Precision shaft industrial equipment maintenance method and system based on industrial element universe

Abstract

The invention provides a precision shaft industrial equipment maintenance method and system based on an industrial element universe, and relates to the technical field of industrial equipment maintenance, an equipment maintenance process is generated based on abnormal information of precision shaft industrial equipment, the equipment maintenance process is divided into a plurality of equipment maintenance operations, and then a collaborative logic relationship in the equipment maintenance process is determined; performing uncertainty evaluation on the maintenance result of each equipment maintenance operation to obtain a collaborative risk index of each equipment maintenance operation; based on all the collaborative risk indexes, carrying out collaborative distribution between virtuality and reality on the equipment maintenance process to obtain a reality maintenance behavior and a virtual simulation behavior; and through the reality maintenance behavior and the virtual simulation behavior, carrying out virtual and reality cooperative cooperation on the equipment maintenance process, and completing the maintenance process of the precision shaft industrial equipment. On the basis of the scheme, cooperative equipment maintenance between virtuality and reality of the precision shaft industrial equipment can be realized, so that the maintenance efficiency of the precision shaft industrial equipment can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of industrial equipment maintenance, and more specifically, to a method and system for maintaining precision shaft industrial equipment based on the industrial metaverse. Background Art

[0002] The maintenance of precision shaft industrial equipment is showing a trend of intelligence, predictiveness and precision. Traditional maintenance methods are mainly based on regular inspections and post-fault repairs, which have problems such as high costs and long downtime. In recent years, with the advancement of the Internet of Things, sensor technology and big data analysis, the maintenance of precision shaft equipment has gradually developed towards predictive and condition monitoring. By real-time monitoring of key parameters such as precision shafts, vibration, temperature, etc., combined with data analysis algorithms, potential failures can be predicted in advance and the reliability of equipment operation can be improved.

[0003] Traditional industrial equipment maintenance is arranged according to fixed cycles or simple conditions, which fails to fully consider the actual operating status and health of the equipment, and does not take into account the use environment, load changes and workload dynamics of industrial equipment. It is impossible to flexibly adjust the maintenance process according to the actual working conditions, which not only wastes material and human resources, but may also have a negative impact on production progress and quality. Especially in high-precision, high-load industrial equipment such as precision shaft equipment, the accuracy of maintenance is particularly important. Any unnecessary downtime or delay may affect the accuracy and reliability of the equipment. Equipment maintenance that cooperates with virtual and real equipment can accurately simulate the health status of industrial equipment, thereby effectively solving the above problems. Therefore, how to achieve equipment maintenance that cooperates between virtual and real equipment for precision shaft industrial equipment, so as to improve the maintenance efficiency of precision shaft industrial equipment, is a difficult problem faced by the industry. Summary of the invention

[0004] The present application provides a precision shaft industrial equipment maintenance method and system based on the industrial metaverse, which can realize equipment maintenance with coordinated cooperation between virtual and real aspects of precision shaft industrial equipment, thereby improving the maintenance efficiency of precision shaft industrial equipment.

[0005] In a first aspect, the present application provides a method for maintaining precision shaft industrial equipment based on the industrial metaverse, the maintenance method comprising the following steps: Collect the equipment operation status of precision shaft industrial equipment; Extracting abnormal information of precision shaft industrial equipment from the equipment operation status, generating an equipment maintenance process of the precision shaft industrial equipment based on the digital twin technology in the industrial metaverse combined with the abnormal information, and then dividing the equipment maintenance process into multiple equipment maintenance operations; Determine the collaborative logical relationship in the equipment maintenance process according to the operational association between each equipment maintenance operation, and perform uncertainty assessment on the maintenance result of each equipment maintenance operation through the collaborative logical relationship and the operational attributes of each equipment maintenance operation to obtain the collaborative risk index of each equipment maintenance operation; Based on all collaborative risk indexes, all equipment maintenance operations in the equipment maintenance process are collaboratively allocated between virtual and real to obtain real maintenance behaviors and virtual simulation behaviors; Through the actual maintenance behavior and the virtual simulation behavior, all equipment maintenance operations in the equipment maintenance process are coordinated between virtual and real to complete the maintenance process of precision shaft industrial equipment.

[0006] In this embodiment, extracting abnormal information of precision shaft industrial equipment from the equipment operation status specifically includes: Determine abnormal status characteristics of precision shaft industrial equipment through historical maintenance records in the precision shaft industrial equipment; The abnormality matching of the equipment operation state is performed according to the abnormal state characteristics to obtain abnormal information of the precision shaft industrial equipment.

