Intelligent factory maturity evaluation method and system

Abstract

The invention discloses an intelligent factory maturity evaluation method, which comprises the steps of constructing a capability domain index system and a sub-capability domain index system corresponding to the capability domain index system, and enabling one capability domain index to correspond to a plurality of sub-capability domain indexes; calculating the weight of the capability domain index of the capability domain index system; calculating the weight of the sub-capability domain index of the sub-capability domain index system; obtaining evaluation data of the intelligent factory; based on the evaluation data, satisfaction degree scoring coefficients of the intelligent factory are determined, and one sub-capability domain index corresponds to one satisfaction degree scoring coefficient; and based on the weight of the capability domain index, the weight of the sub-capability domain index and the satisfaction degree scoring coefficient, calculating an intelligent factory maturity evaluation total score of the intelligent factory, the intelligent factory maturity evaluation total score being used for evaluating the maturity level of the intelligent factory.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent manufacturing information processing, and in particular to an intelligent factory maturity evaluation method and system. Background Art

[0002] Intelligent manufacturing is the main direction of building a strong manufacturing country, and its development level is directly related to the quality level of my country's manufacturing industry. Building intelligent scenarios and intelligent factories, accelerating the deep integration of new-generation information technology with the entire process and all factors of manufacturing, promoting manufacturing technology breakthroughs and process innovation, promoting lean management and business process reengineering, and realizing ubiquitous perception, data penetration, integrated interconnection, human-machine collaboration and analytical optimization are effective ways to focus on the transformation and upgrading needs of enterprises and industries and create value-added.

[0003] At present, different smart factories have different construction focuses and uneven levels. In the context of digital transformation, establishing a comprehensive smart factory maturity evaluation method can help smart factories analyze the effects of intelligent construction more accurately, and identify the advantages and gaps of intelligent construction based on the smart factory maturity index score, providing important reference for the design, construction implementation and improvement of smart factory solutions. Summary of the invention

[0004] In order to overcome the above technical defects, the present invention provides a smart factory maturity evaluation method and system.

[0005] In order to solve the above problems, the present invention is implemented according to the following technical solutions:

[0006] In a first aspect, the present invention provides a method for evaluating the maturity of a smart factory, comprising:

[0007] Construct a capability domain indicator system and a sub-capability domain indicator system corresponding to the capability domain indicator system. One capability domain indicator corresponds to several sub-capability domain indicators.

[0008] Calculate the weights of capability domain indicators in the capability domain indicator system;

[0009] Calculate the weights of the sub-capability domain indicators of the sub-capability domain indicator system;

[0010] Obtain evaluation data of smart factories;

[0011] Based on the evaluation data, determine the satisfaction rating coefficient of the smart factory, where one sub-capability domain indicator corresponds to one satisfaction rating coefficient;

[0012] Based on the weights of the capability domain indicators, the weights of the sub-capability domain indicators and the satisfaction rating coefficients, the overall score of the smart factory maturity evaluation of the smart factory is calculated, and the overall score of the smart factory maturity evaluation is used to evaluate the maturity level of the smart factory.

[0013] In combination with the first aspect, the present invention further provides a first specific implementation manner of the first aspect, specifically, calculating the overall score of the smart factory maturity evaluation of the smart factory, specifically including:

[0014] Based on the weights of the capability domain indicators, the weights of the sub-capability domain indicators and the satisfaction rating coefficient, the sub-capability domain evaluation scores of the smart factory are calculated;

[0015] Based on the sub-capability domain evaluation scores, calculating the capability domain evaluation score of the smart factory;

[0016] Based on the capability domain scores, an overall smart factory maturity evaluation score of the smart factory is calculated, and the overall smart factory maturity evaluation score is used to evaluate the maturity level of the smart factory.

[0017] In combination with the first aspect, the present invention further provides a second specific implementation of the first aspect. Specifically, the calculation formula of the sub-capability domain evaluation score is X=α×β×10 2 ×γ;

[0018] Where X is the sub-competency domain evaluation score of the ith sub-competency domain indicator, α is the weight of the competency domain indicator j corresponding to the ith sub-competency domain indicator, β is the weight of the ith sub-competency domain indicator, and γ is the satisfaction rating coefficient corresponding to the ith sub-competency domain indicator.

[0019] In combination with the first aspect, the present invention further provides a third specific implementation of the first aspect. Specifically, the calculation formula of the capability domain evaluation score is:

[0020] Where Y is the capability domain evaluation score of the j-th capability domain indicator, X is the sub-capability domain evaluation score of the i-th sub-capability domain indicator corresponding to the j-th capability domain indicator, and m is the number of sub-capability domains corresponding to the j-th capability domain indicator.

[0021] In combination with the first aspect, the present invention further provides a fourth specific implementation of the first aspect. Specifically, the calculation formula for the overall score of the smart factory maturity evaluation is:

[0022] Where Z is the overall score of the smart factory maturity evaluation, Y is the capability domain evaluation score of the jth capability domain indicator, and n is the number of capability domain indicators.

[0023] In combination with the first aspect, the present invention also provides a fifth specific implementation scheme of the first aspect. Specifically, the capability domain indicator system includes management system indicators, intelligent design indicators, intelligent production indicators, intelligent logistics indicators, intelligent management indicators, system integration indicators, resource indicators and performance indicators.

[0024] In combination with the first aspect, the present invention further provides a sixth specific implementation of the first aspect, specifically, corresponding to the management system indicator, the sub-capability domain indicator system includes a sub-management system indicator;

[0025] Corresponding to the intelligent design index, the sub-capability domain index system includes product design index and process design index;

[0026] Corresponding to the intelligent production indicators, the sub-capability domain indicator system includes production planning and scheduling indicators, production execution indicators, quality control indicators and equipment operation and maintenance indicators;

[0027] Corresponding to the smart logistics indicators, the sub-capability domain indicator system includes smart warehousing indicators and smart distribution indicators;

[0028] Corresponding to the intelligent management indicators, the sub-capability domain indicator system includes procurement management indicators, sales management indicators, customer service indicators, product service management indicators, asset management indicators, energy management indicators, safety management indicators and environmental management indicators;

[0029] Corresponding to the system integration indicator, the sub-capability domain indicator system includes sub-system integration indicators;

[0030] Corresponding to resource indicators, the sub-capability domain indicator system includes digital indicators, networking indicators and information indicators;

[0031] Corresponding to resource indicators, the sub-capability domain indicator system includes benefit indicators and sustainable development indicators.

[0032] In a second aspect, the present invention provides a smart factory maturity evaluation system, comprising:

[0033] A construction module is used to construct a capability domain indicator system and a sub-capability domain indicator system corresponding to the capability domain indicator system. One capability domain indicator corresponds to several sub-capability domain indicators.

