Charging pile safety monitoring method and system

Abstract

The invention relates to the technical field of monitoring or controlling charging stations, in particular to a charging pile safety monitoring method and system, and the method comprises the following steps: 1, a monitoring stage; step 2, primary feedback; 3, a tracing analysis stage: determining a fire point and a fire reason; step 3.1, field condition extraction; step 3.2, fire point analysis: through the acquired on-site video data, based on image feature judgment, when the brightness L of a pixel in an image is greater than Lthresh and the color is within Crange, determining the region where the pixel is located as a suspected flame region F; according to the invention, data is collected in real time in an omnibearing manner through multiple sensors, and accurate and comprehensive monitoring and safety early warning are realized. By means of traceability analysis, fire points and reasons can be rapidly determined, and responsibilities can be defined. Primary feedback gives an alarm in time and assists in judgment, secondary feedback is specifically processed according to a traceability result, a double-feedback mechanism optimizes an emergency process, the user experience is improved, and the operation safety of the charging pile is comprehensively guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of monitoring or controlling charging stations, and particularly to a method and system for safety monitoring of charging piles. Background Art

[0002] The safety monitoring of charging piles is a technical means used to ensure the safety of charging piles during operation, real-time monitor various operating parameters of charging piles and the surrounding environment conditions, timely discover and handle potential safety hazards, so as to ensure the stable and safe operation of charging piles and provide reliable charging services for users.

[0003] Traditional monitoring methods and systems have many deficiencies. They often can only perform simple electrical parameter monitoring, and the monitoring of key safety factors such as temperature, smoke, flame, and the insertion and extraction force of the charging gun is not comprehensive or accurate enough. After an accident occurs, it is difficult to quickly and accurately determine the ignition point and the cause of the fire, and there is a lack of an effective traceability mechanism; at the same time, the feedback mechanism is imperfect, unable to convey alarm information to relevant personnel in a timely manner, nor can it conduct targeted processing according to the cause of the accident, resulting in low emergency handling efficiency and poor user experience.

[0004] Therefore, the present invention proposes a method and system for safety monitoring of charging piles, which realizes all-round and accurate monitoring through multi-type high-precision sensors, uses advanced traceability analysis to clarify the ignition point and cause, and relies on a perfect dual feedback mechanism to give an alarm in a timely manner and conduct targeted processing according to the traceability results, achieving comprehensive, efficient, and intelligent monitoring of the safety of charging piles, and improving the safety, reliability, and user experience of charging pile operation. Summary of the Invention

[0005] Technical problems to be solved: The problem that it often can only perform simple electrical parameter monitoring, and the monitoring of key safety factors such as temperature, smoke, flame, and the insertion and extraction force of the charging gun is not comprehensive or accurate enough.

[0006] In view of the deficiencies of the prior art, the present invention provides a method and system for safety monitoring of charging piles, thereby solving the technical problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions:

[0008] A method for safety monitoring of charging piles includes the following steps:

[0009] Step 1, monitoring stage: By means of sensors and installing thermal imaging binocular cameras, real-time monitor leakage, overload, short circuit, smoke, flame, and the surrounding environment, providing a basis for subsequent abnormal judgment;

[0010] Step 2, primary feedback: Alarm the abnormal situations monitored in the monitoring stage of Step 1;

[0011] Step 3: Source tracing and analysis phase, determine the fire point and cause;

[0012] Step 3.1, extracting the on-site situation;

[0013] Step 3.2: Fire point analysis: Based on the acquired on-site video data and image feature judgment, when the pixels in the image satisfy the brightness L>L thresh And the color is in C range When the pixel is within, the area where the pixel is located is determined as the suspected flame area F. At the same time, the motion vector V of the smoke area between adjacent frames is calculated by using the optical flow algorithm. t , the fire point is determined by combining the analysis results of flame morphology and smoke movement trajectory;

[0014] Step 3.3: Analyze the cause of fire;

[0015] Step 3.3.1: Internal cause analysis, check the leakage current I detected by the leakage sensor leak According to the charging line current I monitored by the current transformer and the key part temperature T monitored by the temperature sensor, the vehicle battery management system data is obtained to analyze the current I during the charging process. thaege , battery temperature T battery , voltage V battery , state of charge parameter. When the monitored value is greater than the set threshold, it indicates that there is an abnormality in the monitored area or it may be the cause of the fire;

[0016] Step 3.3.2, external cause analysis, by dividing the half-hour thermal imaging video data extracted by the thermal imaging binocular camera in the environmental visual monitoring module into a two-dimensional image matrix I (x, y, t), screen abnormal temperature points for abnormal temperature analysis; at the same time, use the behavior analysis algorithm based on deep learning to process the image sequence, use the convolutional neural network to learn the training data, extract the characteristics of different behaviors for abnormal behavior analysis; then, intercept the video segment from the abnormal point to the issuance of the fire alarm;

[0017] Step 4: Secondary feedback: After completing the source tracing analysis in step 3, relevant information needs to be processed and fed back in a targeted manner based on the cause of the fire.

