An electric vehicle instrument electronic guide book implementation method, device and electronic equipment

Abstract

The application provides an electric vehicle instrument electronic guide book implementation method and device and electronic equipment, and relates to the field of data processing. The working procedure position and working content of a worker in the production work of an electric vehicle instrument are acquired, and the worker wears AR glasses; electronic guide content is determined from the electronic guide book according to the working procedure position and working content, and a first image is generated; the first image is displayed to the worker through the AR glasses; quality detection information of the electric vehicle instrument sent by a detection device is received, the quality detection information includes operated step information, operated step completion time and defective product conditions, and the quality detection information is used to indicate whether the quality of the electric vehicle instrument is qualified; the quality detection information is marked in the first image to generate a second image; and the second image is displayed to the worker through the AR glasses. Through the reasonable combination of the electronic guide book and product traceability, the beneficial implementation of the electronic guide book is facilitated.

Description

Technical Field

[0001] This application relates to the technical field of data processing, specifically to a method, device, and electronic device for implementing an electronic instruction manual for an electric vehicle instrument panel. Background Technology

[0002] Ensuring consistent product quality is crucial in the production of electric vehicle dashboards. However, traditional production line inspection and guidance management methods have certain limitations.

[0003] On the one hand, traditional inspection relies heavily on manual experience and offline testing equipment, resulting in a lack of real-time and accurate process guidance data after defective products are detected. On the other hand, the production process involves multiple complex stages, such as welding, assembly, and testing. The operator behavior at each stage can affect product quality, but these operator behaviors are often not systematically and in real-time guided in traditional systems. Therefore, related technical approaches fail to properly integrate electronic instruction manuals with product traceability, hindering the effective implementation of electronic instruction manuals.

[0004] Therefore, there is an urgent need for a method, device, and electronic equipment for implementing electronic instruction manuals for electric vehicle instruments. Summary of the Invention

[0005] This application provides a method, device, and electronic device for implementing an electronic instruction manual for electric vehicle instruments. By reasonably combining the electronic instruction manual with product traceability, the beneficial implementation of the electronic instruction manual is facilitated.

[0006] A first aspect of this application provides a method for implementing an electronic instruction manual for an electric vehicle instrument panel. The method includes: acquiring the process position and work content of a worker in the production of the electric vehicle instrument panel, the worker wearing AR glasses; determining electronic instruction content from an electronic instruction manual based on the process position and work content, and generating a first image; displaying the first image to the worker through the AR glasses; receiving quality inspection information for the electric vehicle instrument panel sent by a testing device, the quality inspection information including information on the operated steps, the completion time of the operated steps, and the defective product status, the quality inspection information being used to indicate whether the quality of the electric vehicle instrument panel is qualified; annotating the quality inspection information into the first image to generate a second image; and displaying the second image to the worker through the AR glasses.

[0007] By adopting the above technical solution, the system automatically identifies the operator's work position and content, extracts the corresponding operation instructions from the electronic manual, and presents them in real time in the AR glasses. This ensures that the operator can accurately execute the process standards at each step, avoiding operational deviations due to unfamiliarity with the process. Through the first image displayed by the AR glasses, the operator does not need to refer to paper or handheld manuals; they can directly see the operation steps and precautions in their field of vision, reducing operation interruptions and improving efficiency. The system receives quality inspection information from the inspection equipment, including completed operation steps, defective product information, etc., and determines whether the current product meets quality standards. Real-time annotation of inspection information helps operators to promptly identify problems and prevent defective products from entering the next process. When an anomaly is detected, the system will visually present the defective product information through the AR glasses, enabling the operator to quickly adjust or redo the non-conforming steps, reducing rework waste and improving overall production efficiency. Quality inspection information is automatically recorded and annotated into the corresponding operation instruction image. This data traceability helps to achieve transparent traceability in the production chain, making it easy to find the specific link that produces defective products. The second image, incorporating quality information, not only includes real-time detection data but also integrates the completion status of specific operational steps. This facilitates post-operational traceability analysis of each step and quality inspection, providing data support for process improvement. Upon detecting a quality problem, a second image is generated through real-time annotation, displaying specific defect information to the operator. This feedback loop allows operators to immediately understand and correct errors, effectively reducing the probability of batch defects. The system records every operational step and defective product detection result, which can be analyzed to optimize the electronic instruction manual content, enhancing the practicality and relevance of work instructions, thereby gradually improving process quality standards. Therefore, by rationally combining electronic instruction manuals with product traceability, the beneficial implementation of electronic instruction manuals is facilitated.

[0008] Optionally, obtaining the worker's process position and work content in the production of electric vehicle instruments specifically includes: obtaining the location information of the AR glasses; determining the first position of the worker based on the location information; obtaining a work image of the worker captured by the AR glasses; determining the environment of the worker from the work image to obtain a second position; combining the first position and the second position to determine the process position; and performing image feature recognition on the work image to determine the work content.

[0009] By adopting the above technical solution, the system can more accurately determine the operator's work process position by combining the location information of the AR glasses with environmental recognition in the work image. Combining the first position of the AR glasses with the second position identified by environmental features can effectively compensate for the error caused by single positioning, improve positioning accuracy, and ensure that the guidance content is completely matched with the current process. The dual positioning mechanism ensures the accuracy of process identification, avoids erroneous operations caused by positioning deviations, and improves operator safety and production consistency. By performing feature recognition on the work image captured by the AR glasses, the system can dynamically obtain the specific work content of the operator. This method not only ensures that the system's identification of the actual work content is more accurate, but also lays the foundation for personalized real-time work guidance. By automatically identifying work content instead of manual judgment, it can reduce the work errors caused by operator lack of experience or negligence, and ensure that each process is strictly carried out according to standardized procedures. Based on the accurate identification of process position and work content, the AR glasses can display guidance content that best matches the current work step. Operators can receive dynamic and accurate guidance at each step, thereby ensuring operation quality and avoiding the problem of lagging guidance content in traditional methods. The system updates work instructions based on the operator's location and job content in different processes, enabling seamless workflow guidance, reducing operator switching and waiting time, and improving work efficiency. Through image capture via AR glasses, the system not only records the operator's work status but also archives this image information in real time, providing complete traceable data for the production process. In the event of defective products or operational abnormalities, the system can quickly trace back to the specific process location and job content, improving traceability efficiency. Precise process and job content recording makes quality management more transparent. Managers can view and analyze production data in real time, identify potential problems, and make preventative adjustments.

