A lithium battery detection method, system, electronic device and medium

Abstract

The present invention provides a lithium battery detection method, system, electronic device and medium, which relate to the field of lithium battery detection technology. The method includes using a generative adversarial network to generate a real-life image of a lithium battery in a battery compartment; determining the lithium battery installation information based on a real-life image processing model; constructing a lithium battery installation graph structure based on the lithium battery installation information, the lithium battery installation graph structure including three nodes and three edges between the three nodes, the three nodes including a battery compartment node, a battery compartment spring node, and a lithium battery node, the node feature of the battery compartment node including battery compartment information, the node feature of the battery compartment spring node including battery compartment spring information, the node feature of the lithium battery node including lithium battery information, and the edge between two nodes indicating the positional relationship between the two nodes; using a graph convolutional network to process the lithium battery installation graph structure to determine whether the installation status of the lithium battery is normal. The method can quickly and accurately detect the installation status of the lithium battery.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium batteries, and in particular to a lithium battery detection method, system, electronic equipment and medium. Background Art

[0002] With the popularity of electronic products such as electric vehicles, smart phones, and wearable devices, lithium batteries, as an important energy source for these devices, have received widespread attention for their safety, stability, and reliability. However, during the installation process, lithium batteries may be installed abnormally due to improper human operation, damage to the battery compartment spring, and other reasons, which may even cause serious safety hazards such as battery short circuit, overheating, and even explosion.

[0003] Traditional lithium battery testing methods usually rely on manual visual inspection or physical measurement using simple sensors, which have many shortcomings. First, manual visual inspection is easily affected by subjective factors, and the test results may be inaccurate; second, simple sensor measurements can only obtain limited physical information and cannot fully reflect the installation status of the lithium battery; finally, these methods are usually unable to accurately judge the installation status of the lithium battery.

[0004] Therefore, how to quickly and accurately detect the installation status of lithium batteries is a problem that needs to be solved urgently. Summary of the invention

[0005] The main technical problem solved by the present invention is how to quickly and accurately detect the installation status of a lithium battery.

[0006] According to a first aspect, the present invention provides a lithium battery detection method, comprising: obtaining an X-ray detection image of a lithium battery in a battery compartment; generating a real-life image of the lithium battery in the battery compartment using a generative adversarial network based on the X-ray detection image of the lithium battery in the battery compartment; processing the real-life image of the lithium battery in the battery compartment based on a real-life image processing model to determine lithium battery installation information, wherein the lithium battery installation information includes battery compartment information, battery compartment spring information, lithium battery information, a positional relationship between the battery compartment and the battery compartment spring, a positional relationship between the battery compartment and the lithium battery, and a positional relationship between the lithium battery and the battery compartment spring; constructing a lithium battery installation graph structure based on the lithium battery installation information, wherein the lithium battery installation graph structure includes three nodes and three edges between the three nodes, wherein the three nodes include a battery compartment node, a battery compartment spring node, and a lithium battery node, wherein the node feature of the battery compartment node includes the battery compartment information, the node feature of the battery compartment spring node includes the battery compartment spring information, the node feature of the lithium battery node includes the lithium battery information, and the edge between two nodes represents the positional relationship between the two nodes; processing the lithium battery installation graph structure using a graph convolutional network to determine whether the installation status of the lithium battery is normal.

[0007] Furthermore, the X-ray detection image is obtained by scanning the lithium battery at different angles using an X-ray imaging device.

[0008] Furthermore, the graph convolution network includes a graph convolution layer and a fully connected layer, the output of the graph convolution layer is connected to the fully connected layer, and the fully connected layer performs classification and outputs whether the installation status of the lithium battery is normal.

[0009] Furthermore, the battery compartment information includes the segmented image, size, shape, structural design, and positive and negative terminal contact positions of the battery compartment; the battery compartment spring information includes the segmented image size, position, and compression degree of the spring; and the lithium battery information includes the segmented image, size, shape, and positive and negative terminal contact positions of the lithium battery.

[0010] Furthermore, the real-scene image processing model is a convolutional neural network model.

[0011] According to a second aspect, the present invention provides a lithium battery detection system, comprising: an acquisition module, for acquiring an X-ray detection image of a lithium battery in a battery compartment;

[0012] A generation module, used to generate a real-life image of the lithium battery in the battery compartment using a generative adversarial network based on the X-ray detection image of the lithium battery in the battery compartment;

[0013] A real-scene image processing module, used to process the real-scene image of the lithium battery in the battery compartment based on the real-scene image processing model to determine the lithium battery installation information, wherein the lithium battery installation information includes battery compartment information, battery compartment spring information, lithium battery information, positional relationship between the battery compartment and the battery compartment spring, positional relationship between the battery compartment and the lithium battery, and positional relationship between the lithium battery and the battery compartment spring;

[0014] a graph structure construction module, for constructing a lithium battery installation graph structure based on the lithium battery installation information, wherein the lithium battery installation graph structure includes three nodes and three edges between the three nodes, wherein the three nodes include a battery compartment node, a battery compartment spring node, and a lithium battery node, wherein the node feature of the battery compartment node includes battery compartment information, the node feature of the battery compartment spring node includes battery compartment spring information, the node feature of the lithium battery node includes lithium battery information, and the edge between two nodes indicates a positional relationship between the two nodes;

[0015] The status determination module is used to process the lithium battery installation graph structure using a graph convolutional network to determine whether the installation status of the lithium battery is normal.

[0016] Furthermore, the X-ray detection image is obtained by scanning the lithium battery at different angles using an X-ray imaging device.