[0018] In a possible implementation, the monitoring phase is specifically as follows:

[0019] Monitor the current data of the charging pile through a leakage sensor to capture leakage information; monitor the current change data of the charging line through a current transformer to detect abnormal overload or short - circuit conditions; monitor the temperature data of the key heat - generating components inside the charging pile through a temperature sensor; monitor the fire situation in the surrounding area of the charging pile by installing an ionization smoke sensor and an infrared flame detector; obtain the video information and temperature change situation in the surrounding area of the charging pile through a thermal imaging binocular camera; obtain the key data of the vehicle battery through the communication connection with the vehicle battery management system.

[0020] In a possible implementation manner, step 2 utilizes the ionization smoke sensor and the infrared flame detector in step 1. When the detected smoke concentration exceeds the set threshold or the flame spectrum is detected, the alarm mechanism is triggered, and the alarm information is fed back to the remote monitoring center and the client.

[0021] In a possible implementation manner, step 4 specifically includes the following situations:

[0022] 4.1. If the cause of the fire is internal, deeply organize the relevant data related to the abnormal internal circuit of the charging pile or the vehicle, and feedback it to the remote control center through network communication technology. Display this information in the form of intuitive charts and detailed data reports, and feedback the detailed fault information and repair suggestions to the client of the operation and maintenance personnel; feedback the cause of the accident and the estimated time to resume service to the user client;

[0023] 4.2. If the cause of the fire is external, organize and feedback the video clips of the abnormal temperature analysis and abnormal behavior analysis intercepted by the external cause analysis to the remote control center. The staff in the remote control center view the videos on the monitoring platform, and combine with the analysis report to judge the severity and potential risks of the accident. The illegal information involved is promptly connected to the law enforcement department.

[0024] In a possible implementation manner, a monitoring system for implementing the above - mentioned charging pile safety monitoring method, the system includes a perception layer, a data transmission layer, a data processing layer, and a user interaction layer;

[0025] The perception layer is responsible for collecting various types of data, covering a variety of sensors and devices; the data transmission layer undertakes the task of transmitting the data of the perception layer, and the data processing layer receives the data transmitted by the perception layer and conducts processing and analysis; the user interaction layer provides different services for operation and maintenance personnel and ordinary users.

[0026] Beneficial effects compared with the prior art:

[0027] 1. In this solution, by means of a high-definition camera, the surveillance video is retrieved, and technologies such as image feature judgment and smoke motion vector calculation are combined to determine the ignition point. Considering the flame shape, smoke motion trajectory, and the relevant sensor data of the charging pile and the vehicle comprehensively, it is accurately determined whether the ignition point is the vehicle, the charging pile, or surrounding foreign objects. At the same time, the cause of the fire is analyzed in depth from internal and external factors. Internally, the internal circuit faults of the charging pile and the vehicle are judged by analyzing electrical parameters, and externally, the video analysis of abnormal temperature and abnormal behavior is used to explore human factors. Such a comprehensive and in-depth traceability analysis can not only quickly clarify the cause of the fire after the accident, provide a basis for subsequent maintenance and improvement, but also accurately define responsibilities and avoid disputes, which is of great significance for improving the safety management system of charging piles;

[0028] 2. In this solution, through a primary feedback mechanism, when smoke or flame anomalies are detected, multiple alarm methods are quickly triggered. The local audible and visual alarm warns the surrounding personnel to evacuate, and the alarm information is transmitted to the remote monitoring center and the client at the same time. The remote monitoring center automatically retrieves historical data for auxiliary judgment, and uses artificial intelligence algorithms to classify and sort the alarm information to give priority to handling emergency events. The client provides different information according to the user role. The operation and maintenance personnel obtain detailed fault analysis and handling suggestions, and ordinary users receive concise safety reminders and information about nearby charging points. A voice alarm function is also added; the secondary feedback is targeted processing according to the traceability results. The internal factor feedback provides detailed faults and maintenance information, and the external factor provides videos and risk prompts. The cooperation of the dual feedback mechanism realizes the efficient connection from the accident occurrence to the handling, optimizes the emergency handling process, ensures the safety of personnel and property, improves the user experience at the same time, and enhances the user's sense of security and satisfaction with the use of charging piles. Brief Description of the Drawings

[0029] The above description is only an overview of the technical solution of the present invention. In order to be able to understand the technical means of the present invention more clearly and implement it according to the content of the specification, the following describes the preferred embodiments of the present invention in detail with reference to the accompanying drawings.

