Battery pack detection system and method, electronic equipment and vehicle

Abstract

The embodiment of the invention provides a battery pack detection system and method, electronic equipment and a vehicle. The system comprises a shooting module, an image processing module, an image recognition module and a risk assessment module, wherein the shooting module is used for shooting a vehicle bottom image, and the vehicle bottom image at least comprises a battery pack image and a fixing structure image used for fixing a battery pack; the image processing module is used for processing the vehicle bottom image, so that the vehicle bottom image has unified image parameters; the image recognition module is used for recognizing the image with the unified image parameters based on the deep learning and / or computer vision technology so as to recognize the battery pack and the fixing structure in the image; and the risk assessment module is used for carrying out risk identification on the images of the battery pack and the fixed structure so as to determine the risk level of the vehicle. According to the battery pack detection system provided by the embodiment of the invention, the real-time performance and the accuracy of battery pack detection are improved.

Description

Technical Field

[0001] The present application relates to the technical field of electric vehicles, and in particular to a battery pack detection system, method, electronic equipment and vehicle. Background Art

[0002] New energy vehicle battery packs are the core components of electric vehicles and are directly related to the safety and reliability of the vehicle.

[0003] In the existing technology, professional technicians are required to conduct manual visual inspections of the bottom of the vehicle and determine whether there is a risk of bottoming out by observing the battery pack and its fixing position. This method is inefficient, relies on personnel experience, has subjective judgment errors, and is difficult to monitor in real time. Summary of the invention

[0004] In order to solve the above technical problems, the embodiments of the present invention provide a battery pack detection system, method, electronic device and vehicle to improve the automation and accuracy of battery pack detection.

[0005] According to a first aspect of an embodiment of the present application, a battery pack detection system is provided, which is applied to a vehicle detection station. The system includes: a shooting module, an image processing module, an image recognition module and a risk assessment module; wherein:

[0006] The shooting module is used to shoot an image of the bottom of the vehicle, where the image of the bottom of the vehicle at least includes an image of the battery pack and an image of a fixing structure for fixing the battery pack;

[0007] The image processing module is used for processing the vehicle bottom image so that the vehicle bottom image has uniform image parameters;

[0008] The image recognition module is used to recognize images with uniform image parameters based on deep learning and / or computer vision technology to identify the battery pack and the fixing structure in the image;

[0009] The risk assessment module is used to perform risk identification on images of battery packs and fixed structures to determine the risk level of the vehicle.

[0010] In one embodiment, a shooting module shoots an image of the bottom of a vehicle, including: the shooting module determines a first angle according to the type of battery pack, and determines a second angle according to the fixed structure; and shoots the bottom image of the vehicle at the first angle and the second angle respectively, to obtain multiple images of the bottom of the vehicle.

[0011] In one embodiment, the image parameters include a file name, and the image processing module processes the vehicle bottom image so that the vehicle bottom image has unified image parameters, including: the image processing module unifies the file name of the image, and the file name includes a timestamp, environmental data, and a shooting device identifier.

[0012] In one embodiment, the image parameters include image size, and the image processing module processes the vehicle bottom image so that the vehicle bottom image has uniform image parameters, including: the image processing module crops the vehicle bottom image according to the image size determined based on the region of interest.

[0013] In one embodiment, the image recognition module is also used to identify the location information and status information of the battery pack, and send the location information and status information to the risk assessment module.

[0014] In one embodiment, the risk assessment module includes a risk level determination unit, which is used to determine a first risk level of the battery pack based on location information and status information of the battery pack, determine a second risk level of the fixed structure based on the location information and status information of the fixed structure, and determine the risk level of the vehicle based on the first risk level and the second risk level.

[0015] In one embodiment, the risk assessment module includes a risk judgment unit, which is used to judge whether there is a bottoming risk based on the distance between the lowest point of the battery pack or the fixed structure and the ground.

