Industrial equipment digital twin model management method and system based on model database
Through the digital twin model management method of industrial equipment based on model database, the problem of poor scalability of existing digital twin workshop systems is solved, and more efficient system updates and stronger universality are achieved.
Patent Information
- Application Number
- CN202411423486.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-12
- Publication Date
- 2025-02-18
AI Technical Summary
When facing changes in the production line and internal space of the workshop, the existing digital twin workshop system has poor expansion, requiring a large amount of manpower and material resources to update and secondary development, resulting in universality and inefficiency.
The digital twin model management method of industrial equipment based on model database is adopted. By standardizing the model and data transmission format, and using specific model points and data structures, the program automatically parses and generates new device scenarios, improving the scalability of digital twin scenarios.
It improves the scalability of digital twin scenarios, reduces the time and cost of system updates and secondary development, and enhances the universality and adaptability of the system.
Smart Images
Figure CN119475675A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of industrial data and digital twin technology, and in particular to a method and system for managing a digital twin model of industrial equipment based on a model database. Background Art
[0002] The mainstream digital twin implementation solution consists of modeling real equipment - placing models in scenes - collecting real equipment data - transmitting real data to the twin model - driving the digital twin process - and publishing the program.
[0003] The current research in the field of digital twin workshop systems can basically achieve the completion of some tasks for the data generated by the state monitoring and prediction evaluation and early warning systems. At present, the typical digital twin workshop system framework is mainly composed of five dimensions: physical entity, virtual model, service system, twin data and real-time interactive connection. These five dimensions are mainly focused on the use of digital twin systems on the basis of the integration of virtual space and physical space, through the use of various information processing methods, the use of various decision-making algorithms until the operation of the physical world production workshop. However, in actual production applications, customized needs often cannot open up the application market well, and the system is often affected by the upgrading of production line products and the changes in the internal space of the workshop with demand over time. On the one hand, when the physical machines in the production line and workshop fail and are replaced with new machines, the internal architecture of the entire digital twin workshop often needs to adjust the action to adapt to the new machines. From the proposal of system update requirements to the completion of secondary development and testing, it usually takes a lot of manpower and material resources, and the scalability is poor. On the other hand, in the face of the needs of the same type of workshop, it is often necessary to customize and develop multiple digital twin systems, which consumes additional time costs, restricts the development space of digital twin systems in production and use, and leads to poor universality. Summary of the invention
[0004] The embodiments of the present application provide a method and system for managing a digital twin model of industrial equipment based on a model database, which is used to standardize the format of the model and corresponding data transmission, and use specific model points and data structures to allow the program to automatically parse and generate new equipment scenarios, thereby improving the scalability of digital twin scenarios.
[0005] The present application embodiment provides a method for managing a digital twin model of industrial equipment based on a model database, including:
[0006] According to different types of workshop industrial equipment, corresponding data structures are used for data structure modeling; and,
[0007] According to the conditions of different workshops, data models are built for workshop industrial equipment;
[0008] The modeled workshop industrial equipment is packaged and compressed, and the packaged and compressed resource package includes a header and a data segment, wherein the header includes relevant information and a unique identifier of the entire package, and the header is also used to provide a byte index based on a balanced search tree for each item in the resource package;
[0009] Deploy the packaged and compressed resource package to the model database, and establish a user scenario factory based on the model database, so as to decompress the resources used in the user scenario factory and instantiate objects respectively;
[0010] Collect data information of workshop industrial equipment and transmit the data information to the established user scenario factory to realize the digital twin of workshop industrial equipment on the model database.
