An internet of things practical training method and system

Abstract

The application discloses an Internet of Things practical training work method and system. In the method, first, an Internet of Things scheme demand sent by a target enterprise is acquired, and then a matching degree of the Internet of Things scheme demand and a practical training target is determined. If it is judged that the matching degree is greater than a preset matching degree threshold, at least one practical training task is generated according to the Internet of Things scheme demand, and the practical training task is issued to a target Internet of Things practical training terminal. The target Internet of Things practical training terminal can execute the practical training task to meet the demand of the target enterprise. Then, first scheme information generated by the target Internet of Things practical training terminal according to the practical training task is acquired, and the first scheme information is sent to the target enterprise, so as to cooperate with the research and development process of the target enterprise. The practical training work system of the Internet of Things can organically integrate the actual demand of the target enterprise and the education resources of the target Internet of Things practical training terminal, improve the research and development process of the target enterprise, and is beneficial to the education and teaching of the target Internet of Things practical training terminal.

Description

Technical Field

[0001] This invention relates to the technical field of Internet of Things (IoT) training, and in particular to a method and system for IoT training. Background Technology

[0002] With the continuous development of artificial intelligence technology, the content and methods of IoT teaching in universities are also constantly changing. IoT training equipment is an IoT training platform that integrates teaching, competition, industrial control, and environmental monitoring development. It can comprehensively assess students' mastery of knowledge and formulate corresponding teaching plans.

[0003] Currently, there are many problems in the use of IoT training equipment. For example, the IoT training courses are not scientifically and rationally designed and cannot be adapted to the actual R&D process of IoT companies; the IoT training process lacks input of high-quality resources from IoT companies, making it impossible to organically integrate industrial resources and educational resources. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for Internet of Things (IoT) training.

[0005] To achieve the above objectives, the technical solution provided by this invention is as follows:

[0006] A practical training method for the Internet of Things (IoT) includes:

[0007] Obtain the IoT solution requirements sent by the target enterprise, which include business functions in specific scenarios;

[0008] Determine the matching degree between the requirements of the IoT solution and the training objectives, wherein the training objectives include several key IoT technologies that need to be mastered through training;

[0009] If the matching degree is greater than the preset matching degree threshold, at least one training task is generated according to the IoT solution requirements. The training task includes functional requirements, security requirements, and reliability requirements. Otherwise, it indicates that the design of the specific IoT solution is inconsistent with the training objectives of the IoT training control center. In this case, it is necessary to provide feedback to the target enterprise on the matching degree between the IoT solution requirements and the training objectives, as well as the preset matching degree threshold, to inform the target enterprise that the design of the specific IoT solution is inconsistent with the training objectives of the IoT training control center, for the target enterprise's reference.

[0010] The training task is then sent to the target IoT training terminal.

[0011] The system obtains the first scheme information generated by the target IoT training terminal according to the training task, and sends the first scheme information to the target enterprise. The first scheme information includes first perception layer information, first network transmission layer information, and first application layer information.

[0012] Furthermore, it also includes:

[0013] Obtain feedback information from the target enterprise regarding the first solution information, and send the feedback information to the target IoT training terminal. The feedback information includes security vulnerability information, functional vulnerability information, and reliability information.

[0014] The second scheme information generated by the target IoT training terminal based on the feedback information is obtained, and the second scheme information is sent to the target enterprise. The second scheme information includes second perception layer information, second network transmission layer information, and second application layer information.

[0015] Furthermore, the target IoT training terminal is determined in the following way:

[0016] Determine the task tag information for the training task;

[0017] Determine the training course label information for the candidate IoT training terminals;

[0018] The correlation between the task label information and the training course label information is analyzed by semantic model to determine the IoT training terminal with the highest correlation to the training task. Different IoT training terminals are configured with different training sensors.

[0019] The IoT training terminal that is most relevant to the training task is identified as the target IoT training terminal.

[0020] Furthermore, before distributing the training task to the target IoT training terminal, the following steps are also included:

[0021] The deadline for the IoT solution is determined based on the solution requirements, and the deadline for the training task is determined based on the solution deadline.