[0010] Optionally, determining the electronic guidance content from the electronic instruction manual based on the process location and the work content, and generating the first image, specifically includes: determining the electronic guidance content based on the process location and the work content; performing text conversion and image conversion on the electronic guidance content to generate voice information and motion information; inputting the voice information and the motion information into an initial digital human model to generate a target digital human containing the voice information for explaining the voice information and the motion information for demonstrating the motion information; and generating the first image based on the target digital human.

[0011] By employing the aforementioned technical solutions, a digital human model incorporating voice information and gesture demonstrations provides human-like operational guidance. This approach not only allows operators to "see" the guidance content but also to "hear" step-by-step voice explanations, making it more intuitive and easier to understand than traditional text or static illustrations. The digital human demonstrates complex operational steps through gestures, clearly indicating key operational gestures or tool usage methods, helping operators accurately understand operational requirements. This is particularly important for processes involving delicate manual operations, effectively reducing operational deviations. Customized guidance based on process location and work content: The system determines the guidance content based on the operator's current specific process location and operational content, thus providing "tailor-made" operational guidance for each process. This personalized guidance significantly improves operator efficiency, ensuring that every step meets standard requirements. By generating digital humans in real time and demonstrating corresponding actions and explanations, the system can dynamically adjust the guidance content according to the operator's progress. Operators do not need to consult documents or ask superiors; they can simply follow the digital human's guidance to complete standardized operations. For beginners or unfamiliar tasks, the digital human's voice and gesture demonstrations provide a learn-and-use approach. New operators can quickly learn and master processes on the production line, reducing training time and costs and enabling them to get up to speed faster. Operators can accurately complete complex or detail-oriented operations with real-time guidance from digital humans, avoiding errors caused by unfamiliarity or misunderstanding of steps, thereby improving overall production quality. Digital humans utilize voice and gesture demonstrations, conveying information to operators through both visual and auditory means. Multi-sensory input enhances the effectiveness of information delivery, helping operators understand and remember complex steps, further improving operational proficiency. Compared to traditional static electronic manuals, dynamic digital humans offer a more interactive experience, allowing operators to maintain focus, reducing boredom, and contributing to a better overall work experience and engagement. The operations and explanations demonstrated by digital humans follow standardized procedures set by the system, helping all operators achieve consistent operating standards across different shifts and environments. This not only improves the stability of the production process but also reduces process deviations caused by individual differences. The standardized procedures through unified explanations and gesture demonstrations by digital humans are applicable to multiple production lines, ensuring that every production line and every operator performs tasks according to the same standards.

[0012] Optionally, receiving the quality inspection information for the electric vehicle instrument panel sent by the testing device specifically includes: generating quality inspection information after the first testing step corresponding to the testing device has completed testing the electric vehicle instrument panel; determining the next associated testing step from the electronic instruction manual to obtain a second testing step, wherein the testing result corresponding to the first testing step and the testing result corresponding to the second testing step have a causal relationship; and acquiring and storing the quality inspection information for the electric vehicle instrument panel sent by the testing device based on the second testing step, wherein the testing device corresponding to the second testing step includes the AR glasses.

[0013] By adopting the above technical solution and recording the causal relationships between each testing step in the electronic instruction manual, the system can automatically identify the subsequent testing requirements that a certain test result may lead to. This function makes the testing information of each step more traceable, facilitating the analysis of the source and cause of specific problems. Because the system can track and display the causal relationships between different steps, quality management personnel can quickly locate the root cause of the problem. For example, if an anomaly is found in the second testing step, the results of the first testing step can be traced back to find possible quality problems, thereby improving the efficiency of troubleshooting. By automatically generating related testing steps, the system ensures that all necessary testing steps are not overlooked, especially for processes with more hidden problems, effectively reducing the risk of missed detection and improving the comprehensiveness and accuracy of overall quality testing. The quality testing information of each testing step is transmitted and stored immediately after the testing equipment completes the test. The system can ensure the integrity and real-time nature of the testing data, so that the test results of the previous steps can be referenced in subsequent testing steps, achieving seamless multi-step data integration. AR glasses can not only display instruction content, but also act as a testing device to transmit testing information to the system in real time. The system combines operational guidance with inspection feedback, ensuring operators are constantly guided during the inspection process and reducing risks caused by operator error. By automatically identifying related inspection steps and causal relationships, the system achieves a closed-loop management process from inspection to problem analysis and resolution. Inspection information not only provides a reference for subsequent steps but also automatically triggers relevant measures when quality anomalies occur, such as increasing inspection frequency or enhancing operational guidance, ensuring continuous quality monitoring and improvement throughout the production process.

[0014] Optionally, the step of annotating the quality inspection information into the first image to generate a second image specifically includes: classifying the quality inspection information to obtain the information of the operated steps, the completion time of the operated steps, and the defective product status; annotating the information of the operated steps and the completion time of the operated steps into a dynamic area to obtain the content of the first image, wherein the dynamic area is used to display or hide the information of the operated steps and the completion time of the operated steps; determining the coordinate information of the defective product according to the defective product status; annotating the coordinate information of the defective product on the electric vehicle instrument panel in a spatial coordinate system using a color coding method to generate the content of the second image; and generating the second image based on the content of the first image and the content of the second image.

[0015] By adopting the above technical solutions, and by setting dynamic areas in the first image to display or hide information on completed steps and completion times, operators and managers can flexibly view and focus on specific inspection information, avoiding information overload and making the progress and time information of each step more intuitive. This approach provides a hierarchical presentation of information, helping personnel focus on the current process or inspection content and improving work efficiency. By using color coding to mark the coordinates of defective products on the electric vehicle dashboard, managers and operators can quickly identify problematic areas, avoiding complex text descriptions or numerical judgments, and improving operators' sensitivity to problems. The intuitiveness of color makes the location of defective products clear at a glance, helping to accelerate quality repair and processing. By dynamically marking the information on completed steps and completion times on the image, staff can receive immediate feedback on the operation progress, understanding the execution status and completion time of each step. This helps operators confirm whether the operation is completed on time and adjust the work progress in a timely manner, avoiding quality problems caused by missed or delayed steps. By marking the coordinates of defective products and using color coding, personnel on the production line can quickly identify problem areas. This instant feedback not only enhances operators' awareness but also provides managers with crucial data support, enabling rapid remedial action and preventing defective products from flowing into the next stage. By categorizing quality inspection information, the system presents information more clearly to relevant personnel. Categorized information helps managers understand and analyze it more quickly, reducing information screening and filtering time and improving the efficiency of quality management. The coordinates and color-coded markings of defective products help staff quickly locate problems, avoiding tedious inspection and judgment processes and reducing human error. By displaying defective product information directly on the image, operators can clearly identify which parts or locations require further inspection or repair, ensuring timely resolution of problems.

