An automatic report generation engine and method for a production and manufacturing system based on TSN

Abstract

The present invention discloses an automatic report generation engine and method for a production and manufacturing system based on TSN, and relates to the field of intelligent factory network collaborative manufacturing. The invention comprises a user input module, a scheduling generation module, a configuration module, a performance analysis module and a report export module. The invention supplements the field of intelligent factory network collaborative manufacturing, especially the lack of automatic report generation method in the production and manufacturing system based on TSN, and can flexibly adjust the content of the report to meet the needs of different users.

Description

Technical Field

[0001] The present invention relates to the field of intelligent factory network collaborative manufacturing, and in particular to an automatic report generation engine and method for a production manufacturing system based on TSN. Background Art

[0002] With the continuous progress of modern science and technology, a new round of global industrial transformation and upgrading competition is becoming more and more fierce. The complexity and application scale of manufacturing systems are constantly increasing, and the differentiated needs of users are also increasing. The types of data streams existing in the manufacturing site are becoming more and more complex, and the requirements for real-time and determinism are different. In order to enhance the determinism and real-time performance of IEEE802.1 standard Ethernet, Time Sensitive Networking (TSN) was derived. TSN is a series of extensions to standard Ethernet standards. It integrates information technology (IT) networks and industrial technology (OT) networks, enabling periodic and non-periodic, real-time and non-real-time data to be mixed and transmitted, eliminating the need for physical isolation of its network and realizing the interconnection of the data link layer. In response to the traffic scheduling problems in different scenarios, the academic and industrial communities have designed corresponding scheduling algorithms based on the most commonly used traffic scheduling protocols in the two TSN sub-standards of IEEE 802.1Qbv and Qch, realizing deterministic transmission of time-triggered (TT) flows with low latency and low jitter. However, although there are many existing scheduling algorithms, there is no standardized presentation form for the analysis report of the results after the algorithm is deployed and run; even though the TSN working group has standardized the YANG model related to network configuration based on the NETCONF (Network Configuration Protocol) network configuration management protocol, this only standardizes the configuration parameters and their classification in the single stage of network configuration. There is no unified format standard for managing the data in other stages of the entire network system, such as the relevant parameter data of the initialization device, performance analysis data, etc., and there is a lack of good support for personalized data selection and analysis. Therefore, in order to more intuitively and conveniently display the scheduling, performance analysis results and other necessary parameter information of the network, how to design a method to unify and standardize the data information characteristics of the TSN-based manufacturing system and automatically produce system status, performance and other reports is a key problem that needs to be solved urgently. In addition, by comparing and analyzing the traffic scheduling operation data of the twin or simulation system and the actual system, and combining the results of historical scheduling for knowledge reasoning, it is of great help to improve the network scheduling plan, predict the success of scheduling and failure conditions, etc. Therefore, making full use of relevant information for knowledge reasoning can improve and supplement the content of the automatically generated report.

[0003] After searching the existing literature, it was found that the most similar implementation scheme is the Chinese patent application number 201811142363.3, named: A method and system for automatic report generation for ECG monitoring, and its specific approach is: automatically analyze the individual's ECG data through an artificial intelligence (AI) algorithm, identify abnormal segments in the ECG, and select information according to the report output configuration parameters, and finally generate a corresponding report. However, it is an automatic report generation method in the medical and health field. The specific parameter settings and report type structure are not applicable to the communication network field. The method of building a template is not specifically explained, and it does not meet the requirements of universality in the industrial production and manufacturing field; and its intelligent AI module cannot be applied to data analysis in the network. The patent application number is 202110612694.4, and its name is: A method and system for automatically generating special equipment inspection reports based on a form model. Its specific approach is: establish a report sub-template library suitable for special equipment inspection, and after visual automatic configuration, automatically associate the configuration file to generate a report. However, it is an automatic report generation in the field of special equipment and is not suitable for the field of industrial production and manufacturing. Its template parameter configuration and sub-template construction methods are not clear. There is no intelligent module to further process the data, and it simply organizes the inspection results. The patent application number is 202010496813.X, and the name is: A vertical integration gateway for production line edge devices based on OPC UA and its implementation method. The specific approach is: establish data communication with the equipment, obtain operation data, perform edge computing, and store raw data and preprocessing results; based on the Open Platform Communication Unified Architecture (OPC UA), the production line edge devices are information modeled, the model nodes are associated with the underlying data and interfaces, and the OPC UA server is constructed. The unified device data interface, and feedback control of the edge device is realized through parameter writing. However, it only constructs the OPC UA information model for a single production line edge device, which cannot meet the needs of information model construction for different data at each stage in automatic report generation.

