Experiment teaching method and system based on science and technology dispatcher scene

Abstract

The invention relates to the technical field of experiment teaching evaluation, in particular to an experiment teaching method and system based on a science and technology dispatcher scene. The method comprises the steps that an experiment scene can be created based on experiment teaching target and content fusion; generating an experiment scene activation value based on the node information, and judging whether the student experiment scene operation node meets an experiment scene receiving requirement or not by the cloud distribution center based on the experiment scene activation value; acquiring an operation video generated by the student based on the experiment scene, reading the operation video, and performing student face recognition on the operation video based on the student face recognition model to obtain a student face recognition result; the node information of the experiment scene operation node of the student can be researched and judged, the experiment scene operation node of the student can meet the experiment operation requirement, and the experiment efficiency is further improved. Students can better master professional skills and operation methods required by the production line through experimental teaching of science and technology dispatchers in real scenes and simulation of role playing.

Description

Technical Field

[0001] The present invention relates to the field of experimental teaching evaluation technology, and in particular to an experimental teaching method and system based on a science and technology commissioner scenario. Background Art

[0002] In today's rapidly developing technological era, higher education and vocational education are facing unprecedented challenges and opportunities. Traditional teaching methods often focus on imparting theoretical knowledge, while neglecting the cultivation of practical skills and the stimulation of innovative ability. In order to cultivate high-quality scientific and technological talents that meet the needs of future society, it is particularly important to explore a teaching model that closely combines theoretical knowledge with practical application.

[0003] By constructing multiple simulated work scenarios for science and technology commissioners, such as agricultural technology promotion, enterprise technology upgrades, community science and technology services, etc., each scenario contains real or virtual project tasks and challenges. Combined with the work process of science and technology commissioners, trainees can experience the front-line production scenarios through role-playing, including demand research, solution design, technology implementation, effect evaluation and other links.

[0004] In the prior art, there is no experimental teaching method and system based on science and technology commissioner scenarios to train and cultivate students' skills through simulated experimental scenarios. Summary of the invention

[0005] The purpose of the present invention is to provide an experimental teaching method and system based on a science and technology commissioner scenario: to solve the technical problems that the existing solutions lack front-line scenarios such as science and technology commissioner scenarios in experimental teaching, cannot timely evaluate the students' experimental operation logs and provide experimental feedback, resulting in poor experimental teaching results.

[0006] The purpose of the present invention can be achieved by the following technical solutions: On the one hand, an experimental teaching method based on a science and technology commissioner scenario includes: Obtain experimental teaching objectives and content, and create experimental scenarios based on the integration of experimental teaching objectives and content; The experimental scenario is uploaded to the cloud distribution center. The cloud distribution center obtains the node information of the student's experimental scenario operation node, generates the experimental scenario activation value based on the node information, and determines whether the student's experimental scenario operation node meets the experimental scenario reception requirements based on the experimental scenario activation value; The student experiment scenario operation node receives the experiment scenario, the student operates based on the experiment scenario, and generates an experiment operation log based on the student operation record; Obtain the operation video generated by the trainee based on the experimental scene, read the operation video, perform trainee face recognition on the operation video based on the trainee face recognition model, and obtain the trainee face recognition result; Based on the student's face recognition result, the corresponding experimental operation log of the student is determined, the student's experimental operation score is generated based on the experimental operation log, and whether to generate an experimental diagnosis and improvement report is determined based on the experimental operation score.

