A method and system for intelligently constructing digital twin scenes of industrial equipment

Abstract

The present application relates to the field of digital twin technology, and in particular to a method and system for intelligently constructing a digital twin scenario of industrial equipment, the steps of which include: obtaining data collection information and equipment association information of industrial equipment; analyzing the data collection method of industrial equipment through data collection information to evaluate whether the industrial equipment meets the data availability requirements; evaluating the data collection quality of industrial equipment in combination with the data availability requirements evaluation results; analyzing the association between industrial equipment through equipment association information to evaluate the necessity of twin construction of industrial equipment; evaluating the feasibility of twin construction of industrial equipment in combination with the data collection quality evaluation results and the twin construction necessity evaluation results; constructing a digital twin scenario of industrial equipment based on the twin construction feasibility evaluation results. The present application improves the efficiency of constructing digital twin scenarios of industrial equipment by quantifying the feasibility of twin construction of industrial equipment.

Description

Technical Field

[0001] The present application relates to the field of digital twin technology, and in particular to a method and system for intelligently constructing digital twin scenarios of industrial equipment. Background Art

[0002] With the continuous development of the new generation of industrial revolution characterized by digitalization, networking and intelligence, the need for traditional equipment manufacturing companies to achieve digital transformation is becoming increasingly urgent, and the application of digital twin technology is an important means for manufacturing companies to achieve digitalization.

[0003] Digital Twin technology (DT) is aimed at the entire life cycle of equipment, playing the role of a bridge and link between the physical world and the information world. It integrates multiple technical advantages such as data sensing, virtual simulation and artificial intelligence, and is driven by intelligent perception to achieve high-fidelity mapping of physical equipment in virtual space.

[0004] The application of digital twin technology is highly dependent on the data collection capability and quality of industrial equipment. Efficient and accurate data collection is the key to achieving dynamic consistency between digital twin models and industrial equipment. At present, some equipment cannot provide all the data required to support digital twin functions. For example, in traditional manufacturing, some industrial equipment is not equipped with sufficient sensors and data transmission interfaces, and cannot collect key operating data in real time, which limits the function of digital twins and makes it difficult to achieve true data-driven. In the process of intelligent construction of digital twin scenarios for industrial equipment, existing technologies have failed to effectively quantify the data collection capability and quality of industrial equipment, limiting the construction efficiency and accuracy of digital twin scenarios for industrial equipment. Summary of the invention

[0005] In order to overcome the defects and shortcomings of the prior art, the present application provides a method and system for intelligently constructing digital twin scenarios of industrial equipment. By quantitatively analyzing the data collection quality and the feasibility of twin construction of industrial equipment, the construction efficiency of digital twin scenarios of industrial equipment is improved.

[0006] In order to achieve the above objectives, this application adopts the following technical solutions: In a first aspect, the present application provides a method for intelligently constructing a digital twin scene of industrial equipment, comprising the following steps: Obtain data collection information and equipment-related information of industrial equipment; Analyze the data collection methods of industrial equipment through data collection information and evaluate whether the industrial equipment meets the data availability requirements; Combined with the data availability requirement assessment results, the data collection quality of industrial equipment is assessed; Analyze the correlation between industrial equipment through equipment correlation information and evaluate the necessity of twin construction of industrial equipment; Combine the data collection quality assessment results and the twin construction necessity assessment results to conduct a feasibility assessment of twin construction of industrial equipment; Construct digital twin scenarios for industrial equipment based on the feasibility assessment results of twin construction.

[0007] Optionally, the step of evaluating whether the industrial equipment meets the data availability requirement specifically includes: Obtain data collection information of industrial equipment, including data collection method, data collection frequency, data collection delay and data collection accuracy; Whether the industrial equipment meets the data availability requirements is determined by judging the data collection method of the industrial equipment. If the data collection method of the industrial equipment is electrical data collection, the industrial equipment meets the data availability requirements; otherwise, the industrial equipment does not meet the data availability requirements.

[0008] Optionally, the data collection quality assessment of industrial equipment specifically includes: Obtain data collection information and data availability requirement assessment results of industrial equipment. Data collection information includes data collection method, data collection frequency, data collection delay and data collection accuracy. When industrial equipment meets the data availability requirements, the data collection quality index is calculated through data collection frequency, data collection delay and data collection accuracy; When the industrial equipment does not meet the data availability requirements, the data collection quality index is 0, where the data collection quality index is used to evaluate the data collection quality of the industrial equipment.

