Complex equipment task cooperative control method based on model and model machine

Abstract

The invention discloses a complex equipment task cooperative control method based on a model and a model machine, which aims at a complex equipment cooperative design process, takes the model as a core, takes a BOM as a data main line, constructs a demand model based on a sysml language, and realizes cooperative decomposition and refinement of cross-organization demand entries and full-period tracing of demands and indexes. The method comprises the following steps: starting dynamically and rapidly iterated task-driven flattening collaboration through demand items, constructing a demand catalog, a task execution catalog, an APP task-driven catalog and a prototype collaboration task-driven catalog, receiving real-time demands of a user, realizing distributed prototype construction and packaging, and realizing cross-domain collaboration verification.

Description

Technical Field

[0001] The invention relates to a complex equipment task collaborative control method based on models and prototypes, and belongs to the field of complex equipment collaborative development control. Background Art

[0002] The development of complex equipment has the characteristics of long cycle, multiple fields and wide regions. The development cycle of complex equipment is divided into the stages of initial design, testing and finalization, each of which takes 1-2 years; the development team is large, belonging to different professional fields, the development tools are diverse, and the generated data is heterogeneous; the development units are scattered all over the country, the development results are distributed and stored in each development unit, and the intellectual property rights belong to the development unit. In short, the development of complex equipment is a typical virtual development organization form.

[0003] Due to the long development cycle, multiple fields and wide geographical areas of complex equipment, a black box development method has gradually been formed, which is based on the collaborative development of complex equipment, driven by R&D tasks, constrained by interfaces and using files as carriers.

[0004] As the development cycle of complex equipment shortens, the types of equipment increase, professional fields become increasingly subdivided, and the number of participating research units increases, this method has the following problems.

[0005] (1) Demand indicators are decomposed layer by layer, with too many levels and too great depth; the cycle is too long and the process is uncontrollable when tracing back layer by layer, and demand coordination lacks flatness.

[0006] The demand indicators of complex equipment are decomposed from top to bottom by the overall unit. The participating research units receive the demand indicators and further decompose and refine them according to their own research and development practices. They use this as input to carry out equipment research and development, or the lower-level units continue to decompose them, and then relay the decomposition in turn. In the stage of demand indicator decomposition, the coordinated decomposition of demand indicators within the unit has not been achieved, nor has the coordinated decomposition of demand indicators between participating research units. In short, the flat coordination of demand indicator decomposition has not been achieved.

[0007] (2) The execution process of development tasks is trivial and discrete. In the traditional management that focuses on results rather than processes, the collaborative status of the actual execution route of development tasks is not accurately recorded and reflected.

[0008] Traditional task development management focuses on business control points such as the production, review, and archiving of outputs such as documents and drawings. The task development process is a typical business management process. The actual process of developing tasks is trivial and discrete. The existing development process is solidified and not optimized, focusing on results rather than processes, highlighting management elements, and failing to truthfully record and reflect the actual collaborative status of development tasks.

[0009] (3) During the use of R&D tools, people are the main focus and people are in the loop, while tools are not the main focus and tools are not automated and service-oriented in the loop, which cannot improve the overall R&D efficiency.

[0010] At present, in the process of executing R&D tasks, people mainly operate R&D tools, and even automatic tools cannot exclude people in the loop. R&D tasks involve many types of tools, and the technical approaches used vary greatly. In different professional fields, the data formats and types generated by R&D tools are different, and heterogeneous tools generate heterogeneous data. Due to the factors of R&D tool types, fields, and human participation, an application scenario with tools as the core, service-oriented invocation and automated operation of tools in the loop has not been formed, resulting in low overall R&D efficiency.

[0011] (4) Joint computing relies on complete prototypes of complex equipment. Prototype data collection is difficult and time-consuming, intellectual property rights are difficult to protect, results cannot be fed back in real time, latency is uncontrollable, and efficiency, performance, and operation virtualization cannot be achieved when tools and data are in a heterogeneous distribution state.