[0030] Figure 1 It is a flowchart of the steps of a method for monitoring the safety of a charging pile;

[0031] Figure 2 It is a system framework diagram of a system for monitoring the safety of a charging pile. Detailed Embodiments

[0032] The preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. However, the present invention can be implemented in various different forms, so the present invention is not limited to the embodiments described below;

[0033] The technical solutions in the embodiments of the present application are to solve the problems in the above background technology, and the general idea is as follows:

[0034] Embodiment 1:

[0035] Please refer to Figure 1 As shown, this embodiment introduces a method for safety monitoring of charging piles, including the following steps:

[0036] Step 1: Monitoring stage

[0037] The core of this stage is to use various sensors and devices to collect comprehensive and real-time data on the charging pile and its surrounding environment, providing a basis for subsequent anomaly judgment;

[0038] 1.1 Electrical parameter monitoring

[0039] Leakage current monitoring: Install highly sensitive leakage sensors in the main circuit and branch circuits of the charging pile, and collect leakage current data every 10 milliseconds; this is because leakage may cause electrical fires, and it is crucial to capture leakage information in a timely manner;

[0040] Current change monitoring: Use current transformers with appropriate turns ratios to collect current change data on the charging line every 5 milliseconds; by monitoring the current in real time, abnormal situations such as overload or short circuit can be detected in a timely manner;

[0041] Temperature monitoring: Install high-precision temperature sensors at key heat-generating components inside the charging pile, such as power modules and transformers, and collect temperature data every minute; too high temperature may be a manifestation of equipment failure or abnormal operation;

[0042] 1.2 Smoke and flame monitoring

[0043] Install ionization smoke sensors and infrared flame detectors inside and around the charging pile; the smoke sensor detects the smoke concentration once per second, and the flame detector scans the flame spectrum once every 0.5 seconds; regularly clean and calibrate the smoke sensor and flame detector to ensure their stable performance;

[0044] Set up multiple data transmission channels for the smoke and flame monitoring devices, such as 4G, 5G, Wi-Fi, and Bluetooth. When the main transmission channel fails, automatically switch to the backup channel to ensure the timely transmission of alarm signals; at the same time, locally store the smoke and flame monitoring data to save key information even in case of network failures;

[0045] 1.3 Environmental visual monitoring

[0046] Install thermal imaging binocular cameras at key positions above and around the charging pile, which have both video monitoring and thermal imaging functions, can display the temperature changes of target objects in the monitored environment in real time, conduct 24-hour continuous monitoring and shooting, and set the frame rate to 25fps; use a camera housing with automatic defogging and dustproof functions to reduce the impact of bad weather and environment on the camera;

[0047] Perform local preprocessing on the video data of the thermal imaging binocular camera, adopt image enhancement algorithms to improve image clarity and recognition, and reduce monitoring errors caused by insufficient light or complex backgrounds; at the same time, optimize the video transmission strategy, dynamically adjust the video frame rate and resolution according to the network conditions to ensure the stable transmission of real-time images to the remote monitoring center; store the monitoring videos for at least 2 weeks, and regularly perform data cleaning and backup to prevent data loss;

[0048] 1.4. Vehicle battery data monitoring

[0049] When the charging pile is connected to the vehicle, establish a communication connection with the vehicle battery management system, and obtain the key data of the vehicle battery in real time through standard data transmission protocols (such as CAN bus protocol, etc.), and the data acquisition frequency is set to once per second; add a data verification mechanism to perform CRC verification on the acquired battery data to ensure the accuracy and integrity of the data;

[0050] When the network fails and the battery data cannot be transmitted in real time, cache the battery data locally at the charging pile and automatically resend it after the network is restored; at the same time, perform real-time analysis on the battery data. If abnormal battery data is found, in addition to sending alarm information to the remote monitoring center and the client, also prompt the user about the battery abnormality through the charging pile display screen;

[0051] Step 2. Primary feedback

[0052] When the smoke sensor detects that the smoke concentration exceeds the set smoke alarm threshold, or the infrared flame detector scans that the flame spectrum meets the flame alarm conditions, immediately trigger the alarm mechanism; the alarm signal is transmitted to the local alarm device and the remote monitoring center through wired or wireless communication methods, and is also sent to other nearby charging piles to achieve regional joint defense alarms, expand the alarm range, and remind more people to pay attention to safety;

