An implementation method, device, equipment, medium and product of a teaching assistant

Abstract

The application discloses an implementation method, device, equipment, medium and product of a teaching assistant. The method comprises the following steps: receiving a calling request of a synthetic API generated by a large teaching model according to a student assistance request; calling a preset API of a virtual simulation training software and / or a PID setting software according to the calling request; feeding back a calling result to the large teaching model, so as to instruct the large teaching model to generate an assistance feedback according to the calling result. The embodiment of the application can improve the training efficiency of virtual simulation training.

Description

Technical Field

[0001] This invention relates to the field of simulation training technology, and in particular to a method, apparatus, equipment, medium and product for implementing a teaching assistant. Background Technology

[0002] Operator Training Simulator (OTS) software is based on the industrial processes of real production enterprises. It establishes a "virtual factory" based on dynamic mechanism modeling and uses an advanced computing engine for simulation calculations. The OTS software interface highly replicates the operation of a Distributed Control System (DCS) in a production environment and uses 3D technology to recreate the actual equipment and environment of a real factory. This comprehensively assists schools in training students to operate equipment in factories, enhancing their understanding of production equipment and control strategies. The dynamic process characteristics and equipment parameters of this product are derived from enterprises, making it closer to actual production.

[0003] For most students, operator simulation training software is difficult to master. Typically, trainers are responsible for demonstrating the software based on students' questions. However, different students have varying levels of understanding of operator simulation training, and each person's questions are different. Inefficiently having trainers answer each question and demonstrate the software individually is difficult to meet actual training needs. Furthermore, when process parameter optimization is required in simulation training, PID tuning software is also needed, further increasing the difficulty for students to use the operator simulation training software independently. Summary of the Invention

[0004] This invention provides a method, apparatus, device, medium, and product for implementing a teaching assistant, so as to improve the training efficiency of virtual simulation training.

[0005] According to one aspect of the present invention, a method for implementing a teaching assistant is provided, comprising:

[0006] Receive the call request to the synthesis API generated by the teaching model based on student assistance requests;

[0007] The virtual simulation training software and / or PID tuning software's preset APIs are invoked according to the invocation request;

[0008] The call result is fed back to the teaching model, which instructs the teaching model to generate assistance feedback based on the call result.

[0009] According to another aspect of the present invention, an apparatus for implementing a teaching assistant is provided, comprising:

[0010] The receiving module is used to receive the call requests to the synthesis API generated by the teaching model based on student assistance requests;

[0011] The calling module is used to call the preset APIs of the virtual simulation training software and / or PID tuning software according to the calling request;

[0012] The feedback module is used to provide the call results to the teaching model and to instruct the teaching model to generate assistance feedback based on the call results.

[0013] According to another aspect of the present invention, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the method for implementing a teaching assistant according to any embodiment of the present invention.

[0014] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to execute the implementation method of the teaching assistant according to any embodiment of the present invention.

[0015] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the teaching assistant implementation method according to any embodiment of the present invention.

[0016] This invention integrates the APIs of virtual simulation training software and PID tuning software, taking into account the operational characteristics of virtual simulation training software in the chemical industry. It fine-tunes the large language model based on these APIs and ultimately uses the large language model to intelligently and indirectly call these APIs to assist students in operation, thus acting as a teaching assistant and improving training efficiency.

[0017] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1AThis is a flowchart of a method for implementing a teaching assistant according to an embodiment of the present invention;

[0020] Figure 1B This is a schematic diagram of a calling relationship provided according to an embodiment of the present invention;

[0021] Figure 2 This is a flowchart of a method for implementing a teaching assistant according to another embodiment of the present invention;

[0022] Figure 3 This is a schematic diagram of the structure of a teaching assistant implementation device according to another embodiment of the present invention;

[0023] Figure 4 This is a schematic diagram of the structure of an electronic device that implements an embodiment of the present invention. Detailed Implementation

[0024] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first," "second," etc., used in this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0026] Figure 1A This is a flowchart illustrating a method for implementing a teaching assistant according to an embodiment of the present invention. This embodiment is applicable to situations where students complete simple teaching tasks using a large teaching model during virtual simulation training. The method can be executed by a device that implements the teaching assistant, which can be implemented in hardware and / or software. This device can be configured in an electronic device with corresponding data processing capabilities. Figure 1A As shown, the method includes:

[0027] S110: Receive the call request to the synthesis API generated by the teaching model based on the student assistance request.

[0028] S120. Call the preset API of the virtual simulation training software and / or PID tuning software according to the call request.

[0029] S130. Feedback the call result to the teaching model to instruct the teaching model to generate assistance feedback based on the call result.

