Charging management method and device based on reservation management, equipment and medium
By using image acquisition equipment and prediction models in the charging area for automatic appointment management, the charging equipment occupation problem is solved, the charging efficiency and equipment utilization rate are improved, and the user's reasonable use behavior is promoted.
Patent Information
- Application Number
- CN202311515615.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-11-14
- Publication Date
- 2025-01-14
AI Technical Summary
The resources of charging equipment in the charging area are limited, making it difficult for users to find idle devices, and the equipment takes up a long time after charging, forming a vicious cycle, affecting charging efficiency.
The method based on reservation management is adopted to obtain user and vehicle information through the image acquisition device, use automatic identification and prediction models to predict the charging time and frequency, automatically make an appointment for the charging device, and adjust priority and billing according to the default value to promote reasonable use.
It improves the efficiency of charging equipment, reduces long-term occupation behavior, improves user behavior norms through reward and punishment mechanisms, and promotes the virtuous cycle of charging equipment.
Smart Images
Figure CN119312948A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the fields of Internet of Things and artificial intelligence, and more specifically to a charging management method, device, equipment and medium based on reservation management. Background Art
[0002] As the country vigorously develops new energy, new energy has become the development trend at this stage. Lithium batteries are the only choice for many charging vehicle users. The number of charging vehicles in the charging area is also increasing. However, the charging equipment resources in the charging area are relatively limited. When people need to charge the vehicles to be charged, it is often difficult to find idle charging equipment. The reason is that there is a widespread problem of occupation before and after charging. Many existing charging devices are activated by scanning codes. However, due to the increase in the number of people charging every day, the occupation of charging equipment has become a phenomenon. People save the code map on the charging device and scan the code in advance to activate the charging device, so as to achieve the purpose of early occupation. The occupation problem after charging is more prominent. The battery of the vehicle to be charged generally does not need to be charged for more than 8 hours. The battery of most people only needs to be charged for 2-5 hours to be fully charged. However, many users will not release the charging equipment in time after receiving the reminder of charging completion, which results in more and more people occupying in advance every day, forming a vicious circle. Summary of the invention
[0003] In view of the above problems, embodiments of the present disclosure provide a charging management method, apparatus, device and medium based on reservation management.
[0004] According to the first aspect of the present disclosure, a charging management method based on reservation management is provided, comprising: in response to a user and a vehicle to be charged entering a charging area, obtaining image information of the user and the vehicle to be charged by using an image acquisition device arranged in the charging area, wherein a plurality of charging devices are arranged in the charging area; identifying the identity information of the user, the model of the vehicle to be charged and the power of the vehicle to be charged by using a pre-trained automatic recognition model according to the image information of the user and the vehicle to be charged; predicting the charging time and frequency of the vehicle to be charged by using a pre-trained prediction model according to the identified model of the vehicle to be charged and the power of the vehicle to be charged; automatically reserving the charging device to charge the vehicle to be charged according to the predicted charging time and the charging frequency; and in response to a successful automatic reservation, sending charging reservation information to the user, so that the user can charge the vehicle to be charged according to the charging reservation information.
[0005] According to an embodiment of the present disclosure, the prediction model includes a charging time prediction model and a charging frequency prediction model; the prediction model is used to predict the charging time and charging frequency of the vehicle to be charged based on the identified model of the vehicle to be charged and the power of the vehicle to be charged, using a pre-trained prediction model, specifically including: inputting the model of the vehicle to be charged and the power of the vehicle to be charged into the charging time prediction model to obtain the charging time required for the vehicle to be charged; and in response to the charging time being greater than 0, inputting the identity information of the user into the charging frequency prediction model to obtain the charging frequency of the user.
[0006] According to an embodiment of the present disclosure, automatically reserving a charging device to charge the vehicle to be charged based on the predicted charging duration and the charging frequency specifically includes: judging the time when the user will use the charging device next time based on the charging frequency and the time when the charging device was last used; and automatically reserving the charging device to charge the vehicle to be charged when the time when the user will use the charging device next time is no later than the time when the user and the vehicle to be charged enter the charging area.
[0007] According to an embodiment of the present disclosure, in response to a successful automatic reservation, charging reservation information is sent to the user so that the user can charge the vehicle to be charged according to the charging reservation information, specifically including: in response to a successful automatic reservation, the charging reservation information is pushed to the user through a preset client so that the user can learn and modify the charging reservation information, wherein the charging reservation information includes the number of the charging device, the activation key of the charging device, the charging start time and the charging duration; and in response to the user confirming the charging reservation information, the charging device is locked within a predetermined time period
[0008] According to an embodiment of the present disclosure, in response to the user failing to charge the vehicle to be charged within a predetermined time period, the user is determined to be in breach of trust, and the frequency of the user's breach of trust is recorded, and the breach of trust value is accumulated; when the user makes an appointment, the priority of the user's appointment is processed according to the user's breach of trust value; and when the user's breach of trust frequency is greater than a first preset value, no automatic appointment is made for the user within the specified time period.
[0009] According to an embodiment of the present disclosure, when the user makes a reservation, the priority of the user's reservation is processed according to the user's credibility value, specifically including: in response to multiple users making reservations for the same charging device at the same time, comparing the credibility value of each user; assigning a higher priority to the user with a low credibility value so that the user with a high priority can make a reservation for the charging device first.
