Micro-service training method and device for simulating production line simulation practical training, equipment, medium and product

Abstract

The invention discloses a micro-service training method and device for simulating production line simulation practical training, equipment, a medium and a product, and relates to the technical field of technician training, and the method comprises the steps: receiving a training request of a user; and analyzing the training request to obtain training content, selecting a micro-service set based on the training content, and providing training service for the user through the micro-service set. Therefore, after the training request of the user is received, the training request is analyzed to obtain the training content required by the user, finally, the training content is responded to select the micro-service to obtain the micro-service set, and the training service is provided for the user through the micro-service set, so that the problem that no flexible micro-service architecture is available for coping with the training request of the user is solved; therefore, the problem of poor training effect is solved, and the training effect is improved.

Description

Technical Field

[0001] The present application relates to the technical field of technical personnel training, and in particular to a microservice training method, device, equipment, medium and product for simulating production line simulation training. Background Art

[0002] The digital transformation of the energy industry has put forward an urgent need for the cultivation of digital talents. The modern energy industry needs talents with a wide range of skills and knowledge, including expertise in digital technology, data analysis, automated control and sustainable energy management. The challenge is that the supply of digital talents in the energy field is relatively insufficient, so a lot of education and training must be carried out to achieve and meet the needs of cultivating high-skilled talents.

[0003] At present, the mainstream model for this demand is online training or virtual training platform model, including virtual laboratories, simulators and online learning systems. However, the core problem of the current training model is that digital transformation requires cross-border cooperation between the energy industry and other industries such as information technology and the Internet. At the same time, the core key technology of smart mine construction lies in data interoperability and the interconnection of things. Traditional teaching methods can only solve the problem of single knowledge points and skill points, but cannot solve the problem of cross-border collaboration and data interconnection. This means that when faced with special training situations, there is no flexible architecture to deal with it, resulting in poor final training results.

[0004] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of this application is to provide a microservice training method, device, equipment, medium and product for simulating production line simulation training, aiming to solve the technical problem that there is no flexible microservice architecture to respond to user training requests, resulting in poor training results.

[0006] To achieve the above objectives, the present application proposes a microservice training method for simulating production line simulation training, which is applied to a training provider and includes:

[0007] Receive training requests from users;

[0008] The training request is parsed to obtain training content, a microservice set is selected based on the training content, and a training service is provided to the user through the microservice set.

[0009] In one embodiment, the steps of parsing the training request to obtain training content, selecting a microservice set based on the training content, and providing training services to the user through the microservice set include:

[0010] Parsing the training request to obtain training content, where the training content includes training scenarios, business requirements, and training processes;

[0011] Based on the training scenario, simulation modeling is performed through the perception execution layer, the edge computing layer, and the platform layer to obtain a virtual training scenario;

[0012] A microservice set is selected according to the virtual training scenario, business requirements and training process;

[0013] Provide training services to the user after integrating the microservice set through the application layer

[0014] In one embodiment, the step of performing simulation modeling based on the training scenario through the perception execution layer, the edge computing layer, and the platform layer to obtain a virtual training scenario includes:

[0015] Based on the training scenario, data collection is performed through the perception execution layer to obtain training environment data and training equipment data;

[0016] Extracting features from the training environment data through the edge computing layer to obtain a first data feature, and extracting features from the training device data through the edge computing layer to obtain a second data feature;

[0017] According to the training environment data, the training equipment data, the first data feature and the second data feature, simulation modeling is performed through the platform layer to obtain a virtual training scene.

[0018] In one embodiment, after the step of selecting and obtaining microservices according to the virtual training scenario, business requirements, and training process, the method further includes:

[0019] If the microservice set cannot meet the business requirements, a business microservice is constructed according to the business requirements;

[0020] The business microservice is orchestrated into the microservice set through a container orchestration tool, and the step of: integrating the microservice set through an application layer and providing training services to the user is performed.