[0007] In this embodiment, the equipment maintenance process of precision shaft industrial equipment generated based on the digital twin technology in the industrial metaverse combined with the abnormal information specifically includes: Initialize the digital twin model based on the digital twin technology in the industrial metaverse; Determining an abnormal function in the digital twin model through the abnormal information; The digital twin model is used to generate the equipment maintenance process of precision shaft industrial equipment.

[0008] In this embodiment, the equipment maintenance process is divided into multiple equipment maintenance operations, specifically including: Determining the relevance of each maintenance action in the equipment maintenance process; The equipment maintenance process is divided into a plurality of equipment maintenance operations according to all the association degrees.

[0009] In this embodiment, determining the collaborative logical relationship in the equipment maintenance process according to the operation association between each equipment maintenance operation specifically includes: For each equipment maintenance operation in the equipment maintenance process, a logical association diagram of the equipment maintenance operation is extracted from the operation associations between the various equipment maintenance operations; Determine the coordination matrix of the equipment maintenance operation to the equipment maintenance process through the logical association diagram, and then obtain the coordination matrix of each equipment maintenance operation to the equipment maintenance process; Determine the collaborative logical relationship in the equipment maintenance process based on all collaborative matrices.

[0010] In this embodiment, uncertainty assessment is performed on the maintenance results of each equipment maintenance operation through the collaborative logical relationship and the operation attributes of each equipment maintenance operation, and the collaborative risk index of each equipment maintenance operation is obtained, which specifically includes: For each equipment maintenance operation, a coordination matrix of the equipment maintenance operation to the equipment maintenance process is obtained from the coordination logical relationship; The collaborative risk index of the equipment maintenance operation is determined by the collaborative matrix and the operation attributes of the equipment maintenance operation, and then the collaborative risk index of each equipment maintenance operation is obtained.

[0011] In this embodiment, all equipment maintenance operations in the equipment maintenance process are collaboratively allocated between virtual and real based on all collaborative risk indexes, and the real maintenance behaviors and virtual simulation behaviors obtained specifically include: Determine the risk threshold of collaboration between virtual and reality; The set of all equipment maintenance operations corresponding to the collaborative risk index less than the collaborative risk threshold is taken as the actual maintenance behavior; A set of all equipment maintenance operations corresponding to collaborative risk indexes greater than or equal to the collaborative risk threshold is used as a virtual simulation behavior.

[0012] In this embodiment, the virtual and real coordination of all equipment maintenance operations in the equipment maintenance process through the real maintenance behavior and the virtual simulation behavior specifically includes: When the precision shaft industrial equipment is maintained according to the various equipment maintenance operations in the equipment maintenance process; If the equipment maintenance operation belongs to actual maintenance behavior, then the equipment maintenance operation is directly used to perform equipment maintenance; If the equipment maintenance operation belongs to the real maintenance behavior, virtual reality technology is used to iteratively adjust the equipment maintenance operation until there is no risk in the virtual reality environment, and then the equipment maintenance is performed.

[0013] In this embodiment, a sensor network in the precision shaft industrial equipment is used to collect the equipment operation status of the precision shaft industrial equipment.

[0014] In a second aspect, the present application provides a precision shaft industrial equipment maintenance system based on the industrial metaverse, which is used to perform a precision shaft industrial equipment maintenance method based on the industrial metaverse, and the equipment maintenance system includes: Status acquisition module, used to collect the equipment operation status of precision shaft industrial equipment; A process division module is used to extract abnormal information of precision shaft industrial equipment from the equipment operation status, generate an equipment maintenance process of the precision shaft industrial equipment based on the digital twin technology in the industrial metaverse combined with the abnormal information, and then divide the equipment maintenance process into multiple equipment maintenance operations; A risk assessment module, which is used to determine the collaborative logical relationship in the equipment maintenance process according to the operational association between each equipment maintenance operation, and to perform uncertainty assessment on the maintenance result of each equipment maintenance operation through the collaborative logical relationship and the operational attributes of each equipment maintenance operation, so as to obtain the collaborative risk index of each equipment maintenance operation; A collaborative allocation module, used for collaboratively allocating all equipment maintenance operations in the equipment maintenance process between virtual and real based on all collaborative risk indexes, to obtain real maintenance behaviors and virtual simulation behaviors; The coordination module is used to coordinate between virtual and real for all equipment maintenance operations in the equipment maintenance process through the real maintenance behavior and the virtual simulation behavior, so as to complete the maintenance process of precision shaft industrial equipment.

