Industrial 5.0 comprehensive performance evaluation method and system, electronic equipment and program product

Abstract

The invention relates to the technical field of industrial automation, in particular to an industrial 5.0 comprehensive performance evaluation method and system, electronic equipment and a program product, and the method comprises the steps: constructing an industrial performance evaluation framework of an industrial system, and enabling the industrial performance evaluation framework to integrate a digital twin system, an IIOT system and an XR system; a working scene of an industrial system is simulated by using a digital twin system, and working data of the industrial system is obtained through an IIOT system to calculate overall equipment efficiency (OEE); the IIOT system is utilized to track the task completion time and the resource use condition of the industrial system, and the human-machine cooperation efficiency HMCE is calculated; and according to the data of the IIOT system, the data of the digital twin system and the data of the XR system, calculating the equipment fault probability DTPM of the industrial system within the preset time. Therefore, the problems of low interactivity, low integration level, low intelligence and the like of an industrial ecological system are solved, enhancement of man-machine cooperation and a sustainable self-adaptive production process is facilitated, and enhanced scene sense and immersion sense are provided for operators.

Description

Technical Field

[0001] The present application relates to the field of industrial automation technology, and in particular to an Industry 5.0 comprehensive performance evaluation method, system, electronic equipment and program product. Background Art

[0002] With the advancement of computer and communication technology, the fourth industrial revolution, Industry 4.0, has arrived. This paradigm shift is characterized by the integration of various technological innovations that are fundamentally reshaping traditional production models and promoting a more interconnected and intelligent industrial ecosystem. Thanks to the widespread implementation of smart machines and smart factories, the core of Industry 4.0 is to enhance the automation of the manufacturing process, which is essential for moving towards the next generation of Industry 5.0, industrial Internet of Things, intelligence and digital twin technology. Summary of the invention

[0003] The present application provides an Industry 5.0 comprehensive performance evaluation method, system, electronic equipment and program product to solve the problems of low interactivity, integration and intelligence of the industrial ecosystem.

[0004] In a first aspect, an embodiment of the present application provides an Industry 5.0 comprehensive performance evaluation method, comprising the following steps: constructing an industrial performance evaluation framework for an industrial system, the industrial performance evaluation framework integrating a digital twin system, an IIoT (Industrial Internet of Things) system, and an XR (Extended Reality, Virtual Reality / Augmented Reality / Mixed Reality [VR / AR / MR]) system; using the digital twin system to simulate the working scene of the industrial system, and obtaining the working data of the industrial system through the IIoT system to calculate the overall equipment efficiency OEE; using the IIoT system to track the task completion time and resource usage of the industrial system, and calculate the human-machine collaboration efficiency HMCE; and calculating the equipment failure probability DTPM of the industrial system within a preset time based on the data of the IIoT system, the data of the digital twin system, and the data of the XR system.

[0005] Optionally, after calculating the overall equipment efficiency OEE, the method includes: using the XR system to display the overall equipment efficiency OEE of the industrial system and improvement suggestions for the industrial system.

[0006] Optionally, after calculating the equipment failure probability DTPM of the industrial system within a preset time, the method includes: receiving a guiding maintenance instruction from an operator; and performing predictive maintenance on the industrial system based on the guiding maintenance instruction.

[0007] Optionally, the calculation formula of the overall equipment efficiency OEE is:

[0008] y OEE =f(A, P, Q, DTA);

[0009] Among them, A is the production line availability of the industrial system, P is the production performance, Q is the product quality, DTA is the digital twin accuracy, and f(·) is the nonlinear function.

[0010] Optionally, the calculation formula of the human-machine collaboration efficiency HMCE is:

[0011] y HMCE =g(V h , V m ,T,R,XR efficiency );

[0012] Among them, V h is the value of the manual task, V m is the value of the machine task, T is the time taken for the task, R is the resources used for the task, XR efficiency is the efficiency gain assisted by XR technology, and g(·) is a nonlinear function.