[0007] Furthermore, creating an experimental scenario based on experimental teaching objectives and content integration specifically includes the following processes: Determine the experimental elements in the experimental element library based on the experimental teaching objectives and contents, wherein the experimental elements include laboratory equipment, low-value consumables, experimental materials, and database-type components, wherein different experimental teaching objectives and contents correspond to different experimental elements in the experimental element library; The experimental components in the experimental component library are displayed on the construction interface by selecting instructions, and the virtual experimental process is obtained by combining experimental components through construction instructions: Receive the virtual experiment construction instructions from the science and technology commissioner and execute the display experiment component library interface and construction interface; The experimental components in the experimental component library interface are displayed in the construction interface after receiving the selection command or the drag command; the experimental components in the construction interface display the component attributes after receiving the attribute selection command and determine the component attributes through the determination command to obtain the experimental components with confirmed component attributes; the experimental components with confirmed component attributes receive the connection command to connect the experimental components to obtain the virtual experimental process; Publish the virtual experiment process and obtain the experimental scene.

[0008] Furthermore, generating the experimental scene activation value based on the node information specifically includes the following process: Get the node load rate based on node information ; Obtain node computing power based on node information, and count the predicted computing power corresponding to the experimental scenario, calculate the difference between the node computing power and the predicted computing power, and record the difference as computing power redundancy A; Based on the node information, obtain the node delay B and the data transmission distance C from the node to the cloud distribution center; The load factor , computing power redundancy A, delay B and data transmission distance C are substituted into the experimental scenario activation value calculation formula to obtain the experimental scenario activation value. The calculation formula is as follows: ; in, is the activation value of the experimental scene, and the value of e is 2.72.

[0009] Furthermore, the cloud distribution center determines whether the student's experimental scenario operation node meets the experimental scenario reception requirements based on the experimental scenario activation value, specifically including the following process: Obtain the experimental scenario activation value threshold, where the experimental scenario activation value threshold is stored in the cloud distribution center, and determine whether the experimental scenario activation value exceeds the experimental scenario activation value threshold. If so, it is determined that the student's experimental scenario operation node meets the experimental scenario reception requirements. If not, it is determined that the student's experimental scenario operation node does not meet the experimental scenario reception requirements.

[0010] Furthermore, based on the student face recognition model, the operation video is subjected to student face recognition, and obtaining the student face recognition result specifically includes the following process: The MTCNN network is used to detect the face position of the student in the first frame of the operation video. The particle filter is initialized according to the face position of the first frame, and random particle resampling is performed to update the weight distribution of the particles. According to the different weights assigned and the adopted state transfer matrix, the face pre-selection box is predicted by the particle swarm, and the pre-selection box is input as the face recommendation area into the R-net and O-net networks for further detection and identification to obtain the student face recognition result.

[0011] Furthermore, according to the different weights assigned and the adopted state transfer matrix, the face pre-selection box is predicted by the particle swarm, which specifically includes the following processes: ; in, is the particle position corresponding to the next moment, is the particle position corresponding to the current moment, , is the state transfer matrix, is the weight corresponding to the particle, is the control input; The position of the particle group is determined by the positions of the N particles, and the position of the face pre-selection box is determined based on the position of the particle group.

[0012] Furthermore, generating the student's experimental operation score based on the experimental operation log specifically includes the following process: Based on the experimental operation log, obtain the number of process steps actually operated by the trainees and the number of process steps that the trainees should operate, and calculate the ratio of the number of process steps actually operated by the trainees to the number of process steps that the trainees should operate ; Based on the experimental operation log, obtain the number of components actually used by the students and the number of components that the students should use, and calculate the ratio of the number of components actually used by the students to the number of components that the students should use. ; Based on the experimental operation log, the student's operation start time T0 and operation end time T1 are obtained, and the time period T from time T0 to time T1 is calculated; Based on the experimental operation log, the process step sequence of the student's actual operation is obtained, and it is determined whether the process step sequence of the student's actual operation is consistent with the process step sequence of the predetermined operation. If so, the student's operation score coefficient R is 1, otherwise, the student's operation score coefficient R is 0; The ratio ,Proportion , time period T and the student's operation score coefficient R are substituted into the student's experimental operation score LMS calculation formula to obtain the student's experimental operation score LMS, which is as follows: ; in, This is the basic score value corresponding to the experimental scenario. Different basic score values ​​correspond to different experimental scenarios and are set by the science and technology commissioners.

