Intelligent charging management system and management method

Abstract

The invention discloses an intelligent charging management system and management method, and belongs to the technical field of intelligent charging, and the system comprises an identity authentication module, a charging control module and a safety monitoring module. The identity authentication module is used for acquiring video data around the charging pile, verifying the identity of a target user according to the video data and determining a target electric vehicle model; the charging control module is used for acquiring a voice instruction, determining a charging strategy according to the voice instruction and a target electric vehicle model, adjusting the charging strategy according to a plurality of real-time charging parameters in a charging process, predicting a charging fault according to peripheral video data and the plurality of real-time charging parameters, and feeding back charging progress and charging fault information; the safety monitoring module is used for determining a danger category according to the video data around the charging pile and a plurality of real-time charging parameters, and determining danger emergency measures according to the danger category. According to the invention, video identity authentication, voice interaction and safety monitoring are combined, so that the operation convenience, safety and reliability of the charging pile are improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent charging technology, and in particular to an intelligent charging management system and management method. Background Art

[0002] As the electric vehicle market continues to expand, the demand for the construction of charging infrastructure is also continuing to grow. As an important part of the charging infrastructure, the market demand for electric vehicle charging piles is becoming increasingly strong, and their level of intelligence directly affects user experience and operational efficiency.

[0003] Traditional charging piles use physical buttons to send operating instructions and use display screens to display information. Physical button operations are complicated, and information displayed on the display screen is difficult to read due to the influence of light. In addition, it is impossible to predict faults of charging piles. During the charging process, it is impossible to monitor charging accidents, such as electric vehicle fires and spontaneous combustion. Therefore, the development of a charging pile system that can automatically identify user needs, provide voice interaction guidance, and has intelligent safety monitoring functions can improve user experience and safety performance, which is of great practical significance.

[0004] Therefore, there is an urgent need for an intelligent charging management system that can combine video identity authentication, voice interaction and safety monitoring to improve the operational convenience, safety and reliability of charging piles. Summary of the invention

[0005] In view of this, it is necessary to provide an intelligent charging management system and management method that can combine video identity authentication, voice interaction and safety monitoring to improve the operational convenience, safety and reliability of charging piles.

[0006] In order to solve the above technical problems, on the one hand, the present invention provides an intelligent charging management system, including an identity authentication module, a charging control module and a safety monitoring module: The identity authentication module is used to obtain video data around the charging pile, verify the identity of the target user based on the video data, and determine the target electric vehicle model; The charging control module is used to obtain voice instructions, determine the charging strategy of the target electric vehicle according to the voice instructions and the target electric vehicle model, adjust the charging strategy according to multiple real-time charging parameters during charging, predict charging faults according to the surrounding video data and multiple real-time charging parameters, and feedback charging progress information and charging fault information; The safety monitoring module is used to determine the danger category according to the video data around the charging pile and multiple real-time charging parameters, and determine the danger emergency measures according to the danger category.

[0007] In one possible implementation, the identity authentication module includes a video monitoring submodule and an identity recognition submodule; The video monitoring submodule is used to obtain the surrounding video data of the charging pile, and extract the target user's facial image and the target electric vehicle image from the surrounding video data; The identity recognition submodule is used to recognize the facial features of the target user according to the target user facial image, determine the target user identity recognition result, and after the target user identity recognition result is passed, recognize the target electric vehicle model according to the target electric vehicle image.

[0008] In a possible implementation, the identity recognition submodule includes a feature extraction unit and a verification unit; The feature extraction unit is used to extract the body contour and detail features in the target electric vehicle image based on an edge detection algorithm and an object recognition algorithm, and to extract the facial features of the target user in the target user facial image based on a facial recognition algorithm; The verification unit is used to identify the target electric vehicle model according to the vehicle body contour and detail features, and to determine the target user identity recognition result according to the target user facial features and a preset user identity information database.

[0009] In one possible implementation, the charging control module includes a voice interaction submodule, a charging status monitoring submodule, and an AI processing submodule; The voice interaction submodule is used to collect and process voice data and broadcast charging information and safety information; The charging status monitoring submodule is used to monitor multiple real-time charging parameters of the charging pile in the charging state; The AI ​​processing submodule is used to parse the voice data, generate control instructions according to the voice data and the target electric vehicle model, determine the charging optimization strategy of the charging pile according to the multiple real-time charging parameters, and predict charging failures according to the video data and the multiple real-time charging parameters.

[0010] In one possible implementation, the AI ​​processing submodule includes a data receiving unit, a charging strategy optimization unit, and a fault prediction unit; The data receiving unit is used to obtain and integrate the video data, voice instructions and multiple real-time charging parameters; The charging strategy optimization unit is used to determine the model of the target electric vehicle according to the video data, determine the charging strategy according to the model of the target electric vehicle and the voice command, determine the optimal charging voltage and charging current in combination with the multiple real-time charging parameters, and generate a charging optimization strategy; The fault prediction unit is used to predict charging faults according to the video data and abnormal data in the multiple real-time charging parameters, and generate a processing plan.

