Augmented reality and digital twin hybrid drive equipment maintenance method and system

Abstract

The invention discloses an augmented reality and digital twin hybrid drive equipment maintenance method and system, and relates to the technical field of equipment maintenance, and the method comprises the steps: obtaining the feature data of target equipment; inputting the feature data into a preset equipment digital twinborn model, and generating a digital mirror image of the target equipment, namely a target equipment twinborn body; a hybrid drive maintenance mode is established through augmented reality hardware equipment and target equipment twin; making a maintenance strategy of the target equipment based on the hybrid drive maintenance mode; and the target equipment is maintained through the maintenance strategy that the augmented reality hardware equipment projects the target equipment step by step. The maintenance efficiency and quality can be improved, and the maintenance cost is reduced.

Description

Technical Field

[0001] The present application relates to the technical field of equipment maintenance, and in particular to an equipment maintenance method and system driven by a hybrid of augmented reality and digital twins. Background Art

[0002] In the modern industrial field, the maintenance of various complex equipment faces many challenges. On the one hand, on-site maintenance personnel may have difficulty in diagnosing and repairing problems quickly and accurately due to lack of experience or facing new types of faults. On the other hand, expert resources are often concentrated in specific regions or institutions and cannot arrive at the site in time to provide support.

[0003] Maintenance assistance methods in related technologies, such as paper manuals or simple video guidance, can no longer meet the increasingly complex equipment maintenance needs. Although augmented reality (AR) technology can provide visual assistance to on-site personnel to a certain extent, it lacks comprehensive and in-depth digital model support for equipment. Digital twin technology can build an accurate digital model of equipment, but it lacks in real-time interaction with the site and convenience.

[0004] Nowadays, the prospect of combining augmented reality with digital twin technology and applying it to the field of equipment maintenance has become apparent. There is an urgent need for a technical method that can integrate the advantages of both, which can not only provide real-time, visual maintenance assistance, but also rely on precise digital models for in-depth fault analysis and maintenance strategy formulation to meet the maintenance challenges of complex equipment in the modern industrial field. Summary of the invention

[0005] The purpose of this application is to provide an equipment maintenance method and system driven by a hybrid of augmented reality and digital twins, which can realize equipment maintenance driven by a hybrid of augmented reality and digital twins, improve maintenance efficiency and quality, and reduce maintenance costs.

[0006] To achieve the above objectives, this application provides the following solutions: In a first aspect, the present application provides an equipment maintenance method driven by a hybrid of augmented reality and digital twins, comprising: Acquire characteristic data of the target equipment; the characteristic data includes geometric structure data, physical property data, operation characteristic data and manufacturing process characteristic data; Input the characteristic data of the target equipment into the preset equipment digital twin model to obtain the target equipment twin; the preset equipment digital twin model is based on three-dimensional modeling software, and is built by using digital twin technology in combination with the design drawings, technical manuals and characteristic data of the target equipment, and is used to simulate the operating state of the target equipment; the target equipment twin is a digital image of the target equipment; Formulate a hybrid drive maintenance mode through augmented reality hardware devices and target equipment twins; wherein the augmented reality hardware devices are determined based on the application scenarios and demand information of the target equipment; Formulate maintenance strategies for target equipment based on the hybrid drive maintenance model; The repair strategy of the target device is projected step by step through the augmented reality hardware device to repair the target device.

[0007] In the second aspect, the present application provides an equipment maintenance system driven by a hybrid of augmented reality and digital twins, including: A feature data acquisition module is used to obtain feature data of target equipment; the feature data includes geometric structure data, physical property data, operation characteristic data and manufacturing process characteristic data; A target equipment twin acquisition module is used to input the characteristic data of the target equipment into a preset equipment digital twin model to obtain a target equipment twin; the preset equipment digital twin model is based on three-dimensional modeling software and is built using digital twin technology in combination with the design drawings, technical manuals and characteristic data of the target equipment, and is used to simulate the operating state of the target equipment; the target equipment twin is a digital image of the target equipment; A hybrid drive maintenance mode formulation module is used to formulate a hybrid drive maintenance mode through an augmented reality hardware device and a target equipment twin; wherein the augmented reality hardware device is determined according to the application scenario and demand information of the target equipment; A maintenance strategy formulation module, used to formulate a maintenance strategy for target equipment based on the hybrid drive maintenance mode; The equipment maintenance execution module is used to project the maintenance strategy of the target equipment step by step through the augmented reality hardware device to repair the target equipment.

