User charging demand identification method and device, charging pile and storage medium

Abstract

The invention discloses a user charging demand identification method and device, a charging pile and a storage medium, and belongs to the technical field of new energy. The method comprises the following steps: when a charging signal is identified, acquiring image data and equipment use data of a charging station; identifying identity data and action data of a target user according to the image data, wherein the target user is a charging user corresponding to the charging signal; determining behavior data of the target user according to the equipment use data and the action data; and determining the charging demand of the user according to the identity data and the behavior data, so that the time required by different types of crowds on specific behaviors can be identified, the charging demand of the user is determined, the distribution reasonability of electric power resources in the charging process is improved, and the charging experience of the user can be effectively improved.

Description

Technical Field

[0001] This invention relates to the field of new energy, and in particular to a method, device, charging pile, and storage medium for identifying user charging needs. Background Technology

[0002] The development of power distribution networks varies to varying degrees across regions. A common issue is the weak power supply capacity resulting from the small cross-section and low current carrying capacity of older lines. Replacing cables can be resource-intensive. Currently, the common power distribution method is average distribution, where the total power of the power supply line is evenly distributed among multiple charging piles at a charging station within the rated charging power range of electric bicycles. However, because different users have different needs, some users' electric bicycles finish charging too early, while users who urgently need charging experience slow charging speeds, resulting in a poor charging experience for users.

[0003] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main objective of this invention is to provide a method, device, charging pile, and storage medium for identifying user charging needs, aiming to improve the user's charging experience when using a charging pile to charge an electric bicycle.

[0005] To achieve the above objectives, the present invention provides a user charging demand identification method, applied to a charging station, the user charging demand identification method comprising the following steps:

[0006] When a charging signal is detected, image data and device usage data of the charging station are acquired;

[0007] Based on the image data, the identity data and action data of the target user are identified, and the target user is the charging user corresponding to the charging signal;

[0008] The behavioral data of the target user is determined based on the device usage data and the action data;

[0009] The user's charging needs are determined based on the identity data and the behavioral data.

[0010] Optionally, the number of device usage data is multiple, and the step of determining the target user's behavioral data based on the device usage data and the action data includes:

[0011] Extract the feature data from the motion data, which includes: motion type data, motion location data, motion time data, and motion rate data;

[0012] Based on the action feature data, the target device usage data of the target user is determined from multiple device usage data.

[0013] Behavioral data is determined based on the motion data, the motion feature data, and the target device usage data.

[0014] Optionally, the step of determining behavioral data based on the action data, the action feature data, and the target device usage data includes:

[0015] Extract the target device feature data from the target device usage data;

[0016] Generate behavioral sequence data based on the action type data and the target device usage data;

[0017] A user behavior vector is generated based on the behavior sequence data, the action feature data, and the target device feature data.

[0018] The user behavior vector is used as the behavior data.

[0019] Optionally, the step of determining the user's charging needs based on the identity data and the behavioral data includes:

[0020] The duration of the first behavior is determined based on the identity data and the behavior data, where the first behavior is the behavior performed by the target user at the current moment.

[0021] The probability and duration of the second behavior are determined based on the identity data and the behavior data. The second behavior is the behavior performed by the target user after the first behavior.

[0022] The average waiting time of the target user is calculated based on the duration of the first behavior, the probability of the second behavior, and the duration of the second behavior, and the average waiting time is used as the charging demand.

[0023] Optionally, the step of determining the probability of the second behavior and the duration of the second behavior based on the identity data and the behavior data includes:

[0024] Based on the identity data and preset statistical data, determine the historical behavior statistics corresponding to the target user;

[0025] The probability and duration of the second behavior are determined based on the historical behavior statistics and the behavior data.

[0026] Optionally, the step of acquiring the image data of the charging station includes:

[0027] The charging terminal location information is determined based on the charging signal;

[0028] The first image acquisition device is controlled to acquire a first image based on the charging terminal location information, and the second image acquisition device is determined to acquire a second image based on the first walking recognition result of the first image.

[0029] The first image and the second image are used as the image data.

[0030] Optionally, after the step of determining the user's charging needs based on the identity data and the behavior data, the method further includes:

[0031] The power allocation strategy for the charging station is generated based on the charging demand, equipment charging parameters, and power grid data.