[0004] Most existing automatic report generation methods rely on one or a group of specific XML-based template libraries, which have poor readability, lack intuitive presentation, and are difficult for users to modify quickly. In addition, the report template content does not fully match the TSN-based production and manufacturing system. It is mainly used for data analysis in the fields of medical care and smart grids, and has no specific applications in the field of smart factory network collaborative manufacturing, especially in the field of TSN-based production and manufacturing.

[0005] Even though the TSN working group has defined the network configuration-related YANG model based on the NETCONF standard, this only standardizes the configuration parameters and their classification in the single stage of network configuration. There is no unified format standard for managing the data in other stages of the entire network system, such as the relevant parameter data of the initialization device, performance analysis data, etc., and it is impossible to achieve semantic interoperability and difficult to realize standardized and flexible adjustment of report templates, which cannot meet the user's needs for personalized data selection and analysis.

[0006] The existing OPC UA information model is mainly used to construct information models for a single industrial device. It does not perform integrated information modeling for generating data for different devices in the manufacturing system or data required at different stages of the manufacturing system, such as relevant data parameters required for performance analysis, and lacks a unified model standard.

[0007] The AI ​​module or big data analysis module in the existing automatic report generation method is mainly used for automatic classification and semantic generation of data, and cannot be applied to data analysis and optimization in TSN-based production and manufacturing systems, such as scheduling scheme optimization, network failure prediction, network consistency improvement, and other functions.

[0008] Therefore, technical personnel in this field are committed to developing an automatic report generation engine and method for TSN-based production and manufacturing systems, which can more standardizedly and conveniently construct an overall information model as a template for automatic report generation; flexibly adjust the content to be displayed in the report template; and improve the deficiency that OPC UA usually only constructs a single industrial production equipment information model; thereby improving the network scheduling plan, optimizing the system's latency, jitter, packet loss and other network performance, and realizing network consistency testing and other functions. Summary of the invention

[0009] In view of the above-mentioned defects of the prior art, the technical problem to be solved by the present invention is how to design an automatic report generation engine that can flexibly adjust the report template settings according to different user needs while ensuring transmission security, and use the knowledge reasoning engine to optimize the scheduling plan, obtain the results of performance analysis in further production and manufacturing, and automatically generate a report on the TSN-based production and manufacturing system. How to use appropriate template construction methods to build a standardized, flexibly adjustable TSN-based production and manufacturing system report template with data information at different stages of system initialization, network configuration, and performance analysis according to the characteristics of TSN-based production and manufacturing system performance analysis and user needs. How to use knowledge reasoning methods to compare and analyze the traffic scheduling operation data of twin or simulation systems and actual systems, and combine the results of historical scheduling for knowledge reasoning, iteratively adjust the network scheduling plan, and optimize the overall performance of the production and manufacturing system.

[0010] To achieve the above-mentioned object, the present invention provides an automatic report generation engine for a production and manufacturing system based on TSN, including a user input module, a scheduling generation module, a configuration module, a performance analysis module and a report export module;

[0011] The user input module: provides an interface for users to input specific report requirements and network scheduling related parameters;

[0012] The scheduling generation module: after receiving the information input by the user, according to the network scheduling related parameter information, uses the corresponding scheduling algorithm to obtain the scheduling plan, thereby generating a specific configuration plan;

[0013] The configuration module maps the generated configuration scheme into a standard format file, deploys it into the network system, and performs specific traffic scheduling;

[0014] The performance analysis module: after completing the deployment of the traffic scheduling scheme, obtains relevant performance data in the simulation or actual system, uses the knowledge reasoning method to further analyze and process, and improves the entire scheduling scheme;

[0015] The report export module automatically generates corresponding reports based on the collected and analyzed data according to the user's needs.