[0013] Furthermore, judging whether to generate an experimental diagnosis and improvement report according to the experimental operation score specifically includes the following steps: Load the experimental operation score threshold to determine whether the student's experimental operation score exceeds the preset experimental operation score threshold. If so, do not generate an experimental diagnosis and improvement report. If not, generate an experimental diagnosis and improvement report.

[0014] On the other hand, an experimental teaching system based on a science and technology commissioner scenario includes an experimental teaching scenario creation module, a cloud distribution center, an experimental operation log recording module, a face recognition module, and an experimental operation score calculation module; The experimental teaching scenario creation module is used to obtain the experimental teaching objectives and content, and create the experimental scenario based on the integration of the experimental teaching objectives and content; The cloud distribution center is used to upload the experimental scenario to the cloud distribution center, the cloud distribution center obtains the node information of the student's experimental scenario operation node, generates the experimental scenario activation value based on the node information, and the cloud distribution center determines whether the student's experimental scenario operation node meets the experimental scenario reception requirements based on the experimental scenario activation value; The experimental operation log recording module is used for the student's experimental scenario operation node to receive the experimental scenario, the student operates based on the experimental scenario, and the experimental operation log is generated based on the student's operation record; The face recognition module is used to obtain the operation video generated by the trainees based on the experimental scene, read the operation video, perform trainee face recognition on the operation video based on the trainee face recognition model, and obtain the trainee face recognition result; The experimental operation score calculation module is used to determine the experimental operation log corresponding to the student based on the student's face recognition result, generate the student's experimental operation score based on the experimental operation log, and determine whether to generate an experimental diagnosis and improvement report based on the experimental operation score.

[0015] Compared with the existing solutions, the present invention achieves the following beneficial effects: The present invention can create an experimental scene based on the fusion of experimental teaching objectives and contents; generate an experimental scene activation value based on node information, and a cloud distribution center judges whether the student's experimental scene operation node meets the experimental scene reception requirement based on the experimental scene activation value; obtains the student's operation video generated based on the experimental scene, reads the operation video, performs student face recognition on the operation video based on the student face recognition model, and obtains the student face recognition result; can judge the node information of the student's experimental scene operation node, can ensure that the student's experimental scene operation node meets the experimental operation requirement, and further improves the efficiency of the experiment.

[0016] The present invention can generate the trainees' experimental operation scores based on the experimental operation logs, and generate experimental diagnosis and improvement reports according to the experimental operation scores. Through the real scenes of scientific and technological commissioners' experimental teaching and simulated role-playing, trainees can better master the professional skills and operating methods required for the production line. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0018] Figure 1 It is a workflow diagram of an experimental teaching method based on a science and technology commissioner scenario in an embodiment of the present invention; Figure 2 It is a workflow diagram of another experimental teaching method based on the science and technology commissioner scenario in an embodiment of the present invention; Figure 3 It is a workflow diagram of another experimental teaching method based on the science and technology commissioner scenario in an embodiment of the present invention; Figure 4 It is a system block diagram of an experimental teaching system based on a science and technology commissioner scenario in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments 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.

[0020] In addition, the described features, structures or characteristics may be combined in one or more example embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the example embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or other methods, components, steps, etc. may be adopted. In other cases, well-known structures, methods, implementations or operations are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0021] This embodiment provides an experimental teaching method based on the science and technology commissioner scenario. Figure 1 is a workflow diagram of an experimental teaching method based on a science and technology commissioner scenario in an embodiment of the present invention, such as Figure 1 As shown, the method comprises the following steps: Step S101: Acquire the experimental teaching objectives and contents, and create an experimental scene based on the integration of the experimental teaching objectives and contents; Step S102: Upload the experimental scenario to the cloud distribution center, the cloud distribution center obtains the node information of the student's experimental scenario operation node, generates the experimental scenario activation value based on the node information, and the cloud distribution center determines whether the student's experimental scenario operation node meets the experimental scenario reception requirements based on the experimental scenario activation value; Step S103: The student's experiment scenario operation node receives the experiment scenario, the student operates based on the experiment scenario, and generates an experiment operation log based on the student's operation record; Step S104: obtaining the operation video generated by the trainee based on the experimental scenario, reading the operation video, performing trainee face recognition on the operation video based on the trainee face recognition model, and obtaining the trainee face recognition result; Step S105: Determine the experimental operation log corresponding to the student based on the student face recognition result, generate the student's experimental operation score based on the experimental operation log, and determine whether to generate an experimental diagnosis and improvement report based on the experimental operation score.

