Vehicle data processing method, system, electronic device and readable storage medium

Abstract

Embodiments of the present application disclose a vehicle data processing method and system, electronic equipment and a readable storage medium. After an image of a battery mounting area of a target vehicle is acquired, the image is subjected to image recognition based on a pre-trained image recognition model to determine whether the battery mounting area of the target vehicle is iced, and whether the battery mounting area of the target vehicle is subjected to temperature raising processing is determined according to at least one of an image recognition result of the image and meteorological information of an environment in which the target vehicle is located. Embodiments of the present application can combine the image recognition result of the battery mounting area of the vehicle and the meteorological information of the environment in which the vehicle is located to perform targeted temperature raising processing on the vehicle, so that the vehicle with a part of the battery mounting area not iced can directly replace the power supply battery, thereby effectively alleviating the situation of energy waste and improving the replacement efficiency of the power supply battery.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and more specifically to a vehicle data processing method, system, electronic device, and readable storage medium. Background Technology

[0002] To facilitate users in replacing the batteries of their new energy vehicles, the number of battery swapping stations is gradually increasing. In existing technology, battery swapping stations heat up the battery installation area of ​​new energy vehicles during cold seasons before replacing the batteries. However, this method undoubtedly wastes energy and reduces the efficiency of battery swapping. Summary of the Invention

[0003] In view of this, embodiments of the present invention provide a vehicle data processing method, system, electronic device, and readable storage medium to combine image recognition results of the vehicle battery installation area with meteorological information of the vehicle's environment to perform targeted heating treatment on the vehicle, thereby effectively alleviating energy waste and improving the replacement efficiency of the power supply battery.

[0004] In a first aspect, embodiments of the present invention provide a vehicle data processing method, the method comprising:

[0005] Acquire an image to be detected, wherein the image to be detected is an image of a first region of the target vehicle, and the first region is the battery installation area of ​​the target vehicle;

[0006] The first image recognition result of the image to be detected is obtained based on a pre-trained image recognition model. The first image recognition result is used to characterize whether the first region is icy. The image recognition model is trained based on a training sample set. The training sample set includes multiple sample images and sample labels corresponding to each sample image. The sample labels are used to characterize whether the second region of the non-target vehicle in the sample image is icy. The second region is the battery installation area of ​​the non-target vehicle.

[0007] Obtain meteorological information about the environment in which the target vehicle is located;

[0008] The processing strategy for the target vehicle is determined based on at least one of the first image recognition results and the meteorological information, and the processing strategy is used to characterize whether to perform a temperature-raising process on the first area.

[0009] In one optional implementation, obtaining the meteorological information of the environment in which the target vehicle is located includes:

[0010] Identify the battery swapping station where the target vehicle is located;

[0011] The meteorological information is obtained based on the location information of the battery swapping station.

[0012] In one optional implementation, determining the processing strategy for the target vehicle based on at least one of the first image recognition result and the meteorological information includes:

[0013] In response to the first image recognition result indicating that the first area is icy, and / or the meteorological information meets preset meteorological conditions, the processing strategy is determined to be to heat up the first area.

[0014] In one optional implementation, determining the processing strategy for the target vehicle based on at least one of the first image recognition result and the meteorological information includes:

[0015] In response to the inability to determine whether the first area is icy based on the first image recognition result, determine whether the meteorological information meets the preset meteorological conditions;

[0016] In response to the meteorological information meeting the preset meteorological conditions, the processing strategy is determined to be to warm up the first area.

[0017] In one alternative implementation, the sample image is determined as follows:

[0018] Acquire a first type of image, which is an image of ice formation occurring in the second region;

[0019] Determine the icing area and the occlusion area of ​​the second region in the first type of image;

[0020] The first type of image whose icing area satisfies the first area condition and whose occlusion area satisfies the second area condition is determined as the sample image.

[0021] In one alternative implementation, the sample image is determined as follows:

[0022] Acquire a second type of image, which is an image of the unfrozen area within the second region;

[0023] The second image recognition result of the second type of image is obtained based on the image recognition model, and the second image recognition result is used to characterize whether the second region is frozen.

[0024] The second image, characterized by the second image recognition result, representing the second type of image where ice forms in the second region, is determined as the sample image.