[0011] Optionally, data structure modeling is performed using corresponding data structures according to different types of workshop industrial equipment, including:
[0012] For robot equipment, the data required by the robot are divided into: operating system data, welding process data, and offline simulation data. The operating system data includes: CPU usage, memory occupancy, disk occupancy, network throughput, power consumption, task response time, task processing time, sensor data processing delay, and control command issuance delay; welding process data includes: response time, speed, energy consumption, pressure wheel pressure, gas flow, welding current, arc voltage, swing amplitude, and swing frequency; offline simulation data includes: terminal X coordinate, terminal Y coordinate, terminal Z coordinate, first axis, second axis, third axis, fourth axis, fifth axis, and sixth axis;
[0013] For PLC: PLC data address bit types are divided into DBD, DBX, and DBW, and the corresponding multiple bit offsets are taken.
[0014] Optionally, according to different workshop conditions, data modeling of workshop industrial equipment includes:
[0015] According to the actual situation of the workshop, a data model with a set resolution is created, in which the rotatable and movable joints in the created data model are compared with the format in the data structure.
[0016] Optionally, packaging and compressing the modeled workshop industrial equipment includes using LZ4 compression algorithm to compress each resource to obtain a compressed resource package, and the resources under different resource packages are managed by a resource list, and all resource packages are managed by a total resource package list.
[0017] Optionally, establishing a user scenario factory based on the model database includes:
[0018] A user scenario factory is established in the model database, and the resource packages used are packaged with associated resources, so that the resource packages used are packaged and decompressed and objects are instantiated respectively, and a mapping relationship with the physical entity is established through a mapping table.
[0019] Optionally, collecting data information of industrial equipment in the workshop and transmitting the data information to the established user scenario workshop includes:
[0020] Based on Message Queue Telemetry Transport (MQTT), after aggregating field data and device information, the aggregated field data and device information are packaged into JSON format for transmission to perform data communication through MQTT.
[0021] An embodiment of the present application also proposes an industrial equipment digital twin model management system based on a model database, including a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the steps of the industrial equipment digital twin model management method based on the model database as described above are implemented.
[0022] The method of the embodiment of the present application standardizes the format of the model and the corresponding data transmission, and uses specific model points and data structures to allow the program to automatically parse and generate new device scenarios, thereby improving the scalability of the digital twin scenario.
[0023] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS
[0024] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:
[0025] Figure 1 The following is a basic flow chart of the industrial equipment digital twin model management method according to the embodiment of the present application;
[0026] Figure 2 This is a schematic diagram of the classification of data required by the robot in the industrial equipment digital twin model management method according to the embodiment of the present application;
[0027] Figure 3 This is a schematic diagram of the classification of the digital twin model management method of industrial equipment according to the embodiment of the present application according to the PLC data address type. DETAILED DESCRIPTION
[0028] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.
[0029] The present application embodiment provides a method for managing a digital twin model of industrial equipment based on a model database, such as Figure 1 As shown, the following steps are included:
[0030] In step S101, data structure modeling is performed using corresponding data structures according to different types of workshop industrial equipment. In some specific examples, data structure modeling is performed using corresponding data structures according to different types of workshop industrial equipment, including:
[0031] For robot equipment, the data required by the robot is divided into: operating system data, welding process data, and offline simulation data, such as Figure 2 As shown, the operating system data includes: CPU usage, memory occupancy, disk occupancy, network throughput, power consumption, task response time, task processing time, sensor data processing delay, control command issuance delay; welding process data includes: response time, speed, energy consumption, pressure wheel pressure, gas flow, welding current, arc voltage, swing amplitude, swing frequency; offline simulation data includes: terminal X coordinate, terminal Y coordinate, terminal Z coordinate, first axis, second axis, third axis, fourth axis, fifth axis, sixth axis;
[0032] For PLC: PLC data address bit types are divided into DBD, DBX, and DBW, and the corresponding multiple bit offsets are taken. Figure 3 As shown, for example, in a specific example for PLC: divided into: DBD, DBX, DBW according to the PLC data address bit type, and take the corresponding 1st, 3rd, 5th, 7th, 9th, 13th, 15th, 17th bit offsets.