[0021] In one embodiment, the step of constructing a business microservice according to the business requirements includes:

[0022] Select and obtain technology stack components according to the business requirements;

[0023] Analyze the business requirements to obtain a data interface;

[0024] A storage database is constructed based on the business requirements;

[0025] Business microservices are constructed through the technology stack components, data interfaces and storage databases.

[0026] In one embodiment, after the step of integrating the microservice set through the application layer to provide training services to the user, the method further includes:

[0027] Performing performance testing on the training provider in real time to obtain test results;

[0028] When the test result shows that a performance bottleneck occurs, analyzing the microservice set to obtain high-frequency microservices;

[0029] Based on the traffic demand of the high-frequency microservice, traffic is allocated to the microservice set through a container orchestration tool.

[0030] In addition, to achieve the above purpose, the present application also proposes a microservice training device for simulating production line simulation training, and the microservice training device for simulating production line simulation training includes:

[0031] A receiving module, used for receiving a user's training request;

[0032] The training module is used to parse the training request to obtain training content, select a microservice set based on the training content, and provide training services to the user through the microservice set.

[0033] In addition, to achieve the above-mentioned purpose, the present application also proposes a microservice training device for simulating production line simulation training, the device comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the microservice training method for simulating production line simulation training as described above.

[0034] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the microservice training method for simulated production line simulation training as described above are implemented.

[0035] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the microservice training method for simulated production line simulation training as described above.

[0036] One or more technical solutions proposed in this application have at least the following technical effects:

[0037] The embodiment of the present application proposes a microservice training method, device, equipment, storage medium and computer program product for simulating production line simulation training, which receives a training request from a user; parses the training request to obtain training content, selects a microservice set based on the training content, and provides training services to the user through the microservice set. Thus, after receiving the training request from the user, the training request is parsed to obtain the training content required by the user, and finally the microservice is selected in response to the training content to obtain a microservice set, and the training service is provided to the user through the microservice set, which solves the problem of poor training effect caused by the lack of a flexible microservice architecture to respond to the user's training request, and improves the training effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0040] Figure 1 A flow chart of the first embodiment of the microservice training method for simulating production line simulation training of this application;

[0041] Figure 2 The microservice training method for the simulated production line simulation training for this application involves a schematic diagram of the microservice architecture of the PLC automatic control course system;

[0042] Figure 3 A flow chart of the second embodiment of the microservice training method for simulating production line simulation training of this application;

[0043] Figure 4 A schematic diagram of the module structure of a microservice training device for simulating production line simulation training in an embodiment of the present application;

[0044] Figure 5 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the microservice training method for simulating production line simulation training in an embodiment of the present application.

[0045] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0046] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0047] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0048] The main solution of the embodiment of the present application is: parse the training request to obtain the training content, which includes the training scenario, business requirements and training process; based on the training scenario, simulate and model the training through the perception execution layer, edge computing layer and platform layer to obtain a virtual training scenario; select and obtain a microservice set according to the virtual training scenario, business requirements and training process; integrate the microservice set through the application layer to provide training services for the user. Based on the training scenario, data collection is performed through the perception execution layer to obtain training environment data and training equipment data; feature extraction is performed on the training environment data through the edge computing layer to obtain a first data feature, and feature extraction is performed on the training equipment data through the edge computing layer to obtain a second data feature; according to the training environment data, training equipment data, the first data feature and the second data feature, simulation modeling is performed through the platform layer to obtain a virtual training scenario. If the microservice set cannot meet the business requirements, a business microservice is constructed according to the business requirements; the business microservice is orchestrated to the microservice set through the container orchestration tool, and the step is performed: the microservice set is integrated through the application layer to provide training services for the user. Select and obtain technology stack components according to the business requirements; analyze the business requirements to obtain data interfaces; construct a storage database based on the business requirements; and construct business microservices through the technology stack components, data interfaces, and storage databases. Perform performance testing on the training provider in real time to obtain test results; when the test results show that a performance bottleneck has occurred, analyze the microservice set to obtain high-frequency microservices; and distribute traffic to the microservice set through a container orchestration tool based on the traffic requirements of the high-frequency microservices. This solves the problem of poor training results due to the lack of a flexible microservice architecture to respond to user training requests, and implements a microservice training method that simulates production line simulation training, thereby improving the training effect. Based on the scheme of the present invention, from the reality that the core of the current training model is the digital transformation that requires cross-border cooperation between the energy industry and other industries such as information technology and the Internet, at the same time, the core key technology of smart mine construction lies in data interoperability and object-to-object connectivity. Traditional teaching methods can only solve the problem of single knowledge points and skill points, but cannot solve the problem of cross-border collaboration and data interconnection, thus resulting in poor results, a microservice training method for simulated production line simulation training is designed, and the effectiveness of the microservice training method for simulated production line simulation training of the present invention is verified when training users. Finally, the effect of training through the method of the present invention is significantly improved.