[0015] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects: Collect the equipment operation status of precision shaft industrial equipment; extract the abnormal information of precision shaft industrial equipment from the equipment operation status, generate the equipment maintenance process of precision shaft industrial equipment based on the digital twin technology in the industrial metaverse combined with the abnormal information, and then divide the equipment maintenance process into multiple equipment maintenance operations; determine the collaborative logical relationship in the equipment maintenance process according to the operational association between each equipment maintenance operation, and perform uncertainty assessment on the maintenance result of each equipment maintenance operation through the collaborative logical relationship and the operational attributes of each equipment maintenance operation to obtain the collaborative risk index of each equipment maintenance operation; based on all the collaborative risk indices, all the equipment maintenance operations in the equipment maintenance process are collaboratively allocated between virtual and real to obtain real maintenance behavior and virtual simulation behavior; all the equipment maintenance operations in the equipment maintenance process are collaboratively coordinated between virtual and real through the real maintenance behavior and the virtual simulation behavior to complete the maintenance process of precision shaft industrial equipment.

[0016] It can be seen that in this application, all equipment maintenance operations in the equipment maintenance process are coordinated between virtual and real through real maintenance behaviors and virtual simulation behaviors to complete the maintenance process of precision shaft industrial equipment; first, the collaborative risk index comprehensively considers factors such as the interdependence, execution order, and resource usage between equipment operations, so as to evaluate the potential problems of failure risk and performance degradation that each equipment maintenance operation may bring in the maintenance process. Through uncertainty assessment, an accurate collaborative risk index can be assigned to each equipment maintenance operation, which helps maintenance personnel to prioritize high-risk operations, allocate resources reasonably, and avoid inefficient or repetitive maintenance, thereby greatly improving the maintenance efficiency of precision shaft industrial equipment; then, by dividing the equipment maintenance operations in the equipment maintenance process into It is divided into real maintenance behavior and virtual simulation behavior, so as to coordinate and cooperate between virtual and reality, ensure that high-risk operations in the virtual environment are fully simulated and optimized, and avoid potential failures in actual operations. Through virtual simulation technology, operations can be iteratively adjusted in the simulation environment to ensure that each operation is accurately optimized, reducing equipment downtime and maintenance costs. Through reasonable coordination between virtual and reality, the equipment maintenance process becomes more efficient and accurate, effectively promoting the improvement of the maintenance efficiency of precision shaft industrial equipment, and greatly improving the maintenance efficiency of precision shaft industrial equipment. In summary, the above scheme can realize the coordinated equipment maintenance between the virtual and reality of precision shaft industrial equipment, thereby improving the maintenance efficiency of precision shaft industrial equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0018] Figure 1 It is a flow chart of a method for maintaining precision shaft industrial equipment based on the industrial metaverse provided by the present application; Figure 2 is an exemplary flow chart for determining collaborative logical relationships provided by the present application; Figure 3 is an exemplary flow chart of collaborative allocation provided in accordance with the present application; Figure 4 It is a module structure diagram of the precision shaft industrial equipment maintenance system based on the industrial metaverse provided in this application. DETAILED DESCRIPTION

[0019] 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.

[0020] The embodiment of the present application provides a method and system for maintaining precision shaft industrial equipment based on the industrial metaverse, the core of which is to collect the equipment operation status of precision shaft industrial equipment; extract the abnormal information of precision shaft industrial equipment from the equipment operation status, generate the equipment maintenance process of precision shaft industrial equipment based on the digital twin technology in the industrial metaverse combined with the abnormal information, and then divide the equipment maintenance process into multiple equipment maintenance operations; determine the collaborative logical relationship in the equipment maintenance process according to the operation association between each equipment maintenance operation, and evaluate the uncertainty of the maintenance result of each equipment maintenance operation through the collaborative logical relationship and the operation attribute of each equipment maintenance operation to obtain the collaborative risk index of each equipment maintenance operation; based on all the collaborative risk indexes, all equipment maintenance operations in the equipment maintenance process are collaboratively allocated between virtual and reality to obtain real maintenance behavior and virtual simulation behavior; all equipment maintenance operations in the equipment maintenance process are collaboratively coordinated between virtual and reality through the real maintenance behavior and the virtual simulation behavior to complete the maintenance process of precision shaft industrial equipment. The above scheme can realize the equipment maintenance of precision shaft industrial equipment with collaborative cooperation between virtual and reality, thereby improving the maintenance efficiency of precision shaft industrial equipment. Example

[0021] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods. Figure 1 As shown, this figure is an exemplary flow chart of a precision shaft industrial equipment maintenance method based on the industrial metaverse according to this embodiment of the present application, and the maintenance method includes the following steps: In step S1, the equipment operating status of the precision shaft industrial equipment is collected.