[0017] Furthermore, the graph convolution network includes a graph convolution layer and a fully connected layer, the output of the graph convolution layer is connected to the fully connected layer, and the fully connected layer performs classification and outputs whether the installation status of the lithium battery is normal.

[0018] According to a third aspect, an embodiment of the present invention provides an electronic device, comprising: a processor; a memory; and a computer program; wherein the computer program is stored in the memory and is configured to be executed by the processor to implement a method as described above, the method comprising: obtaining an X-ray detection image of a lithium battery in a battery compartment; generating a real-life image of the lithium battery in the battery compartment using a generative adversarial network based on the X-ray detection image of the lithium battery in the battery compartment; processing the real-life image of the lithium battery in the battery compartment based on a real-life image processing model to determine lithium battery installation information, the lithium battery installation information including battery compartment information, battery compartment spring information, lithium battery information, and battery compartment and battery compartment spring information. The positional relationship between the battery compartment and the lithium battery, and the positional relationship between the lithium battery and the battery compartment spring; based on the lithium battery installation information, a lithium battery installation graph structure is constructed, and the lithium battery installation graph structure includes three nodes and three edges between the three nodes. The three nodes include a battery compartment node, a battery compartment spring node, and a lithium battery node. The node feature of the battery compartment node includes the battery compartment information, the node feature of the battery compartment spring node includes the battery compartment spring information, and the node feature of the lithium battery node includes the lithium battery information. The edge between two nodes represents the positional relationship between the two nodes; the lithium battery installation graph structure is processed using a graph convolutional network to determine whether the installation status of the lithium battery is normal.

[0019] According to the fourth aspect, the present embodiment provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the lithium battery detection method provided above, the method comprising: obtaining an X-ray detection image of a lithium battery in a battery compartment; generating a real-life image of the lithium battery in the battery compartment using a generative adversarial network based on the X-ray detection image of the lithium battery in the battery compartment; processing the real-life image of the lithium battery in the battery compartment based on a real-life image processing model to determine lithium battery installation information, the lithium battery installation information comprising battery compartment information, battery compartment spring information, lithium battery information, a positional relationship between the battery compartment and the battery compartment spring, and a relationship between the battery compartment and the lithium The positional relationship of the battery, and the positional relationship between the lithium battery and the battery compartment spring; constructing a lithium battery installation graph structure based on the lithium battery installation information, the lithium battery installation graph structure includes three nodes and three edges between the three nodes, the three nodes include a battery compartment node, a battery compartment spring node, and a lithium battery node, the node feature of the battery compartment node includes battery compartment information, the node feature of the battery compartment spring node includes battery compartment spring information, the node feature of the lithium battery node includes lithium battery information, and the edge between two nodes represents the positional relationship between the two nodes; using a graph convolutional network to process the lithium battery installation graph structure to determine whether the installation status of the lithium battery is normal.

[0020] The present invention provides a lithium battery detection method, system, electronic device and medium, the method comprising obtaining an X-ray detection image of a lithium battery in a battery compartment; generating a real-life image of the lithium battery in the battery compartment using a generative adversarial network based on the X-ray detection image of the lithium battery in the battery compartment; processing the real-life image of the lithium battery in the battery compartment based on a real-life image processing model to determine lithium battery installation information, the lithium battery installation information comprising battery compartment information, battery compartment spring information, lithium battery information, a positional relationship between the battery compartment and the battery compartment spring, a positional relationship between the battery compartment and the lithium battery, and a positional relationship between the lithium battery and the battery compartment spring; based on the lithium battery installation The lithium battery installation graph structure is constructed based on the installation information, the lithium battery installation graph structure includes three nodes and three edges between the three nodes, the three nodes include a battery compartment node, a battery compartment spring node, and a lithium battery node, the node feature of the battery compartment node includes the battery compartment information, the node feature of the battery compartment spring node includes the battery compartment spring information, the node feature of the lithium battery node includes the lithium battery information, and the edge between two nodes represents the positional relationship between the two nodes; the lithium battery installation graph structure is processed using a graph convolutional network to determine whether the installation status of the lithium battery is normal. The method can quickly and accurately detect the installation status of the lithium battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A schematic diagram of a lithium battery detection method provided by an embodiment of the present invention;

[0022] Figure 2 A schematic diagram of a lithium battery installation structure provided by an embodiment of the present invention;

[0023] Figure 3 A schematic diagram of a lithium battery detection system provided by an embodiment of the present invention;

[0024] Figure 4 A schematic diagram of an electronic device provided by an embodiment of the present invention;

[0025] Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In an embodiment of the present invention, there is provided Figure 1 A lithium battery detection method is shown, the lithium battery detection method comprises steps S1 to S5:

[0027] Step S1, obtaining an X-ray detection image of a lithium battery in a battery compartment.

[0028] The X-ray detection image is obtained by scanning the lithium battery at different angles using an X-ray imaging device.

[0029] Step S2, using a generative adversarial network to generate a real-life image of the lithium battery in the battery compartment based on the X-ray detection image of the lithium battery in the battery compartment.

[0030] The Generative Adversarial Network (GAN) includes a generator and a discriminator. In lithium battery testing, the generator receives an X-ray inspection image as input and generates a real-life image of a lithium battery in a battery compartment. The discriminator analyzes the generated real-life image and tries to distinguish whether it is a real lithium battery image or an image generated by the generator. The input of the GAN is the X-ray inspection image of the lithium battery in the battery compartment, and the output of the GAN is a real-life image of the lithium battery in the battery compartment.