[0008] According to the specific embodiments provided in this application, this application has the following technical effects: The present application provides an equipment maintenance method and system driven by a hybrid of augmented reality and digital twins. By acquiring the characteristic data of the target equipment and inputting the characteristic data of the target equipment into the preset equipment digital twin model, the target equipment twin is obtained, which can highly restore the real state of the equipment. As a digital mirror image of the target equipment, the target equipment twin can simulate and test various maintenance plans for the equipment without affecting the actual operation of the equipment, thereby improving the accuracy and safety of the maintenance; by formulating a hybrid drive maintenance mode through augmented reality hardware equipment and the target equipment twin, the problem of virtual and reality being out of touch and maintenance guidance being unintuitive in the traditional maintenance mode is solved, and the deep integration of virtual information and real equipment is achieved, providing maintenance personnel with more intuitive and convenient maintenance guidance, solving the problem of not being able to understand the internal operation of the equipment and potential faults in real time and intuitively, and achieving accurate simulation and visual display of the operating status of the target equipment. The hybrid drive maintenance mode comprehensively considers the simulation results of the target equipment twin, the real-time feedback of the augmented reality hardware equipment, and the actual operation experience of the maintenance personnel. Through scientific analysis and decision-making, it formulates the most suitable maintenance strategy for the target equipment. The target equipment is repaired by projecting the maintenance strategy of the target equipment step by step through the augmented reality hardware equipment, which solves the problems of non-standard maintenance process and difficult to ensure maintenance quality, and realizes standardized and high-quality maintenance operations according to the established strategy. During the maintenance process, the maintenance personnel strictly follow the maintenance strategy, and use the augmented reality hardware equipment and the target equipment twin for real-time monitoring and guidance to ensure the accuracy and standardization of the maintenance work, improve the maintenance quality, extend the service life of the equipment, and reduce the maintenance cost and failure rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0010] Figure 1 This is an application environment diagram of an equipment maintenance method driven by a hybrid of augmented reality and digital twins in one embodiment of the present application.

[0011] Figure 2 A flowchart of an equipment maintenance method driven by a hybrid of augmented reality and digital twins is provided in one embodiment of the present application.

[0012] Figure 3 A schematic diagram of a process for obtaining a maintenance efficiency improvement index according to an embodiment of the present application.

[0013] Figure 4A schematic diagram of a flow chart for obtaining a maintenance quality improvement index according to an embodiment of the present application.

[0014] Figure 5 A schematic diagram of a process for obtaining a troubleshooting accuracy index provided in an embodiment of the present application.

[0015] Figure 6 A schematic diagram of the functional modules of an equipment maintenance system driven by a hybrid of augmented reality and digital twins provided in one embodiment of the present application.

[0016] Figure 7 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0018] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0019] The equipment maintenance method of the augmented reality and digital twin hybrid drive provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the characteristic data of the target equipment to the server 104. After the server 104 receives the characteristic data of the target equipment, the characteristic data of the target equipment is input into the preset equipment digital twin model. The model is based on three-dimensional modeling software and uses digital twin technology to combine the design drawings, technical manuals and characteristic data of the target equipment to simulate the operating state of the target equipment, thereby obtaining the target equipment twin, which is a digital mirror of the target equipment. Afterwards, the server 104 determines the augmented reality hardware device according to the application scenario and demand information of the target equipment, and formulates a hybrid drive maintenance mode through the device and the target equipment twin. Based on the hybrid drive maintenance mode, the server 104 further formulates a maintenance strategy for the target equipment and feeds the strategy back to the terminal 102. In addition, in some embodiments, the equipment maintenance method driven by a hybrid of augmented reality and digital twins can also be implemented independently by the server 104 or the terminal 102. For example, the terminal 102 can directly complete feature data acquisition, twin construction, and maintenance mode formulation locally; or the server 104 obtains relevant data of the target equipment from the data storage system and directly completes all operations from twin construction to maintenance strategy formulation on the server side.

[0020] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0021] In an exemplary embodiment, Figure 2 As shown, an equipment maintenance method driven by a hybrid of augmented reality and digital twins is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate, including the following steps 201 to 205. Among them: Step 201, acquiring characteristic data of the target equipment; the characteristic data includes geometric structure data, physical property data, operation characteristic data and manufacturing process characteristic data. The geometric structure data includes shape characteristic data, size characteristic data and spatial position data of the target equipment; the physical property data includes material property data and physical performance test data of the target equipment; the operation characteristic data includes sensor characteristic data of the target equipment and operation log data of the target equipment; the manufacturing process characteristic data includes processing technology data and assembly technology data of the target equipment.