[0016] Optionally, the method further includes: performing action recognition on the work image to determine the production action corresponding to the worker; calculating the action similarity between the production action and a preset action corresponding to the work content, and determining whether the key production action included in the production action is consistent with the key production action included in the preset action; if it is determined that the action similarity between the production action and the preset action is greater than or equal to a preset threshold, and the key production action included in the production action is consistent with the key production action included in the preset action, then the production action corresponding to the worker is determined to be qualified.

[0017] By adopting the above technical solution and utilizing motion recognition technology, the system can automatically determine whether workers are performing production tasks according to prescribed standard actions, eliminating the need for manual monitoring or intervention. This automated detection method enhances the intelligence level of the production line, making the production process more efficient. Traditional production lines may require manual inspection of each process, but now, with automatic computer recognition and comparison of production actions, manual intervention is reduced, improving overall production efficiency and accuracy. Through precise motion recognition of work images, the system can accurately capture the actual operations of workers. By calculating similarity and judging the consistency of key production actions, the system ensures that each action meets preset standard requirements, thereby improving the accuracy of the production process. Preset actions are established through process flow and production standards. Using motion recognition and standard comparison ensures that operators always operate according to standards, thus avoiding quality problems caused by differences in personal experience or non-standard operations. If a worker's production actions do not conform to the preset standards, the system can detect and provide feedback immediately. This means that non-conforming production operations can be quickly identified when they occur, reducing the risk of non-conforming products flowing into the next process or being delivered, thereby improving the level of quality control.

[0018] Optionally, the method further includes: if it is determined that the action similarity between the production action and the preset action is greater than or equal to a preset threshold, and the key production action included in the production action is inconsistent with the key production action included in the preset action, then the production action corresponding to the worker is determined to be unqualified; or, if it is determined that the action similarity between the production action and the preset action is less than a preset threshold, then the production action corresponding to the worker is determined to be unqualified, and an abnormal action alarm message is generated; the abnormal action alarm message is displayed to the worker through the AR glasses.

[0019] By adopting the above technical solutions, the system can detect potential errors in the production process in real time when the similarity between the worker's production actions and the preset actions is lower than a preset threshold, or when key production actions are inconsistent. This real-time feedback mechanism ensures that workers are aware of their operational problems in a timely manner, allowing for immediate correction and reducing the generation of defective products. By comparing the similarity between preset actions and actual production actions and determining whether key production actions are included, the system ensures that each operator operates according to standardized procedures, thereby improving production consistency and accuracy. Even with varying operator experience, the system helps them maintain consistent operating standards. The system can clearly identify when and why a certain action does not meet requirements, relying not only on human experience, making production standardization and quality control more reliable and avoiding human error. By judging the similarity of actions, potential operational errors can be discovered. Although these errors may not be obvious on the surface, if they are not detected in time, they may eventually affect product quality. This intelligent monitoring effectively reduces quality problems caused by improper operation. The generation and real-time feedback mechanism of abnormal action alarm information can prevent non-conforming actions from continuing to affect production, ensuring that product quality can be controlled at an early stage. This prevents the spread of defective products along the production line, reducing the risk of rework and scrap. Timely identification and correction of non-conforming actions can reduce material waste and rework caused by operational errors, improving production line efficiency. This allows companies to utilize resources more efficiently, reduce production costs, and increase production effectiveness. Rapid detection and feedback can prevent production delays caused by operational errors, ensuring the production line operates at its optimal pace and improving overall production efficiency.

[0020] A second aspect of this application provides an electronic instruction manual implementation device for electric vehicle instrument panels, characterized in that the device includes an acquisition module and a processing module, wherein the acquisition module is used to acquire the process position and work content of a worker in the production of electric vehicle instrument panels, the worker wearing AR glasses; the processing module is used to determine electronic instruction content from the electronic instruction manual based on the process position and the work content, and generate a first image; the processing module is also used to display the first image to the worker through the AR glasses; the acquisition module is also used to receive quality inspection information for the electric vehicle instrument panel sent by a testing device, the quality inspection information including information on the operated steps, the completion time of the operated steps, and the defective product status, the quality inspection information being used to indicate whether the quality of the electric vehicle instrument panel is qualified; the processing module is also used to annotate the quality inspection information into the first image to generate a second image; the processing module is also used to display the second image to the worker through the AR glasses.

[0021] A third aspect of this application provides an electronic device including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, and both the user interface and the network interface are used to communicate with other devices. The processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method described above.

[0022] A fourth aspect of this application provides a computer-readable storage medium storing instructions that, when executed, perform the method described above.

[0023] In summary, one or more technical solutions provided in this application have at least the following technical effects or advantages:

[0024] By automatically identifying the operator's work position and tasks, the system extracts corresponding operational guidelines from electronic manuals and presents them in real-time on AR glasses. This ensures that operators can accurately execute process standards at each step, avoiding operational deviations due to unfamiliarity with the process. Through the initial image displayed on the AR glasses, operators do not need to consult paper or handheld manuals; they can directly see the operational steps and precautions in their field of vision, reducing operational interruptions and improving efficiency. The system receives quality inspection information from the inspection equipment, including completed operational steps, defective product information, etc., and determines whether the current product meets quality standards. Real-time annotation of inspection information helps operators promptly identify problems and prevent defective products from entering the next process. When an anomaly is detected, the system visually presents the defective product information through the AR glasses, allowing operators to quickly adjust or redo the non-conforming steps, reducing rework waste and improving overall production efficiency. Quality inspection information is automatically recorded and annotated into the corresponding operational guidance image. This data traceability facilitates transparent traceability in the production chain, making it easy to find the specific link that generated the defective product. The second image, incorporating quality information, not only includes real-time detection data but also integrates the completion status of specific operational steps. This facilitates post-operational traceability analysis of each step and quality inspection, providing data support for process improvement. Upon detecting a quality problem, a second image is generated through real-time annotation, displaying specific defect information to the operator. This feedback loop allows operators to immediately understand and correct errors, effectively reducing the probability of batch defects. The system records every operational step and defective product detection result, which can be analyzed to optimize the electronic instruction manual content, enhancing the practicality and relevance of work instructions, thereby gradually improving process quality standards. Therefore, by rationally combining electronic instruction manuals with product traceability, the beneficial implementation of electronic instruction manuals is facilitated. Attached Figure Description

[0025] Figure 1This is a flowchart illustrating a method for implementing an electronic instruction manual for an electric vehicle instrument panel, as provided in an embodiment of this application.

[0026] Figure 2 This is another flowchart illustrating a method for implementing an electronic instruction manual for an electric vehicle instrument panel, as provided in an embodiment of this application.