[0022] In summary, the present invention can create an experimental scene based on the fusion of experimental teaching objectives and content; generate an experimental scene activation value based on node information, and the cloud distribution center determines whether the student's experimental scene operation node meets the experimental scene reception requirement based on the experimental scene activation value; obtain the student's operation video generated based on the experimental scene, read the operation video, perform student face recognition on the operation video based on the student face recognition model, and obtain the student face recognition result; can analyze and judge the node information of the student's experimental scene operation node, can ensure that the student's experimental scene operation node meets the experimental operation requirements, and further improve the efficiency of the experiment.

[0023] The present invention can generate the trainees' experimental operation scores based on the experimental operation logs, and generate experimental diagnosis and improvement reports according to the experimental operation scores. Through the real scenes of scientific and technological commissioners' experimental teaching and simulated role-playing, trainees can better master the professional skills and operating methods required for the production line.

[0024] In some embodiments, in step S101, Figure 2 is a workflow diagram of another experimental teaching method based on the science and technology commissioner scenario in an embodiment of the present invention, such as Figure 2 As shown in the figure, creating an experimental scenario based on experimental teaching objectives and content integration specifically includes the following steps: Step S201: Determine the experimental elements in the experimental element library based on the experimental teaching objectives and contents, wherein the experimental elements include laboratory equipment, low-value consumables, experimental materials, and database-type components, wherein different experimental teaching objectives and contents correspond to different experimental elements in the experimental element library; Step S202: The experimental components in the experimental component library are displayed on the construction interface through the selection command, and the experimental components are combined through the construction command to obtain a virtual experimental process; The specific process includes the following: Receive the virtual experiment construction instructions from the science and technology commissioner and execute the display experiment component library interface and construction interface; The experimental components in the experimental component library interface are displayed in the construction interface after receiving the selection command or the drag command; the experimental components in the construction interface display the component attributes after receiving the attribute selection command and determine the component attributes through the determination command to obtain the experimental components with confirmed component attributes; the experimental components with confirmed component attributes receive the connection command to connect the experimental components to obtain the virtual experimental process; Step S203: Publish the virtual experiment process to obtain the experiment scene.

[0025] In some embodiments, generating the experimental scenario activation value based on the node information specifically includes the following process: Get the node load rate based on node information ; Obtain node computing power based on node information, and count the predicted computing power corresponding to the experimental scenario, calculate the difference between the node computing power and the predicted computing power, and record the difference as computing power redundancy A; Based on the node information, obtain the node delay B and the data transmission distance C from the node to the cloud distribution center; The load factor , computing power redundancy A, delay B and data transmission distance C are substituted into the experimental scenario activation value calculation formula to obtain the experimental scenario activation value. The calculation formula is as follows: ; in, is the activation value of the experimental scene, and the value of e is 2.72.

[0026] Furthermore, the cloud distribution center determines whether the student's experimental scenario operation node meets the experimental scenario reception requirements based on the experimental scenario activation value, specifically including the following process: Obtain the experimental scenario activation value threshold, where the experimental scenario activation value threshold is stored in the cloud distribution center, and determine whether the experimental scenario activation value exceeds the experimental scenario activation value threshold. If so, it is determined that the student's experimental scenario operation node meets the experimental scenario reception requirements. If not, it is determined that the student's experimental scenario operation node does not meet the experimental scenario reception requirements.

