CN115984059A - Collaborative fusion education attention training method, system, device and medium - Google Patents
Collaborative fusion education attention training method, system, device and medium Download PDFInfo
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Abstract
The utility model provides a collaborative fusion education attention training method, a system, equipment and a medium, which relate to the technical field of information-based construction and intelligent upgrade, and the method comprises the steps of establishing an intelligent teaching aid, an attention training course and an attention training course flow under the state of an information data platform; converting the established attention training course flow into an informatization and intelligentization operation form, designing an interaction scene, carrying out attention training on students according to the attention training course flow, identifying limb actions in the attention training process of the students through an intelligent perception algorithm, analyzing data in the attention training process, and associating the attention training data of different modes by using a knowledge graph; and (4) scoring the attention training process by using the trained data, and finally storing the attention training data in a graph data mode.
Description
Technical Field
The disclosure relates to the technical field of information-based construction and intelligent upgrading, in particular to a fusion education attention training method, system, equipment and medium based on five-domain collaboration.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
With the rapid development of informatization and intelligent technologies, the deep fusion of artificial intelligence technology and the field of education has become an important trend, but the informatization and intelligent systems applied to special education or fusion education are lack of practicability and popularity. At present, some artificial intelligence technologies are applied to special education and fusion education, and training intelligent teaching aids serving special teachers and auxiliary tools providing life convenience for specially-needed groups are generated. However, the feedback of the demands of the professional and expert in the special education field is difficult to solve the practical demands, and especially in the context of the integrated education, the following problems still exist in the existing information intelligent platform:
(1) The traditional teaching and assisting tool or part of the intelligent teaching and assisting tool cannot evaluate and count data in the training process; (2) The interaction process of the teaching aid is single, the self-made teaching aid is often demonstrated by operation, the information teaching aid is often subjected to screen touch control, and more abundant interaction forms and rich special education courses are needed; (3) The types and the degrees of the handicaps of the students are different, the personalized teaching needs to be strengthened, and the teaching aid is not personalized; (4) The special teaching teachers are relatively weak, and especially in the fusion education background, the specialization level of teachers in the common school is urgently needed to be strengthened; (5) Teaching materials, teaching aids and teaching experiences of special education and fusion education lack a sharing platform. And the technical scheme of coupling multiple technical projects of service platform construction, intelligent teaching and assistance tool terminal development, artificial intelligence algorithm and knowledge map construction in the fields of special education and fusion education is lacked, and multi-technology cooperation cannot be realized.
Disclosure of Invention
In order to solve the problems, the disclosure provides a fusion education attention training method, a fusion education attention training system, a fusion education attention training device, a fusion education intelligent system and a fusion education intelligent system medium based on five-domain collaboration, wherein the five domains are respectively a professional domain, an equipment domain, a data domain, a model domain and a knowledge domain, and the problems that an intelligent education assistive device is single in interaction form and the training process is lack of evaluation are solved.
According to some embodiments, the following technical scheme is adopted in the disclosure:
the five-domain cooperation based fusion education attention training method comprises the following steps:
establishing an attention training course and an attention training course flow under the states of an intelligent teaching aid and an information data platform;
converting the established attention training course flow into an information and intelligent operation form, designing an interaction scene, carrying out attention training on students according to the attention training course flow, identifying limb actions in the attention training process of the students through an intelligent perception algorithm, analyzing data in the attention training process, and associating attention training data of different modes by using a knowledge graph;
and grading the attention training process by using the trained data, and finally storing the attention training data in a graph data mode.
According to some embodiments, the following technical scheme is adopted in the disclosure:
five-domain collaboration based fusion education attention training system, comprising:
the training initialization module is used for establishing attention training courses and attention training course flows under the states of intelligent teaching aids and an information data platform;
the training module is used for converting the established attention training course flow into an informatization and intelligentized operation form, designing an interactive scene, carrying out attention training on students according to the attention training course flow, identifying limb actions in the attention training process of the students through an intelligent perception algorithm, analyzing data in the attention training process, and associating attention training data of different modes by using a knowledge graph;
and the evaluation module is used for grading the attention training process by using the trained data and finally storing the attention training data in a graph data mode.
According to some embodiments, the following technical scheme is adopted in the disclosure:
a non-transitory computer readable storage medium for storing computer instructions that, when executed by a processor, implement the five-domain collaboration based fusion education attention training method.