[0032] The charging station is controlled to operate according to the power allocation strategy.

[0033] Furthermore, to achieve the above objectives, the present invention also provides a user charging demand identification device, the user charging demand identification device comprising:

[0034] The acquisition module is used to acquire image data and equipment usage data of the charging station when a charging signal is detected.

[0035] The matching module is used to identify the identity data and action data of the target user based on the image data, wherein the target user is the charging user corresponding to the charging signal;

[0036] The identification module is used to determine the behavioral data of the target user based on the device usage data and the action data;

[0037] The detection module is used to determine the user's charging needs based on the identity data and the behavior data.

[0038] In addition, to achieve the above objectives, the present invention also provides a charging station, the charging station comprising: a memory, a processor, and a user charging demand identification program stored in the memory and executable on the processor, the user charging demand identification program being configured to implement the steps of the user charging demand identification method described in any of the above claims.

[0039] In addition, to achieve the above objectives, the present invention also provides a storage medium storing a user charging demand identification program, wherein the user charging demand identification program, when executed by a processor, implements the steps of the user charging demand identification method described in any of the above claims.

[0040] This invention proposes a method for identifying user charging needs. When a charging signal is detected, the method acquires image data and device usage data of the charging station, identifies the target user's identity data and action data based on the image data, determines the target user's behavior data based on the device usage data and action data, and finally determines the user's charging needs based on the identity data and behavior data. Compared to evenly distributing charging power, this method can identify the time required for different types of people to perform specific behaviors based on identity data and behavior data, thereby determining the user's charging needs, improving the rationality of power resource allocation during the charging process, and ultimately effectively improving the user's charging experience. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the structure of a charging station in the hardware operating environment involved in the embodiments of the present invention;

[0042] Figure 2 This is a flowchart illustrating a first embodiment of a user charging demand identification method according to the present invention.

[0043] Figure 3 This is a flowchart illustrating a second embodiment of a user charging demand identification method according to the present invention.

[0044] Figure 4 This is a flowchart illustrating a third embodiment of a user charging demand identification method according to the present invention.

[0045] Figure 5 This is a rendering of a charging pile according to an embodiment of the present invention.

[0046] Figure 6 This is a rendered schematic diagram of a charging station according to an embodiment of the present invention.

[0047] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0048] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0049] Reference Figure 1 , Figure 1 This is a schematic diagram of the charging station structure in the hardware operating environment involved in the embodiments of the present invention.

[0050] like Figure 1As shown, the charging station may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, an interactive device 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The interactive device 1003 may include a display screen or an input unit such as a keyboard. Optionally, the interactive device 1003 may also be connected to the communication bus via a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0051] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the charging station and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0052] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a data storage module, a network communication module, a user interface module, and a user charging demand identification program.

[0053] exist Figure 1 In the charging station shown, the network interface 1004 is mainly used for data communication with other devices; the interactive device 1003 is mainly used for data interaction with users; the processor 1001 and the memory 1005 in the charging station of the present invention can be set in the charging station. The charging station calls the user charging demand identification program stored in the memory 1005 through the processor 1001 and executes the user charging demand identification method provided in the embodiment of the present invention.

[0054] This invention provides a method for identifying user charging needs, referring to... Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of a user charging demand identification method according to the present invention.

[0055] In this embodiment, the user charging demand identification method includes:

[0056] Step S1: When a charging signal is detected, acquire image data and device usage data of the charging station;

[0057] In this embodiment, when charging is detected, a charging signal is identified, and the triggering method of the charging signal is not limited. Optionally, when the charging port of the electric bicycle is connected to the discharge port of the charging station, the vehicle information of the electric bicycle is obtained and the charging signal is confirmed. Optionally, when the user scans the QR code of the charging port and selects to perform the charging function, the charging signal is confirmed to have been identified. Preferably, when a charging signal is identified and the current load of the charging station is higher than a preset load, the steps of acquiring the image data and device usage data of the charging station are executed. Generally, the image acquisition device of the charging station, such as a camera, is usually in a continuous working state. Therefore, specifically, when a charging signal is identified, the image information corresponding to the moment the charging signal is identified by the camera is saved to the corresponding data storage space. In this embodiment, it should be noted that the equipment of the charging station includes: vending machines, rest room equipment, elevators, catering room equipment, and smart device charging ports, etc. Commonly, equipment usage data includes vending machine order data, which can identify the products purchased by users, such as self-heating hot pot and beverages. In addition, the equipment in the lounge includes an access control system, which generally includes information on the duration of users' use of the lounge and the usage status of equipment in the dining room.

