Self-detection method for visual information collection device, and related apparatus

Abstract

A self-detection method for a visual information collection device. The method is executed by the visual information collection device, the visual information collection device is provided with an information collection panel, and the information collection panel is provided with an image collection component. The method comprises: acquiring a visual image collected by an image collection component (S301); when it is determined, on the basis of the visual image collected by the image collection component, that a visual information collection device satisfies a self-detection condition, collecting a mirror image of an information collection panel by means of the image collection component (S302); and on the basis of the mirror image, detecting components comprised in the visual information collection device so as to determine whether the components comprised in the visual information collection device are anomalous (S303).

Description

Self-testing methods and related equipment for visual information acquisition devices

[0001] Related applications

[0002] This application claims priority to Chinese patent application filed on June 18, 2024, application number 202410792561.3, entitled "Self-testing method and related equipment for visual information acquisition device", the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application relates to the field of computer technology, specifically to a self-testing method, apparatus, electronic device, computer-readable storage medium, and computer program product for a visual information acquisition device. Background Technology

[0004] With the development of hardware technology, electronic devices used for acquiring visual information are gradually attracting attention because they can be applied in various fields that require visual information acquisition. Electronic devices can acquire visual information with the help of configured visual information acquisition devices. For example, electronic devices can acquire images based on configured sensors, and then further process the acquired images to meet the needs of various application scenarios based on visual information processing.

[0005] As electronic devices are used over time, their internal components may malfunction. When users notice a malfunction, they often lack the relevant technical knowledge to accurately identify the faulty component and can only rely on the official after-sales personnel for testing and repair, resulting in low efficiency in calibration and repair of electronic devices.

[0006] Therefore, improving the efficiency of fault detection and repair of electronic equipment has become an urgent technical problem to be solved. Summary of the Invention

[0007] This application provides a self-testing method, apparatus, electronic device, computer-readable storage medium, and computer program product for a visual information acquisition device. It can determine whether the visual information acquisition device needs self-testing based on the acquired visual images, and can also perform self-testing based on the acquired mirror images. It does not require after-sales personnel to perform testing and repair, which helps to improve the efficiency of calibration and repair of visual information acquisition devices.

[0008] In a first aspect, embodiments of this application provide a self-testing method for a visual information acquisition device, executed by the visual information acquisition device, which has an information acquisition panel and an image acquisition component disposed on the information acquisition panel. The method includes:

[0009] Acquire the visual image captured by the image acquisition component;

[0010] If, based on the visual image acquired by the image acquisition component, the visual information acquisition device is determined to meet the self-test conditions, then a mirror image of the information acquisition panel is acquired by the image acquisition component; and

[0011] The visual information acquisition device is inspected based on the mirrored image to determine whether any of its components are abnormal.

[0012] Secondly, embodiments of this application provide a self-testing device for a visual information acquisition device, the visual information acquisition device having an information acquisition panel, an image acquisition component being disposed on the information acquisition panel, and the device comprising:

[0013] The acquisition unit is used to acquire the visual image acquired by the image acquisition component;

[0014] The acquisition unit is configured to, when determining that the visual information acquisition device meets self-test conditions based on the visual image acquired by the image acquisition component, acquire a mirror image of the information acquisition panel through the image acquisition component; and

[0015] The detection unit is used to detect each component included in the visual information acquisition device based on the mirror image, so as to determine whether each component included in the visual information acquisition device is abnormal.

[0016] Thirdly, embodiments of this application provide an electronic device, which includes one or more processors; and a memory for storing one or more computer programs, wherein when the one or more computer programs are executed by the one or more processors, the electronic device enables the self-testing method of the visual information acquisition device described in the first aspect.

[0017] Fourthly, embodiments of this application provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the self-testing method for the visual information acquisition device described in the first aspect.

[0018] Fifthly, embodiments of this application provide a computer program product, which includes a computer program or computer instructions. When the computer program or computer instructions are executed by a processor, they implement the self-testing method of the visual information acquisition device as described in the first aspect.

[0019] Details of one or more embodiments of this application are set forth in the following drawings and description. Other features, objects, and advantages of this application will become apparent from the specification, drawings, and claims. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or the conventional technology, the drawings used in the description of the embodiments or the conventional technology will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the disclosed drawings without creative effort.

[0021] Figure 1 is a schematic diagram of the architecture of a self-testing system for a visual information acquisition device provided in an embodiment of this application;

[0022] Figure 2 is a schematic diagram of the hardware environment of a visual information acquisition device provided in an embodiment of this application;

[0023] Figure 3 is a flowchart illustrating a self-testing method for a visual information acquisition device provided in an embodiment of this application;

[0024] Figure 4 is a schematic diagram of a scene for acquiring visual images provided in an embodiment of this application;

[0025] Figure 5 is a schematic diagram of a visual image acquisition scenario provided in an embodiment of this application;

[0026] Figure 6 is a schematic diagram of a self-inspection permission control method provided in an embodiment of this application;

[0027] Figure 7 is a schematic diagram of a detection device provided in an embodiment of this application;

[0028] Figure 8 is a schematic diagram of a reference brightness value calibration for a lighting unit provided in an embodiment of this application;

[0029] Figure 9 is a detection schematic diagram of a detection distance sensor assembly provided in an embodiment of this application;

[0030] Figure 10 is a detection schematic diagram of an image acquisition component provided in an embodiment of this application;

[0031] Figure 11 is a schematic diagram of a reference test pattern provided in an embodiment of this application;

[0032] Figure 12 is a timing diagram of a self-calibration process provided in an embodiment of this application;

[0033] Figure 13 is a structural schematic diagram of a self-testing device for a visual information acquisition equipment provided in an embodiment of this application;

[0034] Figure 14 is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0035] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0036] It should be noted in advance that, in order to enable those skilled in the art to better understand the technical solutions proposed in the embodiments of this application, the embodiments of this application will be described clearly and completely in conjunction with one or more accompanying drawings. Furthermore, the various drawings shown in the embodiments of this application are merely illustrative examples; for example, the execution order of each step in the drawings can be adaptively adjusted according to the actual application scenario. In addition, in the embodiments of this application, the block diagrams shown in the various drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, or in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0037] In this application embodiment, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0038] It should be noted that "multiple" in this article refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0039] It is understood that this application may display a prompt interface or pop-up window before and during the collection of user-related data (such as collecting user feature information through visual information acquisition devices). This prompt interface or pop-up window is used to inform the user that their relevant data is being collected. This ensures that the application only begins executing the steps related to acquiring user-related data after receiving confirmation from the user regarding the prompt interface or pop-up window; otherwise (i.e., without receiving confirmation from the user), the steps to acquire user-related data are terminated, meaning no user-related data is acquired. In other words, all user data collected in this application is collected with the user's consent and authorization, and the collection, use, and processing of relevant user data must comply with the relevant laws, regulations, and standards of the relevant countries and regions.

[0040] The technical solution of this application embodiment is applied to the field of visual information acquisition equipment. Specifically, after a period of use, the components (core devices) of a visual information acquisition device may malfunction, requiring testing and calibration. Currently, the calibration process for visual information acquisition devices is mainly performed before the device leaves the factory; calibration is not supported for devices already manufactured. In other words, after a period of time, the visual information acquisition device lacks self-testing and self-recovery capabilities. Since visual information acquisition devices are distributed worldwide, when users notice a malfunction, due to a lack of relevant technical knowledge, it is difficult to accurately identify the faulty component. Users struggle to pinpoint the problem, and after-sales personnel cannot quickly provide support. Users can only rely on official after-sales personnel for testing and repair. Furthermore, when users report device malfunctions to after-sales personnel, inaccurate fault descriptions may occur, leading to high after-sales maintenance costs (high service costs), low calibration and repair efficiency (low timeliness), and the inability of users to perform self-testing and the device to report test results. After the equipment has been used by users for a period of time, its quality may decline to some extent, and its availability may also decrease, which may affect the effectiveness of users using the visual information acquisition equipment.

[0041] Based on this, embodiments of this application provide a self-testing method for a visual information acquisition device. This method can determine whether the visual information acquisition device needs self-testing based on the acquired visual images, and can also perform self-testing based on acquired mirror images. It provides a rapid and effective testing and calibration mechanism that eliminates the need for after-sales personnel (user self-service), enabling the visual information acquisition device to perform self-testing on its core components. This supports self-testing and self-repair attempts, allowing for rapid identification of problems and reducing after-sales service costs (communication costs), thus improving the efficiency of calibration and repair.

[0042] The self-testing method for visual information acquisition devices proposed in this application involves technologies such as cloud computing and databases, wherein:

[0043] Cloud computing is a computing model that distributes computing tasks across a large pool of computers, enabling various application systems to access computing power, storage space, and information services as needed. The network providing these resources is called the "cloud." From the user's perspective, resources in the "cloud" appear infinitely scalable, readily available, on-demand, and expandable, with payment based on usage.

[0044] As a provider of fundamental cloud computing capabilities, a cloud resource pool is established, which can be simply referred to as a cloud platform, generally known as an Infrastructure as a Service (IaaS) platform. Various types of virtual resources are deployed in the resource pool for external customers to choose from. The cloud resource pool mainly includes: computing devices (virtualized machines containing operating systems), storage devices, and network devices.

[0045] Based on logical function, a Platform as a Service (PaaS) layer can be deployed on top of the IaaS layer, followed by a Software as a Service (SaaS) layer. Alternatively, SaaS can be deployed directly on top of IaaS. PaaS is the platform for running software, such as databases and web containers. SaaS comprises various types of business software, such as web portals and bulk SMS senders. Generally, SaaS and PaaS are upper layers compared to IaaS.

[0046] A database, simply put, can be viewed as an electronic filing cabinet—a place to store electronic files, where users can perform operations such as adding, querying, updating, and deleting data. A "database" is a collection of data stored together in a certain way, capable of being shared by multiple users, with minimal redundancy, and independent of application programs.

[0047] A Database Management System (DBMS) is a computer software system designed to manage databases, generally possessing basic functions such as storage, retrieval, security, and backup. DBMSs can be classified according to the database model they support, such as relational or Extensible Markup Language (XML); or according to the type of computer they support, such as server clusters or mobile devices; or according to the query language used, such as Structured Query Language (SQL) or XQuery; or according to performance priorities, such as maximum scale or maximum operating speed; or other classification methods. Regardless of the classification method used, some DBMSs can cross categories, for example, supporting multiple query languages ​​simultaneously.

[0048] Based on the above description, this application provides a self-testing scheme for a visual information acquisition device, which can improve the efficiency of calibration and repair of the visual information acquisition device. Please refer to Figure 1, which is a schematic diagram of the architecture of a self-testing system for a visual information acquisition device provided in this application. As shown in Figure 1, the self-testing system includes a visual information acquisition device 101, a server 102, and a database storing relevant parameters for self-testing. The visual information acquisition device 101 can be directly or indirectly connected to the server 102 via wired or wireless means. It should be noted that the number and form of the devices shown in Figure 1 are for illustrative purposes only and do not constitute a limitation on the embodiments of this application. In this application embodiment, the visual information acquisition device 101 is used as an electronic device in a biometric recognition scenario for explanation. Figure 1 illustrates the visual information acquisition device 101's ability to collect user's palm features. This visual information acquisition device can also be called a palm-scanning device. This application does not limit the application scenarios of the visual information acquisition device 101.

[0049] In some embodiments, the visual information acquisition device 101 may acquire images for self-testing and perform detection based on the acquired images. The visual information acquisition device 101 may also acquire images for self-testing and send the acquired images to the server 102 to instruct the server 102 to detect the components contained in the visual information acquisition device 101 based on the received images to determine whether the individual components contained in the visual information acquisition device are abnormal.

[0050] The visual information acquisition device 101 can be a terminal device, which may include, but is not limited to, smartphones (such as Android phones, iOS phones, etc.), tablet computers, portable personal computers, mobile internet devices (MIDs), smart voice interaction devices, smart home appliances, vehicle terminals, aircraft, wearable devices, etc. This embodiment does not limit the specific type of terminal device. The server 102 can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms. This embodiment does not limit the specific type of server. The database can be a database of relevant parameters used for self-testing, a database stored in the cloud, or a database stored locally. This embodiment does not limit the specific type of database.

[0051] Please refer to Figure 2. Figure 2 is a schematic diagram of the hardware environment of a visual information acquisition device provided in an embodiment of this application. As shown in Figure 2, the visual information acquisition device 101 may include an information acquisition panel, on which an image acquisition component is provided. The visual information acquisition device 101 may also include an illumination component, an information output component, a distance sensor (Psensor) component, etc. This application does not limit these components.

[0052] The image acquisition component may include image acquisition sub-components, such as the two image acquisition sub-components shown in Figure 2. These sub-components can be different types of cameras, such as color cameras (Red, Green, Blue Camera, RGB Camera) or infrared cameras (IR Camera). Different image acquisition sub-components can acquire different visual information for subsequent processing. For example, if the visual information acquisition device 101 is an electronic device used to capture user biometrics (such as palm features), then the color camera can be used to acquire palm print images of the user's palm, and the infrared camera can be used to acquire palm vein images of the user's palm.

[0053] The lighting component is an illumination element capable of illuminating at least one color, such as the light ring shown in Figure 2. It can be used to output light prompts to interact with the user and guide user input, such as biometric input. This lighting component can also supplement light to enhance the image acquisition component's ability to collect visual information. The lighting component may also include an infrared lighting component, such as an infrared light ring, which can supplement infrared light to enhance the infrared camera's ability to collect visual information.

[0054] The information output component can be a component for outputting prompts, such as a display screen, which can output image prompts to the user or specific patterns (special textures). Optionally, the display screen can be a touch screen capable of receiving user touch input. The information output component can also be a speaker, which can output sound prompts to the user.