[0022] The process of distributing the training tasks to the target IoT training terminal also includes:

[0023] The task deadline and the training task are sent together to the target IoT training terminal.

[0024] Furthermore, it also includes: identifying abnormal training tasks that have exceeded the deadline and the abnormal IoT training terminals corresponding to the abnormal training tasks;

[0025] Obtain the description information of the abnormal training task from the abnormal IoT training terminal;

[0026] If the explanatory information is of the first type, then the deadline for the task shall be extended in accordance with the deadline of the proposed solution.

[0027] If the description information is of the second type, the abnormal training task will be redistributed to other IoT training terminals.

[0028] Furthermore, it also includes:

[0029] If the description information is the second type of description information, then the course generation information is sent to the target enterprise, the target training course produced by the target enterprise based on the course generation information is obtained, and the target training course is sent to the abnormal IoT training terminal.

[0030] Furthermore, it also includes:

[0031] Obtain the evaluation information of the abnormal IoT training terminal on the target training course;

[0032] The evaluation information is sent to the target company.

[0033] Furthermore, it also includes:

[0034] Obtain updated information from the target company regarding the target training course based on the evaluation information;

[0035] The update information is sent to the abnormal IoT training terminal.

[0036] Furthermore, determining the degree of matching between the IoT solution requirements and the training objectives includes:

[0037] Determine the requirement tag information for the IoT solution;

[0038] Determine the target label information for the practical training objectives;

[0039] The matching degree between the requirement tag information and the target tag information is analyzed by semantic model, and the matching degree between the requirement tag information and the target tag information is determined as the matching degree between the IoT solution requirement and the training objective.

[0040] During semantic model training, the following loss function F is used to measure the difference between the semantic model's predictions and the true labels, where a represents the main clause, p represents the positive sentence, n represents the negative sentence, S represents the sentence embedding vectors of a, p, and n, ||·|| represents the distance, and d represents the distance between the two sentences. p With S n The distance between them;

[0041] F = max(||S) a -S p ||-||S a -S n ||+d,0).

[0042] To achieve the above objectives, the present invention further provides an Internet of Things (IoT) training system for implementing the aforementioned IoT training method, comprising:

[0043] The acquisition module is used to acquire IoT solution requirements sent by the target enterprise, which include business functions in specific scenarios;

[0044] The determination module is used to determine the matching degree between the IoT solution requirements and the training objectives, wherein the training objectives include multiple key IoT technologies that need to be mastered through training.

[0045] The generation module is used to generate at least one training task according to the requirements of the IoT solution if the matching degree is greater than a preset matching degree threshold. The training task includes at least functional requirements, security requirements and reliability requirements.

[0046] The distribution module is used to distribute the training tasks to the target IoT training terminal;

[0047] The sending module is used to obtain the first scheme information generated by the target IoT training terminal according to the training task, and send the first scheme information to the target enterprise. The first scheme information includes first perception layer information, first network transmission layer information and first application layer information.

[0048] Compared with existing technologies, the principles and advantages of this technical solution are as follows:

[0049] First, the IoT solution requirements sent by the target company are obtained. Then, the matching degree between the IoT solution requirements and the training objectives is determined. If the matching degree is greater than a preset matching degree threshold, at least one training task is generated based on the IoT solution requirements and sent to the target IoT training terminal. The target IoT training terminal can execute the training task to meet the needs of the target company. Next, the first solution information generated by the target IoT training terminal based on the training task is obtained and sent to the target company, thereby cooperating with the target company's R&D process. Through the IoT training system, the actual needs of the target company can be organically integrated with the educational resources of the target IoT training terminal, improving the target company's R&D process and facilitating the teaching and learning activities of the target IoT training terminal. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the services required in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description 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.

[0051] Figure 1 This is a connection block diagram of an Internet of Things (IoT) training system according to an embodiment of the present invention.

[0052] Figure 2 This is a flowchart illustrating the principle of an IoT training method according to an embodiment of the present invention. Detailed Implementation

[0053] The present invention will be further described below with reference to specific embodiments:

[0054] like Figure 1 As shown in the figure, the IoT training system described in this embodiment includes an acquisition module, a determination module, a generation module, a distribution module, and a sending module.