[0022] As an optional implementation, based on equipment shape feature data, equipment size feature data, equipment spatial position data, equipment material property data, physical performance test data, sensor characteristic data, and equipment operation log data, combined with processing technology data and assembly process data, a target equipment twin is obtained through a preset equipment digital twin model, and the target equipment twin and target equipment physical parameters are displayed.

[0023] Among them, this implementation method is to carry out comprehensive digital modeling of the equipment, including the appearance shape, internal structure, mechanical parts, electrical circuits, hydraulic systems and other aspects of the equipment. Using 3D modeling software, a high-precision digital twin model is constructed according to the equipment's design drawings, technical manuals and actual measurement data. This model not only needs to reflect the geometric shape of the equipment, but also needs to integrate the equipment's physical characteristics, performance parameters, operation logic and manufacturing process information, such as the material properties, strength parameters, motion range, power transmission relationship, etc. of each component of the equipment, so that the digital twin model can truly simulate the operating status of the equipment under different working conditions. Therefore, according to the equipment shape, equipment size, equipment spatial position, equipment material properties, physical performance test, sensor characteristics and equipment operation log data, combined with the processing technology data and assembly process data, the digital twin model of the preset equipment is used to obtain the target equipment twin and display the target equipment twin and the physical parameters of the target equipment.

[0024] Step 202, input the characteristic data of the target equipment into the preset equipment digital twin model to obtain the target equipment twin; the preset equipment digital twin model is based on three-dimensional modeling software, and is built using digital twin technology in combination with the design drawings, technical manuals and characteristic data of the target equipment, and is used to simulate the operating status of the target equipment; the target equipment twin is a digital image of the target equipment.

[0025] Step 203: Formulate a hybrid drive maintenance mode through the augmented reality hardware device and the target equipment twin. Specifically, the target equipment twin uses a machine learning algorithm to analyze the historical fault data of the target equipment and predict the potential failure nodes of the target equipment; the potential failure nodes of the target equipment and the real-time operating parameters of the target equipment are superimposed and displayed through the augmented reality hardware device interface to formulate a hybrid drive maintenance mode.

[0026] Among them, the augmented reality hardware equipment is determined according to the application scenario and demand information of the target equipment. Historical fault data includes but is not limited to the fault type, fault occurrence time, operating parameters when the fault occurs, fault repair method and fault cause analysis recorded in the past operation of the target equipment. The real-time operating parameters include but are not limited to the current temperature, speed, pressure, vibration, current and voltage and other key operating indicators of the target equipment. Based on the prediction results and real-time operating parameters, combined with the interactive function of the augmented reality hardware equipment, so that maintenance personnel can intuitively understand the operating status and potential problems of the target equipment, this mode integrates the simulation prediction capabilities of digital twins and the intuitive display and interactive capabilities of augmented reality to improve the accuracy and efficiency of maintenance.

[0027] Step 204 : formulating a maintenance strategy for the target device based on the hybrid drive maintenance mode.

[0028] Step 205: Use the augmented reality hardware device to project the maintenance strategy of the target device in steps, and repair the target device. Use the augmented reality hardware device to project the maintenance animation in steps to guide the technician to complete the complex operation.

[0029] By implementing the above steps 201 to 205, the present application can significantly improve the efficiency and accuracy of equipment maintenance, reduce maintenance costs and risks, and enhance the overall operational reliability and service life of the equipment.

[0030] In another exemplary embodiment of the present application, in order to accurately and scientifically formulate a maintenance strategy for a target device based on a hybrid drive maintenance mode, a strategy parameter combination that can optimize the comprehensive maintenance effectiveness index of the target device can be obtained based on information such as the operating characteristics, maintenance resource status, and historical maintenance data of the target device, and a specific maintenance plan corresponding to the strategy parameter combination is determined as the maintenance strategy for the target device. The above step 204 specifically includes: Based on the hybrid drive maintenance mode, combined with the historical maintenance records of the target equipment, fault occurrence patterns, current operating status and available maintenance resources, alternative maintenance strategies are generated through comprehensive judgment through data analysis, simulation and expert experience.