[0055] Among them, the distance sensor can be a component used to measure the distance between objects. For example, it can emit infrared light or laser light and convert the light or signal reflected back by the infrared light or laser light into a distance value.

[0056] In some implementations, the information acquisition panel of the visual information acquisition device can be printed with specific patterns (special textures), which can be used to trigger the visual information acquisition device to enter the self-test process.

[0057] The software environment of the visual information acquisition device 101 will be described below:

[0058] The visual information acquisition device 101 may possess the ability to run code-based programs, select algorithms, render graphics, and store data in a database, and may also include other capabilities, which are not limited in this application. The ability to run code-based programs can be understood as the visual information acquisition device 101's ability to recognize the performance of logical operations and to run programs written by technicians to achieve self-testing. The algorithm selection capability refers to the visual information acquisition device 101's ability to acquire multiple detection images and select the optimal one for self-testing during the acquisition of images for self-testing. The graphics rendering capability refers to the visual information acquisition device 101's ability to render views for display on a screen. The database storage capability refers to the visual information acquisition device 101's ability to store acquired images and detection results, etc.

[0059] In some embodiments, the visual information acquisition device 101 also has the ability to connect to a network, which can support accessing the network and establishing a communication connection with the server 102. The server 102 can be used as a device to query when confirming with the visual information acquisition device 101 whether a self-test is required.

[0060] In some embodiments, when the visual information acquisition device 101 is applied to a specific scenario, it can access a network to establish a communication connection with the server 102, and the server 102 can be used as a backend service server in that specific scenario. For example, if the visual information acquisition device 101 is an electronic device used to collect users' biometric information (such as palm features), the visual information acquisition device 101 can set up a backend recognition service, and the server 102 can be used as a backend server for recognizing the user's identity corresponding to the biometric information.

[0061] The general process of the self-testing method for the visual information acquisition device provided in this application is as follows:

[0062] The device information acquisition device 101 can acquire visual images captured by the image acquisition component of the device on its information acquisition panel, and determine whether the device information acquisition device 101 needs to perform a self-test based on the visual images. If it is determined that the device information acquisition device 101 needs to perform a self-test, it acquires a mirror image of the information acquisition panel through the image acquisition component. This mirror image can be acquired based on a reflective device positioned opposite the information acquisition panel, as shown in Figure 1. Furthermore, the device information acquisition device 101 can detect each component it contains based on the mirror image to determine whether any component of the device information acquisition device 101 is abnormal. After acquiring the visual images, the device information acquisition device 101 can send a self-test request to the server 102, and upon receiving the response information returned by the server 102, determine that the visual information acquisition device meets the self-test conditions.

[0063] In one implementation, the aforementioned visual images, mirrored images, and other data can be stored in the blockchain, preventing tampering with this information. Blockchain is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Essentially, it is a decentralized database, a chain of data blocks linked using cryptographic methods. Each data block contains information about a batch of network transactions, used to verify the validity of the information (anti-counterfeiting) and generate the next block.

[0064] Specifically, visual information acquisition devices can use a blockchain client to encrypt visual images, mirror images, and other data using encryption algorithms to generate a data digest. This data digest, along with device identifiers, timestamps, and other information, is then packaged into transaction information and transmitted peer-to-peer to various nodes in the blockchain network. Each node verifies and confirms the transaction information according to a consensus mechanism. Once confirmed, the transaction is added to a data block in the blockchain, thus achieving data preservation and tamper-proofing.

[0065] It is understood that the self-testing system of the visual information acquisition device described in the embodiments of this application is for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and does not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the evolution of system architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0066] Based on the self-testing system of the aforementioned visual information acquisition device, please refer to Figure 3. Figure 3 is a flowchart illustrating a self-testing method for a visual information acquisition device provided in this embodiment of the application. This self-testing method can be implemented by the visual information acquisition device, which has an information acquisition panel and an image acquisition component. The visual information acquisition device can be the visual information acquisition device 101 shown in Figure 1. The self-testing method of this visual information acquisition device can include the following steps S301-S303, wherein:

[0067] S301. Acquire the visual image captured by the image acquisition component.

[0068] In this embodiment, the visual image, also known as a self-test flag, can refer to an image used to trigger a self-test by the visual information acquisition device. For example, it can be an image containing specific visual information, such as an image containing a specific pattern. The visual image can also carry identification information used to trigger the self-test process of the visual information acquisition device. The image acquisition component mounted on the information acquisition panel of the visual information acquisition device can be, for example, a camera, used to acquire visual images. The visual information acquisition device can then obtain the visual images acquired by the image acquisition component and further process the visual images to initiate the self-test process. Here, self-testing can also be called self-calibration, which refers to the process by which an electronic device detects itself and attempts to repair itself.

[0069] Please refer to Figure 4, which is a schematic diagram of a scenario for acquiring visual images provided by an embodiment of this application. As shown in Figure 4, based on specific business needs, the information acquisition panel of the visual information acquisition device can be printed with visual images, that is, the visual images are the appearance elements of the information acquisition panel. The visual information acquisition device can also output visual images through an information output component (such as a display screen). The visual image can be an image including specific patterns, such as custom random hash points, lines, and patterns. The visual image can also be an image including recognition information, such as a coded image, such as a QR code or barcode. This application does not limit this. Figure 4 illustrates and explains a visual image including random hash points and lines as an example.

[0070] In one possible implementation, the image acquisition component of the visual information acquisition device can acquire visual images based on a reflective device arranged parallel to and opposite to the information acquisition panel. Please refer to Figure 5, which is a schematic diagram of a visual image acquisition scenario provided in an embodiment of this application. As shown in Figure 5, a reflective device is arranged above the visual information acquisition device, parallel to and opposite to the information acquisition panel. The reflective device can be, for example, a mirror. Based on this reflective device, a virtual image corresponding to the visual information acquisition device (information acquisition panel) can be obtained, as shown by the reflected virtual image (dashed line) in Figure 5. Specifically, a virtual image corresponding to the visual image printed on the information panel or displayed on the screen can be acquired. Thus, the image acquisition component can acquire the virtual image of the visual image reflected by the reflective device, obtaining a visual image. Optionally, the visual information acquisition device can perform mirror processing on the acquired virtual image to obtain a visual image. Mirror processing refers to symmetrical processing of the acquired image, such as flipping it horizontally or vertically, so that the processed image meets the detection requirements.

[0071] Optionally, the visual information acquisition device can be located in a specific testing environment, such as a device specifically designed for self-testing of the visual information acquisition device, for example, a darkroom. The visual information acquisition device can be placed within this device to acquire visual images displayed on the device's internal screen or printed via an image acquisition component.

[0072] Furthermore, after the visual information acquisition device obtains the visual image captured by the image acquisition component, it can determine whether the visual information acquisition device needs to perform a self-test based on the visual image. Specifically, the visual information acquisition device can send a self-test request to the server based on the acquired visual image. This self-test request carries the identification information of the visual information acquisition device. After receiving the self-test request, the server can determine whether the visual information acquisition device has self-test permissions based on the identification information. For example, it can query a preset table of identification information with self-test permissions to see if the identification information is included. This identification information table can be configured by after-sales personnel, meaning that self-test permissions can be managed by after-sales personnel. Then, if the server determines that the visual information acquisition device has self-test permissions based on the identification information, it returns response information. This response information can be an indication that the visual information acquisition device has self-test permissions, or it can be a trigger that causes the visual information acquisition device to display a hidden self-test control. Self-inspection permission refers to the authorization of visual information acquisition equipment to perform self-inspection operations. Its management methods can be divided into after-sales management and user management. For example, after-sales personnel can manage it by configuring a table of identification information with self-inspection permission on the server, or the visual information acquisition equipment system information can indicate whether the user has self-inspection permission through flag bits.

[0073] Correspondingly, the visual information acquisition device can receive a response message from the server in response to the self-test request, and output self-test prompt information based on the response message. The self-test prompt information can be a prompt indicating to the user that the visual information acquisition device has self-test permissions, and can also prompt the user to input a self-test operation. This self-test prompt information can be displayed on the user interface of the visual information acquisition device's screen, or it can be an audio prompt output by the visual information acquisition device's speaker. Furthermore, the visual information acquisition device can receive self-test operations received by the user based on the self-test prompt information; for example, the user can input a confirmation operation in response to the prompt information in the user interface. Another example is that the user can find a self-test control that was previously hidden but is now displayed in the relevant settings interface, and input a self-test operation by targeting that self-test control. Subsequently, the visual information acquisition device can respond to the self-test operation and determine that the visual information acquisition device meets the self-test conditions.

[0074] Please refer to Figure 6, which is a schematic diagram of a self-test permission control method provided in an embodiment of this application. As shown in Figure 6, the self-test permission (self-calibration permission) control method 1 is after-sales management, and the self-test permission control method 2 is user management. In method 1, the visual information acquisition device can send a self-test request to the server so that the server can determine whether the visual information acquisition device has self-test permission. If it has self-test permission, the server will return response information. That is, whether the visual information acquisition device has self-test permission can be set by after-sales personnel, and the server can query based on the configuration of the after-sales personnel. In method 2, the visual information acquisition device can determine whether it has self-test permission. For example, the system information of the visual information acquisition device can have a flag indicating whether it has self-test permission. For example, it can be assumed that the visual information acquisition device has self-test permission, or it can be assumed that the visual information acquisition device does not have self-test permission. Users can also set whether the visual information acquisition device has self-test permission, etc. It can be seen that method 1 is more secure, while method 2 is less secure, but more convenient for users to perform self-tests.

[0075] It is understood that the above two methods are merely examples, and there may be other control methods with self-inspection permissions, such as user self-service switch control, etc. This application does not limit these methods.

[0076] It should be noted that before sending a self-test request to the server based on the acquired visual image, the visual information acquisition device can perform an inspection on the acquired visual image. Only when the visual image meets specific conditions will the operation of sending a self-test request to the server be executed.

[0077] In one implementation, the visual information acquisition device can perform recognition processing on the visual image. Only when the pattern included in the visual image matches a set reference pattern will it send a self-check request to the server. Specifically, the visual information acquisition device can extract pixel values ​​from a specific region within the visual image and match these pixel values ​​with the pixel values ​​of the set reference image. For example, it can determine whether the pixel values ​​in the specific region are the same as the pixel values ​​of corresponding pixels in the reference pattern, or whether the proportion of pixels with the same pixel values ​​in the specific region exceeds a preset threshold. If the determination is correct, the pattern included in the visual image is determined to match the set reference pattern; otherwise, it is determined that the pattern included in the visual image does not match the set reference pattern. The visual information acquisition device can also be configured with a neural network model for recognizing the set reference pattern. The visual image can be input into the neural network model to obtain the image region corresponding to the reference pattern in the visual image recognized by the neural network model, thereby determining whether the pattern included in the visual image matches the set reference pattern. If the visual image output by the neural network model does not output the image region corresponding to the reference pattern in the visual image, then it is determined that the pattern included in the visual image does not match the set reference pattern.

[0078] In another implementation, the visual information acquisition device can input a visual image into a pre-trained image classification model, allowing the model to classify the image and output a classification result. This result can indicate whether the image belongs to a specified category or not, and may include categories such as A, B, and C. If the classification result indicates the image belongs to a specified category, a self-check request is sent to the server; otherwise, no request is sent. The image classification model can be trained on custom patterns and textures using machine learning before classifying the image, enabling it to recognize patterns and textures.

[0079] Below, using the aforementioned image classification model as an example, we will introduce its training and classification process. Specifically, it can be divided into steps such as data preprocessing, feature extraction, classifier training, and pattern and texture recognition.

[0080] Data preprocessing can involve acquiring images with different patterns and textures as a training dataset, and cropping these images to a predefined size for uniformity. It can also perform denoising and grayscale conversion on these images to reduce irrelevant noise and complexity. Furthermore, users or electronic devices used for image annotation can annotate these images, assigning them specific categories, thus completing the data preprocessing and obtaining the training database.

[0081] Feature extraction can be performed on the preprocessed image based on a neural network structure capable of extracting image features. Such a neural network structure could be, for example, a Convolutional Neural Network (CNN) or a convolutional layer; this application does not limit this specific type. For ease of description, CNN is used as an example. It is a deep learning model that can learn and extract image features from each training image in the training dataset. For example, the formula for the convolution operation of CNN can be found in Equation 1: (F*K)(i,j)=∑∑F(a,b)K(ia,jb) Equation 1

[0082] In Formula 1, F is the input image, K is the convolution kernel, i and j are pixel positions in the image, and a and b are pixel positions of the convolution kernel. After convolution processing, the extracted image features of the training image are obtained, which can be vector representations of the image features.

[0083] Here, classifier training can refer to training a classifier for classification processing based on the vector representation of extracted image features, such as mathematical model-based classifiers: Support Vector Machine (SVM), logistic regression models, etc. This application uses SVM as an example for explanation. The formula for SVM is shown in Equation 2: min 1 / 2||w||^2+C∑ξ_i Equation 2

[0084] In Formula 2, w represents the normal vector of the classification hyperplane, and ||w||^2 is the square of the magnitude of the normal vector w, representing the complexity of the hyperplane. By minimizing it, we can find the simplest hyperplane for separating data (i.e., for classification). ξ_i represents the slack variable, used to handle inseparable data. When a data point is on the error side of the hyperplane (i.e., misclassified), the corresponding slack variable will be greater than 0, allowing these data points to have some "errors". C represents the penalty parameter, used to balance the simplicity of the hyperplane and the number of classification errors. A larger C value means a higher penalty for classification errors, while a smaller C value emphasizes the simplicity of the hyperplane. After training the classifier, we can obtain an image classification model for classification processing, thus completing the training.