[0025] In a second aspect, embodiments of the present invention provide a vehicle data processing system, the system comprising:

[0026] An image acquisition device is configured to acquire images of the battery mounting area of ​​a vehicle;

[0027] A heating device is configured to heat the battery mounting area; and

[0028] A control device is configured to perform the method as described in any one of the first aspects.

[0029] Thirdly, embodiments of the present invention provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method as described in any one of the first aspects.

[0030] Fourthly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method as described in any one of the first aspects.

[0031] Fifthly, embodiments of the present invention provide a computer program product that, when run on a computer, causes the computer to perform the method as described in any one of the first aspects.

[0032] In this embodiment of the invention, after acquiring an image of the battery mounting area of ​​a target vehicle, image recognition is performed on the image based on a pre-trained image recognition model to determine whether the battery mounting area of ​​the target vehicle is icy. Based on at least one of the image recognition results and meteorological information of the target vehicle's environment, it is determined whether to perform a heating treatment on the battery mounting area of ​​the target vehicle. This embodiment of the invention can combine the image recognition results of the vehicle's battery mounting area with the meteorological information of the vehicle's environment to perform targeted heating treatment on the vehicle, allowing vehicles with some battery mounting areas that are not icy to directly replace the power supply battery. Therefore, it can effectively alleviate energy waste and improve the replacement efficiency of the power supply battery. Attached Figure Description

[0033] The above and other objects, features and advantages of the present invention will become clearer from the following description of embodiments of the invention with reference to the accompanying drawings, in which:

[0034] Figure 1 This is a schematic diagram of the hardware system architecture according to an embodiment of the present invention;

[0035] Figure 2 This is a flowchart of a vehicle data processing method according to an embodiment of the present invention;

[0036] Figure 3 This is a flowchart of a vehicle data processing method according to an embodiment of the present invention;

[0037] Figure 4This is a flowchart of a vehicle data processing method according to an embodiment of the present invention;

[0038] Figure 5 This is a schematic diagram of the judgment process in an embodiment of the present invention;

[0039] Figure 6 This is a schematic diagram of a vehicle data processing device according to an embodiment of the present invention;

[0040] Figure 7 This is a schematic diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0041] The present application is described below based on embodiments, but it is not limited to these embodiments. In the detailed description of the present application below, certain specific details are described in detail. Those skilled in the art can fully understand the present application without these details. To avoid obscuring the substance of the present application, well-known methods, processes, flows, elements, and circuits are not described in detail.

[0042] Furthermore, those skilled in the art should understand that the accompanying drawings provided herein are for illustrative purposes only and are not necessarily drawn to scale.

[0043] Unless the context explicitly requires it, words such as "including" or "contains" throughout the application should be interpreted as including rather than exclusive or exhaustive; that is, meaning "including but not limited to".

[0044] In the description of this application, it should be understood that the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance. Furthermore, in the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0045] The solutions described in this specification and embodiments, if involving the processing of personal information, will be processed only under the premise of having a legal basis (such as obtaining the consent of the personal information subject, or being necessary for the performance of a contract), and will only be processed within the scope stipulated or agreed upon. A user's refusal to process personal information beyond what is necessary for basic functions will not affect the user's use of basic functions.

[0046] New energy vehicles are powered by batteries. However, due to limitations in current battery charging technology, charging times for new energy vehicles are generally longer than those for refueling traditional gasoline vehicles, significantly reducing the user experience. This is especially true for new energy vehicles used for commercial purposes, such as ride-hailing services, where charging time can even impact user earnings. To reduce waiting time for users to recharge their batteries, more and more vehicle manufacturers are designing batteries that can be charged off-vehicle. This allows users to replace low-charge batteries with high-charge ones to continue using the vehicle and to recharge low-charge batteries through other means.

[0047] To facilitate battery swapping and charging for users, the number of battery swapping stations is gradually increasing. A battery swapping station is a facility specifically designed to provide battery swapping services for new energy vehicles, especially pure electric vehicles. It allows users to replace their depleted or nearly depleted batteries with fully charged or higher-charged batteries. Some battery swapping stations also offer charging services for existing batteries.