[0030] The synthesis API (Application Programming Interface) includes: synthesis virtual operator navigation API, synthesis DCS screen navigation API, and synthesis process parameter optimization API. The preset APIs include: preset virtual operator navigation API for virtual simulation training software, preset DCS screen navigation API for virtual simulation training software, process data acquisition API for virtual simulation training software, process data setting API for virtual simulation training software, model twin API for virtual simulation training software, and calculation API for PID tuning software.

[0031] Specifically, the developers pre-create an HTTP server within the virtual simulation training software, providing the web API interfaces shown in Table 1 below.

[0032] Table 1

[0033]

[0034] The developers pre-create an HTTP server within the PID tuning software, providing the web API interface shown in Table 1 below.

[0035] Table 2

[0036]

[0037] Developers first create an HTTP server application, and then create the various synthetic APIs shown in Table 3 within the HTTP server application.

[0038] Table 3

[0039]

[0040] Among them, the Synthetic Virtual Operator Navigation API is used to realize the automatic navigation function in the 3D scene by calling the preset virtual operator navigation API; the Synthetic DCS Screen Jump API is used to realize the automatic switching function of the DCS screen by calling the preset DCS screen jump API; and the Synthetic Process Parameter Optimization API is used to call the first calculation API, the second calculation API, the process data acquisition API, the process data setting API, and the model twin API in turn and sequentially according to the preset logic to realize the recommendation of controller parameters.

[0041] The purpose of creating the HTTPS application and its corresponding synthetic API is to account for the limited comprehension capabilities of the large model. Before creating the HTTPS application and its corresponding synthetic API, the large model needed to generate call requests by referring to the seven APIs and their functional descriptions of the virtual simulation training software and PID tuning software, based on its understanding of the student problem. After creating the HTTPS application and its corresponding synthetic API, the large model only needs to generate call requests by referring to the three APIs of the HTTPS application and their functional descriptions, based on its understanding of the student problem, significantly reducing the requirements for the large model's comprehension and expression abilities.

[0042] The three synthesis APIs that the large language model will eventually call are used to prepare training data for fine-tuning the large model. Scripts are written to fine-tune the large model using the training data to obtain the fine-tuned large model as the teaching model. The preparation of the teaching model is now complete.

[0043] like Figure 1B As shown, student assistance requests are input into the large-scale teaching model. The model understands these requests, determines the necessary synthetic APIs to be called, and generates API call requests, sending them to the HTTPS server application to indirectly call the virtual simulation training software and PID tuning software. Upon receiving the call requests from the large-scale teaching model, the HTTPS application determines which APIs it needs to call from the virtual simulation training software and PID tuning software, and also generates corresponding call requests to directly call these software programs. The HTTPS application returns the call results to the large-scale teaching model, which then processes the results and generates a natural language description to display to the student.

[0044] This invention integrates the APIs of virtual simulation training software and PID tuning software, taking into account the operational characteristics of virtual simulation training software in the chemical industry. It fine-tunes the large language model based on these APIs and ultimately uses the large language model to intelligently and indirectly call these APIs to assist students in operation, thus acting as a teaching assistant and improving training efficiency.

[0045] Based on the above embodiments, optionally, if the synthetic API is a synthetic virtual operator navigation API, then the call result indicates whether the navigation is successful.

[0046] Specifically, if the call request generated by the large model is a call to the synthetic virtual operator navigation API, then according to the bit number specified in the call request, the preset virtual operator navigation API is called to navigate the virtual operator in the virtual simulation training software to the corresponding position, and the call result returned by the software is forwarded to the large model.

[0047] Based on the above embodiments, optionally, if the teaching software synthesis API is a synthesis DCS screen jump API, the call result indicates whether the screen jump was successful.

[0048] Specifically, if the call request generated by the large model is a call to the synthetic DCS screen jump API, then according to the bit number specified in the call request, the preset DCS screen jump API is called to jump the screen of the virtual simulation training software to the corresponding position, and the call result returned by the software is forwarded to the large model.

[0049] Figure 2 This is a flowchart illustrating a method for implementing a teaching assistant according to another embodiment of the present invention. This embodiment is an optimization and improvement upon the above embodiment. Figure 2 As shown, the method includes:

[0050] S210: Receive the call request to the synthesis API generated by the teaching model based on the student assistance request.

[0051] S220. Determine the target process model in the virtual simulation training software according to the call request; call the process data acquisition API to obtain the initial process data of the target process model; call the first calculation API of the PID tuning software to determine the first controller parameters of the initial process data.

[0052] S230. The model twin API is called to generate a first twin process model of the target process model in the virtual simulation training software; the process data setting API is called to set the first controller parameters into the first twin process model; the process data acquisition API is called to acquire the first target process data of the first twin process model.

