault characteristic parameter corresponding to the safety detection result; the fault characteristic parameter includes at least one of a fault type, a fault location, and a fault range; Generate a reminder task according to the fault characteristic parameters and determine the priority corresponding to the reminder task; According to the priority of the reminder task, the alarm information corresponding to the reminder task is output in sequence.

5. The method according to claim 4, characterized in that The obtaining of the fault characteristic parameter corresponding to the safety detection result includes: Obtain the number of faults, charging failures, charging fault temperature and charging fault current value that occur in the same charging pile within a preset time period; The generating a reminder task and determining a priority corresponding to the reminder task according to the fault characteristic parameter includes: The priority corresponding to the reminder task is determined according to at least one of the fault quantity, the charging failure number, the charging fault temperature and the charging fault current value.

6. The method according to claim 5, characterized in that The determining the priority corresponding to the reminder task according to at least one of the fault quantity, the charging failure number, the charging fault temperature, and the charging fault current value includes: Determine the charging pile whose number of faults is greater than or equal to a first threshold as a first candidate charging pile; Selecting a charging pile whose number of charging failures is greater than or equal to a second threshold from the first candidate charging piles as a second candidate charging pile; wherein the priority corresponding to the reminder task of the second candidate charging pile is higher than the priority corresponding to the reminder task of the first candidate charging pile; The second candidate charging piles are sorted according to at least one of the charging fault temperature and the charging fault current value, and the priorities corresponding to the reminder tasks of the second candidate charging piles are determined in sequence according to the sorting results.

7. The method according to claim 4, characterized in that The sequentially outputting the alarm information corresponding to the reminder task includes at least one of the following: The first method is to output the warning information in sequence based on the voice of the voice device of the charging pile; The second method: sending the alarm information to a third-party terminal; the alarm information is used for display by the third-party terminal; The third type: sending the alarm information to the third-party terminal; the alarm information is used for motor vibration output based on the third-party terminal.

8. A charging safety detection device, characterized in that: The device comprises: An acquisition module is used to acquire charging data of a charging pile; the charging data includes charging features of different feature types; The detection module is used to input charging data into the multi-task prediction model to obtain the safety detection results of the charging pile; Among them, the multi-task prediction model includes an expert network and a target prediction network; the expert network includes at least one public expert network and multiple private expert networks; the public expert network is used to extract features and fuse charging data related to physical faults of charging piles and charging data related to virtual faults to obtain public feature vectors; the private expert network is used to extract features of charging data of charging piles in specific scenarios and / or specific tasks respectively to obtain private feature vectors; the target prediction network is used to perform multi-task prediction on the target feature vector obtained by fusion of the public feature vector and the private feature vector to obtain a safety detection result.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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