Industrial element universe virtual and real interactive teaching system and method

Abstract

The invention relates to the technical field of element universe teaching application, in particular to an industrial element universe virtual and real interactive teaching system and method. Comprising a data acquisition and analysis module, a work type matching test module, an on-duty scheduling module and an extra skill training module. The on-duty scheduling module formulates an on-duty plan according to the skill types and obtains the authenticity of sampling, obtains real skills and weak skills, and executes the real skills in the virtual reality test platform according to the skill type levels and the on-duty plan. The method comprises the following steps: dividing a corresponding skill on-duty scheduling area by combining marked skill difficulty levels in a key skill set, applying an on-duty plan to a virtual reality test platform according to an on-duty period and frequency, collecting related data in a test process, performing evaluation, continuously optimizing the on-duty plan according to an evaluation result, and obtaining an optimal skill on-duty scheduling area. Therefore, the students can be optimally matched with the stations, so that the expected training effect of virtual simulation is achieved.

Description

Technical Field

[0001] The present invention relates to the field of metaverse teaching application technology, and in particular to an industrial metaverse virtual-reality interactive teaching system and method. Background Art

[0002] Industrial Metaverse is an advanced system that combines virtual reality (VR), augmented reality (AR), Internet of Things (IoT) and other technologies. It aims to provide a new experience and method for practical training in the field of industrial processing. This technology can simulate a real factory environment, allowing students or employees to learn and practice various industrial processing skills through virtual operations, improve their practical operation capabilities and safety awareness. Through Industrial Metaverse technology, users can interact with machines and production lines in a virtual environment, learn how to properly operate and maintain equipment, and understand production processes and safety regulations.

[0003] In traditional virtual-reality interactive teaching, in order to understand the skills and proficiency of trainees, trainees with corresponding skills are matched with workstations so that the entire work line can run smoothly and facilitate subsequent management. For this purpose, a form is usually used to collect trainee information, which includes the skills possessed by the trainees and the proficiency of the corresponding skills. Then, through relevant theoretical test questions or using simulation software and simulation technology, operation scenarios similar to the real environment are created, and trainees are asked to perform simulated operations to verify the authenticity of the forms filled out by the trainees.

[0004] Because the existing industrial metaverse virtual-reality interactive teaching system mostly simulates the skills recorded in the forms filled out by the students when teaching and simulating students, the simulated skills are relatively single, which is not convenient for fully exploring the students' skill status. Moreover, during the simulation, students will fill in according to their favorite workstations, but it is not certain whether the skill proficiency corresponding to the workstation matches the student. When simulating through a virtual environment, the virtual system matches the student to the position he likes, but cannot match the student to the position he is most suitable for, thereby failing to achieve the expected training effect of virtual simulation. Summary of the invention

[0005] The purpose of the present invention is to provide an industrial metaverse virtual-reality interactive teaching system and method to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above-mentioned purpose, the present invention aims to provide an industrial metaverse virtual-reality interactive teaching system, including a data collection and analysis module, a job matching test module, a rotation scheduling module and an additional skill training module, wherein:

[0007] The data collection and analysis module is used to collect the trainee's pre-filled form information, the actual factory scene and the information of each workstation corresponding to the actual factory scene, store the pre-filled form information in a relational database, and query the skill proficiency in the pre-filled form information through SQL;

[0008] The job matching test module uses data sampling technology to extract a preset number of skill types and the skill proficiency corresponding to the skill types from the database, divides the skill types into levels, and establishes a virtual reality test platform according to the actual factory scene, formulates matching standards, and applies the extracted skill types and skill proficiency to the virtual reality test platform in combination with the matching standards to verify the authenticity of the sampling;

[0009] The rotation scheduling module formulates a rotation plan according to the skill types and obtains the authenticity of the sampling, obtains the real skills and weak skills, executes the real skills in the virtual reality test platform according to the skill type level and the rotation plan, collects data through the virtual reality test platform, and optimizes the rotation plan;

[0010] The additional skill training module acquires weak skills, trains trainees through a virtual reality testing platform combined with actual operations, builds a skill assessment model based on statistical methods, and evaluates the current trainees' skills and proficiency based on this model to determine whether this model meets the assessment standards. If so, the authenticity is verified through the job matching test module. If not, the trainees continue learning through the additional skill training module.

