Quantitative analysis system for realizing intelligent manufacturing capability maturity evaluation

Abstract

The invention discloses a quantitative analysis system for realizing intelligent manufacturing capability maturity evaluation, which comprises the following steps of: data acquisition: collecting real-time data by using a sensor and an equipment interface; data processing: in the Node-RED, using a function node to process and convert the collected data according with the parameter specification of ISO 22400-2-2014 Index System, and using GraphQL to receive factory model data collected by the Node-RED; kPI API configuration of a factory model is realized in a customized manner, KPI API parameters are called by a Plant Simulation to execute a KPI panel and simulation, and evaluation is carried out according to a Plant Simulation result and a gap is analyzed; a set of qualitative analysis system based on a factory model and surrounding production management performance indicators is designed by taking an intelligent manufacturing capability maturity three-level evaluation standard as a starting point, so that the system integration difficulty is simplified, the evaluation time is saved, the evaluation accuracy is improved, and the beneficial technical effect of effectively guaranteeing the evaluation correctness is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of computer-aided enterprise production systems, and in particular to a quantitative analysis system for realizing maturity assessment of intelligent manufacturing capabilities. Background Art

[0002] At present, assessment agencies all use the "GB∕T 39116-2020 Intelligent Manufacturing Capability Maturity Assessment Method" to conduct CMMM ratings on enterprises' intelligent manufacturing capability maturity assessments. The overall decision-making level includes 3 areas of personnel, technology, and resources, with a total of 7 sub-areas. The manufacturing level includes 4 areas of design, production, logistics, sales and services, with a total of 13 sub-areas. There are so many business areas and dozens of information software or business modules.

[0003] However, the evaluation clauses in the standard use qualitative descriptions as the basis for scoring, and face a series of problems such as how to convert the descriptions into an actual set of information system functions, how to unify information and data storage specifications, and how to ensure the correct extraction and use of production data through such business definition specifications.

[0004] How can the results of the current intuitive scoring system based on on-site observation and real-time operation experience ensure that enterprises adopt real and continuous operations in production to produce effective data management factories? The accuracy of the evaluation is greatly affected by the information technology research and development capabilities of the evaluation agency personnel and their factory management practice level and experience. There is a huge gap hidden behind the facts, and the evaluation and rating have generally become a formality. Summary of the invention

[0005] In view of the above technical problems in the related art, the present invention proposes a quantitative analysis system for realizing intelligent manufacturing capability maturity assessment, which can overcome the above shortcomings of the prior art.

[0006] To achieve the above technical objectives, the technical solution of the present invention is implemented as follows: A quantitative analysis system for realizing maturity assessment of intelligent manufacturing capabilities; The quantitative analysis system for realizing intelligent manufacturing capability maturity assessment specifically includes the following steps: Step S1: Data collection: collect real-time data using sensors and device interfaces; Step S2: Data processing: In Node-RED, function nodes are used to process and transform the collected data to comply with the parameter specifications of the ISO 22400-2-2014 indicator system, and GraphQL is used to receive the factory model data collected by Node-RED; Step S3: Customized factory model KPI API configuration: GraphQL allows the client to accurately specify the data structure it needs, which is used to build a flexible data query interface, so that the evaluation system can obtain specific data sets according to the national standard specifications of factory management performance indicators; Step S4: Plant Simulation calls the KPI API parameters to execute the KPI panel and simulation; Step S5: Evaluate and analyze the gaps based on the Plant Simulation results.

[0007] Furthermore, in step S1, the method of collecting real-time data is: implementing Modbus and OPC UA protocols through Node-RED, communicating with PLC to obtain equipment status and production data; Node-RED uses HTTP nodes to call the API of external systems or uses database nodes to realize data collection with ERP, MES, PDM, SRM, and CRM systems.

[0008] Furthermore, in step S3, the participating enterprises select the parameters of the GraphQL data collection API on the operation interface and design a performance indicator query interface that complies with the ISO 22400-2-2014 indicator system.

[0009] Furthermore, the integration with PLC is realized in Node-RED by using a dedicated node to communicate with PLC.