[0016] Further, the scheduling generation module includes a data receiving unit, an algorithm scheduling unit and a solution generation unit;

[0017] The data receiving unit receives the data input by the user and stores it in a standardized manner into the established OPC UA information model;

[0018] The algorithm scheduling unit: uses the scheduling algorithm to obtain the necessary data required to generate the scheduling plan according to the constraints of the network scheduling related parameters;

[0019] The solution generation unit converts the obtained corresponding data into a specific configuration solution and stores it in a standardized manner in the OPC UA information model.

[0020] Furthermore, the data receiving unit selects required objects and variables according to the report content requirements input by the user and the information model structure of the automatic report class, establishes corresponding sub-models, and then stores the acquired data into corresponding variables;

[0021] The algorithm scheduling unit applies a corresponding scheduling algorithm, establishes a corresponding constraint set according to the input related network scheduling parameters, and solves and obtains the necessary data required to generate a scheduling solution;

[0022] The scheme generating unit converts the obtained corresponding data, including the time when the data stream arrives at each node and the setting of the time slot, into a corresponding GCL list, and sets the path and priority of each data stream, thereby obtaining a specific scheduling scheme.

[0023] Furthermore, the performance analysis module includes a data collection unit, an indicator analysis unit and a solution optimization unit;

[0024] The data collection unit collects relevant performance data obtained after configuring the scheduling scheme to the twin or simulation system and the actual system is running;

[0025] The index analysis unit further analyzes and processes the collected data according to the required indexes;

[0026] The scheme optimization unit combines historical information to optimize the scheduling scheme and improve the performance of the generated manufacturing system.

[0027] Further, the data acquisition unit stores the data collected from the twin or simulation system and the actual system into the variables under the corresponding simulation indicator object and actual indicator object in the sub-model respectively;

[0028] The index analysis unit further processes and analyzes the obtained data, including but not limited to overall network load analysis, consistency analysis, and reliability analysis;

[0029] The scheme optimization unit combines historical performance information, scheduling information, and knowledge reasoning to obtain an optimized scheduling scheme, and has the ability to predict link failures.

[0030] The present invention also provides an automatic report generation method for a production and manufacturing system based on TSN, comprising the following steps:

[0031] Step 1: The user inputs the corresponding system parameters and the required report content and board requirements, and sends them to the scheduling generation module through the user input module using the OPC UA client-server transmission mechanism;

[0032] Step 2: The data receiving unit in the scheduling generation module selects the required objects and variables according to the report content required by the user and the information model of the automatic report class, and establishes a specific sub-model; the obtained system parameter information and board information are respectively written into the variables under the system parameter object and board object in the established sub-model;

[0033] Step 3: According to the data in the sub-model system parameter object, call the algorithm scheduling unit and use the corresponding TSN traffic scheduling algorithm to obtain the necessary data required to generate the scheduling plan;

[0034] Step 4: Map the corresponding data obtained into a specific scheduling plan through the plan generation unit, including the GCL list of each network switch node and the data flow priority and path setting, and store this information in the variables under the network configuration object in the sub-model;

[0035] Step 5: Make a judgment based on the report content required by the user obtained in step 2: If the user only needs a scheduling plan (no need to send the scheduling plan to the system for operation), call the report export module based on the information in the existing sub-model to automatically generate the final report, and the step ends; if the user still needs a specific performance analysis, continue with the following steps;

[0036] Step 6: According to the YANG format defined by IEEE802.1Qcc and Qcp in TSN, the configuration module is called. Through the configuration software, the configuration information stored in the sub-model network configuration object is converted into configuration files for switch ports and terminal devices using CNC centralized network configuration. The configuration is then sent to the actual system composed of TSN switches and terminal devices and the system obtained by twin simulation based on the actual device model.