[0013] Optionally, the calculation formula of the probability of device failure DTPM is:

[0014] P(F(t)|X,D,E)=h(q(X),r(D),s(E),t);

[0015] where P(F(t)|X, D, E) is the failure probability given the input variables X, D, E, X is the input data of the IIoT system, D is the data from the digital twin system, E is the data of the XR system, q(X) is the function that processes the data of the IIoT system, r(D) is the function that processes the digital twin data, s(E) is the function related to the additional input from the operator through the XR interface, h(·) is the function that maps q, r, s, t to failure probabilities, and F(t) is the failure within time t.

[0016] The second aspect of the present application provides an Industry 5.0 comprehensive performance evaluation system, including: a construction module, used to construct an industrial performance evaluation framework for an industrial system, the industrial performance evaluation framework integrating a digital twin system, an IIoT system and an XR system; a calculation module, used to use the digital twin system to simulate the working scenario of the industrial system, and obtain the working data of the industrial system through the IIoT system to calculate the overall equipment efficiency OEE, use the IIoT system to track the task completion time and resource usage of the industrial system, calculate the human-machine collaboration efficiency HMCE, and calculate the equipment failure probability DTPM of the industrial system within a preset time based on the data of the IIoT system, the data of the digital twin system and the data of the XR system.

[0017] Optionally, after calculating the overall equipment efficiency OEE, the calculation module is further used to: use the XR system to display the overall equipment efficiency OEE of the industrial system and improvement suggestions for the industrial system.

[0018] Optionally, after calculating the equipment failure probability DTPM of the industrial system within a preset time, the calculation module is further used to: receive a guiding maintenance instruction from an operator; and perform predictive maintenance on the industrial system based on the guiding maintenance instruction.

[0019] Optionally, the calculation formula of the overall equipment efficiency OEE is:

[0020] y OEE =f(A, P, Q, DTA);

[0021] Among them, A is the production line availability of the industrial system, P is the production performance, Q is the product quality, DTA is the digital twin accuracy, and f(·) is the nonlinear function.

[0022] Optionally, the calculation formula of the human-machine collaboration efficiency HMCE is:

[0023] y HMCE =g(V h , V m ,T,R,XR efficiency );

[0024] Among them, V h is the value of the manual task, V m is the value of the machine task, T is the time taken for the task, R is the resources used for the task, XR efficiency is the efficiency gain assisted by XR technology, and g(·) is a nonlinear function.

[0025] Optionally, the calculation formula of the probability of device failure DTPM is:

[0026] P(F(t)|X,D,E)=h(q(X),r(D),s(E),t);

[0027] where P(F(t)|X, D, E) is the failure probability given the input variables X, D, E, X is the input data of the IIoT system, D is the data from the digital twin system, E is the data of the XR system, q(X) is the function that processes the data of the IIoT system, r(D) is the function that processes the digital twin data, s(E) is the function related to the additional input from the operator through the XR interface, h(·) is the function that maps q, r, s, t to failure probabilities, and F(t) is the failure within time t.

[0028] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the comprehensive performance evaluation method of Industry 5.0 as described in the above embodiment.

[0029] The fourth aspect of the present application provides a computer program product on which a computer program is stored, and the program is executed by a processor to implement the comprehensive performance evaluation method of Industry 5.0 as described in the above embodiment.

[0030] In the above implementation, an industrial performance evaluation framework for industrial systems is constructed, and the industrial performance evaluation framework integrates the digital twin system, the IIoT system, and the XR system; the digital twin system is used to simulate the working scene of the industrial system, and the working data of the industrial system is obtained through the IIoT system to calculate the overall equipment efficiency OEE; the IIoT system is used to track the task completion time and resource usage of the industrial system, and the human-machine collaboration efficiency HMCE is calculated; the equipment failure probability DTPM of the industrial system within a preset time is calculated based on the data of the IIoT system, the data of the digital twin system, and the data of the XR system. In this way, the problems of low interactivity, integration, and intelligence of the industrial ecosystem are solved, which helps to enhance human-machine collaboration and sustainable adaptive production processes, and provide operators with an enhanced sense of presence and immersion.