According to some embodiments, the following technical scheme is adopted in the disclosure:
an electronic device, comprising: a processor, a memory, and a computer program; wherein, the processor is connected with the memory, the computer program is stored in the memory, when the electronic device runs, the processor executes the computer program stored in the memory, so that the electronic device executes the method for implementing the five-domain cooperation-based fusion education attention training method.
Compared with the prior art, the beneficial effect of this disclosure is:
the intelligent service system is constructed according to the actual requirements of special education and fusion education, and comprises the service platform construction, intelligent teaching aid terminal development, artificial intelligence algorithm and knowledge map construction in the fields of special education and fusion education. The actual requirements are coupled with a plurality of technical projects, so that the cooperative combination is realized, and an intelligent system meeting the actual requirements is constructed.
The five domains are respectively 'professional domain', 'equipment domain', 'data domain', 'model domain' and 'knowledge domain', the multiple domains are cooperated to provide actual requirements of the fusion education system, research and development guidance is provided according to professional knowledge of special education, and the intelligent system of the fusion education is ensured to be in line with use requirements and use habits; the intelligent teaching and assisting tool is connected with the data platform; the sharing requirements of resources and courses in the fields of integrated education and special education are met; a deep learning algorithm is introduced, so that the problems that the interactive form of the intelligent teaching assistive device is single and the training process is lack of evaluation are solved; the knowledge graph of the integrated education is constructed, the knowledge graph comprises teaching resources, teaching data and theoretical knowledge of the special education and assessment data generated by intelligent education, physical and mental development characteristics of special children are identified through the knowledge graph, and personalized teaching is achieved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
Fig. 1 is a flow chart of an attention training system according to an embodiment of the present disclosure.
The specific implementation mode is as follows:
the present disclosure is further described with reference to the following drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The "five domains" in this disclosure are the "professional domain", "device domain", "data domain", "model domain", and "knowledge domain", respectively. The 'professional domain' is the leading of workers at the first line of special education and experts in the field of special education, mainly provides the actual requirements of the fusion education system, provides research and development guidance according to professional knowledge of special education, and ensures that the intelligent system of the fusion education conforms to the use requirements and use habits; the 'equipment domain' mainly relates to the interconnection and intercommunication between the intelligent teaching and assisting tool and the data platform; the data field mainly relates to the collection, storage and analysis of intelligent teaching and assisting tool data, and solves the sharing requirements of resources and courses in the fields of integrated education and special education; the 'model domain' is a deep learning algorithm, and solves the problems of single interaction form of intelligent teaching aids and lack of evaluation in the training process; the knowledge domain is used for constructing a knowledge graph of the fusion education, which comprises teaching resources, teaching data and theoretical knowledge of the special education and evaluation data generated by intelligent teaching assistance, and the physical and mental development characteristics of the special children are identified through the knowledge graph to realize the personalized teaching.
According to the construction idea of 'five domains' cooperation, the intelligent integrated education system is realized through the following technical scheme.
Example 1
An embodiment of the present disclosure provides a five-domain collaboration-based fusion education attention training method, including:
the method comprises the following steps: establishing an attention training course and an attention training course flow under the states of an intelligent teaching aid and an information data platform;
step two: converting the established attention training course flow into an information and intelligent operation form, designing an interaction scene, carrying out attention training on students according to the attention training course flow, identifying limb actions in the attention training process of the students through an intelligent perception algorithm, analyzing data in the attention training process, and associating attention training data of different modes by using a knowledge graph;
step three: and grading the attention training process by using the trained data, and finally storing the attention training data in a graph data mode.
As an embodiment, a five-domain cooperation-based fusion education attention training method is realized according to a construction idea of the five-domain cooperation, wherein the realization content of the five-domain mainly comprises the following steps:
on the first hand, the process of 'professional domain' is combed and decomposed, and a design process of a special education course or a formulation process of an individual education plan of a student is provided in the 'professional domain' through special education professional knowledge. The process is arranged into a flow chart, the education content and the education purpose of each link of a special education course are described, all processes related in the flow chart are converted into a form of auxiliary teaching by adopting an informatization and intelligentization means, specific technical requirements are provided for relevant effects to be achieved in the informatization and intelligentization processes, and the flow chart is refined into technical key points according to the technical requirements and is associated to a data domain, an equipment domain, a model domain and a knowledge domain.
In a second aspect, the "data domain" constructs a suitable data storage structure and communication interface according to the technical requirements set forth in the first aspect, and supports the construction of the platform for integrating education data.