[0048] To ensure vehicle balance, improve interior space utilization, facilitate battery system integration and protection, and ensure vehicle heat dissipation, vehicle manufacturers typically place the battery installation area for new energy vehicles in the vehicle chassis. However, this arrangement exposes the battery to the outside of the vehicle, making it prone to icing in cold weather. To avoid increased difficulty in battery removal due to icing and to prevent damage to the vehicle or battery from the ice during removal, battery swapping stations use heating equipment to warm the battery installation area of ​​new energy vehicles to remove the ice. However, current technology heats the battery installation area of ​​all new energy vehicles, resulting in energy waste (electricity and heat) and time consumption, thus reducing battery replacement efficiency.

[0049] This embodiment of the invention uses the battery installation area of ​​a new energy vehicle as an example of the vehicle chassis. It should be understood that this embodiment is not limited to this. Battery installation areas that can support corresponding functions, such as the vehicle roof, the vehicle trunk, or battery installation areas that can support corresponding functions with future technological development, are all within the protection scope of this embodiment of the invention.

[0050] To address the aforementioned problems, embodiments of the present invention provide a vehicle data processing method, system, electronic device, and readable storage medium. In these embodiments, an example is provided using a server of a battery swapping station management platform as the control device. However, those skilled in the art will readily understand that the method of this embodiment is equally applicable when the control device is other electronic devices, such as a terminal.

[0051] Figure 1 This is a schematic diagram of the hardware system architecture of an embodiment of the present invention. Figure 1 The hardware system architecture shown may include a server 11, an image acquisition device 12, and a heating device 13 on the battery swapping station management platform side.

[0052] Server 11, image acquisition device 12, and heating device 13 can establish a communication connection through a network, thereby enabling the exchange of information and data. It should be understood that, although... Figure 1 Only a certain number of servers 11, image acquisition devices 12 and heating devices 13 are shown, but this does not mean that the number of each is limited. This system may contain multiple servers, multiple image acquisition devices and multiple heating devices.

[0053] Server 11 should be understood as a device that provides data processing, database, and communication facilities. For example, server 11 may refer to a single physical server with associated communication, data storage, and database facilities, or it may refer to a networked or aggregated collection of processors, associated networks, and storage devices that operate software and one or more database systems and application software supporting the services provided by the server. Server 11 may be a monolithic server or a distributed server spanning multiple computers or computer data centers, or it may be various types of cloud servers. In some embodiments, each server may include hardware, software, or embedded logical components for performing suitable functions supported or implemented by the server, or a combination of two or more such components.

[0054] The image acquisition device 12 can be any existing image acquisition device, such as an industrial camera, fisheye camera, or high-definition network PTZ camera, used to acquire images of the battery installation area of ​​the vehicle. In practical applications, the image acquisition device 12 can be set in the vehicle battery swapping (i.e., battery replacement) area of ​​a battery swapping station, such as the bottom of the battery swapping container. The image acquisition device 12 can be fixed or not, and this embodiment of the invention does not impose any restrictions on this.

[0055] The heating device 13 is a temperature-controllable heating device, such as a hot air blower, battery heating belt, or heater, used to heat the battery installation area of ​​the vehicle. In practical applications, the heating device 13 can be set up in the vehicle battery swapping area of ​​a battery swapping station. The heating device 13 can be fixed or not, and this embodiment of the invention does not impose any restrictions on this.

[0056] Therefore, in this embodiment of the invention, after the server 11 obtains the image of the battery installation area of ​​the target vehicle collected by the image acquisition device 12, it can perform image recognition on the image based on the pre-trained image recognition model to determine whether the battery installation area of ​​the target vehicle is frozen, and determine whether to control the heating device 13 to heat up the battery installation area of ​​the target vehicle based on at least one of the image recognition results of the image and the meteorological information of the environment where the target vehicle is located.

[0057] The embodiments of the present invention can combine the image recognition results of the vehicle battery installation area with the meteorological information of the vehicle's environment to carry out targeted heating treatment on the vehicle, so that vehicles with some battery installation areas that are not frozen can directly replace the power supply battery. Therefore, it can effectively alleviate energy waste and improve the replacement efficiency of the power supply battery.

[0058] The following describes the method through examples. Figure 2 This is a flowchart of a vehicle data processing method according to an embodiment of the present invention. Figure 2 As shown, the method in this embodiment includes the following steps:

[0059] Step S201: Obtain the image to be detected.

[0060] When a vehicle arrives at the battery swapping station to replace its power supply battery, the image acquisition equipment installed at the station can capture an image of the vehicle's battery installation area, i.e., the first area, and upload this image to the server. Therefore, in this step, the server can identify this image as the image to be detected and determine the vehicle corresponding to the image as the target vehicle.