[0053] Specifically, the HTTP server application first determines the student's expected optimization target, i.e., the target process model in the simulation training software, based on the call request. Then, it calls the API to obtain the initial process data of the target process model. Finally, it calls the first calculation API of the PID tuning software to determine the first controller parameters of the initial process data.

[0054] To avoid disrupting the normal operation of the target process model and prevent repeated parameter tuning, the first controller parameters are not directly set into the target process model. Instead, a first twin process model of the target process model is generated in the virtual simulation training software using the model twin API. Then, the first controller parameters are set into the first twin process model using the process data setting API. This ensures both the normal operation of the target process model and the setting of the first controller parameters. After setting, the first target process data of the first twin process model is obtained by calling the process data acquisition API. If the first target process data is stable, the first controller parameters are fed back to the larger model as a result for further review by students.

[0055] S240. If the first target process data is not stable, the second calculation API of the PID tuning software is called to determine the second controller parameters of the initial process data; the model twin API is called to generate the second twin process model of the target process model in the virtual simulation training software; the process data setting API is called to set the second controller parameters into the second twin process model; and the process data acquisition API is called to acquire the second target process data of the second twin process model.

[0056] The second calculation API and the first calculation API use different calculation algorithms.

[0057] Specifically, if the first target process data is not stable, a second calculation API, using a different algorithm than the first calculation API, is called to perform another calculation on the initial process data to obtain the second controller parameters. Similar to the process of obtaining the first target process data, the model twin API is first called to generate a second twin process model of the target process model in the virtual simulation training software. Then, the process data setting API is called to set the second controller parameters in the second twin process model. Finally, the process data acquisition API is called to obtain the second target process data of the second twin process model. The stability of the second target process data is analyzed. If the second target process data is stable, the second controller parameters are fed back to the large model as a result for further review by students. If the second target process data is still not stable, optimization failure is fed back to the large model as a result.

[0058] S235. Feedback the call result to the teaching model to instruct the teaching model to generate assistance feedback based on the call result.

[0059] This invention reduces the training difficulty of large teaching models by packaging the calling logic of multiple APIs that adjust parameters into a single synthetic API.

[0060] Figure 3This is a schematic diagram of a teaching assistant implementation device provided in another embodiment of the present invention. Figure 3 As shown, the device includes:

[0061] The receiving module 310 is used to receive the call request to the synthesis API generated by the teaching model based on the student assistance request;

[0062] The calling module 320 is used to call the preset APIs of the virtual simulation training software and / or PID tuning software according to the calling request;

[0063] Feedback module 330 is used to provide feedback on the call results to the teaching model and to instruct the teaching model to generate assistance feedback based on the call results.

[0064] The teaching assistant implementation device provided in the embodiments of the present invention can execute the teaching assistant implementation method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0065] Optionally, the synthesis API includes: a synthesis virtual operator navigation API, a synthesis DCS screen jump API, and a synthesis process parameter optimization API; the preset API includes: a preset virtual operator navigation API of the virtual simulation training software, a preset DCS screen jump API of the virtual simulation training software, a process data acquisition API of the virtual simulation training software, a process data setting API of the virtual simulation training software, a model twin API of the virtual simulation training software, and a calculation API of the PID tuning software.

[0066] Optionally, calling module 320 includes:

[0067] The target model determination unit is used to determine the target process model in the virtual simulation training software according to the call request.

[0068] The initial data acquisition unit is used to call the process data acquisition API to obtain the initial process data of the target process model;

[0069] The first parameter calculation unit is used to call the first calculation API of the PID tuning software to determine the first controller parameters of the initial process data.

[0070] The first model generation unit is used to call the model twin API to generate the first twin process model of the target process model in the virtual simulation training software;

[0071] The first parameter setting unit is used to call the process data setting API to set the first controller parameters into the first twin process model;

[0072] The first data acquisition unit is used to call the process data acquisition API to obtain the first target process data of the first twin process model.

[0073] Optionally, module 320 may also include:

[0074] The second parameter calculation unit is used to call the second calculation API of the PID tuning software to determine the second controller parameters of the initial process data if the first target process data is not stable; wherein the calculation algorithms used by the second calculation API and the first calculation API are different.

[0075] The second model generation unit is used to call the model twin API to generate a second twin process model of the target process model in the virtual simulation training software;

[0076] The second parameter setting unit is used to call the process data setting API to set the second controller parameters into the second twin process model;

[0077] The second data acquisition unit is used to call the process data acquisition API to obtain the second target process data of the second twin process model.

[0078] Optionally, if the synthetic API is a synthetic virtual operator navigation API, the call result indicates whether the navigation was successful.

[0079] Optionally, if the synthesis API is a synthesis DCS screen jump API, the call result indicates whether the screen jump was successful.