[0010] Further, step 101: installing Node-RED and PLC nodes; Step 102: Configure the PLC node: In the flow editor of Node-RED, drag the S7 config node to configure the connection parameters with the PLC; the connection parameters include IP address, rack number, and slot number; Step 103: Write process logic: Use the inject node as the trigger point of the process and set the trigger conditions; use the function node to process the business logic; use the debug node to debug and view the data flow in the process; Step 104: Data reading and writing: Read the data in the PLC through the S7 in node and send the data to other nodes for processing; read the data in the PLC through the S7 in node and send the data to other nodes for processing.

[0011] Furthermore, the step 102 further includes the following steps: Step S1021: Drag in the S7 in node to read the PLC data, configuration mode and variable address; Step S1022: Drag in the S7 out node to write data to the PLC, and configure the variable address and data type to be written.

[0012] Furthermore, the data collection in step S1 includes plant production process data collection, and uses a Node-RED process to receive device data, process the data and return it in the form of an HTTP response. The data format collected by Node-RED is compatible with Tecnomatix Plant Simulation, and data interaction is performed.

[0013] Furthermore, when GraphQL is used to receive the factory model data collected by Node-RED in step S2, GraphQL integration is implemented in Node-RED, specifically including the following steps: Step S201: Install the GraphQL node; in Node-RED, install the node-red-contrib-graphql-server node through the management panel; Step S202: Define GraphQL Schema: Create a GraphQL Schema in JSON format to describe the data structure and available query, mutation, and subscription types; Step S203: Implement the Resolver function: The Resolver function defines how to respond to queries for a specific type of field; in the intelligent manufacturing evaluation system, the Resolver function retrieves data from a database or other data source; Step S204: Configure HTTP nodes: Use Node-RED's HTTP nodes to receive GraphQL requests, and use the node-red-contrib-graphql-server node to process these requests; Step S204: Implement data interaction: Implement data interaction between the client and the Node-RED server through GraphQL queries and changes; for subscriptions, the client maintains connection with the server to receive real-time updates.

[0014] The method of the present invention realizes quantitative evaluation and has the following beneficial effects: 1. Simplify the integration difficulty: Participating enterprises may upload actual data based on their own collection points without considering the standards. The collection information can be added and modified at any time during the evaluation process. Use the system function to select indicators and perform online analysis / conversion / calculation of the collection point data for the indicator parameters. Plant Simulation automatically displays the indicators and executes and configures simulation scenarios related to the indicators.

[0015] 2. Save time: In Plant Simulation, indicators that change with production and operation animations of factory scenes are displayed, making the evaluation process intuitive and clear, avoiding the process of asking, operating, observing, and verifying data.

[0016] 3. Accurate assessment: The data all come from the production process, which can verify the technical architecture and standard feasibility of the participating enterprises, monitor the actual production control, and ensure the authenticity and sustainability of the intelligent manufacturing level of the participating enterprises. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0018] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present invention, the meaning of "several" is two or more, unless otherwise clearly and specifically defined.

[0019] A quantitative analysis system for realizing intelligent manufacturing capability maturity assessment according to the present invention comprises the following steps: Step S1: Data collection: collect real-time data using sensors and device interfaces; Step S2: Data processing: In Node-RED, function nodes are used to process and transform the collected data to comply with the parameter specifications of the ISO 22400-2-2014 indicator system, and GraphQL is used to receive the factory model data collected by Node-RED; Step S3: Customized factory model KPI API configuration: GraphQL allows the client to accurately specify the data structure it needs, which is used to build a flexible data query interface, so that the evaluation system can obtain specific data sets according to the national standard specifications of factory management performance indicators; Step S4: Plant Simulation calls the KPI API parameters to execute the KPI panel and simulation; Step S5: Evaluate and analyze the gaps based on the Plant Simulation results.

[0020] According to a quantitative analysis system for realizing intelligent manufacturing capability maturity assessment described in the present invention, in a specific embodiment, in the step S1, the method of collecting real-time data is: implementing Modbus and OPC UA protocols through Node-RED, communicating with PLC to obtain equipment status and production data; Node-RED uses HTTP nodes to call the API of external systems or uses database nodes to realize data collection with ERP, MES, PDM, SRM, and CRM systems.

[0021] According to a quantitative analysis system for realizing intelligent manufacturing capability maturity assessment described in the present invention, in a specific embodiment, in step S3, the participating enterprise selects the parameters of the GraphQL data collection API on the operation interface to design a performance indicator query interface that complies with the "ISO 22400-2-2014 Index System".