[0034] A first calculation module, which is used to calculate the weight of the capability domain indicator of the capability domain indicator system;

[0035] A second calculation module, which is used to calculate the weights of the sub-capability domain indicators of the sub-capability domain indicator system;

[0036] An acquisition module, which is used to acquire evaluation data of the smart factory;

[0037] A third calculation module is used to determine a satisfaction rating coefficient of the smart factory based on the evaluation data, where one sub-capability domain indicator corresponds to one satisfaction rating coefficient;

[0038] The fourth calculation module is used to calculate the overall score of the smart factory maturity evaluation of the smart factory based on the weights of the capability domain indicators, the weights of the sub-capability domain indicators and the satisfaction rating coefficient. The total score of the smart factory maturity evaluation is used to evaluate the maturity level of the smart factory.

[0039] In combination with the second aspect, the present invention further provides a first specific implementation of the second aspect, specifically, the fourth calculation module calculates the overall score of the smart factory maturity evaluation of the smart factory, specifically including:

[0040] Based on the weights of the capability domain indicators, the weights of the sub-capability domain indicators and the satisfaction rating coefficient, the sub-capability domain evaluation scores of the smart factory are calculated;

[0041] Based on the sub-capability domain evaluation scores, calculating the capability domain evaluation score of the smart factory;

[0042] Based on the capability domain scores, an overall smart factory maturity evaluation score of the smart factory is calculated, and the overall smart factory maturity evaluation score is used to evaluate the maturity level of the smart factory.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] The present invention provides a method for evaluating the maturity of a smart factory, comprising: constructing a capability domain indicator system and a sub-capability domain indicator system corresponding to the capability domain indicator system, wherein one capability domain indicator corresponds to a plurality of sub-capability domain indicators; calculating the weights of the capability domain indicators of the capability domain indicator system; calculating the weights of the sub-capability domain indicators of the sub-capability domain indicator system; acquiring evaluation data of the smart factory; determining a satisfaction rating coefficient of the smart factory based on the evaluation data, wherein one sub-capability domain indicator corresponds to one satisfaction rating coefficient; and calculating an overall score of the maturity of the smart factory based on the weights of the capability domain indicators, the weights of the sub-capability domain indicators and the satisfaction rating coefficients, wherein the overall score of the maturity of the smart factory is used to evaluate the maturity level of the smart factory.

[0045] The present invention provides an evaluation method that meets the characteristics of smart factories and scientifically and objectively evaluates the maturity of smart factories, provides a reference basis for evaluating the maturity level of smart factories, provides guidance for building smart factories, and provides a scientific and reliable analysis and evaluation tool for the evaluation of the maturity of smart factories. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The specific embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings, wherein:

[0047] Figure 1 It is a flow chart of a smart factory maturity evaluation method of the present invention;

[0048] Figure 2 It is a network model structure diagram of four different complexities of YOLOv5 of the present invention. DETAILED DESCRIPTION

[0049] The following specifically illustrates the implementation mode of the present invention in conjunction with the accompanying drawings. The embodiments are provided for illustrative purposes only and cannot be understood as limiting the present invention. The accompanying drawings are provided for reference and illustration only and do not constitute a limitation on the scope of patent protection of the present invention, because many changes can be made to the present invention without departing from the spirit and scope of the present invention.

[0050] Different smart factories have different construction focuses and uneven levels. In the context of digital transformation, establishing a comprehensive smart factory maturity evaluation method can help smart factories analyze the effects of intelligent construction more accurately, and identify the advantages and gaps of intelligent construction based on the smart factory maturity index score, providing important reference for the design, construction implementation and improvement of smart factory solutions.

[0051] Embodiment 1

[0052] refer to Figure 1 , an embodiment of the present invention provides a flow chart of a method for evaluating the maturity of a smart factory. The present invention is used to evaluate the maturity of a smart factory. The method can be executed by a smart factory maturity evaluation system. The system can be implemented in the form of hardware and / or software. The system can be configured in an electronic device. Figure 1 As shown, the method includes:

[0053] S100: Construct a capability domain indicator system and a sub-capability domain indicator system corresponding to the capability domain indicator system. One capability domain indicator corresponds to several sub-capability domain indicators.

[0054] S200: Calculate the weights of the capability domain indicators of the capability domain indicator system.

[0055] S300: Calculate the weights of the sub-capability domain indicators of the sub-capability domain indicator system.

[0056] S400: Obtain evaluation data of the smart factory.

[0057] S500: Based on the evaluation data, determine the satisfaction rating coefficient of the smart factory, where one sub-capability domain indicator corresponds to one satisfaction rating coefficient.

[0058] S600: Based on the weights of the capability domain indicators, the weights of the sub-capability domain indicators and the satisfaction rating coefficient, the overall score of the smart factory maturity evaluation of the smart factory is calculated, and the overall score of the smart factory maturity evaluation is used to evaluate the maturity level of the smart factory.

[0059] The present invention provides an evaluation method that meets the characteristics of smart factories and scientifically and objectively evaluates the maturity of smart factories, provides a reference basis for evaluating the maturity level of smart factories, and provides a guiding direction for building smart factories. It provides a scientific and reliable analysis and evaluation tool for the evaluation of the maturity of smart factories. Specifically, this embodiment provides a detailed description of each step.

[0060] S100: Construct a capability domain indicator system and a sub-capability domain indicator system corresponding to the capability domain indicator system. One capability domain indicator corresponds to several sub-capability domain indicators.

[0061] Specifically, the capability domain index system and sub-capability domain index system of the present invention are shown in Table 1:

[0062] Table 1 Intelligent factory evaluation index system and index weights

[0063]

[0064]

[0065] In the present invention, the capability domain indicator system includes management system indicators, intelligent design indicators, intelligent production indicators, intelligent logistics indicators, intelligent management indicators, system integration indicators, resource indicators and performance indicators.

[0066] Specifically, corresponding to the management system indicators, the sub-capability domain indicator system includes sub-management system indicators. Corresponding to the intelligent design indicators, the sub-capability domain indicator system includes product design indicators and process design indicators. Corresponding to the intelligent production indicators, the sub-capability domain indicator system includes production planning and scheduling indicators, production execution indicators, quality control indicators and equipment operation and maintenance indicators. Corresponding to the intelligent logistics indicators, the sub-capability domain indicator system includes intelligent warehousing indicators and intelligent distribution indicators. Corresponding to the intelligent management indicators, the sub-capability domain indicator system includes procurement management indicators, sales management indicators, customer service indicators, product service management indicators, asset management indicators, energy management indicators, safety management indicators and environmental management indicators. Corresponding to the system integration indicators, the sub-capability domain indicator system includes sub-system integration indicators. Corresponding to the resource indicators, the sub-capability domain indicator system includes digital indicators, networking indicators and information indicators. Corresponding to the resource indicators, the sub-capability domain indicator system includes benefit indicators and sustainable development indicators.

[0067] Specifically, those skilled in the art can form an evaluation index for the maturity of an electronic and electrical appliance smart factory based on the business characteristics of electronic and electrical appliance manufacturing enterprises. The corresponding evaluation model can be formed by selecting corresponding indicators from the capability domain indicator system and the sub-capability domain indicator system in Table 1.

[0068] S200: Calculate the weights of the capability domain indicators of the capability domain indicator system.