[0012] At present, the joint computing of digital prototypes relies on the data aggregation of complete prototypes. The prototype data is composed of data from different fields, types and formats, and the data storage locations are scattered and distributed across the country. With the development of digital technology, digital R&D and data twin technology are constantly applied to the scientific research and production process, but in the joint computing of digital prototypes, the efficiency, performance and operation virtualization of tools and data in a heterogeneous distribution state have not been achieved. Summary of the invention

[0013] The technical problem solved by the present invention is: in view of the problems in the current prior art, such as untimely communication, missing process data, and inability to jointly design and verify models and prototypes across units based on traditional discussion methods and design methods in the complex equipment design process, a complex equipment task collaborative control method based on models and prototypes is proposed.

[0014] The present invention solves the above technical problems by the following technical solutions:

[0015] A complex equipment task collaborative control method based on models and prototypes, comprising:

[0016] Collect user initial requirements, define requirement information based on equipment operation data and user requirements, and generate a requirement catalog;

[0017] Generate a task execution catalog based on demand information and demand catalog;

[0018] Encapsulate and register the engineering APP, import it into the engineering APP according to the task execution directory to drive the task, and obtain the APP task drive directory;

[0019] Import the task execution directory into the digital prototype for task driving, and obtain the prototype collaborative task driving directory;

[0020] Receive real-time user demands and perform collaborative control of equipment tasks based on the demand catalog, task execution catalog, APP task drive catalog, and prototype collaborative task drive catalog.

[0021] The method of defining demand information and generating a demand catalog based on equipment operation data and user needs is as follows:

[0022] Establish a demand model through the historical operation data of the equipment during the mission execution, generate a demand ID according to the user's initial demand, and store it in the R-Model data table;

[0023] Update the requirement ID in the R-Model data table based on the user requirements received in real time, associate all requirement IDs before and after the update with the user requirements, and store them in the R-Data data table;

[0024] Determine user permissions based on the requirement ID in the R-Data data table, substitute it into the requirement model, associate the user permissions with the requirement ID, and generate the R-control data table;

[0025] Combine the R-Model data table, R-Data data table, and R-control data table into a requirements catalog.

[0026] The method to define the task execution directory is:

[0027] Substitute the current user demand into the demand model to generate real-time user tasks, and associate the demand ID of the R-control data table in the demand directory with the real-time user task ID to generate the RT-relation data table;

[0028] Create a task entity model, associate it with the real-time user task ID, and store it in the T-Model data table;

[0029] The real-time user task ID in the T-Model data table is updated through the real-time user task ID received subsequently, and all the real-time user task IDs before and after the update are associated with the task entity model and stored in the T-Data data table;

[0030] Merge the RT-relation data table, T-Model data table, and T-Data data table into the task execution directory.

[0031] The method for encapsulating and registering the engineering APP is:

[0032] Encapsulate interfaces between apps based on parameter transmission requirements between apps;

[0033] After completing the interface encapsulation between APPs, generate APP-ID based on APP information and service host information.

[0034] The method to obtain the APP task driver directory is:

[0035] Generate APP data packets based on APP-ID and each APP service host information and store them in the APP-mate table;

[0036] According to the task execution directory, build the APP computing environment, retrieve the APP information from the APP-mate table, build the upstream APP input parameters and downstream APP output parameters, and use the upstream APP input parameters and downstream APP output parameters to generate a parameter mapping table;

[0037] Generate APP-cooperative data table according to APP computing environment and parameter mapping table;

[0038] After associating the APP-cooperative data table with the real-time user task ID in the task execution directory, the APP-data data table is generated;

[0039] Merge the APP-mate table, APP-cooperative data table, and APP-data data table into an APP task-driven directory.

