Online teaching resource allocation management system based on digital twinning technology

Abstract

The invention discloses an online teaching resource allocation management system based on a digital twinborn technology, relates to the technical field of resource allocation, solves the technical problem that accurate matching cannot be performed according to each dimension analysis type in the prior art, and particularly relates to accurate matching of a to-be-learned subject and online teaching resources through dimension analysis. And real-time matching is monitored and controlled, so that the allocation feasibility of the subject to be learned is ensured, the reasonable allocation efficiency of the online teaching resources is also ensured, and unnecessary resource management cost caused by unreasonable allocation of the online teaching resources due to unbalanced resource occupation is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of resource allocation, and in particular to an online teaching resource allocation and management system based on digital twin technology. Background Art

[0002] Online teaching resource allocation refers to the rational planning, configuration and scheduling of various teaching resources in the online teaching environment to ensure that these resources can effectively meet teaching needs and achieve the best teaching results.

[0003] However, in the prior art, online teaching resources cannot be analyzed in various dimensions for students when being allocated, and cannot be analyzed in various dimensions for teaching resources, so that reasonable matching cannot be performed. In addition, accurate matching cannot be performed according to the analysis type of each dimension, which reduces the rationality and accuracy of the allocation of online teaching resources.

[0004] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to solve the above-mentioned problems and to propose an online teaching resource allocation and management system based on digital twin technology.

[0006] The purpose of the present invention can be achieved through the following technical solutions: An online teaching resource allocation and management system based on digital twin technology, comprising a demand dimension evaluation unit, a supply dimension evaluation unit and a matching degree analysis unit; The demand dimension assessment unit conducts demand dimension assessment on students participating in online teaching, collects and marks students participating in online teaching as subjects to be learned based on online real-name sign-in; divides the demand dimension of the subjects to be learned into learning level dimension, learning style dimension and learning preference dimension according to the data standard of the subjects to be learned, collects demand dimension information, substitutes it into the demand dimension formula to obtain the real-time demand dimension assessment coefficient of the subjects to be learned, and divides the subjects to be learned into complex allocation subjects and simple allocation subjects according to the comparison of coefficient thresholds; The supply dimension evaluation unit evaluates the supply dimension of real-time online teaching resources, marks the online teaching resources that have completed the real-time online registration and obtained the online display permission as the learning subject, and divides the supply dimension of the learning subject into the textbook type dimension and the textbook modality dimension; collects the supply dimension information, and substitutes it into the supply dimension formula to obtain the supply dimension evaluation coefficient of the real-time online teaching resources, and divides the learning subject into a rich supply subject and a single supply subject according to the coefficient threshold comparison; The matching analysis unit accurately matches the learners and online teaching resources through dimensional analysis.

[0007] As a preferred implementation manner of the present invention, the demand dimension information is respectively the growth rate of the mean error rate of the teaching course test assignments when the teaching course video duration in the learning level dimension of the learner increases cumulatively, the corresponding numerical ratio of the proportion of real-time classroom participation time of the teaching course videos of different tutors and the increase span of the error rate of the teaching course test assignments in the learning style dimension of the learner, and the maximum deviation value of the control speed of the error rate of the teaching course test assignments of different subjects of the learner corresponding to the proportion of the same classroom participation time in the teaching course videos of different subjects in the learning preference dimension of the learner, and the labels XW-sp, XW-fg and XW-xh are set respectively.

[0008] As a preferred implementation of the present invention, the demand dimension formula is: , where bn1, bn2 and bn3 are preset proportional coefficients, and S is the contingency correction coefficient of data collection corresponding to each type of demand dimension, and α is used as the error correction factor.

[0009] As a preferred embodiment of the present invention, if the real-time demand dimension evaluation coefficient of the subject to be learned exceeds the demand dimension evaluation coefficient threshold, the corresponding subject to be learned will be marked as a complex allocation subject; if the real-time demand dimension evaluation coefficient of the subject to be learned does not exceed the demand dimension evaluation coefficient threshold, the corresponding subject to be learned will be marked as a simple allocation subject.