[0033] In step S102, data modeling is performed on the workshop industrial equipment according to different workshop conditions. In some embodiments, data modeling of workshop industrial equipment according to different workshop conditions includes: creating a data model of set resolution according to the actual situation of the workshop, wherein the rotatable and movable joints in the created data model correspond to the format in the data structure. For example, according to the actual situation of the workshop, a 4K resolution model is created, and resources such as models, materials, maps, prefabricated bodies, scripts, data, documents, etc. are unified into the same format and stored and managed, so that the model is uniformly stored and managed and dynamically loaded into the scene. In the specific example, the Unity3D engine can be used, and the corresponding resource format standard of Unity is adopted.
[0034] In step S103, the modeled workshop industrial equipment is packaged and compressed, and the packaged and compressed resource package includes a header and a data segment, wherein the header includes relevant information of the entire package and a unique identifier, and the header is also used to provide a byte index based on a balanced search tree for each item in the resource package. In some examples, the unique identifier can be a hash code that uniquely distinguishes a resource package.
[0035] In step S104, the packaged and compressed resource package is deployed to the model database, and a user scenario factory is established based on the model database, so as to decompress the resources used and instantiate objects based on the user scenario factory.
[0036] In step S105, data information of the workshop industrial equipment is collected and transmitted to the established user scenario factory to realize the digital twin of the workshop industrial equipment on the model database.
[0037] The method of the embodiment of the present application standardizes the format of the model and the corresponding data transmission, and uses specific model points and data structures to allow the program to automatically parse and generate new device scenarios, thereby improving the scalability of the digital twin scenario.
[0038] In some embodiments, packaging and compressing the modeled workshop industrial equipment includes using the LZ4 compression algorithm to compress each resource to obtain a compressed resource package, and the resources under different resource packages are managed by a resource list, and all resource packages are managed by a total resource package list. In this way, the performance during subsequent loading can be further improved.
[0039] In some embodiments, establishing a user scenario factory based on the model database includes:
[0040] A user scenario factory is established in the model database, and the resource packages used are packaged with associated resources, so that the resource packages used are packaged, decompressed and instantiated objects respectively, and a mapping relationship with the physical entity is established through a mapping table. In a specific example, the packaged resources are deployed to the model database, and then a new user scenario factory is created on the model database, and the packages used are packaged first with associated resources, the main package is loaded and run, and then the resources used are decompressed and objects are instantiated in the system.
[0041] To collect data information of industrial equipment in the workshop, you can use SDK and various industrial communication protocols to collect real-time data of robots, PLCs and other equipment in the workshop. The collected data also needs to strictly refer to the set workshop industrial equipment data structure. The collection program is written in C# language and uses .net4.6.1 version.
[0042] There are a large number of devices from different manufacturers, brands, and technical directions, and the data output interfaces they provide are also different. Digital twin technology requires the real-time nature of data mapping. In some embodiments, collecting data information of industrial equipment in the workshop and transmitting the data information to the established user scenario workshop includes:
[0043] Based on Message Queue Telemetry Transport (MQTT), after aggregating field data and device information, the aggregated field data and device information are packaged into JSON format for transmission to perform data communication through MQTT.
[0044] The method of the embodiment of the present application uses a "lightweight" communication protocol using a publisher / subscriber model: Message Queuing Telemetry Transport (MQTT) to provide more messages, remove application coupling, provide instant messaging services at low cost and low bandwidth, and provide orderly, lossless, bidirectional transmission based on byte streams. Most existing cloud services or model databases have good adaptation support for the MQTT protocol.
[0045] In the specific application of this application, a data storage server, an MQTT server, and an MQTT client can be set up on a cloud computing platform. The MQTT server is connected to workshop field equipment such as programmable control equipment and industrial robots through industrial Ethernet to obtain data from the data acquisition system, thereby completing the data acquisition work of the bottom-level equipment in the workshop. After summarizing the field data and equipment information, the MQTT server packages it into JSON format and transmits it. After data management and logical operations, the MQTT server provides corresponding services for the MQTT client.