[0022] In this embodiment, the sensor network in the precision shaft industrial equipment is used to collect the equipment operation status of the precision shaft industrial equipment; in specific implementation, the sensor network in the precision shaft industrial equipment is used to collect the component status of each component of the precision shaft industrial equipment in the historical time period (default is the last working hour), and the collection of all component statuses can be used as the equipment operation status of the precision shaft industrial equipment. It should be noted that in this application, the equipment operation status reflects the current working status and health status of each component in the precision shaft industrial equipment. The equipment operation status is composed of multiple parameters and indicators, such as: temperature, pressure, vibration, speed, current and voltage.

[0023] In step S2, abnormal information of the precision shaft industrial equipment is extracted from the equipment operation status, and the equipment maintenance process of the precision shaft industrial equipment is generated based on the digital twin technology in the industrial metaverse combined with the abnormal information, and then the equipment maintenance process is divided into multiple equipment maintenance operations.

[0024] In this embodiment, the abnormal information of the precision shaft industrial equipment is extracted from the equipment operation status in the following manner, namely: Determine abnormal status characteristics of precision shaft industrial equipment through historical maintenance records in the precision shaft industrial equipment; The abnormality matching of the equipment operation state is performed according to the abnormal state characteristics to obtain abnormal information of the precision shaft industrial equipment.

[0025] It should be noted that, in the present application, abnormal information refers to abnormal component information of equipment parameters exceeding the normal range; abnormal state characteristics refer to behavioral characteristics in precision shaft industrial equipment that are different from the normal state.

[0026] In the specific implementation, first, collect the historical maintenance records of the precision shaft industrial equipment within a specified time period (the default is the most recent month), which includes the equipment failure type, operating status when it occurs, maintenance operations and maintenance effect data. Data mining technology (for example, pattern recognition) can be used to extract abnormal features from the historical maintenance data, and the results of the abnormal feature extraction can be used as the state abnormality features of the precision shaft industrial equipment; then, use a machine learning algorithm (for example, a decision tree algorithm based on a support vector machine) to match the state abnormality features with the equipment operating status, so as to identify the matching degree between the status of each component in the equipment operating status and the historical failure mode. When the matching degree exceeds the preset threshold, it is determined that a component in the precision shaft industrial equipment is abnormal, and all abnormal components can be obtained. The collection of information in all abnormal components can be used as the abnormal information of the precision shaft industrial equipment.

[0027] In this embodiment, the equipment maintenance process of precision shaft industrial equipment is generated based on the digital twin technology in the industrial metaverse and combined with the abnormal information, which can be specifically adopted in the following manner, namely: Initialize the digital twin model based on the digital twin technology in the industrial metaverse; Determining an abnormal function in the digital twin model through the abnormal information; The digital twin model is used to generate the equipment maintenance process of precision shaft industrial equipment.

[0028] It should be noted that in this application, the equipment maintenance process refers to the sequence and plan of risk maintenance of precision shaft industrial equipment; the digital twin model is a deep learning model used to simulate the operating status of precision shaft industrial equipment; the abnormality function is a logical function used to evaluate the degree of abnormality in the state of precision shaft industrial equipment.

[0029] In the specific implementation, first, the digital twin technology in the industrial metaverse is used to initialize the digital twin model. The digital twin model can synchronize data with physical equipment through IoT sensors and real-time data acquisition devices to ensure that the virtual model and the actual equipment remain consistent in real time; then, the initial function in the digital twin model is obtained, and the abnormal information is input into the initial function using an unsupervised learning algorithm based on a support vector machine. The initial function is trained to recognize abnormal information to improve the maintenance efficiency of the subsequently generated equipment maintenance process, so that the trained initial function can be used as the abnormal function in the digital twin model; finally, the digital twin model is used to simulate the effects of different maintenance actions to obtain a complete maintenance action, and all maintenance actions are arranged in the order of maintenance procedures as the equipment maintenance process for precision shaft industrial equipment.

[0030] In this embodiment, the device maintenance process is divided into multiple device maintenance operations in the following manner, namely: Determining the relevance of each maintenance action in the equipment maintenance process; The equipment maintenance process is divided into a plurality of equipment maintenance operations according to all the association degrees.

[0031] It should be noted that, in this application, equipment maintenance operations refer to specific tasks performed during the equipment maintenance process; correlation is an indicator that measures the degree of dependency between equipment maintenance operations.

[0032] In the specific implementation, first, for each maintenance action, the execution content, prerequisites and interdependencies of the maintenance action can be obtained from the maintenance manual of the precision shaft industrial equipment, and then the dependency matrix between the maintenance action and the equipment maintenance process is established using the graph theory model, so as to calculate the cosine similarity between the maintenance action and other maintenance actions in the equipment maintenance process in the dependency matrix as the correlation of the maintenance action. The correlation of each maintenance action in the equipment maintenance process can be obtained in the above manner; then, the set of maintenance actions whose absolute difference between the correlations in adjacent maintenance actions is within a preset range is taken as the equipment maintenance operation, and multiple equipment maintenance actions can be obtained.