[0010] As a preferred embodiment of the present invention, the supply dimension information includes the proportion of knowledge points of non-single type in the content display type of each knowledge point in the teaching material type dimension in the real-time online teaching resource, and the number of overlapping knowledge points of the dynamic display knowledge points and the static display knowledge points in the teaching material modality dimension in the real-time online teaching resource, and are marked as GW-zb and GW-sl respectively; As a preferred embodiment of the present invention, the supply dimension formula is: , where vg1 and vg2 are preset proportional coefficients, and e is a natural constant.

[0011] As a preferred implementation of the present invention, if the supply dimension evaluation coefficient of the real-time online teaching resource exceeds the supply dimension evaluation coefficient threshold, the online teaching resource is determined to be a rich supply entity; if the supply dimension evaluation coefficient of the real-time online teaching resource does not exceed the supply dimension evaluation coefficient threshold, the online teaching resource is determined to be a single supply entity.

[0012] As a preferred embodiment of the present invention, the operation process of the matching degree analysis unit is as follows: The number of complex learning subjects and simple allocation subject selections corresponding to the rich supply subjects and single supply subjects in the online teaching resources is obtained, and they are marked as the proportion of complex rich matching combination resources, the proportion of complex single matching combination resources, the proportion of simple rich matching combination resources and the proportion of simple single matching combination resources respectively; the proportion of complex single matching combination resources is marked as the abnormal matching proportion, and the proportion of simple rich matching combination resources is marked as the inefficient matching proportion; the proportion of complex rich matching combination resources and the proportion of simple single matching combination resources are marked as the normal matching proportion.

[0013] As a preferred implementation of the present invention, the numerical growth span of the normal matching ratio when real-time online teaching resources are allocated to the learning subject, the control speed of the real-time numerical increase span of the abnormal matching ratio, and the corresponding speed numerical ratio of the normal matching ratio numerical growth speed and the inefficient matching ratio numerical reduction speed at any allocation moment are obtained; If any of the numerical growth span of the normal matching ratio when real-time online teaching resources are allocated to learning entities, the control speed of the real-time numerical increase span of the abnormal matching ratio, and the corresponding speed ratio of the normal matching ratio growth rate to the inefficient matching ratio decrease rate at any allocation moment does not exceed the corresponding set threshold, then the allocation matching analysis of real-time online teaching resources and learning entities is determined to be abnormal.

[0014] As a preferred implementation manner of the present invention, if any of the numerical growth span of the normal matching ratio when real-time online teaching resources are allocated to learning entities, the control speed of the real-time numerical increase span of the abnormal matching ratio, and the corresponding speed numerical ratio of the normal matching ratio growth rate to the inefficient matching ratio numerical decrease rate at any allocation moment exceeds the corresponding set threshold, then the allocation matching analysis of real-time online teaching resources and learning entities is determined to be normal.

[0015] Compared with the prior art, the present invention has the following beneficial effects: In the present invention, the demand dimension of students participating in online teaching is evaluated, and the resource demand direction of each student is inferred according to the analysis of the student's demand dimension, so that the resource allocation can be guaranteed to be accurate when the resource is allocated, reducing the resource supply without playing a teaching role, wasting network resource channels and affecting the student's online teaching progress; Evaluate the supply dimension of real-time online teaching resources, and reasonably analyze the supply characteristics of teaching resources based on the evaluation of each dimension of real-time teaching resources, so as to accurately match the demand dimensions of learners for resource allocation, improve the allocation and management efficiency of online teaching resources, and ensure the stability of online teaching.

[0016] In the present invention, the learners and online teaching resources are accurately matched through dimensional analysis, and the real-time matching is monitored and controlled to ensure the feasibility of the allocation of the learners and the reasonable allocation efficiency of the online teaching resources, so as to avoid the unreasonable allocation of online teaching resources due to uneven resource occupancy and generate unnecessary resource management costs.