Furthermore, the storage position and the network access of the data are determined by considering the access range of the platform, such as local area network access or public network access.
Further, a data storage type is determined, such as determining whether to be a structured data store, or an object store and a graph data store.
Furthermore, a data structure is designed, an interface for increasing, deleting, modifying and checking data is designed, and data storage of special education and integrated education courses is supported.
And in the third aspect, the equipment domain establishes intelligent terminal equipment meeting the interaction requirements according to the interaction requirements of the teaching and assisting tool provided by the first aspect, and realizes communication between data generated by the intelligent terminal equipment and the data domain, and realizes intercommunication and storage of the data.
Further, the type of the intelligent terminal device is determined, for example, android device, windows co operating system device, or linux operating system device is adopted.
Furthermore, a development tool is selected according to the intelligent terminal device model selection, for example, a programming tool adopts Java, C + +, or python, an android native interface, a Qt interface or a Web interface and the like are adopted for interface development, and the type of the development tool is synchronized with the model domain, so that the adaptation with the model deployment stage is realized.
And further, designing an interactive scene, including design of an interactive prototype and interactive UI (user interface) design, and cutting the material after the content design is finished.
Further, the implementation of scene implementation and interactive control logic is carried out, and an algorithm deployed in a model domain is introduced
Further, a data structure required to be stored by the aid of the teaching aid for the fusion education is designed, such as training duration, training score and total score of a training process of each scene.
Furthermore, a data domain is accessed, a proper network communication protocol is designed, for example, http, tcp, udp, websocket or mqtt and the like are adopted, and a data transmission interface is called to realize the operations of adding, deleting, modifying and checking data in the data domain.
In a fourth aspect, the "model domain" selects the problem to be solved by using machine learning or deep learning according to the technical requirements set forth in the first aspect, and develops the relevant model to support the intelligent perception and interaction process and the intelligent evaluation process.
Furthermore, the domain of the problem to be solved, such as computer vision, natural language processing or data analysis, is considered in terms of the model functionality.
Further, after the domain of the model is determined, an algorithm suitable for a current recognition or detection task and the model weight of the object are selected, if the domain of the algorithm belongs to computer vision and the detection task is limb detection, a BlazePose neural network structure is selected and the corresponding weight is used.
Further, if the relevant neural network structure or model weight solving the problem does not exist, data acquisition, labeling, algorithm design and iterative training are carried out to formulate a model of the task.
And further, model deployment is carried out, the model is converted and exported, and the model is adapted and deployed according to hardware model selection and development tool model selection of the 'model domain'.
Furthermore, combining the algorithm models and adjusting a proper post-processing strategy to meet the service requirements.
In the fifth aspect, the knowledge domain forms a knowledge graph of the special education and the fusion education, and an application algorithm of the knowledge graph is constructed according to the technical requirements of the first aspect.
Furthermore, a knowledge graph framework of special education and fusion education is constructed, such as multi-mode information in the fusion education, text teaching materials, audio materials, video materials, teaching plan materials, IEP schemes, teacher comments and the like.
Furthermore, the association matching of the special education and the fusion education knowledge map is established, and the cross-modal resource domain information matching is realized.
Further, a database in the data field is used as information input to carry out association of training data with comments, teaching techniques and teaching aids.
Further, an application algorithm for generating a knowledge graph is required from the technical requirements set forth from the "professional domain" according to the first aspect.
As an embodiment, an intelligent system for an attention training course is constructed by using a five-domain collaborative method, and each process link is divided into a professional domain, a data domain, an equipment domain, a model domain and a knowledge domain, as shown in fig. 1, the division of the overall process includes:
the 'professional domain' combs the process of an attention training course, then establishes an attention training course flow under the state of using an intelligent teaching and assisting tool and an information data platform, and then divides each flow link into a 'data domain', 'equipment domain', 'model domain', 'knowledge domain';
the 'professional domain' combs the process of an attention training course and mainly comprises the following steps: the method comprises the following steps of (1) making a student attention training plan, (2) training the color and shape attention ability of students, (3) training sequence memory, (4) training limb actions, and (5) training a schulnger square.