[0037] Step 7: Collect the corresponding basic performance data through the data collection unit in the performance analysis module and store it in the sub-model performance analysis object; then, call the indicator analysis unit to further process the collected data and store the analysis results in the sub-model performance analysis object;

[0038] Step 8: Call the solution optimization unit, analyze the differences between the algorithm design results, simulation results and actual results according to the needs of the user, perform knowledge reasoning based on the historical operation results, obtain a new scheduling solution, return the relevant scheduling information to the scheduling generation module for iterative adjustment, optimize the system performance, and update the optimized data information into the corresponding data variables in the sub-model;

[0039] Step 9, call the report export module, select various types of data under the content object in the model according to the template data under the template object in the automatic report information model, and automatically generate the final report.

[0040] Furthermore, the user input method of step 1 can be operated using a visual page; the input content includes the parameters necessary to implement network scheduling and the report content that the user ultimately wants, which can be flexibly adjusted and supplemented according to the characteristics of the user and the network; the report format information can be selectively input, and if no input is made, the default value is used.

[0041] Furthermore, the establishment of the automatic reporting submodel in step 2 needs to be based on the user's input content, according to the information model of the automatic reporting class, inherit or supplement the corresponding objects and variables under the board class, system parameter class, network configuration class, and performance analysis class objects, and establish the submodel; the system parameter class object includes production equipment parameters and network equipment parameters. The equipment parameters can refer to the relevant equipment industry standards defined by OPC UA, and the required objects and variables are selected or supplemented; the specific objects and table quantities under the network configuration class object can refer to the parameter variables in the relevant YANG model defined by the TSN working group and the NETCONF working group, and the required objects and variables are selected or supplemented.

[0042] Furthermore, the data collection objects of step 7 include the twin or simulation system and the actual system; data from the two systems are selectively collected according to the report content requirements of the user.

[0043] Furthermore, the knowledge reasoning in step 9 is combined with the scheduling operation data of the simulation and actual system, as well as the historical scheduling information, with the purpose of optimizing the scheduling plan, predicting the success of scheduling, predicting failures, and improving network consistency.

[0044] In a preferred embodiment of the present invention, the present invention provides an automatic report generation engine and method for a TSN-based manufacturing system, which supplements the field of intelligent factory network collaborative manufacturing, especially the lack of automatic report generation methods in TSN-based manufacturing systems. The system can flexibly adjust the content of the report to meet the needs of different users.

[0045] The engine as a whole consists of a user input module, a schedule generation module, a configuration module, a performance analysis module, and a report export module. Figure 1 shown.

[0046] The user input module sends the system parameters input by the user and the required report content, format and other information to the scheduling generation module deployed on the OPC UA server through the OPC UA client.

[0047] The scheduling generation module includes a data receiving unit, an algorithm scheduling unit, and a solution generation unit.

[0048] The data receiving unit obtains the report content required by the user according to Figure 2The overall information model of the automatic report class shown in the figure selects the required objects and variables, and establishes rules according to the information model structure and variables, adds other required variables, and establishes a specific report sub-model; uses the information model format and transmission communication method defined by the OPC UA specification to standardize the parameter information required for traffic scheduling, including the data flow specification related information contained in the production equipment, such as data flow size, period, etc., as well as the network topology, link speed, etc. of the network equipment, and stores them in the information model of the system parameter object in the sub-model; receives the user's requirements for the report format, including the layout, font, color, etc. of the template, and stores them in the information model of the report object. The algorithm scheduling unit simulates and schedules by applying the relevant TSN traffic scheduling algorithm to obtain the necessary data required to generate the scheduling plan. The plan generation unit generates a specific scheduling plan based on the corresponding data obtained, including the periodic gate control list (GCL) of each network switch node and the data flow priority and path setting, and uniformly stores them in the information model of the network configuration object by using the information model format defined by the OPC UA specification.