[0017] In the present invention, the intelligent allocation and control unit analyzes and controls the real-time resource matching, and performs synchronous analysis through the allocation of various online teaching resources combined with the numerical values ​​of various dimensions of the learning subject, and infers whether the current resource matching needs to be regulated, so as to ensure the rationality of the allocation of online teaching resources, ensure that the learning subject can be allocated with appropriate teaching resources, and improve the efficiency of online teaching. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0019] Figure 1 This is a principle block diagram of Embodiment 1 of the present invention; Figure 2 This is a principle block diagram of the second embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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.

[0021] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments. Embodiment 1

[0022] This embodiment is applicable to the online teaching resource allocation scenario, ensuring that online teaching courses and corresponding students can reasonably allocate resources, avoiding resource occupation or unreasonable resource supply, which leads to a decrease in the efficiency of the entire online teaching. Figure 1 As shown, an online teaching resource allocation and management system based on digital twin technology includes a demand dimension evaluation unit, a supply dimension evaluation unit and a matching degree analysis unit; The teaching resource allocation management platform generates a demand dimension evaluation signal and sends the demand dimension evaluation signal to the demand dimension evaluation unit. After receiving the demand dimension evaluation signal, the demand dimension evaluation unit conducts demand dimension evaluation on the students participating in online teaching, and infers the resource demand direction of each student based on the student's demand dimension analysis, so as to ensure accurate resource allocation when allocating resources, reduce resource supply without playing a teaching role, waste network resource channels and affect students' online teaching progress; Students who participate in online teaching are collected and marked as subjects to be learned based on their real-name sign-in. At the same time, historical online teaching course videos and corresponding teaching course test assignments are collected and stored based on the network terminals of the subjects to be learned and used as data standards for the subjects to be learned; According to the data standard of the subject to be learned, the demand dimensions of the subject to be learned are divided into the learning level dimension, the learning style dimension and the learning preference dimension. This application uses the above three demand dimensions as the standard in the process of allocating online teaching resources. In the actual allocation of online teaching resources, the dimensions of the subject to be learned are diverse. The maintenance types of other scenarios can be substituted into this system to perform dimensional evaluation on the corresponding collected data. It can be understood that the dimensions are expressed as characteristic parameters that reflect the learning ability of the subject to be learned; The growth rate of the mean error rate of the teaching course inspection assignments when the teaching course video duration in the learning level dimension of the learning subject is cumulatively increased is obtained, and at the same time, the numerical ratio corresponding to the proportion of the teaching course video real-time classroom participation time of different tutors and the increase span of the error rate of the teaching course inspection assignments in the learning style dimension of the learning subject is obtained, where the classroom participation time is represented by the time when the online course video responds to the tutor's questions or the tutor adds new knowledge points. The numerical ratio here is represented by the numerical ratio of the two data corresponding to the learning subject, and the influence of the numerical unit is not considered. Only the influence of the numerical fluctuation is analyzed. After the collection is completed, the growth rate of the mean error rate of the teaching course inspection assignments when the teaching course video duration in the learning level dimension of the learning subject is cumulatively increased, and the numerical ratio corresponding to the proportion of the teaching course video real-time classroom participation time of different tutors and the increase span of the error rate of the teaching course inspection assignments in the learning style dimension of the learning subject are marked as XW-sp and XW-fg respectively; At the same time, the maximum deviation value of the control speed of the error rate of the inspection assignments of different subject teaching courses corresponding to the proportion of the same classroom participation time in the teaching course videos of different subject subjects in the learning preference dimension of the learning subject is obtained, and the maximum deviation value of the control speed of the error rate of the inspection assignments of different subject teaching courses corresponding to the proportion of the same classroom participation time in the teaching course videos of different subject subjects in the learning preference dimension of the learning subject is marked as XW-xh; The above collected data are uniformly marked as demand dimension information, and substituted into the formula to obtain the real-time demand dimension evaluation coefficient XW-p of the subject to be learned. The demand dimension formula is: , where bn1, bn2 and bn3 are preset proportional coefficients, and S is the accidental correction coefficient of data collection corresponding to each type of demand dimension. After the accidental probability is obtained through multiple data collection and multiple parameter corrections are performed, the accidental influence is reduced to the tolerance range of data error. Then, the mean value of the multiple parameter correction coefficients is set as the real-time S value. α is used as the error correction factor, which is used to correct the influence of data error when data collection error occurs during data collection. When the influence of data error is reduced, the corresponding system mean value is used as the value of the actual error correction factor. Compare the real-time demand dimension evaluation coefficient of the subject to be learned with the demand dimension evaluation coefficient threshold: If the real-time demand dimension evaluation coefficient of the subject to be learned exceeds the demand dimension evaluation coefficient threshold, the corresponding subject to be learned will be marked as a complex allocation subject; if the real-time demand dimension evaluation coefficient of the subject to be learned does not exceed the demand dimension evaluation coefficient threshold, the corresponding subject to be learned will be marked as a simple allocation subject; It can be understood that the complex allocation subject means that the current student’s demand dimensions are complex and cannot be directly allocated according to the teaching resource needs, while the simple allocation subject means