[0080] The teaching assistant implementation device described in further detail can also execute the teaching assistant implementation method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0081] Figure 4 A schematic diagram of an electronic device 40 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0082] like Figure 4As shown, the electronic device 40 includes at least one processor 41 and a memory, such as a read-only memory (ROM) 42 or a random access memory (RAM) 43, communicatively connected to the at least one processor 41. The memory stores computer programs executable by the at least one processor. The processor 41 can perform various appropriate actions and processes based on the computer program stored in the ROM 42 or loaded from storage unit 48 into the RAM 43. The RAM 43 may also store various programs and data required for the operation of the electronic device 40. The processor 41, ROM 42, and RAM 43 are interconnected via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0083] Multiple components in electronic device 40 are connected to I / O interface 45, including: input unit 46, such as keyboard, mouse, etc.; output unit 47, such as various types of monitors, speakers, etc.; storage unit 48, such as disk, optical disk, etc.; and communication unit 49, such as network card, modem, wireless transceiver, etc. Communication unit 49 allows electronic device 40 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0084] Processor 41 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 41 performs the various methods and processes described above, such as the implementation methods of a teaching assistant.

[0085] In some embodiments, the implementation method of the teaching assistant may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 40 via ROM 42 and / or communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the implementation method of the teaching assistant described above may be performed. Alternatively, in other embodiments, processor 41 may be configured to execute the implementation method of the teaching assistant by any other suitable means (e.g., by means of firmware).

[0086] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0087] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0088] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0089] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0090] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0091] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0092] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0093] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for implementing a teaching assistant, characterized in that, The method includes: Receive the call request to the synthesis API generated by the teaching model based on student assistance requests; The virtual simulation training software and / or PID tuning software's preset APIs are invoked according to the invocation request; Feedback of the call result to the teaching model is used to instruct the teaching model to generate assistance feedback based on the call result; The synthesis API includes: a synthesis virtual operator navigation API, a synthesis DCS screen navigation API, and a synthesis process parameter optimization API; the preset API includes: a preset virtual operator navigation API for virtual simulation training software, a preset DCS screen navigation API for virtual simulation training software, a process data acquisition API for virtual simulation training software, a process data setting API for virtual simulation training software, a model twin API for virtual simulation training software, and a calculation API for PID tuning software.

2. The method according to claim 1, characterized in that, If the synthesis API is a synthesis process parameter optimization API, then calling the preset APIs of the virtual simulation training software and / or PID tuning software according to the call request includes: The target process model in the virtual simulation training software is determined based on the call request; Call the process data acquisition API to obtain the initial process data of the target process model; The first controller parameter of the initial process data is determined by calling the first calculation API of the PID tuning software. The model twin API is invoked to generate a first twin process model of the target process model in the virtual simulation training software; Call the process data setting API to set the parameters of the first controller into the first twin process model; Call the process data acquisition API to obtain the first target process data of the first twin process model.

3. The method according to claim 2, characterized in that, The step of calling the preset API of the virtual simulation training software and / or PID tuning software according to the call request also includes: If the first target process data is not stable, the second calculation API of the PID tuning software is called to determine the second controller parameters of the initial process data; wherein the calculation algorithms used by the second calculation API and the first calculation API are different. The model twin API is invoked to generate a second twin process model of the target process model in the virtual simulation training software; Call the process data setting API to set the parameters of the second controller into the second twin process model; Call the process data acquisition API to obtain the second target process data of the second twin process model.

4. The method according to claim 1, characterized in that, If the synthetic API is a synthetic virtual operator navigation API, then the call result indicates whether the navigation was successful.

5. The method according to claim 1, characterized in that, If the synthesis API is a synthesis DCS screen jump API, the call result indicates whether the screen jump was successful.

6. A device for implementing a teaching assistant, characterized in that, The device includes: The receiving module is used to receive the call requests to the synthesis API generated by the teaching model based on student assistance requests; The calling module is used to call the preset APIs of the virtual simulation training software and / or PID tuning software according to the calling request; The feedback module is used to provide the call results to the teaching model and to instruct the teaching model to generate assistance feedback based on the call results. The synthesis API includes: a synthesis virtual operator navigation API, a synthesis DCS screen navigation API, and a synthesis process parameter optimization API; the preset API includes: a preset virtual operator navigation API for virtual simulation training software, a preset DCS screen navigation API for virtual simulation training software, a process data acquisition API for virtual simulation training software, a process data setting API for virtual simulation training software, a model twin API for virtual simulation training software, and a calculation API for PID tuning software.

7. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the implementation method of the teaching assistant according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that are used to cause a processor to execute the method for implementing the teaching assistant as described in any one of claims 1-5.

9. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method for implementing the teaching assistant as described in any one of claims 1-5.

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