[0011] In a possible implementation, the safety monitoring module includes a safety risk identification submodule and an emergency measures submodule: The safety risk identification submodule is used to determine the charging abnormality category according to the video data around the charging pile and multiple real-time charging parameters; The emergency measures submodule is used to generate corresponding emergency measures according to the abnormality category.

[0012] In a possible implementation, the safety risk identification submodule includes a charging pile abnormality identification unit, a vehicle abnormality identification unit and an environmental abnormality identification unit; The charging pile abnormality recognition unit is used to construct a charging pile abnormality recognition model according to the image features of the charging pile in normal state and abnormal state and their corresponding classification labels, and to output the first abnormality category of the charging pile using the surrounding video data of the charging pile as input, and to determine the second abnormality category of the charging pile according to multiple real-time charging parameters, wherein the first abnormality category and the second abnormality category constitute the charging pile abnormality category; The vehicle abnormality recognition unit is used to construct an electric vehicle abnormality recognition model based on the image features of the electric vehicle in normal and abnormal states and their corresponding classification labels, and determine the abnormal category of the charging pile based on the real-time monitoring video data; The environmental anomaly recognition unit is used to build an environmental anomaly recognition model based on the image features of the surrounding environment of the charging pile in normal and abnormal states and their corresponding classification labels, and determine the environmental anomaly category based on real-time monitoring video data.

[0013] In a possible implementation, the intelligent charging management system further includes a human-computer interaction module; The human-computer interaction module is used to send control instructions using multi-touch buttons and display real-time charging information, fault causes and emergency measures.

[0014] In one possible implementation, the intelligent charging management system further includes a cloud service module; The cloud service module is used for distributed storage and management of user identity information data, electric vehicle model data, video monitoring data, voice command data and charging parameter data, building a remote monitoring platform, and remotely displaying information with a graphical interface.

[0015] In a second aspect, the present invention further provides an electric vehicle intelligent charging management method, which is applicable to any one of the above-mentioned intelligent charging management systems, and the method comprises: Obtain video data around the charging pile, verify the identity of the target user based on the video data, and determine the target electric vehicle model; Acquire a voice command, determine a charging strategy for the target electric vehicle according to the voice command and the target electric vehicle model, adjust the charging strategy according to a plurality of real-time charging parameters during charging, predict charging failures according to the surrounding video data and the plurality of real-time charging parameters, and feed back charging progress information and charging failure information; The danger category is determined according to the video data around the charging pile and a plurality of real-time charging parameters, and the danger emergency measures are determined according to the danger category.

[0016] The beneficial effects of the present invention are as follows: the intelligent charging management system provided by the present invention, through the identity authentication module, sets a high-definition camera to obtain video data around the charging pile, verifies the identity of the target user according to the video data, determines the target electric vehicle model, and improves the convenience and safety of identity authentication and vehicle authentication; obtains voice instructions through the high-sensitivity voice collection device of the charging control module equipment, determines the charging strategy of the target electric vehicle according to the voice instructions and the target electric vehicle model, adjusts the charging strategy according to multiple real-time charging parameters during charging, predicts charging faults according to the surrounding video data and multiple real-time charging parameters, feeds back charging progress information and charging fault information, and controls the charging pile by voice instructions, thereby improving the convenience of the charging pile; predicts charging faults through the collected video data and multiple real-time charging parameters, can predict faults in advance, and take corresponding treatment measures in time before or at the early stage of the fault, thereby improving the safety and reliability of the charging pile; determines the danger category according to the surrounding video data and multiple real-time charging parameters through the safety monitoring module, determines the danger emergency measures according to the danger category, and can avoid safety risks in all directions through real-time safety monitoring, thereby improving the safety and reliability of the charging pile. From the above content, it can be seen that the present invention combines video identity authentication, voice interaction and safety monitoring, thereby improving the convenience, safety and reliability of the operation of the charging pile. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 A schematic diagram of the structure of an embodiment of the intelligent charging management system provided by the present invention; Figure 2 A schematic diagram of the structure of an embodiment of the identity recognition submodule provided by the present invention; Figure 3 A schematic diagram of the structure of an embodiment of the AI ​​processing submodule provided by the present invention; Figure 4A schematic diagram of the structure of an embodiment of the security risk identification submodule provided by the present invention; Figure 5 A schematic diagram of a flow chart of an embodiment of the intelligent charging management method provided by the present invention. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0020] In the description of the embodiments of the present invention, unless otherwise specified, “plurality” means two or more than two.