[0027] Figure 3 This is a schematic diagram of a module for implementing an electronic instruction manual for an electric vehicle instrument panel, provided in an embodiment of this application.

[0028] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0029] Explanation of reference numerals in the attached figures: 31. Acquisition module; 32. Processing module; 41. Processor; 42. Communication bus; 43. User interface; 44. Network interface; 45. Memory. Detailed Implementation

[0030] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification 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.

[0031] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0032] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0033] Ensuring consistent and stable product quality is crucial in the production of electric vehicle instruments. However, existing traditional production line inspection and guidance management methods have significant limitations in practical applications, making it difficult to meet the demands of modern production processes for efficiency, real-time performance, and accuracy.

[0034] On the one hand, traditional inspection relies heavily on manual experience and offline testing equipment, resulting in a lack of real-time and accurate process guidance data after defective products are detected. On the other hand, the production process involves multiple complex stages, such as welding, assembly, and testing. The operator behavior at each stage can affect product quality, but these operator behaviors are often not systematically and in real-time guided in traditional systems. Therefore, related technical approaches fail to properly integrate electronic instruction manuals with product traceability, hindering the effective implementation of electronic instruction manuals.

[0035] To address the aforementioned technical problems, this application provides a method for implementing an electronic instruction manual for an electric vehicle instrument panel, referring to... Figure 1 , Figure 1 This is a flowchart illustrating a method for implementing an electronic instruction manual for an electric vehicle instrument panel, provided in an embodiment of this application. The method is applied to a server and includes steps S110 to S160, as follows:

[0036] S110. Obtain the process location and work content of the staff in the production of electric vehicle instruments. The staff are wearing AR glasses.

[0037] Specifically, the server is a centralized computing and data processing system responsible for receiving, processing, and analyzing data from various devices. In this embodiment, the server's primary task is to receive data from AR glasses and provide decision support during the production process based on this data. "Worker" refers to an operator or technician performing tasks on the electric vehicle instrument production line. Workers wear AR glasses to receive work instructions and view process information in real time. "Process location" refers to the worker's position during production, i.e., a specific workstation or process step on the production line. For example, a worker might be responsible for assembling a part of an electric vehicle instrument at an assembly station, or performing instrument function testing at a testing station. Obtaining the process location helps the server understand the worker's current position to provide correct operational guidance. "Work content" refers to the tasks that the worker needs to perform in a specific process. For example, a worker might need to complete specific tasks such as welding, installation, calibration, and inspection at a certain production stage. By obtaining the work content, the system can provide precise operating steps and requirements to ensure that workers perform their work according to specifications.

[0038] In this embodiment, AR glasses are devices integrating augmented reality technology that can display work-related virtual information in real time and overlay this information onto the actual field of vision to assist workers in completing tasks. The AR glasses and a server can transmit data wirelessly or via wired connection, and the server can control the operation of the AR glasses. After wearing the AR glasses, workers can receive real-time process instructions, quality standards, and operating procedures from the server. Furthermore, the AR glasses can capture workers' movements and process information through built-in cameras and sensors, and transmit this information to the server in real time for analysis and feedback.

[0039] In one possible implementation, obtaining the process position and work content of the worker in the production of electric vehicle instruments specifically includes: obtaining the location information of AR glasses; determining the first position of the worker based on the location information; obtaining the work image of the worker captured by the AR glasses; determining the environment in which the worker is located from the work image to obtain the second position; combining the first position and the second position to determine the process position; and performing image feature recognition on the work image to determine the work content.

[0040] Specifically, AR glasses have a built-in positioning system, such as GPS, inertial measurement units, accelerometers, and gyroscopes, which can track the physical position of the glasses in real time. For example, the glasses can determine the current location of a worker using these sensors. Location information can be provided in the form of coordinates, such as the location of a workstation or a specific area of ​​a production line. The server, using the location information obtained from the AR glasses, can determine the worker's primary location. This could be the worker's specific position on the production line, such as their position in the assembly process or in the testing phase. The server determines which workstation the worker is at and what task they are performing based on the location information. The AR glasses are equipped with cameras that can capture the worker's current working environment and operational status. The server can further analyze the worker's working state using the captured images. These images can include a panoramic view of the production environment, components on the workbench, and the worker's movement information. By analyzing the captured images, the server can identify the worker's environment. For example, the images can show whether the worker is on the correct workbench, whether they have the correct tools and parts, and even confirm the worker's correct position by recognizing the characteristics of equipment or workpieces. This process generates a secondary location, which is a further judgment and positioning of the worker's actual working environment. By combining a worker's primary and secondary locations, the server can accurately determine the worker's current work process position. For example, a worker might be at an assembly station for electric vehicle dashboards or at a functional testing station; the system uses the combination of these two locations to determine the worker's current work process. The work images captured by the AR glasses are not only used for positioning but can also be used to identify key elements in the images through image feature recognition technologies such as computer vision and deep learning. For instance, the server can identify the parts, tools, or equipment the worker is operating through the image, thereby determining the specific task or work content the worker is performing. This might include operations such as welding, assembly, and testing.

[0041] S120. Based on the process location and work content, determine the electronic instruction content from the electronic instruction manual and generate the first image.

[0042] Specifically, the electronic instruction manual contains information such as operating procedures, precautions, and quality standards for each process on the production line. It serves as an "operation manual" for workers during production. The electronic instruction manual typically provides corresponding guidance based on different process positions and job content. The server automatically filters relevant guidance content from the electronic instruction manual based on the worker's process position and job content. For example, if a worker is at the "assembly station" and their job is "installing the display screen," the server will extract specific steps and precautions for "display screen installation" from the electronic instruction manual. The first image is a visualization generated by the server based on the content extracted from the electronic instruction manual. It can be a guidance screen containing text and graphics, a flowchart, step-by-step instructions, or even a virtual character demonstration. This image shows the operating steps that workers need to perform and displays them to workers through devices such as AR glasses, helping them accurately complete their tasks.

[0043] In one possible implementation, the electronic instruction content is determined from the electronic instruction manual based on the process location and work content, and a first image is generated. Specifically, this includes: determining the electronic instruction content based on the process location and work content; performing text conversion and image conversion on the electronic instruction content to generate voice information and motion information; inputting the voice information and motion information into an initial digital human model to generate a target digital human containing explanatory voice information and demonstration motion information; and generating the first image based on the target digital human.