[0022] According to a quantitative analysis system for realizing intelligent manufacturing capability maturity assessment described in the present invention, in a specific embodiment, integration with PLC is realized in Node-RED, and communication with PLC is performed by using a dedicated node.

[0023] According to a quantitative analysis system for realizing intelligent manufacturing capability maturity assessment according to the present invention, in a specific embodiment, the dedicated node communicates with the PLC, specifically comprising the following steps: Step 101: Install Node-RED and PLC nodes; Step 102: Configure the PLC node: In the flow editor of Node-RED, drag the S7 config node to configure the connection parameters with the PLC; the connection parameters include IP address, rack number, and slot number; Step 103: Write process logic: Use the inject node as the trigger point of the process and set the trigger conditions; use the function node to process the business logic; use the debug node to debug and view the data flow in the process; Step 104: Data reading and writing: Read the data in the PLC through the S7 in node and send the data to other nodes for processing; read the data in the PLC through the S7 in node and send the data to other nodes for processing.

[0024] According to a quantitative analysis system for realizing intelligent manufacturing capability maturity assessment according to the present invention, in a specific embodiment, step 102 further includes the following steps: Step S1021: Drag in the S7 in node to read the PLC data, configuration mode and variable address; Step S1022: Drag in the S7 out node to write data to the PLC, and configure the variable address and data type to be written.

[0025] According to a quantitative analysis system for realizing intelligent manufacturing capability maturity assessment described in the present invention, in a specific embodiment, the data collection in step S1 includes factory production process data collection, and a Node-RED process is used to receive device data, process data and return it in the form of an HTTP response. The data format collected by Node-RED is compatible with TecnomatixPlant Simulation, and data interaction is performed.

[0026] According to a quantitative analysis system for realizing intelligent manufacturing capability maturity assessment according to the present invention, in a specific embodiment, when using GraphQL to receive the plant model data collected by Node-RED in step S2, implementing GraphQL integration in Node-RED specifically includes the following steps: Step S201: Install the GraphQL node; in Node-RED, install the node-red-contrib-graphql-server node through the management panel; Step S202: Define GraphQL Schema: Create a GraphQL Schema in JSON format to describe the data structure and available query, mutation, and subscription types; Step S203: Implement the Resolver function: The Resolver function defines how to respond to queries for a specific type of field; in the intelligent manufacturing evaluation system, the Resolver function retrieves data from a database or other data source; Step S204: Configure HTTP nodes: Use Node-RED's HTTP nodes to receive GraphQL requests, and use the node-red-contrib-graphql-server node to process these requests; Step S204: Implement data interaction: Implement data interaction between the client and the Node-RED server through GraphQL queries and changes; for subscriptions, the client maintains connection with the server to receive real-time updates.

[0027] In order to facilitate understanding of the above technical solutions of the present invention, the above technical solutions of the present invention are described in detail in all aspects through specific solutions below.

[0028] This system mainly solves the problem of converting the qualitative analysis method based on the intelligent manufacturing capability maturity assessment method of GB∕T 39116-2020 into a quantitative analysis method. This system involves factory IOT data collection and information system data integration technology, factory model level definition, business reorganization and indicator reorganization technology of collected data, factory model simulation technology, implementation process quality and data quality assessment technology.

[0029] 1. Brief description of the implementation process The factory information model is established to achieve the virtual factory business, in accordance with the information model establishment method and model attribute information requirements. The factory information model library includes an object model library with personnel, equipment facilities, materials, site environment and other information as the main content, and a rule model library with production process, production management, product information, production logistics, and technical knowledge as the main content.

[0030] Maintain a unified set of core key indicators based on the product life cycle and continuously analyze the current production process in order to control and manage it after the start of production (SOP).