[0021] The descriptions of "first", "second", etc. involved in the embodiments of the present invention are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the technical features defined as "first" or "second" may explicitly or implicitly include at least one of the features.

[0022] Reference to an "embodiment" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiment may be included in at least one embodiment of the present invention. The appearance of the phrase in various places 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.

[0023] The present invention provides an intelligent charging management system and a management method, which are respectively described below.

[0024] Figure 1 A schematic diagram of the structure of an embodiment of the intelligent charging management system provided by the present invention is shown in FIG. Figure 1 As shown, the intelligent charging management system includes an identity authentication module 100, a charging control module 200 and a safety monitoring module: The identity authentication module 100 is used to obtain video data around the charging pile, verify the identity of the target user based on the video data, and determine the target electric vehicle model; The charging control module 200 is used to obtain voice instructions, determine the charging strategy of the target electric vehicle according to the voice instructions and the target electric vehicle model, adjust the charging strategy according to multiple real-time charging parameters during charging, predict charging faults according to the surrounding video data and multiple real-time charging parameters, and feedback charging progress information and charging fault information; The safety monitoring module 300 is used to determine the danger category based on the video data around the charging pile and multiple real-time charging parameters, and determine the danger emergency measures according to the danger category.

[0025] It should be noted that, in this embodiment, a high-definition camera is installed on the charging pile and its surroundings to obtain surrounding video data, a high-sensitivity voice receiving device is installed in the charging pile to receive voice commands, the voice receiving device can be a microphone but is not limited to a microphone, and various sensors are installed in the charging pile, such as a current sensor, a voltage sensor, a temperature sensor, etc. to obtain multiple real-time charging parameters, and the multiple real-time charging parameters include real-time charging current, real-time charging voltage, and real-time target electric vehicle battery temperature data. The video data is cut and extracted by the video processing software on the mobile terminal to obtain a feature image, and the feature image is analyzed by the image processing software to obtain the target user identity information, vehicle information, and the surrounding environment information of the charging pile, etc. The voice command is parsed by the voice recognition algorithm, and the voice command is converted into a text command that can be recognized by the mobile terminal. The text command is analyzed and processed by the mobile terminal to obtain a text control command of the charging pile, and the charging pile is controlled to charge the electric vehicle according to the text control command, and the charging progress is converted into voice information, which is sent to the voice broadcast device on the charging pile and broadcasted, wherein the mobile terminal can be a variety of electronic devices that support various video image voice data analysis and storage, including but not limited to smart phones, tablet computers, laptop portable computers, and desktop computers.

[0026] It should be further explained that facial recognition data of all different users and data of various electric vehicle models, as well as charging parameters corresponding to each vehicle signal, are collected in advance, and a distributed database is constructed to store user information data, vehicle model data, and charging parameters corresponding to the vehicle model; infrared sensors and distance sensors are installed in the charging pile to sense environmental changes. When the user and the electric vehicle approach the charging pile and reach a preset distance range, the identity authentication module 100 automatically starts running; when the target user's identity authentication is passed, the charging control module 200 will start to receive voice commands from the user.

[0027] Specifically, when the sensor installed in the charging pile, such as an infrared sensor or a distance sensor, senses that the target user and the target electric vehicle are close to the preset distance range of the charging pile, the identity authentication module 100 is automatically started, starts to obtain the video data around the charging pile, extracts the facial image features of the target user in the video data, and performs identity recognition based on the facial image features of the target user based on the preset target user identity information database. After the identity recognition is passed, the electric vehicle image features in the video data are extracted, and the model of the electric vehicle is identified; after the identity recognition is passed, the charging control module 200 is automatically turned on, senses the user's voice command, and determines the charging strategy according to the voice command and the electric vehicle model. The voice command includes charging voice command, charging status query voice command, reservation charging voice command, and fault alarm voice command, etc. Different control operations are performed according to different voice commands, and a charging fault recognition model is constructed according to multiple charging parameters and surrounding video data obtained by various sensors. The fault is predicted at the early stage of the fault or before the fault occurs, and the charging fault is processed accordingly in time; during the charging process, the safety monitoring module 300 is started to extract the danger features in the real-time monitored video data around the charging pile and multiple real-time charging parameters, build an abnormality recognition model, determine the danger category, and generate different danger emergency measures according to different danger categories.