[0069] S300: Calculate the weights of the sub-capability domain indicators of the sub-capability domain indicator system.

[0070] In a specific implementation, the analytic hierarchy process can be used to determine the indicator weights in S200-S300. The analytic hierarchy process is a conventional technique. In order to avoid affecting the evaluation results, the entropy method can also be used to determine the indicator weights.

[0071] In another specific implementation, the triangular fuzzy AHP method can be used to determine the weight of each indicator. The basic idea of ​​the triangular fuzzy number hierarchical analysis method is: the element C jk (k=1,2,3,…,n) relative to the previous layer element B j (j=1, 2, 3, ..., n) are compared pairwise and the comparison results are quantitatively expressed using triangular fuzzy numbers, thereby obtaining a fuzzy judgment matrix composed of triangular fuzzy numbers.

[0072] Definition: is a triangular fuzzy number, and its membership function can be expressed as:

[0073]

[0074] Where: x∈R; l and u are its upper and lower bounds; m is its value; ul represents the degree of blur, the larger the ul, the greater the degree of blur.

[0075] Based on the triangular fuzzy number analytic hierarchy process, the basic method for determining the weights of various indicators in this paper is as follows:

[0076] 1. Experts score in the form of triangular fuzzy numbers, and based on this, they compare the importance of each factor pairwise to form a judgment matrix R N

[0077] 2. According to the average value formula List the mean value matrix and calculate the mean value matrix by m ij The median matrix Perform consistency check.

[0079] 3. According to the formula Calculate the comprehensive fuzzy value of element i at the kth layer And using the formula Compare the comprehensive importance values ​​pairwise.

[0080] 4. Calculate indicator B i A pure measure that is superior to other indicators. According to the formula d(B i )=V(D Bi ≥D B1 , …, D Bi-1, D Bi+1 , …, D Bn )=min{V(D Bi ≥D Bk )}(k=1,2,…,n,and k≠i)get the weight fuzzy vector of the index W'=(d(B1),d(B2),…,d(Bn)) T , and then normalize the weight value to get W B .

[0081] 5. According to formula W Cij =W Bi W' Cij Find the total weight vector of this level.

[0082] Specifically, the indicator weights of each indicator are shown in Table 1.

[0083] S400: Obtain evaluation data of the smart factory.

[0084] In a specific implementation, the evaluation data includes the degree of satisfaction of the sub-capability domain requirements of each sub-capability domain indicator, and the degree of satisfaction of the sub-capability domain requirements includes four types of evaluation: completely unsatisfied, partially satisfied, mostly satisfied, and completely satisfied.

[0085] S500: Based on the evaluation data, determine the satisfaction rating coefficient of the smart factory, where one sub-capability domain indicator corresponds to one satisfaction rating coefficient.

[0086] In the specific implementation, as shown in Table 2, the relationship table between the satisfaction degree scoring coefficient and the evaluation data.

[0087] Table 2

[0088] Sub-competency domain requirements met Satisfaction Rating Coefficient Totally dissatisfied 0 Partially satisfied 0.3 Mostly satisfied 0.7 Completely satisfied 1

[0089] In a specific implementation, the evaluation data can be formed and stored after evaluating the requirements of each indicator one by one for subsequent use. In one example, the evaluation data of the sub-management system indicator: the degree of satisfaction of the sub-capability domain requirements is mostly satisfied; and then the satisfaction degree score coefficient of the sub-management system indicator can be determined to be 0.7.

[0090] In a specific implementation, each sub-capability domain requirement satisfaction degree corresponds to a different scoring interval, and the sub-capability domain indicator is scored to determine the sub-capability domain requirement satisfaction degree of the sub-capability domain indicator based on the scoring value and the scoring interval, as shown in Table 3.

[0091] Table 3

[0092] Sub-competency domain requirements met Rating range Totally dissatisfied 0 points Partially satisfied (0，50] Mostly satisfied (50，90] Completely satisfied (90,100]

[0093] In the present invention, the sub-capability domain indicators are scored based on the indicator requirements. It can be understood that based on the score value of the indicator requirements, the evaluation data of the satisfaction degree of the sub-capability domain requirements are obtained. In an example, when the score of the indicator requirements of the sub-management system indicator is 95, that is, the evaluation data of the sub-management system indicator is mostly satisfied, it can be determined that the satisfaction degree scoring coefficient of the sub-management system indicator is 0.7.

[0094] On the other hand, when a sub-capability domain indicator system has multiple indicator requirements, each indicator requirement is scored one by one to obtain a score value for each indicator requirement, and then the average score of all indicator requirement scores is calculated (average score = total score value / number of indicator requirements) to obtain the final score value. Based on the final score value, the score range is determined to finally obtain evaluation data on the degree of satisfaction of the sub-capability domain requirements.

[0095] In one example, the sub-capability domain indicator [safety management indicator] includes a, b, c, d, e, f, g, h, i and j, a total of 10 indicator requirements. You can first get the score value of each indicator requirement one by one, and then get the score value of the safety management indicator based on the average of the score values ​​of all indicator requirements.

[0096] Similarly, when an indicator requirement has multiple evaluation items, the above method can be used to calculate the score value of the indicator requirement. In one example, the h indicator requirements of [Security Management Indicators] include evaluation items for networks, servers, and industrial control systems. The scores of each evaluation item of the network, server, and industrial control system can be obtained one by one, and the score value of the h indicator requirement of the security management indicator can be obtained based on the average of the scores of all evaluation items.

[0097] Specifically, the indicator requirements for the sub-capability domain indicators of each sub-capability domain indicator system are as follows:

[0098] (I) Management system indicators:

[0099] Sub-management system indicators: The factory should establish, implement and maintain a quality management system and an environmental management system. The factory's quality management system should meet the requirements of GB / T19001, the environmental management system should meet the requirements of GB / T 24001, and should pass the third-party certification of the quality management system.

[0100] (II) Intelligent design indicators: Intelligent design in smart factories mainly refers to the use of digital technology and intelligent technology to achieve intelligent design and digital simulation of products and processes.

