Electric vehicle charging station safety monitoring system and method

Abstract

The present application discloses a safety monitoring system and method for an electric vehicle charging station. The safety monitoring system for an electric vehicle charging station provided by the present application includes a charging station safety management and control cloud platform, a charging pile status warning unit, and a charging station video monitoring unit. The charging pile status warning unit and the charging station video monitoring unit are embedded with a deep learning algorithm for realizing real-time warning prediction of the charging pile status and real-time monitoring and identification of abnormal status of the charging station, replacing the traditional manual real-time video monitoring method, and using deep learning theory to identify dangerous sources near the charging station in real time, thereby improving the utilization efficiency of personnel. When a dangerous source is detected near the charging station or there is an abnormality in the operation of the charging pile, the electric vehicle charger in the charging pile is disconnected in real time by the charging pile controller to prevent the dangerous source from causing more serious impact on the charging station, thereby solving the technical problem of low reliability of existing electric vehicle charging station monitoring.

Description

Technical Field

[0001] The present application relates to the technical field of electric vehicle charging safety monitoring, and in particular to a safety monitoring system and method for electric vehicle charging stations. Background Art

[0002] Replacing traditional LPG vehicles with cleaner electric vehicles is of great significance to my country. In recent years, with the gradual increase in the number of electric vehicles, electric vehicles have become an important means of transportation for residents' daily commutes. Electric vehicle charging stations are one of the key means of charging electric vehicles. However, electric vehicle charging stations are equipped with numerous devices and experience heavy traffic. Failure to strengthen the daily management of electric vehicle charging stations will cause damage to the stations, negatively impacting the normal operation of the electric vehicle charging business.

[0003] At present, the management of electric vehicle charging stations usually adopts video surveillance. Although video surveillance can record the real-time operation status of the charging station, if you want to know the operation status of the station at all times, you need to send someone to check the surveillance specifically, which has the technical problem of low monitoring reliability. Summary of the Invention

[0004] The present application provides a safety monitoring system and method for an electric vehicle charging station, which is used to solve the technical problem of low reliability of existing electric vehicle charging station monitoring.

[0005] To solve the above technical problems, the first aspect of the present application provides an electric vehicle charging station safety monitoring system, comprising: a charging pile status early warning unit, a charging station video monitoring unit, a charging pile control unit, and a charging station safety management and control cloud platform;

[0006] The charging pile status warning unit is configured to: obtain device operation data of the charging pile through a sensor device built into the charging pile, and then obtain a status evaluation result of the charging pile based on the device operation data and a charging pile status evaluation model carried by the charging pile status warning unit;

[0007] The charging station video surveillance unit is configured to: obtain monitoring video data of the charging station through video surveillance equipment in the charging station, and then obtain a hazard source identification result of the charging station based on the monitoring video data and a built-in hazard source identification model of the charging station video surveillance unit;

[0008] The charging pile control unit is in communication with the charging pile status warning unit and the charging station video monitoring unit, and is configured to perform real-time disconnection control of the charging pile according to the status assessment result or the hazard source identification result when the result displayed by the status assessment result or the hazard source identification result is abnormal;

[0009] The charging station safety management and control cloud platform is connected to the charging pile status warning unit and the charging station video monitoring unit through a communication link, and is used to receive data uploaded by the charging pile status warning unit and the charging station video monitoring unit.

[0010] Preferably, the equipment operation data includes: temperature data, gas concentration data and / or power parameter data.

[0011] Preferably, the system further comprises: the hazard source identification model building unit, specifically configured to:

[0012] Acquire surveillance video sample data containing hazard sources, and construct a hazard source database based on the surveillance video sample data;

[0013] Extracting features from the surveillance video sample data to obtain local features of the sample;

[0014] The local features of the sample are secondary fused with the original surveillance video sample data to obtain the global features of the sample;

[0015] The initial deep learning model is trained based on the local features of the samples and the global features of the samples to obtain a hazard source identification model.