[0055] like Figure 2 As shown, the working principle of the IoT training system is as follows:

[0056] S1. Obtain the IoT solution requirements sent by the target enterprise through the acquisition module;

[0057] The IoT solution requirements include business functions in specific scenarios. These requirements originate from the actual needs of IoT enterprises in their production and operation activities. In the traditional model, IoT enterprises rely on internal designers to complete specific IoT solution designs. When the business volume of an IoT enterprise is large, relying on internal designers to complete specific IoT solution designs leads to inefficiency. In this embodiment, when the target enterprise has a large business volume, it can send its IoT solution requirements to the IoT training system.

[0058] S2. Determine the matching degree between the IoT solution requirements and the training objectives through the determination module;

[0059] The training objectives include several key IoT technologies that need to be mastered through training, such as key technologies of industrial IoT, key technologies of IoT sensing, key technologies of embedded technology, key technologies of machine vision and sorting technology, robot-related skills, and key technologies of drones.

[0060] The requirements for an IoT solution involve the design of a specific IoT solution. This design may or may not align with the training objectives of the IoT training system. This implementation method determines whether the design of the specific IoT solution aligns with the training objectives of the IoT training system by assessing the degree of matching between the IoT solution requirements and the training objectives.

[0061] Furthermore, when determining the matching degree between IoT solution requirements and training objectives, the requirement label information of IoT solution requirements is first determined, then the target label information of training objectives is determined, and finally the matching degree between requirement label information and target label information is analyzed through semantic model. The matching degree between requirement label information and target label information is determined as the matching degree between IoT solution requirements and training objectives.

[0062] During semantic model training, the following loss function F is used to measure the difference between the semantic model's predictions and the true labels, where a represents the main clause, p represents the positive sentence, n represents the negative sentence, S represents the sentence embedding vectors of a, p, and n, ||·|| represents the distance, and d represents the distance between the two sentences. p With S n The distance between them;

[0063] F = max(||S) a -S p ||-||S a -S n ||+d,0);

[0064] Analyzing the matching degree between demand label information and target label information using semantic models has high accuracy. When training the semantic model, the training data can be divided into training set, test set, and validation set, and the parameters of the semantic model can be cross-validated using the training set, test set, and validation set.

[0065] S3. If the matching degree is greater than the preset matching degree threshold, at least one training task is generated by the generation module according to the IoT solution requirements. The training task includes at least functional requirements, security requirements, and reliability requirements. Otherwise, it indicates that the design of the specific IoT solution is inconsistent with the training objectives of the IoT training control center. In this case, it is necessary to provide feedback to the target enterprise on the matching degree between the IoT solution requirements and the training objectives, as well as the preset matching degree threshold, to inform the target enterprise that the design of the specific IoT solution is inconsistent with the training objectives of the IoT training control center, for the target enterprise's reference.

[0066] Specifically, when the matching degree between the IoT solution requirements and the training objectives is greater than a preset matching degree threshold, at least one training task is generated based on the IoT solution requirements. The number of training tasks generated based on the IoT solution requirements is determined according to the actual situation.

[0067] S4. The training task is sent to the target IoT training terminal through the distribution module;

[0068] When determining the target IoT training terminal, the task tag information for the training task is first determined, followed by the training course tag information for the candidate IoT training terminals. Semantic modeling is used to analyze the correlation between the task tag information and the training course tag information to identify the IoT training terminal with the highest relevance to the training task. This IoT training terminal with the highest relevance to the training task is then selected as the target IoT training terminal. Different IoT training terminals typically offer different training courses, and the training courses offered by each IoT training terminal are adapted to the IoT modules installed on the terminal.

[0069] Furthermore, the IoT solution requirements of target enterprises often have certain deadlines, and exceeding these deadlines will seriously affect the production and operation activities of the target enterprises. Before distributing the training tasks to the target IoT training terminals, the deadline for the IoT solution can be determined based on the solution requirements, and the deadline for the training tasks can be determined based on the solution deadline. When distributing the training tasks to the target IoT training terminals, the deadline and the corresponding training tasks should be distributed to the target IoT training terminals together.