[0101] 1. Product design indicators: Product design and simulation in smart factories should meet the following requirements: a) Determine the function / performance and structure of the product to ensure that the product has good manufacturability and assemblability; b) Standardize and normalize product design processes, methods, product definitions, data and knowledge; c) Establish a product digital design simulation platform to achieve parametric design, simulation and testing of product structure, function / performance in a virtual digital environment; d) Establish a product data management system so that product-related design data and information can be shared and interacted in the entire life cycle of design, manufacturing, quality, logistics, etc., and the relevant data should maintain consistency and uniqueness; e) Establish a big data platform for the entire product life cycle, use big data analysis and knowledge engineering technology, and continuously optimize product research and development design based on the dynamic changes in customer needs, real-time dynamics of the entire product life cycle, and production line dynamics;

[0102] 2. Process design indicators: Process design and simulation in smart factories should meet the following requirements: a) Digital design simulation tools should be used to carry out process design, and process routes and process layout simulations should be carried out in a virtual digital environment; b) The simulation and optimization and improvement of the processing or assembly process should be completed in a digital simulation environment, and visualization should be achieved; c) A process information management system should be established, including process-related specifications, process design case libraries, expert knowledge bases, etc., and information management should be achieved; d) Process documents can be automatically sent to the production site to guide production; e) The process BOM should be automatically output to the manufacturing execution system;

[0103] (III) Intelligent production indicators:

[0104] 1. Production plan and scheduling indicators: The production plan and scheduling of the factory should meet the following requirements: a) Establish an intelligent production plan scheduling system, obtain the availability of workshop equipment, personnel, materials and other resources in real time according to the company's sales plan, customer orders and other information, and analyze and calculate according to the product bill of materials and process BOM, and automatically generate the optimal production schedule; b) Obtain dynamic information such as operation plan execution, equipment status, material supply, etc. in real time, comprehensively consider changes in order demand and quality inspection during production, and dynamically adjust the operation plan and production schedule; c) Automatically send the production schedule to the production execution management system to guide the operation site to complete the production tasks according to the plan and process; d) Realize the visualization of the production plan, including plan visualization, monitoring data visualization, real-time execution data visualization, plan completion status visualization, etc.

[0105] 2. Production execution indicators: The production execution of the factory should meet the following requirements: a) Establish a production execution management system, automatically receive the operation plan issued by the intelligent scheduling system, and confirm the preparation status of various production factors (people, machines, materials) required for the production plan; b) Real-time monitoring of the status of various production factors (people, machines, materials), timely response to abnormal information on the production site, agile handling of unpredictable conditions in the production plan, and adjustment of production resource inputs such as personnel, equipment, and materials to ensure that orders are delivered on time; c) Automatically collect relevant production data information such as personnel, equipment input, materials, and energy consumption during the production execution process, and conduct real-time cost and benefit analysis to optimize production execution management; d) Through the production process kanban management, realize the visualization of production progress, process, quality, materials, energy consumption, equipment failure, equipment utilization, personnel in place and other data; e) Provide electronic process documents and guidance operation documents;

[0106] 3. Quality control indicators: The factory's quality control should meet the following requirements: a) Control the quality of the production process and collect quality data such as raw material inspection, sample inspection, product inspection, and quality statistics; b) Establish a digital quality file to record quality information throughout the product life cycle and ensure traceability of each link; c) Use information management methods to realize the informatization of quality management and realize the automatic collection and interactive sharing of relevant quality data; d) Establish a product quality traceability system to collect data in product research and development, production, and after-sales service, establish a quality model, and form a data closed loop;

[0107] 4. Equipment operation and maintenance indicators: The factory's equipment operation and maintenance should meet the following requirements: a) Automatically collect equipment operating parameters to achieve real-time monitoring of equipment operating status and statistical analysis of overall efficiency (OEE), and be able to adjust equipment operating parameters online; b) Use information tools to establish a complete equipment archive to achieve full life cycle management and maintenance of equipment information; c) Establish a digital model of equipment operation and a fault knowledge base, and carry out equipment predictive maintenance based on equipment operating status and fault prediction analysis; d) Automatically generate maintenance work orders based on equipment operating status and equipment maintenance plan to achieve closed-loop management of maintenance based on equipment operating status; e) Use intelligent mobile point inspection terminals to conduct equipment point inspections, and establish an equipment intelligent point inspection system; f) Automatically statistically analyze and visualize data such as equipment overall efficiency, equipment failure and early warning information, equipment maintenance, and maintenance progress.

[0108] (IV) Intelligent logistics indicators

[0109] 1. Smart warehousing indicators: Smart warehousing in factories should meet the following requirements: a) Use RFID, QR codes, labels and other Internet of Things technologies to realize digital identification of raw materials, semi-finished products and finished products; b) Establish a warehouse management system to realize the information management of the basic information of raw materials, semi-finished products and finished products (coding, name, specification model, storage location information, etc.), as well as warehousing, storage and outbound management; c) Timely respond to material needs of the production site and provide real-time feedback on material distribution information; d) Realize the traceability of information throughout the life cycle of materials; e) Real-time monitoring of material inventory, timely analysis and optimization of inventory through information sharing and interaction in production planning, procurement and other links, and conduct low and high inventory warnings; f) Realize visual management of material demand information, distribution information, inventory information, etc.

[0110] 2. Intelligent distribution indicators: The logistics distribution of the factory should meet the following requirements: a) Use automated distribution equipment such as AGV, hanging chains, and conveyor belts to realize automatic distribution of materials; b) Be able to plan logistics distribution routes and transportation modes based on production line layout and material demand, and continuously optimize logistics distribution routes and loading methods to achieve accurate distribution; c) Monitor the status of logistics distribution and distribution equipment, and be able to locate and track in real time, and visualize relevant information.

[0111] (V) Intelligent management indicators

[0112] 1. Procurement management indicators: Factory procurement management should meet the following requirements: a) Establish a procurement management information system to realize the information management of procurement plans, procurement processes, suppliers, and procurement contracts; b) Automatically obtain real-time information such as product orders, material BOMs, production schedules, raw material inventory, procurement cycles, etc., automatically generate procurement plans based on procurement budgets combined with material forecasts and analysis, and realize visual dynamic monitoring of the entire procurement process; c) Establish a supplier management system to form a multi-dimensional comprehensive evaluation model for supplier products, services, prices, delivery capabilities, etc., realize comprehensive dynamic evaluation of suppliers, and optimize supplier management; d) Based on digital technology, realize the identification, location, early warning or efficient disposal of risks and hidden dangers in the entire procurement process; e) Use big data to build real-time procurement reports and annual portraits, including raw material procurement and cost distribution, key materials and their suppliers, procurement budget analysis, procurement progress, contract disputes, etc., and realize visual monitoring to provide a basis for accurate decision-making on procurement needs or dynamic optimization of procurement plans;

[0113] 2. Sales management indicators: The sales management of the factory should meet the following requirements: a) Establish an information management system and a detailed digital customer profile to achieve unified management and maintenance of records such as customer tracking, relationships, orders, product maintenance, sales process, payment collection, and services, and achieve real-time data updates; b) Use digital technology to achieve unified management of product sales channels (distributors), and achieve full-process management and tracking of sales business and orders; c) Sales business management should be integrated with financial and other information systems to form dynamic accounting and management of expenses, performance, cost assessment, etc. in sales business and processes; d) Apply data analysis technology to conduct statistical analysis and mining of sales plans, customer information, orders, contracts and other data, and achieve market forecasts based on data analysis or formulate accurate sales plans; e) Realize the interconnection and interoperability of sales, procurement, production, R&D and other data, apply digital technology, analyze the dynamic changes of market or customer needs, and adjust product development, production, procurement and other plans in a timely manner; f) Use information technology to achieve unified management of product delivery and after-sales, conduct real-time statistical analysis of product after-sales information, and feedback to R&D and production departments;

[0114] 3. Customer service management indicators: The factory's customer service management should meet the following requirements: a) Establish customer feedback channels and service satisfaction evaluation service system, and use information systems to achieve closed-loop management of customer service; b) Build a customer service platform to support mobile clients to provide real-time online customer service; c) Establish a customer service information database, automatically analyze customer service information and feedback it to relevant departments;

[0115] 4. Product service management indicators: The factory's product service management should meet the following requirements: a) Establish a knowledge base of product failures and maintenance methods, establish a product remote operation and maintenance management platform, and realize remote monitoring and predictive maintenance of products; b) Establish a product after-sales service information system to realize digital management such as product return and exchange quality management, customer experience surveys, and customer satisfaction surveys; c) Through in-depth interaction with users, provide customized product design, production and personalized services that meet users' personalized needs.