[0044] Specifically, the server converts text descriptions or steps in electronic manuals into audio information, enabling workers to understand task requirements through hearing. For example, the system might translate "Step 1: Connect the display to the circuit board" into a spoken announcement. The server also converts images, diagrams, or flowcharts in the electronic manuals into dynamic images or motion information, helping workers understand the operating steps visually. For example, the system might generate an image showing how to correctly install a display, or display a dynamic demonstration of hand movements. Audio information refers to the explanation of operating steps played through audio, helping workers acquire information through hearing and reducing the need to take their eyes off the workbench to consult the manual. Motion information refers to the operating steps shown through animation or video, demonstrating how to correctly perform a task, such as how to properly place and secure a display with hands. The initial digital human model is a virtual character, a 3D model created using computer graphics technology. This model can be seen as a virtual assistant that can be used to explain operating steps and demonstrate actions. The target digital human, combining audio and motion information, will be used to demonstrate operating steps to workers. This virtual assistant will use appropriate voice and animation to explain how to perform the task. For example, the digital human model can simulate how to correctly place a part or use a tool.

[0045] For example, the target digital human can provide explanations based on the voice information of the electronic instructions, such as "First, connect the display screen interface to ensure it is not loose." The target digital human can demonstrate operating steps and simulate actions, such as how to correctly align the display screen with the circuit board. The server converts the images generated by the target digital human, including its voice explanations and action demonstrations, into a visual image or animation, and presents it to staff through AR devices. Staff can see the target digital human and its operating demonstrations through AR glasses, receiving detailed guidance and feedback in real time.

[0046] S130: Display the first image to staff via AR glasses.

[0047] Specifically, the server generates an initial image based on the process location and work content, which is then displayed to the worker through AR glasses. These images or animations are related to the operational steps and may include diagrams, 3D models, animated demonstrations, etc., to help workers understand how to perform specific tasks. The initial image may be a static image showing the operational steps, or a dynamic 3D model showing how to install or inspect a component. The AR glasses display these images in real time through a monitor, providing real-time guidance to workers during operation. For example, after wearing AR glasses, workers can see a demonstration image of the display installation steps on the lenses, or see a target digital avatar demonstrating how to install the display.

[0048] S140. Receive quality inspection information for electric vehicle instrument sent by the testing equipment. The quality inspection information includes the operation steps, the completion time of the operation steps, and the defective product status. The quality inspection information is used to indicate whether the quality of the electric vehicle instrument is qualified.

[0049] Specifically, the server serves as a central control and data processing system and is responsible for receiving data from different devices. The detection devices may be automated detection tools, sensors, scanning devices, quality inspection machines, etc. on the production line. These devices transmit quality inspection information to the server in real time through the network. The operation step information indicates the production or inspection steps that have been completed. For example, the production process of an electric vehicle instrument may include multiple steps such as welding, assembly, and testing. The inspection information records whether each step has been successfully completed. The completion time of the operation steps records the completion time of each production step. The time information helps to evaluate the production progress and can also be used to monitor the production efficiency. For example, if the completion time of a certain step is abnormally long, it may indicate a problem. The defective product situation records the status in the quality inspection of the product and indicates whether the electric vehicle instrument is qualified. If the detection device identifies problems such as a damaged circuit board or a non-lighting display screen, the instrument is marked as "unqualified". After the server aggregates these inspection information, it can be used to evaluate the quality of the entire product. If all operation steps are completed on time and correctly and no defective products are detected, the server will determine that the product is qualified; otherwise, if problems are found in any step, the server can mark it as "unqualified" and issue an alarm.

[0050] In a possible implementation manner, receiving the quality inspection information of the electric vehicle instrument sent by the detection device specifically includes: when the first detection link corresponding to the detection device finishes detecting the electric vehicle instrument, generating the quality inspection information; determining the next associated detection link of the first detection link from the electronic instruction book to obtain the second detection link, and there is a causal relationship between the detection result corresponding to the first detection link and the detection result corresponding to the second detection link; based on the second detection link, obtaining and storing the quality inspection information of the electric vehicle instrument sent by the detection device, and the detection device corresponding to the second detection link includes an AR glasses.

[0051] Specifically, the production process of electric vehicle dashboards involves a series of inspection steps. The first inspection step typically refers to the initial quality inspection step in the production process, such as checking whether the circuit board is securely soldered and whether the screen is clear. Upon completion of this step, the inspection equipment generates relevant quality inspection information. This information includes whether the inspection step was completed, whether it passed, and any defects requiring repair. Once the result of the first inspection step is confirmed, the electronic instruction manual (EIM) determines the next inspection step based on this result. For example, if the first inspection step discovers a soldering problem, the EIM might instruct the soldering to be repaired or further inspected in the next step. The second inspection step follows the first step and may involve inspecting another part of the electric vehicle dashboard or verifying the repairs found in the first step. The causal relationship means that the result of the first inspection step directly affects the judgment of the second inspection step. For example, if the first inspection step discovers a poor connection in the electric vehicle dashboard display, the second step may need to verify whether the problem has been repaired or directly replace the relevant components. This relationship ensures that the inspection results of each step are closely linked to subsequent steps, effectively resolving quality issues. The operation and results of the second inspection step are generated by second inspection equipment, which may include visual inspection equipment, sensors, AR glasses, etc. These devices are used to collect quality data from electric vehicle dashboards and send this data to a server for processing and storage. AR glasses serve as one of the devices in the second inspection stage. They not only help workers view guidance information in real time during the inspection process but also assist in recording specific quality inspection information, helping workers identify problems and providing immediate feedback during the inspection. The quality inspection information acquired by the server, such as whether the inspection is qualified or not, and the effect of repairs, will be stored on the server for subsequent analysis, traceability, and reporting. This information can provide data support for quality control on the production line, helping to analyze and prevent similar problems from recurring.

[0052] For example, during the production process, the welding of electric vehicle dashboards is inspected by specialized testing equipment. If the equipment detects loose welds, it generates quality inspection information, marking the issue as "poor welding." The electronic instruction manual (EIM) determines the next step based on the results of this first inspection, instructing the operator to proceed to the display screen testing phase. Display screen testing might include checking brightness, resolution, and color. If welding problems are found in the first phase, the EIM might also require checking for connection issues caused by poor welding. Since poor welding found in the first phase directly affects the display's connection quality, the results of the second phase will be influenced by the first. If the display screen malfunctions, the system will determine that the problem likely stems from poor welding, requiring the operator to repair the welding and retest. During the display screen testing phase, the operator might wear AR glasses. Through AR glasses, the operator can not only view the steps and repair suggestions in the EIM in real time but also see the display screen's testing data. For example, the AR glasses might mark areas requiring special attention in the corners of the display screen or prompt "Check display brightness." All quality inspection information collected through AR glasses and other testing devices, such as display brightness test results and display and circuit connectivity, will be stored on a server. This data will support quality traceability and problem analysis during the production process, helping to confirm whether electric vehicle dashboards meet quality standards and providing a basis for future production improvements.