[0031] Use alternative maintenance strategies to maintain the target equipment twin, and obtain operation monitoring data of the target equipment twin within a preset time period; the operation monitoring data includes maintenance efficiency characteristic data, maintenance quality characteristic data, maintenance cost characteristic data and fault characteristic data.

[0032] Based on the operational monitoring data, the model effectiveness improvement index is calculated.

[0033] If the mode effectiveness improvement index is greater than or equal to the preset mode effectiveness improvement threshold, the performance improvement effect of the alternative maintenance strategy meets the standard, and the alternative maintenance strategy is used as the maintenance strategy for the target device.

[0034] If the mode effectiveness improvement index is less than the preset mode effectiveness improvement threshold, corresponding optimization measures are taken for the alternative maintenance strategy until the performance improvement effect of the alternative maintenance strategy meets the standard, and the maintenance strategy of the target equipment is obtained; the optimization measures include adjusting the maintenance steps (such as disassembly sequence, spare parts allocation), replacing maintenance tools, and improving maintenance methods.

[0035] In another exemplary embodiment of the present application, the model effectiveness improvement index is calculated based on the operation monitoring data, specifically including: A maintenance efficiency improvement index is determined based on maintenance efficiency characteristic data in the operation monitoring data; the maintenance efficiency characteristic data includes post-mode average maintenance time data and post-mode maintenance task completion rate data.

[0036] The maintenance quality improvement index is determined based on the maintenance quality characteristic data in the operation monitoring data; the maintenance quality characteristic data includes one-time maintenance success rate data, post-mode mean time between failures data and post-mode failure interval time data.

[0037] The maintenance cost reduction index is determined based on the maintenance cost characteristic data in the operation monitoring data; the maintenance cost characteristic data includes maintenance material cost reduction rate data and maintenance labor cost reduction rate data.

[0038] The fault troubleshooting accuracy index is determined based on the fault characteristic data in the operation monitoring data; the fault characteristic data includes post-mode fault location accuracy data and post-mode fault cause diagnosis accuracy data.

[0039] According to the maintenance efficiency improvement index, maintenance quality improvement index, maintenance cost reduction index and troubleshooting accuracy index, a model effectiveness improvement index is obtained.

[0040] In another exemplary embodiment of the present application, Figure 3 As shown, a flow chart of obtaining the maintenance efficiency improvement index is provided. According to the maintenance efficiency characteristic data in the operation monitoring data, the maintenance efficiency improvement index is determined, specifically including: Step 301, obtaining pre-mode average maintenance time data and pre-mode maintenance task completion rate data.

[0041] Step 302: Based on the pre-mode average maintenance time data, the pre-mode maintenance task completion rate data, the post-mode average maintenance time data, and the post-mode maintenance task completion rate data, a maintenance efficiency improvement index is obtained. The calculation formula of the maintenance efficiency improvement index is: .

[0042] in, To improve the maintenance efficiency index, is the mean maintenance time data after the mode, is the maintenance task completion rate data after the mode, is the mean maintenance time data before the mode, is the maintenance task completion rate data before the mode, and They are respectively the first preset characteristic coefficient and the second preset characteristic coefficient.

[0043] Among them, when implementing this implementation method, the average maintenance time refers to the average time from the occurrence of equipment failure to the completion of maintenance and restoration of normal operation. In the maintenance mode driven by a hybrid of augmented reality and digital twins, this time is expected to be significantly shortened through precise fault location and visual maintenance guidance. The maintenance task completion rate refers to the proportion of maintenance tasks completed within the specified time. Since the digital twin model can plan maintenance tasks in advance, augmented reality provides real-time operation guidance, allowing maintenance personnel to better grasp the maintenance progress and task requirements, thereby improving the maintenance task completion rate. Finally, the average maintenance time data before the mode and the maintenance task completion rate data before the mode are combined with the average maintenance time data after the mode and the maintenance task completion rate data after the mode to obtain the maintenance efficiency improvement index.

[0044] In another exemplary embodiment of the present application, Figure 4 As shown, a flow chart of obtaining the maintenance quality improvement index is provided. According to the maintenance quality characteristic data in the operation monitoring data, the maintenance quality improvement index is determined, which specifically includes: Step 401, according to the post-mode mean time between failures data and the post-mode failure interval data, obtain equipment reliability improvement rate data. The calculation formula of the equipment reliability improvement rate data is: .

[0045] in, The equipment reliability improvement rate data is: is the mean time between failures data after the mode, is the time between failures after the mode, is the third preset characteristic coefficient.