[0061] Step S202: Obtain the first image recognition result of the image to be detected based on the pre-trained image recognition model.

[0062] In this step, the server can use the image to be detected as input to the image recognition model to determine the first image recognition result of the image to be detected based on the image recognition model, that is, whether the battery installation area of ​​the target vehicle is icy.

[0063] The image recognition model in this embodiment can be a deep learning model, such as a convolutional neural network (CNN) and a model based on a convolutional neural network architecture, a recurrent neural network (RNN) and a model based on a recurrent neural network architecture, an attention mechanism model, a residual network (ResNet), etc., or a traditional machine learning model, such as a support vector machine (SVM), a decision tree, etc.

[0064] Furthermore, the image recognition model can be an object detection model. Taking YOLO (You Only Look Once) v8 as an example, YOLOv8 is a model based on a convolutional neural network architecture that can output information such as the bounding box and confidence score of a specific object in an image as the image recognition result. Therefore, optionally, the server can use YOLOv8 to obtain the bounding box and confidence score of the icy region in the image to be detected as the image recognition result of the image to be detected, that is, the first image recognition result.

[0065] In this embodiment, the image recognition model can be trained based on a training sample set. The training sample set includes multiple sample images and sample labels for each sample image, wherein the sample labels are used to characterize the battery installation area of ​​the non-target vehicle in the sample image, i.e., whether the second area is icy. Optionally, if the image recognition model in this embodiment is trained in a supervised manner, the sample labels for each sample image are manually labeled.

[0066] Figure 3 This is a flowchart of a vehicle data processing method according to an embodiment of the present invention. Figure 3 As shown, in one optional implementation, this embodiment can determine the sample image in the following way:

[0067] Step S301: Obtain the first type of image.

[0068] In this embodiment, images of icing occurring in the battery mounting area of ​​each non-target vehicle can be collected as the first type of images. Optionally, to enhance the image recognition capability of the image recognition model, images with blurred icing outlines and images with obvious interference (such as glass) can be removed from the first type of images.

[0069] Step S302: Determine the icing area of ​​the second region and the occlusion area of ​​the second region in the first type of image.

[0070] After determining the first type of image, the minimum bounding rectangle of the ice condensed in the battery mounting area of ​​the non-target vehicle and the minimum bounding rectangle of the obstruction can be determined in each first type of image. The icing area of ​​the battery mounting area of ​​the non-target vehicle can be calculated based on the minimum bounding rectangle of the ice, and the obstruction area of ​​the battery mounting area of ​​the non-target vehicle can be calculated based on the minimum bounding rectangle of the obstruction.

[0071] Step S303: The first type of image, whose icing area satisfies the first area condition and whose occlusion area satisfies the second area condition, is determined as the sample image.

[0072] In this embodiment, when the icing area of ​​the battery mounting area of ​​a non-target vehicle meets the first area condition, the icing area occupies a larger area in the first type of image. When the occlusion area of ​​the battery mounting area of ​​a non-target vehicle meets the second area condition, the occlusion area occupies a smaller area in the first type of image. This makes the ice condensed in the battery mounting area easier to identify, effectively enhancing the image recognition capability of the image recognition model. Therefore, the first area condition can be set to an icing area greater than (or not less than) a first area, and the second area condition can be set to an occlusion area less than (or not more than) a second area. Optionally, the first area (or second area) can be determined based on the product of the area of ​​the first type of image and a preset ratio, and the preset ratio can be determined according to actual needs; this embodiment does not impose any restrictions on this.

[0073] Therefore, in this step, if the icing area of ​​any first-class image satisfies the first area condition and the occlusion area satisfies the second area condition, the server can determine the first-class image as a sample image.

[0074] After determining the sample images based on the first type of images, the minimum bounding rectangle of the ice condensed in the battery installation area of ​​the non-target vehicle in each sample image can be determined as the ice bounding box. The sample images are used as the input of the image recognition model, and the ice bounding boxes and sample labels corresponding to the sample images are used as training targets to train the image recognition model until the image recognition model meets the preset training conditions, such as the loss function convergence and the accuracy is higher than the preset threshold.