[0053] In the remote monitoring center, in addition to popping up an alarm interface to display the location of the charging pile, the alarm type, and the alarm time, it will also automatically retrieve the monitoring data of this charging pile in the past period of time, including electrical parameters, temperature, vehicle battery data, etc., to assist the staff in quickly judging the accident situation; at the same time, use artificial intelligence algorithms to classify and prioritize the alarm information, and give priority to handling alarm events with a high degree of urgency;

[0054] On the client side, alarm information with different levels of detail is displayed according to the different roles of users (operation and maintenance personnel, ordinary users); for operation and maintenance personnel, detailed alarm data and fault analysis suggestions are provided; for ordinary users, they are reminded to pay attention to safety in a concise and clear manner, and information on nearby safe charging points is provided; at the same time, a voice alarm function is added to the client side to facilitate users to receive alarm prompts even when they cannot view the mobile phone screen in time;

[0055] Step 3: Traceability analysis stage

[0056] This stage is the key to the whole method. By deeply analyzing the on-site situation, the ignition point and the cause of the fire are determined;

[0057] 3.1. Extraction of on-site situation

[0058] Starting from the moment of the fire, the monitoring video within the past 30 minutes is retrieved with the help of the high-definition camera unit to comprehensively extract on-site information, including the position and status of the vehicle, the appearance of the charging pile, the activities of people and the distribution of objects in the surrounding environment, etc.;

[0059] 3.2. Analysis of the ignition point

[0060] 3.2.1. Judgment based on image features

[0061] Determination of the suspected flame area: Using the monitoring video images obtained by the high-definition camera, the suspected flame area is determined by setting the brightness threshold L thresh and the color range C range When the pixels in the image satisfy the brightness L > L thresh and the color is within C range the area where the pixel is located is determined as the suspected flame area F, which is expressed by the formula F = {(x, y)|L(x, y) > L thresh ∧C(x, y) ∈ C range}; for example, in practical applications, through a large number of analyses and studies on flame images, the brightness threshold is set to a specific value, and the color range covers the common red, yellow and other color tone ranges of flames to accurately screen out the areas that may be flames;

[0062] Calculation of the smoke motion vector: The motion vector V t of the smoke area between adjacent frames is calculated with the help of the optical flow algorithm; The optical flow algorithm calculates the motion vector based on the pixel brightness change caused by the movement of objects in the image. It assumes that the movement of objects is continuous in a short period of time. By comparing the brightness changes of the same pixel points in adjacent frames, the movement direction and speed of the pixel points are calculated, so as to obtain the motion vector of the smoke area; The ignition point is usually located in the starting direction of the smoke motion vector because the smoke spreads from the ignition point and its starting direction of movement often points to the ignition point;

[0063] Determination of the ignition point location: The ignition point is determined by comprehensively analyzing the results of the flame shape and the smoke movement trajectory; if the flame area and the starting point of the smoke movement coincide in height in the spatial position, it can be basically determined that this position is the ignition point; if there are multiple flame areas, the center of the flame area most concentratedly pointed to by the smoke movement vector is selected as the ignition point; this is because the area most concentratedly pointed to by the smoke movement vector is most likely the source of the smoke, that is, the location of the ignition point;

[0064] 3.2.2. Judgment of the ignition point: If the flame is mainly concentrated in key positions such as the battery part and the engine compartment of the vehicle, and abnormal conditions such as smoking and sparking occur during the charging process of the vehicle, the ignition point is determined to be the vehicle;

[0065] When the flame mainly ignites from the electrical connection parts inside or on the surface of the charging pile, and abnormal data records are available from the leakage sensor, current transformer or temperature sensor on the charging pile before the fire, the ignition point is determined to be the charging pile;

[0066] If the flame originates from foreign objects around the charging pile and there is no direct electrical connection between the foreign objects and the charging pile and the vehicle, the ignition point is determined to be the surrounding foreign objects;

[0067] 3.3. Analysis of the cause of the fire

[0068] 3.3.1. Analysis of internal causes, mainly by analyzing whether there are abnormalities in the internal circuits of the charging pile and the vehicle that may lead to a fire;

[0069] 3.3.1.1. Analysis of the cause of the charging pile fire

[0070] Check the leakage current I detected by the leakage sensor leak , when I leak is greater than the set leakage current threshold I leak-thresh (such as 30 mA), it indicates that there is a leakage fault and a fire may be caused;

[0071] According to the charging line current I monitored by the current transformer, combined with the rated current I of the charging pile rated set the overload current threshold I rated-thresh = 1.2I rated and the short - circuit current threshold I short-thresh , when I > I over-thresh and the duration exceeds the overload duration threshold T over (such as 6 seconds), it is determined as overload; when I > I short-thresh , it is determined as short - circuit. Both overload and short - circuit may cause a fire;