[0031] Establish a factory model based on ISO 22400 and other national standards and form a QCDPSM (quality, cost, delivery, production efficiency, safety, sustainability, employee morale) North Star indicator system: 1) Field equipment and instruments (layer 0); Definition: This layer includes the basic equipment and instruments in the factory, such as sensors, actuators, etc. Attributes: equipment status, performance parameters, maintenance records, etc. KPIs: equipment availability, failure rate, maintenance cost, etc. 2) Control devices and PLC (Layer 1); Definition: This layer involves control logic and automation processes, usually managed by a PLC (Programmable Logic Controller); Attributes: program running status, input / output signals, cycle time, etc.; KPIs: control accuracy, response time, program change frequency, etc. 3) Supervisory control (Layer 2); Definition: This layer is responsible for monitoring and optimizing the production process and may include SCADA (Supervisory Control and Data Acquisition) systems; Attributes: production process monitoring, quality control parameters, energy consumption, etc.; KPIs: production efficiency, quality pass rate, energy utilization rate, etc.; 4) Manufacturing Execution System (MES, Layer 3); Definition: MES is the bridge between the enterprise system and the factory floor, responsible for production scheduling, tracking and reporting; Attributes: order fulfillment, production planning, material tracking, etc. KPIs: on-time order delivery rate, production cost, inventory turnover rate, etc. 5) Enterprise systems (Layer 4); Definition: This layer includes ERP (Enterprise Resource Planning) and other business management systems; Attributes: financial data, human resources information, market analysis, etc. KPIs: Profit margin, customer satisfaction, market share, etc.

[0032] To implement these levels of the North Star Indicator system, we need to follow these steps: Step 1: Data Collection: Use sensors and device interfaces to collect real-time data, such as implementing Modbus, OPC UA and other protocols through Node-RED, and communicating with PLC to obtain device status and production data. Node-RED uses HTTP nodes to call the API of external systems or use database nodes to realize data collection with ERP, MES, PDM, SRM, CRM and other systems.

[0033] Step 2: Data Processing: In Node-RED, function nodes are used to process and transform the collected data to comply with the parameter specifications of the ISO 22400-2-2014 Index System, and GraphQL is used to receive the factory model data collected by Node-RED.

[0034] Step 3: Customize the factory model KPI API configuration; GraphQL allows the client to precisely specify the data structure it needs, and is used to build a flexible data query interface, so that the evaluation system can obtain specific data sets according to the national standard of factory management performance indicators. In the operation interface of this system, the participating enterprises select the parameters of the GraphQL data collection API and design a performance indicator (QCDPSM) query interface that complies with the ISO 22400-2-2014 indicator system.

[0035] Step 4: Plant Simulation calls the KPI API parameters to execute the KPI panel and simulation.

[0036] Step 5: Evaluate and analyze gaps based on Plant Simulation results.

[0037] Regarding the code implementation, the details are as follows: 2.1. Integration with factory automation system in Node-RED; Integration method 1: Modbus TCP communication Modbus TCP communication can be achieved using the node-red-node-modbus node of Node-RED. The following is a simple flow for reading data from a Modbus TCP device: / / First, you need to install the node-red-node-modbus node / / You can install it through the Node-RED interface, or use the npm command: npm install node-red-node-modbus / / In Node-RED, add a Modbus TCP client node (modbus-client) and configure its connection parameters / / Then, add a Modbus read node (modbus-read) and set the register address and quantity / / Finally, add a function node (function) to process the read data and display the results in the debug window (debug) / / Function node code example: function(msg) { / / Process the data read from the Modbus device console.log("Data read:",msg.payload); return msg; } Integration method 2: OPC UA communication For the OPC UA protocol, you can use the node-red-contrib-opcua node. The following is a simple flow for connecting to an OPC UA server and reading node data: / / First, you need to install the node-red-contrib-opcua node / / You can install it through the Node-RED interface, or use the npm command: npm install node-red-contrib-opcua / / In Node-RED, add an OPC UA client node (OPCUA-Client) and configure its connection parameters / / Then, add an OPC UA read node (OPCUA-read) and set the node ID to read / / Finally, add a function node (function) to process the read data and display the results in the debug window (debug) / / Function node code example: function(msg) { / / Process the data read from the OPC UA server console.log("Data read:", msg.payload); return msg; } Integration method 3: MQTT communication Node-RED has built-in support for the MQTT protocol, which allows MQTT communication with other systems. The following is a simple process for subscribing to an MQTT topic and processing received messages: / / In Node-RED, add an MQTT input node (mqtt in) and configure the topic it subscribes to / / Then, add a function node to process the received message / / Finally, add a debug node to display the processed message / / Function node code example: function(msg) { / / Process messages received from MQTT topics console.log("Received message:",msg.payload); return msg; } Integration method 4: HTTP API integration If you need to integrate with a system that provides an HTTP API, you can use Node-RED's HTTP node. The following is a simple flow for calling an HTTP API and handling the response: / / In Node-RED, add an HTTP input node (http in) and configure its path and method / / Then, add a function node to handle the request and call the HTTP API / / Finally, add an HTTP response node (http response) to return the response to the client / / Function node code example: function(msg) { / / Call the HTTP API and process the response var options = { url: "http: / / example.com / api / data", / / API URL method: "GET", / / or "POST", "PUT", "DELETE", etc. headers: { "Content-Type": "application / json" } }; var response = http.request(options, function(res) { var result = ""; res.setEncoding("utf8"); res.on("data", function(chunk) { result += chunk; }); res.on("end", function() { msg.payload =JSON.parse(result); node.send(msg); }); }); response.on("error", function(e) { console.error(e); }); response.end(); } 2.2 Integration with PLC Integration with PLC is achieved in Node-RED by using a dedicated node to communicate with the PLC.