[0010] According to an embodiment of the present disclosure, the method further includes: dividing the user into levels of dishonesty based on the user's dishonesty value, with different levels of dishonesty having different billing coefficients; dividing the time that the user occupies the charging device for more than a predetermined time period according to a preset billing interval, with different billing intervals corresponding to different billing rates; and charging the dishonest user additionally based on the billing coefficient, the billing rate and the time exceeding the predetermined time period.
[0011] According to an embodiment of the present disclosure, the method also includes: storing the identity information and image information of the user, as well as the model of the vehicle to be charged, the power of the vehicle to be charged, the charging time and the charging frequency in a database, and creating a charging equipment table containing the basic information of the charging equipment, wherein the charging equipment table is updated in real time based on the reservation behavior of the user; regularly scanning the information of the charging equipment table in the database to obtain the status of the charging equipment; and in response to the acquired status of the charging equipment being empty, uniformly pushing the information of the charging equipment that can be reserved to the user.
[0012] According to an embodiment of the present disclosure, pre-training the automatic recognition model includes: obtaining image information of users and vehicles to be charged within a historical time period to obtain a first training sample set; inputting the first training sample set into a pre-constructed automatic recognition model, and obtaining the output of the automatic recognition model, wherein the automatic recognition model is constructed based on a convolutional neural network model, and the output includes the user's identity information, the model of the vehicle to be charged, and the power of the vehicle to be charged; and when the difference between the output of the automatic recognition model and the actual identity information of the user, the model of the vehicle to be charged, and the power of the vehicle to be charged is greater than a first prescribed threshold, adjusting the parameters and structure of the automatic recognition model until the difference between the output of the automatic recognition model and the actual identity information of the user, the model of the vehicle to be charged, and the power of the vehicle to be charged is less than or equal to the first prescribed threshold.
[0013] According to an embodiment of the present disclosure, pre-training the prediction model includes: obtaining the model of the vehicle to be charged and the power of the vehicle to be charged within a historical time period to obtain a second training sample set; inputting the second training sample set into a pre-constructed prediction model, and obtaining the output of the prediction model, wherein the prediction model is constructed based on LDA-SVM, and the output includes the charging time and charging frequency of the vehicle to be charged; and when the difference between the output of the prediction model and the actual charging time and charging frequency of the vehicle to be charged is greater than a second prescribed threshold, adjusting the parameters and structure of the prediction model until the difference between the output of the prediction model and the actual charging time and charging frequency of the vehicle to be charged is less than or equal to the second prescribed threshold.
[0014] The second aspect of the present disclosure provides a charging management device based on reservation management, including: a collection module, used to: in response to a user and a vehicle to be charged entering a charging area, obtain image information of the user and the vehicle to be charged by using an image collection device arranged in the charging area, wherein a plurality of charging devices are arranged in the charging area; an identification module, used to: identify the identity information of the user, the model of the vehicle to be charged and the power of the vehicle to be charged according to the image information of the user and the vehicle to be charged by using a pre-trained automatic recognition model; a prediction module, used to: predict the charging time and frequency of the vehicle to be charged according to the identified model of the vehicle to be charged and the power of the vehicle to be charged by using a pre-trained prediction model; an automatic reservation module, used to: automatically reserve a charging device to charge the vehicle to be charged according to the predicted charging time and charging frequency; and a message sending module, used to: in response to a successful automatic reservation, send charging reservation information to the user, so that the user can charge the vehicle to be charged according to the charging reservation information.
[0015] The third aspect of the present disclosure provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors execute the above-mentioned charging management method based on reservation management.
[0016] A fourth aspect of the present disclosure further provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, causes the processor to execute the above-mentioned charging management method based on reservation management.
[0017] In the disclosed embodiment, a reservation function is provided to automatically make reservations for users, thereby reducing the bad behavior of occupying charging cabinets for a long time and improving the efficiency of charging cabinet use. At the same time, economic constraints and penalties are imposed through a reward and punishment mechanism to increase the cost of illegal use by users, improve the standardization of their behavior, and promote users to use charging cabinets rationally. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] The above contents and other purposes, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:
[0019] Figure 1 The application scenario diagram of the charging management method, apparatus, device and medium based on reservation management according to the embodiment of the present disclosure is schematically shown;
[0020] Figure 2 The flowchart of the charging management method based on reservation management according to the embodiment of the present disclosure is schematically shown;
[0021] Figure 3 The structure block diagram of the charging management device based on reservation management according to the embodiment of the present disclosure is schematically shown;
[0022] Figure 4 A block diagram of an electronic device according to a charging management method based on reservation management according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION
[0023] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.
[0024] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.
[0025] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification, and should not be interpreted in an idealized or overly rigid manner.
[0026] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).
[0027] In the technical solution of the present invention, the user information (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data comply with the relevant laws, regulations and standards of relevant countries and regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.
[0028] Figure 1 The application scenario diagram of the charging management method, apparatus, device and medium based on reservation management according to an embodiment of the present disclosure is schematically shown.
[0029] like Figure 1 As shown, the application scenario 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for a communication link between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.
[0030] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only examples).
[0031] The terminal devices 101 , 102 , and 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers.
[0032] The server 105 may be a server that provides various services, such as a background management server (only an example) that provides support for websites browsed by users using the terminal devices 101, 102, and 103. The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.