[0085] Pattern and texture recognition refers to the process where, after training the classifier (i.e., obtaining an image classification model), a visual image is input into the pre-trained image classification model. The model automatically extracts image features from the visual image and performs classification processing based on the pre-trained classifier, such as the SVM mentioned above, to obtain the classification result, thereby completing the recognition of the custom pattern and texture. Then, based on the classification, it can determine whether to send a self-check request to the server.

[0086] In another implementation, the visual information acquisition device can process the recognition information in the visual image. This recognition information carries a trigger command, and the visual information acquisition device can trigger a self-test request to be sent to the server based on the recognized trigger command. This recognition information can be links or encoded images included in the visual image, such as QR codes or barcodes. For example, if the visual image includes a QR code carrying a trigger command, the visual information acquisition device can recognize the QR code and send a self-test request to the server based on the recognized trigger command. It should be noted that the above explanation uses three implementation methods as examples; other methods are also possible, and this application does not limit this. The visual information acquisition device can execute at least one of the above implementation methods.

[0087] Optionally, in a visual image acquisition method where the image acquisition component acquires the visual image based on a reflective device that is parallel and opposite to the information acquisition panel, such as the acquisition method shown in Figure 5, the visual information acquisition device can perform mirror processing on the visual image before processing it, such as the aforementioned recognition processing and input image classification model. That is, the virtual image in the acquired mirror can be mirrored, for example, by flipping it horizontally, and further recognition processing and input image classification model can be performed based on the mirrored visual image.

[0088] In one possible implementation, the visual information acquisition device responds to a self-test operation received based on self-test prompts, such as a user confirmation operation based on the self-test prompts. In determining whether the visual information acquisition device meets the self-test conditions, it can also confirm that the user intends to initiate the self-test. Specifically, the visual information acquisition device includes an illumination component. Responding to the received self-test operation, the visual information acquisition device controls the illumination component to illuminate according to a specified flashing frequency and a specified illumination color, and acquires a set of illumination images captured by the image acquisition component. Then, based on each illumination image in the illumination image set, it determines whether the illumination component is illuminated according to the specified flashing frequency and the specified illumination color. If it is, the visual information acquisition device is determined to meet the self-test conditions; otherwise, it is determined that the visual information acquisition device does not need to perform a self-test.

[0089] The visual information acquisition device can control the lighting components to illuminate at a specified flashing frequency and with a specified lighting color for a duration (e.g., 10 seconds, 20 seconds, etc.). During this process, the image acquisition component can continuously acquire an image stream to obtain a set of lighting images. Optionally, the visual information acquisition device can acquire the image stream acquired by the image acquisition component and filter the image stream, for example, removing blurry, unclear, or unprocessable images, and using the filtered images as lighting images in the lighting image set.

[0090] Specifically, the visual information acquisition device can perform recognition processing on each lighting image in the lighting image set to determine the image region corresponding to the lighting component in each lighting image. Then, based on the pixel values ​​within each image region in each lighting image, it determines the test flicker frequency and test lighting color of the lighting component. If the test flicker frequency matches a specified flicker frequency and the test lighting color matches a specified lighting color, the visual information acquisition device is deemed to have met the self-test conditions. The recognition processing can be based on a segmentation algorithm, such as inputting each lighting image into a model for segmentation processing to obtain the image region corresponding to the lighting component output by the model. The recognition processing can also be based on the shape of the lighting component, such as performing edge detection and Hough transform on each lighting image to identify the image region corresponding to the lighting component in each lighting image. Other methods of recognition processing are also possible, and this application does not limit them.

[0091] Below, we will take shape-based recognition of lighting components as an example to introduce the specific implementation method of the recognition process. Taking a circular lighting component as an example, such as a light ring, the circular recognition can be achieved based on edge detection and Hough transform, identifying the image region corresponding to the light ring. Specifically, the recognition process can be divided into steps such as data preprocessing, edge detection, Hough transform, and image recognition.

[0092] Data preprocessing refers to acquiring each illumination image from the illumination image set and further performing processes such as grayscale conversion and denoising. Grayscale conversion involves converting the information contained in the red, green, and blue channels of each illumination image (color image) into a single grayscale value, thus transforming the color image into a grayscale image. Denoising refers to eliminating noise in the image to improve the accuracy of subsequent edge detection.

[0093] Edge detection refers to the process of identifying object boundaries in an image, with the aim of identifying points in a digital image where brightness changes significantly. For example, the Canny edge detection algorithm can be used to find edge information in various lighting images to identify shapes. Taking the Canny edge detection algorithm as an example, it can be specifically explained in three steps: a. Noise Reduction: Smoothing is performed based on filters to eliminate noise in the image. This filter can be a Gaussian filter, a low-pass filter that reduces high-frequency noise in the image. b. Image Gradient Calculation: The gradient of an image represents the rate of change of pixel intensity. At edges, the rate of change of pixel intensity is usually high, so calculating the magnitude and direction of the image gradient can help locate edges. c. Non-Maximum Suppression: During edge detection, multiple response points may correspond to the same edge. The purpose of non-maximum suppression is to eliminate these redundant response points, retaining only the points with the maximum gradient value along the edge direction. d. Dual Thresholding and Edge Connection: The Canny algorithm uses two thresholds (a high threshold and a low threshold) to further filter and connect edge points. First, points with gradient values ​​higher than a high threshold can be identified as true edge points. Then, points with gradient values ​​lower than the high threshold but higher than the low threshold can be identified and connected to the true edge points to improve edge continuity. This completes edge detection, resulting in a binary image containing the main edges of the image. Subsequent steps, such as Hough transform, can then be used to identify and analyze circular features within the edges.

[0094] The Hough Transform refers to obtaining a set of shapes conforming to a specific shape by calculating the local maximum value of the accumulated results in a parameter space. Taking the shape of the lighting component as a circle as an example, this application can detect circles in an image based on the Hough Circle Transform. The Hough Circle Transform uses a voting mechanism to find the optimal center and radius parameters in the parameter space. This can be understood as follows: for each edge point detected by edge detection, according to the standard equation of a circle, all possible combinations of center and radius are traversed, and a vote is taken in the parameter space (i.e., the three-dimensional space of center coordinates and radius). Then, in the parameter space, the combination of center and radius that receives more votes is taken as the detected circle. The formula for the Hough Circle Transform is shown in Equation 3: (xa)^2 + (yb)^2 = r^2 Equation 3

[0095] In Formula 3, (a,b) are the coordinates of the circle's center, r is the radius, and (x,y) are points on the circle. Therefore, after the Hough circle transformation, circles can be detected in each illumination image.

[0096] In this context, pattern recognition refers to the ability to further filter out circles that meet specific requirements based on the center and radius obtained from the Hough transform. For example, specific thresholds can be set to filter out circles that meet the requirements, such as setting a range for the radius or a range for the position of the center, to filter out circles that do not meet the requirements.

[0097] Furthermore, after identifying the image region corresponding to the lighting component, the visual information acquisition device can determine the test flicker frequency and test lighting color of the lighting component based on the pixel values ​​of each pixel located within the image region in each lighting image. It is understood that the lighting images in the lighting image set can be arranged in chronological order. Specifically, the flicker frequency of the lighting component can be determined based on the image region corresponding to the lighting component in multiple lighting images, thus obtaining the test flicker frequency. Furthermore, the visual information acquisition device can determine the color of the lighting component based on the pixel values ​​of the region corresponding to the lighting image, thereby obtaining the test lighting color. Subsequently, the visual information acquisition device can compare the test flicker frequency with a specified flicker frequency. If the frequencies are consistent, or the error is within a preset error range, the test flicker frequency and the specified flicker frequency are determined to match. Moreover, the visual information acquisition device can compare the test lighting color with a specified lighting color. If the colors are consistent, or the chromaticity values ​​are within a preset error range, the test lighting color and the specified lighting color are determined to match. In the case of a match, the visual information acquisition device can determine that the visual information acquisition device meets the self-test conditions.

[0098] In some embodiments, the visual information acquisition device can first acquire lighting images through an image acquisition component, and then identify the image region corresponding to the lighting component based on the lighting images. Subsequently, it can control the lighting component to illuminate according to a specified flashing frequency and a specified lighting color, and acquire a set of lighting images. When processing each lighting image in the lighting image set later, no further identification processing is required. That is, identification processing is performed first, and subsequent identification processing is unnecessary.

[0099] Alternatively, if the visual information acquisition device does not acquire a visual image, further analysis can be performed simply by controlling the lighting components to illuminate, thereby triggering the self-test process.

[0100] It should be noted that since the lighting component is located on the information acquisition panel of the visual information acquisition device, the images acquired by the image acquisition device in the visual information acquisition device, such as the aforementioned visual images and the various lighting images in the lighting image set, can all be acquired based on a reflective device that is set parallel and opposite to the information acquisition panel. During this process, the user can hold the reflective device to allow the image acquisition component to acquire the corresponding image. Since controlling the lighting component to illuminate at a specified flashing frequency and specified lighting color may continue for a period of time, if the test flashing frequency matches the specified flashing frequency and the test lighting color matches the specified lighting color, and it is determined that the user has been holding the reflective device throughout this process, it can be confirmed that the user has agreed to enter the self-test, i.e., the user has confirmed entering the self-test.

[0101] S302. If the visual information acquisition device is determined to meet the self-test conditions based on the visual image acquired by the image acquisition component, then a mirror image of the information acquisition panel is acquired by the image acquisition component.

[0102] Self-inspection conditions refer to the conditions that a visual information acquisition device must meet to perform a self-inspection. These conditions are determined by comprehensively judging factors such as whether a specific visual image has been acquired, whether the device has self-inspection authority, and whether the lighting components are lit as specified.

[0103] In this embodiment, after confirming the self-test authorization and verifying it through the lighting component, it can be determined that the visual information acquisition device meets the self-test conditions. Then, the image acquisition component can acquire a mirror image of the information acquisition panel, which is the image acquired for self-testing. The mirror image can be acquired by the image acquisition component based on a reflective device positioned opposite the information acquisition panel, as shown in Figure 5. Therefore, the acquired image can be called a mirror image. Thus, the image acquisition component can acquire a mirror image including the components positioned on the information acquisition panel.

[0104] Optionally, the image acquisition component may include at least one image acquisition sub-component, which can acquire mirror images of the information acquisition panel respectively to obtain mirror images corresponding to each image acquisition sub-component.

[0105] S303. Based on the above-mentioned mirror image, the various components included in the above-mentioned visual information acquisition device are detected to determine whether the various components included in the above-mentioned visual information acquisition device are abnormal.

[0106] In this embodiment, the visual information acquisition device may include multiple components, such as an illumination component, a distance sensor component, and an image acquisition component. These components can be individually tested based on the acquired mirror image to determine if any abnormality has occurred. For example, the illumination component may exhibit inaccurate color (color cast). For colored illumination and infrared illumination components, uneven brightness or failure to illuminate (damaged light) may occur. The distance sensor component may experience inaccurate distance sensing, i.e., decreased sensing accuracy. The image acquisition component may exhibit decreased image quality or fail to acquire images. This embodiment uses the illumination component, distance sensor component, and image acquisition component as examples to illustrate the self-test process.

[0107] In one possible implementation, the lighting component of the visual information acquisition device includes multiple lighting units. The visual information acquisition device can control the lighting component to light up when a preset current value is applied. That is, the mirror image acquired by the image acquisition component includes the image of the lighting component under the applied preset current value. Furthermore, the visual information acquisition device can detect the multiple lighting units included in the lighting component based on the mirror image to obtain the detection result corresponding to the lighting component. Here, the lighting component can be understood as a light on the information acquisition panel, such as a light ring, and the lighting units can be understood as LED beads, light bulbs, etc., within the lighting component. Optionally, the image acquisition component can acquire multiple images when the lighting component is subjected to a preset current value, i.e., acquire an image stream, and then select one image from the image stream as the mirror image, for example, selecting the clearest image as the mirror image.

[0108] Specifically, the visual information acquisition device can first identify the image region corresponding to the lighting component from the mirror image. The specific implementation of identifying the image region corresponding to the lighting component can be found in the detailed description of step S301, and will not be repeated here. Further, based on the preset positional relationship between the lighting component and multiple lighting units, the image regions corresponding to each of the multiple lighting units can be determined from the mirror image, thus obtaining multiple lighting unit regions. For example, if the lighting component is a light ring and the lighting units are LED beads arranged in the lighting component at preset intervals, then multiple lighting unit regions can be determined in the image region corresponding to the lighting component based on the preset positional relationship between the lighting component and multiple lighting units. Then, the visual information acquisition device can calculate the average pixel value in each lighting unit region based on the pixel values ​​contained within each lighting unit region. The average pixel value is the average pixel value of each pixel point in the lighting unit region, which can be understood as the brightness value of the determined lighting unit. Finally, the visual information acquisition device can determine the detection results of each lighting unit contained in the lighting component based on the average pixel value in each lighting unit region, thus obtaining the detection results corresponding to the lighting component.

[0109] In the process of determining the detection results of each lighting unit, the visual information acquisition device can first obtain the ambient brightness of the detection environment in which the visual information acquisition device is located. That is, the visual information acquisition device can obtain the average pixel value of other image regions in the mirror image besides the areas of multiple lighting units, which can be calculated by averaging the pixel values ​​of each pixel in the other image regions of the mirror image. After determining the current ambient brightness (the average pixel value of other image regions), the device can obtain the reference brightness value of each lighting unit under a preset current value, i.e., the calibrated value, and then compare them to obtain the detection result. Specifically, if the average pixel value of other image regions matches the reference ambient brightness value, the visual information acquisition device can obtain the reference brightness value of each lighting unit under the preset current value. This reference brightness value is correlated with the reference ambient brightness value. Therefore, the visual information acquisition device can determine the brightness error of each lighting unit under the preset current value based on the average pixel value in each lighting unit region and the reference brightness value of each lighting unit under the preset current value, and determine the detection result of each lighting unit based on the brightness error of each lighting unit under the preset current value.