[0031] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0033] Figure 1 A flowchart of an industrial 5.0 comprehensive performance evaluation method provided according to an embodiment of the present application;

[0034] Figure 2 is a schematic diagram of an Industry 5.0 monitoring system according to an embodiment of the present application;

[0035] Figure 3 A schematic diagram of XR providing support for digital twin interaction in the context of Industry 5.0 according to an embodiment of the present application;

[0036] Figure 4 This is an example diagram of an Industry 5.0 comprehensive performance evaluation system according to an embodiment of the present application;

[0037] Figure 5 Schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0038] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0039] The following describes the industrial 5.0 comprehensive performance evaluation method, system, electronic device and program product of the embodiment of the present application with reference to the accompanying drawings. In view of the low interactivity, integration and intelligence of the industrial ecosystem mentioned in the above background technology, the present application provides an industrial 5.0 comprehensive performance evaluation method, in which an industrial system industrial performance evaluation framework is constructed, and the industrial performance evaluation framework integrates a digital twin system, an IIoT system and an XR system; the digital twin system is used to simulate the working scene of the industrial system, and the working data of the industrial system is obtained through the IIoT system to calculate the overall equipment efficiency OEE; the IIoT system is used to track the task completion time and resource usage of the industrial system, and the human-machine collaboration efficiency HMCE is calculated; the equipment failure probability DTPM of the industrial system within a preset time is calculated based on the data of the IIoT system, the data of the digital twin system and the data of the XR system. As a result, the problems of low interactivity, integration and intelligence of the industrial ecosystem are solved, which helps to enhance human-machine collaboration and sustainable adaptive production processes, and provide operators with an enhanced sense of presence and immersion.

[0040] In the context of the Industry 5.0 manufacturing paradigm, the production system integrates a comprehensive Industrial IoT ecosystem, including all production equipment with embedded smart sensors and actuators, auxiliary equipment, and advanced robotics, which helps enhance human-machine collaboration and sustainable adaptive production processes. As a virtual replica of a physical system, digital twin technology has been a hot topic in various industrial fields, such as advanced manufacturing, and can be used for various functions such as monitoring, control strategy design, fault detection, positioning, and recovery. The integration of extended reality technology allows immersive monitoring of industrial processes and equipment, which can provide operators with an enhanced sense of presence and immersion. Industrial processes and equipment can also be controlled through various XR accessories, such as XR controllers and data gloves.

[0041] Specifically, Figure 1 A flowchart of an Industry 5.0 comprehensive performance evaluation method provided in an embodiment of the present application.

[0042] Before specifically introducing the comprehensive performance evaluation method of Industry 5.0, the embodiment of the present application also proposes an Industry 5.0 monitoring system, which is specifically described as follows:

[0043] First, build a distributed, modular, and scalable multi-layer architecture that integrates digital twins, industrial IoT, and XR technologies. Figure 1 The following figure shows the Industry 5.0 architecture enabled by digital twins, IIoT, and XR. This architecture is a comprehensive three-layer architecture that considers the integration of humans, machines, and objects. Humans refer to the monitoring layer, machines refer to the computing layer, and objects refer to the physical layer. From a bottom-up perspective, these three layers constitute the components from on-site industrial scenes to remote monitoring with remote operation capabilities. Figure 2 As shown in the figure, at the physical layer, workshops are interconnected to form IIoT and flexible manufacturing paradigms. Digital twins are also developed based on this layer of physical devices. At the computing layer, server clusters are deployed to support IIoT configuration and communication, as well as interactions from the monitoring layer. Users (such as technicians, engineers, and data analysts) can monitor and operate through digital twins and real-time videos collected by cameras deployed at industrial sites. The computing layer can be placed in a cloud environment. The monitoring layer can support typical devices such as tablets and smartphones, and can be enhanced by XR devices.