[0031] Generative adversarial networks have the ability to capture and reproduce complex details in X-ray images, such as the internal structure of batteries and battery compartments. Generative adversarial networks can achieve end-to-end learning from X-ray images to real-life images without the need for additional preprocessing or feature extraction steps. The training of generative adversarial networks involves an adversarial process between the generator and the discriminator, and this dynamic training can improve the quality of generated images.

[0032] Step S3, based on the real-scene image processing model, the real-scene image of the lithium battery in the battery compartment is processed to determine the lithium battery installation information, wherein the lithium battery installation information includes battery compartment information, battery compartment spring information, lithium battery information, the position relationship between the battery compartment and the battery compartment spring, the position relationship between the battery compartment and the lithium battery, and the position relationship between the lithium battery and the battery compartment spring.

[0033] The real-scene image processing model is a convolutional neural network model, the input of the real-scene image processing model is the real-scene image of the lithium battery in the battery compartment, and the output of the real-scene image processing model is the lithium battery installation information, which includes battery compartment information, battery compartment spring information, lithium battery information, the positional relationship between the battery compartment and the battery compartment spring, the positional relationship between the battery compartment and the lithium battery, and the positional relationship between the lithium battery and the battery compartment spring.

[0034] A convolutional neural network (CNN) may be a multi-layer neural network (e.g., including at least two layers). The at least two layers may include at least one of a convolutional layer (CONV), a rectified linear unit (ReLU) layer, a pooling layer (POOL), or a fully connected layer (FC). A convolutional neural network can extract useful features from an image and gradually understand and learn contextual information of the image.

[0035] The lithium battery installation information includes battery compartment information, battery compartment spring information, lithium battery information, the positional relationship between the battery compartment and the battery compartment spring, the positional relationship between the battery compartment and the lithium battery, and the positional relationship between the lithium battery and the battery compartment spring.

[0036] The battery compartment information includes the segmented image, size, shape, structural design, and positive and negative terminal contact positions of the battery compartment; the battery compartment spring information includes the segmented image size, position, and compression degree of the spring; the lithium battery information includes the segmented image, size, shape, and positive and negative terminal contact positions of the lithium battery.

[0037] The lithium battery installation information refers to the specific installation status information of the lithium battery in the battery compartment.

[0038] The segmented image of the battery compartment refers to an image of the battery compartment portion separated from the real scene image.

[0039] The dimensions of the battery compartment refer to the physical dimensions of the battery compartment, including length, width and height. As an example, the dimensions of the battery compartment are 70mm x 50mm x 8mm.

[0040] The shape of the battery compartment refers to the geometric shape of the battery compartment. As an example, the shape of the battery compartment can be rectangular, cylindrical, or other specially designed shapes. The structural design of the battery compartment refers to the design features inside the battery compartment, such as partitions, protrusions, etc. As an example, a partition is designed inside the battery compartment to separate the positive and negative poles of the battery.

[0041] The positive and negative contact positions of the battery compartment refer to the specific positions of the positive and negative contact positions in the battery compartment. As an example, the positive contact is located on the left side of the battery compartment and the negative contact is located on the right side.

[0042] The segmented image of the battery compartment spring refers to an image of the battery compartment spring portion separated from the real scene image.

[0043] The size of the battery compartment spring refers to the physical dimensions of the battery compartment spring, such as diameter and length. As an example, the spring has a diameter of 3mm and a length of 20mm.

[0044] The location of the battery compartment spring refers to the specific location of the spring in the battery compartment. As an example, the spring is located in the center of the bottom of the battery compartment to support the bottom of the battery.

[0045] The battery compartment spring compression refers to the current compression state of the spring, and the battery compartment spring compression affects its support strength for the battery. As an example, the battery compartment spring is compressed to 70% of its original length to ensure contact between the battery and the battery compartment.

[0046] The lithium battery segmentation image refers to the image of the lithium battery part separated from the real scene image.

[0047] Lithium battery size refers to the physical dimensions of the lithium battery, including length, width and thickness.

[0048] Lithium battery shape refers to the geometric shape of the lithium battery.

[0049] The positive and negative terminal contact positions of lithium batteries refer to the specific positions of the positive and negative terminal contact positions on lithium batteries.

[0050] Convolutional neural networks automatically learn features in images through multiple convolutional layers, and can gradually extract useful information from raw pixels to complex structures.

[0051] X-ray images are not easy to observe and interpret directly because they are usually black and white images and lack intuitive color and texture information. The real-life images generated by generative adversarial networks can provide more intuitive and real-world images, which are convenient for subsequent analysis. Generative adversarial networks can help generate real-life images with color and texture, which can better highlight the characteristics of lithium batteries and their components, and provide richer information for subsequent processing by convolutional neural networks. When generating real-life images, generative adversarial networks may also enhance or add some information that is useful to convolutional neural networks, such as improving image contrast to make features more obvious. Generative adversarial networks have the ability to capture and reproduce complex details in X-ray images, such as the internal structure of batteries and battery compartments, which is crucial for the analysis of convolutional neural networks.

[0052] In some embodiments, the real-life image processing model includes a battery compartment position recognition layer, a position determination layer, an image segmentation layer, and an information determination layer. The battery compartment position recognition layer, the position determination layer, the image segmentation layer, and the information determination layer all include convolutional neural networks. The input of the battery compartment position recognition layer is the real-life image of the lithium battery in the battery compartment, and the output of the battery compartment position recognition layer is the real-life image of the lithium battery in the battery compartment selected by the frame, the input of the position determination layer is the real-life image of the lithium battery in the battery compartment selected by the frame, the output of the position determination layer is the position relationship between the battery compartment and the battery compartment spring, the position relationship between the battery compartment and the lithium battery, and the position relationship between the lithium battery and the battery compartment spring, the input of the image segmentation layer is the real-life image of the lithium battery in the battery compartment selected by the frame, the output of the image segmentation layer is the segmented battery compartment image, the segmented battery compartment spring image, and the segmented lithium battery image, the input of the information determination layer is the segmented battery compartment image, the segmented battery compartment spring image, and the segmented lithium battery image, and the output of the information determination layer is the battery compartment information, the battery compartment spring information, and the lithium battery information.