[0116] 5. Asset management indicators: The asset management of the factory should meet the following requirements: a) The enterprise asset management system and management methods should be formulated, and various types of assets related to the enterprise's production and operation activities should be graded and classified; b) Digital archives of assets should be established, and intelligent information management systems should be used to achieve full life cycle management of software and hardware assets, including equipment planning, selection, installation and commissioning, measurement, use, maintenance, idleness, and scrap management; c) Data analysis technology should be used to collect and analyze data such as asset utilization rate, idleness rate, failure rate, and scrap rate; d) The full life cycle management of other intangible assets (data assets) should be achieved, including collection, storage, application, security protection, inventory, disposal, and destruction;

[0117] 6. Energy management indicators: The energy management of the factory should meet the following requirements: a) Establish an enterprise energy management system to realize dynamic monitoring and measurement of key energy consumption such as water, electricity and gas; b) Automatically collect and monitor energy consumption data of high-energy-consuming equipment and systems, realize dynamic operation monitoring, measure equipment with energy-saving optimization requirements in real time, and carry out energy-saving transformation according to the measurement results; c) Conduct statistical analysis on energy consumption data of high-energy-consuming equipment and realize visual management;

[0118] 7. Safety management indicators: The safety management of the factory should meet the following requirements: a) Formulate the overall policy and safety strategy for safety management, establish an industrial information security management system and technical protection system, and have information security protection capabilities such as network protection and emergency response; b) Use digital technology to identify, track and trace the activity paths of people entering and leaving; c) Use information management system to realize the electronic recording and management of the qualifications, training and safety protection equipment information of operators; d) Monitor the safety status of the operation site in real time, realize process safety monitoring and risk warning, and improve the visualization level of process safety management; e) Establish a material safety management system, and all materials used in production are electronically recorded, tracked and traced throughout the process; f) Use data analysis technology to analyze data such as safe operations and risk control, and realize automated monitoring, management and early warning of hazardous sources; g) Divide and manage areas according to safety control levels; h) The security protection of networks, servers, industrial control systems, data, etc. has boundary protection capabilities; i) Establish a safety-related alarm performance monitoring and operation and maintenance system; j) The safe operation rate of personnel on the operation site during the statistical period.

[0119] 8. Environmental management indicators: The environmental management of the factory should meet the following requirements: a) Formulate an enterprise environmental management system and have relevant documents on environmental operating procedures; b) Use digital technology to realize data collection of the entire process from clean production to end-of-pipe treatment, real-time monitoring and alarm, and conduct visual analysis.

[0120] (VI) System integration indicators:

[0121] 1. Subsystem integration indicators: Factory system integration should meet the following requirements: a) Establish a continuous and interconnected industrial network covering factories, workshops, production lines, equipment, and warehousing links; b) Establish an enterprise-level unified data management center to realize real-time data collection, storage, and monitoring among workshops, systems, and levels of the enterprise; c) Integrate and share data of various intelligent information systems for design, production, logistics, and management, realize collaborative management of procurement, planning, production, sales, and integrated operation of business processes, and promote intelligent management of enterprise production, operations, and decision-making.

[0122] 7. Resource indicators

[0123] 1. Digital indicators:

[0124] (1) Personnel digitization rate: The personnel digitization rate refers to the proportion of production-related personnel whose records are recorded in digital form, calculated according to formula (1):

[0125]

[0126] Where: P: personnel digitization rate; P1: number of production personnel who have achieved digital description and record; P all : Total number of production personnel.

[0127] (2) Equipment digitalization rate: The equipment digitalization rate refers to the proportion of equipment that has achieved automatic perception of operating data in the total number of production equipment included in the statistics of the factory (workshop), calculated according to formula (2):

[0128]

[0129] Where: E: equipment digitization rate; E1: number of equipment that realizes automatic collection of operating status; E all :The total number (units) of equipment with specific functions related to production included in the statistics of the factory (workshop).

[0130] (3) Material digitization rate: The material digitization rate refers to the proportion of raw materials, work-in-progress, finished products, production auxiliary materials, tooling, tools and equipment spare parts for which material digitization models have been established. It is calculated according to formula (3):

[0131]

[0132] Where: M: material digitization rate; M1: material types that can be identified and tracked in real time by the system; M all : Total material types.

[0133] 2. Networking indicators:

[0134] (1) Network coverage: Network coverage refers to the percentage of industrial network facilities deployed by factories in key business activities related to production and operation, such as R&D, production, procurement, logistics, warehousing, quality inspection, etc., and is calculated according to formula (4):

[0135]

[0136] Where: N: network coverage; N1: number of key business activities in the deployed network; N all : The total number of all key business activities related to production and operations.

[0137] (2) Equipment networking rate: The equipment networking rate refers to the proportion of equipment that has been connected to the network and has been assigned a network address within the total number of production equipment included in the statistics, calculated using formula (5).

[0138]

[0139] Where: n e : Equipment networking rate; E net : The number of production equipment connected to the network or assigned network addresses; E all :The total number (units) of equipment with specific functions related to production included in the statistics of the factory (workshop).

[0140] 3. Informationization indicators:

[0141] (1) Information system application rate: The information system application rate refers to the proportion of factories that have established information systems in key business activities related to production and operation, such as R&D, production, procurement, logistics, warehousing, quality inspection, etc., and is calculated according to formula (6):

[0142]

[0143] Where: S: information system application rate; S1: number of key business activities where information systems are deployed; S all : The total number of all key business activities related to production and operations.

[0144] In specific implementation, the evaluation data of each sub-capability domain indicator of the resource indicator can be calculated and obtained in the following way:

[0145] Calculate the score of the digitalization index, score = (personnel digitalization rate + equipment digitalization rate + material digitalization rate) / 3 × 100;

[0146] Calculate the score of the networking index, score = (network coverage + device networking rate) / 2 × 100;

[0147] Calculate the score of the informatization index, score = information system application rate × 100;

[0148] Among them, the scoring values ​​of the digitalization index, networking index and informationization index are the scores of the sub-capability domain indicators of the resource index, corresponding to the scoring range in Table 3, to obtain the evaluation data of the resource index.

[0149] (VIII) Performance indicators

[0150] 1. Benefit indicators:

[0151] (1) Product qualification rate: The product qualification rate refers to the ratio of the number of qualified products submitted for inspection to the total number of products submitted for inspection in the batch, calculated according to formula (7):

[0152]

[0153] Where: Q: product qualification rate; Q1: the number of qualified products in the statistical batch of products that have been inspected; Q all :To count the total number of products in a batch that have been inspected.