[0033] It should be noted that the charging management method based on reservation management provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the charging management device based on reservation management provided in the embodiment of the present disclosure can generally be set in the server 105. The charging management method based on reservation management provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and / or the server 105. Accordingly, the charging management device based on reservation management provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101, 102, 103 and / or the server 105.
[0034] It should be understood that Figure 1The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.
[0035] Figure 2 The flowchart of the charging management method based on reservation management according to the embodiment of the present disclosure is schematically shown.
[0036] The embodiment of the present disclosure provides a charging management method based on reservation management. The flowchart of the method can be found in Figure 2 As shown, it specifically includes operations S1-S5.
[0037] In operation S1 , in response to a user and a vehicle to be charged entering a charging area, image information of the user and the vehicle to be charged is acquired using an image acquisition device disposed in the charging area, wherein a plurality of charging devices are disposed in the charging area.
[0038] In the embodiments provided in the present disclosure, before obtaining the image information of the user and the vehicle to be charged, the user's consent or authorization may be obtained. For example, before operation S1, a request to obtain information may be issued to the user. When the user agrees or authorizes that the image information of the user and the vehicle to be charged may be obtained, the operation S1 is performed.
[0039] In operation S2, based on the image information of the user and the vehicle to be charged, the identity information of the user, the model of the vehicle to be charged, and the power level of the vehicle to be charged are identified using a pre-trained automatic recognition model.
[0040] In operation S3, the charging time and charging frequency of the vehicle to be charged are predicted according to the identified model of the vehicle to be charged and the power level of the vehicle to be charged by using a pre-trained prediction model.
[0041] In operation S4, a charging device is automatically reserved to charge the vehicle to be charged according to the predicted charging duration and the charging frequency.
[0042] In operation S5, in response to the automatic reservation being successful, charging reservation information is sent to the user, so that the user can charge the vehicle to be charged according to the charging reservation information.
[0043] In the method provided by the embodiment of the present disclosure, it is first necessary to obtain image information of the user and the vehicle to be charged through an image acquisition device, such as installing a camera at a preset position in the charging area, and taking images in real time when the user enters the charging area. Then, the image information of the user and the vehicle to be charged is converted into the user's identity information, the model of the vehicle to be charged, and the power of the vehicle to be charged using a pre-trained automatic recognition model. Based on the above information, the charging time and charging frequency of the vehicle to be charged are predicted, and whether the vehicle to be charged needs to be charged is determined according to the charging time, and the time of the user's next charge is determined according to the charging frequency, thereby realizing the automatic reservation function. At the same time, when the automatic reservation is successful, the charging reservation information is sent to the client, so that the user can charge the vehicle to be charged according to the reservation information.
[0044] In the disclosed embodiment, by making automatic reservations for users, it is possible to avoid wasting time looking for available charging equipment in the charging area, and to release occupied charging equipment in a timely manner, thereby improving charging efficiency and the utilization rate of charging equipment. In addition, charging reservation information is sent so that users can easily know when they can charge their vehicles without having to check the charging status frequently, which improves user convenience to a certain extent. At the same time, by using the charging time and charging frequency predicted by the model, charging equipment resources can be allocated more reasonably so that more vehicles can be charged when needed. In short, by providing an automatic reservation function, users are encouraged to use charging equipment reasonably, reduce the bad behavior of occupying charging equipment for a long time, improve the efficiency of charging equipment use, and establish a virtuous cycle mechanism for the use of charging equipment in the charging area.
[0045] In the embodiment of the present disclosure, the prediction model includes a charging duration prediction model and a charging frequency prediction model.
[0046] Furthermore, operation S3 specifically includes S31-S32.
[0047] In operation S31, the model of the vehicle to be charged and the power of the vehicle to be charged are input into the charging time prediction model to obtain the charging time required for the vehicle to be charged.
[0048] In operation S32, in response to the charging duration being greater than 0, the user's identity information is input into the charging frequency prediction model to obtain the user's charging frequency.
[0049] Specifically, the model and power of the vehicle to be charged are transmitted to the charging time prediction model, and the time required for the current vehicle to be charged is output. If the required charging time is 0, no further step is required. If it is greater than 0, the user's identity information is transmitted to the charging frequency prediction model to obtain the user's charging frequency.
[0050] By using two independent prediction models (charging time prediction model and charging frequency prediction model), the charging time required for the vehicle to be charged and the charging frequency of the user can be predicted more accurately. Then, based on the predicted charging time and charging frequency, the charging equipment resources can be more effectively allocated and managed to meet the charging needs of different users and vehicles. At the same time, by predicting the charging frequency of different users, personalized charging service recommendations and suggestions can be provided to users, such as recommending appropriate charging time or charging plan, thereby enhancing user satisfaction and convenience with charging services.
[0051] Further, operation S4 specifically includes S41-S42.
[0052] In operation S41, based on the charging frequency and the time when the charging device was last used, the time when the user will use the charging device next time is determined.
[0053] In operation S42, when the time when the user uses the charging device next time is not later than the time when the user and the vehicle to be charged enter the charging area, the charging device is automatically reserved to charge the vehicle to be charged.