[0049] In this embodiment, for ease of description, the following description is made using a microservice training device for simulating production line simulation training as the execution subject.

[0050] Since the digital transformation of the energy industry in existing technologies has put forward an urgent need for the cultivation of digital talents, the modern energy industry needs talents with a wide range of skills and knowledge, including professional knowledge in digital technology, data analysis, automated control and sustainable energy management. The challenge is that the supply of digital talents in the energy field is relatively insufficient, so a large amount of education and training must be carried out to achieve and meet the needs of high-skilled talent training. The current mainstream model for this demand is online training or virtual training platform models, including virtual laboratories, simulators and online learning systems. However, the core of the current training model is the problem that digital transformation requires cross-border cooperation between the energy industry and other industries such as information technology and the Internet. At the same time, the core key technology of smart mine construction lies in data interoperability and the connection of things. Traditional teaching methods can only solve the problems of single knowledge points and skill points, but cannot solve the problems of cross-border collaboration and data interconnection. This leads to unsatisfactory training results.

[0051] The present application provides a solution, which utilizes a microservice architecture to respond to users' training requests in a training platform, selects or creates microservices in response to users' training requests, and provides users with corresponding training services. While providing users with better services, it also ensures that the system can handle a variety of different training scenarios.

[0052] As can be seen from the above embodiments, the present application receives a training request from a user; parses the training request to obtain training content, selects a microservice set based on the training content, and provides training services to the user through the microservice set. Thus, after receiving the training request from the user, the training request is parsed to obtain the training content required by the user, and finally the microservice is selected in response to the training content to obtain a microservice set, and the training service is provided to the user through the microservice set, which solves the problem of poor training effect caused by the lack of a flexible microservice architecture to respond to the user's training request, and improves the training effect.

[0053] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device, an intention device, etc. that can realize the above functions. The following takes a microservice training device for simulating production line simulation training as an example to illustrate this embodiment and the following embodiments.

[0054] Based on this, the embodiment of the present application provides a microservice training method for simulating production line simulation training, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the microservice training method for simulating production line simulation training in this application.

[0055] In this embodiment, the microservice training method for simulating production line simulation training is applied to the training provider, and the method includes steps S01 to S02:

[0056] Step S01, receiving a user's training request;

[0057] It should be clear that the digital transformation of the energy industry has put forward an urgent need for the cultivation of digital talents. The modern energy industry needs talents with a wide range of skills and knowledge, including expertise in digital technology, data analysis, automated control and sustainable energy management. The challenge is that the supply of digital talents in the energy field is relatively insufficient, so a lot of education and training must be carried out to achieve and meet the needs of cultivating high-skilled talents.

[0058] At present, the mainstream model for this demand is online training or virtual training platform model, including virtual laboratories, simulators and online learning systems. However, the core problem of the current training model is that digital transformation requires cross-border cooperation between the energy industry and other industries such as information technology and the Internet. At the same time, the core key technology of smart mine construction lies in data interoperability and the interconnection of things. Traditional teaching methods can only solve the problem of single knowledge points and skill points, but cannot solve the problem of cross-border collaboration and data interconnection. This means that when faced with special training situations, there is no flexible architecture to deal with it, resulting in poor final training results.