[0053] In some embodiments, the information determination layer includes a battery compartment information determination sublayer, a lithium battery information determination sublayer, and a battery compartment spring information determination sublayer. The input of the battery compartment information determination sublayer is the segmented battery compartment image, and the output of the battery compartment information determination sublayer is the battery compartment information. The input of the lithium battery information determination sublayer is the segmented lithium battery image, and the output of the lithium battery information determination sublayer is the lithium battery information. The input of the battery compartment spring information determination sublayer is the segmented battery compartment spring image, and the output of the battery compartment spring information determination sublayer is the battery compartment spring information.

[0054] The framed real-life image of the lithium battery in the battery compartment refers to an image of the key areas including the lithium battery, lithium battery spring, and battery compartment that are identified and framed from the original real-life image after being processed by the battery compartment part recognition layer.

[0055] The hierarchical processing can gradually filter out irrelevant information and improve the accuracy of the final information determination layer. Through this hierarchical model design, the installation information of lithium batteries can be extracted from real-life images more systematically and efficiently.

[0056] Step S4, constructing a lithium battery installation diagram structure based on the lithium battery installation information, the lithium battery installation diagram structure includes three nodes and three edges between the three nodes, the three nodes include a battery compartment node, a battery compartment spring node, and a lithium battery node, the node characteristics of the battery compartment node include battery compartment information, the node characteristics of the battery compartment spring node include battery compartment spring information, the node characteristics of the lithium battery node include lithium battery information, and the edge between two nodes represents the positional relationship between the two nodes.

[0057] The lithium battery installation diagram structure is a diagram structure used to represent the relationship between a lithium battery, a battery compartment and a battery compartment spring.

[0058] The graph structure includes nodes and edges. A node represents an entity or object. The three nodes include a battery compartment node, a battery compartment spring node, and a lithium battery node. An edge between two nodes represents the positional relationship between the two nodes. For example, the edge between a battery compartment node and a battery compartment spring node represents the spatial positional relationship between them.

[0059] Figure 2 A schematic diagram of a lithium battery installation structure provided by an embodiment of the present invention is shown in FIG. Figure 2 As shown, nodes A, B, and C represent the battery compartment node, the battery compartment spring node, and the lithium battery node, respectively, and the edges between nodes A, B, and C represent the positional relationship between the two nodes.

[0060] Step S5: Use a graph convolutional network to process the lithium battery installation graph structure to determine whether the installation status of the lithium battery is normal.

[0061] The graph convolution network includes a graph convolution layer and a fully connected layer. The output of the graph convolution layer is connected to the fully connected layer. The fully connected layer performs classification and outputs whether the installation status of the lithium battery is normal.

[0062] The input of the graph convolutional network is the lithium battery installation graph structure, and the output of the graph convolutional network is whether the installation status of the lithium battery is normal or abnormal.

[0063] The graph structure can naturally represent the complex relationship between lithium batteries, battery compartments, and battery compartment springs, including the spatial position relationship between them. The graph structure allows the features of different components (such as the size of the battery compartment and the compression state of the spring) to be integrated together to provide comprehensive input information for state analysis. The graph convolutional network (GCN) can handle the complexity of graph structured data and capture the dependencies between nodes, which is crucial for understanding the installation status of lithium batteries. Through the graph structure, end-to-end analysis from raw installation information to installation status judgment can be achieved without additional preprocessing steps. When the lithium battery installation status is abnormal, the graph structure helps to quickly locate the problem and facilitate fault diagnosis and repair.

[0064] Based on the same inventive concept, Figure 3 A schematic diagram of a lithium battery detection system provided in an embodiment of the present invention, the lithium battery detection system comprising:

[0065] An acquisition module 31 is used to acquire an X-ray detection image of a lithium battery in a battery compartment;

[0066] A generating module 32, configured to generate a real-life image of the lithium battery in the battery compartment using a generative adversarial network based on the X-ray detection image of the lithium battery in the battery compartment;

[0067] A real-scene image processing module 33 is used to process the real-scene image of the lithium battery in the battery compartment based on the real-scene image processing model to determine the lithium battery installation information, wherein the lithium battery installation information includes battery compartment information, battery compartment spring information, lithium battery information, positional relationship between the battery compartment and the battery compartment spring, positional relationship between the battery compartment and the lithium battery, and positional relationship between the lithium battery and the battery compartment spring;

[0068] A graph structure building module 34 is used to build a lithium battery installation graph structure based on the lithium battery installation information, wherein the lithium battery installation graph structure includes three nodes and three edges between the three nodes, wherein the three nodes include a battery compartment node, a battery compartment spring node, and a lithium battery node, wherein the node feature of the battery compartment node includes the battery compartment information, the node feature of the battery compartment spring node includes the battery compartment spring information, and the node feature of the lithium battery node includes the lithium battery information, and the edge between two nodes indicates the positional relationship between the two nodes;

[0069] The state determination module 35 is used to use a graph convolutional network to process the lithium battery installation graph structure to determine whether the installation state of the lithium battery is normal.