[0049] The configuration module uses the YANG format defined by TSN and related configuration software to convert the configuration information stored in the network configuration object into configuration files for switch ports and terminal devices in a centralized network configuration (CNC) manner, and sends the configuration to the actual system composed of TSN switches and terminal devices, as well as to the system twinned or simulated based on the actual device model.

[0050] The performance analysis module includes a data collection unit, an indicator analysis unit, and a solution optimization unit.

[0051] The data acquisition unit collects information such as the delay, jitter, traffic conditions of each switch port, and device synchronization accuracy of each data stream in the actual TSN network operation through a network tester, as well as the corresponding information output by the twin or simulation system, and uses the information model format defined by the OPC UA specification to store them in the information models of the simulation indicator object and the actual indicator object under the performance analysis object. The indicator analysis unit further processes the collected performance parameters and stores the analysis results of these indicators in a standardized manner in the information model of the performance analysis object according to the indicator requirements required by the user, such as the real-time load of each device in the network, the maximum jitter and delay of the network, etc. The solution optimization unit analyzes the differences between the algorithm design results, simulation results, and actual results according to the needs of the user, and performs knowledge reasoning based on the historical operation results. It optimizes some network nodes and links that do not meet the performance requirements in a targeted manner to obtain a new scheduling solution, and returns the relevant scheduling information to the scheduling generation module for iterative adjustment to optimize the system performance. The optimized and adjusted relevant data is updated in the information model to obtain the information model of the final report.

[0052] The report export module generates the corresponding report after local rendering based on the final information model.

[0053] The present invention is achieved through the following technical solution, which comprises the following steps:

[0054] Step 1: The user inputs the corresponding system parameters and the required report content, format and other requirements, and sends them to the scheduling generation module through the user input module using the OPC UA client-server transmission mechanism.

[0055] Step 2: The data receiving unit in the scheduling generation module obtains the report content required by the user according to Figure 2 The information model of the automatic report class shown in the figure selects the required objects and variables to establish a specific sub-model; the obtained system parameter information and plate type information are respectively written into the variables under the system parameter object and plate type object in the established sub-model.

[0056] Step 3: Based on the data in the sub-model system parameter object, call the algorithm scheduling unit and use the corresponding TSN traffic scheduling algorithm to obtain the necessary data required to generate a scheduling plan.

[0057] Step 4: Through the scheme generation unit, the corresponding data is mapped into a specific scheduling scheme, including the GCL list of each network switch node, as well as the data flow priority and path settings, and this information is stored in the variables under the network configuration object in the sub-model.

[0058] Step 5: Make a judgment based on the report content required by the user obtained in the second step: If the user only needs a scheduling plan (there is no need to send the scheduling plan to the system for operation), then call the report export module based on the information in the existing sub-model, automatically generate the final report, and the step ends; if the user also needs specific performance analysis, continue with the following steps.

[0059] Step 6: According to the YANG format defined by IEEE802.1Qcc and Qcp in TSN, call the configuration module, and through the relevant configuration software, use the CNC centralized network configuration method to convert the configuration information stored in the sub-model network configuration object into configuration files for switch ports and terminal devices, and send the configuration to the actual system composed of TSN switches and terminal devices, as well as the system twinned or simulated based on the actual device model.

[0060] Step 7: The corresponding basic performance data of the actual system and the twin or simulation system are collected through the data collection unit in the performance analysis module and stored in the sub-model performance analysis object. Then, the indicator analysis unit is called to further process the collected data and store the analysis results in the sub-model performance analysis object.

[0061] Step 8: Call the scheme optimization unit to analyze the differences between the algorithm design results, simulation results and actual results according to user needs, perform knowledge reasoning based on historical operation results, obtain a new scheduling scheme, and return the relevant scheduling information to the scheduling generation module for iterative adjustment to optimize system performance. Update the optimized data information into the corresponding data variables in the sub-model.

[0062] Step 9: Call the report export module, select various types of data under the content object in the model according to the template data under the template object in the automatic report information model, and automatically generate the final report.