[0016] According to a second aspect of an embodiment of the present application, a battery pack detection method is provided, the method comprising:

[0017] Acquire a vehicle bottom image, the vehicle bottom image including at least a battery pack image and an image of a fixing structure for fixing the battery pack;

[0018] Processing the vehicle bottom image so that the image has uniform image parameters;

[0019] Identify battery packs and fixing structures in vehicle bottom images with uniform image parameters based on deep learning and / or computer vision techniques;

[0020] Based on the images of the battery pack and mounting structures, the risk level of the vehicle is determined.

[0021] According to a third aspect of an embodiment of the present application, there is provided an electronic device, including a memory and a processor;

[0022] The memory is connected to the processor and is used for storing programs;

[0023] The processor is used to implement the method in the second aspect by running the program in the memory.

[0024] According to a fourth aspect of an embodiment of the present application, a vehicle is provided, the vehicle comprising the system in the first aspect or any possible implementation of the first aspect.

[0025] The battery pack detection system, method, electronic device and vehicle provided in the embodiments of the present invention use a shooting module to capture an image of the bottom of the vehicle, an image processing module to process the image of the bottom of the vehicle so that the image of the bottom of the vehicle has uniform image parameters; an image recognition module processes the image with uniform image parameters to identify the battery pack and fixed structure in the image; and a risk assessment module performs risk identification on the image with uniform image parameters to determine the risk level of the vehicle. Since the image of the bottom of the vehicle includes at least an image of the battery pack and an image of a fixed structure for fixing the battery pack, the battery pack detection system provided in the embodiments of the present invention can not only realize risk detection of the battery pack itself, but also realize detection of the fixed structure, making the detection result more comprehensive and accurate.

[0026] The battery pack detection system provided in the embodiment of the present invention can automatically complete the detection after the vehicle enters the inspection station, thereby improving the automation level of battery pack detection. In addition, the image recognition module in the system is based on the image recognition algorithm of deep learning and / or computer vision technology, which can quickly identify the status of the battery pack and the fixed structure. The algorithm can accurately identify various potential damages and abnormal conditions, reduce false alarms and missed alarms, and improve the reliability of the overall detection. The vehicle risk level generated by the risk identification module in the embodiment of the present invention can help car owners and maintenance personnel make scientific decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0028] Figure 1 A schematic diagram of an implementation environment according to the present invention;

[0029] Figure 2 A structural block diagram of a battery pack detection system provided by an embodiment of the present invention;

[0030] Figure 3 A flowchart of a battery pack detection method provided by an embodiment of the present invention;

[0031] Figure 4 A structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0033] New energy vehicle battery packs are the core components of electric vehicles and are directly related to the safety and reliability of the vehicle.

[0034] In the existing technology, professional technicians are required to conduct manual visual inspections of the bottom of the vehicle and determine whether there is a risk of bottoming out by observing the battery pack and its fixing position. This method is inefficient, relies on personnel experience, has subjective judgment errors, and is difficult to monitor in real time.

[0035] Since the vehicle bottom image includes at least a battery pack image and an image of a fixed structure for fixing the battery pack, the battery pack detection system provided by the embodiment of the present invention can not only realize risk detection of the battery pack itself, but also realize detection of the fixed structure, making the detection result more comprehensive and accurate.

[0036] The battery pack detection system provided in the embodiment of the present invention can automatically complete the detection after the vehicle enters the inspection station, thereby improving the automation level of battery pack detection. In addition, the image recognition module in the system is based on the image recognition algorithm of deep learning and / or computer vision technology, which can quickly identify the status of the battery pack and the fixed structure. The algorithm can accurately identify various potential damages and abnormal conditions, reduce false alarms and missed alarms, and improve the reliability of the overall detection. The vehicle risk level generated by the risk identification module in the embodiment of the present invention can help car owners and maintenance personnel make scientific decisions.

[0037] Exemplary Implementation Environment

[0038] Please refer to Figure 1 , Figure 1 It is a schematic diagram of an implementation environment involved in the present invention.