[0028] In this embodiment, a high-definition camera is set through the identity authentication module to obtain video data around the charging pile, and the target user identity is verified according to the video data, and the target electric vehicle model is determined, thereby improving the convenience and safety of identity authentication and vehicle authentication; a voice command is obtained through a high-sensitivity voice collection device of the charging control module device, and the charging strategy of the target electric vehicle is determined according to the voice command and the target electric vehicle model. During the charging process, the charging strategy is adjusted according to multiple real-time charging parameters, and charging failures are predicted according to the surrounding video data and multiple real-time charging parameters. Charging progress information and charging failure information are fed back, and the charging pile is controlled by voice commands, thereby improving the convenience of the charging pile. Charging failure prediction is performed through the collected video data and multiple real-time charging parameters, and the failure can be predicted in advance. Corresponding treatment measures are taken in time before or at the initial stage of the failure, thereby improving the safety and reliability of the charging pile; the safety monitoring module determines the danger category according to the surrounding video data of the charging pile and multiple real-time charging parameters, and the danger emergency measures are determined according to the danger category. Through real-time safety monitoring, safety risks can be avoided in all directions, thereby improving the safety and reliability of the charging pile. From the above content, it can be seen that the present invention combines video identity authentication, voice interaction and safety monitoring, thereby improving the convenience, safety and reliability of the operation of the charging pile.

[0029] In some embodiments of the present invention, the identity authentication module 100 includes a video monitoring submodule 110 and an identity recognition submodule 120; The video monitoring submodule 110 is used to obtain video data around the charging pile, and extract the target user's facial image and the target electric vehicle image from the surrounding video data; The identity recognition submodule 120 is used to recognize the facial features of the target user according to the target user's facial image, determine the target user's identity recognition result, and after the target user's identity recognition result is passed, recognize the target electric vehicle model according to the target electric vehicle image.

[0030] Specifically, in the present embodiment, the video monitoring submodule 110 collects video data of the surrounding area of ​​the charging pile through a high-definition camera, and uses image processing software to process the video data. The processing process includes denoising, grayscale processing, enhancement operation, compression operation, etc., to obtain processed image data, and the processed image data is segmented to obtain a target user's facial image, a charging pile image, an electric car image, and a surrounding environment image, etc. The model of the electric car is determined according to the electric car image, and the target user's identity information is identified according to the target user's facial image. After the target user's identity is successfully identified, the charging control module 200 starts to work.

[0031] This embodiment obtains the target user's facial image and electric vehicle image through the video monitoring submodule 110, authenticates the user, and identifies the model of the electric vehicle, thereby realizing automatic identity authentication and electric vehicle model identification, and improving the convenience and safety of the charging pile.

[0032] In some embodiments of the present invention, Figure 2 As shown, Figure 2 This is a schematic diagram of the structure of an embodiment of the identity recognition submodule provided by the present invention. The identity recognition submodule 120 includes a feature extraction unit 121 and a verification unit 122; The feature extraction unit 121 is used to extract the body contour and detail features in the target electric vehicle image based on the edge detection algorithm and the object recognition algorithm, and to extract the facial features of the target user in the target user facial image based on the facial recognition algorithm; The verification unit 122 is used to identify the target electric vehicle model according to the body contour and detail features, and determine the target user identity recognition result according to the target user facial features and a preset user identity information database.

[0033] Specifically, the outline of the electric vehicle, such as the body shape of the electric vehicle, is determined based on the edge detection algorithm. On this basis, the object recognition algorithm is used to further extract the detailed features of the electric vehicle, such as the color and license plate number of the electric vehicle. The extracted body outline and detailed features are analyzed and compared with the preset vehicle database. Through the matching algorithm, the model and related information of the target electric vehicle are identified, where the relevant information includes charging parameters; based on the facial recognition algorithm, the facial image of the target user is analyzed to extract key features, such as the position and shape features of the eyes, nose, and mouth; the key features extracted from the body are compared with the preset target user identity information database, which includes the user's facial feature template and identity information, and the target user identity recognition result is determined through the facial matching algorithm.

[0034] This embodiment identifies the model of the electric vehicle by extracting the outline and detail features of the electric vehicle, and identifies the target user by identifying the key facial features of the target user, thereby improving the accuracy of identity authentication and further improving the safety of the charging pile.

[0035] In some embodiments of the present invention, the charging control module 200 includes a voice interaction submodule 210, a charging status monitoring submodule 230, and an AI processing submodule 220; The voice interaction submodule 210 is used to collect and process voice data and broadcast charging information and safety information; The charging state monitoring submodule 230 is used to monitor multiple real-time charging parameters of the charging pile under the charging state, and the real-time charging parameter data includes real-time charging current, real-time charging voltage, real-time target electric vehicle battery temperature data and battery SOC of the target electric vehicle model; The AI ​​processing submodule 220 is used to parse the voice data, generate control instructions according to the voice data and the target electric vehicle model, determine the charging optimization strategy of the charging pile according to multiple real-time charging parameters, and predict charging failures according to the video data and multiple real-time charging parameters.