[0053] S150. Mark the quality inspection information into the first image to generate the second image.

[0054] Specifically, quality inspection information refers to specific data about the quality of electric vehicle dashboards generated by inspection equipment, such as visual inspection instruments and sensors. Quality inspection information can include multiple aspects, such as completed operation steps, the time of completion, whether defective products were found, and whether repairs are needed. The first image is displayed to workers through AR glasses or other devices; it is a real-time image of the production process, such as a worker operating a part of the electric vehicle dashboard, or a graphical display generated by the system. The server integrates quality inspection information into the first image, for example, marking quality problems in a certain area of ​​the image, reminding workers of parts requiring special attention, or displaying other quality-related information. In this way, workers can obtain quality inspection-related data in real time during production operations and adjust their operations accordingly. The second image is a new image containing both the quality inspection information and the content of the first image. The purpose of generating the second image is to allow workers to see the product's quality status more intuitively, helping them make decisions. For example, if a part is found to be defective, the server will mark that part in the image, possibly using color markings, such as red, or arrows, labels, etc., to indicate to workers that there is a problem with that part or to request repair. This method of combining quality information with images allows staff to quickly identify product problems through visual feedback, without relying on traditional text descriptions or waiting for subsequent manual inspections.

[0055] In one possible implementation, quality inspection information is annotated into a first image to generate a second image. Specifically, this includes: classifying the quality inspection information to obtain information on the steps already performed, the completion time of those steps, and the status of defective products; annotating the information on the steps already performed and the completion time of those steps into a dynamic area to obtain the content of the first image, where the dynamic area is used to display or hide the information on the steps already performed and the completion time of those steps; determining the coordinate information of defective products based on their status; annotating the coordinate information of defective products on the electric vehicle's instrument panel using color coding in a spatial coordinate system to generate the content of the second image; and generating the second image based on the content of the first image and the content of the second image.

[0056] Specifically, "Operated Steps" information refers to the various operational steps completed during the production process. "Completion Time of Operated Steps" indicates the time taken to complete each step, used to record the production process's timeline. "Defective Product Status" records information on defective products detected during production, such as a component failing to meet standards or the entire product having defects. "Dynamic Area" refers to a region in the image used to display or hide relevant information. "Operated Steps" information and "Completion Time of Operated Steps" are marked in this dynamic area. Specifically, workers can see descriptions of operational steps and the specific completion time of each step in a certain part of the image. This marking method is dynamic and can be shown or hidden under different circumstances. For example, when workers view the image, they can show a specific step or hide irrelevant information as needed. "Defective Product Status" refers to non-conforming products discovered through quality inspection. In this case, the server determines the location of the defective product based on information provided by the quality inspection equipment. These non-conforming products can be located using coordinates. For example, if a problem is found at a contact point of an electric vehicle's instrument panel during the welding process, the system will generate the coordinate information of that component, accurate to its specific location on the production line. The server will use a spatial coordinate system to mark the location of the defective product. This means that the location of defective products will be represented by coordinates on the actual equipment or virtual model of the production line. Color coding is used to distinguish different quality states. For example, red represents non-conforming, green represents conforming, and yellow represents products requiring inspection. For instance, if the display screen on an electric vehicle's dashboard has a defect, the system may mark the specific location on the screen in red to clearly indicate the non-conforming part.

[0057] The server combines the above information to generate a new image for staff to view. This image not only shows the current work environment and progress but also displays dynamic information related to quality inspection, such as which steps have been completed, which parts have quality issues, and uses color markers and coordinates to indicate the location of defective products. Finally, the server integrates the content of the first and second images to generate a final image. This image contains all real-time feedback information and can be displayed to staff via AR glasses or other devices, helping them to promptly identify quality problems and make appropriate adjustments.

[0058] S160: Display a second image to staff via AR glasses.

[0059] Specifically, the server generates a second image containing information such as quality inspection data, the status of operational steps, completion time, and the coordinates of defective products. This second image is then transmitted to AR glasses worn by the worker and displayed to them. This means that workers can see important information relevant to their current work in real time, without relying on traditional display devices or paper instructions. The AR glasses, acting as a display device, overlay the second image onto the actual environment in the worker's field of vision using augmented reality technology. While working, workers can see not only the actual production environment but also digital information related to it. For example, when assembling an instrument, a worker can simultaneously see a red marker on the equipment's display screen indicating the location of a defective component. Therefore, by rationally combining electronic instructions with product traceability, the beneficial implementation of electronic instructions becomes easier.

[0060] In one possible implementation, refer to Figure 2 , Figure 2 This application provides another flowchart illustrating a method for implementing an electronic instruction manual for an electric vehicle instrument panel, specifically including steps S210 to S230, as follows: S210: Perform action recognition on the work image to determine the production action corresponding to the worker; S220: Calculate the action similarity between the production action and the preset action corresponding to the work content, and determine whether the key production action included in the production action is consistent with the key production action included in the preset action; S230: If it is determined that the action similarity between the production action and the preset action is greater than or equal to a preset threshold, and the key production action included in the production action is consistent with the key production action included in the preset action, then the production action corresponding to the worker is determined to be qualified.

[0061] Specifically, in this step, the server uses AR glasses or other camera devices to capture the movements of workers. These movements can be various production operations such as welding, assembly, and testing. Motion recognition technology analyzes the worker's body movements, such as hand and body postures, to determine the current production task they are performing. For example, the server might recognize the worker's hand movements through video streams or real-time image processing to determine if they are performing welding operations. The server compares the worker's actual movements with preset standard movements. Preset movements refer to standardized operating procedures set by the company or production line. These preset movements include specific operational specifications for each step, such as how to correctly use tools and how to perform precise assembly. The server calculates the similarity between the actual movements and the preset movements. Similarity can be measured using different algorithms, such as time-series analysis based on movements, key point detection (e.g., posture, gestures), or machine learning models. If the similarity between the actual production movement and the preset movement is greater than or equal to a set threshold, it indicates that the worker's movement meets expectations. Key production movements refer to movements that must be strictly performed in a specific operation. For example, in a welding process, the angle, pressure, and speed of the welding torch may be key factors. If workers fail to correctly execute these critical steps while performing a task, product quality may be affected. The server also checks whether the actual action includes key production actions consistent with those in the preset action list. If workers omit these key actions during operation, the server will identify and provide a prompt. If the similarity between the actual production action and the preset action meets the preset standard (i.e., the similarity is greater than or equal to the preset threshold), and the actual action includes the key production actions in the preset action list, then the action is considered "qualified." If both conditions are met, the server will confirm that the worker's operation is compliant and will not affect product quality.