[0009] Optionally, calculating the data acquisition quality index by using data acquisition frequency, data acquisition delay and data acquisition accuracy specifically includes: The data collection frequency coefficient is determined by the data collection frequency and the target data collection frequency. When the data collection frequency is greater than or equal to the target data collection frequency, the data collection frequency coefficient is 1. When the data collection frequency is less than the target data collection frequency, the data collection frequency coefficient is the ratio of the data collection frequency to the target data collection frequency. The data collection delay coefficient is determined by the data collection delay and the target data collection delay. When the data collection delay is less than or equal to the target data collection delay, the data collection delay coefficient is 1. When the data collection delay is greater than the target data collection delay, the data collection delay coefficient is the ratio of the target data collection delay to the data collection delay. The data collection accuracy coefficient is determined by the data collection accuracy and the target data collection accuracy. When the data collection accuracy is greater than or equal to the target data collection accuracy, the data collection accuracy coefficient is 1. When the data collection accuracy is less than the target data collection accuracy, the data collection accuracy coefficient is the ratio of the data collection accuracy to the target data collection accuracy. The sum of the data collection frequency weighting coefficient, the data collection delay weighting coefficient and the data collection accuracy weighting coefficient is taken as the data collection quality index, wherein the data collection frequency weighting coefficient is the product of the data collection frequency coefficient and the data collection frequency weight, the data collection delay weighting coefficient is the product of the data collection delay coefficient and the data collection delay weight, and the data collection accuracy weighting coefficient is the product of the data collection accuracy coefficient and the data collection accuracy weight.

[0010] Optionally, the necessity assessment of twin construction of industrial equipment specifically includes: Obtain equipment-related information and data collection quality index of industrial equipment; An industrial equipment operation topology diagram for describing the association relationship between different industrial equipment is constructed through equipment association information of the industrial equipment; When the data collection quality index is greater than or equal to the preset data collection quality threshold, the twin construction necessity index is 1. When the data collection quality index is less than the preset data collection quality threshold, the betweenness centrality of the industrial equipment is calculated through the industrial equipment operation topology diagram, and the ratio of the betweenness centrality of the industrial equipment to the average betweenness centrality of all industrial equipment is used as the twin construction necessity index of the industrial equipment. The twin construction necessity index is used to evaluate the necessity of twin construction of industrial equipment.

[0011] Optionally, the feasibility assessment of twin construction of industrial equipment specifically includes: Obtain the data collection quality index and twin construction necessity index of industrial equipment; The sum of the data collection quality weighted index and the twin construction necessity weighted index is taken as the twin construction feasibility index. The twin construction feasibility index is used to evaluate the feasibility of twin construction of industrial equipment. Among them, the data collection quality weighted index is the product of the data collection quality index and the data collection quality weight, and the twin construction necessity weighted index is the product of the twin construction necessity index and the twin construction necessity weight.

[0012] Optionally, the digital twin scenario construction of industrial equipment according to the feasibility assessment result of twin construction specifically includes: When the feasibility index of twin construction of industrial equipment is greater than or equal to the preset feasibility threshold of twin construction, the twin construction of industrial equipment is carried out; When the twin construction feasibility index of industrial equipment is less than the preset twin construction feasibility threshold, the twin construction of industrial equipment will not be performed.

[0013] In a second aspect, the present application provides a digital twin scene intelligent construction system for industrial equipment, including: Information acquisition module, used to obtain data collection information and equipment related information of industrial equipment; The first evaluation module is used to analyze the data collection method of the industrial equipment through the data collection information and evaluate whether the industrial equipment meets the data availability requirements; The second evaluation module is used to evaluate the data collection quality of industrial equipment in combination with the data availability requirement evaluation results; The third evaluation module is used to analyze the association relationship between industrial equipment through equipment association information and conduct a necessity assessment on the twin construction of industrial equipment; The fourth evaluation module is used to evaluate the feasibility of twin construction of industrial equipment by combining the data collection quality evaluation results and the twin construction necessity evaluation results; The twin construction module is used to construct digital twin scenarios for industrial equipment based on the feasibility assessment results of twin construction.

[0014] In a third aspect, the present application provides an electronic device comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a method for intelligently constructing a digital twin scene of industrial equipment by calling the computer program stored in the memory.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium storing instructions, which, when executed on a computer, enables the computer to execute a method for intelligently constructing a digital twin scenario of industrial equipment.