[0046] The model is further instantiated in the scene in the model database, and data communication is carried out through MQTT. Because the data of the data acquisition program and the model database program in the workshop are strictly in accordance with the agreed data structure, the model database digital twin program can form a mapping relationship with the actual equipment in the workshop, thereby realizing the digital twin of the workshop on the model database.
[0047] This application stores data structures and models in a standardized manner, which is convenient for secondary development and utilization, and improves time efficiency. The data and models are packaged and compressed to reduce the cost of uploading to the model database and reduce resource loss.
[0048] An embodiment of the present application also proposes an industrial equipment digital twin model management system based on a model database, including a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the steps of the industrial equipment digital twin model management method based on the model database as described above are implemented.
[0049] It should be noted that in the various embodiments of the present application, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, method, article or device including the element.
[0050] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.
[0051] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in each embodiment of the present application.
[0052] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.
Claims
1. A method for managing a digital twin model of industrial equipment based on a model database, characterized in that: include: According to different types of workshop industrial equipment, corresponding data structures are used for data structure modeling; as well as, According to the conditions of different workshops, data models are built for workshop industrial equipment; The modeled workshop industrial equipment is packaged and compressed, and the packaged and compressed resource package includes a header and a data segment, wherein the header includes relevant information and a unique identifier of the entire package, and the header is also used to provide a byte index based on a balanced search tree for each item in the resource package; Deploy the packaged and compressed resource package to the model database, and establish a user scenario factory based on the model database, so as to decompress the resources used in the user scenario factory and instantiate objects respectively; Collect data information of workshop industrial equipment and transmit the data information to the established user scenario factory to realize the digital twin of workshop industrial equipment on the model database.
2. The industrial equipment digital twin model management method based on the model database according to claim 1, characterized in that: According to different types of workshop industrial equipment, corresponding data structures are used for data structure modeling, including: For robot equipment, the data required by the robot are divided into: operating system data, welding process data, and offline simulation data. The operating system data includes: CPU usage, memory occupancy, disk occupancy, network throughput, power consumption, task response time, task processing time, sensor data processing delay, and control command issuance delay; welding process data includes: response time, speed, energy consumption, pressure wheel pressure, gas flow, welding current, arc voltage, swing amplitude, and swing frequency; offline simulation data includes: terminal X coordinate, terminal Y coordinate, terminal Z coordinate, first axis, second axis, third axis, fourth axis, fifth axis, and sixth axis; For PLC: PLC data address bit types are divided into DBD, DBX, and DBW, and the corresponding multiple bit offsets are taken.
3. The industrial equipment digital twin model management method based on the model database according to claim 2, characterized in that: According to different workshop conditions, data modeling of workshop industrial equipment includes: According to the actual situation of the workshop, a data model with a set resolution is created, in which the rotatable and movable joints in the created data model are compared with the format in the data structure.
4. The industrial equipment digital twin model management method based on the model database according to claim 1, characterized in that: Packaging and compressing the modeled workshop industrial equipment includes using the LZ4 compression algorithm to compress each resource to obtain a compressed resource package, and the resources under different resource packages are managed by a resource list, and all resource packages are managed by a total resource package list.
5. The industrial equipment digital twin model management method based on the model database according to claim 1, characterized in that: Establishing a user scenario workshop based on the model database includes: A user scenario factory is established in the model database, and the resource packages used are packaged with associated resources, so that the resource packages used are packaged and decompressed and objects are instantiated respectively, and a mapping relationship with the physical entity is established through a mapping table.
6. The industrial equipment digital twin model management method based on the model database according to claim 1, characterized in that: Collecting data information of industrial equipment in the workshop and transmitting the data information to the established user scenario workshop includes: Based on Message Queue Telemetry Transport (MQTT), after aggregating field data and device information, the aggregated field data and device information are packaged into JSON format for transmission to perform data communication through MQTT.
7. An industrial equipment digital twin model management system based on a model database, characterized in that: It includes a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the steps of the industrial equipment digital twin model management method based on the model database are implemented as described in any one of claims 1 to 6.