[0033] In step S3, the collaborative logical relationship in the equipment maintenance process is determined based on the operational association between each equipment maintenance operation, and the maintenance result of each equipment maintenance operation is evaluated for uncertainty through the collaborative logical relationship and the operational attributes of each equipment maintenance operation to obtain the collaborative risk index of each equipment maintenance operation.

[0034] Preferably, in this embodiment, reference Figure 2 As shown, this figure is an exemplary flow chart for determining collaborative logical relationships in an embodiment of the present application. In this embodiment, the collaborative logical relationships in the equipment maintenance process are determined according to the operational associations between the various equipment maintenance operations, which can be implemented by the following steps: First, in step S31, for each equipment maintenance operation in the equipment maintenance process, a logical association diagram of the equipment maintenance operation is extracted from the operation associations between the equipment maintenance operations; Then, in step S32, the coordination matrix of the equipment maintenance operation to the equipment maintenance process is determined through the logical association diagram, and then the coordination matrix of each equipment maintenance operation to the equipment maintenance process is obtained; Finally, in step S33, the collaborative logical relationship in the equipment maintenance process is determined based on all the collaborative matrices.

[0035] It should be noted that in this application, the collaborative logical relationship reflects the mutual dependence, cooperation and coordination between equipment maintenance operations; the logical association diagram represents the graph structure of the relationship dependency between equipment maintenance operations; and the collaborative matrix is ​​a matrix that describes the collaborative relationship between equipment maintenance operations.

[0036] In the specific implementation, first, for each equipment maintenance operation in the equipment maintenance process, the operation association of the equipment maintenance operation can be obtained from all dependency matrices. The operation association refers to the dependency relationship between each maintenance operation. For example, a certain equipment maintenance operation needs to be performed after another equipment maintenance operation is completed, or two equipment maintenance operations can be executed in parallel. The graph theory model can be used to convert the operation association into a logical association graph of the equipment maintenance operation; then, the logical association graph can be used to calculate the cosine similarity between the equipment maintenance operation and other equipment operations in the equipment maintenance process as the synergy value, and all synergy values ​​can be constructed into a first-order matrix as the synergy matrix of the equipment maintenance operation to the equipment maintenance process. The synergy matrix of the equipment maintenance operation to the equipment maintenance process can be obtained in the above manner; finally, the set of all synergy matrices can be used as the synergy logical relationship in the equipment maintenance process.

[0037] In this embodiment, the uncertainty of the maintenance result of each equipment maintenance operation is evaluated through the collaborative logical relationship and the operation attribute of each equipment maintenance operation to obtain the collaborative risk index of each equipment maintenance operation, which can be specifically implemented in the following manner, namely: For each equipment maintenance operation, a coordination matrix of the equipment maintenance operation to the equipment maintenance process is obtained from the coordination logical relationship; The collaborative risk index of the equipment maintenance operation is determined by the collaborative matrix and the operation attributes of the equipment maintenance operation, and then the collaborative risk index of each equipment maintenance operation is obtained.

[0038] It should be noted that the collaborative risk index is an indicator that measures the risk level of equipment maintenance operations under collaborative relationships; the collaborative matrix is ​​a matrix that measures the collaborative relationship between equipment maintenance operations; and the operation attributes are attributes that describe the characteristics of each maintenance operation in the equipment maintenance process, including operation difficulty, resource requirements, operation time, risk assessment, and probability of operation failure.

[0039] In the specific implementation, first, for each equipment maintenance operation, the coordination matrix of the equipment maintenance operation to the equipment maintenance process is obtained from the collaborative logical relationship; then, a fuzzy logic model based on the Bayesian network is initialized, and each coordination value in the coordination matrix is ​​used as the input variable in the fuzzy logic model, and the operation attribute of the equipment maintenance operation is used as the fuzzy rule in the fuzzy logic model. The fuzzy logic model is used to perform uncertainty assessment on the maintenance result of the equipment maintenance operation. The quantitative value of the uncertainty assessment result can be used as the collaborative risk index of the equipment maintenance operation. The collaborative risk index of each equipment maintenance operation can be obtained in the above manner.