[0046] Step 402: Obtain a maintenance quality improvement index based on the one-time maintenance success rate data and the equipment reliability improvement rate data. The calculation formula of the maintenance quality improvement index is: .

[0047] in, The maintenance quality improvement index is is the first-time repair success rate data, The equipment reliability improvement rate data is: and They are the fourth preset characteristic coefficient and the fifth preset characteristic coefficient respectively.

[0048] Among them, in this implementation method, the one-time repair success rate refers to the proportion of equipment that can operate normally after one-time repair and no longer have the same fault within a specified time (one month). Through the fault simulation and accurate diagnosis of digital twins, as well as the precise operation guidance of augmented reality, the one-time repair success rate can be effectively improved. The equipment reliability improvement rate after repair refers to the measurement of repair quality by comparing the reliability indicators of equipment before and after repair (such as mean time between failures, time between failures). If the reliability indicators of the equipment after repair are significantly improved, it means that the repair quality is high. Then, according to the one-time repair success rate data and the equipment reliability improvement rate data, the repair quality improvement index is obtained.

[0049] In another exemplary embodiment of the present application, determining the maintenance cost reduction index according to the maintenance cost characteristic data in the operation monitoring data specifically includes: According to the maintenance material cost reduction rate data and the maintenance labor cost reduction rate data, the maintenance cost reduction index is obtained. The calculation formula of the maintenance cost reduction index is: .

[0050] in, is the maintenance cost reduction index, The data for the reduction rate of maintenance material costs is: For maintenance labor cost reduction rate data, and They are the sixth preset characteristic coefficient and the seventh preset characteristic coefficient respectively.

[0051] Among them, by implementing this implementation method, the digital twin model can accurately analyze the cause of the fault, determine the parts that need to be replaced, and avoid unnecessary waste of materials. Augmented reality helps to accurately install parts and reduce part damage caused by operational errors. Through the combination of the two, the proportion of reduced maintenance material costs can be calculated. On the other hand, due to the improvement in maintenance efficiency, maintenance personnel can complete maintenance tasks in a shorter time, thereby reducing labor costs. At the same time, the remote collaboration function of augmented reality enables maintenance personnel to share on-site conditions with remote experts in real time through augmented reality devices. Experts provide remote guidance based on the digital twin model and project operation prompts or annotations to on-site personnel through AR devices to achieve remote collaborative maintenance, reducing dependence on on-site guidance from senior technicians and further reducing labor costs. The reduction rate can be calculated by comparing the labor costs under traditional maintenance and hybrid drive maintenance modes. The maintenance cost reduction index can be obtained by processing the maintenance material cost reduction rate data and the maintenance labor cost reduction rate data.

[0052] In another exemplary embodiment of the present application, Figure 5 As shown, a flow chart of obtaining the troubleshooting accuracy index is provided. According to the fault characteristic data in the operation monitoring data, the troubleshooting accuracy index is determined, specifically including: Step 501, obtaining the pre-mode fault location accuracy data and the pre-mode fault cause diagnosis accuracy data.

[0053] Step 502, based on the pre-mode fault location accuracy data, the pre-mode fault cause diagnosis accuracy data, the post-mode fault location accuracy data and the post-mode fault cause diagnosis accuracy data, obtain a fault troubleshooting accuracy index. The calculation formula of the fault troubleshooting accuracy index is: .

[0054] in, is the troubleshooting accuracy index, is the fault location accuracy data after the mode, is the fault cause diagnosis accuracy data after the mode, is the fault location accuracy data before the mode, is the fault cause diagnosis accuracy data before the mode, and They are the eighth preset characteristic coefficient and the ninth preset characteristic coefficient respectively.

[0055] Among them, in this implementation method, the fault location accuracy refers to the proportion of accurately finding the fault location. The digital twin model simulates the fault propagation path and analyzes the operation data, and the augmented reality device provides an intuitive view of the internal structure of the equipment. The combination of the two can greatly improve the accuracy of fault location. On the other hand, the fault mechanism analysis and big data auxiliary diagnosis function of the digital twin are combined with the real-time data display of augmented reality and the remote collaboration of experts to more accurately determine the cause of the fault. Finally, the pre-mode fault location accuracy data and the pre-mode fault cause diagnosis accuracy data are obtained, and the post-mode fault location accuracy data and the post-mode fault cause diagnosis accuracy data are combined for processing to obtain the fault troubleshooting accuracy index.