[0070] S5. Obtain the first scheme information generated by the target IoT training terminal according to the training task through the sending module, and send the first scheme information to the target enterprise. The first scheme information includes first perception layer information, first network transmission layer information and first application layer information.

[0071] In this system, after receiving the training task from the IoT training system, the target IoT training terminal executes the task using teacher and student resources and corresponding hardware resources, ultimately generating a first solution information, which is then sent to the IoT training system. The IoT training system further sends the first solution information to the target enterprise to accelerate its design process.

[0072] It should be noted that the initial solution information provided by the target IoT training terminal to the target enterprise may not be the final version and may contain some flaws. In this case, the IoT training system can obtain feedback from the target enterprise regarding the initial solution information and send this feedback to the target IoT training terminal. The target IoT training terminal will then further optimize the initial solution information based on the feedback, generate a second solution, and send the second solution to the IoT training system. Feedback information includes, but is not limited to, information on security vulnerabilities, functional vulnerabilities, and reliability issues.

[0073] The second solution information includes second perception layer information, second network transmission layer information, and second application layer information. The IoT training system further acquires the second solution information generated by the target IoT training terminal based on the feedback information, and sends the second solution information to the target enterprise.

[0074] Furthermore, the IoT training system can also monitor the completion status of training tasks, identify, and promptly handle abnormal training tasks. When identifying and handling abnormal training tasks, the system first determines the abnormal training tasks that have exceeded their deadlines and the corresponding abnormal IoT training terminals. The abnormal IoT training terminals will then send explanatory information to the IoT training system, explaining why the task exceeded its deadline. This explanatory information includes two types: the first type indicates that the reason for exceeding the deadline is a surmountable technical problem, and the second type indicates that the reason is an insurmountable technical problem.

[0075] The IoT training system further obtains explanatory information about abnormal training tasks from abnormal IoT training terminals. If the explanatory information is determined to be of the first type, the IoT training system extends the task deadline according to the plan's deadline. If the explanatory information is determined to be of the second type, the IoT training system reissues the abnormal training task to other IoT training terminals, which then resume the processing of the abnormal training task.

[0076] Furthermore, if the information is classified as Type II, the target enterprise needs to provide the abnormal IoT training terminal with target training courses to assist the abnormal IoT training terminal in conducting targeted training. Additionally, if the information is classified as Type II, the IoT training system will further send course generation information to the target enterprise, obtain the target training courses created by the target enterprise based on the course generation information, and finally send the target training courses to the abnormal IoT training terminal.

[0077] Furthermore, the abnormal IoT training terminal can also evaluate benchmark training courses. Specifically, the IoT training system obtains the evaluation information of the abnormal IoT training terminal on the target training course and sends the evaluation information to the target enterprise.

[0078] Furthermore, the IoT training system can obtain the target enterprise's updated information on the target training course based on the evaluation information, and send the updated information to the abnormal IoT training terminal, so that the abnormal IoT training terminal can obtain high-quality teaching resources in the process of executing the enterprise's actual production tasks.

[0079] In this embodiment, the IoT solution requirements sent by the target enterprise are first obtained, and then the matching degree between the IoT solution requirements and the training objectives is determined. If the matching degree is greater than a preset matching degree threshold, at least one training task is generated based on the IoT solution requirements, and the training task is sent to the target IoT training terminal. The target IoT training terminal can execute the training task to meet the needs of the target enterprise. Next, the first solution information generated by the target IoT training terminal based on the training task is obtained, and the first solution information is sent to the target enterprise, thereby cooperating with the target enterprise's R&D process. The IoT training system can organically integrate the actual needs of the target enterprise with the educational resources of the target IoT training terminal, improve the target enterprise's R&D process, and facilitate the teaching and learning activities of the target IoT training terminal.