[0040] The method for obtaining the prototype collaborative task driver directory is:

[0041] Build a digital prototype and confirm the collaborative control method based on the type of digital prototype;

[0042] Generate an M-Inforation data table according to the digital prototype ID and the parameter mapping table of the digital prototype;

[0043] Build a digital prototype computing environment and generate an M-cooperative data table based on the real-time user task ID and digital prototype computing environment information in the task execution directory;

[0044] According to the M-cooperative data table, the collaborative operation is performed once, and the single digital prototype test data and the real-time user task ID are associated and the M-Model data table is generated;

[0045] After the collaborative operation data of different types of digital prototypes are associated with the digital prototype ID, they are stored in the M-Model data table to generate the M-Data data table;

[0046] The M-Inforation data sheet, M-cooperative data sheet, M-Model data sheet, and M-Data data sheet are merged into a prototype collaborative task driver directory.

[0047] In the process of generating the M-Inforation data table according to the digital prototype ID, an XML file is generated by automatic or manual association according to the digital prototype ID of each digital prototype and the parameter mapping table of the digital prototype, and stored in the M-Inforation data table.

[0048] The demand directory, task execution directory, APP task drive directory, and prototype collaborative task drive directory all construct corresponding BOMs through XBOM; equipment task collaborative control realizes the reading and calling of the root directory through the corresponding BOM.

[0049] The collaborative control method of the digital prototype includes black box collaborative control and white box collaborative control. The method of generating the prototype collaborative task driving directory by the black box collaborative control method and the white box collaborative control method is consistent. Before generating the M-Inforation data table according to the digital prototype ID, each digital prototype in the black box collaborative control method is encapsulated to meet confidentiality requirements.

[0050] The equipment operation data is obtained from a database and is the historical operation data of the equipment system; all operation data of the equipment task collaborative control process are stored and backed up in the database.

[0051] The advantages of the present invention compared with the prior art are:

[0052] (1) The present invention provides a complex equipment task collaborative control method based on models and prototypes. Aiming at the complex equipment collaborative design process, the present invention takes the model as the core and BOM as the data main line, introduces the MBSE design concept, builds the demand model based on the sysml language, realizes the collaborative decomposition and refinement of cross-organizational demand items, and traces the demand and indicators throughout the whole cycle. It can start dynamic and fast iterative task-driven flat collaboration based on the demand items, introduces prototype collaboration into the MBSE design process, completes the distributed prototype construction and packaging, and realizes the cross-domain collaborative verification of geometric models, Class I performance prototypes, and Class II performance prototypes;

[0053] (2) The present invention constructs two prototype collaborative development methods, using models and data to guide the main line, realizes the association and traceability of demand model-task model-prototype model, provides demand entity creation, demand data caching, demand data calculation and loading services, and has the capabilities of demand decomposition and traceability, demand change management, demand indicator verification, etc. It supports both system recommendation and manual selection to realize the input of demand indicator information, and establishes an association relationship with the task activity to cache it into a task model, and drives it to the relevant executors. A prototype collaborative method is proposed to complete the construction and packaging of distributed prototypes, and realize cross-domain collaborative verification of geometric models, Class I performance prototypes, and Class II performance prototypes. It has the capabilities of lightweight prototype models, distributed collaboration, and centralized visualization. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A schematic diagram of a collaborative control method based on model and data drive provided by the present invention;

[0055] Figure 2 A schematic diagram of the BOM structure provided by the present invention;

[0056] Figure 3 A schematic diagram of collaborative control of task requirements provided by the present invention;

[0057] Figure 4 A schematic diagram of task-driven flat collaboration provided by the present invention;

[0058] Figure 5 A schematic diagram of collaborative control of engineering APP provided by the present invention;

[0059] Figure 6 Schematic diagram of the prototype collaborative control development provided by the present invention. DETAILED DESCRIPTION

[0060] A collaborative control method for complex equipment tasks based on models and prototypes, aimed at the collaborative design process of complex equipment, takes the model as the core and BOM as the data main line, builds a demand model based on the sysml language, realizes the collaborative decomposition and refinement of cross-organizational demand items, and traces the full cycle of requirements and indicators. Through the demand items, dynamic and fast iterative task-driven flat collaboration is enabled, and a demand catalog, task execution catalog, APP task-driven catalog, and prototype collaborative task-driven catalog are constructed to receive real-time user requirements, realize distributed prototype construction and packaging, and achieve cross-domain collaborative verification.