[0058] Step S2: Identify the identity data and action data of the target user based on the image data, wherein the target user is the charging user corresponding to the charging signal;

[0059] Specifically, the identity data of the target user is determined through face recognition and gait recognition algorithms. The face region is located in the image data, face data is extracted and standardized, face features are extracted, and the similarity between these features and data in a face database is calculated. Based on this similarity, the target user's identity data is determined. Optionally, the outline of the person is determined through background subtraction, and the gait feature data of the target user is extracted using an image extraction algorithm. The similarity between these gait features and data in a gait database is calculated. Furthermore, human joint coordinates are extracted from each frame of the image, and the keypoint sequence of consecutive frames is input into a deep learning model to identify the user's actions. Commonly, since multiple users may charge electric bicycles at the same time in a charging station, it is necessary to distinguish between different target users. This ensures that both identity data and action data are associated with the target user.

[0060] Step S3: Determine the target user's behavioral data based on the device usage data and the action data;

[0061] In this embodiment, by matching the device usage data and the action data, the complete behavior actually performed by the target user after connecting the charging pile and the electric bicycle can be determined, thereby determining the user's behavior data within the charging station or between adjacent areas, ensuring the accuracy and relevance of the behavior data.

[0062] Step S4: Determine the user's charging needs based on the identity data and the behavior data.

[0063] In this embodiment, the behavioral data is used to predict the target user's charging efficiency needs. Users often use the time while charging to complete other tasks. For example, food delivery riders typically charge their electric bicycles at charging stations after the lunch rush, during which time they eat and rest. Conversely, when riders charge before the lunch rush, they usually require faster charging. Common behavioral data types include eating, resting, leaving, and waiting. For different types of target users, such as company employees, residents, food delivery riders, and couriers, the target user's identity and behavioral data are used to determine their current charging power needs. Optionally, the identity data and behavioral data are input into a preset recognition model to determine the user's charging time needs. Optionally, the average time taken by the user type corresponding to the identity data to perform the corresponding behavior is calculated, and this average time is used as the charging demand. After determining the user's charging demand, the corresponding charging power is allocated to achieve the desired charging efficiency.

[0064] In this embodiment, when a charging signal is detected, image data and device usage data of the charging station are acquired. Based on the image data, the identity data and action data of the target user are identified. Based on the device usage data and action data, the behavioral data of the target user is determined. Based on the identity data and behavioral data, the user's charging needs are determined. Compared with the average allocation of charging power, the identity data and behavioral data can identify the time required for different types of people to perform specific behaviors, thereby determining the user's charging needs, improving the rationality of power resource allocation during the charging process, and thus effectively improving the user's charging experience.

[0065] Furthermore, based on the first embodiment, a second embodiment of the user charging demand identification method of the present invention is proposed. In this embodiment, reference is made to... Figure 3 The number of device usage data is multiple, and the step of determining the target user's behavioral data based on the device usage data and the action data includes:

[0066] Step S31: Extract the feature data of the motion data. The motion feature data includes: motion type data, motion location data, motion time data, and motion rate data.

[0067] Specifically, after identifying the target user's action data through the image data, the action data is analyzed and corresponding features are extracted. In the common process of identification, only the user's action type data is identified. In addition, it is also necessary to determine the action location data based on the target user's location, determine the action time data based on the duration of the action, and determine the action rate data based on the action time data.

[0068] Step S32: Determine the target device usage data of the target user based on the action feature data and multiple device usage data;

[0069] Specifically, the device operated by the target user is determined based on action location data and action time data, and then the target device usage data of the target user is determined from multiple device usage data. It should be noted that preset location information corresponding to each device is stored in advance. The preset location information is compared with the action location data, and the action time data is compared with the operation time of the device usage data, so as to determine the target device usage data based on the above two comparison results.