[0059] Therefore, in this application, a training request from a user is received through a training provider, wherein the training request here refers to a variety of application scenarios. In this embodiment, it is mainly aimed at the training of technical personnel in mining, so in the subsequent description, the training request is illustrated by taking a mining scenario as an example;

[0060] The training request sent by the user at least includes the training project, training scenario, training requirements and training content, etc., which should be set according to the actual business needs. For example, in the training of miners, in order to make the miners familiar with the operating equipment and operating scenarios, these requirements will be included in the training request.

[0061] Step S02: parsing the training request to obtain training content, selecting a microservice set based on the training content, and providing training services to the user through the microservice set.

[0062] After receiving the training request, the training provider parses the training request to obtain the training content to be provided this time. As described in the above embodiment, the training content in this embodiment includes but is not limited to training scenarios, business requirements and training processes. Subsequently, the corresponding microservices are selected based on the obtained training content to obtain a set of microservices for training, wherein microservices are a software architecture style that splits an application into a set of small, independent services, each of which focuses on a specific business function. These services interact with each other through network communications, usually through APIs. The advantages of microservice architecture include higher scalability, flexible technology selection, and easier maintenance and deployment.

[0063] In addition, it should be clear that the interaction mechanism between microservices in this embodiment is:

[0064] In the microservice architecture, service modules interact with each other through lightweight communication protocols. Common communication methods include HTTP / REST and message queues:

[0065] (1) Synchronous communication based on HTTP / REST: The service consumer directly requests the service provider's API interface through the HTTP protocol (usually GET, POST, etc.). After the service provider processes the request, it returns the result through HTTP response. This is a synchronous blocking communication model. The service consumer needs to wait for the service provider's response. For example, in Figure 2 In the PLC automatic control training platform shown, after the user selects a virtual device and clicks the start button, the training resource management module (consumer) needs to notify the virtual device module (provider) to initialize and start the device of a specific model. This process can be done by the training resource management module through HTTP / POST request, passing the necessary parameters such as the device ID, and calling the " / devices / {id} / start" interface of the virtual device module. The advantage is that the communication process is intuitive and easy to understand, and the implementation is simple.

[0066] (2) Asynchronous communication based on message queues: Service modules use a third-party message broker (MessageBroker) to transmit messages instead of calling directly. Service consumers (producers) publish messages to the message queue, and service providers (consumers) actively pull messages from the message queue and process them. This is an asynchronous non-blocking communication model. Taking the virtual device module as an example, after the training resource management module issues a startup instruction to the virtual device, it does not directly call the startup interface, but writes a message to start the device to a specific Topic in the message queue (such as Kafka). The virtual device module subscribes to the Topic. Once a new message is found, the consumption logic is triggered to parse the device ID from the message, and then drive the virtual device to start. The advantage is that the message producer and consumer are decoupled, and neither party needs to be aware of the existence of the other. Even if the virtual device module crashes, it will not affect other processes of the training resource management module. After it recovers, it can continue to process the accumulated messages in the queue.

[0067] In addition to the typical request / response interaction, the training process also involves a large amount of data interaction. For example, the 3D simulation module needs to continuously synchronize the status data of the virtual device to the VR rendering module. The two service modules maintain a long connection and use full-duplex communication protocols such as WebSocket to exchange data.

[0068] Specifically, the above step S02, parsing the training request to obtain training content, selecting a microservice set based on the training content, and providing training services to the user through the microservice set, includes:

[0069] Step S021, parsing the training request to obtain training content, where the training content includes training scenarios, business requirements, and training processes;

[0070] Step S022, based on the training scenario, simulation modeling is performed through the perception execution layer, the edge computing layer and the platform layer to obtain a virtual training scenario;

[0071] Step S023, selecting a microservice set according to the virtual training scenario, business requirements, and training process;

[0072] Step S026, integrating the microservice set through the application layer to provide training services for the user.