[0039] like Figure 1 As shown, the implementation environment of an embodiment of the present invention involves a terminal and a server, wherein the terminal can take a photo of a vehicle to obtain an image of the bottom of the vehicle, for example: the terminal can be a camera; the server is used to process the image of the bottom of the vehicle to obtain the risk level of the battery pack of the vehicle.

[0040] The vehicle is a new energy vehicle, which is equipped with a useful fixed structure and a battery pack. The battery pack is used to provide energy for the vehicle. The fixed structure is a key component used to fix, support and protect the battery pack. The fixed structure may include a battery bracket, a support frame and pillars, a protective plate, and cooling system components.

[0041] Server 1 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. The server can also become a node of the blockchain.

[0042] The server 1 may be one or more servers. When there are multiple servers 1, there are at least two servers for providing different services, and / or there are at least two servers for providing the same service, such as providing the same service in a load balancing manner, which is not limited in the embodiment of the present invention.

[0043] The terminal device and the server can be directly or indirectly connected by wired or wireless communication, and this application does not limit this. This application does not limit the number of servers or terminal devices. The solution provided by this application can be completed independently by the terminal device, or by the server, or by the terminal device and the server in cooperation, and this application does not limit this.

[0044] It should be understood that Figure 1 This is only an exemplary description and does not specifically limit the application scenarios of the embodiments of the present application. Figure 1 One vehicle and one server are shown as examples, but other numbers of vehicles and servers may actually be included, and this application does not impose any limitation on this.

[0045] Exemplary Systems

[0046] Figure 2 This is a structural block diagram of the battery pack detection system provided by the embodiment of the present invention. Figure 2In an exemplary embodiment, a battery pack detection system is provided, which is applied to a vehicle inspection station. The system may include: a shooting module 202, an image processing module 204, an image recognition module 206 and a risk assessment module 208. In this embodiment, the shooting module 202 can be used as a terminal to shoot the bottom of the vehicle, and the shooting module 202 can send the image of the bottom of the vehicle to the server. The image processing module 204, the image recognition module 206 and the risk assessment module 208 can all be arranged in the server, so that after the server receives the image of the bottom of the vehicle sent by the shooting module 202, it processes and analyzes the image to determine the risk level of the vehicle battery pack. The server in this embodiment can be a cloud server or a local processing unit, which can analyze images in real time and quickly identify abnormal situations.

[0047] In this embodiment, the shooting module 202 is used to capture the image of the bottom of the vehicle, and the image of the bottom of the vehicle includes at least the image of the battery pack and the image of the fixed structure used to fix the battery pack; the image processing module 204 is used to process the image of the bottom of the vehicle so that the image of the bottom of the vehicle has uniform image parameters; the image recognition module 206 is used to identify the image with uniform image parameters based on deep learning and / or computer vision technology to identify the battery pack and the fixed structure in the image; the risk assessment module 208 is used to perform risk identification on the images of the battery pack and the fixed structure to determine the risk level of the vehicle.

[0048] The inspection station in the embodiment of the present invention can be a place for inspecting battery packs, such as a maintenance station. After the vehicle enters the maintenance station, it drives directly into the inspection area for a quick bottom inspection. Another example: a parking lot, the driver can park the vehicle at a designated location, and the bottom of the vehicle is just above the tunnel. In practical applications, a special tunnel can be set up on the ground of a parking lot or a maintenance station, and the width and length of the tunnel are adapted to different types of new energy vehicles. Tracks and support structures are set inside the tunnel, and cameras and other shooting equipment can be installed so that when the vehicle drives into the tunnel, the shooting equipment can shoot the vehicle from the bottom. In order to further ensure the driving safety of the vehicle, an anti-skid layer and a waterproof layer can be laid on the surface of the tunnel to ensure the safety of the vehicle entering and the impact of the external environment on the equipment.