[0061] The complex equipment task collaborative control method based on model and prototype has the following specific steps:

[0062] Collect initial user requirements, define requirement information based on equipment operation data and user requirements, and generate a requirement catalog;

[0063] Generate a task execution catalog based on demand information and demand catalog;

[0064] Encapsulate and register the engineering APP, import it into the engineering APP according to the task execution directory to drive the task, and obtain the APP task drive directory;

[0065] Import the task execution directory into the digital prototype for task driving, and obtain the prototype collaborative task driving directory;

[0066] Receive real-time user demands and perform collaborative control of equipment tasks based on the demand catalog, task execution catalog, APP task drive catalog, and prototype collaborative task drive catalog.

[0067] The method of defining demand information and generating a demand catalog based on equipment operation data and user needs is as follows:

[0068] Establish a demand model through the historical operation data of the equipment during the mission execution, generate a demand ID according to the user's initial demand, and store it in the R-Model data table;

[0069] Update the requirement ID in the R-Model data table based on the user requirements received in real time, associate all requirement IDs before and after the update with the user requirements, and store them in the R-Data data table;

[0070] Determine user permissions based on the requirement ID in the R-Data data table, substitute it into the requirement model, associate the user permissions with the requirement ID, and generate the R-control data table;

[0071] Combine the R-Model data table, R-Data data table, and R-control data table into a requirements catalog.

[0072] The method to define the task execution directory is:

[0073] Substitute the current user demand into the demand model to generate real-time user tasks, and associate the demand ID of the R-control data table in the demand directory with the real-time user task ID to generate the RT-relation data table;

[0074] Create a task entity model, associate it with the real-time user task ID, and store it in the T-Model data table;

[0075] The real-time user task ID in the T-Model data table is updated through the real-time user task ID received subsequently, and all the real-time user task IDs before and after the update are associated with the task entity model and stored in the T-Data data table;

[0076] Merge the RT-relation data table, T-Model data table, and T-Data data table into the task execution directory.

[0077] The method for encapsulating and registering the engineering APP is:

[0078] Encapsulate interfaces between apps based on parameter transmission requirements between apps;

[0079] After completing the interface encapsulation between APPs, generate APP-ID based on APP information and service host information.

[0080] The method to obtain the APP task driver directory is:

[0081] Generate APP data packets based on APP-ID and each APP service host information and store them in the APP-mate table;

[0082] According to the task execution directory, build the APP computing environment, retrieve the APP information from the APP-mate table, build the upstream APP input parameters and downstream APP output parameters, and use the upstream APP input parameters and downstream APP output parameters to generate a parameter mapping table;

[0083] Generate APP-cooperative data table according to APP computing environment and parameter mapping table;

[0084] After associating the APP-cooperative data table with the real-time user task ID in the task execution directory, the APP-data data table is generated;

[0085] Merge the APP-mate table, APP-cooperative data table, and APP-data data table into an APP task-driven directory.

[0086] The method for obtaining the prototype collaborative task driver directory is:

[0087] Build a digital prototype and confirm the collaborative control method based on the type of digital prototype;

[0088] Generate an M-Inforation data table according to the digital prototype ID and the parameter mapping table of the digital prototype;

[0089] Build a digital prototype computing environment and generate an M-cooperative data table based on the real-time user task ID and digital prototype computing environment information in the task execution directory;

[0090] According to the M-cooperative data table, the collaborative operation is performed once, and the single digital prototype test data and the real-time user task ID are associated and the M-Model data table is generated;

[0091] After the collaborative operation data of different types of digital prototypes are associated with the digital prototype ID, they are stored in the M-Model data table to generate the M-Data data table;

[0092] The M-Inforation data sheet, M-cooperative data sheet, M-Model data sheet, and M-Data data sheet are merged into a prototype collaborative task driver directory.