[0110] The reference ambient lightness value is a brightness value used to match the average pixel value of other image areas in the mirror image acquired by the visual information acquisition device, excluding the area of ​​the lighting unit or the area corresponding to the specified component. Based on this, the reference brightness value of the lighting unit under a preset current value can be obtained to determine the detection result of the lighting unit, or to determine the relevant parameters when the distance sensor component is detected.

[0111] Brightness Value Error (BVError) refers to the difference between the average pixel value within the illumination unit area and the reference brightness value of the illumination unit under a preset current value. It is used to determine whether the illumination unit is abnormal. If the brightness value error is within the preset error range, the illumination unit is not abnormal; otherwise, it is abnormal.

[0112] Specifically, matching the pixel mean of other image areas with the reference ambient brightness value means that the pixel mean corresponds to an ambient brightness value, and the difference between the corresponding ambient brightness value and the reference ambient brightness value is within a preset range. The reference ambient brightness value can be a single pixel value or a range of pixel values. If the pixel mean is the same as the reference ambient brightness value or falls within the pixel value range, the pixel mean of other image areas is determined to match the reference ambient brightness value. Furthermore, the reference brightness value of each lighting unit under the reference ambient brightness value and at a preset current value can be obtained. This reference brightness value can be a brightness value associated with both the reference ambient brightness value and the preset current value, and can be a calibrated pixel value. For example, a mapping relationship can be obtained: Lighting Unit 1 - Actual Ambient Brightness (REL) - Actual Brightness Value (RL) - Actual Electric Current (RE) for subsequent analysis.

[0113] Furthermore, the visual information acquisition device can determine the brightness error of each lighting unit at a preset current value based on the average pixel value within each lighting unit area and the reference brightness value of each lighting unit at the preset current value. This brightness error can be obtained, for example, by subtracting the average pixel value within each lighting unit area from the reference brightness value of each lighting unit at the preset current value. Subsequently, the visual information acquisition device can determine the detection result of each lighting unit based on the brightness error of each lighting unit at the preset current value. For example, if the brightness error of a certain lighting unit is within the preset error range, it is determined that the lighting unit is not abnormal; if the brightness error of the lighting unit is not within the preset error range, for example, if the brightness of the lighting unit is low after a period of use, it is determined that the lighting unit is abnormal, specifically low brightness.

[0114] In one possible implementation, the reference brightness value of each lighting unit under a preset current value can be pre-calibrated. For example, the current and brightness values ​​of the lighting components can be calibrated before the visual information acquisition device leaves the factory. This can be achieved by fixing the visual information acquisition device in a dark box, adjusting the current of the lighting units, and continuously acquiring images including the lighting components. The visual information acquisition device can be placed in a specific detection environment (detection device) to simulate different ambient brightness scenarios. Please refer to Figure 7, which is a schematic diagram of a detection device provided in an embodiment of this application. As shown in Figure 7, the detection device can be a dark box. Figure 7 is merely an example of the form of the dark box, and this application does not limit its scope. Optionally, the diameter of the dark box, as shown by the black portion in Figure 7, can be equal to the size of the visual information acquisition device, allowing the visual information acquisition device to be fixedly installed in the dark box. Optionally, the height of the dark box can be a fixed value, and it contains a reflective device, such as a mirror. When testing different components of the visual information acquisition device, the reflective device can also be replaced with other images, such as influence test cards (picture cards), etc., and this application does not limit this.

[0115] The darkroom supports multiple ambient brightness levels, such as pure dark, dim, normal, bright, and strong bright. The brightness (lumen) can be set based on the experience of technicians to cover various self-test scenarios. Furthermore, the visual information acquisition device can apply a preset current value to the lighting unit under different ambient brightness conditions, allowing the image acquisition component to capture images of the lighting unit when it is lit. This enables the calculation of a reference brightness value for the lighting unit under a reference ambient brightness value, where the preset current value is applied. Please also refer to Figure 8, which is a schematic diagram of a reference brightness value calibration for a lighting unit according to an embodiment of this application. As shown in Figure 8, which illustrates the lighting components with two different shapes, multiple lighting units are provided in the lighting component. After the reference ambient brightness value is calculated, the lighting unit is given a reference brightness value with a preset current value. The identification of the lighting unit, the ambient brightness value, the brightness value of the lighting unit, and the preset current value can be defined as a mapping relationship. For example, lighting unit 1 - ambient brightness (EL) - brightness value (L) - current value (Electric Current, E). This mapping relationship is then stored.

[0116] In some embodiments, the visual information acquisition device can determine a reference ambient brightness value of the current environment based on the acquired image. This can be specifically divided into steps such as image acquisition, image preprocessing, pixel brightness calculation, and ambient brightness calculation.

[0117] Among them, image acquisition refers to acquiring images of the current environment based on the image acquisition component. In order to improve the accuracy of brightness calculation, the image acquisition component can acquire environmental images at different exposure times and perform fusion processing on them to further calculate the reference environmental brightness.

[0118] Image preprocessing refers to preprocessing the acquired image (the image obtained through fusion processing), such as scaling, denoising, and grayscale conversion, to reduce the complexity of the image and improve the accuracy and efficiency of subsequent calculation of reference brightness values.

[0119] Calculating pixel brightness involves iterating through each pixel in the image and calculating its brightness value. The brightness value is typically an integer between 0 (black) and 255 (white). For grayscale images, the brightness value is the pixel value. For color images, it can be obtained by converting the pixel's R, G, and B components. R, G, and B represent the red, green, and blue components of the pixel, respectively. For example, a pure red pixel can be represented as (255, 0, 0), meaning the red component value is 255 (the maximum value), while the green and blue components are both 0. The conversion formula is shown in Formula 4: Y = 0.299 * R + 0.587 * G + 0.114 * B (Formula 4)

[0120] In Formula 4, Y represents the brightness value, and R, G, and B represent the red, green, and blue components of the pixel, respectively.

[0121] The calculation of ambient brightness refers to the fact that the pixel brightness obtained above is the brightness value of a single pixel in the image. A reference ambient brightness value can be calculated based on the brightness value of each pixel. For example, the average brightness of all pixels can be used as the reference ambient brightness value. This involves adding the brightness values ​​of all pixels and then dividing by the total number of pixels to obtain the average brightness value, which can then be used as an estimate of the ambient brightness. The calculation formula is shown in Formula 5: L=(ΣY) / N Formula 5

[0122] In Formula 5, L represents ambient brightness, Y represents pixel brightness value, and N represents the total number of pixels.

[0123] It should be noted that since the reference ambient brightness value can be obtained based on pixel value conversion, when the visual information acquisition device matches the average pixel value of other image areas with the reference ambient brightness value, it can convert the average pixel value into a brightness value before matching.

[0124] In some embodiments, to improve the accuracy of the reference brightness value calculation, a standard light source with a known brightness value can be used to calibrate the image acquisition component. That is, the image acquisition component can acquire a standard light source and calculate its brightness value, then compare it with the known brightness value to obtain a calibration coefficient. Furthermore, during calibration and actual measurement, this standard coefficient can be added during conversion, such as multiplying the actual measured ambient brightness by the calibration coefficient to obtain a more accurate ambient brightness value.

[0125] In one possible implementation, the preset current value can include multiple current values. The visual information acquisition device can then determine the detection result of each lighting unit at each current value based on the brightness value error corresponding to each lighting unit under the multiple current values ​​and a preset error threshold. Furthermore, based on the detection results of each lighting unit at each current value, the device can determine the detection result of each lighting unit. For example, the visual information acquisition device can determine whether the brightness value error corresponding to each lighting unit at each current value is within the preset error threshold. If not, the detection result at that current value is determined to be abnormal; otherwise, it is determined to be normal. Furthermore, for each lighting unit, the detection result at each current value can be determined. For example, if the proportion of abnormal detection results is greater than a preset proportion threshold, the detection result of that lighting unit is determined to be abnormal. Alternatively, if there are any abnormal detection results, the detection result of that lighting unit is determined to be abnormal. Other methods for determining the detection result are also possible, and this application does not limit this approach.

[0126] In some embodiments, the visual information acquisition device can control the lighting components to display a specified color, specifically controlling each lighting unit to illuminate according to a specified color, to detect whether the brightness of the lighting components is uniform. Furthermore, the visual information acquisition device can acquire a reference chromaticity value for each lighting unit corresponding to a reference ambient brightness value matching the current environment. Then, the visual information acquisition device can compare the reference chromaticity value with the chromaticity value of each lighting unit area, and determine the detection result of each lighting unit based on the chromaticity value error of each lighting unit.

[0127] It should be noted that an illumination unit can consist of multiple monochromatic illumination sub-units. For example, an LED can be composed of red, green, and blue monochromatic LEDs. The accuracy of the color displayed by the illumination unit depends on whether the brightness value of each illumination sub-unit is abnormal when an electric current is applied. That is, if it is determined based on the brightness value that each illumination sub-unit is normal, then it is determined that the illumination unit does not have a color cast problem. The visual information acquisition device can determine whether the illumination unit has a color cast by comparing the brightness value of the illumination sub-unit in the current environment with the corresponding reference brightness value of the current environment.

[0128] The above explanation uses a colored light source as an example to illustrate how to detect a lighting unit and obtain detection results. Lighting components can also be infrared light sources, such as infrared lamps. The visual information acquisition device can control the infrared lamps to illuminate at a preset current value and acquire images using an infrared camera in the image acquisition component, obtaining a mirror image. Based on the mirror image, the average pixel value within each lighting unit area is calculated, thus determining the measured brightness value of each lighting unit. When detecting infrared light sources, reference brightness values ​​corresponding to different ambient brightness scenarios at a preset current value can be pre-calibrated in a dark chamber and stored. During actual detection, the current ambient brightness is determined based on the acquired image, and the corresponding reference brightness value is obtained. This value can then be compared with the reference brightness value to obtain the detection results of each lighting unit, i.e., the detection results of the lighting component. For specific implementation methods, please refer to the specific implementation methods of the colored light source mentioned above; they will not be repeated here.

[0129] Furthermore, if the visual information acquisition device identifies a target lighting unit among multiple lighting units whose brightness value error exceeds a preset error threshold (i.e., the difference between the target lighting unit's RL and L is too large), calibration can be performed. For example, the visual information acquisition device can adjust the applied current value to the target lighting unit. Generally, the brightness of a lighting unit decreases after a period of use; therefore, increasing the applied current value can increase the brightness value of the target lighting unit. A dim target lighting unit indicates uneven brightness. Another example is that the visual information acquisition device can increase the exposure time of the image acquisition component to make the mirror image acquired by the image acquisition component brighter. Then, the visual information acquisition device can re-acquire the mirror image containing the target lighting unit and repeatedly compare it based on the re-acquired mirror image to determine additional detection results for the target lighting unit. Thus, the detection of the lighting component ends, achieving self-calibration of the lighting component.

[0130] In another possible implementation, the visual information acquisition device can detect the included distance sensor component. The acquired mirror image includes an image region corresponding to a designated component. This designated component can be, for example, the aforementioned illumination component, used as a reference point to calculate a reference distance. This reference distance is then compared with the distance value measured by the distance sensor component to obtain the detection result corresponding to that distance sensor component.

[0131] Specifically, first obtain the size information (e.g., the pixel values ​​of width and height) of the image region corresponding to the specified component (such as the lighting component) in the mirrored image, denoted as U. d Simultaneously, the shooting focal length F of the image acquisition component is obtained. The shooting focal length can be determined by acquiring objects of known distance and size using the image acquisition component, and then using the formula... The calculation yields (where D is the known distance between the object and the camera, W is the actual size of the object, and P is the pixel size of the object in the image). The actual size R of the specified component is known. d According to the formula W = R d P = U d By substituting the values, the reference distance D between the specified component and the image acquisition component can be calculated.

[0132] Please also refer to Figure 9, which is a detection schematic diagram of a detection distance sensor component provided in an embodiment of this application. As shown in Figure 9, the image acquisition component of the visual information acquisition device can acquire a mirror image based on a reflective device that is parallel to and opposite to the information acquisition panel, and calculate the distance between the specified component and the visual information acquisition device, i.e., the actual distance, based on the mirror image. Subsequent comparisons can then be made based on this actual distance.

[0133] Specifically, the visual acquisition component can acquire the average pixel value of other image regions in the mirrored image, excluding the image region corresponding to the specified component. If the average pixel value of other image regions matches the reference ambient brightness value, then the acquisition parameters of the image acquisition component and the size information of the specified component are acquired. It should be noted that calculating the average pixel value of other regions and matching it with the reference ambient brightness value is to determine the environment in which the visual acquisition component is located. For example, if the brightness value of the darkroom is a specific ambient brightness value, and a match is found, the fixed height of the darkroom, i.e., a fixed distance, can be acquired. If a match is found with a reference ambient brightness value (which can be understood as an ambient brightness value other than the specific ambient brightness value), then the distance value needs to be calculated.

[0134] Specific Ambient Lightness Value (SALV) refers to a specific brightness value, such as the brightness value corresponding to a darkroom environment. Visual information acquisition devices determine whether to perform corresponding detection on the distance sensor component or image acquisition component by judging whether the average pixel value of a specific area in the mirror image matches this value.

[0135] In this scenario, when the visual information acquisition device determines that a distance value needs to be calculated, it can obtain the acquisition parameters of the image acquisition component and the size information of a specified component. The specified component can be, for example, an illumination component, and the acquisition parameters can be the shooting focal length of the image acquisition component. This shooting focal length can be calculated by acquiring an object of known distance and size using the image acquisition component. The formula for calculating the shooting focal length is shown in Formula 6: F=(P×D) / W Formula 6

[0136] In Formula 6, F represents the shooting focal length, P represents the pixel size of the object in the image, which can be obtained based on recognition processing, D represents the known distance between the object and the camera, and W represents the actual size of the object.