[0054] Specifically, the system retrieves the specific time of the user's last charging and determines the next charging time based on the charging frequency. At the same time, if the user's next use of the charging device is no later than when entering the charging area for self-service charging, the charging device will be automatically reserved for the user. The charging start time can be customized according to the user's charging habits, such as the average of the user's historical charging start time, and the charging duration is the duration output by the charging duration prediction model.
[0055] By considering the user's charging frequency and the last time they used the charging device, the next time they will use the charging device can be predicted more accurately, thereby improving the accuracy of the reservation. Since the next time a user will use the charging device can be predicted in advance, the system can automatically reserve the charging device before the user arrives at the charging area, ensuring that the charging device is available when the user arrives, thereby avoiding the user's time wasted looking for available charging devices after arriving at the charging area and improving charging efficiency.
[0056] Furthermore, operation S5 specifically includes S51 - S52 .
[0057] In operation S51, in response to the successful automatic reservation, the charging reservation information is pushed to the user through the preset client, so that the user can obtain and modify the charging reservation information, wherein the charging reservation information includes the number of the charging device, the activation key of the charging device, the charging start time and the charging duration.
[0058] In operation S52 , in response to the user confirming the charging reservation information, the charging device is locked for a predetermined period of time.
[0059] After the automatic reservation, the charging reservation information is pushed to the user through the preset client (such as mobile phone APP, web page, etc.), and the user obtains the number of the charging device, the unlocking key of the charging device, the charging start time and the charging duration. At the same time, the user can also modify the reservation information. When you click to confirm the reservation, a charging device in the scheduled time period is locked to ensure that other users cannot use the device. After the reservation is successful, the charging device will be reserved for a certain period of time, and the user can make a reservation for the released charging device in real time.
[0060] By pushing charging reservation information through the preset client, users can be ensured to receive relevant information about charging reservations in a timely manner. Allowing users to modify charging reservation information increases user participation and convenience, improves the interactivity between users and the system and the transparency of information, and users can adjust the charging time and duration according to their needs. At the same time, the charging device is locked within the predetermined time period to ensure the user's exclusivity to the charging device and improve the safety of device use. Finally, improvements in timely notification, user participation, and device safety have enhanced user satisfaction and convenience with charging services.
[0061] In the embodiment provided by the present disclosure, in response to the user failing to charge the vehicle to be charged within a predetermined time period, the user is determined to be in default, and the frequency of the user's default is recorded, and the default value is accumulated; when the user makes an appointment, the priority of the user's appointment is processed according to the user's default value; and when the user's default frequency is greater than a first preset value, no automatic appointment is made for the user within the specified time period.
[0062] Specifically, if the vehicle to be charged is not charged within the scheduled time period, the user is judged to be dishonest. Each time the user fails to fulfill his / her promise, the user's dishonesty frequency increases by one. When the user's dishonesty frequency is greater than a first preset value, no automatic reservation will be made within the specified time period. At the same time, based on the user's dishonesty frequency, the dishonesty value is accumulated, and the priority of the user's reservation is processed according to the user's dishonesty value.
[0063] By determining and recording dishonesty, users can be reminded to charge their vehicles on time, thereby increasing their sense of responsibility for charging management. By adjusting reservation priorities and limiting automatic reservations, resources can be more fairly allocated to trustworthy users and the utilization of charging equipment can be optimized. Then, by accumulating dishonesty values and taking corresponding measures, invalid reservations and idle charging equipment can be reduced, thereby improving the overall efficiency of the system.
[0064] Furthermore, the priority of the user's reservation is processed according to the user's credibility value, specifically including: in response to multiple users reserving the same charging device at the same time, comparing the credibility value of each user; assigning a higher priority to the user with a lower credibility value so that the user with a higher priority can reserve the charging device first.
[0065] The time when the user clicks to confirm the appointment determines who has made a successful appointment. If multiple users make appointments for the same charging device at the same time, the system will make a judgment based on the credibility score. Those with lower credibility scores will be given priority to obtain charging equipment, while those with higher credibility scores will require appointment response time. The appointment response time increases proportionally according to the credibility score (negative points have no effect).
[0066] By comparing the default values and assigning priorities accordingly, fair distribution can be achieved when multiple users make reservations for the same charging equipment at the same time, ensuring that users with lower default values receive priority treatment, thereby optimizing resource allocation, improving the utilization rate of charging equipment, and ensuring that trustworthy users receive a better service experience, and encouraging other users to actively fulfill their responsibilities of making reservations and managing charging equipment, ultimately promoting the healthy development of the entire charging service system.
[0067] In the embodiments provided by the present disclosure, based on the user's credibility value, the user is divided into credibility levels, and different credibility levels have different billing coefficients; the time that the user occupies the charging device exceeding a predetermined time period is divided according to a preset billing interval, and different billing intervals correspond to different billing rates; and based on the billing coefficient, the billing rate and the time exceeding the predetermined time period, the dishonest user is additionally charged.
[0068] If the user does not release the fully charged device within the specified time, additional billing management will be performed. Different users have different usage habits and concepts, which can easily lead to waste of space. In order to regulate user behavior, this disclosure adjusts the billing method for users based on the default value. First, the user's default value is calculated based on the user's default frequency and timeout time (unit: min).