[0016] Compared with the prior art, this application has the following advantages and beneficial effects: This application quantitatively evaluates the data availability of industrial equipment by analyzing the data collection methods of industrial equipment, and then evaluates the data collection quality of industrial equipment based on the data availability evaluation results. The necessity of twin construction is quantified by analyzing the correlation between industrial equipment. Finally, the feasibility of twin construction of industrial equipment is evaluated based on the comprehensive data collection quality and twin construction necessity evaluation results, which effectively improves the construction efficiency of digital twin scenarios for industrial equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings: Figure 1It is a schematic diagram of the overall process of a method for intelligently constructing a digital twin scene of industrial equipment provided in an embodiment of the present application; Figure 2 It is a schematic diagram of the digital twin structure provided in the embodiment of the present application; Figure 3 It is a structural schematic diagram of a digital twin scene intelligent construction system for industrial equipment provided in an embodiment of the present application; Figure 4 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] The technical solution of the present application is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0019] See also Figure 1 , Figure 1 This is a schematic diagram of the overall process of a method for intelligently constructing a digital twin scene of industrial equipment provided in an embodiment of the present application, which specifically includes the following steps: S110: Acquire data collection information and equipment-related information of industrial equipment.

[0020] S120: Analyze the data collection method of industrial equipment through data collection information, and evaluate whether the industrial equipment meets the data availability requirements; Electrical data collection of industrial equipment is the premise for building industrial equipment twins. Electrical data collection refers to real-time and accurate acquisition of equipment operating status data through the data transmission interface or sensor of industrial equipment. Efficient data collection methods can not only ensure the synchronization between industrial equipment and industrial equipment twins, but also provide high-precision data support for dynamic updates and real-time decision-making of industrial equipment twins, thereby realizing accurate monitoring of the operating status of industrial equipment, performance optimization and fault prediction, providing technical support for intelligent management and full life cycle optimization of industrial equipment, and evaluating whether industrial equipment meets data availability requirements, including: Obtain data collection information of industrial equipment, including data collection method, data collection frequency, data collection delay and data collection accuracy; Whether the industrial equipment meets the data availability requirements is determined by judging the data collection method of the industrial equipment. If the data collection method of the industrial equipment is electrical data collection, the industrial equipment meets the data availability requirements; otherwise, the industrial equipment does not meet the data availability requirements.

[0021] S130: Conduct data collection quality assessment of industrial equipment based on the data availability requirement assessment results; See also Figure 2 , Figure 2 : is a schematic diagram of the digital twin structure provided in the embodiment of the present application. The digital twin structure includes a physical system in real space, a virtual system in virtual space, and data interconnection between the physical system and the virtual system. Therefore, the data acquisition quality of industrial equipment directly determines the accuracy and reliability of the construction of the industrial equipment twin. High-quality data acquisition not only requires the real-time, accuracy and integrity of the data, but also needs to meet the data acquisition frequency, delay and accuracy requirements to ensure that the data can fully reflect the actual operating status of the equipment. High-quality data acquisition can provide accurate model input for the industrial equipment twin, support its real-time monitoring, dynamic update and precise simulation of physical equipment. At the same time, the quality of data acquisition affects the performance of the industrial equipment twin in fault diagnosis, performance prediction and optimization decision-making. If there is a delay or error in data acquisition, the twin cannot truly reflect the operating status of the equipment. The data acquisition quality assessment of industrial equipment specifically includes: Obtain data collection information and data availability requirement assessment results of industrial equipment. Data collection information includes data collection method, data collection frequency, data collection delay and data collection accuracy. When industrial equipment meets the data availability requirements, the data collection quality index is calculated through data collection frequency, data collection delay and data collection accuracy; When the industrial equipment does not meet the data availability requirements, the data collection quality index is 0, where the data collection quality index is used to evaluate the data collection quality of the industrial equipment; The data collection quality index is calculated by data collection frequency, data collection delay and data collection accuracy, including: The data collection frequency coefficient is determined by the data collection frequency and the target data collection frequency. When the data collection frequency is greater than or equal to the target data collection frequency, the data collection frequency coefficient is 1. When the data collection frequency is less than the target data collection frequency, the data collection frequency coefficient is the ratio of the data collection frequency to the target data collection frequency. The calculation formula of the data collection frequency coefficient can be: ; In the formula represents the data acquisition frequency coefficient, Indicates the frequency of data collection, represents the target data collection frequency, represents the minimum function; The data collection delay coefficient is determined by the data collection delay and the target data collection delay. When the data collection delay is less than or equal to the target data collection delay, the data collection delay coefficient is 1. When the data collection delay is greater than the target data collection delay, the data collection delay coefficient is the ratio of the target data collection delay to the data collection delay. The calculation formula of the data collection delay coefficient can be: ; In the formula represents the data collection delay coefficient, represents the target data acquisition delay, Indicates the data collection delay, represents the minimum function; The data collection accuracy coefficient is determined by the data collection accuracy and the target data collection accuracy. When the data collection accuracy is greater than or equal to the target data collection accuracy, the data collection accuracy coefficient is 1. When the data collection accuracy is less than the target data collection accuracy, the data collection accuracy coefficient is the ratio of the data collection accuracy to the target data collection accuracy. The calculation formula of the data collection accuracy coefficient can be: ; In the formula represents the data acquisition accuracy coefficient, Indicates the data collection accuracy, represents the target data collection accuracy, represents the minimum function; The sum of the data collection frequency weighting coefficient, the data collection delay weighting coefficient and the data collection accuracy weighting coefficient is taken as the data collection quality index, wherein the data collection frequency weighting coefficient is the product of the data collection frequency coefficient and the data collection frequency weight, the data collection delay weighting coefficient is the product of the data collection delay coefficient and the data collection delay weight, and the data collection accuracy weighting coefficient is the product of the data collection accuracy coefficient and the data collection accuracy weight; Among them, the target data collection frequency, target data collection delay and target data collection accuracy are determined by the business needs of building a digital twin scenario for industrial equipment.