[0072] Based on the temperature T of the key parts monitored by the temperature sensor, set the normal operating temperature range [T min , T max(such as [70°C, 80°C]) and the dangerous temperature threshold T danger (such as 90°C), when T is not within the normal operating temperature range, it indicates abnormal temperature; when T > T danger , it may cause a fire due to overheating;

[0073] 3.3.1.2. Analysis of the causes of vehicle fires

[0074] Obtain the current I during the charging process charge and voltage V charge data, set the normal charging current range [I c-min , I c-max and voltage range [V c-min , V c-max , when I charge is not within the normal charging current range or V charge is not within the normal voltage range, it is judged that the charging parameters are abnormal, and it may be that the abnormal charging process causes the vehicle to catch fire;

[0075] Obtain the vehicle battery management system data and analyze the battery temperature T battery , voltage V battery , SOC (state of charge) S SOC and other parameters, set the normal battery temperature range [T b-min , T b-max , voltage range [V b-min , V b-max and SOC range [S s-min , S s-max , when T battery , V battery or S SOC is not within the corresponding normal range, it is judged that the battery has a risk of failure and may cause the vehicle to catch fire;

[0076] 3.3.2. External factor analysis, mainly focusing on whether there are human factors causing the fire. By analyzing the video in the first half hour before the abnormality is discovered, starting from two aspects of abnormal temperature and abnormal behavior, potential fire inducing factors are explored;

[0077] 3.3.2.1. Abnormal temperature analysis;

[0078] By dividing the half-hour thermal imaging video data extracted by the thermal imaging binocular camera in the environmental vision monitoring module into a two-dimensional image matrix I(x, y, t), where x and y represent the coordinates of the image pixels and t represents the video time frame. First, set a suitable temperature threshold T threshold for screening abnormal temperature points; each pixel point (x, y) in the thermal imaging image corresponds to a temperature value T(x, y, t). When the temperature value of the pixel point satisfies T(x, y, t) > T thresholdWhen , the pixel is marked as a suspicious point;

[0079] For example, considering the temperature range of common fire sources (such as cigarette butts and lighter flames), combined with the fluctuation of ambient temperature in actual scenarios, T threshold Set it to 200℃ (this threshold needs to be calibrated based on a lot of experiments and practical experience in actual applications); start from the starting frame of the video, analyze the image frame by frame, and once a suspicious point is found, capture the video clip from this frame, and then continue to analyze the changes of these suspicious points;

[0080] For the continuity judgment of suspicious points, the overlap rate O of the suspicious point regions of two adjacent frames is calculated. Assume that the suspicious point region in the tth frame is S t , the suspicious point area of ​​the t+1th frame is S t+1 , the calculation formula of overlap rate is: When the overlap rate O is greater than the set continuity threshold O continue (For example, continue =0, indicating that the suspicious point areas in the two frames have at least 60% overlap), it is considered that the suspicious point is continuous, that is, there is a possibility of continuous combustion;

[0081] To determine whether the suspicious point area is enlarged, calculate the area of ​​the suspicious point area. Let the area of ​​the suspicious point area in the tth frame be A t , the area of ​​the suspicious point region in the t+1th frame is A t+1 , when satisfied (where α is the area expansion ratio threshold, assuming α = 0.3, which means that the area of ​​the suspicious point region in the next frame is at least 30% larger than that in the previous frame), it is judged that the combustion has an expansion trend;

[0082] Extract the video segments where suspicious points continue or expand as feedback for abnormal temperature analysis. These feedback contents can intuitively show the abnormal temperature change process that may cause fire, providing important basis for subsequent processing and decision-making;

[0083] 3.3.2.2, Abnormal behavior analysis;

[0084] The half-hour surveillance video data extracted by the thermal imaging binocular camera in the environmental visual monitoring module is divided into a two-dimensional image matrix I (x, y, t), where x and y represent the coordinates of the image pixels, and t represents the time frame of the video;

[0085] The image sequences are processed using a behavior analysis algorithm based on deep learning. Taking human behavior analysis as an example, a large amount of video data containing normal and abnormal behaviors is first labeled to construct a training data set. A convolutional neural network (CNN) is used to learn the training data and extract the characteristics of different behaviors.