that the student’s demand dimensions are single and reflect the student’s strong ability, so it can be directly allocated according to the teaching resource needs; Subsequently, the teaching resource allocation management platform generates a supply dimension evaluation signal and sends the supply dimension evaluation signal to the supply dimension evaluation unit. After receiving the supply dimension evaluation signal, the supply dimension evaluation unit performs supply dimension evaluation on the real-time online teaching resources, and reasonably analyzes the supply characteristics of teaching resources based on the evaluation of each dimension of the real-time teaching resources, so as to accurately match the demand dimension of the learning subject for resource allocation, thereby improving the allocation management efficiency of online teaching resources and ensuring the stability of online teaching. Online teaching resources that have completed real-time online registration and obtained online display permissions are marked as learning subjects, and the supply dimension of learning subjects is divided into teaching material type dimension and teaching material modality dimension; among them, online teaching resources are represented by resources such as real-time display of teaching material texts and teaching material display tutor classroom videos; The proportion of the number of knowledge points of non-single type in the content display type of each knowledge point in the textbook type dimension in the real-time online teaching resources is obtained, and at the same time, the number of overlapping knowledge points of the number of dynamically displayed knowledge points and the number of statically displayed knowledge points in the textbook modality dimension in the real-time online teaching resources are obtained, and the proportion of the number of knowledge points of non-single type in the content display type of each knowledge point in the textbook type dimension in the real-time online teaching resources and the number of overlapping knowledge points of the number of dynamically displayed knowledge points and the number of statically displayed knowledge points in the textbook modality dimension in the real-time online teaching resources are marked as GW-zb and GW-sl respectively; wherein the display type is represented by text, graphics, etc., and the modality is represented by static content display and dynamic simulation display of knowledge points, such as software dynamic simulation, experimental demonstration, etc.; The above collected data are uniformly marked as supply dimension information, and substituted into the formula to obtain the supply dimension evaluation coefficient GW-p of real-time online teaching resources. The supply dimension formula is: , where vg1 and vg2 are preset proportional coefficients, and e is a natural constant; Compare the supply dimension evaluation coefficient of real-time online teaching resources with the supply dimension evaluation coefficient threshold: If the supply dimension evaluation coefficient of the real-time online teaching resource exceeds the supply dimension evaluation coefficient threshold, the online teaching resource is determined to be a rich supply entity; if the supply dimension evaluation coefficient of the real-time online teaching resource does not exceed the supply dimension evaluation coefficient threshold, the online teaching resource is determined to be a single supply entity; After completing the dimensional evaluation of online teaching resources and the dimensional evaluation of the subject to be taught, the teaching resource allocation management platform generates a matching analysis signal and sends the matching analysis signal to the matching analysis unit. After receiving the matching analysis signal, the matching analysis unit accurately matches the subject to be taught with the online teaching resources through dimensional analysis, and monitors and controls the real-time matching to ensure the feasibility of the allocation of the subject to be taught, and also ensure the reasonable allocation efficiency of online teaching resources, so as to avoid the unreasonable allocation of online teaching resources due to uneven resource occupation and generate unnecessary resource management costs; The number of complex learning subjects and simple allocation subjects selected by rich supply subjects and single supply subjects in online teaching resources is obtained, and they are marked as the proportion of complex rich matching combination resources, the proportion of complex single matching combination resources, the proportion of simple rich matching combination resources, and the proportion of simple single matching combination resources respectively; The proportion of complex single matching combination resources is marked as abnormal matching proportion, and the proportion of simple rich matching combination resources is marked as inefficient matching proportion; the proportion of complex rich matching combination resources and the proportion of simple single matching combination resources are marked as normal matching proportion; The numerical growth span of the normal matching ratio when real-time online teaching resources are allocated to learning entities, the control speed of the real-time numerical increase span of the abnormal matching ratio, and the corresponding speed ratio of the normal matching ratio numerical growth speed and the inefficient matching ratio numerical reduction speed at any allocation moment are obtained, and the above collected data are analyzed: If any of the numerical growth span of the normal matching ratio when real-time online teaching resources are allocated to the learning subject, the control speed of the real-time numerical increase span of the abnormal matching ratio, and the corresponding speed numerical ratio of the normal matching ratio growth speed and the inefficient matching ratio decrease speed at any allocation moment does not exceed the corresponding set threshold, then the allocation matching degree analysis of the real-time online teaching resources and the learning subject is determined to be abnormal, and a matching degree abnormality signal is generated and sent to the teaching resource allocation management platform. After receiving the matching degree abnormality signal, the teaching resource allocation management platform reallocates the online teaching resources; If any of the numerical growth span of the normal matching ratio when real-time online teaching resources are allocated to learning entities, the control speed of the real-time numerical increase span of the abnormal matching ratio, and the corresponding speed numerical ratio of the normal matching ratio numerical growth speed and the inefficient matching ratio numerical decrease speed at any allocation moment exceeds the corresponding set threshold, then the allocation matching degree analysis of the real-time online teaching resources and the learning entities is determined to be normal, and a normal matching degree signal is generated and sent to the teaching resource allocation management platform; the set threshold in this application refers to the numerical growth span threshold of the normal matching ratio, the control speed threshold of the real-time numerical increase span, and the corresponding speed numerical ratio threshold. Embodiment 2