[0073] After receiving the training request, the training request is parsed to obtain the training scenarios, business requirements and training processes included in this training. It should be clear that the system architecture should be scalable and able to accommodate different types of energy industry equipment and processes. Common choices include cloud-based architecture to support remote access and multi-user sharing. In this embodiment, the virtual production line simulation training platform usually adopts a layered architecture, dividing the entire system into multiple levels, including the perception execution layer, edge computing layer, platform layer and application layer. This layered architecture can decouple the various components of the system from each other, thereby improving the maintainability and scalability of the system.

[0074] Once the training scenario required for this training is known (for example, equipment operation training inside a mine, that is, the training scenario is the physical scene of the mine and the operating equipment), simulation modeling can be performed through the perception execution layer, edge computing layer, and platform layer to obtain a virtual training scenario;

[0075] Then the application layer is adopted, which is oriented to the end users and provides a human-computer interaction interface. The virtual reality module renders an immersive three-dimensional scene and responds to natural interaction. In addition, the online training module integrates courseware, test questions, assessments, etc., supports mobile office on the mobile portal, and also includes a data analysis module to provide multi-dimensional data perspective and visual reports. That is, the application layer in this embodiment can integrate and process multiple modules and interfaces, and finally present them to the user.

[0076] More specifically, after the above step S026, the step of integrating the microservice set through the application layer to provide training services to the user, the method further includes:

[0077] Step S027, performing a performance test on the training provider in real time to obtain a test result;

[0078] Step S028, when the test result shows that a performance bottleneck occurs, analyzing the microservice set to obtain high-frequency microservices;

[0079] Step S029: Based on the traffic demand of the high-frequency microservice, traffic is distributed to the microservice set through a container orchestration tool.

[0080] In order to provide users with better training services, this embodiment performs performance testing on the training provider (i.e., the entire system) in real time when providing training services to users, and obtains corresponding performance test results, wherein the content of the performance test on the microservice architecture may include: inter-service communication (testing the communication delay and throughput between microservices to ensure that they can work together effectively under high load), single service load (simulating the impact of high load on a single microservice to identify bottlenecks), overall system load (testing the performance of the entire microservice architecture under high concurrency, including the automatic expansion and fault recovery capabilities of the service), data storage and caching (evaluating the performance of data storage (such as database) and cache system under load) and monitoring and logging (real-time monitoring of service performance indicators and analysis of logs to quickly identify problems).

[0081] When the test results show that the current performance is sufficient to maintain normal operation, it is continuously monitored and no adjustment measures are taken at this time. However, when performance bottlenecks or concurrent pressures occur, specific microservices (microservices used frequently) can be expanded in a targeted manner to improve system throughput without having to expand the entire platform. Specifically, microservices are stateless and can be flexibly deployed in a distributed manner. With the help of container orchestration tools such as Kubernetes, microservices can be quickly and elastically scaled. For example, when the number of people online on the platform surges, the number of container instances (Pods) of the VR rendering module and the training resource management module can be increased, while the relatively idle evaluation module can be kept as a single instance.

[0082] This embodiment adopts the above scheme, specifically by receiving the training request of the user; parsing the training request to obtain the training content, selecting a microservice set based on the training content, and providing the training service to the user through the microservice set. Thus, after receiving the training request of the user, the training request is parsed to obtain the training content required by the user, and finally the microservice is selected in response to the training content to obtain the microservice set, and the training service is provided to the user through the microservice set, which solves the problem of poor training effect caused by the lack of a flexible microservice architecture to respond to the training request of the user, and improves the training effect.

[0083] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 3 In step S022, based on the training scenario, simulation modeling is performed through the perception execution layer, the edge computing layer and the platform layer to obtain a virtual training scenario. The microservice training method for simulating production line simulation training also includes steps S0221 to S0223:

[0084] Step S0221, based on the training scenario, data collection is performed through the perception execution layer to obtain training environment data and training equipment data;

[0085] Step S0222, extracting features from the training environment data through the edge computing layer to obtain a first data feature, and extracting features from the training device data through the edge computing layer to obtain a second data feature;

[0086] Step S0223, based on the training environment data, the training equipment data, the first data feature and the second data feature, simulation modeling is performed through the platform layer to obtain a virtual training scene.