The teaching purposes corresponding to each process are as follows: the method comprises the following steps of (1) forming an individual education plan taking a child individual as a core, (2) improving the capability of a student to eliminate interference items of colors and shapes, such as interference caused by elimination of shapes when the student is guided to pay attention to the colors, (3) improving the special attention by the important reason of attention deficiency, namely the deficiency of short-term memory and sequential memory, (4) improving the special attention by the response speed of simulation of training limb actions, and (5) improving the capability of visual search rapidly by training a Schuler square.
The course flow is further converted into an operation form of an information and intelligent system, course design is carried out on a data platform, a course scheme is issued to an intelligent teaching and assisting tool terminal to realize the course content, and the final training process data domain evaluation result can be collected to the data platform. The above operation process is converted into specific technical requirements, and the technical requirements are related to a data domain, a device domain, a model domain and a knowledge domain.
The "data field" puts forward the following requirements: the method comprises the steps that (1) an information platform needs to have a login function, (2) a scheme of a child course needs to be designed, (3) a scheme needs to be supported and issued, (4) a scheme needs to be supported and received, and (5) a training result needs to be displayed;
the following requirements are made for the "device domain": the method comprises the following steps of (1) realizing a color distinguishing training scene, (2) realizing a shape distinguishing training scene, (3) realizing a sequence memory training scene, (4) realizing a limb movement training scene, and (5) realizing a schulter square training scene;
the following requirements are made for the "model domain": (1) Realizing limb work recognition through an intelligent perception algorithm, (2) analyzing data in a training process and giving a score; the following requirements are put forward for the "knowledge domain": (1) Personalized training scheme recommendation is realized, and (2) personalized resource recommendation is realized.
From the angle of construction of a data service platform, the data domain is combined with technical requirements to carry out architecture design, database design and data access interface design of network communication.
In consideration of the access range of the platform, in this embodiment, interconnection and intercommunication between the data platform and the terminal device are required, the intelligent teaching and assistance device terminal generates training data records, and has a certain requirement on network bandwidth, and the application range of the data management platform is limited only in classrooms of special education courses, so that the data management platform is determined to be a local area network scheme.
And determining the storage type of the data, wherein the training data mainly comprises training scene names, training process statistical data, training scene scores, comprehensive scene scores and other data, and therefore the structured database is selected for storage. Meanwhile, the situation that a knowledge domain needs to recommend fusion education personalized teaching resources and a knowledge map is used for associating different modal data is considered, and therefore data storage is carried out by using the map data.
And designing a database table and designing an adding, deleting, modifying and checking interface of data. The interfaces designed in the embodiment mainly include: the method comprises the steps of (1) obtaining a course sequencing interface, (2) modifying the course sequencing interface, (3) obtaining training result information, (4) adding a training result, (5) modifying the training result, (6) deleting the training result, and (7) obtaining a training result list.
The equipment domain establishes intelligent terminal equipment meeting interaction requirements according to the technical requirements of the professional domain, and realizes communication between data generated by the intelligent terminal equipment and the data domain, and intercommunication and storage of the data are realized.
According to the technical requirements, the type of the terminal equipment is selected, the interactive training requirement in a classroom is considered, the requirement is flexible and convenient, and therefore the android equipment is selected as a carrier of the attention training intelligent teaching aid terminal.
And selecting a development tool according to the intelligent terminal equipment type selection, wherein the programming tool adopts Java and uses gradle to carry out engineering organization because the hardware carrier of the terminal selects android. Meanwhile, considering that a computer vision algorithm is accessed in the model domain and runs at a terminal, C + + is used as a development tool for neural network model deployment, a JNI interface is written for calling by a Java layer, and CMake is used for engineering organization, so that related algorithm functions can be accessed quickly.
And designing an interactive scene, designing an interactive prototype and an interactive UI, and expressing the training task in a scene picture form, so that children with special needs can more easily accept course contents.
In the process of realizing the limb movement training scene, the limb movement scene needs a model domain algorithm interface to read the real-time video of the student, feed back the limb movement state of the student, and realize the real-time interaction of the scene through the feedback of the BlazePose target detection algorithm on the real-time movement state. Meanwhile, after all attention training scenes are finished, a scoring algorithm interface realized by a model domain needs to be called to realize scoring of the attention training result.
According to the technical requirements of a 'professional domain', training data need to be stored and fed back by the system and need to be displayed on a data platform, the training data can be input as algorithm data of a knowledge domain, a data structure which needs to be stored needs to be designed, and related data content designed by the implementation case mainly comprises training scene names, training duration, training scores and total scores of a training process.