[0049] The battery pack detection system of the embodiment of the present invention is mainly used for vehicle chassis detection, mechanical equipment maintenance, building structure inspection, etc. By identifying the fixed structure, it is detected whether there are problems such as damage and deformation, and repair and maintenance work is guided. The battery pack detection system provided by the embodiment of the present invention can also be applied to electric vehicle battery detection, production line quality control, repair and maintenance, etc. By identifying the position and status of the battery pack, it is detected whether there are problems such as damage, deformation, leakage, etc., to ensure the safety and reliability of the electric vehicle.

[0050] The camera module provided in the embodiment of the present invention can be a camera. The camera can be arranged at the bottom of the tunnel through an electric or pneumatic device, can be extended and retracted, and the shooting range covers the battery pack and fixed structure at the bottom of the vehicle. In order to further ensure the accuracy of shooting, the camera can use a high-resolution camera to ensure the clarity of the image at the bottom of the vehicle and to capture subtle damage and abnormalities. In addition, the camera can also achieve multi-angle coverage. For example, the camera can flexibly adjust the angle in the horizontal and vertical directions to ensure shooting of the entire area of ​​the bottom of the vehicle.

[0051] In one embodiment, a shooting module shoots an image of the bottom of a vehicle, including: the shooting module determines a first angle according to the type of battery pack, and determines a second angle according to the fixed structure; and shoots the bottom image of the vehicle at the first angle and the second angle respectively, to obtain multiple images of the bottom of the vehicle.

[0052] In practical applications, the camera can adjust the angle to shoot the bottom of the vehicle multiple times and obtain images from different angles to achieve shooting of the bottom of the vehicle. Specifically, it may include:

[0053] The camera module determines the first angle according to the type of the battery pack. It should be noted that the first angle and the second angle in this embodiment are both the angles between the axis of the camera and the horizontal plane. In actual applications, due to different types of battery packs, the installation method and size information of the battery packs are different, and the first angle can be determined according to the battery type. The mapping relationship between the battery type and the first angle can be pre-stored in the server so that the first angle can be determined after the battery type is obtained.

[0054] The shooting module determines the second angle based on the fixed structure. Since the fixed structure includes components such as a battery bracket, a support frame and pillars, a protective plate, and cooling system components, in actual applications, the second angle may include multiple angles. For example, the shooting module can shoot separately at multiple locations such as the battery bracket, support frame, pillars, and protective plates to achieve separate shooting of each part, so as to quickly locate the risk later.

[0055] It should be noted that the shooting module of the embodiment of the present invention can also manually adjust the camera angle, allowing the user to fine-tune according to actual conditions. The shooting module provided by the embodiment of the present invention can also automatically optimize the angle, for example: according to the shooting effect of the image, automatically adjust the camera angle to obtain a clearer image.

[0056] In one embodiment, the image parameters include a file name, and the image processing module processes the vehicle bottom image so that the vehicle bottom image has unified image parameters, including: the image processing module unifies the file name of the image, and the file name includes a timestamp, environmental data, and a shooting device identifier.

[0057] The unified image parameters may include image quality parameters, such as contrast, brightness, image size, etc. The image processing module may adjust the brightness and contrast of the image by applying techniques such as histogram equalization and automatic gain control, so as to make the details clearer and adapt to different lighting conditions.

[0058] The image processing module can also use filters to remove random noise in the image to improve the clarity and quality of the image. The image processing module can also use camera calibration parameters to perform geometric correction and color correction to correct geometric distortion and color distortion in the image. Geometric distortion can include: barrel distortion, pincushion distortion, etc.

[0059] The image processing module can also use image decoding and encoding technology to convert the image format, for example: converting from RAW format to RGB or grayscale.

[0060] The unified image parameters may further include a file name, and the image processing module may further use a timestamp, environmental data, and a shooting device identifier as the file name so that the file name may be quickly located in subsequent processing and analysis.

[0061] In one embodiment, the image parameters include image size, and the image processing module processes the vehicle bottom image so that the vehicle bottom image has uniform image parameters, including: the image processing module crops the vehicle bottom image according to the image size determined based on the region of interest.