[0137] After determining the shooting focal length, a first reference distance value can be determined based on the acquisition parameters, the size information of the specified component, and the size information of the image area corresponding to the specified component. Specifically, the size of the specified component can be, for example, the actual size Rd of the lighting component, and the size information of the image area corresponding to the specified component can refer to the pixel size Ud of the image area of ​​the lighting component in the mirror image, that is, the width and height dimensions in the mirror image, which can be obtained based on recognition processing. Furthermore, the first reference distance value can be calculated based on the acquisition parameters, the size information of the specified component, and the size information of the image area corresponding to the specified component. The specific calculation method is shown in Formula 7: D=(W×F) / P Formula 7

[0138] In Formula 7, D represents the distance between the specified component and the image acquisition component, W represents the actual size of the specified component, F represents the acquisition parameter (shooting focal length) which can be calculated by Formula 6, and P represents the pixel size of the specified component in the mirror image.

[0139] Furthermore, the visual information acquisition device can acquire the first test distance value measured by the distance sensor component, and determine the corresponding detection result of the distance sensor component based on the distance error between the first test distance value and the first reference distance value. This detection result can determine whether the distance sensor component is malfunctioning based on whether the distance error is within a preset error range. If it is, the detection result indicates that the distance sensor component is normal; otherwise, if it is not, the detection result indicates that the distance sensor component is malfunctioning.

[0140] Distance Value Error (DVO) refers to the difference between the test distance value measured by the distance sensor component and the reference distance value calculated based on relevant parameters. It is used to determine whether the distance sensor component is malfunctioning. If the distance value error is within the preset error range, the distance sensor component is normal; otherwise, it is malfunctioning.

[0141] In some embodiments, when the visual information acquisition device determines that the average pixel value of other image areas matches a specific brightness value, it can determine that the visual information acquisition device is in a dark box. It can then acquire a second reference distance value corresponding to the specific ambient brightness value, i.e., acquire the fixed height (fixed distance value) of the dark box, and acquire a second test distance value measured by the distance sensor component. Furthermore, based on the distance error between the second test distance value and the second reference distance value, the detection result corresponding to the distance sensor component is determined. This detection result can also determine whether the distance sensor component is abnormal based on whether the distance error is within a preset error range, i.e., to calibrate the accuracy of the distance sensor. Thus, the detection of the distance sensor component ends.

[0142] Alternatively, the aforementioned specified component may also be a special mark set on the information acquisition panel, as long as the size information is known, and this application does not limit this.

[0143] In another possible implementation, the visual information acquisition device can inspect the image acquisition component. This inspection, also known as image effect detection or image quality self-detection, refers to detecting whether the image acquisition component has experienced image quality degradation or damage based on a mirrored image. The image acquisition component may experience quality degradation and damage. It's important to note that when acquiring a mirrored image based on the image acquisition component, if the visual information acquisition device determines that it cannot acquire a mirrored image, it can directly determine that the image acquisition component is abnormal, i.e., damaged. If a mirrored image is acquired, further inspection of the image acquisition component can be performed based on that image. However, during the inspection of the image acquisition component, image quality detection may be significantly affected by ambient light intensity. Therefore, inspection can only be performed in specific environments, such as a darkroom environment. Thus, the visual information acquisition device can obtain the average pixel value of all image regions in the mirrored image except for the area corresponding to the lighting component, and determine whether the average pixel value matches the specific ambient brightness value. If they match, it indicates a darkroom environment, and further inspection can be performed; if they do not match, the image acquisition component is not verified.

[0144] Specifically, the visual information acquisition device can identify a reference test pattern in the mirrored image. This reference test pattern, also known as a chart, can be a pattern including special textures and colors, such as a standard color chart and texture chart, like a standard image quality inspection chart. The reference test pattern can be printed on the information acquisition panel or displayed on the screen. Please refer to Figure 10, which is a detection schematic diagram of an image acquisition component provided in this embodiment. As shown in Figure 10, the image acquisition component of the visual information acquisition device can acquire a mirrored image based on a reflective device parallel and opposite to the information acquisition panel. This mirrored image can include the reference test pattern. After the visual information acquisition device identifies the reference test pattern, it can detect the image acquisition component based on the reference test pattern to obtain the corresponding detection result. Optionally, after obtaining the detection result, the image signal processor (ISP) parameters of the image acquisition component can be adjusted, such as white balance and exposure control parameters, to attempt recovery.

[0145] It should be noted that, in the embodiments of this application, during the process of detecting each component based on a mirror image, the visual information acquisition device can first perform mirror processing on the mirror image, such as symmetry processing, and then detect each component based on the mirror image after mirror processing. Since the acquired mirror image may be reversed from the actual situation, in order to facilitate subsequent detection and comparison with standard situations, the mirror image is mirrored, such as by performing symmetry processing along the horizontal or vertical direction, so that the processed mirror image meets the detection requirements.

[0146] Specifically, when detecting the image acquisition component, the visual attribute information of the image acquisition component can be determined based on the pixel values ​​of each pixel within a specified area in the reference test pattern, and the detection result corresponding to the image acquisition component can be determined based on the visual attribute information. The specified area can be an image region containing a specified color. The visual attribute information can include chroma indices, brightness indices, and contrast indices, etc. Please refer to Figure 11, which is a schematic diagram of a reference test pattern provided in an embodiment of this application. As shown in Figure 11, the reference test pattern includes black and white squares. During the process of determining the visual attribute information, the specified area of ​​the information acquisition device can be either a black or white image region. Understandably, a visual information acquisition device can determine the grayscale value (read_graylevel) of a specified area based on the position information of the specified area, such as the position of the black grid in the reference test pattern. This grayscale value can be compared with the reference grayscale value (grayscale mean) to obtain the detection result corresponding to the brightness index. For example, the difference between the acquired grayscale value and the reference grayscale value can be used as the detection result.

[0147] The designated area can also be an image region of other colors, or it can be an image region containing only one color, such as the squares of a specific color shown in Figure 11. The visual information acquisition device can determine the chromaticity value of the designated area based on the pixel values ​​within that area. For example, it can identify color parameters and compare them with reference chromaticity values ​​to obtain the detection result corresponding to the chromaticity index. Alternatively, the difference between the acquired chromaticity value and the reference chromaticity value can be used as the detection result. The chromaticity index is a type of visual attribute information, obtained by comparing the chromaticity value determined by the pixel values ​​within the designated color area of ​​the reference test pattern with the reference chromaticity value. It is used to reflect the accuracy of the image acquisition component in color reproduction.

[0148] The visual information acquisition device can obtain the brightness differences between different regions in an image to obtain the contrast ratio. For example, if the specified region includes white squares and black squares, the average or median grayscale value (which can be determined based on pixel values) of the white and black squares can be calculated to obtain the average or median value of the two. Then, the ratio of this average or median value is taken to obtain the grayscale ratio, which is the contrast ratio. Similarly, the measured contrast ratio is compared with a reference contrast ratio to obtain the detection result corresponding to the contrast ratio index. For example, the difference between the obtained contrast ratio and the reference contrast ratio is used as the detection result. The contrast ratio is a type of visual attribute information. It is obtained by acquiring the brightness differences between different regions in an image (such as white and black squares in a reference test pattern) and comparing it with a reference contrast ratio. It is used to reflect the image acquisition component's ability to reproduce the brightness contrast of different regions.

[0149] Specifically, when detecting the image acquisition component, the visual information acquisition device can perform edge detection on the reference test pattern to determine the image region corresponding to a specific shape in the reference test pattern. Based on the pixel values ​​within the image region corresponding to the specific shape, the device determines the image quality attribute information of the image acquisition component, and then determines the detection result based on this image quality attribute information. The image quality attribute information refers to information reflecting the performance of the image acquisition component, determined by edge detection on the reference test pattern based on the pixel values ​​within the image region corresponding to the specific shape. This information includes deformation / distortion indices, field of view indices, sharpness indices, and signal-to-noise ratio indices. In the process of detecting deformation indices based on the reference test pattern shown in Figure 11, the specific shape can refer to the shape of a grid. After detecting the grid, the visual information acquisition device can determine the number of grids in each row and column. Optionally, after edge detection, the color of the grid can be determined based on the pixel values ​​within the image region corresponding to the specific shape to improve the accuracy of determining the number of grids.

[0150] The Distortion Index is an image quality attribute. It is calculated by performing edge detection on a reference test pattern to determine the image region corresponding to a specific shape (such as a square), and comparing the number of detected squares with the number of reference squares. It reflects whether shape distortion occurs when the image acquisition component captures the image. The Field of View Index (FOV) is another image quality attribute. It is calculated by performing edge detection on a reference test pattern to determine the image region corresponding to a specific shape (such as a marker point in the reference test image), and calculating the field of view angle. It reflects the visible range of the image acquisition component. The Sharpness Index is also an image quality attribute. It is calculated by performing edge detection on a reference test pattern to determine the image region corresponding to a specific shape (such as a region containing sharp edges), and calculating the Spatial Frequency Response (SFR) based on the pixel values ​​of that region. It reflects the sharpness of the image captured by the image acquisition component. The signal-to-interference-plus-noise ratio (SNR) is a type of image quality attribute. It is calculated by performing edge detection on a reference test pattern to determine the image region corresponding to a specific shape, obtaining the mean and standard deviation of pixels in that region, and then calculating the SNR to reflect the signal quality of the image acquired by the image acquisition component.

[0151] Furthermore, the number of detected squares can be compared with the number of reference squares in each row and column to obtain the detection result corresponding to the deformation index. For example, the detection result may include a difference in the number of squares in a specific row or column. It should be noted that each point of the detected edge square can be called a corresponding point, and each point of the corresponding reference square can be called a standard point. The number of points that can be matched between the corresponding points and the standard points can be determined based on the number of squares. Optionally, to reduce the computational complexity of the detection, the width or height can be used as the criterion, that is, only the number of squares in each row or column is compared.

[0152] The specific shape can be a marker point (also called a chart calibration point) in the reference test image, such as the five marker points (five gray squares) shown in Figure 11. Optionally, after edge detection, the marker points in the reference test image can be further determined based on the pixel values ​​of each pixel within the image region corresponding to the specific shape. For example, the pixel values ​​of the five marker points can be compared with the pixel values ​​of preset marker points to improve the accuracy of the determined marker points. The visual information acquisition device can calculate the field of view and use the field of view as the detection result corresponding to the field of view index. For example, the visual information acquisition device can calculate the field of view based on the difference in the abscissa of the center coordinates of the left and right marker points, combined with the parameters of the image acquisition component, the actual distance between the image acquisition component and the reference test pattern in the mirror (i.e., the distance between the image acquisition component and the reference test pattern in the mirror), and relevant mathematical theorems. Specifically, the field of view can be calculated based on the difference in x-coordinates of the left and right marker points as Δx (in pixels), the actual distance between the left and right marker points, the shooting focal length, and other parameters.

[0153] The specific shape can be a region containing sharp edges, such as the diagonal edge from black to white in Figure 11. Then, based on the pixel values ​​of the image region with the specific shape, the Spatial Frequency Response (SFR) can be calculated, and the SFR can be used as the detection result corresponding to the sharpness index. Specifically, the SFR can be obtained through steps such as calculating the pixel center, linear fitting, obtaining the Edge Spread Function (ESF), oversampling the ESF, calculating the Line Spread Function (LSF), applying a Hamming window, and using the Discrete Fourier Transform.

[0154] The visual information acquisition device can determine the signal-to-noise ratio (SNR) based on the pixel values ​​within an image region corresponding to a specific shape, and use the SNR as the detection result corresponding to the SNR index. Specifically, the visual information acquisition device can obtain the pixel mean and standard deviation of a specific shape. The SNR is then determined based on the obtained pixel mean and standard deviation, as shown in Formula 8: SNR = 20 * log10(mean / (stddev + 0.000001)) Formula 8

[0155] In Formula 8, SNR represents the signal-to-noise ratio, mean represents the average pixel value for a specific shape, and stddev represents the standard deviation of pixel values ​​for a specific shape. Thus, the detection results corresponding to the deformation index, field of view index, sharpness index, and signal-to-noise ratio index are obtained respectively.

[0156] Specifically, the image acquisition component of the visual information acquisition device may include multiple image acquisition sub-components, such as a color camera and an infrared camera. During the process of acquiring mirror images, the image acquisition component can acquire mirror images separately through each image acquisition sub-component, resulting in a set of mirror images. Each image acquisition sub-component corresponds to its own acquired mirror images. When inspecting the image acquisition component, the visual information acquisition device can identify and process the reference test patterns included in each mirror image in the mirror image set, determine the position information of the reference marker points in each mirror image, determine the alignment parameters of the image acquisition component based on the position information of the reference marker points in each mirror image, and determine the corresponding detection result of the image acquisition component based on the alignment parameters.