[0022] S140: Analyze the association relationship between industrial equipment through equipment association information and conduct a necessity assessment on the construction of twins of industrial equipment; Evaluating the necessity of twin construction of industrial equipment through the industrial equipment operation topology diagram can improve the efficiency of twin construction and the rationality of resource allocation. The industrial equipment operation topology diagram takes data interaction as the core, reveals the correlation and frequency of interaction between different equipment, and helps identify key equipment that requires a large amount of data interaction in the production process. Key equipment usually has high requirements for the real-time and reliability of operation data. Therefore, building the corresponding industrial equipment twin can achieve efficient data management and in-depth optimization, improve production efficiency and accuracy. Therefore, evaluating the necessity of twin construction of industrial equipment through the industrial equipment operation topology diagram is helpful to concentrate resources to prioritize the construction of twins of key equipment, improve the intelligence level and collaborative efficiency of industrial equipment, and conduct a necessity evaluation of twin construction of industrial equipment, including: Obtain equipment-related information and data collection quality index of industrial equipment; An industrial equipment operation topology diagram for describing the association relationship between different industrial equipment is constructed through equipment association information of the industrial equipment; When the data collection quality index is greater than or equal to the preset data collection quality threshold, the twin construction necessity index is 1. When the data collection quality index is less than the preset data collection quality threshold, the betweenness centrality of the industrial equipment is calculated through the industrial equipment operation topology diagram, and the ratio of the betweenness centrality of the industrial equipment to the average betweenness centrality of all industrial equipment is used as the twin construction necessity index of the industrial equipment. The twin construction necessity index is used to evaluate the necessity of twin construction of industrial equipment. Among them, the betweenness centrality calculation formula of industrial equipment is: ; In the formula Represents industrial equipment in the industrial equipment operation topology diagram The betweenness centrality of Represents a node in the industrial equipment operation topology diagram To Node The number of shortest paths, Represents a node in the industrial equipment operation topology diagram To Node The shortest path passes through the node The number of shortest paths, Indicates the total number of nodes in the industrial equipment operation topology diagram; The calculation formula for the average betweenness centrality of all industrial equipment is: ; In the formula represents the average betweenness centrality of all industrial equipment in the industrial equipment operation topology graph, Represents industrial equipment in the industrial equipment operation topology diagram The betweenness centrality of Indicates the total number of nodes in the industrial equipment operation topology diagram; The calculation formula for the necessity index of twin construction of industrial equipment is: ; In the formula Represents the necessity index of twin construction of industrial equipment, Represents industrial equipment in the industrial equipment operation topology diagram The betweenness centrality of Represents the average betweenness centrality of all industrial equipment in the industrial equipment operation topology graph.