[0080] The above-described embodiments are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Therefore, any changes made in accordance with the shape and principle of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for IoT training, characterized in that, include: Obtain the IoT solution requirements sent by the target enterprise, which include business functions in specific scenarios; Determine the matching degree between the requirements of the IoT solution and the training objectives, wherein the training objectives include several key IoT technologies that need to be mastered through training; If the matching degree is greater than the preset matching degree threshold, at least one training task is generated according to the IoT solution requirements. The training task includes functional requirements, security requirements, and reliability requirements. Otherwise, the matching degree between the IoT solution requirements and the training objectives and the preset matching degree threshold are fed back to the target enterprise to inform the target enterprise that the design of the specific IoT solution is inconsistent with the training objectives of the IoT training control center, for the target enterprise to refer to. The training task is then sent to the target IoT training terminal. Obtain the first scheme information generated by the target IoT training terminal according to the training task, and send the first scheme information to the target enterprise. The first scheme information includes first perception layer information, first network transmission layer information and first application layer information. Obtain feedback information from the target enterprise regarding the first solution information, and send the feedback information to the target IoT training terminal. The feedback information includes security vulnerability information, functional vulnerability information, and reliability information. Obtain the second scheme information generated by the target IoT training terminal based on the feedback information, and send the second scheme information to the target enterprise. The second scheme information includes second perception layer information, second network transmission layer information, and second application layer information. The target IoT training terminal was determined in the following way: Determine the task tag information for the training task; Determine the training course label information for the candidate IoT training terminals; The correlation between the task label information and the training course label information is analyzed by semantic model to determine the IoT training terminal with the highest correlation to the training task. Different IoT training terminals are configured with different training sensors. The IoT training terminal that is most relevant to the training task is identified as the target IoT training terminal. Before distributing the training task to the target IoT training terminal, the following steps are also included: The deadline for the IoT solution is determined based on the solution requirements, and the deadline for the training task is determined based on the solution deadline. The process of distributing the training tasks to the target IoT training terminal also includes: The task deadline and the training task are sent together to the target IoT training terminal. It also includes: identifying abnormal training tasks that have exceeded the deadline and the abnormal IoT training terminals corresponding to the abnormal training tasks; Obtain the description information of the abnormal training task from the abnormal IoT training terminal; If the explanatory information is of the first type, then the deadline for the task shall be extended in accordance with the deadline of the proposed solution. If the description information is the second type of description information, then the abnormal training task will be redistributed to other IoT training terminals. Also includes: If the description information is the second type of description information, then the course generation information is sent to the target enterprise, the target training course produced by the target enterprise based on the course generation information is obtained, and the target training course is sent to the abnormal IoT training terminal.

2. The IoT training method according to claim 1, characterized in that, Also includes: Obtain the evaluation information of the abnormal IoT training terminal on the target training course; The evaluation information is sent to the target company.

3. The IoT training method according to claim 2, characterized in that, Also includes: Obtain updated information from the target company regarding the target training course based on the evaluation information; The update information is sent to the abnormal IoT training terminal.

4. The IoT training method according to claim 1, characterized in that, Determining the match between the IoT solution requirements and the training objectives includes: Determine the requirement tag information for the IoT solution; Determine the target label information for the practical training objectives; The matching degree between the requirement tag information and the target tag information is analyzed by semantic model, and the matching degree between the requirement tag information and the target tag information is determined as the matching degree between the IoT solution requirement and the training objective. During semantic model training, the following loss function F is used to measure the difference between the semantic model's predictions and the true labels, where a represents the main clause, p represents the positive sentence, n represents the negative sentence, S represents the sentence embedding vectors of a, p, and n, ||·|| represents the distance, and d represents the distance between the two sentences. p With S n The distance between them; F=max(||S a -S p ||-||S a -S n ||+d,0)。 5. An Internet of Things (IoT) training system for implementing the IoT training method according to any one of claims 1-4, characterized in that, include: The acquisition module is used to acquire IoT solution requirements sent by the target enterprise, which include business functions in specific scenarios; The determination module is used to determine the matching degree between the IoT solution requirements and the training objectives, wherein the training objectives include multiple key IoT technologies that need to be mastered through training. The generation module is used to generate at least one training task according to the requirements of the IoT solution if the matching degree is greater than a preset matching degree threshold. The training task includes at least functional requirements, security requirements and reliability requirements. The distribution module is used to distribute the training tasks to the target IoT training terminal; The sending module is used to obtain the first scheme information generated by the target IoT training terminal according to the training task, and send the first scheme information to the target enterprise. The first scheme information includes first perception layer information, first network transmission layer information and first application layer information.

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