[0011] As a further improvement of the present technical solution, the skill types and levels can be divided into elementary, intermediate, advanced and expert levels according to the difficulty of the skills.

[0012] As a further improvement of the technical solution, the job type matching test module includes a proficiency level sampling unit and a workstation information matching unit, wherein the proficiency level sampling unit is used to determine the number of samples in combination with random sampling, and obtain the trainee's skills and the skill proficiency corresponding to the skills from the samples;

[0013] The workstation information matching unit specifies matching criteria according to the difficulty of skills, obtains the skills and skill proficiency in the sampling and the workstation information in the virtual reality test platform, performs matching in combination with the matching criteria, and generates a matching result.

[0014] As a further improvement of the present technical solution, an evaluation standard indicator for the proficiency of each skill in the proficiency level sampling unit is set. The evaluation standard indicator is the specific operation of the trainees in the virtual reality testing platform, including operation speed, accuracy, and error rate. The evaluation standard indicator is compared with a preset threshold to determine whether it is greater than the preset threshold. A yes result indicates proficiency, and a no result indicates unproficiency.

[0015] As a further improvement of the technical solution, the matching steps in the workstation information matching unit are as follows:

[0016] S10. According to the matching criteria, prioritize the matching of skills with high difficulty and students whose corresponding proficiency of the skills is higher than the preset threshold, mark the skills that do not match or whose proficiency is lower than the preset threshold, and monitor the marked skills and proficiency;

[0017] S20. When the student's skills and the skill proficiency corresponding to the skills can match the corresponding positions, it is defined as a successful match, the skill is a real skill, and the student can work normally in the position. When the student's skills and skill proficiency cannot match the corresponding position, that is, one of the student's skills and skill proficiency cannot be adapted to the corresponding position, and the skill type and level are gradually reduced until the match is abandoned and still cannot be matched, it is defined as an unsuccessful match, and the skill is a weak skill.

[0018] As a further improvement of the technical solution, the rotation scheduling module rotates different positions according to the rotation plan. The specific method steps are as follows:

[0019] S110, obtain the skills possessed by the sampled trainees and the corresponding proficiency of each skill;

[0020] S111. Put the skills and identifiers of each student into a set Q, where the set Q includes {(A1, s1, skilled), (B2, s2, skilled), (C3, s3, unskilled), ..., (Zn, sn, unskilled)}, where n represents the number of skills the student has, Zn represents that the nth skill corresponds to the Zn skill, and sn represents the identifier corresponding to the nth skill;

[0021] S112, matching is performed preferentially according to the matching criteria, the elements in the set Q that can match the workstation are deleted, and the identifier corresponding to each deleted element is extracted and put into the key skill set;

[0022] S113, marking the skills corresponding to the identifiers in the key skill set in all sampled samples, and monitoring the marked skills;

[0023] S114. According to the actual production needs of the factory and the ability of the trainees, simulation is carried out in the virtual reality test platform, and the rotation cycle and frequency are formulated. The rotation cycle and frequency are set according to the actual situation;

[0024] S115. Divide the corresponding skill rotation scheduling areas according to the difficulty levels of the marked skills in the key skill set, wherein a corresponding rotation sequence is set, and the skill rotation scheduling areas are adjusted from skills with high difficulty levels to skills with low difficulty levels. Based on the rotation cycle and frequency, the rotation plan is applied to the virtual reality test platform, and relevant data in the test process is collected and evaluated, and the rotation plan is continuously optimized based on the evaluation results.

[0025] As a further improvement of the present technical solution, the additional skill training module obtains the skills and proficiency of trainees who have not been successfully matched, inputs the trainees' skills and proficiency into the evaluation model, analyzes and predicts the results, and if they meet the standards, proceeds to the next step of the job matching test module; if they do not meet the standards, they continue to enter the additional skill training module for training.