[0044] Among them, this multi-layer architecture integrates key enabling technologies: including digital twins, IIoT and XR, as detailed below:

[0045] Digital twin technology: Digital twins create virtual copies of physical assets and processes, which can simulate various scenarios in real time. Digital twins also provide data for predictive maintenance and process optimization, and are used to monitor and remotely control industrial systems.

[0046] IIoT technology: IIoT is a key feature of Industry 5.0, which includes low-latency communications, for which smart sensors, controllers, and actuators are deployed to collect and analyze data, coordinate processes, and execute commands throughout the manufacturing process. IIoT provides data input for digital twins and analytical models.

[0047] XR technology: In the era of Industry 5.0, XR has the characteristics of low latency, so interactive and display functions with good user experience can be realized through XR. XR can provide visual overlay of digital twin data in the physical environment and enhance human-computer interaction, which is helpful for maintenance procedures and training, and Figure 3 Figure 2 shows the XR-enabled digital twin interaction in an Industry 5.0 environment. Communication constraints, such as latency and packet loss, can be actively compensated through network predictive control strategies.

[0048] Furthermore, the comprehensive performance evaluation method of Industry 5.0 includes the following steps:

[0049] In step S101, an industrial performance evaluation framework for the industrial system is constructed, and the industrial performance evaluation framework integrates the digital twin system, the IIoT system, and the XR system.

[0050] In step S102, the digital twin system is used to simulate the working scenario of the industrial system, and the working data of the industrial system is obtained through the IIoT system to calculate the overall equipment efficiency (OEE).

[0051] In step S103, the IIoT system is used to track the task completion time and resource usage of the industrial system and calculate the human-machine collaboration efficiency HMCE.

[0052] In step S104, the equipment failure probability DTPM of the industrial system within a preset time is calculated based on the data of the IIoT system, the data of the digital twin system, and the data of the XR system.

[0053] Optionally, in some embodiments, after calculating the overall equipment efficiency OEE, the method includes: utilizing an XR system to display the overall equipment efficiency OEE of the industrial system and improvement suggestions for the industrial system.

[0054] Optionally, in some embodiments, after calculating the equipment failure probability DTPM of the industrial system within a preset time, the method includes: receiving a guiding maintenance instruction from an operator; and performing predictive maintenance on the industrial system based on the guiding maintenance instruction.

[0055] Optionally, in some embodiments, the calculation formula of overall equipment efficiency OEE is:

[0056] y OEE =f(A, P, Q, DTA); (1)

[0057] Among them, A is the production line availability of the industrial system, P is the production performance, Q is the product quality, DTA is the digital twin accuracy, and f(·) is the nonlinear function.

[0058] It should be noted that the nonlinear function f(·) can be set by relevant persons in the art and is not specifically limited here.

[0059] Optionally, in some embodiments, the calculation formula of human-machine collaboration efficiency HMCE is:

[0060] y HMCE =g(V h , V m ,T,R,XR efficiency ); (2)

[0061] Among them, V h is the value of the manual task, V mis the value of the machine task, T is the time taken for the task, R is the resources used for the task, XR efficiency is the efficiency gain assisted by XR technology, and g(·) is a nonlinear function.

[0062] It should be noted that the nonlinear function g(·) can be set by relevant persons in the art and is not specifically limited here.

[0063] Optionally, in some embodiments, the calculation formula of the probability of device failure DTPM is:

[0064] P(F(t)|X,D,E)=h(q(X),r(D),s(E),t); (3)

[0065] where P(F(t)|X, D, E) is the failure probability given the input variables X, D, and E, X is the input data of the IIoT system, D is the data from the digital twin system, and E is the data of the XR system. q(X) is the function that processes the data of the IIoT system, r(D) is the function that processes the digital twin data, s(E) is the function related to the additional input from the operator through the XR interface, h(·) is the function that maps q, r, s, t to failure probabilities, F(t) is the failure within time t, and r(D) is closely related to DTA.

[0066] Where q(X) is the fault function related to IIoT data processing and can be:

[0067] q(X)=1-exp(-λ||X||);

[0068] Among them, X is the data stream input to the IIoT system, ||X|| is the "load measurement" of the data, which can be data volume, complexity, etc., and λ is the system sensitivity parameter, which reflects the system's ability to withstand data load. The larger the data load, the higher the probability of system failure, and it grows exponentially.