[0075] Since the first type of images are all positive samples, in order to enhance the image recognition capability of the image recognition model through negative samples, this embodiment can also determine the sample images through other methods. Figure 4 This is a flowchart of a vehicle data processing method according to an embodiment of the present invention. Figure 4 As shown, in one optional implementation, this embodiment can determine the sample image in the following way:

[0076] Step S401: Obtain the second type of image.

[0077] In this embodiment, images of the non-ice-covered areas within the battery mounting regions of each non-target vehicle can be collected as a second type of image.

[0078] Step S402: Obtain the second image recognition result of the second type of image based on the image recognition model.

[0079] In this step, the server can use the second type of image as input to the image recognition model to determine the second image recognition result of the second type of image based on the image recognition model, that is, whether the battery installation area of ​​the non-target vehicle is icy.

[0080] Step S403: The second image representing the second region where ice forms, as identified by the second image recognition result, is determined as the sample image.

[0081] If any second-type image is identified as having ice formation in the battery installation area of ​​the corresponding non-target vehicle, it indicates that the image recognition model's ability to recognize the second-type image is insufficient. Therefore, in order to improve the image recognition model's ability, the server can also identify the second-type images that represent ice formation in the battery installation area of ​​the non-target vehicle as sample images, so as to perform secondary training on the image recognition model based on these sample images.

[0082] After determining the sample images based on the second type of images, the sample images can be used as the input to the image recognition model, and the sample labels corresponding to the sample images can be used as the training targets to train the image recognition model until the image recognition model meets the preset training conditions.

[0083] Step S203: Obtain meteorological information about the environment where the target vehicle is located.

[0084] The meteorological information of the target vehicle's environment is strongly correlated with whether the battery installation area of ​​the target vehicle is icy. Therefore, in this step, the server can obtain the meteorological information of the target vehicle's environment through various methods. In this embodiment, the meteorological information of the target vehicle's environment may include the temperature, humidity, precipitation, etc., of the target vehicle's environment.

[0085] In practical applications, to manage each battery swapping station, the server records the location information of each station and its equipment, including image acquisition and heating devices. Therefore, optionally, the server can determine the battery swapping station where the target vehicle is located and obtain meteorological information about the target vehicle's environment based on the station's location information.

[0086] Furthermore, new energy vehicle users can create a battery swap order for a target vehicle through the terminal. The battery swap order will carry the identifier of the battery swap station where the target vehicle is located. Therefore, the server can determine the location information of the battery swap station based on the identifier of the battery swap station where the target vehicle is located, and then obtain the meteorological information of the environment where the target vehicle is located.

[0087] Furthermore, when uploading the image to be detected, the image acquisition device will report its own device identifier. Therefore, the server can also determine the battery swapping station where the image acquisition device is located as the battery swapping station where the target vehicle is located based on the device identifier of the image acquisition device, and obtain the meteorological information of the environment where the target vehicle is located based on the location information of the battery swapping station.

[0088] Alternatively, the server may also obtain meteorological information about the environment where the target vehicle is located through other means, such as determining it based on the target vehicle's location data. This embodiment does not impose any restrictions on this.

[0089] It is easy to understand that in this embodiment, steps S201 and S203 can be executed simultaneously or sequentially, and this embodiment does not impose any restrictions on this.

[0090] Step S204: Determine the processing strategy for the target vehicle based on at least one of the first image recognition results and meteorological information.

[0091] In this step, the server can determine the processing strategy for the target vehicle based on at least one of the image recognition results of the image to be detected and the meteorological information of the environment where the target vehicle is located, that is, whether to heat up the battery installation area of ​​the target vehicle.

[0092] The confidence score output by the image recognition model represents the probability that the battery mounting area of ​​the target vehicle is icy. Therefore, if the confidence score of the image to be detected meets the first confidence score condition, the server can determine that the image recognition result of the image to be detected indicates that the battery mounting area of ​​the target vehicle is icy. The first confidence score condition can be that the confidence score of the image to be detected is greater than (or not lower than) a first confidence threshold.

[0093] If the meteorological information of the target vehicle's environment meets the preset meteorological conditions, it indicates that the probability of icing in the target vehicle's battery installation area is relatively high under the current weather conditions. Optionally, if the meteorological information includes the temperature of the target vehicle's environment, the preset meteorological conditions can be that the temperature is less than (or not higher than) a preset temperature threshold; if the meteorological information includes the humidity of the target vehicle's environment, the preset meteorological conditions can be that the humidity is higher than (or not lower than) a preset humidity threshold; if the meteorological information includes precipitation information of the target vehicle's environment, the preset meteorological conditions can be that precipitation has occurred in the target vehicle's environment within a preset time period, such as 1 day or 7 days, or that the precipitation in the target vehicle's environment within a preset time period is higher than (or not lower than) a preset precipitation threshold, etc.