[0036] It should be noted that the user's voice commands are captured through the voice acquisition device, and the voice commands are converted into text commands that can be understood by the mobile terminal through the voice recognition algorithm, providing the processed voice data to the AI ​​processing submodule 220. At the same time, when the charging pile receives the voice command, the AI ​​processing submodule 220 will also process the text command recognized by the voice recognition algorithm, and provide voice feedback, and broadcast the voice feedback through the speaker.

[0037] Specifically, the voice commands include commands such as start charging, charge status query, scheduled charging and fault alarm. When the charging control module 200 receives the voice command of "start charging", the voice interaction submodule 210 converts "start charging" into a text command according to the voice recognition algorithm and sends it to the AI ​​processing submodule 220. The AI ​​processing submodule 220 analyzes and processes the text command and sends a feedback message "the command has been received and charging will start soon, please confirm" to the voice interaction submodule 210. The voice interaction submodule 210 broadcasts the voice feedback through the speaker. The user inputs a confirmation voice command, such as "confirm charging". The AI ​​processing submodule 220 According to the confirmation voice command, the charging parameters of the target electric vehicle are determined in combination with the target electric vehicle model, and the charging pile is controlled to charge the target electric vehicle according to the charging parameters. During the charging process, the charging state monitoring submodule 230 monitors the real-time charging current, real-time charging voltage, real-time target electric vehicle battery temperature data and battery SOC of the target electric vehicle model in real time during the charging process, and sends these real-time charging parameters to the AI ​​processing submodule 220. In combination with the target electric vehicle model, a charging optimization strategy is generated. According to the charging optimization strategy, the output current and voltage of the charging pile are adjusted in real time to make the target electric vehicle in the best charging state.

[0038] Furthermore, the AI ​​processing submodule 220 predicts potential charging failures by extracting images of the charging pile, the target electric vehicle, and the connection images of the charging pile and the target electric vehicle in the video data, observing whether the plug is tightly plugged in, whether there are foreign objects in the interface, and whether the charging pile and the target electric vehicle are normal. At the same time, by analyzing the changes in current, voltage, and temperature data during the charging process, possible charging failures can be discovered and warned in a timely manner. For example, when the current suddenly rises abnormally or the voltage fluctuates greatly, a charging failure occurs. At this time, the specific cause of the failure is analyzed, a text instruction of the cause of the failure is generated and sent to the voice interaction submodule 210, and the cause of the failure is announced through the speaker. The AI ​​processing submodule 220 converts the charging failure and the cause of the failure into an information text format and sends it to the user's mobile phone and the charging pile remote system monitoring platform so that the user and the charging pile manager can take timely countermeasures.

[0039] In this embodiment, through the collaborative work of multiple sub-modules such as voice interaction, real-time charging status monitoring and AI processing, users can interact with the intelligent charging management system through voice commands. At the same time, the intelligent charging management system can also monitor the charging status in real time and predict potential faults, ensuring the safety and efficiency of the charging process, thereby improving the efficiency, safety and intelligence of charging.

[0040] In some embodiments of the present invention, Figure 3 As shown, Figure 3This is a schematic diagram of the structure of an embodiment of the AI ​​processing submodule provided by the present invention. The AI ​​processing submodule 220 includes a data receiving unit 221, a charging strategy optimization unit 222, and a fault prediction unit 223; The data receiving unit 221 is used to obtain and integrate video data, voice commands and multiple real-time charging parameters; The charging strategy optimization unit 222 is used to determine the model of the target electric vehicle according to the video data, determine the charging strategy according to the model of the target electric vehicle and the voice command, determine the optimal charging voltage and charging current in combination with multiple real-time charging parameters, and generate a charging optimization strategy; The fault prediction unit 223 is used to predict charging faults according to the video data and abnormal data in a plurality of real-time charging parameters, and generate a processing solution.

[0041] Specifically, the data receiving unit 221 receives and preliminarily processes various video data, voice commands and multiple real-time charging parameter data in real time, and uses error detection and correction algorithms to deeply clean and verify the data to ensure the accuracy and completeness of the data and improve the efficiency of the AI ​​algorithm; the charging strategy optimization unit 222 determines specific control instructions according to different voice commands, such as starting charging, querying charging progress, making an appointment for charging, stopping charging or fault alarming. Different control instructions are combined with the model of the target electric vehicle to determine the charging strategy, query data information, make an appointment for charging information, etc., that is, the output parameters of the charging pile, and these charging strategies, query data information, and appointment charging information are displayed on the display screen installed on the charging pile. During the charging process, the charging strategy optimization unit 222 collects multiple real-time charging parameters, including but not limited to charging current, charging voltage, battery and charging pile temperature, and remaining battery capacity, and calculates and analyzes these real-time charging parameter data through AI algorithms. When it is found that the data deviates from the preset safety range, for example, excessive charging current will cause battery overheating and thus affect battery life, and unstable voltage will lead to low charging efficiency and damage to the equipment, the charging strategy optimization unit 222 obtains the optimal charging strategy through AI algorithm analysis, and quickly adjusts and optimizes the current charging parameters based on this optimal charging measurement.