[0063] Compared with the prior art, the present invention has the following obvious substantial features and significant advantages:

[0064] 1. Based on the data information of different stages such as system initialization, network scheduling and performance analysis in the TSN-based manufacturing system, OPC UA is used to build an overall information model in a more standardized and convenient way through object-oriented development as a template for automatic report generation.

[0065] 2. In view of the flexible and diverse user needs and the characteristics of data at different stages in TSN-based production systems, OPC UA supports the establishment of multiple connection methods and hierarchical structures between elements of the information model, as well as the addition and deletion of objects and variables in the information model, thereby flexibly adjusting the content to be displayed in the report template.

[0066] 3. The automatic reporting information model of the manufacturing system based on TSN makes up for the deficiency of the YANG model defined based on the NETCONF protocol that only standardizes network configuration-related data, and improves the deficiency of OPC UA that usually only builds a single industrial production equipment information model.

[0067] 4. Based on the comparative analysis of the system's historical scheduling plans, simulations, and actual performance results, knowledge reasoning was performed to improve the network's scheduling plans, optimize the system's network performance such as latency, jitter, and packet loss, and implement functions such as network consistency testing.

[0068] The concept, specific structure and technical effects of the present invention will be further described below in conjunction with the accompanying drawings to fully understand the purpose, characteristics and effects of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 is an overall engine architecture diagram of a preferred embodiment of the present invention;

[0070] Figure 2 This is an information model diagram of an automatic reporting class of a preferred embodiment of the present invention;

[0071] Figure 3 It is an information model diagram of a specific automatic report of a preferred embodiment of the present invention;

[0072] Figure 4 It is a device connection topology diagram of a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0073] The following describes several preferred embodiments of the present invention with reference to the drawings in the specification, so that the technical content is clearer and easier to understand. The present invention can be embodied in many different forms of embodiments, and the protection scope of the present invention is not limited to the embodiments mentioned in the text.

[0074] In the drawings, components with the same structure are indicated by the same numerical reference numerals, and components with similar structures or functions are indicated by similar numerical reference numerals. The size and thickness of each component shown in the drawings are arbitrarily shown, and the present invention does not limit the size and thickness of each component. In order to make the illustration clearer, the thickness of the components is appropriately exaggerated in some places in the drawings.

[0075] The network topology of the equipment in the TSN-based manufacturing system in this embodiment is as follows Figure 4 As shown, assuming that the path of the scheduled data flow is predetermined in advance, the user does not need to input it. The data flows in the network are all time-sensitive periodic flows, and all need to strictly guarantee the end-to-end deadline requirements.

[0076] Table 1 Information entered by the user

[0077]

[0078] Step 1: The user inputs specific requirements through a visual interface, as shown in Table 1. In Table 1, the user has no specific requirements for the font and color of the board, so the system uses the default values: Songti, black; since the scheduling of this system does not need to consider the data flow path, there is no input of flow path information; the user only requires the jitter and delay of the data flow in the simulation and actual system for performance analysis. Then, the user input module sends the information to the calling configuration module through the wireless transmission of the device.

[0079] Step 2: The data receiving unit in the scheduling generation module obtains user needs according to the following Figure 2 The information model of the automatic report class shown in the figure generates corresponding sub-models, such as Figure 3 The received data of flow period, flow size, end-to-end deadline, and network topology are stored in the corresponding variables in the sub-model one by one, and according to the user's needs, the default values ​​of the font size and page layout variables under the template object are modified to the values ​​set by the user.

[0080] Step 3: Call the algorithm scheduling unit, establish the corresponding mathematical model and constraints based on the information of flow period, flow size, end-to-end deadline, and network topology, and use the SMT solver to obtain the scheduling plan.

[0081] Step 4: According to the obtained scheduling scheme, the scheme generation unit maps it into the required GCL list of each network switch node, as shown in Table 2. And set the three data flow priorities to {6, 7, 8}. Then store the GCL table and priority in the corresponding variables under the network configuration object in the sub-model.

[0082] Table 2 GCL schedule configuration results

[0083]

[0084] Step 5: Since users need to simulate and measure the maximum jitter and latency in the actual system, they need to send the configuration and obtain the corresponding data through the performance analysis module before generating a report.