[0027] In some embodiments, performing student face recognition on the operation video based on the student face recognition model to obtain the student face recognition result specifically includes the following process: The MTCNN network is used to detect the face position of the student in the first frame of the operation video. The particle filter is initialized according to the face position of the first frame, and random particle resampling is performed to update the weight distribution of the particles. According to the different weights assigned and the adopted state transfer matrix, the face pre-selection box is predicted by the particle swarm, and the pre-selection box is input as the face recommendation area into the R-net and O-net networks for further detection and identification to obtain the student face recognition result.

[0028] It is worth mentioning that MTCNN (Multi-task Cascaded Convolutional Networks) is a classic face detection and key point location network. It consists of three cascaded convolutional neural networks, namely P-Net (Proposal Network), R-Net (Refine Network) and O-Net (Output Network), which are used to implement face recognition tasks.

[0029] In some embodiments, using the MTCNN network to detect the face position of the first frame of the trainee in the operation video specifically includes the following process: The image pyramid technology is used to scale the first frame of the trainee's face image in the operation video to different scales, and then input into the P-net network to generate the face candidate frame. The MTCNN network includes a three-layer cascade neural network structure, which consists of a P-net network, an R-net network, and an O-net network. Specifically, image pyramid technology is an effective multi-scale image representation method, which is often used in scale space analysis in image processing, including image enhancement, feature extraction, and target detection.

[0030] The P-net network uses FCN (Fully Convolutional Networks) to initially extract features and calibrate positions, and then uses non-maximum suppression to eliminate candidate boxes with high overlap. The candidate boxes are corrected through bounding box regression, and the candidate boxes obtained by the P-net network are intercepted in the original image and used as the input of R-net to obtain the face position of the student in the first frame. Among them, the bounding box regression uses Euclidean distance as the loss function of the distance metric.

[0031] In some embodiments, in step S103, Figure 3 is a workflow diagram of another experimental teaching method based on the science and technology commissioner scenario in an embodiment of the present invention, such as Figure 3 As shown, the particle filter is initialized according to the face position of the first frame, and random particle resampling is performed. The weight distribution of the updated particles specifically includes the following steps: Step S301: Initialize the particle filter according to the face position in the first frame. Particle initialization is to place particles in the image in a given or random manner, and specify that the initial state of the particles is consistent with the tracking area. Step S302: using the previous prior probability to estimate the posterior probability density in the current scenario; Step S303: Calculate the similarity of particles, and perform similarity calculation between each particle and the tracking area; Step S304: update the weight of each particle according to the similarity and normalize it; Step S305: resampling particles, eliminating particles with low weights, and generating more particles by complex particles with high weights; Step S306: Set the number of iterations, and stop resampling when the number of iterations is reached.

[0032] Furthermore, particle resampling specifically includes the following processes: 1) Particle screening, N is the number of particles in the set, and the particles are sorted according to their weights. Sort the weight of each particle, set a weight threshold, eliminate particles below the threshold, and retain particles with weights above the threshold as replication objects; 2) Particle replication: a fixed space is reserved each time to add random particles, that is, the number of replicated particles is kept constant at i, and the number of replicated particles with different weights is determined by comparing the relationship between the number of the screened particle group and i; 3) Add random particles and divide the particles into intervals. If the number of intervals is greater than Ni, randomly select Ni intervals for uniform sampling, sample Ni particles with different weights, and add the i particles copied before. The total number of particles after sampling remains unchanged at N.

[0033] In some embodiments, according to the different weights assigned and the adopted state transfer matrix, the face pre-selection box is predicted by the particle swarm, specifically including the following process: ; in, is the particle position corresponding to the next moment, is the particle position corresponding to the current moment, , is the state transfer matrix, is the weight corresponding to the particle, is the control input; The position of the particle group is determined by the positions of the N particles, and the position of the face pre-selection box is determined based on the position of the particle group.