[0070] Step S33: Determine behavioral data based on the action data, the action feature data, and the target device usage data.

[0071] In this embodiment, it should be noted that the action data and action feature data reflect the current user's actions, but cannot actually completely determine the user's complete actions. Therefore, target device usage data is also needed to determine behavioral data, thereby improving the accuracy of user behavior, such as the scheduled time in the rest area. Whether a vending machine sells a beverage or food cannot be accurately determined from image data due to factors such as occlusion. Therefore, by generating behavioral data from the action data, action feature data, and target device usage data, the accuracy of the data can be improved.

[0072] In this embodiment, by extracting feature data from the action data, including action type data, action location data, action time data, and action rate data; determining the target device usage data of the target user based on the action feature data across multiple device usage data; and determining behavioral data based on the action data, the action feature data, and the target device usage data, the accuracy of data recognition can be improved.

[0073] Furthermore, the step of determining behavioral data based on the action data, the action feature data, and the target device usage data includes:

[0074] Extract the target device feature data from the target device usage data;

[0075] Generate behavioral sequence data based on the action type data and the target device usage data;

[0076] A user behavior vector is generated based on the behavior sequence data, the action feature data, and the target device feature data, and the user behavior vector is used as the behavior data.

[0077] Shopping information is extracted from the target device usage data. Specifically, the shopping type and quantity are determined based on the shopping information, commonly including food types. The food type and quantity determine the corresponding consumption time. Furthermore, user data is not limited to single behavioral data; users often also use the smart device's power port to charge smart devices, such as mobile phones. Specifically, the time is divided into multiple intervals based on each target device feature data and action type data. Each time interval corresponds to a sub-step marker, resulting in a set of sequence data, including time and corresponding markers. The behavioral sequence data, the action feature data, and the target device feature data are concatenated to obtain the user behavior vector, which is used as the behavioral data.

[0078] In this embodiment, by extracting target device feature data from the target device usage data, generating behavior sequence data based on the action type data and the target device usage data, complete user action data can be obtained. Furthermore, a user behavior vector can be generated based on the behavior sequence data, the action feature data, and the target device feature data, thereby obtaining a complete user behavior vector describing the user behavior.

[0079] Furthermore, based on the first or second embodiment, a third embodiment of the user charging demand identification method of the present invention is proposed. In this embodiment, reference is made to... Figure 4 The step of determining the user's charging needs based on the identity data and the behavior data includes:

[0080] Step S41: Determine the duration of the first behavior based on the identity data and the behavior data, wherein the first behavior is the behavior performed by the target user at the current moment;

[0081] In this embodiment, the historical charging records of each user prior to the current moment include the connection time when the user connects to the charging station and the electric bicycle, and the disconnection time when the user disconnects from the charging station and the electric bicycle. Furthermore, the historical charging records also include user tagging data, specifically, user tagging data includes user historical identity data and user historical behavior statistics. One charging record corresponds to a set of user tagging data. The tagging data is recorded in the same way as in step S1 of this application, which obtains the image data and device usage data of the charging station, thus ensuring that the historical charging records collected under the same conditions do not have significant errors compared to the image data and device usage data. Commonly, historical charging records with the same conditions are filtered from a database storing multiple historical charging records based on the identity data and the behavior data, and these are used as the first matching charging records. The average charging time of the multiple first matching charging records is calculated as the first behavior duration. It should be further noted that the first behavior here can include multiple specific behaviors, not limited to a single behavior. The specific behavior corresponds to the sequence data of the behavior data, and the second behavior is also not limited to a single specific behavior.

[0082] Step S42: Determine the probability and duration of the second behavior based on the identity data and the behavior data. The second behavior is the behavior performed by the target user after the first behavior.

[0083] Optionally, based on the identity data and behavior data, the behavior with the highest probability of execution after the first behavior is completed is identified as the second behavior in the database of multiple historical charging records. The average charging time for executing the second behavior in the database of multiple historical charging records using the identity data is taken as the duration of the second behavior.