[0040] It should be noted that the fuzzy logic model is a reasoning and decision-making model based on fuzzy set theory. By processing uncertainty, fuzziness and fuzzy relationships, it can simulate and analyze uncertain information in complex systems. In the maintenance of precision shaft industrial equipment, the fuzzy logic model can process incomplete or fuzzy data to form a reasoning process based on experience and rules. Through the fuzzy logic model, the various attributes and collaborative relationships of equipment maintenance operations can be quantified, and the impact of uncertain factors on the results of maintenance operations during equipment operation can be considered, thereby evaluating the risks of equipment maintenance operations. The role of this model is to provide reasonable decision support in the absence of precise data through fuzzy rules and reasoning mechanisms, help evaluate the collaborative risks of equipment maintenance operations, and provide a scientific basis for the optimization and implementation of maintenance strategies.

[0041] In step S4, all equipment maintenance operations in the equipment maintenance process are collaboratively allocated between virtual and real based on all collaborative risk indexes to obtain real maintenance behaviors and virtual simulation behaviors.

[0042] Preferably, in this embodiment, reference Figure 3 As shown, this figure is an exemplary flow chart of collaborative allocation in an embodiment of the present application. In this embodiment, all equipment maintenance operations in the equipment maintenance process are collaboratively allocated between virtual and real based on all collaborative risk indexes, and the real maintenance behavior and virtual simulation behavior can be specifically implemented by the following steps: First, in step S41, a collaborative risk threshold between virtual and reality is determined; Then, in step S42, a set of all equipment maintenance operations corresponding to the collaborative risk index less than the collaborative risk threshold is taken as the actual maintenance behavior; Finally, in step S43, a set of all equipment maintenance operations corresponding to collaborative risk indices greater than or equal to the collaborative risk threshold is taken as a virtual simulation behavior.

[0043] It should be noted that in this application, actual maintenance behavior refers to maintenance operations that are performed directly; virtual simulation behavior refers to maintenance operations that require virtual simulation to eliminate maintenance risks; and the collaborative risk threshold is a dividing value for measuring the risk level of equipment maintenance operations.

[0044] In specific implementation, first, the result of dividing the sum of the mean and mode of all collaborative risk indices by 2 can be used as the collaborative risk threshold between virtual and reality. In order to improve the adaptability of the collaborative risk threshold to different devices, in other embodiments, the weights of the means and modes of all collaborative risk indices can be set based on historical experience, so that the weighted sum of the means and modes of all collaborative risk indices can be used as the collaborative risk threshold; then, the set of all equipment maintenance operations corresponding to the collaborative risk index less than the collaborative risk threshold is used as the actual maintenance behavior; finally, the set of all equipment maintenance operations corresponding to the collaborative risk index greater than or equal to the collaborative risk threshold can be used as the virtual simulation behavior.

[0045] In step S5, all equipment maintenance operations in the equipment maintenance process are coordinated between virtual and real through the actual maintenance behavior and the virtual simulation behavior to complete the maintenance process of the precision shaft industrial equipment.

[0046] In this embodiment, the virtual and real coordination of all equipment maintenance operations in the equipment maintenance process through the real maintenance behavior and the virtual simulation behavior can be specifically carried out in the following manner, namely: When the precision shaft industrial equipment is maintained according to the various equipment maintenance operations in the equipment maintenance process; If the equipment maintenance operation belongs to actual maintenance behavior, then the equipment maintenance operation is directly used to perform equipment maintenance; If the equipment maintenance operation belongs to the real maintenance behavior, virtual reality technology is used to iteratively adjust the equipment maintenance operation until there is no risk in the virtual reality environment, and then the equipment maintenance is performed.

[0047] It should be noted that by dividing the equipment maintenance process of precision shaft industrial equipment into real maintenance behavior and virtual simulation behavior, and through the collaborative cooperation between virtual and reality, the safety and efficiency of equipment maintenance are significantly improved. For low-risk real maintenance behaviors, directly executing equipment maintenance operations can efficiently complete routine maintenance tasks; for high-risk virtual simulation operations, virtual simulation technology is used for iterative adjustments, and the operation process is fully simulated in a virtual environment. Potential risks are identified and optimized, thereby reducing the probability of failure in actual maintenance operations. The collaborative cooperation method can effectively reduce equipment downtime, improve the work efficiency of maintenance personnel, and ensure that equipment maintenance operations are carried out under the most optimized conditions, thereby improving the reliability of precision shaft industrial equipment and the efficiency of the maintenance process.