Wherein, the mentioned training score and the total score of the training process are realized by a regularization calculation formula, and the calculation mode of the correctness rule is that
Wherein Score is 1 As the score of the correctness rule, righ is the number of correct responses, wrong is the number of incorrect responses; if the result is then based on the completion rule>
Wherein Score is 2 Righ is the number of correct responses and Goal is the target number of completions for the task, which is the score of the completion rule. The final overall score is the above rule scoreWeighted combinations of scores, e.g. Score =0.4 × Score 1 +0.6*core 2 。
The data generated by the equipment is accessed to a data domain, the implementation case adopts an http network communication protocol, and the data uploading process is realized by calling a data transmission interface established by the data domain.
The "model domain" mainly solves two problems in this implementation case according to the technical requirements set forth by the "professional domain": (1) Limb motion state recognition based on computer vision, and (2) training scene data analysis to calculate objective attention training process scores. Aiming at the problem that the selection needs to be solved by using machine learning or deep learning, a relevant model is developed to support an intelligent perception and interaction process and an intelligent evaluation process.
Considering the field of problems to be solved in terms of model functions, the limb action recognition algorithm belongs to the field of computer vision; a scoring result calculated by training scene process data belongs to the field of data analysis.
In the embodiment, the detection task is limb detection, a BlazePose neural network structure is selected and the corresponding weight is used for detecting key points of a human body.
The assessment result algorithm of the attention training course belongs to a regression problem, training process data is used as input, a scoring result is used as output, and a regression machine learning model is trained. In the embodiment, a scoring model is obtained by collecting nearly 200 training data and performing model training by using a support vector regression machine.
In order to realize the evaluation of the child action simulation, the key points of the human body need to be further processed, and because the simulation action designs the unfolding angles of the two arms and the lifting angles of the two legs, the values of the four limb angles of the human body are obtained by adopting the post-processing calculation of the key points of the human body, and the virtual image of the child action state is driven through the value interval.
The knowledge domain is a knowledge graph aiming at special education and fusion education, and intelligent recommendation service based on the knowledge graph is established according to the technical requirements of personalized training scheme recommendation and personalized resource recommendation provided by the professional domain.
The knowledge graph framework of special education and fusion education is constructed, and the addition of the following steps can be supported: the system comprises a structured training evaluation data, a text teaching material, an audio material, a video material, a teaching plan material, an IEP scheme, a teacher comment and other multi-mode information.
And constructing the association matching of the special education and the fusion education knowledge graph, and realizing the cross-modal resource domain information matching. In this embodiment, the newly added data includes scoring result data of a process data field of attention training, and the data can be matched with attention-related resources after being put in storage, and when the training process reflects factors of attention deficiency, subsequent training plans and teaching resources can be automatically matched.
And inputting the training process data as information, and associating the training data with comments, teaching techniques and teaching aids.
The method for realizing the personalized recommendation is realized by searching a graph database, and firstly, the association between the behavior characteristics of the children and the intelligent teaching and assisting tool equipment is established, for example, the association between the instruction of two stages and the intelligent teaching and assisting tool for attention training cannot be completed due to the attention defect. And calling a newly-added recommendation scheme interface of a data domain, pushing training courses and teaching material resources suitable for the children to a data management platform, and directly displaying an intelligent recommendation scheme in the course of course design and teaching aid selection next time.
Example 2
In one embodiment of the present disclosure, a fusion education attention training system based on five-domain collaboration is provided, including:
the training initialization module is used for establishing attention training courses and attention training course flows under the states of intelligent teaching aids and informatization data platforms;
the training module is used for converting the established attention training course flow into an informatization and intelligentized operation form, designing an interactive scene, carrying out attention training on students according to the attention training course flow, identifying limb actions in the attention training process of the students through an intelligent perception algorithm, analyzing data in the attention training process, and associating attention training data of different modes by using a knowledge graph;
and the evaluation module is used for grading the attention training process by using the trained data and finally storing the attention training data in a graph data mode.
As an embodiment, the five-domain cooperation-based integrated education attention training system is an integrated education intelligent system with a data management platform at the cloud end, attention training process data belonging to the embodiment 1 is stored on the integrated education intelligent system, the system adopts the steps of the five-domain cooperation-based intelligent system design method in the embodiment 1 during construction, and the difference is that a data domain is deployed on a cloud server, and the data service platform has a public network IP and a domain name which can be accessed by the internet.