[0038] Step 1: Install Node-RED and PLC Node First, make sure Node-RED is installed on your system. Then, install the nodes required to communicate with your PLC. For Siemens PLCs, you can use the node-red-contrib-s7 node library.

[0039] npm install node-red-contrib-s7 In the Node-RED interface, go to "Node Management" and install the node-red-contrib-s7 node library.

[0040] Step 2: Configure PLC Node In the Node-RED flow editor, drag the S7 config node to configure the connection parameters with the PLC, such as IP address, rack number, slot number, etc.

[0041] Drag in the S7 in node to read data from the PLC, configure the mode (e.g. read a single variable, all variables, etc.) and the variable address.

[0042] Drag in the S7 out node to write data to the PLC and configure the variable address and data type to be written.

[0043] Step 3: Write the process logic Use the inject node as the trigger point of the process and set the trigger conditions, such as timed triggering or triggering when receiving a specific message.

[0044] Use function nodes to process business logic, such as data conversion, calculation, etc.

[0045] Use the debug node to debug and view the data flow in the process.

[0046] Step 4: Data Reading and Writing The data in the PLC is read through the S7 in node and sent to other nodes for processing.

[0047] The processed data is written back to the PLC via the S7 out node.

[0048] 2.3 Factory production process data collection A basic Node-RED process is implemented to receive device data, process the data and return it in the form of HTTP response, ensuring that the data format collected by Node-RED is compatible with Tecnomatix Plant Simulation and achieving efficient data interaction. / / HTTP input node in Node-RED, used to receive device data [ { "id": "1", "type": "http in", "z": "4957c8f4.03ab7", "name": "Device Data", "url": " / device-data", "method": "POST", "upload": false, "swaggerDoc": "", "x": 110, "y": 80, "wires": [["2"]] }, { "id": "2", "type": "function", "z": "4957c8f4.03ab7", "name": "Process Data", "func": "msg.payload = JSON.parse(msg.payload);\nreturnmsg;", "outputs": 1, "noerr": 0, "x": 290, "y": 80, "wires": [["3"]] }, { "id": "3", "type": "http response", "z": "4957c8f4.03ab7", "name": "", "statusCode": "", "headers": {}, "x": 470, "y": 80, "wires": [] } ] 2.3 Node-RED realizes data collection with ERP, MES, PDM, SRM, CRM and other systems In Node-RED, you can use HTTP nodes to call the API of external systems, or use database nodes to query data in the database. The collected data may need to be converted and processed to meet the needs of subsequent processing. You can use the function node in Node-RED to implement this step.

[0049] Data collection with ERP system: The ERP system provides an API endpoint to obtain order data: / / HTTP input node in Node-RED, used to call the API of the ERP system [ { "id": "1", "type": "http in", "z": "4957c8f4.03ab7", "name": "ERP Orders", "url": " / erp / orders", "method": "GET", "upload": false, "swaggerDoc": "", "x": 110, "y": 80, "wires": [["2"]] }, { "id": "2", "type": "function", "z": "4957c8f4.03ab7", "name": "Process ERP Data", "func": "msg.payload = JSON.parse(msg.payload);\nreturnmsg;", "outputs": 1, "noerr": 0, "x": 290, "y": 80, "wires": [["3"]] }, { "id": "3", "type": "http response", "z": "4957c8f4.03ab7", "name": "", "statusCode": "", "headers": {}, "x": 470, "y": 80, "wires": [] } ] Data collection with MES system: Use the following code to query data in the database: / / Database query node in Node-RED, used to obtain data from the database of the MES system [ { "id": "4", "type": "mysql", "z": "4957c8f4.03ab7", "name": "MES Production Data", "host": "mes-server-ip", "port": "3306", "database": "mes_db", "query": "SELECT * FROM production_data WHERE status='active'", "x": 110, "y": 160, "wires": [["5"]] }, { "id": "5", "type": "function", "z": "4957c8f4.03ab7", "name": "Process MES Data", "func": "msg.payload = JSON.stringify(msg.payload);\nreturnmsg;", "outputs": 1, "noerr": 0, "x": 290, "y": 160, "wires": [["6"]] }, { "id": "6", "type": "http response", "z": "4957c8f4.03ab7", "name": "", "statusCode": "", "headers": {}, "x": 470, "y": 160, "wires": [] } ] For other systems such as PDM, SRM, CRM, etc., the implementation method is similar. The key is to determine the data source and access method, and then use the corresponding nodes of Node-RED to implement data collection and processing.