[0048] It can be seen that in this application, all equipment maintenance operations in the equipment maintenance process are coordinated between virtual and real through real maintenance behaviors and virtual simulation behaviors to complete the maintenance process of precision shaft industrial equipment; first, the collaborative risk index comprehensively considers factors such as the interdependence, execution order, and resource usage between equipment operations, so as to evaluate the potential problems of failure risk and performance degradation that each equipment maintenance operation may bring in the maintenance process. Through uncertainty assessment, an accurate collaborative risk index can be assigned to each equipment maintenance operation, which helps maintenance personnel to prioritize high-risk operations, allocate resources reasonably, and avoid inefficient or repetitive maintenance, thereby greatly improving the maintenance efficiency of precision shaft industrial equipment; then, by dividing the equipment maintenance operations in the equipment maintenance process into It is divided into real maintenance behavior and virtual simulation behavior, so as to coordinate and cooperate between virtual and reality, ensure that high-risk operations in the virtual environment are fully simulated and optimized, and avoid potential failures in actual operations. Through virtual simulation technology, operations can be iteratively adjusted in the simulation environment to ensure that each operation is accurately optimized, reducing equipment downtime and maintenance costs. Through reasonable coordination between virtual and reality, the equipment maintenance process becomes more efficient and accurate, effectively promoting the improvement of the maintenance efficiency of precision shaft industrial equipment, and greatly improving the maintenance efficiency of precision shaft industrial equipment. In summary, the above scheme can realize the coordinated equipment maintenance between the virtual and reality of precision shaft industrial equipment, thereby improving the maintenance efficiency of precision shaft industrial equipment. Example

[0049] This application provides a precision shaft industrial equipment maintenance system based on the industrial metaverse, referring to Figure 4 As shown, this figure is a schematic diagram of the equipment maintenance system shown in this embodiment of the present application, and the equipment maintenance system includes: The status acquisition module 100 is used to acquire the equipment operation status of the precision shaft industrial equipment; A process division module 200 is used to extract abnormal information of precision shaft industrial equipment from the equipment operation status, generate an equipment maintenance process of the precision shaft industrial equipment based on the digital twin technology in the industrial metaverse combined with the abnormal information, and then divide the equipment maintenance process into multiple equipment maintenance operations; The risk assessment module 300 is used to determine the collaborative logical relationship in the equipment maintenance process according to the operational association between each equipment maintenance operation, and to perform uncertainty assessment on the maintenance result of each equipment maintenance operation through the collaborative logical relationship and the operational attributes of each equipment maintenance operation to obtain the collaborative risk index of each equipment maintenance operation; A collaborative allocation module 400 is used to collaboratively allocate all equipment maintenance operations in the equipment maintenance process between virtual and real based on all collaborative risk indexes to obtain real maintenance behaviors and virtual simulation behaviors; The coordination module 500 is used to coordinate between virtual and real for all equipment maintenance operations in the equipment maintenance process through the real maintenance behavior and the virtual simulation behavior, so as to complete the maintenance process of precision shaft industrial equipment.

[0050] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0051] A person skilled in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, the storage medium including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically-erasable programmable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0052] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

Claims

1. A method for maintaining precision shaft industrial equipment based on the industrial metaverse, characterized in that: The maintenance method comprises the following steps: Collect the equipment operation status of precision shaft industrial equipment; Extracting abnormal information of precision shaft industrial equipment from the equipment operation status, generating an equipment maintenance process of the precision shaft industrial equipment based on the digital twin technology in the industrial metaverse combined with the abnormal information, and then dividing the equipment maintenance process into multiple equipment maintenance operations; Determine the collaborative logical relationship in the equipment maintenance process according to the operational association between each equipment maintenance operation, and perform uncertainty assessment on the maintenance result of each equipment maintenance operation through the collaborative logical relationship and the operational attributes of each equipment maintenance operation to obtain the collaborative risk index of each equipment maintenance operation; Based on all collaborative risk indexes, all equipment maintenance operations in the equipment maintenance process are collaboratively allocated between virtual and real to obtain real maintenance behaviors and virtual simulation behaviors; Through the actual maintenance behavior and the virtual simulation behavior, all equipment maintenance operations in the equipment maintenance process are coordinated between virtual and real to complete the maintenance process of precision shaft industrial equipment.

2. A method for maintaining precision shaft industrial equipment based on industrial metaverse according to claim 1, characterized in that: Extracting abnormal information of precision shaft industrial equipment from the equipment operation status specifically includes: Determine abnormal status characteristics of precision shaft industrial equipment through historical maintenance records in the precision shaft industrial equipment; The abnormality matching of the equipment operation state is performed according to the abnormal state characteristics to obtain abnormal information of the precision shaft industrial equipment.

3. A method for maintaining precision shaft industrial equipment based on industrial metaverse according to claim 1, characterized in that: The equipment maintenance process of precision shaft industrial equipment generated based on the digital twin technology in the industrial metaverse combined with the abnormal information specifically includes: Initialize the digital twin model based on the digital twin technology in the industrial metaverse; Determining an abnormal function in the digital twin model through the abnormal information; The digital twin model is used to generate the equipment maintenance process of precision shaft industrial equipment.