[0069] r(D) is the fault function of digital twin data processing and can be:

[0070] r(D)=1-exp(-η|D-Ds| / Ds);

[0071] Among them, D is the data volume of the current digital twin system, Ds is the standard processing capacity of the system, and η is the sensitivity parameter of the system to deviation from the standard processing capacity. When the amount of processed data deviates from the standard processing capacity of the system, the probability of failure increases.

[0072] s(E) is the fault function of XR human-computer interaction, which can be:

[0073] s(E)=1-exp(-μ|E-Eoptimal|)

[0074] Among them, E is the interaction load of the current XR system, Eoptimal is the optimal interaction load level, and μ is the system's sensitivity parameter to deviation from the optimal interaction state. The more the interaction load deviates from the optimal level, the higher the probability of failure.

[0075] h(q, r, s) is the comprehensive function of the overall failure probability of the system, which can be:

[0076] h(q, r, s)=1-(1-q)(1-r)(1-s).

[0077] Specifically, in order to provide a foundation for implementing and optimizing Industry 5.0, the embodiment of the present application proposes an overall Industry 5.0 performance evaluation framework that integrates digital twins, IIoT and XR, which includes three aspects, namely overall equipment effectiveness (OEE), human-machine collaboration efficiency (HMCE) and digital twin-assisted predictive maintenance (DTPM).

[0078] Among them, OEE is used to measure the overall efficiency of manufacturing equipment in using its resources and time to produce high-quality products. Its definition is shown in formula (1). In formula (1), A, P, and Q can be expressed as follows:

[0079] A=T o / T p ;

[0080] P=N a / (T o *R i );

[0081] Q=N g / N a ;

[0082] Among them, T o is the production line operation time, T p Planned production time, N a The actual number of products produced, R i is the ideal production rate, N g is the production yield, and P compares the actual production rate with the ideal (theoretical maximum) production rate.

[0083] DTA can be defined as follows:

[0084] DTA=1-(|V p -V d | / V p );

[0085] Among them, V p is the value measured by the physical system, V d is the value predicted by the twin system, which measures the accuracy of the digital twin model in predicting the results of the physical system.

[0086] For Industry 5.0 application scenarios, digital twins, IIoT, and XR can be used to calculate OEE. The specific scenarios are: digital twins simulate ideal performance scenarios, IIoT sensors provide real-time data for the calculation of A, P, and Q, and XR can display the OEE indicators of industrial systems and improvement suggestions for industrial systems to operators in real time.

[0087] HMCE aims to quantify the efficiency of human-machine collaboration. The higher the HMCE, the higher the efficiency of human-machine collaboration. It can be used to optimize the task allocation between humans and machines and help identify bottlenecks in the collaboration process. The definition of HMCE is shown in formula (2).

[0088] In the context of Industry 5.0, the integration of digital twins, IIoT, and XR can be described as follows: IIoT sensors track task completion time and resource usage, digital twins provide optimal task allocation between humans and machines, and XR interfaces guide human workers through complex tasks such as equipment setup or quality control checks.

[0089] DTPM: To predict the probability of equipment failure based on various input data, as shown in formula (3), in formula (3), in order to make a more specific evaluation of HMCE, formula (3) can be calculated using the following formula:

[0090]

[0091] Considering the relative importance of each manual task, w i This can be determined by factors such as the criticality of the process. In order to quantify the output of the machine automation process, w j R provides a complete picture of resource consumption based on task complexity, cost savings, etc., and can be extended to include other costs such as maintenance and depreciation.

[0092] For applications in Industry 5.0, digital twins predict potential failures based on historical data and current conditions, IIoT sensors monitor equipment parameters such as vibration, temperature, and performance indicators of physical systems, and engineers or operators use XR interfaces to input observations and receive guided maintenance instructions.

[0093] It should be noted that there are two possible implementation methods for the use of digital twins.