[0034] In some embodiments, generating the student's experimental operation score based on the experimental operation log specifically includes the following process: Based on the experimental operation log, obtain the number of process steps actually operated by the trainees and the number of process steps that the trainees should operate, and calculate the ratio of the number of process steps actually operated by the trainees to the number of process steps that the trainees should operate ; Based on the experimental operation log, obtain the number of components actually used by the students and the number of components that the students should use, and calculate the ratio of the number of components actually used by the students to the number of components that the students should use. ; Based on the experimental operation log, the student's operation start time T0 and operation end time T1 are obtained, and the time period T from time T0 to time T1 is calculated; Based on the experimental operation log, the process step sequence of the student's actual operation is obtained, and it is determined whether the process step sequence of the student's actual operation is consistent with the process step sequence of the predetermined operation. If so, the student's operation score coefficient R is 1, otherwise, the student's operation score coefficient R is 0; The ratio ,Proportion , time period T and the student's operation score coefficient R are substituted into the student's experimental operation score LMS calculation formula to obtain the student's experimental operation score LMS, which is as follows: ; in, This is the basic score value corresponding to the experimental scenario. Different basic score values ​​correspond to different experimental scenarios and are set by the science and technology commissioners.

[0035] Furthermore, judging whether to generate an experimental diagnosis and improvement report according to the experimental operation score specifically includes the following steps: Load the experimental operation score threshold to determine whether the student's experimental operation score exceeds the preset experimental operation score threshold. If so, do not generate an experimental diagnosis and improvement report. If not, generate an experimental diagnosis and improvement report.

[0036] Generating an experimental diagnosis and improvement report specifically includes: Experimental preparation stage Analyze the students’ preparation before the experiment, including their understanding of the preview materials and the preparation of experimental tools.

[0037] Point out whether there are problems such as insufficient preparation and unclear understanding of the purpose of the experiment.

[0038] Experimental operation stage Record the students' performance during the experimental operation in detail, including their mastery of skills, operational standards, reactions when encountering problems, etc.

[0039] Analyze the aspects in which the trainees show deficiencies, such as unskilled operation, neglect of details, lack of innovative thinking, etc.

[0040] Experimental results and analysis Evaluate students' experimental results, including the accuracy of data, rationality of conclusions, etc.

[0041] Analyze the gap between the experimental results and the expected goals, as well as the reasons for the gap.

[0042] The present invention also provides an experimental teaching system based on the science and technology commissioner scenario. Figure 4 is a system block diagram of an experimental teaching system based on a science and technology commissioner scenario in an embodiment of the present invention, such as Figure 4 As shown, the system includes an experimental teaching scene creation module, a cloud distribution center, an experimental operation log recording module, a face recognition module and an experimental operation score calculation module; The experimental teaching scenario creation module is used to obtain the experimental teaching objectives and content, and create the experimental scenario based on the integration of the experimental teaching objectives and content; The cloud distribution center is used to upload the experimental scenario to the cloud distribution center, the cloud distribution center obtains the node information of the student's experimental scenario operation node, generates the experimental scenario activation value based on the node information, and the cloud distribution center determines whether the student's experimental scenario operation node meets the experimental scenario reception requirements based on the experimental scenario activation value; The experimental operation log recording module is used for the student's experimental scenario operation node to receive the experimental scenario, the student operates based on the experimental scenario, and the experimental operation log is generated based on the student's operation record; The face recognition module is used to obtain the operation video generated by the trainees based on the experimental scene, read the operation video, perform trainee face recognition on the operation video based on the trainee face recognition model, and obtain the trainee face recognition result; The experimental operation score calculation module is used to determine the experimental operation log corresponding to the student based on the student's face recognition result, generate the student's experimental operation score based on the experimental operation log, and determine whether to generate an experimental diagnosis and improvement report based on the experimental operation score.