[0042] This embodiment uses an AI algorithm to process and analyze the received real-time video data, voice commands, and multiple real-time charging parameters, makes intelligent decisions based on the analysis results, charges with the optimal charging strategy, adjusts charging parameters in real time, and predicts and handles charging failures, thereby improving charging efficiency and safety.

[0043] In some embodiments of the present invention, the safety monitoring module 300 includes a safety risk identification submodule 310 and an emergency measures submodule 320: The safety risk identification submodule 310 is used to determine the charging abnormality category based on the video data around the charging pile and multiple real-time charging parameters; The emergency measures submodule 320 is used to generate corresponding emergency measures according to the abnormality category.

[0044] Specifically, extract the appearance, color and structure images of the charging pile and the target electric vehicle, as well as the surrounding environment images of the charging pile from the real-time monitoring video data, analyze these images, determine whether there are safety risks in the charging pile, the charging car and the surrounding environment, and the corresponding safety risk categories, and generate corresponding emergency measures according to different safety risk categories. For example, when the charging pile is deformed, when the user tries to start charging, the charging pile reminds the user and the administrator of the intelligent charging management system through voice broadcast and text message that the charging pile is abnormally deformed, and there may be safety risks. For example, a circuit short circuit caused by the deformation of the charging pile can cause a fire. When there is a small amount of smoke on the electric vehicle, the charging pile reminds the user and the administrator of the intelligent charging management system through voice broadcast and text message that there is a charging vehicle with smoke. When there is a flame in the charging vehicle or the charging pile, the power is automatically cut off, and the user and the administrator of the intelligent charging management system are reminded through voice broadcast and text message, and the fire protection system is started at the same time. These measures are not limited to sending alarms through voice broadcast, text message, APP push, etc. to notify relevant personnel to respond quickly; automatic power off, that is, when serious safety hazards are detected, such as battery overheating or fire, it is necessary to automatically cut off the power to prevent the situation from worsening; start emergency plans, such as starting the fire protection system.

[0045] This embodiment uses the safety risk identification submodule 310 to monitor the charging process in all directions and promptly takes appropriate emergency measures when an abnormality occurs, thereby ensuring the safety of the charging system.

[0046] In some embodiments of the present invention, Figure 4 As shown, Figure 4 A schematic diagram of the structure of an embodiment of the safety risk identification submodule provided by the present invention, the safety risk identification submodule 310 includes a charging pile abnormality identification unit 311, a vehicle abnormality identification unit 312 and an environmental abnormality identification unit 313; The charging pile abnormality recognition unit 311 is used to build a charging pile abnormality recognition model according to the image features of the charging pile in the normal state and the abnormal state and the corresponding classification labels, and use the video data around the charging pile as input to output the first abnormality category of the charging pile, and determine the second abnormality category of the charging pile according to multiple real-time charging parameters. The first abnormality category and the second abnormality category constitute the charging pile abnormality category; The vehicle abnormality recognition unit 312 is used to construct an electric vehicle abnormality recognition model based on the image features of the electric vehicle in normal and abnormal states and their corresponding classification labels, and determine the abnormal category of the charging pile based on the real-time monitoring video data; The environment anomaly recognition unit 313 is used to build an environment anomaly recognition model based on the image features of the surrounding environment of the charging pile in normal and abnormal states and their corresponding classification labels, and determine the environment anomaly category based on real-time monitoring video data.

[0047] Specifically, firstly, a first charging pile abnormality recognition model is constructed according to the voltage, current, temperature and SOC value of the electric vehicle battery under the normal charging state and the corresponding voltage, current, temperature and SOC value of the electric vehicle battery under different charging pile abnormality categories; and a second charging pile abnormality recognition model is constructed according to the appearance, structure and color and other features of the charging pile under the normal state and the abnormal state. The first charging pile abnormality recognition model and the second charging pile abnormality recognition model constitute the charging pile abnormality recognition model. Based on this model, the real-time monitored charging voltage, current, temperature and battery SOC value are used as input to output the safety risk category of the charging pile, and the appearance, structure and color images of the charging pile in the real-time video data are extracted as the input of the model to identify the safety risk category of the charging pile, wherein the safety risk category of the charging pile includes normal charging pile, damaged charging pile, over-high charging pile temperature, charging pile with smoke and charging pile with flame, etc.; secondly, according to the normal state and the abnormal state Based on the image features of electric vehicles and their corresponding classification labels, an electric vehicle anomaly recognition model is constructed and trained. Based on the model, the appearance, structure and color images of the target electric vehicle in the real-time video data are extracted as the input of the model to identify the safety risk category of the electric vehicle. The image features of the electric vehicle in the abnormal state are smoke, fire, etc., among which the safety risk categories of the electric vehicle include normal charging vehicles, charging vehicles with smoke, and charging vehicles with fire risks, etc.; finally, according to the environmental image features in normal and abnormal states and their corresponding classification labels, an environmental anomaly recognition model is constructed and trained. Based on the model, the environmental feature images in the real-time video data are extracted as the input of the model to identify the environmental risk category. The environmental image features in the abnormal state include smoke and fire in the surrounding environment, flammable and explosive materials in the surrounding environment, and other environmental abnormal phenomena, among which the environmental risk categories include smoky environment, fire risk environment, and flammable and explosive materials.