[0016] Preferably, the deep learning model is specifically a deep learning model based on the R-FCN network.

[0017] Preferably, obtaining the hazard source identification result of the charging station based on the monitoring video data and in combination with the built-in hazard source identification model of the charging station video monitoring unit specifically includes:

[0018] Extracting features from the surveillance video data to obtain local features of the surveillance video data;

[0019] The local features are secondary fused with the original surveillance video data to obtain the global features of the surveillance video data;

[0020] Inputting the sample local features and the sample global features into the hazard source identification model to obtain a hazard source identification result of the charging station through operation of the hazard source identification model;

[0021] According to the hazard source identification result, a secondary identification is performed by combining the hazard source database with the OpenCV algorithm, so as to update the hazard source identification result according to the result of the secondary identification.

[0022] Preferably, the danger sources specifically include: fire and smoke warnings, dangerous vehicles, dangerous personnel intrusion, and human damage to equipment.

[0023] Preferably, the system further comprises: a charging pile status assessment model building unit, specifically configured to:

[0024] Obtain historical device operation data of the charging pile, and extract device operation data and fault records from the historical device operation data;

[0025] A deep learning model is trained based on the device operation data and the fault records to obtain a charging pile status assessment model.

[0026] Preferably, it also includes: a charging station abnormality alarm unit;

[0027] The charging station abnormality alarm unit is configured to: when the status assessment result or the hazard source identification result shows an abnormal result, output a charging station abnormality alarm signal and trigger a corresponding protection action.

[0028] Preferably, the charging station abnormality alarm signal includes: a message notification alarm signal, a sound alarm signal and / or a light alarm signal.

[0029] At the same time, the second aspect of the present application further provides an electric vehicle charging station safety monitoring method, which is applied to the electric vehicle charging station safety monitoring system provided in the first aspect of the present application, comprising:

[0030] Obtaining the device operation data of the charging pile through the built-in sensor device of the charging pile, and then obtaining the status evaluation result of the charging pile based on the device operation data and the charging pile status evaluation model carried by the charging pile status warning unit;

[0031] Obtaining surveillance video data of the charging station through video surveillance equipment within the charging station, and then obtaining a hazard source identification result for the charging station based on the surveillance video data and a built-in hazard source identification model of the charging station video surveillance unit;

[0032] When the status assessment result or the hazard source identification result shows an abnormal result, the charging pile is disconnected and controlled in real time according to the status assessment result or the hazard source identification result;

[0033] The status assessment results and the hazard source identification results are uploaded to the charging station safety management and control cloud platform via a communication link.

[0034] It can be seen from the above technical solutions that this application has the following advantages:

[0035] The electric vehicle charging station safety monitoring system provided by the present application includes a charging station safety management and control cloud platform, a charging pile status warning unit and a charging station video monitoring unit. The charging pile status warning unit and the charging station video monitoring unit are embedded with a deep learning algorithm to realize real-time warning prediction of the charging pile status and real-time monitoring and identification of abnormal status of the charging station, replacing the traditional manual real-time video monitoring method. With the help of deep learning theory, dangerous sources near the charging station are identified in real time, improving the utilization efficiency of personnel. When a dangerous source is detected near the charging station or there is an abnormality in the operation of the charging pile, the electric vehicle charger in the charging pile is disconnected in real time through the charging pile controller to prevent the dangerous source from causing more serious impact on the charging station, solving the technical problem of low reliability of existing electric vehicle charging station monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0037] Figure 1 This is a structural diagram of an embodiment of an electric vehicle charging station safety monitoring system provided in this application.