[0087] The perception execution layer in this embodiment is responsible for collecting data such as underground environment and equipment status through various sensors and instruments, and uploading them to the edge computing layer through the bus. At the same time, this layer can also receive control instructions from the upper layer to drive robots, coal mining machines, hydraulic supports and other equipment to perform actions;

[0088] The edge computing layer in this embodiment is deployed underground to process the sensed data in real time, filter redundant data, compress the data volume, and extract features. At the same time, for delay-sensitive control tasks, this layer directly distributes control instructions to achieve edge control;

[0089] The platform layer in this embodiment is deployed in the ground data center and is the core of the entire system. This layer integrates the twin modeling engine, the Internet of Things platform, and the industrial big data platform to realize data aggregation, storage, computing, visualization and other functions. Among them, the digital twin engine is responsible for virtual scene construction and synchronous simulation, the Internet of Things platform is responsible for device connection management, data collection, edge computing, etc. The knowledge base subsystem is responsible for building a knowledge graph in the coal mine field to provide support for intelligent decision-making.

[0090] After understanding the structure and functions of multiple layers, we can collect data through the perception and execution layer based on the training scenario to obtain the training environment scenario and training equipment data. Then, we can process and extract features of the training environment scenario and training equipment data through the edge computing layer to obtain the corresponding first data features and second data features. Finally, we can perform simulation modeling through the platform layer based on the training environment data, training equipment data, first data features and second data features to obtain a virtual training scenario.

[0091] Specifically, after the above step S023, in which microservices are selected according to the virtual training scenario, business requirements and training process, the method further includes:

[0092] Step S024: if the microservice set cannot meet the business requirements, construct a business microservice according to the business requirements;

[0093] Step S025, orchestrate the business microservice into the microservice set through a container orchestration tool, and execute the step of integrating the microservice set through an application layer to provide training services for the user.

[0094] It should be clear that the core idea of ​​the microservice architecture is "single responsibility", that is, each microservice is built around a specific business capability to achieve "high cohesion and low coupling". Therefore, when the platform wants to launch a new function, it only needs to create a new microservice for it without large-scale reconstruction of the existing system. For example, the platform wants to add a "training effect evaluation" function. Under the traditional architecture, it is necessary to embed evaluation-related code in the original business logic, which has a high degree of coupling and affects the entire system. Under the microservice architecture, it is only necessary to create a new evaluation service, let it subscribe to the training process data and process it asynchronously, without intrusion on other services and without bringing additional performance overhead to the system.

[0095] Therefore, in the specific application of this embodiment, when the current microservice set cannot meet the business needs, microservices will be constructed according to the business needs to obtain business microservices. Finally, the business microservices will be orchestrated into the microservice set through container orchestration tools (such as Kubernetes, Docker Swarm, and OpenShift, etc.). The microservice set can be integrated through the application layer to provide training services to users.

[0096] More specifically, the above step S024, the step of constructing a business microservice according to the business requirements, includes:

[0097] Step S0241, selecting and obtaining technology stack components according to the business requirements;

[0098] Step S0242, analyzing the business requirements to obtain a data interface;

[0099] Step S0243, constructing a storage database based on the business requirements;

[0100] Step S0244, constructing business microservices through the technology stack components, data interfaces and storage databases.

[0101] Since the services are loosely coupled, each service can freely choose the most suitable programming language and technical framework. For example, for compute-intensive AI recommendation services, you can consider using Python and TensorFlow. For high-concurrency rendering services, Go language would be a good choice, and resource management services can use the mature and stable Java technology stack. As long as each service team strictly follows the unified interface specifications to provide external services, they can independently optimize and iterate internally, and it is also easier to introduce emerging technologies.