[0069] User dishonesty value = dishonesty frequency * timeout time / 60
[0070] Dishonest users are divided into three categories according to their dishonesty values: mild dishonesty (dishonesty value <= 1), moderate dishonesty (1 < dishonesty value <= 3) and severe dishonesty (dishonesty value > 3). The system's billing coefficients for the three types of users are 1, 1.5 and 2 respectively. Additional billing management is calculated based on the time the user occupies the charging device and the billing coefficient. The longer the user occupies the device, the more the billing. And the billing is based on intervals, for example, 0-r1min is charged at 0.1 yuan / min, r1-r2min is charged at 0.2 yuan / min. If the user's occupancy time R is in the r1-r2min interval, the fee the user needs to pay is calculated according to the following formula:
[0071] E = charging factor × (0.2 (R-r1) + 0.1r1) = charging factor × (0.2R-0.1r1)
[0072] Through refined billing and additional billing measures, untrustworthy users can be economically constrained and punished, increasing the cost of their illegal use. It can also serve as a warning to other users, improve their behavioral norms, and promote their rational use of charging cabinets.
[0073] In the embodiments provided by the present disclosure, the identity information and image information of the user, as well as the model of the vehicle to be charged, the power of the vehicle to be charged, the charging time and the charging frequency are stored in a database, and a charging equipment table containing the basic information of the charging equipment is created, and the charging equipment table is updated in real time based on the reservation behavior of the user; the information of the charging equipment table in the database is scanned regularly to obtain the status of the charging equipment; and in response to the acquired status of the charging equipment being empty, the information of the charging equipment that can be reserved is uniformly pushed to the user.
[0074] The database contains the basic information of all charging equipment, including the number, cabinet number, status, reservation time, update time (status change time), charging equipment unlocking key, and reservation user. According to this table, the client can visualize the information of all cabinets that are empty at the current time. At the same time, the charging cabinet table in the database updates the status, reservation time, update time, and unlocking key of the charging equipment in real time through scheduled scanning or after the user's reservation is successful. If there are no remaining cabinets available for reservation, the empty cabinet reminder button will be activated, and the user can get the reminder service by clicking the button. When there are remaining cabinets, the system will send information to users who subscribe to the service, and users can make reservations through the reservation unit at that time. This function is realized by setting a scheduled task. The system will continuously scan the information of the charging equipment table in the database at regular intervals to obtain the status of the charging equipment. If the status is empty, the information will be pushed to users who subscribe to the service.
[0075] Based on the status, reservation time, update time and the charging equipment's unlocking key of each record in the charging equipment table in the database, the system can determine whether the user uses the reserved charging equipment within the specified time. If the user loses trust, the system will update the status, reservation time and charging equipment's unlocking key in the record corresponding to the cabinet to empty, and update the "update time" field. Then the original key cannot match the key field in the database, the user cannot open the cabinet, and the cabinet is released. The database contains a user information table, including fields such as user name, default frequency, default value, correction number, and update time. If the user loses trust once, the system will update the default frequency and default value once, and then update the "correction number" field according to the user's performance (if the user keeps his word and uses the charging cabinet, the "correction number" will be +1), the "correction number" will be +1, and the default value will be reduced by Z points.
[0076] By storing and managing charging equipment and user information in the database, centralized data management and efficient query can be achieved. Real-time updating of the charging equipment table can ensure the accuracy and timeliness of information, provide users and administrators with more accurate charging equipment status information, and regularly scan the information in the charging equipment table to obtain the status of the charging equipment, so as to achieve real-time monitoring and management of the charging equipment. Pushing the reservable charging equipment information to users can improve the user's convenience, and remind users which charging cabinets can be reserved, reducing invalid reservations and vacant charging equipment.
[0077] In an embodiment provided in the present disclosure, pre-training the automatic recognition model includes: obtaining image information of users and vehicles to be charged within a historical time period to obtain a first training sample set; inputting the first training sample set into a pre-constructed automatic recognition model, and obtaining the output of the automatic recognition model, wherein the automatic recognition model is constructed based on a convolutional neural network model, and the output includes the user's identity information, the model of the vehicle to be charged, and the power of the vehicle to be charged; and when the difference between the output of the automatic recognition model and the actual identity information of the user, the model of the vehicle to be charged, and the power of the vehicle to be charged is greater than a first prescribed threshold, adjusting the parameters and structure of the automatic recognition model until the difference between the output of the automatic recognition model and the actual identity information of the user, the model of the vehicle to be charged, and the power of the vehicle to be charged is less than or equal to the first prescribed threshold.
[0078] The deep convolutional neural network model is used to train the automatic recognition model, and the identity information of the current user, the model of the vehicle to be charged, and the power of the vehicle to be charged are identified according to the image information. The deep convolutional neural network involved in the present disclosure is an improved Fast R-CNN neural network, and its principle is: establish a fully connected convolutional network (RPN), train a high-quality region proposal in an end-to-end manner, and then share the convolutional features trained by Fast R-CNN and Fast R-CNN through the attention model, wherein RoI Pooling is used in Fast R-CNN to replace the last pooling layer of the classification network, and a continuous convolution operation is performed on the sample to achieve end-to-end training and testing.
[0079] Through the use of automatic recognition models, the efficiency of charging equipment management and reservation can be greatly improved, and manual intervention and operating costs can be reduced.