[0026] The second purpose of the invention is to provide an industrial metaverse virtual-reality interactive teaching method, comprising the following method steps:

[0027] S1. Collect the trainee's pre-filled form information, the actual factory scene, and the information of each workstation corresponding to the actual factory scene, store the pre-filled form information in a relational database, and query the skill proficiency in the pre-filled form information through SQL;

[0028] S2. Use data sampling technology to extract a preset number of skill types and the corresponding skill proficiency levels from the database, divide the skill types into levels, and establish a virtual reality test platform based on the actual factory scene, formulate matching standards, and apply the extracted skill types and skill proficiency levels to the virtual reality test platform in combination with the matching standards to verify the authenticity of the sampling;

[0029] S3. Formulate a rotation plan based on skill types and obtain the authenticity of sampling to obtain real skills and weak skills. Execute the real skills in the virtual reality test platform according to the skill type level and rotation plan, collect data through the virtual reality test platform, and optimize the rotation plan.

[0030] S4. Acquire weak skills and train trainees through a virtual reality testing platform combined with actual operations. Build a skill assessment model based on statistical methods, and evaluate the current trainees' skills and proficiency based on this model to determine whether the model meets the assessment standards. If so, verify the authenticity through the job matching test module. If not, continue learning through additional skill training modules.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] 1. In the industrial metaverse virtual-reality interactive teaching system, a rotation scheduling module is used to formulate a rotation plan according to the skill types and obtain the authenticity of the sampling, so as to obtain real skills and weak skills. The real skills are executed in the virtual reality test platform according to the skill type level and rotation plan. The corresponding skill rotation scheduling area is divided according to the difficulty of the marked skills in the key skill set, and the corresponding rotation sequence is set. The skill rotation scheduling area is adjusted from skills with high difficulty to skills with low difficulty. According to the rotation cycle and frequency, the rotation plan is applied to the virtual reality test platform, and the relevant data in the test process is collected and evaluated. The rotation plan is continuously optimized according to the evaluation results, so that the trainees can be optimally matched with the workstations, thereby achieving the expected training effect of virtual simulation. For weak skills, they are trained through the virtual reality test platform and actual operation, and then evaluated again, which can fully tap the skills of the trainees. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a schematic diagram of the overall system of the present invention;

[0034] Figure 2 It is a schematic diagram of the overall method of the present invention.

[0035] The meaning of each number in the figure is:

[0036] 100. Data collection and analysis module; 200. Job matching test module; 210. Proficiency level sampling unit; 220. Workstation information matching unit; 300. Job rotation scheduling module; 400. Additional skills training module. DETAILED DESCRIPTION

[0037] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] like Figure 1 As shown, an industrial metaverse virtual-reality interactive teaching system is provided, including a data collection and analysis module 100, a job matching test module 200, a rotation scheduling module 300 and an additional skill training module 400, wherein:

[0039] The data collection and analysis module 100 is used to collect the trainee's pre-filled form information, the actual factory scene and the information of each workstation corresponding to the actual factory scene, store the pre-filled form information in a relational database, and query the skill proficiency in the pre-filled form information through SQL;

[0040] The job matching test module 200 uses data sampling technology to extract a preset number of skill types and the skill proficiency corresponding to the skill types from the database, classifies the skill types, and establishes a virtual reality test platform according to the actual factory scene, formulates matching standards, and applies the extracted skill types and skill proficiency to the virtual reality test platform in combination with the matching standards to verify the authenticity of the sampling;

[0041] The rotation scheduling module 300 formulates a rotation plan according to the skill types and obtains the authenticity of the sampling, obtains the real skills and weak skills, executes the real skills according to the skill type level and the rotation plan in the virtual reality test platform, collects data through the virtual reality test platform, and optimizes the rotation plan;

[0042] The additional skill training module 400 acquires weak skills, trains trainees through a virtual reality testing platform combined with actual operations, builds a skill assessment model based on statistical methods, and evaluates the current trainees' skills and proficiency based on this model to determine whether this model meets the assessment standards. If so, the authenticity is verified through the job matching test module 200. If not, the trainees continue to learn through the additional skill training module 400.