[0062] The image processing module can crop according to the preset region of interest (ROI) and crop the image as needed to reduce the processing load of the server. The ROI can be the location area of ​​the actual battery pack or the location area of ​​specific components in the fixed structure. The specific components of the fixed structure can include: battery brackets, support frames and pillars, protective plates, and cooling system components. The image size can be set according to the actual ROI area.

[0063] In one embodiment, the image recognition module is also used to identify the location information and status information of the battery pack, and send the location information and status information to the risk assessment module.

[0064] The location information of the battery pack can be presented in the form of coordinates. In practical applications, it can be represented by a coordinate system. For example, it can be represented by pixel coordinates, such as (x1, y1) for the coordinates of the upper left vertex of the image, and (x2, y2) for the coordinates of the lower right vertex of the image. The location information of the battery pack can also be represented by a bounding box, represented by the coordinates of the upper left vertex (x, y) and the size (width, height). The location information of the battery pack can also be represented by key points, using the coordinates of multiple key points of the battery pack, for example: the coordinates of the four corners [(x1, y1), (x2, y2), (x3, y3), (x4, y4)]. The location information of the battery pack can also be represented by two-dimensional shapes and three-dimensional shapes.

[0065] The battery pack may have various fault conditions such as expansion, deformation, cracks, leakage and leakage. These faults not only affect the function and performance of the battery pack, but also may bring safety hazards. The image recognition module can identify the image of the bottom of the vehicle to determine whether the battery pack has the above fault conditions; when the status information of the battery pack is a fault, it will cause the shape of the battery pack to change, which will bring about changes in the battery pack position information.

[0066] The fixed structure may be loose, cracked, deformed, or have a shape change due to modification, which may affect the fixation of the battery pack and bring safety hazards. The image recognition module can identify the image of the bottom of the vehicle to determine whether the fixed structure has the above deformation; when the status information of the fixed structure is the above deformation, it will cause the shape of the fixed structure to change, and then bring about a change in the position information of the fixed structure.

[0067] When the image recognition module recognizes fixed structures, it can use convolutional neural networks (CNN) to extract geometric features such as edges and corners of fixed structures, and realize recognition through template matching and feature description. Template matching is a method that uses predefined templates to match features in an image to determine the position and shape of fixed structures. For other positions in the fixed structure, local feature detection algorithms such as SIFT and SURF can be used to detect key points and local features in the image to achieve accurate recognition of fixed structures.

[0068] When the image recognition module identifies the battery pack, it can use deep learning models (such as Faster R-CNN, YOLO, Mask R-CNN, etc.) to extract complex features and detect targets. In addition, the image recognition module can also use image segmentation technology (such as U-Net, Mask R-CNN) to accurately segment the shape of the battery pack to identify the boundaries and internal structure of the battery pack, which is helpful for detailed analysis. The image recognition module can also use multi-scale feature fusion to realize the recognition of different levels of details of the battery pack, ensuring the comprehensiveness and accuracy of the recognition.

[0069] In one embodiment, the risk assessment module includes a risk level determination unit, which is used to determine a first risk level of the battery pack based on location information and status information of the battery pack, determine a second risk level of the fixed structure based on the location information and status information of the fixed structure, and determine the risk level of the vehicle based on the first risk level and the second risk level.

[0070] The risk assessment module in the embodiment of the present invention can determine the risk level of the vehicle in combination with the first risk level of the battery pack and the second risk level of the fixed structure. Therefore, the risk level assessment provided by the embodiment of the present invention is more comprehensive and accurate.

[0071] In one embodiment, the risk assessment module includes a risk judgment unit, which is used to judge whether there is a bottoming risk based on the distance between the lowest point of the battery pack or the fixed structure and the ground.