[0157] Alignment parameters are parameters determined based on the positional information of reference markers in each mirror image. For example, they can be the difference between the x and y coordinates of a specific marker in each mirror image. This parameter can be used to determine the alignment status of each image acquisition sub-component within the image acquisition component. The reference marker can be, for example, a specific marker in a reference test pattern, such as the center point among the five markers shown in Figure 11. The positional information of the reference marker in each mirror image can represent the positional information of the specific marker in the mirror images acquired by each image acquisition sub-component, such as a color camera and an infrared camera. The alignment parameter can be, for example, the difference between the x and y coordinates of a specific marker in each mirror image. When the image acquisition component includes a color camera and an infrared camera, this alignment parameter can also be called the color-infrared alignment (color_ir_align) parameter. Furthermore, the visual information acquisition device can obtain the reference alignment parameters of each image acquisition sub-component under aligned conditions, and based on the reference alignment parameters and the alignment parameters, determine the detection result corresponding to the alignment index, thus obtaining the detection result corresponding to the image acquisition component. For example, if the error between the alignment parameter and the reference alignment parameter is within a preset range, each image acquisition sub-component can be determined to be aligned; otherwise, it is considered misaligned. A reference marking point is a specific marking point in a reference test pattern, such as a point used to determine the alignment parameters of the image acquisition component. By determining its position in each mirror image, the alignment status of each image acquisition sub-component within the image acquisition component can be judged. The alignment index is an index determined based on the comparison between the alignment parameter of the image acquisition component and the reference alignment parameter. It is used to determine whether each image acquisition sub-component in the image acquisition component is aligned. If the error between the alignment parameter and the reference alignment parameter is within a preset range, each image acquisition sub-component is considered aligned; otherwise, it is considered misaligned.

[0158] Furthermore, after obtaining the detection results for the lighting component, the distance sensor component, and the image acquisition component, the detection process concludes. The visual information acquisition device can then generate a detection report based on these results and send the report to the server. After-sales personnel can then use the report to determine if any components in the visual information acquisition device have issues, providing further product support.

[0159] Please also refer to Figure 12, which is a timing diagram of a self-calibration process provided in an embodiment of this application. As shown in Figure 12, firstly, the visual information acquisition device can trigger the self-calibration process based on the acquired visual image. Specifically, the user can set a reflective device at a position parallel to the information acquisition panel, and the image acquisition component of the visual information acquisition device can acquire the visual image based on the reflective device.

[0160] Furthermore, the visual information acquisition device can recognize visual images, such as patterns (self-test marks). These patterns can be those set on the information acquisition panel; if the lighting component is set on the information acquisition panel, the pattern including the lighting component can be detected. The visual information recognition device can also input visual images into an image classification model or process the recognition information they contain. Furthermore, the visual information recognition device can determine whether self-test permissions exist, for example, by sending a self-test request to the server. If self-test permissions are granted, the server issues a response message. If self-test permissions are not granted, the server may either not perform any processing or issue an indication that self-test permissions are not granted. Furthermore, the visual information acquisition device can control the lighting component to illuminate according to a specified flashing frequency and a specified lighting color, and detect whether the flashing marks and colors match. If a match is found, the user's willingness to enter the self-test can be confirmed, thus initiating the specific testing process.

[0161] Furthermore, the visual information acquisition device can separately detect its included illumination component, distance sensor component, and image acquisition component, obtaining detection results for each component. The device can also perform calibration attempts, specifically adjusting the current applied to the illumination unit in the illumination component and adjusting the acquisition parameters of the image acquisition component, such as adjusting the exposure time. Finally, the visual information acquisition device can generate a detection report based on the detection results of each component and send the report to a server.

[0162] In some embodiments, during the verification of the lighting component, the visual information acquisition device can control the lighting component to light up when a preset current value is applied, and acquire images of the lighting component through other electronic devices with image acquisition functions, such as capturing image streams or video streams, and then upload the acquired images to the server. The server can then perform detection based on the images acquired by other electronic devices.

[0163] In some embodiments, the visual information acquisition device can upload the acquired mirror images (a set of mirror images) to a server during the detection of its various components. The server then performs the specific detection process, such as using an algorithm deployed on a cloud server. This reduces the power consumption of the visual information acquisition device.

[0164] In some embodiments of this application, the visual information acquisition device includes an information acquisition panel with an image acquisition component. The device acquires visual images captured by the image acquisition component and determines whether it needs self-testing based on these images. If self-testing is required, the image acquisition component captures a mirror image of the information acquisition panel, and then the device detects each component based on this mirror image to determine if any component is abnormal. Therefore, the visual information acquisition device can determine whether self-testing is needed based on the acquired visual images, and it can also perform self-testing based on the acquired mirror image. This eliminates the need for after-sales personnel to inspect and repair the device, allowing for rapid location of faults in its components. This improves the efficiency of calibration and repair, and also enhances the device's usability.

[0165] The methods of the embodiments of this application have been described in detail above. In order to facilitate better implementation of the above solutions of the embodiments of this application, the apparatus of the embodiments of this application is provided below.

[0166] Please refer to Figure 13, which is a schematic diagram of the structure of a self-testing device for a visual information acquisition device according to an embodiment of this application. This self-testing device 130 can be used to execute the corresponding steps in the self-testing methods for the visual information acquisition device shown in Figures 2 and 10. The self-testing device 130 includes the following units:

[0167] The acquisition unit 1301 is used to acquire the visual image acquired by the image acquisition component;

[0168] The acquisition unit 1302 is used to acquire a mirror image of the information acquisition panel through the image acquisition component when it is determined that the visual information acquisition device meets the self-test conditions based on the visual image acquired by the image acquisition component.

[0169] The detection unit 1303 is used to detect each component included in the visual information acquisition device based on the mirror image, so as to determine whether each component included in the visual information acquisition device is abnormal.

[0170] In one possible implementation, the self-testing device 130 of the visual information acquisition device further includes:

[0171] The sending unit 1304 is used to send a self-test request to the server based on the acquired visual image, wherein the self-test request carries the identification information of the visual information acquisition device;

[0172] The receiving unit 1305 is used to receive the response message returned by the server in response to the self-test request, and output self-test prompt information based on the response message. The response message is sent by the server when it determines that the visual information acquisition device has self-test authority based on the identification information.

[0173] The determining unit 1306 is used to determine, in response to a self-test operation received based on the self-test prompt information, that the visual information acquisition device meets the self-test conditions.

[0174] In one possible implementation, the sending unit 1304 is configured to send a self-test request to the server based on the acquired visual image, specifically for at least one of the following:

[0175] The visual image is processed for recognition, and if the pattern in the visual image is found to match a set reference pattern, a self-test request is sent to the server.

[0176] The visual image is input into a pre-trained image classification model. If the classification result output by the image classification model indicates that the visual image belongs to a specified category, the self-test request is sent to the server.

[0177] The trigger command recognition information carried in the visual image is recognized and processed, so that the self-test request is sent to the server based on the recognized trigger command.

[0178] In one possible implementation, the visual information acquisition device includes an illumination component; the determining unit 1306 is configured to determine, in response to a self-test operation received based on the self-test prompt information, that the visual information acquisition device meets the self-test conditions, specifically for:

[0179] In response to a self-test operation received based on the self-test prompt information, the lighting component is controlled to light up according to a specified flashing frequency and a specified lighting color, and the set of lighting images acquired by the image acquisition component is obtained.

[0180] Each lighting image in the lighting image set is identified to determine the image region corresponding to the lighting component in each lighting image;

[0181] Based on the pixel values ​​located within the image area in each lighting image, the test flicker frequency and test lighting color of the lighting component are determined;

[0182] If the test flashing frequency matches the specified flashing frequency and the test lighting color matches the specified lighting color, then the visual information acquisition device is determined to meet the self-test conditions.

[0183] In one possible implementation, the visual information acquisition device includes an illumination component comprising multiple illumination units; the acquisition unit 1302 is configured to acquire a mirror image of the information acquisition panel via the image acquisition component, specifically for:

[0184] The lighting component is controlled to light up when a preset current value is applied, and the mirror image captured by the image acquisition component is obtained.

[0185] The detection unit 1303 is used to detect the various components included in the visual information acquisition device based on the mirror image, specifically for:

[0186] The multiple lighting units contained in the lighting component are detected based on the mirror image to obtain the detection result corresponding to the lighting component.

[0187] In one possible implementation, the detection unit 1303 is configured to detect multiple lighting units included in the lighting component based on the mirror image, and obtain a detection result corresponding to the lighting component, specifically for:

[0188] Based on the preset positional relationship between the lighting component and the plurality of lighting units, the image regions corresponding to the plurality of lighting units are determined from the mirror image to obtain the plurality of lighting unit regions;

[0189] Calculate the average pixel value within each lighting unit area based on the pixel values ​​contained in each lighting unit area;

[0190] The detection results of each lighting unit included in the lighting assembly are determined based on the average pixel value within each lighting unit area.

[0191] In one possible implementation, the detection unit 1303 is configured to determine the detection result of each lighting unit included in the lighting component based on the average pixel value within each lighting unit area, specifically for:

[0192] Obtain the average pixel value of other image regions in the mirrored image besides the multiple lighting unit regions;

[0193] If the average pixel value of the other image regions matches the reference ambient brightness value, then the reference brightness value of each illumination unit under the preset current value is obtained, and the reference brightness value is associated with the reference ambient brightness value.

[0194] Based on the average pixel value within each lighting unit area and the reference brightness value of each lighting unit under the preset current value, the brightness value error of each lighting unit under the preset current value is determined.

[0195] The detection results of each lighting unit are determined based on the brightness error of each lighting unit under the preset current value.

[0196] In one possible implementation, the preset current value includes multiple current values; the detection unit 1303 is used to determine the detection result of each lighting unit based on the brightness value error of each lighting unit under the preset current value, specifically for:

[0197] Based on the brightness value error of each lighting unit under the multiple current values ​​and the preset error threshold, the detection result of each lighting unit under each current value is determined.

[0198] The detection results of each lighting unit are determined based on the detection results of each lighting unit at each current value.

[0199] In one possible implementation, the self-testing device 130 of the visual information acquisition device further includes: a processing unit, configured to, if there is a target lighting unit among the plurality of lighting units whose corresponding brightness value error is greater than a preset error threshold, adjust the current value applied to the target lighting unit, or increase the exposure time of the image acquisition component and re-acquire a mirror image containing the target lighting unit.

[0200] In one possible implementation, the visual information acquisition device includes a distance sensor component, and the mirrored image includes an image region corresponding to a specified component; the detection unit 1303 is used to detect each component included in the visual information acquisition device based on the mirrored image, specifically for:

[0201] Obtain the average pixel value of other image regions in the mirrored image, excluding the image region corresponding to the specified component;

[0202] If the average pixel value of the other image regions matches the reference ambient brightness value, then the acquisition parameters of the image acquisition component and the size information of the specified component are obtained.

[0203] A first reference distance value is determined based on the acquisition parameters, the size information of the specified component, and the size information of the image region corresponding to the specified component;

[0204] The first test distance value measured by the distance sensor component is obtained, and the detection result corresponding to the distance sensor component is determined based on the distance error between the first test distance value and the first reference distance value.

[0205] In one possible implementation, the acquisition unit 1301 is further configured to: acquire a second reference distance value corresponding to the specific ambient brightness value if the pixel mean value of the other image regions matches the specific ambient brightness value; acquire a second test distance value measured by the distance sensor component; and determine the detection result corresponding to the distance sensor component based on the distance error between the second test distance value and the second reference distance value.

[0206] In one possible implementation, the visual information acquisition device includes an illumination component; the detection unit 1303 is configured to detect the various components included in the visual information acquisition device based on the mirrored image, specifically for:

[0207] Obtain the average pixel value of other image regions in the mirrored image, excluding the image region corresponding to the lighting component;

[0208] If the average pixel value of the other image regions matches the specific ambient brightness value, then the reference test pattern in the mirror image is identified, and the image acquisition component is detected according to the reference test pattern to obtain the detection result corresponding to the image acquisition component.

[0209] In one possible implementation, the detection unit 1303 is configured to detect the image acquisition component according to the reference test pattern and obtain a detection result corresponding to the image acquisition component, specifically for:

[0210] Based on the pixel values ​​of each pixel within a specified area in the reference test pattern, the visual attribute information of the image acquisition component is determined, wherein the specified area is an image area containing a specified color.

[0211] Based on the visual attribute information, the detection result corresponding to the image acquisition component is determined.

[0212] In one possible implementation, the detection unit 1303 is configured to detect the image acquisition component according to the reference test pattern and obtain a detection result corresponding to the image acquisition component, specifically for:

[0213] Edge detection is performed on the reference test pattern to determine the image region corresponding to a specific shape in the reference test pattern;

[0214] Based on the pixel values ​​within the image region corresponding to the specific shape, the image quality attribute information of the image acquisition component is determined;

[0215] Based on the image quality attribute information, the detection result corresponding to the image acquisition component is determined.

[0216] In one possible implementation, the image acquisition component includes multiple image acquisition sub-components; the detection unit 1303 is used to acquire a mirror image of the information acquisition panel through the image acquisition component, specifically for:

[0217] Obtain the mirror images acquired by each of the image acquisition sub-components to obtain a set of mirror images;

[0218] The step of detecting the image acquisition component according to the reference test pattern to obtain the detection result corresponding to the image acquisition component includes:

[0219] The reference test pattern included in each mirror image in the mirror image set is identified and processed to determine the position information of the reference marker points in each mirror image.

[0220] Based on the position information of the reference markers in each of the mirror images, the alignment parameters of the image acquisition component are determined, and based on the alignment parameters, the detection result corresponding to the image acquisition component is determined.

[0221] According to one embodiment of this application, the steps involved in the method shown in FIG3 can be performed by various units in the self-testing device of the visual information acquisition device shown in FIG13. For example, step S301 shown in FIG3 is performed by the acquisition unit 1301 shown in FIG13, step S302 is performed by the acquisition unit 1302 shown in FIG13, and step S303 is performed by the detection unit 1303 shown in FIG13.