[0038] Figure 2 This is a flow chart of an embodiment of a method for safety monitoring of an electric vehicle charging station provided in this application. DETAILED DESCRIPTION

[0039] At present, video surveillance is usually used to manage electric vehicle charging stations. Although video surveillance can record the real-time operation status of the charging station, if you want to understand the operation status of the station at all times, you need to send someone to check the monitoring. On the one hand, it will cause a waste of manpower. The monitoring effect is directly affected by the level of the monitoring personnel, and the monitoring effect is unstable, which leads to the technical problem of low reliability of safety monitoring of electric vehicle charging stations.

[0040] In view of this, embodiments of the present application provide a safety monitoring system and method for an electric vehicle charging station, which are used to solve the technical problem of low reliability of existing electric vehicle charging station monitoring.

[0041] In order to make the purpose, features, and advantages of the invention of this application more obvious and easy to understand, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the embodiments described below are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0042] First, a detailed description of an embodiment of an electric vehicle charging station safety monitoring system provided by this application is as follows:

[0043] See also Figure 1 , this embodiment provides an electric vehicle charging station safety monitoring system, including: a charging pile status warning unit A, a charging station video monitoring unit B, a charging pile control unit C and a charging station safety management and control cloud platform S;

[0044] The charging pile status warning unit A is configured to: obtain the device operation data of the charging pile through the built-in sensor device of the charging pile, and then obtain the status assessment result of the charging pile based on the device operation data and the charging pile status assessment model carried by the charging pile status warning unit A;

[0045] The charging station video surveillance unit B is configured to: obtain monitoring video data of the charging station through the video surveillance equipment in the charging station, and then combine the monitoring video data with the built-in hazard source identification model of the charging station video surveillance unit B to obtain a hazard source identification result of the charging station;

[0046] The charging pile control unit C is in communication with the charging pile status warning unit A and the charging station video monitoring unit B, and is used to control the charging pile to be disconnected in real time according to the status assessment result or the hazard source identification result when the result displayed by the status assessment result or the hazard source identification result is abnormal;

[0047] The charging station safety management and control cloud platform S is connected to the charging pile status warning unit A and the charging station video monitoring unit B through a communication link, and is used to receive data uploaded by the charging pile status warning unit A and the charging station video monitoring unit B.

[0048] It should be noted that the charging pile status warning unit A and the charging station video monitoring unit are embedded with deep learning algorithms to achieve real-time warning prediction of the charging pile status and real-time monitoring and identification of abnormal status of the charging station.

[0049] The charging pile status warning unit A in this system obtains the equipment operation data of the charging pile by communicating with the sensor device built into the charging pile, and then obtains the status assessment results of these charging piles through the charging pile status assessment model installed in the unit. The equipment operation data of the charging pile mentioned in this embodiment includes, but is not limited to: temperature data, gas concentration data, power parameter data, including voltage data, current data, and / or power data of the charging pile.

[0050] Regarding the charging pile status assessment model mentioned in the charging pile status warning unit A, its construction method can be implemented with reference to the following example: by obtaining the historical equipment operation data of the charging pile, extracting the equipment operation data and fault records in the historical equipment operation data; performing deep learning model training based on the equipment operation data and fault records, and after the training is completed, the charging pile status assessment model can be obtained.

[0051] The charging station video surveillance unit B mentioned in this embodiment uses multiple video cameras in the charging station to collect monitoring video data around the charging station, and obtains the hazard source identification results of the charging station based on the monitoring video data and the built-in hazard source identification model of the charging station video surveillance unit B.

[0052] Regarding the hazard source identification model in the charging station video surveillance unit B, its construction method can be implemented with reference to the following example:

[0053] Acquire surveillance video sample data containing hazardous sources, and build a hazardous source database based on the surveillance video sample data; extract features from the annotation information contained in the surveillance video sample data to obtain sample local features; perform secondary fusion of the sample local features with the original surveillance video sample data to obtain sample global features; train the initial deep learning model based on the sample local features and sample global features. After the training is completed, a hazardous source identification model can be obtained, and the global feature prediction module is integrated into the original R-FCN network in parallel to supplement the deficiency of the original network in identifying hazardous sources only through local features.