[0080] In an embodiment provided in the present disclosure, pre-training the prediction model includes: obtaining the model of the vehicle to be charged and the power of the vehicle to be charged within a historical time period to obtain a second training sample set; inputting the second training sample set into a pre-constructed prediction model, and obtaining the output of the prediction model, wherein the prediction model is constructed based on LDA-SVM, and the output includes the charging time and charging frequency of the vehicle to be charged; and when the difference between the output of the prediction model and the actual charging time and charging frequency of the vehicle to be charged is greater than a second prescribed threshold, adjusting the parameters and structure of the prediction model until the difference between the output of the prediction model and the actual charging time and charging frequency of the vehicle to be charged is less than or equal to the second prescribed threshold.
[0081] Use LDA-SVM to train the prediction model. The operation steps of LDA-SVM are to determine the LDA dimension reduction operation for the feature subset after feature selection, and use the reduced dimension features as the input of the classification model. For k-class problems, k(k-1) / 2 decision functions need to be constructed. Let fm,n(x) be the decision function of the mth class and the nth class, and its construction process is as follows:
[0082]
[0083] Get the best solution
[0084] Get the optimal deviation value
[0085] Where x+1 and x-1 are any support vectors from the +1 and -1 classes. Construct the decision function:
[0086]
[0087] Among them, 1=240=6×(16+24).
[0088] After establishing the classification model through the above method, continue to repeat the above steps to adjust the kernel function and penalty coefficient, and finally use 10-fold interactive validation to evaluate the modeling effect.
[0089] Accurate predictions of charging duration and frequency can help charging service providers develop better charging planning and scheduling strategies.
[0090] Figure 3 The structure block diagram of the charging management device based on reservation management according to an embodiment of the present disclosure is schematically shown.
[0091] like Figure 3 As shown, the charging management device 300 based on reservation management according to this embodiment includes a collection module 310 , an identification module 320 , a prediction module 330 , an automatic reservation module 340 and a message sending module 350 .
[0092] The acquisition module 310 is used to obtain image information of the user and the vehicle to be charged by using an image acquisition device arranged in the charging area in response to the user and the vehicle to be charged entering the charging area, wherein a plurality of charging devices are arranged in the charging area.
[0093] The recognition module 320 is used to recognize the identity information of the user, the model of the vehicle to be charged and the power level of the vehicle to be charged based on the image information of the user and the vehicle to be charged using a pre-trained automatic recognition model.
[0094] The prediction module 330 is used to predict the charging time and charging frequency of the vehicle to be charged according to the identified model of the vehicle to be charged and the power of the vehicle to be charged, using a pre-trained prediction model.
[0095] The automatic reservation module 340 is used to automatically reserve a charging device to charge the vehicle to be charged according to the predicted charging time and charging frequency.
[0096] The message sending module 350 is used to send charging reservation information to the user in response to the automatic reservation being successful, so that the user can charge the vehicle to be charged according to the charging reservation information.
[0097] In an embodiment of the present disclosure, the customized driver in the acquisition module 310 is integrated into the system kernel of the Internet of Things device through a loadable kernel module.
[0098] In an embodiment of the present disclosure, the collection module 310 can also be used for: in response to a network communication call from an application process on the IoT device, the customized driver collects the traffic data corresponding to the network communication call, the calling subject of the traffic data and the calling time of the traffic data in real time through a network socket.
[0099] In an embodiment of the present disclosure, the prediction module 330 is specifically used to: input the model of the vehicle to be charged and the power of the vehicle to be charged into the charging time prediction model to obtain the charging time required for the vehicle to be charged; and in response to the charging time being greater than 0, input the identity information of the user into the charging frequency prediction model to obtain the charging frequency of the user.
[0100] In an embodiment of the present disclosure, the automatic reservation module 340 is specifically used to: determine the time when the user will use the charging device next time based on the charging frequency and the last time the charging device was used; and automatically reserve the charging device to charge the vehicle to be charged when the time when the user will use the charging device next time is no later than the time when the user and the vehicle to be charged enter the charging area.
[0101] In an embodiment of the present disclosure, the message sending module 350 is specifically used for: in response to a successful automatic reservation, pushing charging reservation information to the user through a preset client, so that the user can know and modify the charging reservation information, wherein the charging reservation information includes the number of the charging device, the activation key of the charging device, the charging start time and the charging duration; and in response to the user confirming the charging reservation information, locking the charging device within a predetermined time period.
[0102] In an embodiment of the present disclosure, the automatic reservation module 340 can also be used to: in response to the user failing to charge the vehicle to be charged within a predetermined time period, determine that the user has breached trust, record the frequency of breach of trust of the user, and accumulate the breach of trust value; when the user makes a reservation, process the priority of the user's reservation according to the user's breach of trust value; and when the frequency of breach of trust of the user is greater than a first preset value, do not make an automatic reservation for the user within the specified time period.
[0103] In an embodiment of the present disclosure, the automatic reservation module 340 can also be used to: in response to multiple users reserving the same charging device at the same time, compare the credibility value of each user; assign a higher priority to the user with a lower credibility value so that the user with a higher priority can reserve the charging device first.
[0104] In an embodiment of the present disclosure, the automatic reservation module 340 can also be used to: divide the user into default levels based on the default value of the user, and different default levels have different billing coefficients; divide the time that the user occupies the charging device exceeding the predetermined time period according to a preset billing interval, and different billing intervals correspond to different billing rates; and based on the billing coefficient, billing rate and time exceeding the predetermined time period, impose additional billing on the defaulting user.