[0154] (2) Product repair rate: The finished product repair rate refers to the ratio of the number of products that need to be repaired due to quality problems within a specified period of time to the total number of products sold, calculated according to formula (8):

[0155]

[0156] Where: R: product repair rate; R1: the number of products repaired due to quality problems within a specified time; R all : The total number of products sold during the statistical period.

[0157] (3) Product delivery rate: The product delivery rate refers to the ratio of the number of orders delivered on time to the total number of orders that need to be delivered during the statistical period, calculated according to formula (9):

[0158]

[0159] Where: F: product delivery rate; F1: the number of ordered products delivered on time during the statistical period; F all : The total number of order products that need to be delivered during the statistical period.

[0160] (4) Capacity per unit area: Capacity per unit area is the ratio of the factory capacity to the factory area, calculated according to formula (10).

[0161]

[0162] Where: G: production capacity per unit area, unit is set per square meter (m 2 ); N: total production capacity of the factory, in units of units;

[0163] A 用地 :Total factory land area, in square meters (m 2 ).

[0164] (5) Overall Equipment Effectiveness (OEE): Overall Equipment Effectiveness is the ratio of the actual production capacity of the equipment to its theoretical production capacity and is calculated according to formula (11):

[0165] OEE=θ1×θ2×θ3…………………………(11)

[0166] Where:

[0167] θ1: Equipment operating rate, calculated according to formula (11-1);

[0168]

[0169] θ2: Equipment performance utilization rate, calculated according to formula (11-2);

[0170]

[0171] θ3: The qualified rate of products produced by the equipment, calculated according to formula (11-3);

[0172]

[0173] 2. Sustainable development indicators:

[0174] (1) Comprehensive energy consumption per unit of output value: Comprehensive energy consumption per unit of output value refers to the ratio of the comprehensive energy consumption of a factory to the net output value (value) created during the statistical period, calculated according to formula (12):

[0175]

[0176] Where: ε: comprehensive energy consumption per unit output value; all : The actual amount of various energy consumed by the factory during the statistical period, that is, the total comprehensive energy consumption of the main production system, auxiliary production system and ancillary production system, in tons of standard coal; V: The total net output value created by the factory during the statistical period, in ten thousand yuan.

[0177] (2) Comprehensive utilization rate of solid waste: The comprehensive utilization rate of solid waste refers to the ratio of the comprehensive utilization of solid waste generated in the factory's production activities to the total amount of solid waste generated, calculated according to formula (13):

[0178]

[0179] Where, K: comprehensive utilization rate of industrial solid waste; Z r : The comprehensive utilization of industrial solid waste in the factory during the statistical period (excluding external purchases), in tons (t); Z: The amount of industrial solid waste generated by the factory during the statistical period, in tons (t); Z w : The storage volume of industrial solid waste in previous years, in tons (t).

[0180] In specific implementation, the evaluation data of the sub-capability domain indicators of the performance indicators can be calculated and obtained in the following way:

[0181] Calculate the score of the benefit index, score = (product qualification rate + product repair rate + product delivery rate + inventory turnover rate + equipment comprehensive efficiency) / 5 × 100;

[0182] Calculate the score of sustainable development index, score = (comprehensive energy consumption per unit output value + comprehensive utilization rate of solid waste) / 2 × 100;

[0183] The score values ​​of the benefit index and the sustainable development index are the scores of the sub-capability domain indicators of the performance index, corresponding to the score ranges in Table 3, to obtain the relevant evaluation data of the performance index.

[0184] It should be noted that the above sub-capability domain indicators can be combined in any way, and the indicator requirements of the sub-capability domain indicators can also be combined in any way. According to the actual situation of the manufacturing enterprise, the sub-capability domain indicator composition of the sub-capability domain indicator system, the indicator requirement composition of the sub-capability domain indicators, and the composition of the evaluation items of the indicator requirements are dynamically adjusted to make the evaluation more reasonable and objective. For this reason, the present invention does not make any limitation.

[0185] In another specific implementation, based on the description of sub-capability domain indicators (I) to (VIII), since indicators (VII) and (VIII) are different from the previous six indicators and have different evaluation methods, the present invention provides the following different methods for obtaining satisfaction degree scoring coefficients.

[0186] For the management system index, intelligent design index, intelligent production index, intelligent logistics index, intelligent management index, and system integration index, i.e., the above-mentioned indexes (i) to (vi), the same evaluation method based on Table 3 is used to evaluate the satisfaction degree of the sub-capability domain requirements for each index, and then obtain the corresponding satisfaction degree score coefficient. For details, please refer to the relevant records in the above-mentioned step 500.

[0187] For resource indicators and performance indicators, i.e., the above indicators (VII) and (VIII), since the sub-capability domain indicators of resource indicators and performance indicators have definite values, the scoring coefficient of each evaluation requirement can be determined based on the following Table 4 and the value of each evaluation requirement of each sub-capability domain indicator; and then the satisfaction degree scoring coefficient of the sub-capability domain indicator can be obtained based on the average of the scoring coefficients of all evaluation requirements.

[0188] Table 4

[0189]

[0190]

[0191] S600: Based on the weights of the capability domain indicators, the weights of the sub-capability domain indicators and the satisfaction rating coefficient, the overall score of the smart factory maturity evaluation of the smart factory is calculated, and the overall score of the smart factory maturity evaluation is used to evaluate the maturity level of the smart factory.

[0192] In a specific implementation, calculating the overall score of the smart factory maturity evaluation of the smart factory specifically includes:

[0193] S610: Calculate the sub-capability domain evaluation score of the smart factory based on the weight of the capability domain indicator, the weight of the sub-capability domain indicator and the satisfaction degree scoring coefficient.

[0194] Specifically, the calculation formula for the sub-competency domain evaluation score is X=α×β×10 2 ×γ;

[0195] Where X is the sub-competency domain evaluation score of the ith sub-competency domain indicator, α is the weight of the competency domain indicator j corresponding to the ith sub-competency domain indicator, β is the weight of the ith sub-competency domain indicator, and γ is the satisfaction rating coefficient corresponding to the ith sub-competency domain indicator.

[0196] S620: Calculate the capability domain evaluation score of the smart factory based on the sub-capability domain evaluation scores.

[0197] Specifically, the calculation formula for the ability domain evaluation score is:

[0198] Where Y is the capability domain evaluation score of the j-th capability domain indicator, X is the sub-capability domain evaluation score of the i-th sub-capability domain indicator corresponding to the j-th capability domain indicator, and m is the number of sub-capability domains corresponding to the j-th capability domain indicator.

[0199] S630: Based on the capability domain score, calculate the overall smart factory maturity evaluation score of the smart factory, and the overall smart factory maturity evaluation score is used to evaluate the maturity level of the smart factory.