[0086] During the actual monitoring process, for each frame of image I(x, y, t), the CNN model outputs a feature vector F(t), which contains the behavioral feature information of the target (such as a human body) in the current frame; calculate the cosine similarity S between the current frame feature vector F(t) and each vector in the normal behavioral feature vector set N = {N1, N2,..., N m}; The calculation formula of the cosine similarity is: where, F(t)·N i represents the dot product of vectors, and ||F(f)|| and ||N i || represent the norms of vectors F(t) and N i respectively;

[0087] Set an abnormal behavior judgment threshold S threshold (For example, S threshold = 0.8); When for all i (1 ≤ i ≤ m), S(F(t), N i ) < S threshold , it is determined that an abnormal behavior has occurred in the current frame;

[0088] For example, in the charging pile scenario, normal behaviors may include personnel normally plugging and unplugging the charging gun, parking the vehicle orderly, etc. If the model detects that in a certain frame, a person runs quickly towards the charging pile, and the cosine similarity between its behavioral feature vector and the normal behavioral feature vector is lower than the threshold, it is determined that this behavior is an abnormal behavior;

[0089] Once an abnormal behavior is detected, starting from the frame when the abnormal behavior begins to appear, intercept a video segment containing the process of this abnormal behavior as the feedback content for abnormal behavior analysis. These video segments can clearly show the time, location, and specific manifestations of the abnormal behavior, which helps the operation and maintenance personnel quickly understand the on-site situation and take corresponding measures for handling to avoid potential safety accidents;

[0090] Step 4, Secondary feedback

[0091] After completing the traceability analysis in Step 3, it is necessary to perform targeted processing and feedback on relevant information according to whether the cause of the fire is internal or external;

[0092] 4.1. If the cause of the fire is internal

[0093] Data sorting and analysis: Deeply sort out the relevant data related to abnormal internal circuits of charging piles or vehicles, including abnormal data obtained from leakage sensors, current transformers, temperature sensors, as well as current, voltage, and battery management system data during vehicle charging; Detailedly analyze the correlations between these data, and sort out the timeline and development trend of the fault occurrence;

[0094] Feedback to the remote control center: Send the sorted internal cause data, fire origin information, and detailed fault analysis reports to the remote control center through network communication technologies (such as HTTP, MQTT, etc.); on the monitoring platform of the remote control center, display this information in the form of intuitive charts (such as current change curves, temperature trend charts) and detailed data reports, facilitating staff to comprehensively understand the accident situation; based on this information, staff evaluate the damage degree caused by the accident to the charging pile and the vehicle, and formulate corresponding repair and replacement plans;

[0095] Feedback to the client: For the operation and maintenance personnel's client, push detailed fault information and repair suggestions to guide the operation and maintenance personnel to quickly conduct fault troubleshooting and repair work; for example, if it is determined that a certain power module of the charging pile is damaged due to overheating and causes a fire, inform the operation and maintenance personnel to replace the power module and check the surrounding circuits; for the user client, inform the user of the accident reason in an easy-to-understand language (such as "A fire was caused by a malfunction of the internal equipment of the charging pile. The charging service has been stopped, and we are making every effort to repair it"), and explain the estimated time to resume service, soothe the user's emotions, and at the same time remind the user to pay attention to relevant safety tips when using the charging pile in the future;

[0096] 4.2. If the cause of the fire is an external factor

[0097] Video sorting and annotation: Further sort the video clips of abnormal temperature analysis and abnormal behavior analysis intercepted in the external factor analysis, add detailed annotations to each video clip, indicating the time and location of the abnormality, the type of abnormality (such as a high-temperature object approaching, abnormal behavior of personnel, etc.), and the association with the fire origin;

[0098] Feedback to the remote control center: Send the annotated video clips, fire origin information, and external factor analysis reports to the remote control center; the staff in the remote control center view the videos on the monitoring platform and, in combination with the analysis reports, judge the severity and potential risks of the accident. For example, if it is found that someone deliberately set fire near the charging pile, promptly notify the relevant law enforcement departments and strengthen the monitoring of this area; at the same time, record this accident as a case for improving the safety management plan to prevent similar incidents from happening again;

[0099] Feedback to the client: Display the video clips and detailed accident analysis on the operation and maintenance personnel's client, reminding the operation and maintenance personnel to strengthen the daily inspection of this area and pay attention to preventing similar safety hazards; for the user client, inform the user that the accident is caused by external factors (such as "This accident was caused by a fire of foreign objects around. We have taken measures to ensure safety. We apologize for the inconvenience"), and remind the user to pay attention to observing the surrounding environment when using the charging pile. If any suspicious situation is found, notify the operation and maintenance personnel in time; at the same time, some safety science popularization knowledge can be pushed to improve the user's safety awareness.

[0100] Example 2:

[0101] As Figure 2 shown, based on the monitoring method of Example 1, this example constructs a charging pile safety monitoring system, aiming to achieve all-round, efficient, and intelligent safety monitoring of the charging pile, and improve the safety and reliability of the charging pile operation.

[0102] 1. System Architecture

[0103] The system mainly consists of a perception layer, a data transmission layer, a data processing layer, and a user interaction layer.