4. A method for maintaining precision shaft industrial equipment based on industrial metaverse according to claim 1, characterized in that: The equipment maintenance process is divided into multiple equipment maintenance operations, specifically including: Determining the relevance of each maintenance action in the equipment maintenance process; The equipment maintenance process is divided into a plurality of equipment maintenance operations according to all the association degrees.

5. The method for maintaining precision shaft industrial equipment based on industrial metaverse according to claim 1, characterized in that: The collaborative logical relationships in the equipment maintenance process are determined based on the operational associations between the various equipment maintenance operations, including: For each equipment maintenance operation in the equipment maintenance process, a logical association diagram of the equipment maintenance operation is extracted from the operation associations between the various equipment maintenance operations; Determine the coordination matrix of the equipment maintenance operation to the equipment maintenance process through the logical association diagram, and then obtain the coordination matrix of each equipment maintenance operation to the equipment maintenance process; Determine the collaborative logical relationship in the equipment maintenance process based on all collaborative matrices.

6. A method for maintaining precision shaft industrial equipment based on industrial metaverse according to claim 1, characterized in that: The uncertainty of the maintenance results of each equipment maintenance operation is evaluated through the collaborative logical relationship and the operation attributes of each equipment maintenance operation, and the collaborative risk index of each equipment maintenance operation is obtained, which specifically includes: For each equipment maintenance operation, a coordination matrix of the equipment maintenance operation to the equipment maintenance process is obtained from the coordination logical relationship; The collaborative risk index of the equipment maintenance operation is determined by the collaborative matrix and the operation attributes of the equipment maintenance operation, and then the collaborative risk index of each equipment maintenance operation is obtained.

7. A method for maintaining precision shaft industrial equipment based on industrial metaverse as claimed in claim 1, characterized in that: Based on all collaborative risk indexes, all equipment maintenance operations in the equipment maintenance process are collaboratively allocated between virtual and real, and the real maintenance behaviors and virtual simulation behaviors are obtained, which specifically include: Determine the risk threshold of collaboration between virtual and reality; The set of all equipment maintenance operations corresponding to the collaborative risk index less than the collaborative risk threshold is taken as the actual maintenance behavior; A set of all equipment maintenance operations corresponding to collaborative risk indexes greater than or equal to the collaborative risk threshold is used as a virtual simulation behavior.

8. The method for maintaining precision shaft industrial equipment based on industrial metaverse according to claim 1, characterized in that: The virtual and real coordination of all equipment maintenance operations in the equipment maintenance process through the real maintenance behavior and the virtual simulation behavior specifically includes: When the precision shaft industrial equipment is maintained according to the various equipment maintenance operations in the equipment maintenance process; If the equipment maintenance operation belongs to actual maintenance behavior, then the equipment maintenance operation is directly used to perform equipment maintenance; If the equipment maintenance operation belongs to the real maintenance behavior, virtual reality technology is used to iteratively adjust the equipment maintenance operation until there is no risk in the virtual reality environment, and then the equipment maintenance is performed.

9. The method for maintaining precision shaft industrial equipment based on industrial metaverse according to claim 1, characterized in that: Use the sensor network in the precision shaft industrial equipment to collect the equipment operation status of the precision shaft industrial equipment.

10. A precision shaft industrial equipment maintenance system based on industrial metaverse, used to execute a precision shaft industrial equipment maintenance method based on industrial metaverse as claimed in any one of claims 1 to 9, characterized in that: The equipment maintenance system comprises: Status acquisition module, used to collect the equipment operation status of precision shaft industrial equipment; A process division module is used to extract abnormal information of precision shaft industrial equipment from the equipment operation status, generate an equipment maintenance process of the precision shaft industrial equipment based on the digital twin technology in the industrial metaverse combined with the abnormal information, and then divide the equipment maintenance process into multiple equipment maintenance operations; A risk assessment module, which is used to determine the collaborative logical relationship in the equipment maintenance process according to the operational association between each equipment maintenance operation, and to perform uncertainty assessment on the maintenance result of each equipment maintenance operation through the collaborative logical relationship and the operational attributes of each equipment maintenance operation, so as to obtain the collaborative risk index of each equipment maintenance operation; A collaborative allocation module, used for collaboratively allocating all equipment maintenance operations in the equipment maintenance process between virtual and real based on all collaborative risk indexes, to obtain real maintenance behaviors and virtual simulation behaviors; The coordination module is used to coordinate between virtual and real for all equipment maintenance operations in the equipment maintenance process through the real maintenance behavior and the virtual simulation behavior, so as to complete the maintenance process of precision shaft industrial equipment.

Images