[0094] Optionally, if the image recognition result of the image to be detected indicates that the battery installation area of ​​the target vehicle is icy, and / or the meteorological information of the environment where the target vehicle is located meets the preset meteorological conditions, the server can determine that the processing strategy for the target vehicle is to heat up the target vehicle.

[0095] If the confidence level of the image to be detected meets the second confidence level condition, the server can determine that the image recognition result of the image to be detected indicates that the battery installation area of ​​the target vehicle is not icy. The second confidence level condition can be that the confidence level of the image to be detected is less than (or not higher than) a second confidence level threshold. If the meteorological information of the environment where the target vehicle is located does not meet the preset meteorological conditions, it indicates that the probability of icing in the battery installation area of ​​the target vehicle under the current weather conditions is low.

[0096] Therefore, optionally, if the image recognition result of the image to be detected indicates that the battery installation area of ​​the target vehicle is not icy, and the meteorological information of the environment where the target vehicle is located does not meet the preset meteorological conditions, the server can determine that the processing strategy for the target vehicle is not to heat up the target vehicle.

[0097] If the confidence level of the image to be detected does not meet either the first confidence level condition or the second confidence level condition, it means that it is impossible to determine whether the battery installation area of ​​the target vehicle is icy based on the image to be detected. Therefore, optionally, the server can determine whether the meteorological information of the environment where the target vehicle is located meets the preset meteorological conditions. If the weather meets the preset meteorological conditions, the server can determine that the processing strategy for the target vehicle is to heat up the battery installation area of ​​the target vehicle.

[0098] Figure 5 This is a schematic diagram of the judgment process in an embodiment of the present invention. For example... Figure 5As shown, in step S51, the server can determine whether the lowest temperature of the day is greater than 1 degree, that is, whether the temperature is higher than the preset temperature threshold; if the lowest temperature of the day is greater than 1 degree, step S52 is executed, and the server can determine whether there is precipitation on the day, that is, whether precipitation has occurred in the environment where the target vehicle is located within the preset time period; if the lowest temperature of the day is not greater than 1 degree, step S55 is executed, and the server can determine that the probability of icing in the battery installation area of ​​the target vehicle is low. If there is precipitation on that day, proceed to step S56, where the server can determine that the battery installation area of ​​the target vehicle has a high probability of icing; if there is no precipitation on that day, proceed to step S53, where the server can determine whether there has been precipitation in the last seven days, that is, whether precipitation has occurred in the environment where the target vehicle is located within a preset time period; if there has been precipitation in the last seven days, proceed to step S56; if there has been no precipitation in the last seven days, proceed to step S54, where the server can determine whether the air humidity on that day is greater than 80%, that is, whether the humidity is higher than a preset humidity threshold; if the air humidity on that day is greater than 80%, proceed to step S56; if the air humidity on that day is not greater than 80%, proceed to step S55.

[0099] If the processing strategy for the target vehicle is determined to be to heat up the target vehicle, the server can send a corresponding control signal to the heating device to control the heating device to heat up the target vehicle.

[0100] Using the method of this invention, vehicles whose battery installation areas are not icy can have their power supply batteries directly replaced, thus effectively shortening the battery swapping time for such vehicles. It can also reduce the possibility of increased safety hazards due to overheating, effectively reducing energy consumption overall and improving the service frequency and operational efficiency of battery swapping stations.

[0101] In this embodiment of the invention, after acquiring an image of the battery mounting area of ​​a target vehicle, image recognition is performed on the image based on a pre-trained image recognition model to determine whether the battery mounting area of ​​the target vehicle is icy. Based on at least one of the image recognition results and meteorological information of the target vehicle's environment, it is determined whether to perform a heating treatment on the battery mounting area of ​​the target vehicle. This embodiment of the invention can combine the image recognition results of the vehicle's battery mounting area with the meteorological information of the vehicle's environment to perform targeted heating treatment on the vehicle, allowing vehicles with some battery mounting areas that are not icy to directly replace the power supply battery. Therefore, it can effectively alleviate energy waste and improve the replacement efficiency of the power supply battery.