[0200] Specifically, the calculation formula for the overall score of the smart factory maturity evaluation is:

[0201] Where Z is the overall score of the smart factory maturity evaluation, Y is the capability domain evaluation score of the jth capability domain indicator, and n is the number of capability domain indicators.

[0202] In a preferred implementation, the total score of the smart factory maturity evaluation is used to evaluate the maturity level of the smart factory. The present invention divides the maturity level of the smart factory into five levels: "smart factory (three-star)", "smart factory (four-star)", "smart factory (five-star)", "smart factory (six-star)", and "smart factory (seven-star)". The specific level is determined based on the total score Z of the smart factory maturity evaluation. The specific level division is as follows:

[0203] Z<60 points, the enterprise does not meet the smart factory assessment requirements;

[0204] 60≤Z<65 points, the enterprise factory level is determined as "smart factory (three-star)";

[0205] 65≤Z<75 points, the enterprise factory level is judged as "smart factory (four-star)";

[0206] 75≤Z<85 points, the enterprise factory level is judged as "smart factory (five-star)";

[0207] 85≤Z<95 points, the enterprise factory level is judged as "smart factory (six-star)"

[0208] If Z is ≥ 95 points, the enterprise’s factory grade is judged as “smart factory (seven-star)”.

[0209] The present invention also provides a smart factory maturity evaluation system for executing and realizing the smart factory maturity evaluation method, comprising:

[0210] A construction module is used to construct a capability domain indicator system and a sub-capability domain indicator system corresponding to the capability domain indicator system. One capability domain indicator corresponds to several sub-capability domain indicators.

[0211] A first calculation module, which is used to calculate the weight of the capability domain indicator of the capability domain indicator system;

[0212] A second calculation module, which is used to calculate the weights of the sub-capability domain indicators of the sub-capability domain indicator system;

[0213] An acquisition module, which is used to acquire evaluation data of the smart factory;

[0214] A third calculation module is used to determine a satisfaction rating coefficient of the smart factory based on the evaluation data, where one sub-capability domain indicator corresponds to one satisfaction rating coefficient;

[0215] The fourth calculation module is used to calculate the overall score of the smart factory maturity evaluation of the smart factory based on the weights of the capability domain indicators, the weights of the sub-capability domain indicators and the satisfaction rating coefficient. The total score of the smart factory maturity evaluation is used to evaluate the maturity level of the smart factory.

[0216] Embodiment 2

[0217] This embodiment 2 provides a smart factory maturity evaluation method, which is completely the same as the technical solution and principle of embodiment 1. The difference is that the evaluation data includes evaluation information of the safety management index, and based on the evaluation information of the safety management index, the satisfaction degree of the sub-capability domain requirements of the safety management index is determined, and then the satisfaction degree scoring coefficient is obtained.

[0218] Among them, the evaluation information includes the safe operation rate of the on-site personnel during the statistical period. The safe operation rate during the statistical period is mainly calculated by detecting whether the on-site personnel's clothing during the statistical period meets the safety protection standards. The safety protection standard is whether the on-site personnel wear protective equipment correctly according to the factory management requirements. In one example, on-site personnel need to wear the required protective equipment when working on-site, such as safety masks, protective gloves, protective shoes, safety helmets, protective glasses, protective sleeves or protective clothing, etc.

[0219] Specifically, the safe operation rate of the present invention is obtained through the following steps:

[0220] S10: Acquire video data of the work site within the statistical period, wherein the video data is divided into time units, and the time unit is day.

[0221] In the present invention, video data is obtained by multiple cameras that are fixedly deployed on the work site. Specifically, the cameras can be evenly deployed in multiple workshops of the smart factory to comprehensively capture the on-site personnel at the work site.

[0222] S20: Based on the YOLOv5 detection algorithm, detect and identify the on-site personnel in the video data, and obtain several target personnel detection frames and corresponding serial number labels;

[0223] In a preferred implementation, the present invention adopts an improved YOLOv5 on-site personnel detection algorithm based on BiFPN, which uses an improved YOLOv5 deep learning algorithm to identify personnel at the work site in the video stream and outputs the bounding box and serial number mark of the target personnel.

[0224] like Figure 2 As shown, YOLOv5 has four network models of different complexity: s, m, l, and x. The structural frameworks of these four models are consistent, but the network depth and width are different. YOLOv5s has the fastest detection speed, but the detection accuracy is slightly lower, while YOLOv5x has the slowest detection speed, but the model is larger and therefore has a high detection accuracy. A reasonable choice can be made based on the algorithm's usage requirements and application scenarios. To this end, the present invention takes YOLOv5s as an example, which includes four parts: input end, Backbone network, Neck network, and output end. Its network structure is as follows Figure 2 As shown in the figure, the following describes the four main aspects of the YOLOv5 network structure and improves the Neck network.

[0225] (1) The input part is used to input image data and preprocess the image so that it can be passed into the Backbone network for feature extraction. The network requires the input image size to be 640×640. Generally, the image size is not the same as the network input size. Therefore, the input image needs to be scaled to the input size required by the network, and normalization and other operations are performed to accelerate model convergence.

[0226] (2) The main function of the Backbone network is to extract the features of the input image and pass it to the subsequent network for target location and classification. YOLOv5 uses the CSPDarknet53 structure as the backbone network, and uses the Focus concept and SPPF structure to improve the network feature extraction capability and reasoning speed.

[0227] The C3 module contains 3 convolution modules and multiple Bottleneck modules. The C3 module is actually a simplified version of Bottleneck CSP. Both are CSP architectures, but the submodules used are different. Since the structure of each residual block combined in the residual network is similar, it is easy to repeat calculations when updating the model parameters in reverse, which increases the amount of model calculation. Therefore, the CSP structure refers to the calculation idea of ​​the cross-stage local network (CSPNet). The CSPNet structure divides the network transmission into two branches, one branch uses the residual module to extract features from the feature map, and the other branch is directly connected to the output of the feature extraction. This network structure that splits and then merges effectively solves the problem of high repetition of gradient information during reverse update, and the combination of multiple branches enhances the learning ability of the model. At the same time, the CSP structure can reduce the amount of network parameters by 10% to 20% while improving or maintaining accuracy.

[0228] (3) The Neck network is mainly used to fuse the feature information extracted by the Backbone network to improve the feature expression and the robustness of the model, and then send it to the output end for prediction. The Neck network of YOLOv5 uses the FPN+PAN module.

[0229] (4) The output end is responsible for outputting the target detection results. The features extracted by the backbone network are then regressed and classified after multi-scale fusion by Neck. YOLOv5 uses the same Head network as YOLOv3. There are three branches at different depths of the network. After processing with a 1×1 convolution structure, three groups of feature vectors of different sizes are output, which are suitable for detecting targets of different sizes.

[0230] S30: Based on the YOLOv51 detection algorithm, detect and identify the protective equipment in the target person detection frame, and obtain the protective equipment detection frame and the protective equipment category.