[0062] In one possible implementation, if the similarity between the production action and the preset action is determined to be greater than or equal to a preset threshold, and the key production action contained in the production action is inconsistent with the key production action contained in the preset action, then the production action corresponding to the worker is determined to be unqualified; or, if the similarity between the production action and the preset action is determined to be less than a preset threshold, then the production action corresponding to the worker is determined to be unqualified, and an abnormal action alarm message is generated; the abnormal action alarm message is displayed to the worker through AR glasses.

[0063] Specifically, even if the server detects a high similarity between a worker's actions and standard actions, it still needs to check for omissions or inconsistencies in critical production actions. For example, if standard operation requires maintaining a stable hand angle during welding, and the actual operator fails to do so, it will be deemed unqualified even with high similarity. If the similarity is below a threshold—meaning the worker's actions differ significantly from the preset standard actions, or if critical actions are inconsistent (i.e., the worker has not performed critical actions in the standard operation)—the server will determine that production action as "unqualified." The server will generate an abnormal action alarm message, reminding the worker that their operation does not conform to specifications and may affect product quality. The abnormal action alarm message will be displayed to the worker in real time through AR glasses. The AR glasses use augmented reality technology to directly overlay the alarm information into the worker's field of vision, for example, using a prominent red label, text description, or symbol to indicate the unqualified operation. In this way, workers can promptly identify and correct operational errors, ensuring the accuracy and quality of the production process.

[0064] This application also provides a device for implementing an electronic instruction manual for an electric vehicle instrument panel, referring to... Figure 3 , Figure 3 This is a schematic diagram of a module for implementing an electronic instruction manual for an electric vehicle instrument panel, provided in an embodiment of this application. The device is a server, which includes an acquisition module 31 and a processing module 32. The acquisition module 31 acquires the process position and work content of the worker in the production of the electric vehicle instrument panel, and the worker is wearing AR glasses. The processing module 32 determines the electronic instruction content from the electronic instruction manual based on the process position and work content, and generates a first image. The processing module 32 displays the first image to the worker through the AR glasses. The acquisition module 31 receives quality inspection information for the electric vehicle instrument panel sent by the testing equipment. The quality inspection information includes information on the operated steps, the completion time of the operated steps, and the defective product status. The quality inspection information is used to indicate whether the quality of the electric vehicle instrument panel is qualified. The processing module 32 marks the quality inspection information into the first image to generate a second image. The processing module 32 displays the second image to the worker through the AR glasses.

[0065] In one possible implementation, the acquisition module 31 acquires the process position and work content of the worker in the production of electric vehicle instruments, specifically including: the acquisition module 31 acquires the location information of the AR glasses; the processing module 32 determines the first position of the worker based on the location information; the acquisition module 31 acquires the work image of the worker captured by the AR glasses; the processing module 32 determines the environment in which the worker is located from the work image to obtain the second position; the processing module 32 combines the first position and the second position to determine the process position; and the processing module 32 performs image feature recognition on the work image to determine the work content.

[0066] In one possible implementation, the processing module 32 determines the electronic guidance content from the electronic instruction manual based on the process location and work content, and generates a first image. Specifically, the processing module 32 determines the electronic guidance content based on the process location and work content; the processing module 32 performs text conversion and image conversion on the electronic guidance content to generate voice information and action information; the processing module 32 inputs the voice information and action information into an initial digital human model to generate a target digital human containing explanatory voice information and demonstration action information; and the processing module 32 generates the first image based on the target digital human.

[0067] In one possible implementation, the acquisition module 31 receives quality inspection information for the electric vehicle instrument panel sent by the testing device, specifically including: the processing module 32 generating quality inspection information after the first testing stage corresponding to the testing device completes the testing of the electric vehicle instrument panel; the processing module 32 determining the next associated testing stage from the electronic instruction manual to obtain the second testing stage, wherein the testing results corresponding to the first testing stage and the testing results corresponding to the second testing stage have a causal relationship; and the processing module 32 acquiring and storing the quality inspection information for the electric vehicle instrument panel sent by the testing device based on the second testing stage, wherein the testing device corresponding to the second testing stage includes AR glasses.

[0068] In one possible implementation, the processing module 32 annotates the quality inspection information into the first image to generate a second image. Specifically, the processing module 32 classifies the quality inspection information to obtain the information of the operated steps, the completion time of the operated steps, and the defective product status; the processing module 32 annotates the information of the operated steps and the completion time of the operated steps into a dynamic area to obtain the content of the first image, wherein the dynamic area is used to display or hide the information of the operated steps and the completion time of the operated steps; the processing module 32 determines the coordinate information of the defective product based on the defective product status; the processing module 32 annotates the coordinate information of the defective product on the electric vehicle instrument panel in a spatial coordinate system using a color coding method to generate the content of the second image; and the processing module 32 generates the second image based on the content of the first image and the content of the second image.

[0069] In one possible implementation, the processing module 32 performs motion recognition on the work image to determine the production action corresponding to the worker; the processing module 32 calculates the motion similarity between the production action and the preset action corresponding to the work content, and determines whether the key production action contained in the production action is consistent with the key production action contained in the preset action; if the processing module 32 determines that the motion similarity between the production action and the preset action is greater than or equal to a preset threshold, and the key production action contained in the production action is consistent with the key production action contained in the preset action, then the processing module 32 determines that the production action corresponding to the worker is qualified.

[0070] In one possible implementation, if the processing module 32 determines that the similarity between the production action and the preset action is greater than or equal to a preset threshold, and the key production action included in the production action is inconsistent with the key production action included in the preset action, then the processing module 32 determines that the production action corresponding to the worker is unqualified; or, if the processing module 32 determines that the similarity between the production action and the preset action is less than a preset threshold, then the processing module 32 determines that the production action corresponding to the worker is unqualified and generates an abnormal action alarm message; the processing module 32 displays the abnormal action alarm message to the worker through AR glasses.

[0071] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0072] This application also provides an electronic device, with reference to... Figure 4 , Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: at least one processor 41, at least one network interface 44, a user interface 43, a memory 45, and at least one communication bus 42.

[0073] The communication bus 42 is used to enable communication between these components.

[0074] The user interface 43 may include a display screen and a camera. Optionally, the user interface 43 may also include a standard wired interface and a wireless interface.