[0043] When using it specifically, first prepare a pre-filled form information template, clearly specify the types of information included, such as the student's name, position, experience (years of work or specific project experience), skills (such as specific equipment operation, programming ability, etc.), proficiency, etc., send the pre-filled form information template to the students via email or internal communication system, notify the students to fill in the information according to the template requirements, and collect all relevant information submitted by the students before the deadline.

[0044] Secondly, the methods of collecting actual factory scenes include field visits to the factory, taking photos and videos, and recording the information of each workstation during the collection process, so as to match the trainees with the corresponding workstation information later.

[0045] After data collection is completed, since there are many different levels of difficulty in a production line, and different trainees have different proficiency in workstations, it is necessary to obtain the trainee's information and analyze it before matching the trainee with the workstation. Therefore, the skill type level can be divided into elementary, intermediate, advanced, and expert levels according to the difficulty of the skills. Therefore, before matching, first obtain the skills that the trainee is proficient in and the proficiency of the corresponding skills, so that when matching workstations in the later stage, the corresponding trainee can be matched with the workstation more accurately.

[0046] Furthermore, in order to accurately match trainees with corresponding workstations, the job matching test module 200 includes a proficiency level sampling unit 210 and a workstation information matching unit 220. The proficiency level sampling unit 210 is used to determine the number of samples in combination with random sampling, and to obtain the trainee's skills and the skill proficiency corresponding to the skill from the samples.

[0047] The workstation information matching unit 220 specifies matching criteria according to the difficulty of the skills, obtains the skills and skill proficiency in the sample and the workstation information in the virtual reality test platform, matches them in combination with the matching criteria, and generates matching results. Moreover, the skill proficiency of the trainees can be verified not only through practical operation, but also in combination with professional knowledge examinations.

[0048] In specific operations: a certain number of samples are randomly selected from the trainees' proficiency in each skill. Since the proficiency corresponding to each skill is different, according to the sampling results and the needs of the workstations, the trainees with high proficiency are first matched with the corresponding workstations. Among them, the difficulty level corresponding to each workstation is different. Some workstations are simple to operate, such as packaging workstations, and some workstations are difficult to operate, such as welding workstations. Therefore, the matching methods are different.

[0049] Next, the skills, skill proficiency and workstation matching corresponding to the sampled samples are verified, and then the proficiency is judged by the trainee's operation. At this time, the evaluation standard index for the proficiency of each skill in the proficiency level sampling unit 210 is set. The evaluation standard index is the specific operation of the trainee in the virtual reality test platform, including operation speed, accuracy, and error rate. The evaluation standard index is compared with the preset threshold to determine whether it is greater than the preset threshold. The result is yes, indicating proficiency, and the result is no, indicating unskilled, or it is judged by the scoring standard. First, for the operation speed, a standard time range can be set, or an average speed standard can be determined based on past historical data. The average speed is used as a benchmark for judging the trainee's skill proficiency, and then the time required for the trainee to complete the skill is compared with the set average speed, and the trainee's skill proficiency is analyzed. By analogy, accuracy: can be measured by comparing the consistency of the operation result with the expected result, or a standard range of accuracy can be set. Error rate: an acceptable error rate range can be set as a basis for judging proficiency. For example: the skills of student A in the sample are A1, B2, C3, D4, ..., Zn.

[0050] 1. The difficulty level of A1 skill is expert level, and the corresponding proficiency level of this skill is proficient;

[0051] 2. The difficulty level of skill B2 is Advanced, and the corresponding proficiency level of this skill is Proficient;

[0052] 3. The difficulty level of C3 skill is intermediate, and the corresponding proficiency of this skill is unproficient;

[0053] 4. The difficulty level of D4 skill is elementary, and the corresponding proficiency of this skill is unproficient;

[0054] The matching steps in the workstation information matching unit 220 are as follows:

[0055] S10. According to the matching criteria, prioritize the matching of skills with high difficulty and students whose corresponding proficiency of the skills is higher than the preset threshold, mark the skills that do not match or whose proficiency is lower than the preset threshold, and monitor the marked skills and proficiency;

[0056] S20. When the student's skills and the skill proficiency corresponding to the skills can match the corresponding positions, it is defined as a successful match, the skill is a real skill, and the student can work normally in the position. When the student's skills and skill proficiency cannot match the corresponding position, that is, one of the student's skills and skill proficiency cannot be adapted to the corresponding position, and the skill type and level are gradually reduced until the match is abandoned and still cannot be matched, it is defined as an unsuccessful match, and the skill is a weak skill.