[0048] This embodiment, through real-time monitoring of charging piles, electric vehicles and the surrounding environment of the charging piles, identifies safety risks and their causes through models, can comprehensively identify safety risks, refine the causes of risks, accurately locate risks, and take countermeasures in a timely manner, thereby improving the safety of the intelligent charging management system.

[0049] In some embodiments of the present invention, the intelligent charging management system further includes a human-computer interaction module 400; The human-computer interaction module 400 is used to send control instructions using multi-touch buttons and display real-time charging information, fault causes and emergency measures.

[0050] Specifically, a human-computer interaction module 400 is also provided in this embodiment. By providing a highly sensitive touch screen that supports multi-touch and combining it with a voice interaction module, charging information, fault causes and various processing suggestions are displayed, information feedback is supported, and prompt information is sent to users and system platform managers, making it convenient for users and managers to remotely execute and manage charging safety operations.

[0051] In some embodiments of the present invention, the intelligent charging management system further includes a cloud service module 500; The cloud service module 500 is used for distributed storage and management of user identity information data, electric vehicle model data, video monitoring data, voice command data and charging parameter data, building a remote monitoring platform, and remotely displaying information with a graphical interface.

[0052] Specifically, a distributed storage architecture is used to store and manage user identity information, electric vehicle data, real-time monitoring video data, voice command data and charging parameter data. Data mining algorithms, such as association rule mining and cluster analysis, are used to mine the value of user charging behavior and charging station operation data to ensure reliability and scalability. Classified storage and management data is optimized for query. A remote monitoring and management platform is built to monitor charging piles remotely in real time, display information in a graphical interface, support remote control operations and software updates, improve management efficiency, develop secure interfaces and share data with multiple systems, use encryption technology to ensure transmission security, ensure data storage and access security through multi-layer protection measures, and conduct regular backup and audits.

[0053] In order to better implement the intelligent charging management system in the embodiment of the present invention, on the basis of the intelligent charging management system, correspondingly, Figure 5 As shown, the embodiment of the present invention also provides an intelligent charging management method, including: S501, obtaining video data around the charging pile, verifying the identity of the target user based on the video data, and determining the target electric vehicle model; S502, obtaining a voice command, determining a charging strategy for the target electric vehicle according to the voice command and the target electric vehicle model, adjusting the charging strategy according to a plurality of real-time charging parameters during charging, predicting charging failures according to the surrounding video data and the plurality of real-time charging parameters, and feeding back charging progress information and charging failure information; S503: Determine a danger category according to the video data around the charging pile and the multiple real-time charging parameters, and determine a danger emergency measure according to the danger category.

[0054] It should be noted that the intelligent charging management method provided in the above embodiment can implement the technical solution described in the above intelligent charging management system embodiment. The principles or specific implementation details of the above steps can be found in the corresponding contents in the above intelligent charging system embodiment, which will not be described one by one here.

[0055] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware (such as a processor, a controller, etc.) through a computer program, and the computer program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0056] The intelligent charging management method, device, equipment and storage device provided by the present invention are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. An intelligent charging management system, applied to electric vehicle charging piles, characterized in that: Including identity authentication module, charging control module and safety monitoring module; The identity authentication module is used to obtain video data around the charging pile, verify the identity of the target user based on the video data, and determine the target electric vehicle model; The charging control module is used to obtain voice instructions, determine the charging strategy of the target electric vehicle according to the voice instructions and the target electric vehicle model, adjust the charging strategy according to multiple real-time charging parameters during charging, predict charging faults according to the surrounding video data and multiple real-time charging parameters, and feedback charging progress information and charging fault information; The safety monitoring module is used to determine the danger category according to the video data around the charging pile and multiple real-time charging parameters, and determine the danger emergency measures according to the danger category.