[0222] According to one embodiment of this application, the units in the self-testing device 130 of the visual information acquisition device shown in FIG13 can be individually or entirely merged into one or more other units, or some of the units can be further divided into multiple functionally smaller units. This can achieve the same operation without affecting the technical effect of the embodiment of this application. The above-mentioned units are based on logical function division. In practical applications, the function of one unit can also be implemented by multiple units, or the function of multiple units can be implemented by one unit. In other embodiments of this application, the self-testing device 130 of the visual information acquisition device may also include other units. In practical applications, these functions can also be implemented with the assistance of other units, and can be implemented by multiple units working together. According to another embodiment of this application, the self-testing device 130 of the visual information acquisition device shown in FIG13, and the self-testing method of the visual information acquisition device according to the embodiments of this application, can be constructed by running a computer program (including program code) capable of performing the steps involved in the corresponding method shown in FIG3 on a general-purpose computing device including processing elements and storage elements such as a central processing unit (CPU), random access storage medium (RAM), and read-only storage medium (ROM). The computer program can be recorded on, for example, a computer-readable storage medium, and loaded into the visual information acquisition device 101 of the self-testing system of the visual information acquisition device shown in FIG1 via the computer-readable storage medium, and run therein.

[0223] Based on the description of the self-testing method embodiment of the visual information acquisition device above, this application also discloses an electronic device. Referring to FIG14, the electronic device 140 may include at least a processor 1401, an input device 1402, an output device 1403, and a memory 1404. The processor 1401, input device 1402, output device 1403, and memory 1404 within the electronic device 140 may be connected via a bus or other means.

[0224] The aforementioned memory 1404 is a memory device in the electronic device 140, used to store programs and data. It is understood that the memory 1404 here can include the built-in storage medium of the self-testing device of the visual information acquisition device, or it can include an extended storage medium supported by the electronic device 140. The memory 1404 provides storage space that stores the operating system of the electronic device 140. Furthermore, the computer program (including program code) is also stored in this storage space. It should be noted that the computer storage medium here can be a high-speed RAM memory; optionally, it can also be at least one computer storage medium located away from the aforementioned processor, which can be called a Central Processing Unit (CPU), the core and control center of the self-testing device of the visual information acquisition device, used to run the computer program stored in the aforementioned memory 1404.

[0225] In one embodiment, a processor 1401 can load and execute a computer program stored in memory 1404 to implement the corresponding steps of the method in the above-described self-test method embodiment for a visual information acquisition device; wherein, the visual information acquisition device has an information acquisition panel, on which an image acquisition component is disposed; specifically, the processor 1401 loads and executes the computer program stored in memory 1404 for:

[0226] Acquire the visual image captured by the image acquisition component;

[0227] If the visual information acquisition device meets the self-test conditions based on the visual image acquired by the image acquisition component, then the image acquisition component acquires a mirror image of the information acquisition panel.

[0228] The visual information acquisition device is inspected based on the mirrored image to determine whether any of its components are abnormal.

[0229] In one possible implementation, the processor 1401 loads and executes the computer program stored in the memory 1404, and is further configured to: send a self-test request to the server based on the acquired visual image, wherein the self-test request carries the identification information of the visual information acquisition device;

[0230] The server receives a response message in response to the self-test request, and outputs a self-test prompt message based on the response message. The response message is sent by the server when it determines that the visual information acquisition device has self-test permissions based on the identification information.

[0231] In response to a self-test operation received based on the self-test prompt information, it is determined that the visual information acquisition device meets the self-test conditions.

[0232] In one possible implementation, the processor 1401 loads and executes a computer program stored in the memory 1404 to send a self-test request to the server based on the acquired visual image, specifically for at least one of the following:

[0233] The visual image is processed for recognition, and if the pattern in the visual image is found to match a set reference pattern, a self-test request is sent to the server.

[0234] The visual image is input into a pre-trained image classification model. If the classification result output by the image classification model indicates that the visual image belongs to a specified category, the self-test request is sent to the server.

[0235] The trigger command recognition information carried in the visual image is recognized and processed, so that the self-test request is sent to the server based on the recognized trigger command.

[0236] In one possible implementation, the visual information acquisition device includes an illumination component; the processor 1401 loads and executes a computer program stored in the memory 1404, for determining that the visual information acquisition device meets the self-test conditions in response to a self-test operation received based on the self-test prompt information, specifically for:

[0237] In response to a self-test operation received based on the self-test prompt information, the lighting component is controlled to light up according to a specified flashing frequency and a specified lighting color, and the set of lighting images acquired by the image acquisition component is obtained.

[0238] Each lighting image in the lighting image set is identified to determine the image region corresponding to the lighting component in each lighting image;

[0239] Based on the pixel values ​​located within the image area in each lighting image, the test flicker frequency and test lighting color of the lighting component are determined;

[0240] If the test flashing frequency matches the specified flashing frequency and the test lighting color matches the specified lighting color, then the visual information acquisition device is determined to meet the self-test conditions.

[0241] In one possible implementation, the visual information acquisition device includes an illumination component comprising multiple illumination units; the processor 1401 loads and executes a computer program stored in the memory 1404, for acquiring a mirror image of the information acquisition panel through the image acquisition component, specifically for:

[0242] The lighting component is controlled to light up when a preset current value is applied, and the mirror image captured by the image acquisition component is obtained.

[0243] The processor 1401 loads and executes the computer program stored in the memory 1404, which is used to detect the various components included in the visual information acquisition device based on the mirror image, specifically for:

[0244] The multiple lighting units contained in the lighting component are detected based on the mirror image to obtain the detection result corresponding to the lighting component.

[0245] In one possible implementation, the processor 1401 loads and executes a computer program stored in the memory 1404, used to detect multiple lighting units included in the lighting component based on the mirror image, and obtain a detection result corresponding to the lighting component, specifically used for:

[0246] Based on the preset positional relationship between the lighting component and the plurality of lighting units, the image regions corresponding to the plurality of lighting units are determined from the mirror image to obtain the plurality of lighting unit regions;

[0247] Calculate the average pixel value within each lighting unit area based on the pixel values ​​contained in each lighting unit area;

[0248] The detection results of each lighting unit included in the lighting assembly are determined based on the average pixel value within each lighting unit area.

[0249] In one possible implementation, the processor 1401 loads and executes a computer program stored in the memory 1404, used to determine the detection results of each lighting unit included in the lighting assembly based on the average pixel value within each lighting unit area, specifically used for:

[0250] Obtain the average pixel value of other image regions in the mirrored image besides the multiple lighting unit regions;

[0251] If the average pixel value of the other image regions matches the reference ambient brightness value, then the reference brightness value of each illumination unit under the preset current value is obtained, and the reference brightness value is associated with the reference ambient brightness value.

[0252] Based on the average pixel value within each lighting unit area and the reference brightness value of each lighting unit under the preset current value, the brightness value error of each lighting unit under the preset current value is determined.

[0253] The detection results of each lighting unit are determined based on the brightness error of each lighting unit under the preset current value.

[0254] In one possible implementation, the preset current value includes multiple current values; the processor 1401 loads and executes the computer program stored in the memory 1404, which is used to determine the detection result of each lighting unit based on the brightness value error of each lighting unit under the preset current value, specifically for:

[0255] Based on the brightness value error of each lighting unit under the multiple current values ​​and the preset error threshold, the detection result of each lighting unit under each current value is determined.

[0256] The detection results of each lighting unit are determined based on the detection results of each lighting unit at each current value.

[0257] In one possible implementation, the processor 1401 loads and executes the computer program stored in the memory 1404, and is also used for...

[0258] If there is a target lighting unit among the plurality of lighting units whose corresponding brightness value error is greater than a preset error threshold, then the current value applied to the target lighting unit is adjusted, or the exposure time of the image acquisition component is increased, and a mirror image containing the target lighting unit is re-acquired.

[0259] In one possible implementation, the visual information acquisition device includes a distance sensor component, and the mirrored image includes an image region corresponding to a specified component; the processor 1401 loads and executes a computer program stored in the memory 1404 for detecting the various components included in the visual information acquisition device based on the mirrored image, specifically for:

[0260] Obtain the average pixel value of other image regions in the mirrored image, excluding the image region corresponding to the specified component;

[0261] If the average pixel value of the other image regions matches the reference ambient brightness value, then the acquisition parameters of the image acquisition component and the size information of the specified component are obtained.

[0262] A first reference distance value is determined based on the acquisition parameters, the size information of the specified component, and the size information of the image region corresponding to the specified component;

[0263] The first test distance value measured by the distance sensor component is obtained, and the detection result corresponding to the distance sensor component is determined based on the distance error between the first test distance value and the first reference distance value.

[0264] In one possible implementation, the processor 1401 loads and executes the computer program stored in the memory 1404, and is further configured to:

[0265] If the average pixel value of the other image regions matches the specific ambient brightness value, then the second reference distance value corresponding to the specific ambient brightness value is obtained;

[0266] The second test distance value measured by the distance sensor component is obtained, and the detection result corresponding to the distance sensor component is determined based on the distance error between the second test distance value and the second reference distance value.

[0267] In one possible implementation, the visual information acquisition device includes an illumination component; the processor 1401 loads and executes a computer program stored in the memory 1404, for detecting the various components included in the visual information acquisition device based on the mirrored image, specifically for:

[0268] Obtain the average pixel value of other image regions in the mirrored image, excluding the image region corresponding to the lighting component;

[0269] If the average pixel value of the other image regions matches the specific ambient brightness value, then the reference test pattern in the mirror image is identified, and the image acquisition component is detected according to the reference test pattern to obtain the detection result corresponding to the image acquisition component.

[0270] In one possible implementation, the processor 1401 loads and executes a computer program stored in the memory 1404 to detect the image acquisition component according to the reference test pattern and obtain a detection result corresponding to the image acquisition component, specifically for:

[0271] Based on the pixel values ​​of each pixel within a specified area in the reference test pattern, the visual attribute information of the image acquisition component is determined, wherein the specified area is an image area containing a specified color.

[0272] Based on the visual attribute information, the detection result corresponding to the image acquisition component is determined.

[0273] In one possible implementation, the processor 1401 loads and executes a computer program stored in the memory 1404 to detect the image acquisition component according to the reference test pattern and obtain a detection result corresponding to the image acquisition component, specifically for:

[0274] Edge detection is performed on the reference test pattern to determine the image region corresponding to a specific shape in the reference test pattern;

[0275] Based on the pixel values ​​within the image region corresponding to the specific shape, the image quality attribute information of the image acquisition component is determined;

[0276] Based on the image quality attribute information, the detection result corresponding to the image acquisition component is determined.

[0277] In one possible implementation, the image acquisition component includes multiple image acquisition sub-components; the processor 1401 loads and executes a computer program stored in the memory 1404, for acquiring a mirror image of the information acquisition panel through the image acquisition component, specifically for:

[0278] Obtain the mirror images acquired by each of the image acquisition sub-components to obtain a set of mirror images;

[0279] The step of detecting the image acquisition component according to the reference test pattern to obtain the detection result corresponding to the image acquisition component includes:

[0280] The reference test pattern included in each mirror image in the mirror image set is identified and processed to determine the position information of the reference marker points in each mirror image.

[0281] Based on the position information of the reference markers in each of the mirror images, the alignment parameters of the image acquisition component are determined, and based on the alignment parameters, the detection result corresponding to the image acquisition component is determined.

[0282] It should be understood that, in the embodiments of this application, the processor 1401 may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0283] This application provides a computer-readable storage medium storing a computer program, which includes program instructions. When the program instructions are executed by a processor, they can perform the steps described in all the above embodiments.

[0284] This application also provides a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. When the computer instructions are executed by the processor of a computer device, they perform the methods described in all the above embodiments.

[0285] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0286] In summary, this application provides a self-testing method, apparatus, electronic device, computer-readable medium, and computer program product for a visual information acquisition device. The visual information acquisition device has an information acquisition panel with an image acquisition component. The device first acquires a visual image captured by the image acquisition component. Then, when it determines that the self-testing conditions are met based on the visual image, it acquires a mirror image of the information acquisition panel through the image acquisition component. Finally, it tests each component of the device based on the mirror image to determine if any component is abnormal. This method enables the visual information acquisition device to autonomously determine whether a self-test is needed and perform it, without relying on after-sales personnel. It can quickly locate component faults, improve the calibration and repair efficiency of the visual information acquisition device, and enhance its usability.

[0287] Furthermore, the visual information acquisition device sends a self-test request carrying its own identification information to the server based on the acquired visual image. It receives a response message from the server after determining that it has the authority to perform the self-test, and outputs a self-test prompt based on this message. Responding to the self-test operation received based on the prompt, it confirms that the self-test conditions are met. By controlling self-test permissions through the server, the security and standardization of the self-test process are enhanced, unauthorized self-test operations are avoided, and the reliability of the self-test is improved.

[0288] When processing visual images, if a pattern in the visual image is identified as matching a predefined reference pattern, the visual information acquisition device sends a self-test request to the server. This pattern-matching-based recognition method can accurately determine whether a visual image meets the conditions for triggering a self-test, improving the accuracy of self-test triggering and reducing the possibility of false triggering.

[0289] When a visual image is input into a pre-trained image classification model, and the model outputs a classification result indicating that the visual image belongs to a specified category, the visual information acquisition device sends a self-check request to the server. Utilizing the intelligent classification capabilities of the image classification model, visual images meeting the self-check criteria can be selected more efficiently and accurately, improving the automation and accuracy of the self-check process.

[0290] After recognizing and processing the trigger command information carried in the visual image, the visual information acquisition device sends a self-test request to the server based on the recognized trigger command. This trigger command-based recognition method provides more possibilities and flexibility for triggering the self-test request, and can adapt to different application scenarios and needs.

[0291] When the visual information acquisition device includes an illumination component, in response to a self-test operation received based on self-test prompts, the device controls the illumination component to illuminate at a specified flashing frequency and a specified illumination color, and acquires a set of illumination images captured by the image acquisition component. It then performs recognition processing on each illumination image in the set to determine the image region corresponding to the illumination component. Based on the pixel values ​​within the region, it determines the test flashing frequency and test illumination color. When both match specified values, the self-test condition is deemed met. By detecting the flashing frequency and color of the illumination component, the user's willingness to initiate the self-test is further confirmed, improving the rigor and reliability of the self-test process.