[0105] In an embodiment of the present disclosure, the message sending module 350 can also be used to: store the identity information and image information of the user, as well as the model of the vehicle to be charged, the power of the vehicle to be charged, the charging time and the charging frequency into a database, and create a charging equipment table containing the basic information of the charging equipment, and the charging equipment table is updated in real time based on the user's reservation behavior; regularly scan the information of the charging equipment table in the database to obtain the status of the charging equipment; and in response to the acquired status of the charging equipment being empty, uniformly push the information of the charging equipment that can be reserved to the user.
[0106] In an embodiment of the present disclosure, the recognition module 320 can also be used to: obtain image information of users and vehicles to be charged within a historical time period to obtain a first training sample set; input the first training sample set into a pre-constructed automatic recognition model, and obtain the output of the automatic recognition model, wherein the automatic recognition model is constructed based on a convolutional neural network model, and the output includes the user's identity information, the model of the vehicle to be charged, and the power of the vehicle to be charged; and when the difference between the output of the automatic recognition model and the actual identity information of the user, the model of the vehicle to be charged, and the power of the vehicle to be charged is greater than a first prescribed threshold, adjust the parameters and structure of the automatic recognition model until the difference between the output of the automatic recognition model and the actual identity information of the user, the model of the vehicle to be charged, and the power of the vehicle to be charged is less than or equal to the first prescribed threshold.
[0107] In an embodiment of the present disclosure, the prediction module 330 can also be used to: obtain the model of the vehicle to be charged and the power of the vehicle to be charged within a historical time period to obtain a second training sample set; input the second training sample set into a pre-constructed prediction model, and obtain the output of the prediction model, wherein the prediction model is constructed based on LDA-SVM, and the output includes the charging time and charging frequency of the vehicle to be charged; and when the difference between the output of the prediction model and the actual charging time and charging frequency of the vehicle to be charged is greater than a second prescribed threshold, adjust the parameters and structure of the prediction model until the difference between the output of the prediction model and the actual charging time and charging frequency of the vehicle to be charged is less than or equal to the second prescribed threshold.
[0108] Figure 4 A block diagram of an electronic device according to a charging management method based on reservation management according to an embodiment of the present disclosure is schematically shown.
[0109] like Figure 4 As shown, the electronic device 400 according to an embodiment of the present invention includes a processor 401, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 402 or a program loaded from a storage part 408 into a random access memory (RAM) 403. The processor 401 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 401 may also include an onboard memory for caching purposes. The processor 401 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present invention.
[0110] In RAM 403, various programs and data required for the operation of electronic device 400 are stored. Processor 401, ROM 402 and RAM 403 are connected to each other via bus 404. Processor 401 performs various operations of the method flow according to the embodiment of the present invention by executing the programs in ROM 402 and / or RAM 403. It should be noted that the program can also be stored in one or more memories other than ROM 402 and RAM 403. Processor 401 can also perform various operations of the method flow according to the embodiment of the present invention by executing the programs stored in the one or more memories.
[0111] According to an embodiment of the present invention, the electronic device 400 may further include an input / output (I / O) interface 405, which is also connected to the bus 404. The electronic device 400 may further include one or more of the following components connected to the I / O interface 405: an input portion 406 including a keyboard, a mouse, etc.; an output portion 407 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 408 including a hard disk, etc.; and a communication portion 409 including a network interface card such as a LAN card, a modem, etc. The communication portion 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed, so that a computer program read therefrom is installed into the storage portion 408 as needed.
[0112] The present invention also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiment; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present invention is implemented.
[0113] According to an embodiment of the present invention, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present invention, the computer-readable storage medium may include the ROM 402 and / or RAM 403 described above and / or one or more memories other than ROM 402 and RAM 403.
[0114] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system and method according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.
[0115] It will be appreciated by those skilled in the art that the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways, even if such combinations and / or combinations are not explicitly described in the present disclosure. In particular, the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways without departing from the spirit and teachings of the present disclosure. All of these combinations and / or combinations fall within the scope of the present disclosure.
Claims
1. A charging management method based on reservation management, characterized in that: The method comprises: In response to a user and a vehicle to be charged entering a charging area, image information of the user and the vehicle to be charged is acquired using an image acquisition device disposed in the charging area, wherein a plurality of charging devices are disposed in the charging area; According to the image information of the user and the vehicle to be charged, the identity information of the user, the model of the vehicle to be charged and the power level of the vehicle to be charged are identified using a pre-trained automatic recognition model; According to the identified model of the vehicle to be charged and the power of the vehicle to be charged, using a pre-trained prediction model, predicting the charging time and charging frequency of the vehicle to be charged; Automatically booking a charging device to charge the vehicle to be charged according to the predicted charging time and charging frequency; and In response to the automatic reservation being successful, charging reservation information is sent to the user, so that the user can charge the vehicle to be charged according to the charging reservation information.
2. The method according to claim 1, characterized in that The prediction model includes a charging duration prediction model and a charging frequency prediction model; The method of predicting the charging time and frequency of the vehicle to be charged based on the identified model of the vehicle to be charged and the power of the vehicle to be charged using a pre-trained prediction model specifically includes: Inputting the model of the vehicle to be charged and the amount of electricity of the vehicle to be charged into the charging time prediction model to obtain the charging time required for the vehicle to be charged; and In response to the charging duration being greater than 0, the identity information of the user is input into the charging frequency prediction model to obtain the charging frequency of the user.