[0072] The risk level of the vehicle provided in the embodiment of the present invention can be divided into low risk, medium risk, high risk and extremely high risk, wherein low risk can mean that the distance between the battery pack and the ground is greater than the safety threshold, or the distance between any component in the fixed structure and the ground is greater than the safety threshold, and the vehicle is in normal driving condition. Medium risk can mean that the distance between the battery pack and the ground is close to the safety threshold, and the distance between any component in the fixed structure and the ground is close to the safety threshold. At this time, a warning message can be issued to remind the driver to pay attention to the road conditions and it is recommended to slow down or change the driving path. High risk can mean that the distance between the battery pack and the ground is less than the safety threshold, and the distance between any component in the fixed structure and the ground is less than the safety threshold, and there is a high possibility of dragging the ground. At this time, a warning message can be issued, and the driver is strongly advised to slow down, change the driving path or stop the vehicle so that the vehicle automatically adjusts the vehicle height or suspension system to avoid bottoming out. Extremely high risk can mean that the distance between the battery pack and the ground is 0, and the distance between any component in the fixed structure and the ground is 0. At this time, a warning message can be issued, an alarm can be triggered, and the system can intervene urgently to adjust the vehicle height or suspension system, or even force parking to avoid damage.

[0073] The warning information in the embodiment of the present invention can be presented in the form of short text, image annotation, sound or visual prompt, etc.; the warning information can also be delivered instantly through multiple channels such as SMS, email, mobile application notification, sound and light alarm, etc. In addition, the warning information can also be presented in a striking manner such as a pop-up window or a red mark on the user interface to ensure that the user can quickly notice it.

[0074] The battery pack detection system provided by the embodiment of the present invention may also include a detection report generation module for describing the detection target. The detection report may include detailed information of all detected targets, such as location, size, shape, and detection time. The detection report may include images and data of the detection target, including processed images, annotation results, statistical data, charts and other rich content, which can fully display the detection process and results. The detection report may also include analysis results, providing in-depth analysis of the detection results, such as trend analysis, historical comparison, fault cause analysis, etc. In addition, the detection report may include additional information, such as vehicle status, self-test results, operation logs, etc. The detection report can be presented in a variety of forms, such as PDF, Word, Excel and other formats, and may include text, charts, images, data lists and other elements. The detection report can use visualization tools such as icons and images to intuitively display the detection data and analysis results. The detection report provided by the embodiment of the present invention can comprehensively record and analyze the detection results, helping technicians or managers to conduct in-depth analysis and strategy formulation. The detection report provided by the embodiment of the present invention can generate periodic detection reports, archive and track historical data, and facilitate long-term monitoring and trend analysis. The test report provided by the embodiment of the present invention provides detailed data support and analysis basis for repair, maintenance, quality control, etc., and assists in decision-making.

[0075] Exemplary Methods

[0076] Correspondingly, an embodiment of the present application also provides a battery pack detection method. Figure 3 Flow chart of the battery pack detection method provided by the embodiment of the present invention. Figure 3 As shown, the method may include:

[0077] S320: Acquire a vehicle bottom image, where the vehicle bottom image at least includes a battery pack image and an image of a fixing structure for fixing the battery pack;

[0078] S340: Processing the vehicle bottom image so that the image has uniform image parameters;

[0079] S360: Identify battery packs and fixing structures in vehicle bottom images with uniform image parameters based on deep learning and / or computer vision techniques;

[0080] S380: Determine the risk level of the vehicle based on the images of the battery pack and the fixed structure.

[0081] The battery pack detection method provided in this embodiment belongs to the same application concept as the battery pack detection system provided in the above embodiment of this application, and has the same technical effect. For technical details not fully described in this embodiment, please refer to the specific processing content of the battery pack detection system provided in the above embodiment of this application, and will not be repeated here.

[0082] The battery pack detection method provided in the embodiment of the present invention can realize risk detection of the battery pack itself, and can also realize detection of fixed structures, so that the detection results are more accurate. The image recognition algorithm based on deep learning and / or computer vision technology in the embodiment of the present invention can analyze the image in real time after uploading, and quickly identify the status of the battery pack and the fixed structure. The algorithm can accurately identify various potential damages and abnormal conditions, reduce false alarms and missed alarms, and improve the reliability of the overall detection. The determined vehicle risk level in the embodiment of the present invention can help car owners and maintenance personnel make scientific decisions.