[0057] After the corresponding trainees are matched with the workstations, the skill difficulty level of each workstation is different. If the trainees work in a difficult position for a long time, the difficult tasks require more concentration and energy. This will make the trainees feel physically and mentally exhausted, reduce their work efficiency, and even lose their enthusiasm for work. Therefore, the rotation scheduling module 300 rotates different positions according to the rotation plan. The specific method and steps are as follows:

[0058] S110, obtain the skills possessed by the sampled trainees and the corresponding proficiency of each skill;

[0059] S111. Put the skills and identifiers of each student into a set Q, where the set Q includes {(A1, s1, skilled), (B2, s2, skilled), (C3, s3, unskilled), ..., (Zn, sn, unskilled)}, where n represents the number of skills the student has, Zn represents that the nth skill corresponds to the Zn skill, and sn represents the identifier corresponding to the nth skill;

[0060] S112, matching is performed preferentially according to the matching criteria, the elements in the set Q that can match the workstation are deleted, and the identifier corresponding to each deleted element is extracted and put into the key skill set;

[0061] S113, marking the skills corresponding to the identifiers in the key skill set in all sampled samples, and monitoring the marked skills;

[0062] S114. According to the actual production needs of the factory and the ability of the trainees, simulation is carried out in the virtual reality test platform, and the rotation cycle and frequency are formulated. The rotation cycle and frequency are set according to the actual situation;

[0063] S115. Divide the corresponding skill rotation scheduling area according to the difficulty of the skills marked in the key skill set, wherein a corresponding rotation order is set, and the skill rotation scheduling area is adjusted from skills with high difficulty to skills with low difficulty. According to the rotation cycle and frequency, the rotation plan is applied to the virtual reality test platform, relevant data in the test process is collected and evaluated, and the rotation plan is continuously optimized according to the evaluation results;

[0064] For example: Student A is proficient in cutting, welding and packaging, student B is proficient in welding and packaging, and student C is proficient in cutting and welding. The difficulty level of cutting, welding and packaging is cutting>welding>packaging, and cutting, welding and packaging form a production line.

[0065] Three trainees were tested on a virtual reality testing platform. After the test, trainee A's skill scores were cutting (90 points), welding (85 points), and packaging (85 points); trainee B was proficient in welding (85 points) and packaging (90 points); and trainee C was proficient in cutting (85 points) and welding (100 points). A score of 80 points or above is considered proficient, and a score of less than 80 points is considered unskilled. Therefore, during the rotation of the three trainees, although the trainees' skills met the standards, the optimal production line after the rotation was: cutting (90 points) for trainee A, welding (100 points) for trainee C, and packaging (90 points) for trainee B.

[0066] Next, for the trainees who failed to match with the workstation in S20, for example, the trainee has skill f, and the proficiency corresponding to skill f is unskilled. The position matched by the trainee corresponds to skill f, but the proficiency corresponding to the skill does not meet the standard, resulting in a failed match. To this end, the additional skill training module 400 obtains the skills and proficiency of the trainees who failed to match successfully, inputs the trainees' skills and proficiency into the evaluation model, analyzes the prediction results, and enters the next step of the job matching test module 200 if it meets the standard. If it does not meet the standard, it continues to enter the additional skill training module 400 for training. Trainees can perform unlimited simulation operations in a virtual environment, which can quickly improve their proficiency and then apply it to actual positions, reducing training costs and risks.

[0067] If after the virtual environment and real-life operation training simulated by the industrial metaverse, the trainee is able to perform the corresponding work position, then, combined with the above steps S112 and S113, the element in the trainee's corresponding set Q is deleted, and the identifier corresponding to each deleted element is extracted and put into the key skill set, so that a rotation plan can be implemented in multiple key sets later, thereby continuously optimizing human resource allocation.