[0070] Based on the same inventive concept, an embodiment of the present invention provides an electronic device, such as Figure 4 As shown, including:

[0071] The invention comprises: a processor 41; a memory 42; and a computer program; wherein the computer program is stored in the memory 42 and is configured to be executed by the processor 41 to implement the lithium battery detection method provided above, the method comprising: obtaining an X-ray detection image of a lithium battery in a battery compartment; generating a real-life image of the lithium battery in the battery compartment using a generative adversarial network based on the X-ray detection image of the lithium battery in the battery compartment; processing the real-life image of the lithium battery in the battery compartment based on a real-life image processing model to determine lithium battery installation information, the lithium battery installation information comprising battery compartment information, battery compartment spring information, lithium battery information, and position relationship between the battery compartment and the battery compartment spring. system, the positional relationship between the battery compartment and the lithium battery, and the positional relationship between the lithium battery and the battery compartment spring; based on the lithium battery installation information, a lithium battery installation graph structure is constructed, the lithium battery installation graph structure includes three nodes and three edges between the three nodes, the three nodes include a battery compartment node, a battery compartment spring node, and a lithium battery node, the node feature of the battery compartment node includes the battery compartment information, the node feature of the battery compartment spring node includes the battery compartment spring information, the node feature of the lithium battery node includes the lithium battery information, and the edge between two nodes represents the positional relationship between the two nodes; the lithium battery installation graph structure is processed using a graph convolutional network to determine whether the installation status of the lithium battery is normal.

[0072] Based on the same inventive concept, this embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by the processor 41, the lithium battery detection method provided above is implemented. The method includes: Acquire an X-ray detection image of a lithium battery in a battery compartment; generate a real-life image of the lithium battery in the battery compartment using a generative adversarial network based on the X-ray detection image of the lithium battery in the battery compartment; process the real-life image of the lithium battery in the battery compartment based on a real-life image processing model to determine lithium battery installation information, wherein the lithium battery installation information includes battery compartment information, battery compartment spring information, lithium battery information, a positional relationship between the battery compartment and the battery compartment spring, a positional relationship between the battery compartment and the lithium battery, and a positional relationship between the lithium battery and the battery compartment spring; construct a lithium battery installation graph structure based on the lithium battery installation information, wherein the lithium battery installation graph structure includes three nodes and three edges between the three nodes, wherein the three nodes include a battery compartment node, a battery compartment spring node, and a lithium battery node, wherein a node feature of the battery compartment node includes battery compartment information, a node feature of the battery compartment spring node includes battery compartment spring information, a node feature of the lithium battery node includes lithium battery information, and an edge between two nodes indicates a positional relationship between the two nodes; use a graph convolutional network to process the lithium battery installation graph structure to determine whether the installation status of the lithium battery is normal.

[0073] The lithium battery detection method provided in the embodiment of the present application can be applied to terminal devices (such as mobile phones), tablet computers, laptops, ultra-mobile personal computers (ultra-mobile personal computers, UMPCs), handheld computers, netbooks, personal digital assistants (personal digital assistants, PDAs), wearable devices (such as smart watches, smart glasses or smart helmets, etc.), augmented reality (augmented reality, AR) \ virtual reality (virtualreality, VR) devices, smart home devices, car computers and other electronic devices, and the embodiment of the present application does not impose any restrictions on this.

[0074] Taking the mobile phone 100 as an example of the electronic device, Figure 5 A schematic structural diagram of the mobile phone 100 is shown.

[0075] like Figure 5 As shown, the mobile phone 100 may include a processing module 110, an external memory interface 120, an internal memory 121, a universal serial bus (USB) interface 130, a charging management module 140, a power management module 141, a battery 142, an antenna 1, an antenna 2, a mobile communication module 150, a wireless communication module 160, an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, an earphone interface 170D, a sensor module 180, a button 190, a motor 191, an indicator 192, a camera 193, a display screen 194, and a subscriber identification module (SIM) card interface 195, etc.

[0076] The sensor module 180 may include a distance sensor, a proximity light sensor, a fingerprint sensor, a temperature sensor, a touch sensor, an ambient light sensor and other sensors.

[0077] It is to be understood that the structure illustrated in this embodiment does not constitute a specific limitation on the mobile phone 100. In other embodiments of the present application, the mobile phone 100 may include more or fewer components than those illustrated, or combine certain components, or separate certain components, or arrange the components differently. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0078] The processing module 110 may include one or more processing units, for example, the processing module 110 may include an application processor (AP), a modem processor, a graphics processor (GPU), an image signal processor (ISP), a controller, a memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU), etc. Different processing units may be independent devices or integrated into one or more processors.

[0079] The controller can be the nerve center and command center of the mobile phone 100, and is the decision maker that commands the various components of the mobile phone 100 to work in coordination according to the instructions. The controller can generate an operation control signal according to the instruction operation code and timing signal to complete the control of fetching and executing instructions.

[0080] The application processor may be installed with an operating system of the mobile phone 100, which is used to manage the hardware and software resources of the mobile phone 100. For example, it may manage and configure memory, determine the priority of system resource supply and demand, manage file systems, manage drivers, etc. The operating system may also be used to provide an operating interface for users to interact with the system. Various types of software may be installed in the operating system, such as drivers, applications (applications, apps), etc. Exemplarily, the operating system of the mobile phone 100 may be an Android system, a Linux system, etc.

[0081] A memory may also be provided in the processing module 110 for storing instructions and data. In some embodiments, the memory in the processing module 110 is a cache memory. The memory may store instructions or data that have just been used or are cyclically used by the processing module 110. If the processing module 110 needs to use the instruction or data again, it may be directly called from the memory. This avoids repeated access, reduces the waiting time of the processing module 110, and thus improves the efficiency of the system.