[0075] Among them, the network interface 44 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0076] The processor 41 may include one or more processing cores. The processor 41 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 45, and by calling data stored in the memory 45. Optionally, the processor 41 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 41 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also not be integrated into the processor 41 and may be implemented as a separate chip.

[0077] The memory 45 may include random access memory (RAM) or read-only memory. Optionally, the memory 45 may include a non-transitory computer-readable storage medium. The memory 45 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 45 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 45 may also be at least one storage device located remotely from the aforementioned processor 41. Figure 4 As shown, the memory 45, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for implementing an electronic instruction manual for an electric vehicle instrument panel.

[0078] exist Figure 4In the electronic device shown, the user interface 43 is mainly used to provide an input interface for the user and to obtain the user input data; while the processor 41 can be used to call the application program stored in the memory 45 for implementing an electronic manual for an electric vehicle instrument panel. When executed by one or more processors, the electronic device executes one or more methods as described in the above embodiments.

[0079] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0080] This application also provides a computer-readable storage medium storing instructions. When executed by one or more processors, these instructions cause an electronic device to perform one or more of the methods described in the above embodiments.

[0081] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0082] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual couplings or direct couplings or communication connections may be through some service interfaces; indirect couplings or communication connections between apparatuses or units may be electrical or other forms.

[0083] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0084] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0085] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0086] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Those skilled in the art will readily conceive of other embodiments of this disclosure upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A method for implementing an electronic instruction manual for an electric vehicle instrument panel, characterized in that, The method includes: The process location and work content of the staff in the production of electric vehicle instruments are obtained, and the staff are wearing AR glasses; Based on the process location and the work content, determine the electronic instruction content from the electronic instruction manual and generate the first image; The first image is displayed to the staff through the AR glasses; The device receives quality inspection information for the electric vehicle instrument panel sent by the testing equipment. The quality inspection information includes information on the operation steps, the completion time of the operation steps, and the status of defective products. The quality inspection information is used to indicate whether the quality of the electric vehicle instrument panel is qualified. The quality inspection information is annotated onto the first image to generate a second image; The second image is displayed to the staff through the AR glasses; The quality inspection information for the electric vehicle's instrument panel sent by the receiving and testing equipment specifically includes: After the first testing step corresponding to the testing equipment completes the testing of the electric vehicle instrument, quality testing information is generated. The next associated detection step of the first detection step is determined from the electronic instruction manual to obtain the second detection step, and the detection result corresponding to the first detection step and the detection result corresponding to the second detection step have a causal relationship. Based on the second detection step, the quality detection information for the electric vehicle instrument sent by the detection device is acquired and stored, and the detection device corresponding to the second detection step includes the AR glasses.

2. The method for implementing an electronic instruction manual for an electric vehicle instrument panel according to claim 1, characterized in that, The acquisition of the worker's process position and work content in the production of electric vehicle instruments specifically includes: Obtain the location information of the AR glasses; Based on the location information, the first location of the staff member is determined; Acquire images of the staff at work captured by the AR glasses; The second location is obtained by determining the environment in which the worker is located from the working image; The process position is determined by combining the first position and the second position; The work content is determined by performing image feature recognition on the work image.

3. The method for implementing an electronic instruction manual for an electric vehicle instrument panel according to claim 1, characterized in that, The step of determining the electronic guidance content from the electronic instruction manual and generating the first image based on the process location and the work content specifically includes: The electronic guidance content is determined based on the process location and the work content; The electronic guidance content is converted into text and images to generate voice and motion information; The voice information and the action information are input into the initial digital human model to generate a target digital human that includes the voice information and the action information for explaining the voice information and demonstrating the action information; The first image is generated based on the target digital human.

4. The method for implementing an electronic instruction manual for an electric vehicle instrument panel according to claim 1, characterized in that, The step of annotating the quality inspection information onto the first image to generate the second image specifically includes: The quality inspection information is classified and processed to obtain the information on the operated steps, the completion time of the operated steps, and the status of the defective products. The information of the performed steps and the completion time of the performed steps are marked in the dynamic area to obtain the first image content. The dynamic area is used to display or hide the information of the performed steps and the completion time of the performed steps. Based on the described defective product information, determine the coordinates of the defective products; In a spatial coordinate system, the coordinate information of the defective product is marked on the instrument panel of the electric vehicle using a color coding method to generate the second image content; The second image is generated based on the content of the first image and the content of the second image.

5. The method for implementing an electronic instruction manual for an electric vehicle instrument panel according to claim 2, characterized in that, The method further includes: The work image is subjected to motion recognition to determine the production action corresponding to the worker; Calculate the action similarity between the production action and the preset action corresponding to the work content, and determine whether the key production actions included in the production action are consistent with the key production actions included in the preset action; If the similarity between the production action and the preset action is greater than or equal to a preset threshold, and the key production actions included in the production action are consistent with the key production actions included in the preset action, then the production action corresponding to the worker is determined to be qualified.

6. The method for implementing an electronic instruction manual for an electric vehicle instrument panel according to claim 5, characterized in that, The method further includes: If the similarity between the production action and the preset action is greater than or equal to a preset threshold, and the key production actions included in the production action are inconsistent with the key production actions included in the preset action, then the production action corresponding to the worker is determined to be unqualified, or... If the similarity between the production action and the preset action is less than a preset threshold, then the production action corresponding to the worker is determined to be unqualified, and an abnormal action alarm message is generated. The abnormal behavior alarm information is displayed to the staff through the AR glasses.

7. A device for implementing an electronic instruction manual for an electric vehicle instrument panel, characterized in that, The device includes an acquisition module (31) and a processing module (32), wherein, The acquisition module (31) is used to acquire the process position and work content of the staff in the production of electric vehicle instruments. The staff is wearing AR glasses. The processing module (32) is used to determine the electronic guidance content from the electronic instruction book according to the process position and the work content, and generate the first image; The processing module (32) is also used to display the first image to the staff through the AR glasses; The acquisition module (31) is also used to receive quality inspection information for the electric vehicle instrument sent by the testing equipment. The quality inspection information includes the operation steps information, the completion time of the operation steps and the defective product situation. The quality inspection information is used to indicate whether the quality of the electric vehicle instrument is qualified. The processing module (32) is further configured to annotate the quality inspection information into the first image to generate a second image; The processing module (32) is also used to display the second image to the staff through the AR glasses.

8. An electronic device, characterized in that, The electronic device includes a processor (41), a memory (45), a user interface (43), and a network interface (44). The memory (45) is used to store instructions. The user interface (43) and the network interface (44) are both used to communicate with other devices. The processor (41) is used to execute the instructions stored in the memory (45) to cause the electronic device to perform the method as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1 to 6.

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