[0068] The second object of the present invention is to provide an industrial metaverse virtual-reality interactive teaching method, such as Figure 2 As shown, the method comprises the following steps:

[0069] S1. Collect the trainee's pre-filled form information, the actual factory scene, and the information of each workstation corresponding to the actual factory scene, store the pre-filled form information in a relational database, and query the skill proficiency in the pre-filled form information through SQL;

[0070] S2. Use data sampling technology to extract a preset number of skill types and the corresponding skill proficiency levels from the database, divide the skill types into levels, and establish a virtual reality test platform based on the actual factory scene, formulate matching standards, and apply the extracted skill types and skill proficiency levels to the virtual reality test platform in combination with the matching standards to verify the authenticity of the sampling;

[0071] S3. Formulate a rotation plan based on skill types and obtain the authenticity of sampling to obtain real skills and weak skills. Execute the real skills in the virtual reality test platform according to the skill type level and rotation plan, collect data through the virtual reality test platform, and optimize the rotation plan.

[0072] S4. Acquire weak skills, train trainees through a virtual reality testing platform combined with actual operations, build a skill assessment model based on statistical methods, and evaluate the current trainees' skills and proficiency based on this model to determine whether this model meets the assessment standards. If so, verify the authenticity through the job matching test module 200. If not, continue learning through the additional skill training module 400.

[0073] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. An industrial metaverse virtual-reality interactive teaching system, characterized by: The system comprises a data collection and analysis module (100), a job matching test module (200), a job rotation scheduling module (300) and an additional skill training module (400), wherein: The data collection and analysis module (100) is used to collect trainee pre-filled form information, actual factory scenes and information on each workstation corresponding to the actual factory scenes, store the pre-filled form information in a relational database, and query the skill proficiency in the pre-filled form information through SQL; The job matching test module (200) uses data sampling technology to extract a preset number of skill types and skill proficiency levels corresponding to the skill types from a database, classifies the skill types into grades, establishes a virtual reality test platform based on actual factory scenes, formulates matching standards, applies the extracted skill types and skill proficiency levels to the virtual reality test platform in combination with the matching standards, and verifies the authenticity of the sampling; The rotation scheduling module (300) formulates a rotation plan according to the skill types and obtains the authenticity of the sampling, obtains the real skills and weak skills, executes the real skills in the virtual reality test platform according to the skill type level and the rotation plan, collects data through the virtual reality test platform, and optimizes the rotation plan; The additional skill training module (400) acquires weak skills, trains trainees through a virtual reality test platform combined with actual operations, constructs a skill evaluation model based on statistical methods, and evaluates the current skills and proficiency of the trainees based on the model to determine whether the model meets the evaluation criteria. If so, the authenticity is verified through the job matching test module (200); if not, the trainees continue to learn through the additional skill training module (400).

2. The industrial metaverse virtual-reality interactive teaching system according to claim 1 is characterized by: The skill types and levels can be divided into elementary, intermediate, advanced and expert levels according to the difficulty of the skills.

3. The industrial metaverse virtual-reality interactive teaching system according to claim 1 is characterized by: The job type matching test module (200) comprises a proficiency level sampling unit (210) and a workstation information matching unit (220), wherein the proficiency level sampling unit (210) is used to determine the number of samples in combination with random sampling, and to obtain the trainee's skills and the skill proficiency corresponding to the skills from the samples; The workstation information matching unit (220) specifies a matching standard according to the difficulty of the skill, obtains the skills and skill proficiency in the sample and the workstation information in the virtual reality test platform, performs matching in combination with the matching standard, and generates a matching result.

4. The industrial metaverse virtual-reality interactive teaching system according to claim 3 is characterized by: An evaluation standard index for the proficiency of each skill in the proficiency level sampling unit (210) is set. The evaluation standard index is the specific operation of the trainee in the virtual reality test platform, including operation speed, accuracy, and error rate. The evaluation standard index is compared with a preset threshold to determine whether it is greater than the preset threshold. A yes result indicates proficiency, and a no result indicates unproficiency.