[0292] When a visual information acquisition device includes an illumination component comprising multiple illumination units, the device controls the illumination component to illuminate under a preset current value, acquires a mirror image captured by the image acquisition component, and then detects the multiple illumination units based on the mirror image to obtain the corresponding detection results for the illumination component. This method of detecting multiple illumination units can comprehensively and meticulously evaluate the working status of the illumination component and promptly identify potential problems with the illumination units.

[0293] When inspecting multiple lighting units based on a mirror image, the visual information acquisition device determines the image regions corresponding to each lighting unit from the mirror image according to the preset positional relationship between the lighting components and the multiple lighting units. It then calculates the average pixel value within each region to determine the inspection result for each lighting unit. By calculating and analyzing the average pixel value, the brightness of each lighting unit can be accurately assessed, providing a quantitative basis for the inspection of the lighting components.

[0294] When determining the detection results for each lighting unit, the visual information acquisition device obtains the average pixel value of other image regions in the mirror image, excluding the areas of multiple lighting units. If this average value matches the reference ambient brightness value, the reference brightness value of each lighting unit under a preset current value is obtained, the brightness value error is calculated, and the detection result is determined based on the error. By considering the influence of ambient brightness on the detection results and comparing them with the reference brightness value, it is possible to more accurately determine whether the lighting unit is abnormal, thus improving the accuracy of the detection results.

[0295] If the preset current value includes multiple current values, the visual information acquisition device determines the detection result of each lighting unit at each current value based on the brightness value error corresponding to each lighting unit under multiple current values ​​and the preset error threshold, thereby determining the detection result of each lighting unit. Comprehensive analysis of the brightness value error under multiple current values ​​allows for a more comprehensive evaluation of the lighting unit's performance, improving the accuracy and reliability of the detection.

[0296] If a target lighting unit has a corresponding brightness value error greater than a preset error threshold among multiple lighting units, the visual information acquisition device adjusts the current value applied to the target lighting unit or increases the exposure time of the image acquisition component, and then re-acquires a mirror image containing the target lighting unit. This calibration process can promptly adjust and repair abnormal lighting units, improving the stability and reliability of the lighting components.

[0297] When the visual information acquisition device includes a distance sensor component and the mirrored image includes an image region corresponding to a specified component, the device acquires the average pixel value of other image regions in the mirrored image besides the image region corresponding to the specified component. If this average value matches the reference ambient brightness value, the device acquires the acquisition parameters of the image acquisition component and the size information of the specified component, determines a first reference distance value, acquires the first test distance value measured by the distance sensor component, and determines the detection result based on the distance error between the two. By analyzing the distance error, the working state of the distance sensor component can be accurately determined, improving the accuracy of distance measurement.

[0298] If the average pixel value of other image areas matches the specific ambient brightness value, the visual information acquisition device obtains a second reference distance value corresponding to the specific ambient brightness value, and obtains a second test distance value measured by the distance sensor component. The detection result is determined based on the distance error between the two values. Detecting the distance sensor component under specific ambient brightness conditions can further verify its accuracy and reliability, and improve the comprehensiveness of the detection.

[0299] When a visual information acquisition device includes an illumination component, the device acquires the average pixel value of all image regions in the mirrored image except for the image region corresponding to the illumination component. If this average value matches a specific ambient brightness value, a reference test pattern in the mirrored image is identified, and the image acquisition component is detected based on the reference test pattern to obtain the detection result. Detecting the image acquisition component under specific ambient brightness conditions reduces the influence of environmental factors on the detection results and improves the accuracy of the detection.

[0300] When inspecting an image acquisition component based on a reference test pattern, the visual information acquisition device determines the visual attribute information of the image acquisition component based on the pixel values ​​within a specified area of ​​the reference test pattern, and determines the detection result based on this information. By analyzing the visual attribute information, the performance of the image acquisition component, such as color reproduction and brightness, can be comprehensively evaluated, thus improving the accuracy of the detection.

[0301] Edge detection is performed on a reference test pattern to determine the image region corresponding to a specific shape. Based on the pixel values ​​within each region, the image quality attribute information of the image acquisition component is determined, and the detection result is determined based on this information. By analyzing the image quality attribute information, the performance of the image acquisition component can be evaluated more deeply, such as deformation, field of view, and sharpness, thus improving the comprehensiveness and accuracy of the detection.

[0302] When an image acquisition component comprises multiple image acquisition sub-components, the visual information acquisition device acquires mirror images captured by each sub-component, resulting in a set of mirror images. The device then performs recognition processing on the reference test patterns included in each mirror image to determine the position information of the reference marker points within each mirror image. Based on this information, the alignment parameters of the image acquisition component are determined, and the detection result is determined according to these alignment parameters. By analyzing the alignment parameters, the alignment status between multiple image acquisition sub-components can be accurately determined, improving the accuracy and consistency of image acquisition.

[0303] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0304] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A self-testing method for a visual information acquisition device, executed by the visual information acquisition device, the visual information acquisition device having an information acquisition panel, the information acquisition panel being provided with an image acquisition component, the method comprising: Acquire the visual image captured by the image acquisition component; If the visual information acquisition device meets the self-test conditions based on the visual image acquired by the image acquisition component, then the image acquisition component acquires a mirror image of the information acquisition panel. and The visual information acquisition device is inspected based on the mirrored image to determine whether any of its components are abnormal.

2. The method according to claim 1, further comprising: A self-test request is sent to the server based on the acquired visual image, and the self-test request carries the identification information of the visual information acquisition device; The server receives a response message in response to the self-test request, and outputs a self-test prompt message based on the response message. The response message is sent by the server when it determines that the visual information acquisition device has self-test permissions based on the identification information. In response to a self-test operation received based on the self-test prompt information, it is determined that the visual information acquisition device meets the self-test conditions.

3. The method according to claim 2, wherein sending a self-test request to the server based on the acquired visual image includes: The visual image is processed for recognition. If the pattern in the visual image matches a set reference pattern, a self-test request is sent to the server.

4. The method according to claim 2 or 3, wherein sending a self-test request to the server based on the acquired visual image includes: The visual image is input into a pre-trained image classification model. If the classification result output by the image classification model indicates that the visual image belongs to a specified category, the self-test request is sent to the server.

5. The method according to any one of claims 2 to 4, wherein sending a self-test request to the server based on the acquired visual image comprises: The trigger command recognition information carried in the visual image is recognized and processed, so that the self-test request is sent to the server based on the recognized trigger command.

6. The method according to any one of claims 2 to 5, wherein the visual information acquisition device comprises an illumination component; The step of determining that the visual information acquisition device meets the self-test conditions in response to a self-test operation received based on the self-test prompt information includes: In response to a self-test operation received based on the self-test prompt information, the lighting component is controlled to light up according to a specified flashing frequency and a specified lighting color, and the set of lighting images acquired by the image acquisition component is obtained. Each lighting image in the lighting image set is identified to determine the image region corresponding to the lighting component in each lighting image; Based on the pixel values ​​located within the image area in each lighting image, the test flicker frequency and test lighting color of the lighting component are determined; If the test flashing frequency matches the specified flashing frequency and the test lighting color matches the specified lighting color, then the visual information acquisition device is determined to meet the self-test conditions.

7. The method according to any one of claims 1 to 6, wherein the visual information acquisition device comprises an illumination component, the illumination component comprising a plurality of illumination units; The step of acquiring a mirror image of the information acquisition panel through the image acquisition component includes: The lighting component is controlled to light up when a preset current value is applied, and the mirror image captured by the image acquisition component is obtained. The detection of each component of the visual information acquisition device based on the mirrored image includes: The multiple lighting units contained in the lighting component are detected based on the mirror image to obtain the detection result corresponding to the lighting component.

8. The method according to claim 7, wherein detecting a plurality of lighting units included in the lighting component based on the mirror image to obtain a detection result corresponding to the lighting component includes: Based on the preset positional relationship between the lighting component and the plurality of lighting units, the image regions corresponding to the plurality of lighting units are determined from the mirror image to obtain the plurality of lighting unit regions; Calculate the average pixel value within each lighting unit area based on the pixel values ​​contained in each lighting unit area; The detection results of each lighting unit included in the lighting assembly are determined based on the average pixel value within each lighting unit area.

9. The method according to claim 8, wherein determining the detection result of each lighting unit included in the lighting component based on the average pixel value within each lighting unit area comprises: Obtain the average pixel value of other image regions in the mirrored image besides the multiple lighting unit regions; If the average pixel value of the other image regions matches the reference ambient brightness value, then the reference brightness value of each illumination unit under the preset current value is obtained, and the reference brightness value is associated with the reference ambient brightness value. Based on the average pixel value within each lighting unit area and the reference brightness value of each lighting unit under the preset current value, the brightness value error of each lighting unit under the preset current value is determined. The detection results of each lighting unit are determined based on the brightness error of each lighting unit under the preset current value.

10. The method according to claim 9, wherein the preset current value includes multiple current values; and determining the detection result of each lighting unit based on the brightness value error of each lighting unit under the preset current value includes: Based on the brightness value error of each lighting unit under the multiple current values ​​and the preset error threshold, the detection result of each lighting unit under each current value is determined. The detection results of each lighting unit are determined based on the detection results of each lighting unit at each current value.

11. The method according to claim 9 or 10, further comprising: If there is a target lighting unit among the plurality of lighting units whose corresponding brightness value error is greater than a preset error threshold, then the current value applied to the target lighting unit is adjusted, or the exposure time of the image acquisition component is increased, and a mirror image containing the target lighting unit is re-acquired.

12. The method according to any one of claims 1 to 11, wherein the visual information acquisition device includes a distance sensor component, and the mirrored image includes an image region corresponding to a designated component; The detection of each component of the visual information acquisition device based on the mirrored image includes: Obtain the average pixel value of other image regions in the mirrored image, excluding the image region corresponding to the specified component; If the average pixel value of the other image regions matches the reference ambient brightness value, then the acquisition parameters of the image acquisition component and the size information of the specified component are obtained. A first reference distance value is determined based on the acquisition parameters, the size information of the specified component, and the size information of the image region corresponding to the specified component; The first test distance value measured by the distance sensor component is obtained, and the detection result corresponding to the distance sensor component is determined based on the distance error between the first test distance value and the first reference distance value.

13. The method according to claim 12, further comprising: If the average pixel value of the other image regions matches the specific ambient brightness value, then the second reference distance value corresponding to the specific ambient brightness value is obtained; The second test distance value measured by the distance sensor component is obtained, and the detection result corresponding to the distance sensor component is determined based on the distance error between the second test distance value and the second reference distance value.

14. The method according to any one of claims 1 to 13, wherein the visual information acquisition device includes an illumination component; the step of detecting each component included in the visual information acquisition device based on the mirror image includes: Obtain the average pixel value of other image regions in the mirrored image, excluding the image region corresponding to the lighting component; If the average pixel value of the other image regions matches the specific ambient brightness value, then the reference test pattern in the mirror image is identified, and the image acquisition component is detected according to the reference test pattern to obtain the detection result corresponding to the image acquisition component.

15. The method according to claim 14, wherein detecting the image acquisition component based on the reference test pattern to obtain a detection result corresponding to the image acquisition component includes: Based on the pixel values ​​of each pixel within a specified area in the reference test pattern, the visual attribute information of the image acquisition component is determined, wherein the specified area is an image area containing a specified color. Based on the visual attribute information, the detection result corresponding to the image acquisition component is determined.

16. The method according to claim 14, wherein detecting the image acquisition component based on the reference test pattern to obtain a detection result corresponding to the image acquisition component includes: Edge detection is performed on the reference test pattern to determine the image region corresponding to a specific shape in the reference test pattern; Based on the pixel values ​​within the image region corresponding to the specific shape, the image quality attribute information of the image acquisition component is determined; Based on the image quality attribute information, the detection result corresponding to the image acquisition component is determined.

17. The method according to any one of claims 14 to 16, wherein the image acquisition component comprises a plurality of image acquisition sub-components; the step of acquiring a mirror image of the information acquisition panel through the image acquisition component comprises: Obtain the mirror images acquired by each of the image acquisition sub-components to obtain a set of mirror images; The step of detecting the image acquisition component according to the reference test pattern to obtain the detection result corresponding to the image acquisition component includes: The reference test pattern included in each mirror image in the mirror image set is identified and processed to determine the position information of the reference marker points in each mirror image. Based on the position information of the reference markers in each of the mirror images, the alignment parameters of the image acquisition component are determined, and based on the alignment parameters, the detection result corresponding to the image acquisition component is determined.

18. A self-testing device for a visual information acquisition equipment, the visual information acquisition equipment having an information acquisition panel, the information acquisition panel being provided with an image acquisition component, the device comprising: The acquisition unit is used to acquire the visual image acquired by the image acquisition component; The acquisition unit is used to acquire a mirror image of the information acquisition panel through the image acquisition component when it is determined that the visual information acquisition device meets the self-test conditions based on the visual image acquired by the image acquisition component. and The detection unit is used to detect each component included in the visual information acquisition device based on the mirror image, so as to determine whether each component included in the visual information acquisition device is abnormal.

19. An electronic device comprising: One or more processors; A memory for storing one or more computer programs, which, when executed by one or more processors, cause the electronic device to implement the self-testing method of the visual information acquisition device according to any one of claims 1-17.

20. A computer-readable medium having a computer program stored thereon, which, when executed by a processor, implements the self-testing method of the visual information acquisition device according to any one of claims 1-17.

21. A computer program product comprising a computer program stored in a computer-readable storage medium, wherein a processor of an electronic device reads from and executes the computer program, causing the electronic device to perform a self-testing method for a visual information acquisition device according to any one of claims 1-17.

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