3. The method according to claim 1, characterized in that The method of automatically reserving a charging device to charge the vehicle to be charged according to the predicted charging time and charging frequency specifically includes: Based on the charging frequency and the time when the charging device was last used, determining the time when the user will use the charging device next time; and When the time when the user uses the charging device next time is no later than the time when the user and the vehicle to be charged enter the charging area, the charging device is automatically reserved to charge the vehicle to be charged.
4. The method according to claim 1, characterized in that: In response to the automatic reservation being successful, sending charging reservation information to the user so that the user can charge the vehicle to be charged according to the charging reservation information specifically includes: In response to the automatic reservation being successful, the charging reservation information is pushed to the user through a preset client, so that the user can learn and modify the charging reservation information, wherein the charging reservation information includes the serial number of the charging device, the activation key of the charging device, the charging start time and the charging duration; and In response to the user confirming the charging reservation information, the charging device is locked for a predetermined period of time.
5. The method according to claim 1, characterized in that The method further comprises: In response to the user not charging the vehicle to be charged within a predetermined time period, determining that the user has breached trust, recording the frequency of breach of trust of the user, and accumulating a breach of trust value; When the user makes an appointment, the priority of the user's appointment is processed according to the user's dishonesty value; and When the default frequency of the user is greater than a first preset value, no automatic reservation is made for the user within a specified time period.
6. The method according to claim 5, characterized in that When the user makes an appointment, the priority of the user's appointment is processed according to the user's dishonesty value, specifically including: In response to multiple users booking the same charging device at the same time, comparing the dishonesty values of the users; The user with a low dishonesty value is given a higher priority, so that the user with a high priority can make a reservation for the charging device first.
7. The method according to claim 5, characterized in that The method further comprises: Based on the dishonesty value of the user, the user is divided into different dishonesty levels, and different dishonesty levels have different billing coefficients; Dividing the time when the user occupies the charging device for more than a predetermined time period according to a preset charging interval, and different charging intervals correspond to different charging rates; and Based on the billing coefficient, the billing rate and the time exceeding the predetermined time period, the user who has lost trust is additionally charged.
8. The method according to claim 1, characterized in that The method further comprises: The user's identity information and image information, as well as the model of the vehicle to be charged, the power of the vehicle to be charged, the charging time and the charging frequency are stored in a database, and a charging equipment table containing the basic information of the charging equipment is created, and the charging equipment table is updated in real time based on the user's reservation behavior; Periodically scan the information of the charging device table in the database to obtain the status of the charging device; and In response to the acquired status of the charging device being empty, information of the charging devices that can be reserved is uniformly pushed to the user.
9. The method according to claim 1, characterized in that: Pre-training the automatic recognition model includes: Acquire image information of users and vehicles to be charged within a historical time period to obtain a first training sample set; Inputting the first training sample set into a pre-built automatic recognition model, and obtaining an output of the automatic recognition model, wherein the automatic recognition model is built based on a convolutional neural network model, and the output includes the user's identity information, the model of the vehicle to be charged, and the power of the vehicle to be charged; and When the difference between the output of the automatic identification model and the actual identity information of the user, the model of the vehicle to be charged, and the power level of the vehicle to be charged is greater than a first specified threshold, the parameters and structure of the automatic identification model are adjusted until the difference between the output of the automatic identification model and the actual identity information of the user, the model of the vehicle to be charged, and the power level of the vehicle to be charged is less than or equal to the first specified threshold.
10. The method according to claim 1, characterized in that Pre-training the prediction model includes: Obtaining the models of vehicles to be charged and the power of vehicles to be charged within a historical time period to obtain a second training sample set; Inputting the second training sample set into a pre-built prediction model, and obtaining the output of the prediction model, wherein the prediction model is built based on LDA-SVM, and the output includes the charging time and charging frequency of the vehicle to be charged; and When the difference between the output of the prediction model and the actual charging time and charging frequency of the vehicle to be charged is greater than a second specified threshold, the parameters and structure of the prediction model are adjusted until the difference between the output of the prediction model and the actual charging time and charging frequency of the vehicle to be charged is less than or equal to the second specified threshold.
11. A charging management device based on reservation management, characterized in that: The device comprises: A collection module, used to: in response to a user and a vehicle to be charged entering a charging area, obtain image information of the user and the vehicle to be charged by using an image collection device arranged in the charging area, wherein a plurality of charging devices are arranged in the charging area; The recognition module is used to: identify the identity information of the user, the model of the vehicle to be charged and the power level of the vehicle to be charged based on the image information of the user and the vehicle to be charged and using a pre-trained automatic recognition model; A prediction module is used to: predict the charging time and charging frequency of the vehicle to be charged according to the identified model of the vehicle to be charged and the power of the vehicle to be charged, using a pre-trained prediction model; An automatic reservation module, used to: automatically reserve a charging device to charge the vehicle to be charged according to the predicted charging time and charging frequency; and The message sending module is used to: in response to the success of the automatic reservation, send the charging reservation information to the user, so that the user can charge the vehicle to be charged according to the charging reservation information.
12. An electronic device comprising: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors are enabled to execute the method according to any one of claims 1 to 10.
13. A computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, causes the processor to execute the method according to any one of claims 1 to 10.