[0056] In another exemplary embodiment of the present application, the calculation formula of the mode effectiveness improvement index is: .

[0057] in, To improve the model effectiveness index, To improve the maintenance efficiency index, The maintenance quality improvement index is is the maintenance cost reduction index, is the troubleshooting accuracy index, , and They are the tenth preset characteristic coefficient, the eleventh preset characteristic coefficient and the twelfth preset characteristic coefficient respectively.

[0058] As an optional implementation manner, the first to twelfth preset characteristic coefficients mentioned above are all obtained by querying a preset equipment maintenance database.

[0059] The present application also provides an application scenario, which applies the above-mentioned equipment maintenance method driven by a hybrid of augmented reality and digital twins. Specifically: The equipment maintenance method driven by a hybrid of augmented reality and digital twins provided in this embodiment can be applied in remote maintenance scenarios of complex industrial equipment. Complex industrial equipment remote maintenance scenarios include on-site fault diagnosis links, remote maintenance support links, and on-site maintenance execution links; on-site maintenance personnel enter the remote maintenance support link from the on-site fault diagnosis link into the fault information collected on-site, and the expert team analyzes and makes decisions based on the equipment maintenance method driven by a hybrid of augmented reality and digital twins, obtains accurate maintenance strategies and plans, and enters the downstream on-site maintenance execution link. The equipment maintenance method driven by a hybrid of augmented reality and digital twins provided in this embodiment belongs to the core decision-making and guidance links in the remote maintenance support link. Specifically, in the process of remote maintenance support for complex industrial equipment, the characteristic data of the target equipment is first obtained, including geometric structure data, physical property data, operation characteristic data and manufacturing process characteristic data, and these characteristic data are input into the preset equipment digital twin model to obtain the target equipment twin. The preset equipment digital twin model is based on 3D modeling software and is built using digital twin technology in combination with the design drawings, technical manuals and characteristic data of the target equipment to simulate the operating status of the target equipment; then, a hybrid drive maintenance mode is formulated through augmented reality hardware equipment and the target equipment twin, where the augmented reality hardware equipment is determined based on the application scenario and demand information of the target equipment; then, a maintenance strategy for the target equipment is formulated based on the hybrid drive maintenance mode; finally, the target equipment is repaired by projecting the maintenance strategy of the target equipment in steps through the augmented reality hardware equipment, thereby achieving efficient and accurate remote maintenance of complex industrial equipment.

[0060] Based on the same inventive concept, the embodiment of the present application also provides an augmented reality and digital twin hybrid driven equipment maintenance system for implementing the above-mentioned augmented reality and digital twin hybrid driven equipment maintenance method. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more embodiments of the augmented reality and digital twin hybrid driven equipment maintenance system provided below can refer to the limitations of the augmented reality and digital twin hybrid driven equipment maintenance method in the above text, and will not be repeated here.

[0061] In an exemplary embodiment, Figure 6 As shown, an equipment maintenance system driven by a hybrid of augmented reality and digital twins is provided, including: The feature data acquisition module 601 is used to obtain feature data of the target equipment; the feature data includes geometric structure data, physical property data, operation characteristic data and manufacturing process characteristic data.

[0062] The target equipment twin acquisition module 602 is used to input the characteristic data of the target equipment into the preset equipment digital twin model to obtain the target equipment twin; the preset equipment digital twin model is based on three-dimensional modeling software, and is built using digital twin technology in combination with the design drawings, technical manuals and characteristic data of the target equipment, and is used to simulate the operating status of the target equipment; the target equipment twin is a digital mirror image of the target equipment.

[0063] The hybrid drive maintenance mode formulation module 603 is used to formulate a hybrid drive maintenance mode through an augmented reality hardware device and a target equipment twin; wherein the augmented reality hardware device is determined based on the application scenario and demand information of the target equipment.

[0064] The maintenance strategy formulation module 604 is used to formulate a maintenance strategy for the target device based on the hybrid drive maintenance mode.

[0065] The device maintenance execution module 605 is used to project the maintenance strategy of the target device step by step through the augmented reality hardware device to repair the target device.

[0066] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 7 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store processing data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an equipment maintenance method driven by a hybrid of augmented reality and digital twins is realized.

[0067] Those skilled in the art will understand that Figure 7The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0068] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0069] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0070] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0071] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0072] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0073] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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.