[0050] 2.5 Implementing GraphQL integration in Node-RED Step 1: Install GraphQL Node In Node-RED, you can install the node-red-contrib-graphql-server node through the admin panel. This node allows you to run a GraphQL server in Node-RED.

[0051] Step 2: Define the GraphQL Schema Create a GraphQL Schema, defined in JSON format, to describe the data structure and available query, mutation, and subscription types.

[0052] / / GraphQL Schema Definition const typeDefs = ` type Query { sensorData(sensorId: ID!): Sensor } type Mutation { updateSensorData(sensorId: ID!, value: Float!): Sensor } type Subscription { sensorUpdate(sensorId: ID!): Sensor } type Sensor { sensorId: ID! value: Float }`; Step 3: Implement the Resolver function Resolver functions define how to respond to queries for fields of a specific type. In a smart manufacturing evaluation system, these functions may retrieve data from a database or other data source.

[0053] / / GraphQL Resolvers const resolvers = { Query: { sensorData: (_, { sensorId}) =>{ / / This should be a database call, returning data for a specific sensor return { sensorId, value: Math.random() * 100}; / / Sample data }, }, Mutation: updateSensorData: (_, { sensorId, value}) =>{ / / This should be a database call to update the data of a specific sensor return { sensorId, value}; / / Sample data }, }, Subscription: sensorUpdate: { subscribe: (_, { sensorId}) =>{ / / This should be a database call to subscribe to data updates from a specific sensor / / For demonstration purposes, we return a simple asynchronous iterator const asyncIterator = (async function* () { let counter = 0; while (true) { await new Promise(resolve =>setTimeout(resolve, 1000)); / / Push once per second counter += 1; yield { sensorId, value: counter}; / / sample data } })(); return asyncIterator; }, }, }, }; Step 4: Configure HTTP Node Use Node-RED's HTTP node to receive GraphQL requests and the node-red-contrib-graphql-server node to handle these requests.

[0054] Step 5: Implement data interaction Data exchange between the client and the Node-RED server is achieved through GraphQL queries and mutations. For subscriptions, the client will maintain a connection with the server to receive real-time updates.

[0055] 1) Configure an HTTP node in Node-RED to call the GraphQL API. In Plant Simulation, use a custom JavaScript HTTP node function to send HTTP requests to the GraphQL server and process the returned data.

[0056] / / HTTP input node in Node-RED for calling GraphQL API [ { "id": "1", "type": "http in", "z": "4957c8f4.03ab7", "name": "GraphQL API Call", "url": " / graphql", "method": "POST", "upload": false, "swaggerDoc": "", "x": 110, "y": 80, "wires": [["2"]] }, { "id": "2", "type": "function", "z": "4957c8f4.03ab7", "name": "Process GraphQL Response", "func": "msg.payload = JSON.parse(msg.payload);\nreturnmsg;", "outputs": 1, "noerr": 0, "x": 290, "y": 80, "wires": [["3"]] }, { "id": "3", "type": "debug", "z": "4957c8f4.03ab7", "name": "", "active": true, "console": false, "complete": "false", "x": 470, "y": 80, "wires": [] } ] In Tecnomatix Plant Simulation, use SimTalk scripts or other methods to call external HTTP services. Write SimTalk scripts that use HTTP client functions to request the API endpoints created by Node-RED and get data.