[0094] 1) Each physical device has a virtual copy that can be used as a backup to ensure the operation of the industrial system, or for tracking control for monitoring and optimization purposes. In these scenarios, the digital twin is driven by the dynamic model / data of the corresponding physical system.

[0095] 2) Twin systems are used as a complement to physical devices, where the physical system and the twin system play the same role and the twin system is driven by the average model. Figure 2 and attached Figure 3 The system shown has different applications in various scenarios, three examples are provided below:

[0096] Case 1: Remote and collaborative work In the era of Industry 5.0, remote work will become a trend due to the advancement of computer and communication technology. There are two main working modes in this case.

[0097] 1) Individual work mode. Humans, including technicians, engineers, data analysts, etc., are working remotely, and while intelligent and collaborative robots handle most tasks, human operators can monitor and intervene when necessary.

[0098] 2) Collaborative work mode. Due to low-latency communication, multiple engineers can collaborate on manufacturing tasks, whether it is troubleshooting or brainstorming. Digital twins and XR can be beneficial in this mode. This also helps to ensure the continued operation of industrial systems in emergency situations that hinder on-site work. HMCE is closely related to evaluation in this case.

[0099] Case 2: Full Lifecycle Management and Quality Control (QC). This case focuses on traceable manufacturing throughout the product lifecycle, from assembly and operation to maintenance and decommissioning. Key components include the following: Digital twins predict the impact of parameter changes on product quality, IIoT sensors continuously monitor key parameters, and XR interfaces display real-time data and alert QC technicians to potential problems. OEE calculations identify bottlenecks in the quality control process, and DTPM predicts potential equipment failures that could affect product quality.

[0100] Case 3: Scalable training and performance optimization: Figure 3 The distributed and scalable architecture of the system shown supports a variety of terminal devices, including XR technology, which can provide effective training for a large number of workers. The training module can be designed according to the performance evaluation framework proposed by the performance evaluation method, for example, focusing on improving the three components of OEE, namely production line availability, production performance and product quality, and emphasizing effective human-machine interaction.

[0101] According to the comprehensive performance evaluation method of Industry 5.0 proposed in the embodiment of the present application, an industrial performance evaluation framework of the industrial system is constructed, and the industrial performance evaluation framework integrates the digital twin system, the IIoT system and the XR system; the digital twin system is used to simulate the working scene of the industrial system, and the working data of the industrial system is obtained through the IIoT system to calculate the overall equipment efficiency OEE; the IIoT system is used to track the task completion time and resource usage of the industrial system, and the human-machine collaboration efficiency HMCE is calculated; the equipment failure probability DTPM of the industrial system within a preset time is calculated based on the data of the IIoT system, the data of the digital twin system and the data of the XR system. In this way, the problems of low interactivity, integration and intelligence of the industrial ecosystem are solved, which helps to enhance human-machine collaboration and sustainable adaptive production processes, and provide operators with an enhanced sense of presence and immersion.

[0102] Next, the Industry 5.0 comprehensive performance evaluation system proposed according to the embodiment of the present application is described with reference to the accompanying drawings.

[0103] Figure 4 It is a block diagram of the Industrial 5.0 comprehensive performance evaluation system of an embodiment of the present application.

[0104] like Figure 4 As shown, the Industry 5.0 comprehensive performance evaluation system 10 includes: a building module 100 and a calculation module 200.

[0105] Among them, the construction module 100 is used to construct an industrial performance evaluation framework for the industrial system, which integrates the digital twin system, the IIoT system and the XR system; the calculation module 200 is used to use the digital twin system to simulate the working scenario of the industrial system, and obtain the working data of the industrial system through the IIoT system to calculate the overall equipment efficiency OEE, use the IIoT system to track the task completion time and resource usage of the industrial system, calculate the human-machine collaboration efficiency HMCE, and calculate the equipment failure probability DTPM of the industrial system within a preset time based on the data of the IIoT system, the data of the digital twin system and the data of the XR system.