[0074] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. An equipment maintenance method driven by a hybrid of augmented reality and digital twins, characterized in that: The equipment maintenance method driven by hybrid augmented reality and digital twins includes: Acquire characteristic data of the target equipment; the characteristic data includes geometric structure data, physical property data, operation characteristic data and manufacturing process characteristic data; Input the characteristic data of the target equipment into the preset equipment digital twin model to obtain the target equipment twin; the preset equipment digital twin model is based on three-dimensional modeling software, and is built by using digital twin technology in combination with the design drawings, technical manuals and characteristic data of the target equipment, and is used to simulate the operating state of the target equipment; the target equipment twin is a digital image of the target equipment; Formulate a hybrid drive maintenance mode through augmented reality hardware devices and target equipment twins; wherein the augmented reality hardware devices are determined based on the application scenarios and demand information of the target equipment; Formulate maintenance strategies for target equipment based on the hybrid drive maintenance model; The repair strategy of the target device is projected step by step through the augmented reality hardware device to repair the target device.

2. The equipment maintenance method driven by hybrid augmented reality and digital twin according to claim 1, characterized in that: Formulate maintenance strategies for target equipment based on the hybrid drive maintenance model, including: Based on the hybrid drive maintenance mode, combined with the historical maintenance records of the target equipment, the law of failure occurrence, the current operating status and the availability of maintenance resources, an alternative maintenance strategy is generated through comprehensive judgment through data analysis, simulation and expert experience; Use alternative maintenance strategies to repair the target equipment twin and obtain operation monitoring data of the target equipment twin within a preset time period; the operation monitoring data includes maintenance efficiency characteristic data, maintenance quality characteristic data, maintenance cost characteristic data and fault characteristic data; Calculate the model effectiveness improvement index based on operational monitoring data; If the mode effectiveness improvement index is greater than or equal to the preset mode effectiveness improvement threshold, the performance improvement effect of the alternative maintenance strategy meets the standard, and the alternative maintenance strategy is used as the maintenance strategy of the target device; If the mode effectiveness improvement index is less than the preset mode effectiveness improvement threshold, corresponding optimization measures are taken for the alternative maintenance strategy until the performance improvement effect of the alternative maintenance strategy meets the standard and the maintenance strategy of the target equipment is obtained; the optimization measures include adjusting the maintenance steps, replacing the maintenance tools, and improving the maintenance methods.

3. The equipment maintenance method driven by hybrid augmented reality and digital twin according to claim 2, characterized in that: Based on the operational monitoring data, the model effectiveness improvement index is calculated, including: Determine the maintenance efficiency improvement index according to the maintenance efficiency characteristic data in the operation monitoring data; the maintenance efficiency characteristic data includes the average maintenance time data after the mode and the maintenance task completion rate data after the mode; Determine the maintenance quality improvement index according to the maintenance quality characteristic data in the operation monitoring data; the maintenance quality characteristic data includes one-time maintenance success rate data, mean time between failures after mode data and time between failures after mode data; Determine the maintenance cost reduction index according to the maintenance cost characteristic data in the operation monitoring data; the maintenance cost characteristic data includes maintenance material cost reduction rate data and maintenance labor cost reduction rate data; Determine the fault troubleshooting accuracy index according to the fault characteristic data in the operation monitoring data; the fault characteristic data includes the post-mode fault location accuracy data and the post-mode fault cause diagnosis accuracy data; According to the maintenance efficiency improvement index, maintenance quality improvement index, maintenance cost reduction index and troubleshooting accuracy index, a model effectiveness improvement index is obtained.

4. The equipment maintenance method driven by hybrid augmented reality and digital twin according to claim 3, characterized in that: According to the maintenance efficiency characteristic data in the operation monitoring data, the maintenance efficiency improvement index is determined, including: Obtain pre-mode mean maintenance time data and pre-mode maintenance task completion rate data; Based on the average maintenance time data before the mode, the maintenance task completion rate data before the mode, the average maintenance time data after the mode, and the maintenance task completion rate data after the mode, the maintenance efficiency improvement index is obtained using the following formula: ； in, To improve the maintenance efficiency index, is the mean maintenance time data after the mode, is the maintenance task completion rate data after the mode, is the mean maintenance time data before the mode, is the maintenance task completion rate data before the mode, and They are respectively the first preset characteristic coefficient and the second preset characteristic coefficient.