[0023] S150: Conduct feasibility assessment of twin construction of industrial equipment based on the data collection quality assessment results and the twin construction necessity assessment results; The feasibility assessment of twin construction of industrial equipment is a key link to ensure the scientific nature of twin construction and improve construction efficiency. By evaluating the data collection capabilities, data interaction requirements and digital infrastructure conditions of industrial equipment, the technical feasibility of twin construction and the rationality of resource investment can be judged, thereby avoiding construction failures or operation deviations caused by equipment technology limitations or insufficient data accuracy. The feasibility assessment of twin construction of industrial equipment specifically includes: Obtain the data collection quality index and twin construction necessity index of industrial equipment; The sum of the data collection quality weighted index and the twin construction necessity weighted index is taken as the twin construction feasibility index. The twin construction feasibility index is used to evaluate the feasibility of twin construction of industrial equipment. Among them, the data collection quality weighted index is the product of the data collection quality index and the data collection quality weight, and the twin construction necessity weighted index is the product of the twin construction necessity index and the twin construction necessity weight.

[0024] S160: Construct digital twin scenarios for industrial equipment based on the feasibility assessment results of twin construction; For industrial equipment whose twin construction feasibility index is greater than or equal to the preset twin construction feasibility threshold, the construction of twins can significantly improve their data management, operation optimization and decision support capabilities. Conversely, postponing or abandoning the twin construction can concentrate resources on high-priority scenarios. The digital twin scenarios of industrial equipment are constructed based on the feasibility assessment results of the twin construction, including: When the feasibility index of twin construction of industrial equipment is greater than or equal to the preset feasibility threshold of twin construction, the twin construction of industrial equipment is carried out; When the twin construction feasibility index of industrial equipment is less than the preset twin construction feasibility threshold, the twin construction of industrial equipment will not be performed.

[0025] It should be noted that the setting parameters in the embodiments of the present application, such as data acquisition frequency weight, data acquisition delay weight, data acquisition accuracy weight, data acquisition quality weight, twin construction necessity weight, preset data acquisition quality threshold and preset twin construction feasibility threshold, are determined as follows: by acquiring data acquisition information and equipment-related information of industrial equipment to construct a data set, substituting it into the calculation of the twin construction feasibility index of the industrial equipment, obtaining the expert's judgment result on the feasibility of twin construction, importing the twin construction feasibility index and judgment result of the industrial equipment into the fitting software, and outputting the data acquisition frequency weight, data acquisition delay weight, data acquisition accuracy weight, data acquisition quality weight, twin construction necessity weight, preset data acquisition quality threshold and preset twin construction feasibility threshold that meet the maximum judgment accuracy.

[0026] See also Figure 3 , Figure 3 : is a structural diagram of a digital twin scene intelligent construction system for industrial equipment provided in an embodiment of the present application. This embodiment provides a digital twin scene intelligent construction system for industrial equipment, including: Information acquisition module 210, used to acquire data collection information and equipment related information of industrial equipment; A first evaluation module 220 is used to analyze the data collection method of the industrial equipment through the data collection information to evaluate whether the industrial equipment meets the data availability requirements; A second evaluation module 230 is used to evaluate the data collection quality of industrial equipment in combination with the data availability requirement evaluation result; The third evaluation module 240 is used to analyze the association relationship between industrial equipment through equipment association information and evaluate the necessity of twin construction of industrial equipment; The fourth evaluation module 250 is used to evaluate the feasibility of twin construction of industrial equipment by combining the data collection quality evaluation result and the twin construction necessity evaluation result; The twin construction module 260 is used to construct a digital twin scenario of industrial equipment based on the feasibility assessment results of the twin construction.

[0027] In the embodiment of the present application, the first evaluation module 220 is used to analyze the data collection method of the industrial equipment through the data collection information, and evaluate whether the industrial equipment meets the data availability requirements. The evaluation of whether the industrial equipment meets the data availability requirements specifically includes: Obtain data collection information of industrial equipment, including data collection method, data collection frequency, data collection delay and data collection accuracy; Whether the industrial equipment meets the data availability requirements is determined by judging the data collection method of the industrial equipment. If the data collection method of the industrial equipment is electrical data collection, the industrial equipment meets the data availability requirements; otherwise, the industrial equipment does not meet the data availability requirements.