[0093] In the process of generating the M-Inforation data table according to the digital prototype ID, an XML file is generated by automatic or manual association according to the digital prototype ID of each digital prototype and the parameter mapping table of the digital prototype, and stored in the M-Inforation data table.

[0094] The demand directory, task execution directory, APP task driver directory, and prototype collaborative task driver directory all build corresponding BOMs through XBOM; equipment task collaborative control realizes the reading and calling of the root directory through the corresponding BOM.

[0095] The collaborative control methods of digital prototypes include black box collaborative control and white box collaborative control. The methods of generating prototype collaborative task driving directories through the black box collaborative control method and the white box collaborative control method are consistent. Before generating the M-Inforation data table according to the digital prototype ID in the black box collaborative control method, each digital prototype is encapsulated to meet confidentiality requirements.

[0096] The following is further described in conjunction with the accompanying drawings and preferred embodiments of the specification:

[0097] In the current embodiment, the complex equipment task collaborative control method based on the model and the prototype, the overall control requirement diagram is as follows: Figure 1 As shown, including:

[0098] (1) Specific process of demand collaborative control method

[0099] Demand task creation, based on the demand data exchange service method in whiteboard mode, the overall designer completes the editing, decomposition and distribution of top-level requirements, and the system automatically generates the demand ID and the relationship between personnel and permissions, and stores them in the data;

[0100] According to the demand tasks, the demand information is structured to generate a demand model, which is associated with the demand ID and stored in the R-Model data table;

[0101] Based on the demand information, the demand model and the data generated by the demand collaboration process are associated to form a demand data package which is associated with the ID in the R-Model data table and stored in the R-Data data table;

[0102] During the initial demand collaboration process, the overall designer creates the demand BOM based on XBOM and initializes the demand data according to the BOM structure. The overall BOM diagram is as follows: Figure 2 As shown;

[0103] Demand collaborative editing and control, based on the authority control module, performs authority control for each sub-user operation, including locking\unlocking. Associate the authority information with the requirement ID and store it in the R-control data table.

[0104] According to the permission setting, the requirement whiteboard automatically displays the relevant requirement task information according to the designer's permission. After the designer finishes editing the requirement information, the system automatically stores the relevant information in the relevant data table;

[0105] The requirements editing, modification, and completion processes are distinguished by different colors on the requirements whiteboard, prompting upstream and downstream designers to view and edit;

[0106] After the demand collaboration is completed, the overall designer locks the demand to provide conditions for demand-based task collaboration.

[0107] (2) The specific process of task-driven flat collaboration

[0108] Task-driven flat collaboration Figure 4 As shown, including:

[0109] Task creation: The overall designer issues tasks through the task management portal and manually associates them with requirements. You can also log in quickly through the locked requirements and edit the basic information of the task. The system associates the task ID with the requirement ID and stores it in the RT-relation data table;

[0110] The dynamic process engine structures the task information according to the task information, generates a task entity model, associates it with the task ID, and stores it in the T-Model data table;

[0111] After the task is created, the system builds the task BOM based on the task ID, task entity model, and XBOM, and associates the task BOM with the requirement BOM based on the requirement ID. The designer confirms and makes relevant modifications to form a task data package and stores it in the T-data data table.

[0112] The system provides two task processing solutions for task executors: first, to refine the task and decompose it to other designers. Second, to close the task according to the task requirements;

[0113] According to the task information, the executors can conduct flat collaboration based on this information and initiate online discussions, including instant messaging, online meetings and document collaboration. Designers can conduct one-on-one communication and group discussions. The system automatically aggregates the process fragment information into a data package and stores it in the T-informatio data table. It is also associated with personnel information and task information to form a task model data package and update the T-data data table;

[0114] During the task execution process, the system automatically pushes relevant processes and resources to the executor for reference based on the task information;

[0115] After the task is completed, the task initiator can lock the task.