2. The intelligent charging management system according to claim 1, characterized in that: The identity authentication module includes a video monitoring submodule and an identity recognition submodule; The video monitoring submodule is used to obtain the surrounding video data of the charging pile, and extract the target user's facial image and the target electric vehicle image from the surrounding video data; The identity recognition submodule is used to recognize the facial features of the target user according to the target user facial image, determine the target user identity recognition result, and after the target user identity recognition result is passed, recognize the target electric vehicle model according to the target electric vehicle image.

3. The intelligent charging management system according to claim 2, characterized in that: The identity recognition submodule includes a feature extraction unit and a verification unit; The feature extraction unit is used to extract the body contour and detail features in the target electric vehicle image based on an edge detection algorithm and an object recognition algorithm, and to extract the facial features of the target user in the target user facial image based on a facial recognition algorithm; The verification unit is used to identify the target electric vehicle model according to the vehicle body contour and detail features, and to determine the target user identity recognition result according to the target user facial features and a preset user identity information database.

4. The intelligent charging management system according to claim 3, characterized in that: The charging control module includes a voice interaction submodule, a charging status monitoring submodule and an AI processing submodule; The voice interaction submodule is used to collect and process voice data and report charging information and safety information. The charging status monitoring submodule is used to monitor multiple real-time charging parameters of the charging pile in the charging state; The AI ​​processing submodule is used to parse the voice data, generate control instructions according to the voice data and the target electric vehicle model, determine the charging optimization strategy of the charging pile according to the multiple real-time charging parameters, and predict charging failures according to the video data and the multiple real-time charging parameters.

5. The intelligent charging management system according to claim 4, characterized in that: The AI ​​processing submodule includes a data receiving unit, a charging strategy optimization unit and a fault prediction unit; The data receiving unit is used to obtain and integrate the video data, voice instructions and multiple real-time charging parameters; The charging strategy optimization unit is used to determine the model of the target electric vehicle according to the video data, determine the charging strategy according to the model of the target electric vehicle and the voice command, determine the optimal charging voltage and charging current in combination with the multiple real-time charging parameters, and generate a charging optimization strategy; The fault prediction unit is used to predict charging faults according to the video data and abnormal data in the multiple real-time charging parameters, and generate a processing plan.

6. The intelligent charging management system according to claim 5, characterized in that: The safety monitoring module includes a safety risk identification submodule and an emergency measures submodule: The safety risk identification submodule is used to determine the charging abnormality category according to the video data around the charging pile and multiple real-time charging parameters; The emergency measures submodule is used to generate corresponding emergency measures according to the abnormality category.

7. The intelligent charging management system according to claim 6, characterized in that: The safety risk identification submodule includes a charging pile abnormality identification unit, a vehicle abnormality identification unit and an environmental abnormality identification unit; The charging pile abnormality recognition unit is used to construct a charging pile abnormality recognition model according to the image features of the charging pile in normal state and abnormal state and their corresponding classification labels, and to output the first abnormality category of the charging pile using the surrounding video data of the charging pile as input, and to determine the second abnormality category of the charging pile according to multiple real-time charging parameters, wherein the first abnormality category and the second abnormality category constitute the charging pile abnormality category; The vehicle abnormality recognition unit is used to construct an electric vehicle abnormality recognition model based on the image features of the electric vehicle in normal and abnormal states and their corresponding classification labels, and determine the abnormal category of the charging pile based on the real-time monitoring video data; The environmental anomaly recognition unit is used to build an environmental anomaly recognition model based on the image features of the surrounding environment of the charging pile in normal and abnormal states and their corresponding classification labels, and determine the environmental anomaly category based on real-time monitoring video data.

8. The intelligent charging management system according to claim 1, characterized in that: The intelligent charging management system also includes a human-computer interaction module; The human-computer interaction module is used to send control instructions using multi-touch buttons and display real-time charging information, fault causes and emergency measures.

9. The intelligent charging management system according to claim 5, characterized in that: The intelligent charging management system also includes a cloud service module; The cloud service module is used for distributed storage and management of user identity information data, electric vehicle model data, video monitoring data, voice command data and charging parameter data, building a remote monitoring platform, and remotely displaying information with a graphical interface.

10. An intelligent charging management method, characterized in that: Applicable to the intelligent charging management system according to any one of claims 1 to 9, the method comprising: Obtain video data around the charging pile, verify the identity of the target user based on the video data, and determine the target electric vehicle model; Acquire a voice command, determine a charging strategy for the target electric vehicle according to the voice command and the target electric vehicle model, adjust the charging strategy according to a plurality of real-time charging parameters during charging, predict charging failures according to the surrounding video data and the plurality of real-time charging parameters, and feed back charging progress information and charging failure information; The danger category is determined according to the video data around the charging pile and a plurality of real-time charging parameters, and the danger emergency measures are determined according to the danger category.

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