[0085] Step 6: According to the CNC-YANG format, call the configuration module and send the configuration file to the actual system composed of TSN switches and terminal devices and the system simulated by OMNeT based on the actual device model.

[0086] Step 7: The jitter and delay of each data stream in the actual system and the simulation system are collected through the data collection unit in the performance analysis module, as shown in Table 3, and stored in the sub-model. Since the user only needs the jitter and delay of the data stream and no further data processing is required, the indicator analysis unit does not need to be called.

[0087] Table 3 Data flow delay and jitter results

[0088] Performance Indicators Simulation system data results Actual system data results Stream Delay {18,39,35}μs {20,41,37}μs Flow Jitter {24,57,52}μs {25,60,50}μs

[0089] Step 8: Call the solution optimization unit to analyze the differences between the algorithm design results, simulation results and actual results according to user needs. Since the differences between the results are small and meet the user's operating requirements for the network system, there is no need to perform knowledge reasoning based on historical operating results.

[0090] Step 9: Based on the data in the final information model, call the report export module to automatically generate the final report.

[0091] The preferred specific embodiments of the present invention are described in detail above. It should be understood that ordinary technicians in the field can make many modifications and changes based on the concept of the present invention without creative work. Therefore, all technical solutions that can be obtained by technicians in the technical field based on the concept of the present invention through logical analysis, reasoning or limited experiments on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. An automatic report generation engine for a TSN-based manufacturing system, characterized in that: It includes user input module, schedule generation module, configuration module, performance analysis module and report export module; The user input module: provides an interface for users to input specific report requirements and network scheduling related parameters; The scheduling generation module: after receiving the information input by the user, according to the network scheduling related parameter information, uses the corresponding scheduling algorithm to obtain the scheduling plan, thereby generating a specific configuration plan; The configuration module maps the generated configuration scheme into a standard format file, deploys it into the network system, and performs specific traffic scheduling; The performance analysis module: after completing the deployment of the traffic scheduling scheme, obtains relevant performance data in the simulation or actual system, uses the knowledge reasoning method to further analyze and process, and improves the entire scheduling scheme; The report export module automatically generates corresponding reports based on the collected and analyzed data according to the user's needs; The scheduling generation module includes a data receiving unit, an algorithm scheduling unit and a solution generation unit; The data receiving unit receives the data input by the user and stores it in a standardized manner into the established OPC UA information model; The algorithm scheduling unit: uses the scheduling algorithm to obtain the necessary data required to generate the scheduling plan according to the constraints of the network scheduling related parameters; The solution generation unit converts the corresponding data obtained into a specific configuration solution and stores it in the OPC UA information model in a standardized manner; The data receiving unit selects required objects and variables according to the report content requirements input by the user and the information model structure of the automatic report class, establishes corresponding sub-models, and then stores the acquired data into corresponding variables; The algorithm scheduling unit applies a corresponding scheduling algorithm, establishes a corresponding constraint set according to the input related network scheduling parameters, and solves and obtains the necessary data required to generate a scheduling solution; The scheme generating unit converts the obtained corresponding data, including the time when the data flow arrives at each node and the setting of the time slot, into a corresponding GCL list, and sets the path and priority of each data flow, so as to obtain a specific scheduling scheme; The performance analysis module includes a data acquisition unit, an indicator analysis unit and a solution optimization unit; The data collection unit collects relevant performance data obtained after configuring the scheduling scheme to the twin or simulation system and the actual system is running; The index analysis unit further analyzes and processes the collected data according to the required indexes; The scheme optimization unit combines historical information to optimize the scheduling scheme and improve the performance of the generated manufacturing system.

2. The automatic report generation engine for a TSN-based manufacturing system according to claim 1, characterized in that: The data acquisition unit stores the data collected from the twin or simulation system and the actual system into the variables under the corresponding simulation indicator object and the actual indicator object in the sub-model respectively; The index analysis unit further processes and analyzes the obtained data, including but not limited to overall network load analysis, consistency analysis, and reliability analysis; The scheme optimization unit combines historical performance information, scheduling information, and knowledge reasoning to obtain an optimized scheduling scheme, and has the ability to predict link failures.