[0054] Based on the above-mentioned construction method of the hazard source identification model, it can be understood that the specific process of obtaining the hazard source identification result through the hazard source identification model in this embodiment can be: according to the monitoring video data, feature extraction is performed on the monitoring video data to obtain local features of the monitoring video data; the local features are secondary fused with the original monitoring video data to obtain global features of the monitoring video data; the sample local features and the sample global features are input into the hazard source identification model to obtain the hazard source identification result of the charging station through the operation of the hazard source identification model; according to the hazard source identification result, then in order to further optimize the hazard source identification effect, after obtaining the hazard source identification result, this embodiment also performs secondary identification through the OpenCV algorithm combined with the hazard source database to update the hazard source identification result according to the result of the secondary identification, thereby obtaining the final hazard source identification result.

[0055] The hazard source data mentioned in this embodiment mainly includes four types: fire and smoke warning, dangerous vehicles, dangerous personnel intrusion, and human damage to equipment. It can be understood that the hazard source identification model constructed based on the surveillance video sample data containing these four hazard sources includes the following functions:

[0056] 1) Fire and smoke warning identification: Multiple video cameras within the charging station collect video information around the charging station and transmit it in real time to the charging station safety management cloud platform S. A hazard source identification model based on an improved regional fully convolutional network is used to analyze the characteristic images of hazard sources in the charging station and identify fire and smoke warnings around the substation.

[0057] 2) Dangerous Vehicle Identification: Based on the typical database of dangerous sources established within the charging station safety management cloud platform S and the dangerous vehicle data within it, a dangerous source identification model based on an improved regional fully convolutional network is used to analyze the characteristic images of dangerous sources in the charging station, and dangerous vehicles appearing around the charging station are identified in real time.

[0058] 3) Dangerous person intrusion identification: The two-dimensional planar information of the charging station can be divided into dangerous areas and non-dangerous areas within the charging station safety management cloud platform S. The hazard source identification model based on the improved regional fully convolutional network is used to analyze the characteristic images of dangerous sources in the charging station. If a person is detected entering the dangerous area, it can be determined that a dangerous person has intruded.

[0059] 4) Identification of man-made damage to equipment: Multiple video cameras within the charging station are used to monitor the internal equipment status of the charging station in real time. A hazard source identification model based on an improved regional fully convolutional network is used to analyze the characteristic images of hazard sources in the charging station. If any man-made damage to the equipment in the charging station is identified, it can be determined that the equipment has been damaged.

[0060] The charging pile control unit C mentioned in this embodiment can be communicated with the charging pile status warning unit A and the charging station video monitoring unit B, and is used to perform real-time disconnection control of the charging pile according to the status assessment results or the hazard source identification results when the status assessment results or the hazard source identification results display an abnormal result, so as to prevent a secondary accident.

[0061] The charging station safety management and control cloud platform S can be used to receive and store information such as the charging pile operation status and charging station video monitoring information collected by the charging pile status warning system and the charging station video monitoring system, the charging pile original operation data, the danger warning status and possible danger sources in the surrounding environment of the charging station, so that staff can read and check it at any time with the help of mobile phone APP or from the back-end main station to understand the safety status of the charging station.

[0062] Furthermore, the electric vehicle charging station safety monitoring system provided by the present application may further include: a charging station abnormality alarm unit D;

[0063] The charging station abnormality alarm unit D is configured to: when the status assessment result or the hazard source identification result shows an abnormal result, it outputs a charging station abnormality alarm signal and triggers corresponding protection actions.

[0064] The abnormality alarm signal of the charging station includes: a message notification alarm signal, an audible alarm signal and / or a light alarm signal.

[0065] For example, when the status assessment results or hazard source identification results show abnormal results, the charging station's 5G information communication network can be used to feedback the detected hazard information to the staff or the back-end main station through a mobile phone APP, and query the abnormal situation of the charging station in real time. At the same time, it can also issue sound and light alarm signals to remind on-site personnel to avoid risks in time.