5. The equipment maintenance method driven by hybrid augmented reality and digital twin according to claim 3, characterized in that: According to the maintenance quality characteristic data in the operation monitoring data, the maintenance quality improvement index is determined, including: According to the post-mode mean time between failures data and post-mode failure interval data, the equipment reliability improvement rate data is obtained using the following formula; ； in, The equipment reliability improvement rate data is: is the mean time between failures data after the mode, is the time between failures after the mode, is the third preset characteristic coefficient; According to the first-time maintenance success rate data and equipment reliability improvement rate data, the maintenance quality improvement index is obtained using the following formula: ； in, The maintenance quality improvement index is is the first-time repair success rate data, The equipment reliability improvement rate data is: and They are the fourth preset characteristic coefficient and the fifth preset characteristic coefficient respectively.

6. The equipment maintenance method driven by hybrid augmented reality and digital twin according to claim 3, characterized in that: According to the maintenance cost characteristic data in the operation monitoring data, the maintenance cost reduction index is determined, including: According to the maintenance material cost reduction rate data and maintenance labor cost reduction rate data, the maintenance cost reduction index is obtained using the following formula: ； in, is the maintenance cost reduction index, The data for the reduction rate of maintenance material costs is: For maintenance labor cost reduction rate data, and They are the sixth preset characteristic coefficient and the seventh preset characteristic coefficient respectively.

7. The equipment maintenance method driven by hybrid augmented reality and digital twin according to claim 3, characterized in that: According to the fault characteristic data in the operation monitoring data, the fault troubleshooting accuracy index is determined, including: Obtaining the pre-mode fault location accuracy data and the pre-mode fault cause diagnosis accuracy data; Based on the pre-mode fault location accuracy data, pre-mode fault cause diagnosis accuracy data, post-mode fault location accuracy data, and post-mode fault cause diagnosis accuracy data, the following formula is used to obtain the fault troubleshooting accuracy index: ； in, is the troubleshooting accuracy index, is the fault location accuracy data after the mode, is the fault cause diagnosis accuracy data after the mode, is the fault location accuracy data before the mode, is the fault cause diagnosis accuracy data before the mode, and They are the eighth preset characteristic coefficient and the ninth preset characteristic coefficient respectively.

8. The equipment maintenance method driven by hybrid augmented reality and digital twin according to claim 3, characterized in that: The model effectiveness improvement index is: ； in, To improve the model effectiveness index, To improve the maintenance efficiency index, The maintenance quality improvement index is is the maintenance cost reduction index, is the troubleshooting accuracy index, , and They are the tenth preset characteristic coefficient, the eleventh preset characteristic coefficient and the twelfth preset characteristic coefficient respectively.

9. The equipment maintenance method driven by hybrid augmented reality and digital twin according to claim 1, characterized in that: The geometric structure data includes shape feature data, size feature data and spatial position data of the target equipment; The physical property data includes material property data and physical performance test data of the target equipment; The operation characteristic data includes sensor characteristic data of the target equipment and operation log data of the target equipment; The manufacturing process characteristic data includes processing process data and assembly process data of the target equipment.

10. An equipment maintenance system driven by a hybrid of augmented reality and digital twins, characterized in that: The equipment maintenance system driven by a hybrid of augmented reality and digital twins applies the equipment maintenance method driven by a hybrid of augmented reality and digital twins according to any one of claims 1 to 9, and the equipment maintenance system driven by a hybrid of augmented reality and digital twins comprises: A feature data acquisition module is used to obtain feature data of target equipment; the feature data includes geometric structure data, physical property data, operation characteristic data and manufacturing process characteristic data; A target equipment twin acquisition module is used to input the characteristic data of the target equipment into a preset equipment digital twin model to obtain a target equipment twin; the preset equipment digital twin model is based on three-dimensional modeling software and is built using digital twin technology in combination with the design drawings, technical manuals and characteristic data of the target equipment, and is used to simulate the operating state of the target equipment; the target equipment twin is a digital image of the target equipment; A hybrid drive maintenance mode formulation module is used to formulate a hybrid drive maintenance mode through an augmented reality hardware device and a target equipment twin; wherein the augmented reality hardware device is determined according to the application scenario and demand information of the target equipment; A maintenance strategy formulation module, used to formulate a maintenance strategy for target equipment based on the hybrid drive maintenance mode; The equipment maintenance execution module is used to project the maintenance strategy of the target equipment step by step through the augmented reality hardware device to repair the target equipment.

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