[0083] Exemplary Electronic Devices

[0084] Another embodiment of the present application also provides an electronic device, see Figure 4 As shown, the device includes:

[0085] Memory 400 and processor 410;

[0086] The memory 400 is connected to the processor 410 and is used to store programs;

[0087] The processor 410 is used to implement the battery pack detection method disclosed in any of the above embodiments by running the program stored in the memory 400.

[0088] Specifically, the electronic device may further include: a bus, a communication interface 420 , an input device 430 and an output device 440 .

[0089] The processor 410, the memory 400, the communication interface 420, the input device 430 and the output device 440 are connected to each other via a bus.

[0090] A bus may include a pathway that transfers information between components of a computer system.

[0091] Processor 410 may be a general purpose processor, such as a general purpose central processing unit (CPU), a microprocessor, etc., or an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the program of the scheme of the present invention. It may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0092] The processor 410 may include a main processor, and may also include a baseband chip, a modem, and the like.

[0093] The memory 400 stores a program for executing the technical solution of the present invention, and may also store an operating system and other key services. Specifically, the program may include a program code, and the program code includes a computer operation instruction. More specifically, the memory 400 may include a read-only memory (ROM), other types of static storage devices that can store static information and instructions, a random access memory (RAM), other types of dynamic storage devices that can store information and instructions, a disk storage, a flash, and the like.

[0094] The input device 430 may include a device for receiving data and information input by a user, such as a keyboard, a mouse, a camera, a scanner, a light pen, a voice input device, a touch screen, a pedometer, or a gravity sensor.

[0095] Output device 440 may include a device that allows information to be output to a user, such as a display screen, a printer, a speaker, etc.

[0096] The communication interface 420 may include any transceiver or the like to communicate with other devices or communication networks, such as Ethernet, a radio access network (RAN), a wireless local area network (WLAN), etc.

[0097] The processor 410 executes the program stored in the memory 400 and calls other devices, which can be used to implement each step of any battery pack detection method provided in the above embodiments of the present application.

[0098] An embodiment of the present application also proposes a chip, which includes a processor and a data interface. The processor reads and runs a program stored in a memory through the data interface to execute the battery pack detection method introduced in any of the above embodiments. The specific processing process and its beneficial effects can be found in the introduction to the embodiment of the above-mentioned battery pack detection method.

[0099] An embodiment of the present invention further provides a vehicle, which includes the above exemplary system.

[0100] Exemplary computer program products and storage media

[0101] In addition to the above-mentioned methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the battery pack detection method according to various embodiments of the present application described in any of the above embodiments of this specification.

[0102] The computer program product may be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the embodiments of the present application, including object-oriented programming languages, such as Java, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0103] In addition, the embodiment of the present application may also be a storage medium on which a computer program is stored. The computer program is executed by a processor to execute the steps of the battery pack detection method described in any of the above embodiments of this specification, and specifically the following steps may be implemented:

[0104] S320: Acquire a vehicle bottom image, where the vehicle bottom image at least includes a battery pack image and an image of a fixing structure for fixing the battery pack;

[0105] S340: Processing the vehicle bottom image so that the image has uniform image parameters;

[0106] S360: Identify battery packs and fixing structures in vehicle bottom images with uniform image parameters based on deep learning and / or computer vision techniques;

[0107] S380: Determine the risk level of the vehicle based on the images of the battery pack and the fixed structure.

[0108] For the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0109] It should be noted that each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the embodiments can be referred to each other. For the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0110] The steps in the methods of each embodiment of the present application can be adjusted in order, combined and deleted according to actual needs, and the technical features recorded in each embodiment can be replaced or combined.

[0111] The modules and sub-modules in the devices and terminals of the various embodiments of the present application can be combined, divided and deleted according to actual needs.