[0102] In addition, unlike the monolithic architecture where all services share a central database, microservices tend to allow each service to have its own data storage, which makes it possible to decouple services at the application level. On the one hand, as mentioned earlier, the most appropriate database type can be selected for different services to cope with different data access modes. On the other hand, each microservice can expand the database independently. When the number of users and training data surges, the MySQL database of the training resource management service can be divided into libraries and tables, while the Redis used by the VR rendering service does not need to be changed at all. Of course, "one database for each service" is an ideal state. In reality, it is also necessary to consider data consistency, business needs, costs, etc.

[0103] In short, the microservice architecture uses the two powerful tools of "decoupling" and "service-oriented" to enable the platform to maintain the flexibility and scalability of the architecture when facing ever-changing business needs, allowing the development team to focus on business innovation itself. At the same time, it also puts forward higher requirements on the platform's infrastructure, including service orchestration, service discovery, configuration management, call chain monitoring, centralized log management, fault tolerance and disaster recovery.

[0104] This embodiment uses the above scheme, specifically based on the training scenario, to collect data through the perception execution layer to obtain training environment data and training equipment data; to extract features from the training environment data through the edge computing layer to obtain a first data feature, and to extract features from the training equipment data through the edge computing layer to obtain a second data feature; and to perform simulation modeling through the platform layer according to the training environment data, training equipment data, the first data feature and the second data feature to obtain a virtual training scenario. Thus, after receiving the user's training request, the training request is parsed to obtain the training content required by the user, and finally the microservice is selected in response to the training content to obtain a microservice set, and training services are provided to the user through the microservice set, which solves the problem of poor training results due to the lack of a flexible microservice architecture to respond to the user's training requests, and improves the training effect.

[0105] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the microservice training method for simulating production line simulation training in the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0106] This application also provides a microservice training device for simulating production line simulation training. Please refer to Figure 4 The microservice training device for simulating production line simulation training is applied to the training provider, and the device includes:

[0107] A receiving module 10, configured to receive a training request from a user;

[0108] The training module 20 is used to parse the training request to obtain training content, select a microservice set based on the training content, and provide training services to the user through the microservice set.

[0109] The microservice training device for simulating production line simulation training provided by the present application adopts the microservice training method for simulating production line simulation training in the above-mentioned embodiment, which can solve the technical problem that there is no flexible microservice architecture to respond to user training requests, resulting in poor training results. Compared with the prior art, the beneficial effects of the microservice training device for simulating production line simulation training provided by the present application are the same as the beneficial effects of the microservice training method for simulating production line simulation training provided by the above-mentioned embodiment, and the other technical features of the microservice training device for simulating production line simulation training are the same as the features disclosed in the above-mentioned embodiment method, which will not be repeated here.

[0110] The present application provides a microservice training device for simulating production line simulation training, and the microservice training device for simulating production line simulation training includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the microservice training method for simulating production line simulation training in the above-mentioned embodiment one.

[0111] Reference below Figure 5 , which shows a schematic diagram of the structure of a microservice training device suitable for implementing the simulated production line simulation training of the embodiment of the present application. The microservice training device for the simulated production line simulation training in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The microservice training device for simulating production line simulation training shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0112] like Figure 5As shown, the microservice training device for simulating production line simulation training may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM: Read Only Memory) 1002 or the program loaded from the storage device 1003 to the random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the microservice training device for simulating production line simulation training are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other via a bus 1005. The input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the microservice training device for simulating production line simulation training to communicate wirelessly or wired with other devices to exchange data. Although the figure shows a microservice training device for simulating production line simulation training with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have instead.

[0113] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0114] The microservice training device for simulated production line simulation training provided by the present application adopts the microservice training method for simulated production line simulation training in the above-mentioned embodiment, which can solve the technical problem that there is no flexible microservice architecture to respond to user training requests, resulting in poor training results. Compared with the prior art, the beneficial effects of the microservice training device for simulated production line simulation training provided by the present application are the same as the beneficial effects of the microservice training method for simulated production line simulation training provided by the above-mentioned embodiment, and the other technical features of the microservice training device for simulated production line simulation training are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0115] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0116] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0117] The present application provides a computer-readable storage medium having computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the microservice training method for simulated production line simulation training in the above-mentioned embodiment.