[0084] Step S43: Calculate the average waiting time of the target user based on the duration of the first behavior, the probability of the second behavior, and the duration of the second behavior, and use the average waiting time as the charging demand.

[0085] In this embodiment, the first behavior duration, the second behavior probability, and the second behavior duration are weighted and averaged to calculate the average waiting time of the target user.

[0086] Furthermore, the step of determining the probability and duration of the second behavior based on the identity data and the behavior data includes:

[0087] Based on the identity data and preset statistical data, determine the historical behavior statistics corresponding to the target user;

[0088] Specifically, the identity data includes at least one identity tag. Based on the identity tag, the historical users corresponding to the target user are determined. Here, the historical users and the target users may not be the same person, but rather people marked as belonging to the same type through identity tags, such as: deliveryman, courier, resident, student, senior citizen, etc. The historical behavior statistics will also differ depending on the different identity tags and the number of tags.

[0089] The probability and duration of the second behavior are determined based on the historical behavior statistics and the behavior data.

[0090] In this embodiment, the historical behavior statistics corresponding to the target user are determined based on the identity data and preset statistical data; the probability of the second behavior and the duration of the second behavior are determined based on the historical behavior statistics and the behavior data, thereby improving the accuracy of the probability of the second behavior and the duration of the second behavior.

[0091] Furthermore, based on any of the above embodiments, a fourth embodiment of the user charging demand identification method of the present invention is proposed. In this embodiment, the step of acquiring the image data of the charging station includes:

[0092] The charging terminal location information is determined based on the charging signal;

[0093] The first image acquisition device is controlled to acquire a first image based on the charging terminal location information, and the second image acquisition device is determined to acquire a second image based on the first walking recognition result of the first image.

[0094] The first image and the second image are used as the image data.

[0095] Specifically, to limit the amount of image data acquired, the charging terminal location information is determined through the charging signal, the first image acquired by the first image acquisition device is recorded, and the second image acquisition device is determined based on the first walking recognition result of the first image, and the second image is recorded. When the second image has a second walking recognition result, the second image is saved as the first image, and the process returns to the step of determining the associated second image acquisition device based on the first walking recognition result of the first image to acquire the second image. In this embodiment, the first image acquisition device and the second image acquisition device can continuously acquire images and determine the first image and the second image among multiple images.

[0096] Furthermore, based on any of the above embodiments, a fifth embodiment of the user charging demand identification method of the present invention is proposed. In this embodiment, after the step of determining the user's charging demand based on the identity data and the behavior data, the method further includes:

[0097] The power allocation strategy for the charging station is generated based on the charging demand, equipment charging parameters, and power grid data.

[0098] The charging station is controlled to operate according to the power allocation strategy.

[0099] In this embodiment, the power allocation of the charging station is based on the premise that the total output power of the charging station at the current moment is less than the sum of the maximum output power of each charging pile at the current moment. Optionally, when there are a large number of idle charging piles, it is not necessary to activate the power allocation strategy, or an idle power adjustment strategy can be activated. The power allocation strategy here can prioritize user demand, for example: prioritizing the output power of the charging station to users waiting on-site, and giving lower charging priority to users leaving the charging station than to users waiting on-site. Of course, the above allocation strategy is not absolute, and the impact of user charging demand on charging power can be adjusted according to actual conditions.

[0100] Furthermore, embodiments of the present invention also propose a user charging demand identification device, the user charging demand identification device comprising:

[0101] The acquisition module is used to acquire image data and equipment usage data of the charging station when a charging signal is detected.

[0102] The matching module is used to identify the identity data and action data of the target user based on the image data, wherein the target user is the charging user corresponding to the charging signal;

[0103] The identification module is used to determine the behavioral data of the target user based on the device usage data and the action data;

[0104] The detection module is used to determine the user's charging needs based on the identity data and the behavior data.