[0106] Optionally, in some embodiments, after calculating the overall equipment efficiency OEE, the calculation module 200 is further used to: display the overall equipment efficiency OEE of the industrial system and improvement suggestions for the industrial system using the XR system.

[0107] Optionally, in some embodiments, after calculating the equipment failure probability DTPM of the industrial system within a preset time, the calculation module 200 is further used to: receive a guiding maintenance instruction from an operator; and perform predictive maintenance on the industrial system based on the guiding maintenance instruction.

[0108] Optionally, in some embodiments, the calculation formula of overall equipment efficiency OEE is:

[0109] y OEE =f(A, P, Q, DTA);

[0110] Among them, A is the production line availability of the industrial system, P is the production performance, Q is the product quality, DTA is the digital twin accuracy, and f(·) is the nonlinear function.

[0111] Optionally, in some embodiments, the calculation formula of human-machine collaboration efficiency HMCE is:

[0112] y HMCE =g(V h , V m ,T,R,XR efficiency );

[0113] Among them, V h is the value of the manual task, V m is the value of the machine task, T is the time taken for the task, R is the resources used for the task, XR efficiency is the efficiency gain assisted by XR technology, and g(·) is a nonlinear function.

[0114] Optionally, in some embodiments, the calculation formula of the probability of device failure DTPM is:

[0115] P(F(t)|X,D,E)=h(q(X),r(D),s(E),t);

[0116] where P(F(t)|X, D, E) is the failure probability given the input variables X, D, E, X is the input data of the IIoT system, D is the data from the digital twin system, E is the data of the XR system, q(X) is the function that processes the data of the IIoT system, r(D) is the function that processes the digital twin data, s(E) is the function related to the additional input from the operator through the XR interface, h(·) is the function that maps q, r, s, t to failure probabilities, and F(t) is the failure within time t.

[0117] It should be noted that the aforementioned explanation of the embodiment of the industrial comprehensive performance evaluation method is also applicable to the industrial comprehensive performance evaluation system of this embodiment, and will not be repeated here.

[0118] According to the Industry 5.0 comprehensive performance evaluation system proposed in the embodiment of the present application, an industrial performance evaluation framework for industrial systems is constructed, and the industrial performance evaluation framework integrates the digital twin system, the IIoT system, and the XR system; the digital twin system is used to simulate the working scene of the industrial system, and the working data of the industrial system is obtained through the IIoT system to calculate the overall equipment efficiency OEE; the IIoT system is used to track the task completion time and resource usage of the industrial system, and the human-machine collaboration efficiency HMCE is calculated; the equipment failure probability DTPM of the industrial system within a preset time is calculated based on the data of the IIoT system, the data of the digital twin system, and the data of the XR system. In this way, the problems of low interactivity, integration, and intelligence of the industrial ecosystem are solved, which helps to enhance human-machine collaboration and sustainable adaptive production processes, and provide operators with an enhanced sense of presence and immersion.

[0119] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0120] A memory 501 , a processor 502 , and a computer program stored in the memory 501 and executable on the processor 502 .

[0121] When the processor 502 executes the program, the Industry 5.0 comprehensive performance evaluation method provided in the above embodiment is implemented.

[0122] Furthermore, the electronic device further comprises:

[0123] The communication interface 503 is used for communication between the memory 501 and the processor 502 .

[0124] The memory 501 is used to store computer programs that can be executed on the processor 502 .

[0125] The memory 501 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0126] If the memory 501, the processor 502 and the communication interface 503 are implemented independently, the communication interface 503, the memory 501 and the processor 502 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0127] Optionally, in a specific implementation, if the memory 501, the processor 502 and the communication interface 503 are integrated on a chip, the memory 501, the processor 502 and the communication interface 503 can communicate with each other through an internal interface.

[0128] The processor 502 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0129] An embodiment of the present application also provides a computer program product, on which a computer program is stored, and when the program is executed by a processor, the above-mentioned Industry 5.0 comprehensive performance evaluation method is implemented.