[0112] In the several embodiments provided in the present application, it should be understood that the disclosed terminals, devices and methods can be implemented in other ways. For example, the terminal embodiments described above are only schematic, for example, the division of modules or submodules is only a logical function division, and there may be other division methods in actual implementation, for example, multiple submodules or modules can be combined or integrated into another module, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

[0113] The modules or submodules described as separate components may or may not be physically separated, and the components of the modules or submodules may or may not be physical modules or submodules, that is, they may be located in one place, or they may be distributed on multiple network modules or submodules. Some or all of the modules or submodules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0114] In addition, each functional module or submodule in each embodiment of the present application may be integrated into one processing module, or each module or submodule may exist physically separately, or two or more modules or submodules may be integrated into one module. The above-mentioned integrated modules or submodules may be implemented in the form of hardware or in the form of software functional modules or submodules.

[0115] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0116] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly by hardware, software units executed by a processor, or a combination of the two. The software units may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0117] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0118] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A battery pack detection system, applied to a vehicle inspection station, characterized in that: include: Shooting module, image processing module, image recognition module and risk assessment module; among which, The shooting module is used to shoot an image of the bottom of the vehicle, wherein the image of the bottom of the vehicle at least includes an image of a battery pack and an image of a fixing structure for fixing the battery pack; The image processing module is used to process the vehicle bottom image so that the vehicle bottom image has uniform image parameters; The image recognition module is used to recognize the image with uniform image parameters based on deep learning and / or computer vision technology to identify the battery pack and the fixing structure in the image; The risk assessment module is used to perform risk identification on the images of the battery pack and the fixed structure to determine the risk level of the vehicle.

2. The battery pack detection system according to claim 1, characterized in that: The shooting module captures the bottom image of the vehicle, including: the shooting module determines a first angle according to the type of the battery pack, and determines a second angle according to the fixed structure; and captures the bottom image of the vehicle at the first angle and the second angle respectively, to obtain multiple images of the bottom of the vehicle.

3. The battery pack detection system according to claim 1, characterized in that: The image parameters include a file name, and the image processing module processes the vehicle bottom image so that the vehicle bottom image has unified image parameters, including: the image processing module unifies the file name of the image, and the file name includes a timestamp, environmental data, and a shooting device identifier.

4. The battery pack detection system according to claim 1, characterized in that: The image parameters include image size, and the image processing module processes the vehicle bottom image so that the vehicle bottom image has uniform image parameters, including: the image processing module crops the vehicle bottom image according to the image size determined based on the region of interest.

5. The battery pack detection system according to claim 1, characterized in that: The image recognition module is also used to identify the location information and status information of the battery pack, and send the location information and status information to the risk assessment module.

6. The battery pack detection system according to claim 5, characterized in that: The risk assessment module includes a risk level determination unit, which is used to determine a first risk level of the battery pack based on location information and status information of the battery pack, determine a second risk level of the fixed structure based on location information and status information of the fixed structure, and determine the risk level of the vehicle based on the first risk level and the second risk level.

7. The battery pack detection system according to claim 1, characterized in that: The risk assessment module includes a risk judgment unit, which is used to judge whether there is a bottoming risk based on the distance between the lowest point of the battery pack or the fixed structure and the ground.

8. A battery pack detection method, characterized in that: include: Acquire a vehicle bottom image, the vehicle bottom image including at least a battery pack image and an image of a fixing structure for fixing the battery pack; Processing the vehicle bottom image so that the image has uniform image parameters; Identifying the battery pack and the fixing structure in the vehicle bottom image with uniform image parameters based on deep learning and / or computer vision technology; A risk level of the vehicle is determined based on the images of the battery pack and the fixed structure.

9. An electronic device, characterized in that: including memory and processor; The memory is connected to the processor and is used to store programs; The processor is used to implement the battery pack detection method as claimed in claim 8 by running the program in the memory.

10. A vehicle, characterized in that: The vehicle includes the battery pack detection system according to any one of claims 1 to 7.

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