[0082] In some embodiments, the processing module 110 may include one or more interfaces. The interface may include an inter-integrated circuit (I2C) interface, an inter-integrated circuits sound (I2S) interface, a pulse code modulation (PCM) interface, a universal asynchronous receiver / transmitter (UART) interface, a mobile industry processor interface (MIPI), a general-purpose input / output (GPIO) interface, a subscriber identity module (SIM) interface, and / or a universal serial bus (USB) interface, etc.

[0083] The processing module 110 can obtain chronic difficult-to-heal wound images and wound detection reports of multiple users.

[0084] The charging management module 140 is used to receive charging input from a charger. The charger may be a wireless charger or a wired charger. In some wired charging embodiments, the charging management module 140 may receive charging input from a wired charger through the USB interface 130. In some wireless charging embodiments, the charging management module 140 may receive wireless charging input through a wireless charging coil of the mobile phone 100. While the charging management module 140 is charging the battery 142, it may also power the electronic device through the power management module 141.

[0085] The power management module 141 is used to connect the battery 142, the charging management module 140 and the processing module 110. The power management module 141 receives input from the battery 142 and / or the charging management module 140, and supplies power to the processing module 110, the internal memory 121, the external memory, the display screen 194, the camera 193, and the wireless communication module 160. The power management module 141 can also be used to monitor parameters such as battery capacity, battery cycle number, battery health status (leakage, impedance), etc. In some other embodiments, the power management module 141 can also be set in the processing module 110. In other embodiments, the power management module 141 and the charging management module 140 can also be set in the same device.

[0086] The wireless communication function of the mobile phone 100 can be implemented through the antenna 1, the antenna 2, the mobile communication module 150, the wireless communication module 160, the modem processor and the baseband processor.

[0087] Antenna 1 and antenna 2 are used to transmit and receive electromagnetic wave signals. Each antenna in mobile phone 100 can be used to cover a single or multiple communication frequency bands. Different antennas can also be reused to improve the utilization of antennas. For example, antenna 1 can be reused as a diversity antenna for a wireless local area network. In some other embodiments, the antenna can be used in combination with a tuning switch.

[0088] The mobile communication module 150 can provide solutions for wireless communications including 2G / 3G / 4G / 5G applied to the mobile phone 100. The mobile communication module 150 may include at least one filter, a switch, a power amplifier, a low noise amplifier (LNA), etc. The mobile communication module 150 can receive electromagnetic waves from the antenna 1, and filter, amplify, and process the received electromagnetic waves, and transmit them to the modulation and demodulation processor for demodulation. The mobile communication module 150 can also amplify the signal modulated by the modulation and demodulation processor, and convert it into electromagnetic waves for radiation through the antenna 1. In some embodiments, at least some of the functional modules of the mobile communication module 150 can be arranged in the processing module 110. In some embodiments, at least some of the functional modules of the mobile communication module 150 can be arranged in the same device as at least some of the modules of the processing module 110.

[0089] The modem processor may include a modulator and a demodulator. Among them, the modulator is used to modulate the low-frequency baseband signal to be sent into a medium-high frequency signal. The demodulator is used to demodulate the received electromagnetic wave signal into a low-frequency baseband signal. The demodulator then transmits the demodulated low-frequency baseband signal to the baseband processor for processing. After the low-frequency baseband signal is processed by the baseband processor, it is passed to the application processor. The application processor outputs a sound signal through an audio device (not limited to a speaker 170A, a receiver 170B, etc.), or displays an image or video through a display screen 194. In some embodiments, the modem processor may be an independent device. In other embodiments, the modem processor may be independent of the processing module 110 and be set in the same device as the mobile communication module 150 or other functional modules.

[0090] The wireless communication module 160 can provide wireless communication solutions including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), infrared (IR), etc., which are applied to the mobile phone 100. The wireless communication module 160 can be one or more devices integrating at least one communication processing module. The wireless communication module 160 receives electromagnetic waves via the antenna 2, modulates the frequency of the electromagnetic wave signal and performs filtering, and sends the processed signal to the processing module 110. The wireless communication module 160 can also receive the signal to be sent from the processing module 110, modulate the frequency of the signal, amplify it, and convert it into electromagnetic waves for radiation through the antenna 2.

[0091] In some embodiments, the antenna 1 of the mobile phone 100 is coupled to the mobile communication module 150, and the antenna 2 is coupled to the wireless communication module 160, so that the mobile phone 100 can communicate with the network and other devices through wireless communication technology. The wireless communication technology may include global system for mobile communications (GSM), general packet radio service (GPRS), code division multiple access (CDMA), wideband code division multiple access (WCDMA), time-division code division multiple access (TD-SCDMA), long term evolution (LTE), BT, GNSS, WLAN, NFC, FM, and / or IR technology. The GNSS may include a global positioning system (GPS), a global navigation satellite system (GLONASS), a Beidou navigation satellite system (BDS), a quasi-zenith satellite system (QZSS) and / or a satellite based augmentation system (SBAS).

[0092] The mobile phone 100 implements the display function through a GPU, a display screen 194, and an application processor. The GPU is a microprocessor for image processing, which is connected to the display screen 194 and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. The processing module 110 may include one or more GPUs, which execute program instructions to generate or change display information.

[0093] The display screen 194 is used to display images, videos, etc. The display screen 194 includes a display panel. The display panel can be a liquid crystal display (LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode or an active-matrix organic light-emitting diode (AMOLED), a flexible light-emitting diode (FLED), Miniled, MicroLed, Micro-oLed, a quantum dot light-emitting diode (QLED), etc. In some embodiments, the mobile phone 100 may include 1 or N display screens 194, where N is a positive integer greater than 1. In the embodiment of the present application, the display screen 194 can be used to display a remote session window, an image of a chronic, difficult-to-heal wound, and a wound detection report.