[0028] In the embodiment of the present application, the second evaluation module 230 is used to perform data collection quality evaluation of industrial equipment in combination with the data availability requirement evaluation result, and the data collection quality evaluation of industrial equipment specifically includes: Obtain data collection information and data availability requirement assessment results of industrial equipment. Data collection information includes data collection method, data collection frequency, data collection delay and data collection accuracy. When industrial equipment meets the data availability requirements, the data collection quality index is calculated through data collection frequency, data collection delay and data collection accuracy; When the industrial equipment does not meet the data availability requirements, the data collection quality index is 0, where the data collection quality index is used to evaluate the data collection quality of the industrial equipment; The data collection quality index is calculated by data collection frequency, data collection delay and data collection accuracy, including: The data collection frequency coefficient is determined by the data collection frequency and the target data collection frequency. When the data collection frequency is greater than or equal to the target data collection frequency, the data collection frequency coefficient is 1. When the data collection frequency is less than the target data collection frequency, the data collection frequency coefficient is the ratio of the data collection frequency to the target data collection frequency. The data collection delay coefficient is determined by the data collection delay and the target data collection delay. When the data collection delay is less than or equal to the target data collection delay, the data collection delay coefficient is 1. When the data collection delay is greater than the target data collection delay, the data collection delay coefficient is the ratio of the target data collection delay to the data collection delay. The data collection accuracy coefficient is determined by the data collection accuracy and the target data collection accuracy. When the data collection accuracy is greater than or equal to the target data collection accuracy, the data collection accuracy coefficient is 1. When the data collection accuracy is less than the target data collection accuracy, the data collection accuracy coefficient is the ratio of the data collection accuracy to the target data collection accuracy. The sum of the data collection frequency weighting coefficient, the data collection delay weighting coefficient and the data collection accuracy weighting coefficient is taken as the data collection quality index, wherein the data collection frequency weighting coefficient is the product of the data collection frequency coefficient and the data collection frequency weight, the data collection delay weighting coefficient is the product of the data collection delay coefficient and the data collection delay weight, and the data collection accuracy weighting coefficient is the product of the data collection accuracy coefficient and the data collection accuracy weight.

[0029] In the embodiment of the present application, the third evaluation module 240 is used to analyze the association relationship between industrial equipment through equipment association information, and to evaluate the necessity of twin construction of industrial equipment. The necessity of twin construction of industrial equipment is evaluated, which specifically includes: Obtain equipment-related information and data collection quality index of industrial equipment; An industrial equipment operation topology diagram for describing the association relationship between different industrial equipment is constructed through equipment association information of the industrial equipment; When the data collection quality index is greater than or equal to the preset data collection quality threshold, the twin construction necessity index is 1. When the data collection quality index is less than the preset data collection quality threshold, the betweenness centrality of the industrial equipment is calculated through the industrial equipment operation topology diagram, and the ratio of the betweenness centrality of the industrial equipment to the average betweenness centrality of all industrial equipment is used as the twin construction necessity index of the industrial equipment. The twin construction necessity index is used to evaluate the necessity of twin construction of industrial equipment.

[0030] In the embodiment of the present application, the fourth evaluation module 250 is used to perform a feasibility evaluation of twin construction of industrial equipment in combination with the data collection quality evaluation result and the twin construction necessity evaluation result, and the feasibility evaluation of twin construction of industrial equipment is performed, specifically including: Obtain the data collection quality index and twin construction necessity index of industrial equipment; The sum of the data collection quality weighted index and the twin construction necessity weighted index is taken as the twin construction feasibility index. The twin construction feasibility index is used to evaluate the feasibility of twin construction of industrial equipment. Among them, the data collection quality weighted index is the product of the data collection quality index and the data collection quality weight, and the twin construction necessity weighted index is the product of the twin construction necessity index and the twin construction necessity weight.

[0031] In the embodiment of the present application, the twin construction module 260 is used to construct a digital twin scene of industrial equipment according to the feasibility assessment result of the twin construction, and the digital twin scene of industrial equipment is constructed according to the feasibility assessment result of the twin construction, specifically including: When the feasibility index of twin construction of industrial equipment is greater than or equal to the preset feasibility threshold of twin construction, the twin construction of industrial equipment is carried out; When the twin construction feasibility index of industrial equipment is less than the preset twin construction feasibility threshold, the twin construction of industrial equipment will not be performed.