[0116] (3) Specific process of engineering APP collaborative control

[0117] Engineering APP collaborative control Figure 5As shown, including:

[0118] APP encapsulation, based on the APP service-oriented collaborative control method, the APPs involved in APP collaborative design need to be virtualized, and parameter structured design must be completed according to the parameter transfer requirements between APPs. It has remote scheduling capabilities and completes the service encapsulation of engineering APPs;

[0119] APP registration, after APP service packaging, needs to be registered in the APP service store process, including APP information, service host information, etc. The system automatically generates APP_ID based on APP information, and generates APP data packets based on server host information and stores them in the APP-mate table;

[0120] According to the design task requirements, the task initiator builds the APP collaborative computing environment, identifies the APPs that need to participate in the collaborative computing, retrieves the APP information from the APP-mate table, builds the input and output parameters of the upstream and downstream APPs, and builds the parameter mapping table. The system supports manual dragging to complete the parameter mapping and networking.

[0121] After the APP cooperative computing environment is built, the APP cooperative process data package is built according to the task ID and the APP cooperative computing environment, and stored in the APP-cooperative data table. The call instruction is sent to the relevant computer according to the APP information;

[0122] After the collaborative task is completed, the system builds the design BOM based on the XBOM according to the APP-ID and the calculation result data package, and associates the design BOM with the task BOM according to the task ID. The designer confirms and makes relevant modifications to form an APP collaborative data package and stores it in the APP-data data table.

[0123] In the task-driven collaborative process, designers can initiate APP collaboration at any time and update related data packages based on the result data.

[0124] (4) Prototype collaborative control method

[0125] Prototype collaborative control Figure 6 As shown, including:

[0126] Prototype construction, according to the requirements of prototype construction, clarify the integrated test methods of various types of digital prototypes, white box collaboration or black box collaboration. Collaborative control methods such as Figure 3 As shown;

[0127] The specific process of white box collaboration is as follows:

[0128] According to the definition of the digital prototype, system designers conduct discussions through flat collaboration tools to clarify the prototype development environment, and build a full-factor digital prototype and process input and output parameters;

[0129] According to the test verification task requirements, the task initiator builds a prototype collaborative computing environment and specifies the prototypes that need to participate in the collaborative verification. The system automatically creates a prototype ID and stores it in the M-Inforation data table.

[0130] After the prototype collaborative environment is built, a prototype collaborative data package is built based on the task ID and prototype information, and stored in the M-cooperative data table. According to the prototype information, a call instruction is sent to the relevant computer for distributed collaborative verification of the prototype;

[0131] After a prototype collaboration is completed, the prototype collaboration information is structured according to the task information to generate a prototype model, which is associated with the task ID and stored in the M-Model data table;

[0132] Based on the task information, the prototype model and the prototype collaborative process generation process generation data are associated to form a prototype data package which is associated with the ID in the M-Model data table and stored in the M-Data data table.

[0133] The specific process of black box collaboration is as follows:

[0134] According to the definition of the digital prototype, each system designer encapsulates the full-factor digital prototype and completes the parameter structuring process to form a prototype proxy model;

[0135] According to the test verification task requirements, the task initiator builds a prototype collaborative computing environment and specifies the prototypes that need to participate in the collaborative verification. The system automatically creates a prototype ID and stores it in the M-Inforation data table.

[0136] For the parameter linkage of prototypes, the system provides two association modes, including automatic association and manual association. Automatic association is achieved by automatic matching according to the conversion relationship or parameter linkage method of various prototypes. For manual association, according to the conversion relationship or parameter linkage method of various prototypes or models, the system provides a parameter association mapping table, generates a corresponding association information XML file, and stores it in the M-Inforation data table in combination with the prototype ID.