[0094] The mobile phone 100 can realize the shooting function through the ISP, the camera 193, the video codec, the GPU, the display screen 194 and the application processor. In some embodiments, the mobile phone 100 can realize the video communication function through the ISP, the camera 193, the video codec, the GPU and the application processor.

[0095] ISP is used to process the data fed back by camera 193. For example, when taking a photo, the shutter is opened, and the light is transmitted to the camera photosensitive element through the lens. The light signal is converted into an electrical signal, and the camera photosensitive element transmits the electrical signal to ISP for processing and converts it into an image visible to the naked eye. ISP can also perform algorithm optimization on the noise, brightness, and skin color of the image. ISP can also optimize the exposure, color temperature and other parameters of the shooting scene. In some embodiments, ISP can be set in camera 193.

[0096] The camera 193 is used to capture still images or videos. The object generates an optical image through the lens and projects it onto the photosensitive element. The photosensitive element can be a charge coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor. The photosensitive element converts the optical signal into an electrical signal, and then passes the electrical signal to the ISP to be converted into a digital image signal. The ISP outputs the digital image signal to the DSP for processing. The DSP converts the digital image signal into an image signal in a standard RGB, YUV or other format. In some embodiments, the mobile phone 100 may include 1 or N cameras 193, where N is a positive integer greater than 1.

[0097] The digital signal processor is used to process digital signals, and can process not only digital image signals but also other digital signals. For example, when the mobile phone 100 is selecting a frequency point, the digital signal processor is used to perform Fourier transform on the frequency point energy.

[0098] Video codecs are used to compress or decompress digital videos. Mobile phone 100 may support one or more video codecs. Thus, mobile phone 100 may play or record videos in various coding formats, such as Moving Picture Experts Group (MPEG) 1, MPEG2, MPEG3, MPEG4, etc.

[0099] NPU is a neural network (NN) computing processor. By drawing on the structure of biological neural networks, such as the transmission mode between neurons in the human brain, it can quickly process input information and can also continuously self-learn. Through NPU, the intelligent cognition of the mobile phone 100 can be realized, such as image recognition, face recognition, voice recognition, text understanding, etc.

[0100] In some embodiments, the NPU computing processor can run a convolutional neural network model and classify the multiple users to obtain multiple user groups.

[0101] The external memory interface 120 can be used to connect an external memory card, such as a Micro SD card, to expand the storage capacity of the mobile phone 100. The external memory card communicates with the processing module 110 through the external memory interface 120 to implement a data storage function, such as storing music, video and other files in the external memory card.

[0102] The internal memory 121 can be used to store computer executable program codes, which include instructions. The processing module 110 executes various functional applications and data processing of the mobile phone 100 by running the instructions stored in the internal memory 121. The internal memory 121 may include a program storage area and a data storage area. Among them, the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc. The data storage area can store data created during the use of the mobile phone 100 (such as audio data, a phone book, etc.), etc. In addition, the internal memory 121 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, a universal flash storage (UFS), etc.

[0103] The mobile phone 100 can implement audio functions such as music playing and recording through the audio module 170, the speaker 170A, the receiver 170B, the microphone 170C, the earphone interface 170D, and the application processor.

[0104] The audio module 170 is used to convert digital audio information into analog audio signal output, and is also used to convert analog audio input into digital audio signals. The audio module 170 can also be used to encode and decode audio signals. In some embodiments, the audio module 170 can be arranged in the processing module 110, or some functional modules of the audio module 170 can be arranged in the processing module 110.

[0105] The speaker 170A, also called a "speaker", is used to convert an audio electrical signal into a sound signal. The mobile phone 100 can listen to music or listen to a hands-free call through the speaker 170A.

[0106] The receiver 170B, also called a "earpiece", is used to convert audio electrical signals into sound signals. When the mobile phone 100 receives a call or voice message, the voice can be received by placing the receiver 170B close to the ear.

[0107] Microphone 170C, also called "microphone" or "microphone", is used to convert sound signals into electrical signals. When making a call or sending a voice message, the user can speak by putting their mouth close to the microphone 170C to input the sound signal into the microphone 170C. The mobile phone 100 can be provided with at least one microphone 170C. In other embodiments, the mobile phone 100 can be provided with two microphones 170C, which can not only collect sound signals but also realize noise reduction function. In other embodiments, the mobile phone 100 can also be provided with three, four or more microphones 170C to collect sound signals, reduce noise, identify the sound source, realize directional recording function, etc.

[0108] The earphone interface 170D is used to connect a wired earphone and can be a USB interface 130 or a 3.5 mm open mobile terminal platform (OMTP) standard interface or a cellular telecommunications industry association of the USA (CTIA) standard interface.

[0109] The key 190 includes a power key, a volume key, etc. The key 190 can be a mechanical key or a touch key. The mobile phone 100 can receive key input and generate key signal input related to the user settings and function control of the mobile phone 100.

[0110] Motor 191 can generate vibration prompts. Motor 191 can be used for incoming call vibration prompts, and can also be used for touch vibration feedback. For example, touch operations acting on different applications (such as taking pictures, audio playback, etc.) can correspond to different vibration feedback effects. For touch operations acting on different areas of the display screen 194, motor 191 can also correspond to different vibration feedback effects. Different application scenarios (for example: time reminders, receiving messages, alarm clocks, games, etc.) can also correspond to different vibration feedback effects. The touch vibration feedback effect can also support customization.