[0130] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "some examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0131] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "special" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0132] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0133] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer program product for use with an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, a "computer program product" can be any device that can contain, store, communicate, propagate or transmit a program for use with an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer program products (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). Furthermore, the computer program product may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example by optically scanning the paper or other medium and then editing, interpreting or, if necessary, processing in another suitable manner, and then storing it in a computer memory.

[0134] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0135] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer program product, which, when executed, includes one or a combination of the steps of the method embodiment.

[0136] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer program product.

[0137] The computer program product mentioned above may be a read-only memory, a disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A comprehensive performance evaluation method for Industry 5.0, characterized in that: include: Building an industrial performance evaluation framework for industrial systems, wherein the industrial performance evaluation framework integrates a digital twin system, an IIoT system, and an XR system; Using the digital twin system to simulate the working scenario of the industrial system, and obtaining the working data of the industrial system through the IIoT system to calculate the overall equipment efficiency (OEE); Using the IIoT system to track the task completion time and resource usage of the industrial system, and calculate the human-machine collaboration efficiency HMCE; The equipment failure probability DTPM of the industrial system within a preset time is calculated based on the data of the IIoT system, the data of the digital twin system and the data of the XR system.

2. The method according to claim 1, characterized in that After calculating the overall equipment effectiveness OEE, including: The XR system is used to display the overall equipment effectiveness (OEE) of the industrial system and improvement suggestions for the industrial system.

3. The method according to claim 1, characterized in that After calculating the equipment failure probability DTPM of the industrial system within a preset time, the method includes: Receive guidance and maintenance instructions from operators; Predictive maintenance is performed on the industrial system based on the prescriptive maintenance instructions.

4. The method according to claim 1, characterized in that: The calculation formula of the overall equipment efficiency OEE is: y OEE =f(A,P,Q,DTA); Among them, A is the production line availability of the industrial system, P is the production performance, Q is the product quality, DTA is the digital twin accuracy, and f(·) is the nonlinear function.

5. The method according to claim 1, characterized in that The calculation formula of the human-machine collaboration efficiency HMCE is: y HMCE =g(V h ,V m ,T,R,XR efficiency ); Among them, V h is the value of the manual task, V m is the value of the machine task, T is the time taken for the task, R is the resources used for the task, XR efficiency is the efficiency gain assisted by XR technology, and g(·) is a nonlinear function.

6. The method according to claim 1, characterized in that The calculation formula of the probability of equipment failure DTPM is: P(F(t)∣X,D,E)=h(q(X),r(D),s(E),t); where P(F(t)|X,D,E) is the failure probability given the input variables X,D,E, X is the input data of the IIoT system, D is the data from the digital twin system, E is the data of the XR system, q(X) is the function that processes the data of the IIoT system, r(D) is the function that processes the digital twin data, s(E) is the function related to the additional input from the operator through the XR interface, h(·) is the function that maps q,r,s,t to failure probability, and F(t) is the failure in time t.

7. An Industry 5.0 comprehensive performance evaluation system, characterized in that: include: A building module for building an industrial performance evaluation framework for industrial systems, wherein the industrial performance evaluation framework integrates a digital twin system, an IIoT system, and an XR system; A calculation module is used to use the digital twin system to simulate the working scenario of the industrial system, and obtain the working data of the industrial system through the IIoT system to calculate the overall equipment efficiency OEE, use the IIoT system to track the task completion time and resource usage of the industrial system, calculate the human-machine collaboration efficiency HMCE, and calculate the equipment failure probability DTPM of the industrial system within a preset time based on the data of the IIoT system, the data of the digital twin system and the data of the XR system.

8. The system according to claim 7, characterized in that After calculating the overall equipment efficiency OEE, the calculation module is further used to: The XR system is used to display the overall equipment effectiveness (OEE) of the industrial system and improvement suggestions for the industrial system.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for comprehensive performance evaluation of Industry 5.0 as described in any one of claims 1 to 6.

10. A computer program product having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the Industry 5.0 comprehensive performance evaluation method as described in any one of claims 1 to 6.

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