[0032] The above-mentioned parameters and steps for each unit module to implement corresponding functions in the digital twin scene intelligent construction system of an industrial equipment in the present application can refer to the parameters and steps in the embodiment of the digital twin scene intelligent construction method of an industrial equipment mentioned above, and will not be repeated here.

[0033] Please refer to Figure 4 , an embodiment of the present invention further provides an electronic device 300, including a memory 310, a processor 320 and a communication bus 330; the memory 310 and the processor 320 are connected via the communication bus 330. The memory 310 stores a method for intelligently constructing a digital twin scene of industrial equipment as provided in the above embodiment, which can be loaded and executed by the processor 320.

[0034] The memory 310 may be used to store instructions, programs, codes, code sets or instruction sets. The memory 310 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing a method for intelligently constructing a digital twin scene of industrial equipment provided in the above embodiment, etc.; the data storage area may store data involved in the method for intelligently constructing a digital twin scene of industrial equipment provided in the above embodiment, etc.

[0035] The processor 320 may include one or more processing cores. The processor 320 executes various functions and processes data of the present application by running or executing instructions, programs, code sets or instruction sets stored in the memory 310, calling the data stored in the memory 310. The processor 320 may be at least one of an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a central processing unit (CPU), a controller, a microcontroller and a microprocessor. It can be understood that for different devices, the electronic device used to implement the above-mentioned processor 320 function can also be other, and the embodiment of the present application is not specifically limited.

[0036] The communication bus 330 may include a path to transmit information between the above components. The communication bus 330 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The communication bus 330 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one double arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0037] An embodiment of the present application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and execute a method for intelligently constructing a digital twin scene of industrial equipment as provided in the above embodiment.

[0038] In an embodiment of the present application, a computer-readable storage medium may be a tangible device that holds and stores instructions used by an instruction execution device. A computer-readable storage medium may be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. Specifically, a computer-readable storage medium may be a portable computer disk, a hard disk, a USB flash drive, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a podium random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, an optical disk, a magnetic disk, a mechanical encoding device, and any combination thereof.

[0039] The terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or also includes elements inherent to such process, method, article, or apparatus.

[0040] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of application involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the aforementioned application concept. For example, the above features are replaced with (but not limited to) technical features with similar functions applied in the present application.

Claims

1. A method for intelligently constructing a digital twin scene of industrial equipment, characterized in that: The steps include: Obtain data collection information and equipment-related information of industrial equipment; Analyze the data collection methods of industrial equipment through data collection information and evaluate whether the industrial equipment meets the data availability requirements; Combined with the data availability requirement assessment results, the data collection quality of industrial equipment is assessed; Analyze the correlation between industrial equipment through equipment correlation information and evaluate the necessity of twin construction of industrial equipment; Combine the data collection quality assessment results and the twin construction necessity assessment results to conduct a feasibility assessment of twin construction of industrial equipment; Construct digital twin scenarios for industrial equipment based on the feasibility assessment results of twin construction; The data collection quality assessment of industrial equipment specifically includes: Obtain data collection information and data availability requirement assessment results of industrial equipment. Data collection information includes data collection method, data collection frequency, data collection delay and data collection accuracy. When industrial equipment meets the data availability requirements, the data collection quality index is calculated through data collection frequency, data collection delay and data collection accuracy; When the industrial equipment does not meet the data availability requirements, the data collection quality index is 0, where the data collection quality index is used to evaluate the data collection quality of the industrial equipment; The data collection quality index is calculated by using the data collection frequency, data collection delay and data collection accuracy, specifically including: The data collection frequency coefficient is determined by the data collection frequency and the target data collection frequency. When the data collection frequency is greater than or equal to the target data collection frequency, the data collection frequency coefficient is 1. When the data collection frequency is less than the target data collection frequency, the data collection frequency coefficient is the ratio of the data collection frequency to the target data collection frequency. The data collection delay coefficient is determined by the data collection delay and the target data collection delay. When the data collection delay is less than or equal to the target data collection delay, the data collection delay coefficient is 1. When the data collection delay is greater than the target data collection delay, the data collection delay coefficient is the ratio of the target data collection delay to the data collection delay. The data collection accuracy coefficient is determined by the data collection accuracy and the target data collection accuracy. When the data collection accuracy is greater than or equal to the target data collection accuracy, the data collection accuracy coefficient is 1. When the data collection accuracy is less than the target data collection accuracy, the data collection accuracy coefficient is the ratio of the data collection accuracy to the target data collection accuracy. The sum of the data collection frequency weighting coefficient, the data collection delay weighting coefficient and the data collection accuracy weighting coefficient is taken as the data collection quality index, wherein the data collection frequency weighting coefficient is the product of the data collection frequency coefficient and the data collection frequency weight, the data collection delay weighting coefficient is the product of the data collection delay coefficient and the data collection delay weight, and the data collection accuracy weighting coefficient is the product of the data collection accuracy coefficient and the data collection accuracy weight.