[0102] Figure 6 This is a schematic diagram of a vehicle data processing device according to an embodiment of the present invention. Figure 6 As shown, the vehicle data processing device in this embodiment includes an image acquisition unit 601, an image recognition unit 602, an information acquisition unit 603, and a strategy determination unit 604.

[0103] The image acquisition unit 601 is used to acquire an image to be detected, which is an image of a first region of the target vehicle, the first region being the battery installation area of ​​the target vehicle; the image recognition unit 602 is used to acquire a first image recognition result of the image to be detected based on a pre-trained image recognition model, the first image recognition result being used to characterize whether the first region is icy, the image recognition model being trained based on a training sample set, the training sample set including multiple sample images and sample labels corresponding to each sample image, the sample labels being used to characterize whether a second region of the non-target vehicle in the sample image is icy, the second region being the battery installation area of ​​the non-target vehicle; the information acquisition unit 603 is used to acquire meteorological information of the environment where the target vehicle is located; the strategy determination unit 604 is used to determine a processing strategy for the target vehicle based on at least one of the first image recognition result and the meteorological information, the processing strategy being used to characterize whether to perform a heating process on the first region.

[0104] Furthermore, the information acquisition unit 603 includes a site determination subunit and an information acquisition subunit.

[0105] The station determination subunit is used to determine the battery swapping station where the target vehicle is located; the information acquisition subunit is used to acquire the meteorological information based on the location information of the battery swapping station.

[0106] Furthermore, the strategy determination unit 604 includes a first strategy determination subunit.

[0107] The first strategy determination subunit is used to determine the processing strategy as heating the first area in response to the first image recognition result indicating that the first area is icy and / or the meteorological information meets preset meteorological conditions.

[0108] Furthermore, the strategy determination unit 604 includes a judgment subunit and a strategy determination subunit.

[0109] The determination subunit is used to determine whether the meteorological information meets the preset meteorological conditions in response to the inability to determine whether the first area is icy based on the first image recognition result; the strategy determination subunit is used to determine the processing strategy as heating the first area in response to the meteorological information meeting the preset meteorological conditions.

[0110] Furthermore, the sample image is determined by the second image acquisition unit, the area determination unit, and the first image determination unit.

[0111] The second image acquisition unit is used to acquire a first type of image, which is an image of ice forming in the second region; the area determination unit is used to determine the ice area and the occlusion area of ​​the second region in the first type of image; the first image determination unit is used to determine the first type of image where the ice area satisfies a first area condition and the occlusion area satisfies a second area condition as the sample image.

[0112] Furthermore, the sample image is determined by a third image acquisition unit, a second image recognition unit, and a second image determination unit.

[0113] The third image acquisition unit is used to acquire a second type of image, which is an image of the second region that is not frozen; the second image recognition unit is used to acquire a second image recognition result of the second type of image based on the image recognition model, which is used to characterize whether the second region is frozen; and the second image determination unit is used to determine the second type of image, which is characterized by the second image recognition result as the second image of the second region being frozen, as the sample image.

[0114] In this embodiment of the invention, after acquiring an image of the battery mounting area of ​​a target vehicle, image recognition is performed on the image based on a pre-trained image recognition model to determine whether the battery mounting area of ​​the target vehicle is icy. Based on at least one of the image recognition results and meteorological information of the target vehicle's environment, it is determined whether to perform a heating treatment on the battery mounting area of ​​the target vehicle. This embodiment of the invention can combine the image recognition results of the vehicle's battery mounting area with the meteorological information of the vehicle's environment to perform targeted heating treatment on the vehicle, allowing vehicles with some battery mounting areas that are not icy to directly replace the power supply battery. Therefore, it can effectively alleviate energy waste and improve the replacement efficiency of the power supply battery.