[0105] Furthermore, this invention also proposes a charging station, which includes: a memory, a processor, and a user charging demand identification program stored in the memory and executable on the processor. The user charging demand identification program is configured to implement the steps of the user charging demand identification method described above, referring to... Figure 5 , Figure 5 This is a rendered diagram of a charging station. Specifically, the charging station has multiple charging ports, and each charging port is equipped with a vending machine that can offer various food items. (See reference...) Figure 6 , Figure 6 This is a rendered schematic diagram of a charging station. There is no limit to the size of the charging station; the Chinese Academy of Sciences will only set up one reference charging station in each case. Figure 5Charging stations and vending machines are also available. Other charging stations can also be equipped with lounge facilities, elevators in nearby buildings, dining facilities, and charging ports for smart devices.

[0106] Furthermore, embodiments of the present invention also propose a storage medium storing a user charging demand identification program, wherein when the user charging demand identification program is executed by a processor, it implements the steps of the user charging demand identification method described above.

[0107] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0108] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0109] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0110] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A method for identifying user charging needs, characterized in that, Applied to charging stations, the user charging demand identification method includes the following steps: When a charging signal is detected, image data and device usage data of the charging station are acquired; Based on the image data, the identity data and action data of the target user are identified, and the target user is the charging user corresponding to the charging signal; The behavioral data of the target user is determined based on the device usage data and the action data; The user's charging needs are determined based on the identity data and the behavioral data.

2. The user charging demand identification method as described in claim 1, characterized in that, The number of device usage data is multiple, and the step of determining the target user's behavioral data based on the device usage data and the action data includes: Extract the feature data from the motion data, which includes: motion type data, motion location data, motion time data, and motion rate data; Based on the action feature data, the target device usage data of the target user is determined from multiple device usage data. Behavioral data is determined based on the motion data, the motion feature data, and the target device usage data.

3. The user charging demand identification method as described in claim 2, characterized in that, The step of determining behavioral data based on the action data, the action feature data, and the target device usage data includes: Extract the target device feature data from the target device usage data; Generate behavioral sequence data based on the action type data and the target device usage data; A user behavior vector is generated based on the behavior sequence data, the action feature data, and the target device feature data. The user behavior vector is used as the behavior data.

4. The user charging demand identification method as described in claim 1, characterized in that, The step of determining the user's charging needs based on the identity data and the behavioral data includes: The duration of the first behavior is determined based on the identity data and the behavior data, where the first behavior is the behavior performed by the target user at the current moment. The probability and duration of the second behavior are determined based on the identity data and the behavior data. The second behavior is the behavior performed by the target user after the first behavior. The average waiting time of the target user is calculated based on the duration of the first behavior, the probability of the second behavior, and the duration of the second behavior, and the average waiting time is used as the charging demand.

5. The user charging demand identification method as described in claim 4, characterized in that, The step of determining the probability and duration of the second behavior based on the identity data and the behavior data includes: Based on the identity data and preset statistical data, determine the historical behavior statistics corresponding to the target user; The probability and duration of the second behavior are determined based on the historical behavior statistics and the behavior data.

6. The user charging demand identification method as described in claim 1, characterized in that, The step of acquiring the image data of the charging station includes: The charging terminal location information is determined based on the charging signal; The first image acquisition device is controlled to acquire a first image based on the charging terminal location information, and the second image acquisition device is determined to acquire a second image based on the first walking recognition result of the first image. The first image and the second image are used as the image data.

7. The user charging demand identification method as described in any one of claims 1 to 6, characterized in that, After the step of determining the user's charging needs based on the identity data and the behavior data, the method further includes: The power allocation strategy for the charging station is generated based on the charging demand, equipment charging parameters, and power grid data. The charging station is controlled to operate according to the power allocation strategy.

8. A user charging demand identification device, characterized in that, The user charging demand identification device includes: The acquisition module is used to acquire image data and equipment usage data of the charging station when a charging signal is detected. The matching module is used to identify the identity data and action data of the target user based on the image data, wherein the target user is the charging user corresponding to the charging signal; The identification module is used to determine the behavioral data of the target user based on the device usage data and the action data; The detection module is used to determine the user's charging needs based on the identity data and the behavior data.

9. A charging station, characterized in that, The charging station includes: a memory, a processor, and a user charging demand identification program stored in the memory and executable on the processor, the user charging demand identification program being configured to implement the steps of the user charging demand identification method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium stores a user charging demand identification program, which, when executed by a processor, implements the steps of the user charging demand identification method as described in any one of claims 1 to 7.

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