3. An automatic report generation method for a TSN-based manufacturing system, characterized in that: The following steps are involved: Step 1: The user inputs the corresponding system parameters and the required report content and board requirements, and sends them to the scheduling generation module through the user input module using the OPC UA client-server transmission mechanism; Step 2: The data receiving unit in the scheduling generation module selects the required objects and variables according to the report content required by the user and the information model of the automatic report class, and establishes a specific sub-model; the obtained system parameter information and board information are respectively written into the variables under the system parameter object and board object in the established sub-model; Step 3: According to the data in the sub-model system parameter object, call the algorithm scheduling unit and use the corresponding TSN traffic scheduling algorithm to obtain the necessary data required to generate the scheduling plan; Step 4: Map the corresponding data obtained into a specific scheduling plan through the plan generation unit, including the GCL list of each network switch node and the data flow priority and path setting, and store this information in the variables under the network configuration object in the sub-model; Step 5: Make a judgment based on the report content required by the user obtained in step 2: If the user only needs a scheduling plan and does not need to send the scheduling plan to the system for operation, call the report export module based on the information in the existing sub-model to automatically generate the final report, and the step ends; if the user also needs specific performance analysis, continue with the following steps; Step 6: According to the YANG format defined by IEEE802.1Qcc and Qcp in TSN, the configuration module is called. Through the configuration software, the configuration information stored in the sub-model network configuration object is converted into configuration files for switch ports and terminal devices using CNC centralized network configuration. The configuration is then sent to the actual system composed of TSN switches and terminal devices and the system obtained by twin simulation based on the actual device model. Step 7: Collect corresponding basic performance data through the data collection unit in the performance analysis module and store it in the sub-model performance analysis object; Then, the indicator analysis unit is called to further process the collected data and store the analysis results in the sub-model performance analysis object; Step 8: Call the solution optimization unit, analyze the differences between the algorithm design results, simulation results and actual results according to the needs of the user, perform knowledge reasoning based on the historical operation results, obtain a new scheduling solution, return the relevant scheduling information to the scheduling generation module for iterative adjustment, optimize the system performance, and update the optimized data information into the corresponding data variables in the sub-model; Step 9, call the report export module, select various types of data under the content object in the model according to the template data under the template object in the automatic report information model, and automatically generate the final report.

4. The automatic report generation method for a TSN-based manufacturing system according to claim 3, characterized in that: The user input method of step 1 can be operated using a visual page; the input content includes the parameters necessary to implement network scheduling and the report content that the user ultimately wants, which can be flexibly adjusted and supplemented according to the characteristics of the user and the network; the report format information can be selectively input, and if no input is made, the default value is used.

5. The automatic report generation method for a TSN-based manufacturing system according to claim 3, characterized in that: The establishment of the automatic report submodel in step 2 needs to be based on the user's input content, according to the information model of the automatic report class, inherit or supplement the corresponding objects and variables under the board class, system parameter class, network configuration class, and performance analysis class objects, and establish the submodel; the system parameter class object includes production equipment parameters and network equipment parameters. The equipment parameters can refer to the relevant equipment industry standards defined by OPC UA, and the required objects and variables are selected or supplemented; the specific objects and table quantities under the network configuration class object can refer to the parameter variables in the relevant YANG model defined by the TSN working group and the NETCONF working group, and the required objects and variables are selected or supplemented.

6. The automatic report generation method for a TSN-based manufacturing system according to claim 3, characterized in that: The data collection objects in step 7 include the twin or simulation system and the actual system; data from the two systems are selectively collected according to the user's report content requirements.

7. The automatic report generation method for a TSN-based manufacturing system according to claim 3, characterized in that: The knowledge reasoning in step 9 combines the scheduling operation data of the simulation and actual system, as well as the historical scheduling information, with the purpose of optimizing the scheduling plan, predicting the success of scheduling, predicting failures, and improving network consistency.

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