[0066] The above is a detailed description of an embodiment of an electric vehicle charging station safety monitoring system provided by this application. The following is a detailed description of an embodiment of an electric vehicle charging station safety monitoring method provided by this application, which is as follows:

[0067] See also Figure 2 The electric vehicle charging station safety monitoring method provided in this embodiment can be applied to the electric vehicle charging station safety monitoring system provided in the previous embodiment, including:

[0068] Step 101: Obtain device operation data of the charging pile through a built-in sensor device of the charging pile, and then obtain a charging pile status assessment result based on the device operation data and a charging pile status assessment model carried by a charging pile status warning unit;

[0069] Step 102: Obtain surveillance video data of the charging station through the video surveillance equipment in the charging station, and then obtain a hazard source identification result of the charging station based on the surveillance video data and the built-in hazard source identification model of the video surveillance unit of the charging station;

[0070] Step 103: When the status assessment result or the hazard source identification result indicates an abnormal result, the charging pile is disconnected in real time according to the status assessment result or the hazard source identification result;

[0071] Step 104: Upload the status assessment results and hazard source identification results to the charging station safety management and control cloud platform via a communication link.

[0072] More specifically, the hazard source identification model in this embodiment is constructed as follows:

[0073] Acquire surveillance video sample data containing hazardous sources, and build a hazardous source database based on the surveillance video sample data; extract features from the annotation information contained in the surveillance video sample data to obtain sample local features; perform secondary fusion of the sample local features with the original surveillance video sample data to obtain sample global features; train the initial deep learning model based on the sample local features and sample global features. After the training is completed, a hazardous source identification model can be obtained, and the global feature prediction module is integrated into the original R-FCN network in parallel to supplement the deficiency of the original network in identifying hazardous sources only through local features.

[0074] More specifically, in step 102, obtaining a hazard source identification result for the charging station based on the monitoring video data and the built-in hazard source identification model of the charging station video monitoring unit specifically includes:

[0075] According to the monitoring video data, feature extraction is performed on the monitoring video data to obtain local features of the monitoring video data;

[0076] The local features are fused with the original surveillance video data to obtain the global features of the surveillance video data;

[0077] Inputting the sample local features and the sample global features into the hazard source identification model to obtain the hazard source identification result of the charging station through the operation of the hazard source identification model;

[0078] According to the hazard source identification results, secondary identification is performed through the OpenCV algorithm combined with the hazard source database to update the hazard source identification results based on the results of the secondary identification.

[0079] Furthermore, the method provided in this embodiment may further include:

[0080] When the status assessment result or hazard source identification result shows an abnormal result, an abnormal alarm signal of the charging station is output and the corresponding protection action is triggered.

[0081] The abnormality alarm signal of the charging station includes: a message notification alarm signal, an audible alarm signal and / or a light alarm signal.

[0082] In the description of this application, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate the description of this application and simplify the description. They do not indicate or imply that the devices or components referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limitations on this application. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0083] Unless otherwise specified or limited, the terms "mounted," "connected," and "connected" should be interpreted broadly. For example, they can refer to fixed, removable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediary; and internal communication between two components. Those skilled in the art will understand the specific meanings of these terms in this application.