2. According to claim 1, a method for intelligently constructing a digital twin scene of industrial equipment is characterized in that: The assessment of whether industrial equipment meets the data availability requirements specifically includes: Obtain data collection information of industrial equipment, including data collection method, data collection frequency, data collection delay and data collection accuracy; Whether the industrial equipment meets the data availability requirements is determined by judging the data collection method of the industrial equipment. If the data collection method of the industrial equipment is electrical data collection, the industrial equipment meets the data availability requirements; otherwise, the industrial equipment does not meet the data availability requirements.

3. According to claim 1, a method for intelligently constructing a digital twin scene of industrial equipment is characterized in that: The necessity assessment of twin construction of industrial equipment specifically includes: Obtain equipment-related information and data collection quality index of industrial equipment; An industrial equipment operation topology diagram for describing the association relationship between different industrial equipment is constructed through equipment association information of the industrial equipment; When the data collection quality index is greater than or equal to the preset data collection quality threshold, the twin construction necessity index is 1. When the data collection quality index is less than the preset data collection quality threshold, the betweenness centrality of the industrial equipment is calculated through the industrial equipment operation topology diagram, and the ratio of the betweenness centrality of the industrial equipment to the average betweenness centrality of all industrial equipment is used as the twin construction necessity index of the industrial equipment. The twin construction necessity index is used to evaluate the necessity of twin construction of industrial equipment.

4. According to claim 1, a method for intelligently constructing a digital twin scene of industrial equipment is characterized in that: The feasibility assessment of twin construction of industrial equipment specifically includes: Obtain the data collection quality index and twin construction necessity index of industrial equipment; The sum of the data collection quality weighted index and the twin construction necessity weighted index is taken as the twin construction feasibility index. The twin construction feasibility index is used to evaluate the feasibility of twin construction of industrial equipment. Among them, the data collection quality weighted index is the product of the data collection quality index and the data collection quality weight, and the twin construction necessity weighted index is the product of the twin construction necessity index and the twin construction necessity weight.

5. The method for intelligently constructing a digital twin scene of industrial equipment according to claim 1, characterized in that: The digital twin scenario construction of industrial equipment based on the feasibility assessment results of twin construction specifically includes: When the feasibility index of twin construction of industrial equipment is greater than or equal to the preset feasibility threshold of twin construction, the twin construction of industrial equipment is carried out; When the twin construction feasibility index of industrial equipment is less than the preset twin construction feasibility threshold, the twin construction of industrial equipment will not be performed.

6. A digital twin scene intelligent construction system for industrial equipment, applied to a digital twin scene intelligent construction method for industrial equipment according to any one of claims 1 to 5, characterized in that: The system comprises: Information acquisition module, used to obtain data collection information and equipment related information of industrial equipment; The first evaluation module is used to analyze the data collection method of the industrial equipment through the data collection information and evaluate whether the industrial equipment meets the data availability requirements; The second evaluation module is used to evaluate the data collection quality of industrial equipment in combination with the data availability requirement evaluation results; The third evaluation module is used to analyze the association relationship between industrial equipment through equipment association information and conduct a necessity assessment on the twin construction of industrial equipment; The fourth evaluation module is used to evaluate the feasibility of twin construction of industrial equipment by combining the data collection quality evaluation results and the twin construction necessity evaluation results; The twin construction module is used to construct digital twin scenarios for industrial equipment based on the feasibility assessment results of twin construction.

7. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the method for intelligently constructing a digital twin scene of industrial equipment as described in any one of claims 1 to 5 by calling the computer program stored in the memory.

8. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer executes a method for intelligently constructing a digital twin scene of industrial equipment as described in any one of claims 1 to 5.

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