[0104] 1.1 Perception layer: Responsible for collecting various types of data, covering a variety of sensors and devices;

[0105] Install highly sensitive leakage sensors on the main circuit and branch circuits of the charging pile, and equip with backup sensors to collect leakage current data every 10 milliseconds to ensure stable and reliable leakage monitoring; use current transformers with ratio adaptation to collect charging line current change data every 5 milliseconds, and use multiple groups of current transformers for redundant monitoring;

[0106] Install high-precision temperature sensors on key heat-generating components to collect temperature data every minute, increase the number of sensors and adopt distributed monitoring;

[0107] At the same time, install ion type smoke sensors and infrared flame detectors inside and around the charging pile to detect smoke concentration and flame spectrum every second and every 0.5 seconds respectively, and calibrate regularly;

[0108] Deploy thermal imaging binocular cameras at key positions above and around the charging pile, with automatic defogging and dust-proof functions, continuously shoot at a frame rate of 25fps for 24 hours, and obtain real-time monitoring images and temperature change data of target objects;

[0109] When the charging pile is connected to the vehicle, key data such as the temperature, voltage, SOC, and internal resistance of the vehicle battery are obtained every second through standard data transmission protocols such as the CAN bus protocol, and a data verification mechanism is added;

[0110] 1.2 Data transmission layer: Responsible for the transmission of data from the perception layer. Smoke and flame monitoring devices are equipped with multiple data transmission channels such as 4G, 5G, Wi-Fi, and Bluetooth. When the main channel fails, the standby channel is automatically switched to ensure the timely transmission of alarm signals. At the same time, the monitoring data is stored locally;

[0111] After the video data collected by the thermal imaging binocular camera is preprocessed locally, the frame rate and resolution are dynamically adjusted according to the network conditions, and the video data is transmitted to the remote monitoring center through the network. The video data is stored for at least 2 weeks and backed up regularly;

[0112] Vehicle battery data is transmitted in real time when the network is normal. In case of network failure, it is cached locally and resent after the network is restored.

[0113] 1.3. Data processing layer: receives data from the perception layer and processes and analyzes it;

[0114] Once the smoke or flame monitoring equipment triggers an alarm, the system will activate the local sound and light alarm device on the one hand, and transmit the alarm information to the remote monitoring center on the other hand;

[0115] After receiving the alarm information, the remote monitoring center automatically retrieves the monitoring data of the charging pile over the past period of time, uses artificial intelligence algorithms to classify and prioritize the alarm information, and assists staff to quickly determine the accident situation;

[0116] In terms of source tracing analysis, starting from the moment of fire, the surveillance video within 30 minutes was traced back with the help of high-definition cameras to extract on-site information; the suspected flame area was determined by setting the brightness threshold and color range, and the smoke motion vector was calculated using the optical flow algorithm. The fire point was determined by combining the flame shape and smoke motion trajectory; the cause of the fire was analyzed from both internal and external factors. The internal cause was determined by analyzing the relevant parameters of the charging pile and the internal circuit of the vehicle, and the external cause was explored by analyzing the abnormal temperature and abnormal behavior of the video half an hour before the abnormality was discovered;

[0117] 1.4. User interaction layer: provides different services for operation and maintenance personnel and ordinary users;

[0118] The client displays alarm information of different levels of detail according to the user role. Operation and maintenance personnel can obtain detailed alarm data and fault analysis suggestions, while ordinary users receive concise safety reminders and information about nearby safe charging points. The client also adds a voice alarm function.

[0119] In the secondary feedback stage, if the cause of the fire is internal, detailed fault information and repair suggestions will be pushed to the operation and maintenance personnel client, the cause of the accident and the estimated recovery time will be informed to the user client, and a reminder will be given to pay attention to safety tips; if it is an external cause, the operation and maintenance personnel client will be shown video clips and accident analysis to remind them to strengthen inspections, and the user client will be informed that the accident was caused by external factors and reminded to observe the surrounding environment, while safety science knowledge will be pushed;

[0120] 2. System workflow

[0121] 2.1. After the system is started, various sensors and devices in the perception layer continuously collect data and transmit the data to the data processing layer through the data transmission layer;

[0122] 2.2. The data processing layer analyzes data in real time. When abnormal data is detected and an alarm condition is triggered, the alarm feedback mechanism is activated to notify local and remote relevant personnel;

[0123] 2.3. In case of accidents such as fire, enter the traceability analysis process to determine the ignition point and cause of the fire.

[0124] 2.4. According to the results of the traceability analysis, conduct targeted feedback processing at the user interaction layer to provide a basis for maintenance for operation and maintenance personnel and safety tips for users.