[0057] / / SimTalk script example WEB_CLIENT myClient; myClient:=WEB_CLIENT.Create("http: / / your-plant-simulation-api / factory-data"); myClient.Send(); IF myClient.Response()>0 THEN / / Process the response data WRITE myClient.ResponseText(); ENDIF myClient.Dispose(); 2) Use the apollo-server library to create a GraphQL API that receives Tecnomatix PlantSimulation control instructions to update plant equipment and information systems.

[0058] const { ApolloServer, gql} = require('apollo-server'); const fetch = require('node-fetch'); / / Use the node-fetch library to make HTTP requests / / Define the GraphQL Schema const typeDefs = gql` type Query { productionData: [ProductionData] } type ProductionData { productId: ID quantity: Int status: String }`; / / Function to get data from Tecnomatix Plant Simulation const getSimulationData = async () =>{ / / Use fetch to send a request to the Tecnomatix Plant Simulation API const response = await fetch('http: / / your-plant-simulation-api / productionData'); return await response.json(); }; / / Implement the Resolver function const resolvers = { Query: { productionData: async () => { return getSimulationData(); }, }, }; / / Create and start the GraphQL server const server = new ApolloServer({ typeDefs, resolvers}); server.listen().then(({ url}) =>{ / / Feedback result processing }); Implement integration with GraphQL in Node-RED and update industrial automation systems and information systems such as ERP / MES.

[0059] Step 1: Install GraphQL Node In Node-RED, you can install the node-red-contrib-graphql node through the admin panel. This node allows you to execute GraphQL queries and mutations in Node-RED.

[0060] Step 2: Define the GraphQL Schema Create a GraphQL Schema that will define the KPI data structure that your industrial automation system and ERP / MES system can provide. This may include production process, equipment status, material flow, order information, etc.

[0061] Step 3: Implement the Resolver function Write Resolver functions to handle GraphQL queries and mutation requests. These functions will use the interfaces provided by your industrial automation system and ERP / MES system to retrieve or modify data.

[0062] Step 4: Setting up the GraphQL server Use Node.js and the apollo-server library to set up a GraphQL server. This server will handle GraphQL requests from Node-RED.

[0063] Step 5: Configure industrial automation system and ERP / MES system: Configure API endpoints in your industrial automation systems and ERP / MES systems so that Node-RED can query and modify data in these systems via GraphQL.

[0064] Use the node-red-contrib-graphql node to execute GraphQL queries: [ { "id": "1", "type": "graphql", "z": "2", "name": "Query Production Data", "server": "graphql-server-1", "query": "query { productionData { productId quantity status}}", "x": 180, "y": 100, "wires": [["2"]] }, { "id": "2", "type": "debug", "z": "2", "name": "", "active": true, "console": "false", "complete": "false", "x": 360, "y": 100, "wires": [] } ] 2.6 Factory Simulation Verification Indicators This system creates virtual models of factories and production lines in Plant Simulation according to national standards such as ISO 22400, including supply chain, production resources, control strategies, production processes, and business processes. These models implement the "Step 5: Realize Data Interaction" method to conduct experiments, analyze and verify the indicator operation process of each evaluation domain of GB∕T 39116-2020 Intelligent Manufacturing Capability Maturity Assessment Method" without affecting the actual production system, and score them according to the number of indicators that can be achieved in each domain and the data quality.

[0065] Assessment of Level 3 enterprises requires that there is only one performance indicator that can be obtained normally in each of the 13 sub-areas in the four areas of manufacturing, including design, production, logistics, sales and service, to prove that they have achieved horizontal integration within the factory and vertical integration between supply chains.

[0066] Assessment level 4 requires assessment experts to determine whether there are indicators that support enterprise decision-making and factory automation control and enterprise decision-making in the supply chain.

[0067] Participating companies in the assessment level 5 shall demonstrate digital simulation (digital twin) of the product life cycle or implement digital simulation (digital twin) of at least one application scenario of the product life cycle (7 sub-areas at the manufacturing level) on this platform.

[0068] 3. User operations of this system 1) Operate this system in participating enterprises.

[0069] 2) The participating enterprises provide available PLC collection point information, and the system developers or enterprise developers configure the collection points on the system.

[0070] 3) The system developer or enterprise developer selects the collected data based on the indicator system parameter definition to form the API for Plant Simulation to call.