[0043] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0044] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0045] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0046] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only some logical function divisions. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0047] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0048] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An experimental teaching method based on the science and technology commissioner scenario, characterized in that the method include: Obtain experimental teaching objectives and content, and create experimental scenarios based on the integration of experimental teaching objectives and content; The experimental scenario is uploaded to the cloud distribution center. The cloud distribution center obtains the node information of the student's experimental scenario operation node, generates the experimental scenario activation value based on the node information, and determines whether the student's experimental scenario operation node meets the experimental scenario reception requirements based on the experimental scenario activation value; The student experiment scenario operation node receives the experiment scenario, the student operates based on the experiment scenario, and generates an experiment operation log based on the student operation record; Obtain the operation video generated by the trainee based on the experimental scene, read the operation video, perform trainee face recognition on the operation video based on the trainee face recognition model, and obtain the trainee face recognition result; Based on the student's face recognition result, the corresponding experimental operation log of the student is determined, the student's experimental operation score is generated based on the experimental operation log, and whether to generate an experimental diagnosis and improvement report is determined based on the experimental operation score.

2. According to claim 1, an experimental teaching method based on a science and technology commissioner scenario is characterized in that: Creating an experimental scenario based on experimental teaching objectives and content integration specifically includes the following processes: Determine the experimental elements in the experimental element library based on the experimental teaching objectives and contents, wherein the experimental elements include laboratory equipment, low-value consumables, experimental materials, and database-type components, wherein different experimental teaching objectives and contents correspond to different experimental elements in the experimental element library; The experimental components in the experimental component library are displayed on the construction interface by selecting instructions, and the virtual experimental process is obtained by combining experimental components through construction instructions: Receive the virtual experiment construction instructions from the science and technology commissioner and execute the display experiment component library interface and construction interface; The experimental components in the experimental component library interface are displayed in the construction interface after receiving the selection command or the drag command; the experimental components in the construction interface display the component attributes after receiving the attribute selection command and determine the component attributes through the determination command to obtain the experimental components with confirmed component attributes; the experimental components with confirmed component attributes receive the connection command to connect the experimental components to obtain the virtual experimental process; Publish the virtual experiment process and obtain the experimental scene.

3. According to claim 1, the experimental teaching method based on the science and technology commissioner scenario is characterized in that: Generating the experimental scene activation value based on node information specifically includes the following processes: Get the node load rate based on node information ; Obtain node computing power based on node information, and count the predicted computing power corresponding to the experimental scenario, calculate the difference between the node computing power and the predicted computing power, and record the difference as computing power redundancy A; Based on the node information, obtain the node delay B and the data transmission distance C from the node to the cloud distribution center; The load factor , computing power redundancy A, delay B and data transmission distance C are substituted into the experimental scenario activation value calculation formula to obtain the experimental scenario activation value. The calculation formula is as follows: ； in, is the activation value of the experimental scene, and the value of e is 2.

72.

4. According to claim 1, the experimental teaching method based on the science and technology commissioner scenario is characterized in that: The cloud distribution center determines whether the student's experimental scenario operation node meets the experimental scenario reception requirements based on the experimental scenario activation value, which specifically includes the following processes: Obtain the experimental scenario activation value threshold, where the experimental scenario activation value threshold is stored in the cloud distribution center, and determine whether the experimental scenario activation value exceeds the experimental scenario activation value threshold. If so, it is determined that the student's experimental scenario operation node meets the experimental scenario reception requirements. If not, it is determined that the student's experimental scenario operation node does not meet the experimental scenario reception requirements.

5. According to claim 1, the experimental teaching method based on the science and technology commissioner scenario is characterized in that: Based on the student face recognition model, the student face recognition is performed on the operation video to obtain the specific student face recognition results. The process includes: The MTCNN network is used to detect the face position of the student in the first frame of the operation video. The particle filter is initialized according to the face position of the first frame, and random particle resampling is performed to update the weight distribution of the particles. According to the different weights assigned and the adopted state transfer matrix, the face pre-selection box is predicted by the particle swarm, and the pre-selection box is input as the face recommendation area into the R-net and O-net networks for further detection and identification to obtain the student face recognition result.