[0125] Finally, it should be noted that: Obviously, the above embodiments are merely examples for clearly illustrating the present invention and are not limitations on the implementation manners. For those of ordinary skill in the art, other different forms of changes or variations can be made based on the above description. It is not necessary and impossible to enumerate all the implementation manners here. And the obvious changes or variations derived therefrom are still within the protection scope of the present invention.

Claims

1. A method for safety monitoring of a charging pile, characterized in that, It includes the following steps: Step 1, Monitoring stage: By means of sensors and installed thermal imaging binocular cameras, real-time monitoring of leakage, overload, short circuit, smoke, fire and the surrounding environment is carried out to provide a basis for subsequent abnormal judgment; Step 2, Primary feedback: Alarm the abnormal situations monitored in the monitoring stage of Step 1; Step 3, Traceability analysis stage: Determine the fire origin and the cause of the fire; Step 3.1, Extraction of on-site conditions; Step 3.2, Fire origin analysis. Based on the obtained on-site video data and judgment according to image features, when the pixels in the image satisfy the brightness L > L thresh and the color is within C range , the area where the pixel is located is determined as the suspected flame area F. At the same time, the optical flow algorithm is used to calculate the motion vector V t of the smoke area between adjacent frames, and the fire origin is determined by integrating the analysis results of the flame shape and the smoke movement trajectory; Step 3.3, Analysis of the cause of the fire; Step 3.3.1, internal cause analysis, check the leakage current I detected by the leakage sensor leak , according to the charging line current I monitored by the current transformer, and based on the temperature T of the key parts monitored by the temperature sensor, obtain the vehicle battery management system data, and analyze the current I charge , battery temperature T battery , voltage V battery , state of charge parameter. When the monitored value is greater than the set threshold, it indicates that there is an abnormality in the monitored area or it is the cause of the fire; Step 3.3.2, External cause analysis: By dividing the half-hour thermal imaging video data extracted by the thermal imaging binocular camera in the environmental vision monitoring module into a two-dimensional image matrix I(x, y, t), screening abnormal temperature points for abnormal temperature analysis; at the same time, using a behavior analysis algorithm based on deep learning to process the image sequence, using a convolutional neural network to learn the training data, and extracting the characteristics of different behaviors for abnormal behavior analysis; Then, intercept the video segment from the appearance of the abnormal point to the issuance of the fire alarm; Step 4, Secondary feedback: After completing the traceability analysis in Step 3, it is necessary to conduct targeted processing and feedback on relevant information according to the cause of the fire.

2. The method for safely monitoring a charging pile according to claim 1, wherein, The specific monitoring stage is as follows: Monitor the current data of the charging pile through a leakage sensor to capture leakage information; monitor the current change data of the charging line through a current transformer to detect overload or short circuit abnormal situations; monitor the temperature data of the key heat-generating components inside the charging pile through a temperature sensor; monitor the fire situation in the surrounding area of the charging pile through an installed ionization smoke sensor and an infrared flame detector; obtain the video information and temperature change situation in the surrounding area of the charging pile through a thermal imaging binocular camera; obtain the key data of the vehicle battery through the communication connection with the vehicle battery management system.

3. A method for monitoring the safety of a charging pile according to claim 1, wherein: In Step 2, by using the ionization smoke sensor and the infrared flame detector in Step 1, when the detected smoke concentration exceeds the set threshold or the flame spectrum is detected, the alarm mechanism is triggered, and the alarm information is fed back to the remote monitoring center and the client.

4. The method for safely monitoring a charging pile according to claim 1, wherein, Step 4 specifically includes the following situations: 4.

1. If the cause of the fire is an internal cause, deeply organize the relevant data related to the abnormal internal circuit of the charging pile or the vehicle, and feedback it to the remote control center through network communication technology, display this information in the form of intuitive charts and detailed data reports, and feedback the detailed fault information and maintenance suggestions to the client of the operation and maintenance personnel; Feed back the cause of the accident and the estimated time to resume service to the user client; 4.

2. If the cause of the fire is an external cause, organize and feedback the video segments of the abnormal temperature analysis and abnormal behavior analysis intercepted in the external cause analysis to the remote control center. The staff of the remote control center view the video on the monitoring platform, combine the analysis report, judge the severity and potential risks of the accident, and promptly connect the illegal information involved to the law enforcement department.

5. A monitoring system for implementing the charging pile safety monitoring method according to any one of claims 1 to 4, characterized in that, The system includes a perception layer, a data transmission layer, a data processing layer and a user interaction layer; The perception layer is responsible for collecting various types of data, covering a variety of sensors and devices; the data transmission layer undertakes the task of transmitting the data of the perception layer, and the data processing layer receives the data transmitted from the perception layer and conducts processing and analysis; the user interaction layer provides different services for operation and maintenance personnel and ordinary users.

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