[0071] To sum up, with the help of the above-mentioned technical scheme of the present invention, a qualitative analysis system based on the factory model and centered on production management performance indicators is designed with the intelligent manufacturing capability maturity level 3 evaluation standard as the starting point, which simplifies the difficulty of system integration, saves evaluation time, and improves evaluation accuracy, thereby achieving the beneficial technical effect of effectively ensuring the correctness of the evaluation.

[0072] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A quantitative analysis system for realizing intelligent manufacturing capability maturity assessment, characterized in that: The specific steps include: Step S1: Data collection: collect real-time data using sensors and device interfaces; Step S2: Data processing: In Node-RED, function nodes are used to process and transform the collected data to comply with the parameter specifications of the ISO22400-2-2014 indicator system, and GraphQL is used to receive the factory model data collected by Node-RED; Step S3: Customized factory model KPI API configuration: GraphQL allows the client to accurately specify the data structure it needs, which is used to build a flexible data query interface, so that the evaluation system can obtain specific data sets according to the national standard specifications of factory management performance indicators; Step S4: Plant Simulation calls the KPI API parameters to execute the KPI panel and simulation; Step S5: Evaluate and analyze the gaps based on the Plant Simulation results.

2. A quantitative analysis system for realizing intelligent manufacturing capability maturity assessment according to claim 1, characterized in that: In step S1, the real-time data is collected by implementing Modbus and OPC UA protocols through Node-RED to communicate with PLC to obtain device status and production data; Node-RED uses HTTP nodes to call the API of external systems or uses database nodes to realize data collection with ERP, MES, PDM, SRM, and CRM systems.

3. The quantitative analysis system for realizing intelligent manufacturing capability maturity assessment according to claim 1, characterized in that: In step S3, the participating enterprises select the parameters of the GraphQL data collection API on the operation interface and design a performance indicator query interface that complies with the ISO 22400-2-2014 indicator system.

4. A quantitative analysis system for realizing intelligent manufacturing capability maturity assessment according to claim 2, characterized in that: Integration with PLC is achieved in Node-RED by using specialized nodes to communicate with PLC.

5. A quantitative analysis system for realizing intelligent manufacturing capability maturity assessment according to claim 4, characterized in that: The dedicated node communicates with the PLC, specifically comprising the following steps: Step 101: Install Node-RED and PLC nodes; Step 102: Configure the PLC node: In the flow editor of Node-RED, drag the S7 config node to configure the connection parameters with the PLC; the connection parameters include IP address, rack number, and slot number; Step 103: Write process logic: Use the inject node as the trigger point of the process and set the trigger conditions; use the function node to process the business logic; use the debug node to debug and view the data flow in the process; Step 104: Data reading and writing: Read the data in the PLC through the S7 in node and send the data to other nodes for processing; read the data in the PLC through the S7 in node and send the data to other nodes for processing.

6. A quantitative analysis system for realizing intelligent manufacturing capability maturity assessment according to claim 5, characterized in that: The step 102 further includes the following steps: Step S1021: Drag in the S7 in node to read the PLC data, configuration mode and variable address; Step S1022: Drag in the S7 out node to write data to the PLC, and configure the variable address and data type to be written.

7. The quantitative analysis system for realizing intelligent manufacturing capability maturity assessment according to claim 1, characterized in that: The data collection in step S1 includes plant production process data collection, using the Node-RED process to receive device data, process the data and return it in the form of HTTP response. The data format collected by Node-RED is compatible with Tecnomatix PlantSimulation, and data interaction is performed.

8. The quantitative analysis system for realizing intelligent manufacturing capability maturity assessment according to claim 1, characterized in that: When using GraphQL to receive the factory model data collected by Node-RED in step S2, the GraphQL integration is implemented in Node-RED, which specifically includes the following steps: Step S201: Install the GraphQL node; in Node-RED, install the node-red-contrib-graphql-server node through the management panel; Step S202: Define GraphQL Schema: Create a GraphQL Schema in JSON format to describe the data structure and available query, mutation, and subscription types; Step S203: Implement the Resolver function: The Resolver function defines how to respond to queries for a specific type of field; in the intelligent manufacturing evaluation system, the Resolver function retrieves data from a database or other data source; Step S204: Configure HTTP nodes: Use Node-RED's HTTP nodes to receive GraphQL requests, and use the node-red-contrib-graphql-server node to process these requests; Step S204: Implement data interaction: Implement data interaction between the client and the Node-RED server through GraphQL queries and changes; for subscriptions, the client maintains connection with the server to receive real-time updates.