[0111] Indicator 192 may be an indicator light, which may be used to indicate charging status, power changes, messages, missed calls, notifications, etc.

[0112] The SIM card interface 195 is used to connect the SIM card. The SIM card can be connected to and separated from the mobile phone 100 by inserting it into the SIM card interface 195 or pulling it out from the SIM card interface 195. The mobile phone 100 can support 1 or N SIM card interfaces, where N is a positive integer greater than 1. The SIM card interface 195 can support Nano SIM cards, Micro SIM cards, SIM cards, etc. Multiple cards can be inserted into the same SIM card interface 195 at the same time. The types of the multiple cards can be the same or different. The SIM card interface 195 can also be compatible with different types of SIM cards. The SIM card interface 195 can also be compatible with external memory cards. The mobile phone 100 interacts with the network through the SIM card to realize functions such as calls and data communications. In some embodiments, the mobile phone 100 uses an eSIM, i.e., an embedded SIM card. The eSIM card can be embedded in the mobile phone 100 and cannot be separated from the mobile phone 100.

[0113] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements and corrections to this specification. Such modifications, improvements and corrections are suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

[0114] At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures or characteristics in one or more embodiments of this specification can be appropriately combined.

[0115] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this specification, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0116] Similarly, it should be noted that in order to simplify the description disclosed in this specification and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this specification, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0117] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A lithium battery detection method, characterized in that: include: Obtain X-ray inspection images of lithium batteries in the battery compartment; Generate a real-life image of the lithium battery in the battery compartment using a generative adversarial network based on the X-ray detection image of the lithium battery in the battery compartment; Based on the real-scene image processing model, the real-scene image of the lithium battery in the battery compartment is processed to determine the lithium battery installation information, the lithium battery installation information includes the battery compartment information, the battery compartment spring information, the lithium battery information, the positional relationship between the battery compartment and the battery compartment spring, the positional relationship between the battery compartment and the lithium battery, and the positional relationship between the lithium battery and the battery compartment spring. The battery compartment information includes the segmented image, size, shape, structural design, and positive and negative electrode contact positions of the battery compartment. The battery compartment spring information includes the segmented image size, position, and compression degree of the spring. The lithium battery information includes the segmented image, size, shape, and positive and negative electrode contact positions of the lithium battery. The real-scene image processing model is a convolutional neural network model. Constructing a lithium battery installation graph structure based on the lithium battery installation information, the lithium battery installation graph structure includes three nodes and three edges between the three nodes, the three nodes include a battery compartment node, a battery compartment spring node, and a lithium battery node, the node feature of the battery compartment node includes battery compartment information, the node feature of the battery compartment spring node includes battery compartment spring information, the node feature of the lithium battery node includes lithium battery information, and the edge between two nodes represents the positional relationship between the two nodes; A graph convolutional network is used to process the lithium battery installation graph structure to determine whether the installation status of the lithium battery is normal.

2. The lithium battery detection method according to claim 1, characterized in that: The X-ray detection image is obtained by scanning the lithium battery at different angles using an X-ray imaging device.

3. The lithium battery detection method according to claim 1, characterized in that: The graph convolution network includes a graph convolution layer and a fully connected layer. The output of the graph convolution layer is connected to the fully connected layer. The fully connected layer performs classification and outputs whether the installation status of the lithium battery is normal.

4. A lithium battery detection system, characterized in that: include: An acquisition module is used to acquire an X-ray detection image of a lithium battery in a battery compartment; A generation module, used to generate a real-life image of the lithium battery in the battery compartment using a generative adversarial network based on the X-ray detection image of the lithium battery in the battery compartment; A real-scene image processing module, used to process the real-scene image of the lithium battery in the battery compartment based on the real-scene image processing model to determine the lithium battery installation information, the lithium battery installation information includes battery compartment information, battery compartment spring information, lithium battery information, the positional relationship between the battery compartment and the battery compartment spring, the positional relationship between the battery compartment and the lithium battery, and the positional relationship between the lithium battery and the battery compartment spring. The battery compartment information includes the segmented image, size, shape, structural design, and positive and negative electrode contact positions of the battery compartment. The battery compartment spring information includes the segmented image size, position, and compression degree of the spring. The lithium battery information includes the segmented image, size, shape, and positive and negative electrode contact positions of the lithium battery. The real-scene image processing model is a convolutional neural network model; a graph structure construction module, for constructing a lithium battery installation graph structure based on the lithium battery installation information, wherein the lithium battery installation graph structure includes three nodes and three edges between the three nodes, wherein the three nodes include a battery compartment node, a battery compartment spring node, and a lithium battery node, wherein the node feature of the battery compartment node includes battery compartment information, the node feature of the battery compartment spring node includes battery compartment spring information, the node feature of the lithium battery node includes lithium battery information, and the edge between two nodes indicates a positional relationship between the two nodes; The status determination module is used to process the lithium battery installation graph structure using a graph convolutional network to determine whether the installation status of the lithium battery is normal.

5. The lithium battery detection system as claimed in claim 4, characterized in that: The X-ray detection image is obtained by scanning the lithium battery at different angles using an X-ray imaging device.

6. The lithium battery detection system as claimed in claim 4, characterized in that: The graph convolution network includes a graph convolution layer and a fully connected layer. The output of the graph convolution layer is connected to the fully connected layer. The fully connected layer performs classification and outputs whether the installation status of the lithium battery is normal.

7. An electronic device, characterized in that: include: processor; Memory; And a computer program; wherein the computer program is stored in the memory and is configured to be executed by the processor to implement the lithium battery detection method according to any one of claims 1 to 3.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the lithium battery detection method according to any one of claims 1 to 3 is implemented.

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