Example 3
In one embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided for storing computer instructions that, when executed by a processor, implement a five-domain collaboration based fusion education attention training method as described in embodiment 1.
Example 4
An embodiment of the present disclosure provides an electronic device, including: a processor, a memory, and a computer program; wherein a processor is connected with the memory, a computer program is stored in the memory, and when the electronic device runs, the processor executes the computer program stored in the memory, so that the electronic device executes the method for implementing the five-domain collaboration-based fusion education attention training method according to embodiment 1.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Although the present disclosure has been described with reference to specific embodiments, it should be understood that the scope of the present disclosure is not limited thereto, and those skilled in the art will appreciate that various modifications and changes can be made without departing from the spirit and scope of the present disclosure.
Claims (10)
1. A fusion education attention training method based on five-domain collaboration is characterized by comprising the following steps:
establishing attention training courses and attention training course flows under the states of intelligent teaching aids and information data platforms;
converting the established attention training course flow into an information and intelligent operation form, designing an interaction scene, carrying out attention training on students according to the attention training course flow, identifying limb actions in the attention training process of the students through an intelligent perception algorithm, analyzing data in the attention training process, and associating attention training data of different modes by using a knowledge graph;
and (4) scoring the attention training process by using the trained data, and finally storing the attention training data in a graph data mode.
2. The method of claim 1, wherein the attention training course flow comprises: the student attention training plan is made, the color and shape attention ability of students is trained, the sequential memory of students is trained, the limb action is trained, and the schulter square is trained.
3. The five-domain collaboration based fusion education attention training method of claim 1, wherein the attention training data includes training scenario names, training process statistics, training scenario scores, and composite scenario scores.
4. The method for attention training of fusion education based on five-domain collaboration as claimed in claim 1, wherein the attention training data is stored in a graph data manner, and in the storage structure of the graph data, an addition, deletion, modification and check interface of the data is designed.
5. A five-domain collaboration based fusion educational attention training method according to claim 4, wherein the interface designed in the data structure comprises: the method comprises the steps of obtaining an attention training course sequencing interface, modifying the attention training course sequencing interface, obtaining attention training result information, adding an attention training result, modifying the attention training result, deleting the attention training result and obtaining an attention training result list interface.
6. The five-domain collaboration based fusion education attention training method according to claim 1, wherein the interaction scenario is interactive prototype and interactive UI design, and attention training is expressed in the form of a scene picture.
7. The five-domain collaboration based fusion education attention training method according to claim 1, wherein in the process of realizing the limb movement training scene, the limb movement scene needs to realize an algorithm interface, so that the aim of reading a student training real-time video, feeding back the limb movement state of the student is achieved, and the real-time interaction of the scene is realized through the feedback of the algorithm to the real-time movement state.
8. Five-domain collaboration-based fusion education attention training system is characterized by comprising:
the training initialization module is used for establishing attention training courses and attention training course flows under the states of intelligent teaching aids and an information data platform;
the training module is used for converting the established attention training course flow into an informatization and intelligentized operation form, designing an interactive scene, carrying out attention training on students according to the attention training course flow, identifying limb actions in the attention training process of the students through an intelligent perception algorithm, analyzing data in the attention training process, and associating attention training data of different modes by using a knowledge graph;
and the evaluation module is used for grading the attention training process by using the trained data and finally storing the attention training data in a graph data mode.
9. A non-transitory computer-readable storage medium storing computer instructions which, when executed by a processor, implement the five-domain collaboration based fusion education attention training method of any one of claims 1-7.
10. An electronic device, comprising: a processor, a memory, and a computer program; wherein a processor is connected with a memory, a computer program is stored in the memory, and when the electronic device is operated, the processor executes the computer program stored in the memory to make the electronic device execute a method for implementing the five-domain collaboration based fusion education attention training method according to any one of claims 1-7.
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| CN116721738A (en) * | 2023-08-09 | 2023-09-08 | 深圳市心流科技有限公司 | Method and device for controlling movement of target object based on concentration force |
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2023
- 2023-02-07 CN CN202310095143.4A patent/CN115984059A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116721738A (en) * | 2023-08-09 | 2023-09-08 | 深圳市心流科技有限公司 | Method and device for controlling movement of target object based on concentration force |
| CN116721738B (en) * | 2023-08-09 | 2024-01-30 | 深圳市心流科技有限公司 | Method and device for controlling movement of target object based on concentration force |
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