[0084] The units described as separate components may or may not be physically separate, and 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0085] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0086] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0087] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An electric vehicle charging station safety monitoring system, characterized in that: include: Charging pile status warning unit, charging station video monitoring unit, charging pile control unit, hazard source identification model construction unit and charging station safety management and control cloud platform; The charging pile status warning unit is configured to: obtain device operation data of the charging pile through a sensor device built into the charging pile, and then obtain a status evaluation result of the charging pile based on the device operation data and a charging pile status evaluation model carried by the charging pile status warning unit; The charging station video surveillance unit is configured to: obtain monitoring video data of the charging station through video surveillance equipment in the charging station, and then obtain a hazard source identification result of the charging station based on the monitoring video data and a built-in hazard source identification model of the charging station video surveillance unit; The charging pile control unit is in communication with the charging pile status warning unit and the charging station video monitoring unit, and is configured to perform real-time disconnection control of the charging pile according to the status assessment result or the hazard source identification result when the result displayed by the status assessment result or the hazard source identification result is abnormal; The charging station safety management and control cloud platform is connected to the charging pile status warning unit and the charging station video monitoring unit via a communication link, and is used to receive data uploaded by the charging pile status warning unit and the charging station video monitoring unit; The hazard source identification model building unit is specifically used to: obtain monitoring video sample data containing hazard sources, and build a hazard source database based on the monitoring video sample data; perform feature extraction on the monitoring video sample data to obtain sample local features; The sample local features are secondary fused with the original monitoring video sample data to obtain the sample global features; based on the sample local features and the sample global features, the initial deep learning model is trained to obtain a hazard source identification model.

2. The electric vehicle charging station safety monitoring system according to claim 1, characterized in that: The equipment operation data includes: temperature data, gas concentration data and / or power parameter data.

3. The electric vehicle charging station safety monitoring system according to claim 1, characterized in that: The deep learning model is specifically a deep learning model based on the R-FCN network.

4. The electric vehicle charging station safety monitoring system according to claim 3, characterized in that: According to the monitoring video data, combined with the built-in hazard source identification model of the charging station video monitoring unit, the hazard source identification result of the charging station is obtained, which specifically includes: Extracting features from the surveillance video data to obtain local features of the surveillance video data; The local features are secondary fused with the original surveillance video data to obtain the global features of the surveillance video data; Inputting the sample local features and the sample global features into the hazard source identification model to obtain a hazard source identification result of the charging station through operation of the hazard source identification model; According to the hazard source identification result, a secondary identification is performed by combining the hazard source database with the OpenCV algorithm, so as to update the hazard source identification result according to the result of the secondary identification.

5. The electric vehicle charging station safety monitoring system according to claim 1, characterized in that: The danger sources specifically include: fire and smoke warnings, dangerous vehicles, dangerous personnel intrusion and human damage to equipment.

6. The electric vehicle charging station safety monitoring system according to claim 1, characterized in that: Also includes: The charging pile status assessment model building unit is specifically used to: Obtain historical device operation data of the charging pile, and extract device operation data and fault records from the historical device operation data; A deep learning model is trained based on the device operation data and the fault records to obtain a charging pile status assessment model.

7. The electric vehicle charging station safety monitoring system according to claim 1, characterized in that: Also includes: Charging station abnormality alarm unit; The charging station abnormality alarm unit is configured to: when the status assessment result or the hazard source identification result shows an abnormal result, output a charging station abnormality alarm signal and trigger a corresponding protection action.

8. The electric vehicle charging station safety monitoring system according to claim 7, characterized in that: The abnormality warning signal of the charging station includes: a message notification warning signal, a sound warning signal and / or a light warning signal.

9. A safety monitoring method for an electric vehicle charging station, applied to the safety monitoring system for an electric vehicle charging station according to any one of claims 1 to 8, characterized in that: include: Obtaining the device operation data of the charging pile through the built-in sensor device of the charging pile, and then obtaining the status evaluation result of the charging pile based on the device operation data and the charging pile status evaluation model carried by the charging pile status warning unit; Obtaining surveillance video data of the charging station through video surveillance equipment within the charging station, and then obtaining a hazard source identification result for the charging station based on the surveillance video data and a built-in hazard source identification model of the charging station video surveillance unit; When the status assessment result or the hazard source identification result shows an abnormal result, the charging pile is disconnected and controlled in real time according to the status assessment result or the hazard source identification result; The status assessment results and the hazard source identification results are uploaded to the charging station safety management and control cloud platform via a communication link.

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