6. The experimental teaching method based on the science and technology commissioner scenario according to claim 5 is characterized in that: According to the different weights assigned and the state transfer matrix adopted, the face pre-selection box is predicted by particle swarm. The process includes: ； in, is the particle position corresponding to the next moment, is the particle position corresponding to the current moment, , is the state transfer matrix, is the weight corresponding to the particle, is the control input; The position of the particle group is determined by the positions of the N particles, and the position of the face pre-selection box is determined based on the position of the particle group.

7. The experimental teaching method based on the science and technology commissioner scenario according to claim 1 is characterized in that: Generate students' experimental operation scores based on the experimental operation log The process includes: Based on the experimental operation log, obtain the number of process steps actually operated by the trainees and the number of process steps that the trainees should operate, and calculate the ratio of the number of process steps actually operated by the trainees to the number of process steps that the trainees should operate ; Based on the experimental operation log, obtain the number of components actually used by the students and the number of components that the students should use, and calculate the ratio of the number of components actually used by the students to the number of components that the students should use. ; Based on the experimental operation log, the student's operation start time T0 and operation end time T1 are obtained, and the time period T from time T0 to time T1 is calculated; Based on the experimental operation log, the process step sequence of the student's actual operation is obtained, and it is determined whether the process step sequence of the student's actual operation is consistent with the process step sequence of the predetermined operation. If so, the student's operation score coefficient R is 1, otherwise, the student's operation score coefficient R is 0; The ratio ,Proportion , time period T and the student's operation score coefficient R are substituted into the student's experimental operation score LMS calculation formula to obtain the student's experimental operation score LMS, which is as follows: ； in, This is the basic score value corresponding to the experimental scenario. Different basic score values ​​correspond to different experimental scenarios and are set by the science and technology commissioners.

8. The experimental teaching method based on the science and technology commissioner scenario according to claim 1 is characterized in that: Determining whether to generate an experimental diagnosis and improvement report based on the experimental operation score specifically includes the following steps: Load the experimental operation score threshold to determine whether the student's experimental operation score exceeds the preset experimental operation score threshold. If so, do not generate an experimental diagnosis and improvement report. If not, generate an experimental diagnosis and improvement report.

9. An experimental teaching system based on the science and technology commissioner scenario, characterized in that: An experimental teaching method based on a science and technology commissioner scenario applicable to any one of claims 1 to 8, the system comprising an experimental teaching scenario creation module, a cloud distribution center, an experimental operation log recording module, a face recognition module and an experimental operation score calculation module; The experimental teaching scenario creation module is used to obtain the experimental teaching objectives and content, and create the experimental scenario based on the integration of the experimental teaching objectives and content; The cloud distribution center is used to upload the experimental scenario to the cloud distribution center, the cloud distribution center obtains the node information of the student's experimental scenario operation node, generates the experimental scenario activation value based on the node information, and the cloud distribution center determines whether the student's experimental scenario operation node meets the experimental scenario reception requirements based on the experimental scenario activation value; The experimental operation log recording module is used for the student's experimental scenario operation node to receive the experimental scenario, the student operates based on the experimental scenario, and the experimental operation log is generated based on the student's operation record; The face recognition module is used to obtain the operation video generated by the trainees based on the experimental scene, read the operation video, perform trainee face recognition on the operation video based on the trainee face recognition model, and obtain the trainee face recognition result; The experimental operation score calculation module is used to determine the experimental operation log corresponding to the student based on the student's face recognition result, generate the student's experimental operation score based on the experimental operation log, and determine whether to generate an experimental diagnosis and improvement report based on the experimental operation score.

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