5. The industrial metaverse virtual-reality interactive teaching system according to claim 3 is characterized by: The matching steps in the workstation information matching unit (220) are as follows: S10. According to the matching criteria, prioritize the matching of skills with high difficulty and students whose corresponding proficiency of the skills is higher than the preset threshold, mark the skills that do not match or whose proficiency is lower than the preset threshold, and monitor the marked skills and proficiency; S20. When the student's skills and the skill proficiency corresponding to the skills can match the corresponding positions, it is defined as a successful match, the skill is a real skill, and the student can work normally in the position. When the student's skills and skill proficiency cannot match the corresponding position, that is, one of the student's skills and skill proficiency cannot be adapted to the corresponding position, and the skill type and level are gradually reduced until the match is abandoned and still cannot be matched, it is defined as an unsuccessful match, and the skill is a weak skill.

6. The industrial metaverse virtual-reality interactive teaching system according to claim 5 is characterized by: The rotation scheduling module (300) rotates different positions according to the rotation plan. The specific method steps are as follows: S110, obtain the skills possessed by the sampled trainees and the corresponding proficiency of each skill; S111. Put the skills and identifiers of each student into a set Q, where the set Q includes {(A1, s1, skilled), (B2, s2, skilled), (C3, s3, unskilled), ..., (Zn, sn, unskilled)}, where n represents the number of skills the student has, Zn represents that the nth skill corresponds to the Zn skill, and sn represents the identifier corresponding to the nth skill; S112, matching is performed preferentially according to the matching criteria, the elements in the set Q that can match the workstation are deleted, and the identifier corresponding to each deleted element is extracted and put into the key skill set; S113, marking the skills corresponding to the identifiers in the key skill set in all sampled samples, and monitoring the marked skills; S114. According to the actual production needs of the factory and the ability of the trainees, simulation is carried out in the virtual reality test platform, and the rotation cycle and frequency are formulated. The rotation cycle and frequency are set according to the actual situation; S115. Divide the corresponding skill rotation scheduling areas according to the difficulty levels of the marked skills in the key skill set, wherein a corresponding rotation sequence is set, and the skill rotation scheduling areas are adjusted from skills with high difficulty levels to skills with low difficulty levels. Based on the rotation cycle and frequency, the rotation plan is applied to the virtual reality test platform, and relevant data in the test process is collected and evaluated, and the rotation plan is continuously optimized based on the evaluation results.

7. The industrial metaverse virtual-reality interactive teaching system according to claim 5 is characterized by: The additional skill training module (400) obtains the skills and proficiency of the trainees who have not been successfully matched, inputs the skills and proficiency of the trainees into the evaluation model, analyzes the prediction results, and if they meet the standards, enters the next step of the job matching test module (200); if they do not meet the standards, continue to enter the additional skill training module (400) for training.

8. An industrial metaverse virtual-reality interactive teaching method, characterized in that: The method comprises the following steps: S1. Collect the trainee's pre-filled form information, the actual factory scene, and the information of each workstation corresponding to the actual factory scene, store the pre-filled form information in a relational database, and query the skill proficiency in the pre-filled form information through SQL; S2. Use data sampling technology to extract a preset number of skill types and the corresponding skill proficiency levels from the database, divide the skill types into levels, and establish a virtual reality test platform based on the actual factory scene, formulate matching standards, and apply the extracted skill types and skill proficiency levels to the virtual reality test platform in combination with the matching standards to verify the authenticity of the sampling; S3. Formulate a rotation plan based on skill types and obtain the authenticity of sampling to obtain real skills and weak skills. Execute the real skills in the virtual reality test platform according to the skill type level and rotation plan, collect data through the virtual reality test platform, and optimize the rotation plan. S4. Acquire weak skills, train trainees through a virtual reality test platform combined with actual operations, build a skill assessment model based on statistical methods, and evaluate the current skills and proficiency of trainees based on this model to determine whether this model meets the assessment standards. If so, verify the authenticity through a job matching test module (200). If not, continue learning through an additional skill training module (400).

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