[0231] In this invention, OLOv5l uses CSPDarknet53 as its backbone network, and optimizes the performance of small target detection through a series of improvement strategies. The input end of the YOLOv5 series algorithm uses Mosaic data enhancement technology, adaptive anchor box calculation and adaptive image scaling technology, which help improve the detection effect of small targets.

[0232] S40: Matching all protective equipment categories within the target person detection frame with protective equipment wearing standards;

[0233] In the present invention, the protective equipment wearing standard is a collection of protective equipment categories that the smart factory requires on-site personnel to wear when working on-site.

[0234] S50: When the category of protective equipment in the target person detection frame does not match the category and quantity of protective equipment in the protective equipment wearing standard, a result of not meeting the safety protection standard is output.

[0235] S60: Count all the results that do not meet the safety protection standards in the video data within one day, and add them up to get the total number of violations. Calculate the safety protection standard rate for one day, safety protection standard rate = (total number of target detection personnel detected in step S20 - total number of violations) / total number of target detection personnel detected in step S20.

[0236] S70: Calculate the safety protection standard rates of all days in the statistical period one by one; calculate the average based on all safety protection standard rates to obtain the safe operation rate.

[0237] It can be understood that the safe operation rate is one of the evaluation items of the safety management indicators, and the score of this evaluation item = the specific value of the safe operation rate × 100.

[0238] For other structures of the intelligent factory maturity evaluation method and system described in this embodiment, refer to the prior art.

[0239] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Therefore, any modification, equivalent change and modification made to the above embodiment according to the technical essence of the present invention without departing from the technical solution of the present invention shall still fall within the scope of the technical solution of the present invention.

Claims

1. A method for evaluating the maturity of a smart factory, characterized in that: include: Construct a capability domain indicator system and a sub-capability domain indicator system corresponding to the capability domain indicator system. One capability domain indicator corresponds to several sub-capability domain indicators. Calculate the weights of capability domain indicators in the capability domain indicator system; Calculate the weights of the sub-capability domain indicators of the sub-capability domain indicator system; Obtain evaluation data of smart factories; Based on the evaluation data, determine the satisfaction rating coefficient of the smart factory, where one sub-capability domain indicator corresponds to one satisfaction rating coefficient; Based on the weights of the capability domain indicators, the weights of the sub-capability domain indicators and the satisfaction rating coefficients, the overall score of the smart factory maturity evaluation of the smart factory is calculated, and the overall score of the smart factory maturity evaluation is used to evaluate the maturity level of the smart factory.

2. The intelligent factory maturity evaluation method according to claim 1 is characterized in that: Calculate the overall score of the smart factory maturity assessment of the smart factory, including: Based on the weights of the capability domain indicators, the weights of the sub-capability domain indicators and the satisfaction rating coefficient, the sub-capability domain evaluation scores of the smart factory are calculated; Based on the sub-capability domain evaluation scores, calculating the capability domain evaluation score of the smart factory; Based on the capability domain scores, an overall smart factory maturity evaluation score of the smart factory is calculated, and the overall smart factory maturity evaluation score is used to evaluate the maturity level of the smart factory.

3. The intelligent factory maturity evaluation method according to claim 2 is characterized in that: The calculation formula of the sub-competency domain evaluation score is X = α × β × 10 2 ×γ; Where X is the sub-competency domain evaluation score of the ith sub-competency domain indicator, α is the weight of the competency domain indicator j corresponding to the ith sub-competency domain indicator, β is the weight of the ith sub-competency domain indicator, and γ is the satisfaction rating coefficient corresponding to the ith sub-competency domain indicator.

4. The intelligent factory maturity evaluation method according to claim 3 is characterized by: The calculation formula for the ability domain evaluation score is: Where Y is the capability domain evaluation score of the j-th capability domain indicator, X is the sub-capability domain evaluation score of the i-th sub-capability domain indicator corresponding to the j-th capability domain indicator, and m is the number of sub-capability domains corresponding to the j-th capability domain indicator.

5. The intelligent factory maturity evaluation method according to claim 4 is characterized in that: The calculation formula for the overall score of the smart factory maturity evaluation is: Where Z is the overall score of the smart factory maturity evaluation, Y is the capability domain evaluation score of the jth capability domain indicator, and n is the number of capability domain indicators.

6. A smart factory maturity evaluation method according to any one of claims 1 to 5, characterized in that: The capability domain indicator system includes management system indicators, intelligent design indicators, intelligent production indicators, intelligent logistics indicators, intelligent management indicators, system integration indicators, resource indicators and performance indicators.

7. The intelligent factory maturity evaluation method according to claim 6 is characterized by: Corresponding to the management system indicators, the sub-capability domain indicator system includes sub-management system indicators; Corresponding to the intelligent design index, the sub-capability domain index system includes product design index and process design index; Corresponding to the intelligent production indicators, the sub-capability domain indicator system includes production planning and scheduling indicators, production execution indicators, quality control indicators and equipment operation and maintenance indicators; Corresponding to the smart logistics indicators, the sub-capability domain indicator system includes smart warehousing indicators and smart distribution indicators; Corresponding to the intelligent management indicators, the sub-capability domain indicator system includes procurement management indicators, sales management indicators, customer service indicators, product service management indicators, asset management indicators, energy management indicators, safety management indicators and environmental management indicators; Corresponding to the system integration indicator, the sub-capability domain indicator system includes sub-system integration indicators; Corresponding to resource indicators, the sub-capability domain indicator system includes digital indicators, networking indicators and information indicators; Corresponding to resource indicators, the sub-capability domain indicator system includes benefit indicators and sustainable development indicators.

8. A smart factory maturity evaluation system, characterized in that: include: A construction module is used to construct a capability domain indicator system and a sub-capability domain indicator system corresponding to the capability domain indicator system. One capability domain indicator corresponds to several sub-capability domain indicators. A first calculation module, which is used to calculate the weight of the capability domain indicator of the capability domain indicator system; A second calculation module, which is used to calculate the weights of the sub-capability domain indicators of the sub-capability domain indicator system; An acquisition module, which is used to acquire evaluation data of the smart factory; A third calculation module is used to determine a satisfaction rating coefficient of the smart factory based on the evaluation data, where one sub-capability domain indicator corresponds to one satisfaction rating coefficient; The fourth calculation module is used to calculate the overall score of the smart factory maturity evaluation of the smart factory based on the weights of the capability domain indicators, the weights of the sub-capability domain indicators and the satisfaction rating coefficient. The total score of the smart factory maturity evaluation is used to evaluate the maturity level of the smart factory.

9. The intelligent factory maturity evaluation system according to claim 8, characterized in that: The fourth calculation module calculates the overall score of the smart factory maturity evaluation of the smart factory, specifically including: Based on the weights of the capability domain indicators, the weights of the sub-capability domain indicators and the satisfaction rating coefficient, the sub-capability domain evaluation scores of the smart factory are calculated; Based on the sub-capability domain evaluation scores, calculating the capability domain evaluation score of the smart factory; Based on the capability domain scores, an overall smart factory maturity evaluation score of the smart factory is calculated, and the overall smart factory maturity evaluation score is used to evaluate the maturity level of the smart factory.

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