[0115] Figure 7 This is a schematic diagram of an electronic device according to an embodiment of the present invention. (For example...) Figure 7As shown, electronic device 7 is a general-purpose data processing device, which includes a general-purpose computer hardware structure, including at least a processor 701 and a memory 702. The processor 701 and memory 702 are connected via a bus 703. The memory 702 is adapted to store instructions or programs executable by the processor 701. The processor 701 can be a standalone microprocessor or a collection of one or more microprocessors. Thus, the processor 701 executes the instructions stored in the memory 702 to perform the method flow of the embodiments of the present invention as described above, thereby realizing data processing and control of other devices. The bus 703 connects the aforementioned components together, and also connects the aforementioned components to a display controller 704, a display device, and an input / output (I / O) device 705. The input / output (I / O) device 705 can be a mouse, keyboard, modem, network interface, touch input device, motion-sensing input device, printer, and other devices known in the art. Typically, the input / output device 705 is connected to the system via an input / output (I / O) controller 706.

[0116] Those skilled in the art will understand that embodiments of this application can be provided as methods, apparatus (devices), or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0117] This application is described with reference to flowchart illustrations of methods, apparatus (devices), and computer program products according to embodiments of this application. It should be understood that each step in the flowchart can be implemented by computer program instructions.

[0118] These computer program instructions may be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction means, the implementation process of which is described in the instruction means. Figure 1 The function specified in one or more processes.

[0119] These computer program instructions may also be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing device, produce instructions for implementing processes. Figure 1 A device for a function specified in one or more processes.

[0120] Another embodiment of the present invention relates to a non-volatile storage medium for storing a computer-readable program for use by a computer to execute some or all of the above-described method embodiments.

[0121] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program specifying the relevant hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0122] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. For those skilled in the art, the present invention can be modified and varied in various ways. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of protection of the present invention.

Claims

1. A vehicle data processing method, characterized in that, The method includes: Acquire an image to be detected, wherein the image to be detected is an image of a first region of the target vehicle, and the first region is the battery installation area of ​​the target vehicle; The first image recognition result of the image to be detected is obtained based on a pre-trained image recognition model. The first image recognition result is used to characterize whether the first region is icy. The image recognition model is trained based on a training sample set. The training sample set includes multiple sample images and sample labels corresponding to each sample image. The sample labels are used to characterize whether the second region of the non-target vehicle in the sample image is icy. The second region is the battery installation area of ​​the non-target vehicle. Obtain meteorological information about the environment in which the target vehicle is located; The processing strategy for the target vehicle is determined based on at least one of the first image recognition results and the meteorological information, and the processing strategy is used to characterize whether to perform a warming treatment on the first area.

2. The method according to claim 1, characterized in that, The acquisition of meteorological information about the environment in which the target vehicle is located includes: Identify the battery swapping station where the target vehicle is located; The meteorological information is obtained based on the location information of the battery swapping station.

3. The method according to claim 1, characterized in that, The step of determining the processing strategy for the target vehicle based on at least one of the first image recognition result and the meteorological information includes: In response to the first image recognition result indicating that the first area is icy, and / or the meteorological information meets preset meteorological conditions, the processing strategy is determined to be to heat up the first area.

4. The method according to claim 1, characterized in that, The step of determining the processing strategy for the target vehicle based on at least one of the first image recognition result and the meteorological information includes: In response to the inability to determine whether the first area is icy based on the first image recognition result, determine whether the meteorological information meets the preset meteorological conditions; In response to the meteorological information meeting the preset meteorological conditions, the processing strategy is determined to be to warm up the first area.

5. The method according to claim 1, characterized in that, The sample image is determined in the following way: Acquire a first type of image, which is an image of ice formation occurring in the second region; Determine the icing area and the occlusion area of ​​the second region in the first type of image; The first type of image whose icing area satisfies the first area condition and whose occlusion area satisfies the second area condition is determined as the sample image.

6. The method according to claim 1 or 5, characterized in that, The sample image is determined in the following way: Acquire a second type of image, which is an image of the unfrozen area within the second region; The second image recognition result of the second type of image is obtained based on the image recognition model, and the second image recognition result is used to characterize whether the second region is frozen. The second image, characterized by the second image recognition result, representing the second type of image where ice forms in the second region, is determined as the sample image.

7. A vehicle data processing system, characterized in that, The system includes: An image acquisition device is configured to acquire images of the battery mounting area of ​​a vehicle; A heating device is configured to heat the battery mounting area; and A control device is configured to perform the method as described in any one of claims 1-6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1-6.

9. An electronic device comprising a memory and a processor, characterized in that, The memory is used to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method as described in any one of claims 1-6.

10. A computer program product, characterized in that, When the computer program product is run on a computer, it causes the computer to perform the method as described in any one of claims 1-6.

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