[0137] Construct a prototype cooperative data package based on the task ID and prototype information, and store it in the M-cooperative data table. Send a call instruction to the relevant computer based on the prototype information to perform distributed collaborative verification of the prototype;

[0138] After a prototype collaboration is completed, the prototype collaboration information is structured according to the task information to generate a prototype model, which is associated with the task ID and stored in the M-Model data table;

[0139] Based on the task information, the prototype model and the prototype collaborative process generation process generation data are associated to form a prototype data package which is associated with the ID in the M-Model data table and stored in the M-Data data table.

[0140] The defined resource library consists of demand data, task data, design data, test data, message data, meeting data, demand models, architecture models, prototype models, etc., and is used to store the models and data generated during demand collaboration, task-driven flat collaboration, APP collaboration, and prototype collaboration.

[0141] The defined resource library consists of demand data, task data, design data, test data, message data, meeting data, demand model, architecture model, prototype model, etc. It is used to store models and data generated in the process of demand collaboration, task-driven flat collaboration, APP collaboration, and prototype collaboration. It also provides support for data traceability based on XBOM.

[0142] In this embodiment, there are the following innovative features:

[0143] (1) Demand Collaboration Flattening

[0144] Multiple people can collaborate on demand decomposition and tracing at the same time, realize demand item collaboration, and collaborate on the same screen. This changes the demand collaboration mode where subsequent personnel can only download and modify the demand file after it is uploaded.

[0145] (2) Task-driven flat collaboration

[0146] With dynamic processes as the core, it collects business data, identifies specific activities, extracts business logic models, and traces the dynamic association of business activities. It records activity relationships and logics without any sense, and records process execution paths. It optimizes process and sub-process paths to find the best process path.

[0147] (3) Tool Collaboration Service

[0148] Tool collaboration service summarizes automated tools and records expert practical experience. Cross-domain tool collaboration calls, different computing apps, and different regional data not only achieve distributed collaboration, but also protect intellectual property rights.

[0149] (4) Prototype collaborative virtualization

[0150] Prototype collaborative virtualization, in a cross-domain environment, joint computing, to achieve real-time input and output. And provide cloud computing, big data services, prototype collaboration to achieve data available but invisible.

[0151] Although the present invention has been disclosed as above in the form of a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art may make possible changes and modifications to the technical solution of the present invention by using the methods and technical contents disclosed above without departing from the spirit and scope of the present invention. Therefore, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall fall within the protection scope of the technical solution of the present invention.

[0152] The contents not described in detail in the specification of the present invention belong to the common knowledge of those skilled in the art.

Claims

1. A complex equipment task collaborative control method based on models and prototypes, characterized in that include: Collect initial user requirements, define requirement information based on equipment operation data and user requirements, and generate a requirement catalog; Generate a task execution catalog based on demand information and demand catalog; Encapsulate and register the engineering APP, import it into the engineering APP according to the task execution directory to drive the task, and obtain the APP task drive directory; Import the task execution directory into the digital prototype for task driving, and obtain the prototype collaborative task driving directory; Receive real-time user demands and perform collaborative control of equipment tasks based on the demand catalog, task execution catalog, APP task drive catalog, and prototype collaborative task drive catalog.

2. The method for coordinated control of complex equipment tasks based on models and prototypes according to claim 1, characterized in that: The method of defining demand information and generating a demand catalog based on equipment operation data and user needs is as follows: Establish a demand model through the historical operation data of the equipment during the mission execution, generate a demand ID according to the user's initial demand, and store it in the R-Model data table; Update the requirement ID in the R-Model data table based on the user requirements received in real time, associate all requirement IDs before and after the update with the user requirements, and store them in the R-Data data table; Determine user permissions based on the requirement ID in the R-Data data table, substitute it into the requirement model, associate the user permissions with the requirement ID, and generate the R-control data table; Combine the R-Model data table, R-Data data table, and R-control data table into a requirements catalog.