[0118] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0119] The above-mentioned computer-readable storage medium may be included in the microservice training device for simulating production line simulation training; or it may exist independently without being assembled into the microservice training device for simulating production line simulation training.

[0120] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the microservice training device for simulating production line simulation training, the microservice training device for simulating production line simulation training: receives the user's training request; parses the training request to obtain training content, selects a microservice set based on the training content, and provides training services to the user through the microservice set.

[0121] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0122] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0123] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0124] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned microservice training method for simulated production line simulation training, which can solve the technical problem that there is no flexible microservice architecture to respond to user training requests, resulting in poor training results. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the microservice training method for simulated production line simulation training provided by the above-mentioned embodiments, and will not be repeated here.

[0125] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the microservice training method for simulated production line simulation training as described above.

[0126] The computer program product provided by this application can solve the technical problem that there is no flexible microservice architecture to respond to user training requests, resulting in poor training results. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the microservice training method for simulated production line simulation training provided by the above embodiment, which will not be repeated here.

[0127] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A microservice training method for simulating production line simulation training, characterized in that: The method is applied to a training provider, and comprises: Receive training requests from users; The training request is parsed to obtain training content, a microservice set is selected based on the training content, and a training service is provided to the user through the microservice set.

2. The method according to claim 1, characterized in that The steps of parsing the training request to obtain training content, selecting a microservice set based on the training content, and providing training services to the user through the microservice set include: Parsing the training request to obtain training content, where the training content includes training scenarios, business requirements, and training processes; Based on the training scenario, simulation modeling is performed through the perception execution layer, the edge computing layer, and the platform layer to obtain a virtual training scenario; A microservice set is selected according to the virtual training scenario, business requirements and training process; After integrating the microservice set through the application layer, training services are provided to the user.

3. The method according to claim 2, characterized in that The step of performing simulation modeling based on the training scenario through the perception execution layer, the edge computing layer and the platform layer to obtain a virtual training scenario includes: Based on the training scenario, data collection is performed through the perception execution layer to obtain training environment data and training equipment data; Extracting features from the training environment data through the edge computing layer to obtain a first data feature, and extracting features from the training device data through the edge computing layer to obtain a second data feature; According to the training environment data, the training equipment data, the first data feature and the second data feature, simulation modeling is performed through the platform layer to obtain a virtual training scene.

4. The method according to claim 2, characterized in that After the step of selecting and obtaining microservices according to the virtual training scenario, business requirements, and training process, the method further includes: If the microservice set cannot meet the business requirements, a business microservice is constructed according to the business requirements; The business microservice is orchestrated into the microservice set through a container orchestration tool, and the steps are performed: the microservice set is integrated through an application layer to provide training services for the user.

5. The method according to claim 4, characterized in that The step of constructing a business microservice according to the business requirements includes: Select and obtain technology stack components according to the business requirements; Analyze the business requirements to obtain a data interface; A storage database is constructed based on the business requirements; Business microservices are constructed through the technology stack components, data interfaces and storage databases.

6. The method according to claim 4, characterized in that After the step of integrating the microservice set through the application layer to provide training services to the user, the method further includes: Performing performance testing on the training provider in real time to obtain test results; When the test result shows that a performance bottleneck occurs, analyzing the microservice set to obtain high-frequency microservices; Based on the traffic demand of the high-frequency microservice, traffic is allocated to the microservice set through a container orchestration tool.

7. A microservice training device for simulating production line simulation training, characterized in that: The device is applied to a training provider, and includes: A receiving module, used for receiving a user's training request; The training module is used to parse the training request to obtain training content, select a microservice set based on the training content, and provide training services to the user through the microservice set.

8. A microservice training device for simulating production line simulation training, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the microservice training method for simulated production line simulation training as described in any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the microservice training method for simulated production line simulation training as described in any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the microservice training method for simulated production line simulation training as described in any one of claims 1 to 6 are implemented.

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