3. The method for coordinated control of complex equipment tasks based on models and prototypes according to claim 2 is characterized in that: The method to define the task execution directory is: Substitute the current user demand into the demand model to generate real-time user tasks, and associate the demand ID of the R-control data table in the demand directory with the real-time user task ID to generate the RT-relation data table; Create a task entity model, associate it with the real-time user task ID, and store it in the T-Model data table; The real-time user task ID in the T-Model data table is updated through the real-time user task ID received subsequently, and all the real-time user task IDs before and after the update are associated with the task entity model and stored in the T-Data data table; Merge the RT-relation data table, T-Model data table, and T-Data data table into the task execution directory.

4. The method for coordinated control of complex equipment tasks based on models and prototypes according to claim 1 is characterized in that: The method for encapsulating and registering the engineering APP is: Encapsulate interfaces between apps based on parameter transmission requirements between apps; After completing the interface encapsulation between APPs, generate APP-ID based on APP information and service host information.

5. The method for coordinated control of complex equipment tasks based on models and prototypes according to claim 4 is characterized in that: The method to obtain the APP task driver directory is: Generate APP data packets based on APP-ID and each APP service host information and store them in the APP-mate table; According to the task execution directory, build the APP computing environment, retrieve the APP information from the APP-mate table, build the upstream APP input parameters and downstream APP output parameters, and use the upstream APP input parameters and downstream APP output parameters to generate a parameter mapping table; Generate APP-cooperative data table according to APP computing environment and parameter mapping table; After associating the APP-cooperative data table with the real-time user task ID in the task execution directory, the APP-data data table is generated; Merge the APP-mate table, APP-cooperative data table, and APP-data data table into an APP task-driven directory.

6. The method for coordinated control of complex equipment tasks based on models and prototypes according to claim 3 is characterized by: The method for obtaining the prototype collaborative task driver directory is: Build a digital prototype and confirm the collaborative control method based on the type of digital prototype; Generate an M-Inforation data table according to the digital prototype ID and the parameter mapping table of the digital prototype; Build a digital prototype computing environment and generate an M-cooperative data table based on the real-time user task ID and digital prototype computing environment information in the task execution directory; According to the M-cooperative data table, the collaborative operation is performed once, and the single digital prototype test data and the real-time user task ID are associated and the M-Model data table is generated; After the collaborative operation data of different types of digital prototypes are associated with the digital prototype ID, they are stored in the M-Model data table to generate the M-Data data table; The M-Inforation data sheet, M-cooperative data sheet, M-Model data sheet, and M-Data data sheet are merged into a prototype collaborative task driver directory.

7. The method for coordinated control of complex equipment tasks based on models and prototypes according to claim 6 is characterized in that: In the process of generating the M-Inforation data table according to the digital prototype ID, an XML file is generated by automatic or manual association according to the digital prototype ID of each digital prototype and the parameter mapping table of the digital prototype, and stored in the M-Inforation data table.

8. The method for coordinated control of complex equipment tasks based on models and prototypes according to claim 1, characterized in that: The demand directory, task execution directory, APP task drive directory, and prototype collaborative task drive directory all construct corresponding BOMs through XBOM; equipment task collaborative control realizes the reading and calling of the root directory through the corresponding BOM.

9. The method for coordinated control of complex equipment tasks based on models and prototypes according to claim 6, characterized in that: The collaborative control method of the digital prototype includes black box collaborative control and white box collaborative control. The method of generating the prototype collaborative task driving directory by the black box collaborative control method and the white box collaborative control method is consistent. Before generating the M-Inforation data table according to the digital prototype ID, each digital prototype in the black box collaborative control method is encapsulated to meet confidentiality requirements.

10. The method for coordinated control of complex equipment tasks based on models and prototypes according to claim 1, characterized in that: The equipment operation data is obtained from a database and is the historical operation data of the equipment system; all operation data of the equipment task collaborative control process are stored and backed up in the database.

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