[0023] The previous embodiment analyzes and allocates online teaching resources and learning subjects. This embodiment performs intelligent allocation and regulation based on the previous embodiment. Please refer to Figure 2 As shown, the matching degree analysis unit and the teaching resource allocation management platform are connected by an intelligent allocation control unit; The intelligent allocation and control unit analyzes and controls the real-time resource matching. It performs synchronous analysis on the allocation of various online teaching resources combined with the values ​​of various dimensions of the learning subjects, and infers whether the current resource matching needs to be adjusted to ensure the rationality of the allocation of online teaching resources and ensure that the learning subjects can be allocated appropriate teaching resources, thereby improving the efficiency of online teaching. Set a circular model and mark it as a matching manifestation map. According to the three demand dimensions of the subject to be learned, set them as any three points on the boundary of the matching manifestation map, and the three points evenly divide the matching manifestation map; and quantify them in the same proportion according to the specific manifestation values ​​of the demand dimensions, and connect the points of each dimension in the matching manifestation map with the center of the circle as the starting point to construct a matching map of the demand dimensions of the subject to be learned; Set the centers of the rich supply subjects and the single supply subject in the matching graph respectively, and use them to construct the demand dimension matching graph. According to the matching subjects to be learned of the real-time rich supply subjects and the single supply subject, analyze the demand dimension matching graphs corresponding to the rich supply subjects and the single supply subject. Get the maximum deviation value of the area of ​​the region constructed by the corresponding point connection line of any subject to be learned matching embodiment diagram in the rich supply subject corresponding demand dimension matching diagram, and at the same time get the maximum deviation value of the distance of any subject to be learned point connection line in the same dimension of the single supply subject corresponding demand dimension matching diagram, and mark the maximum deviation value of the area of ​​the region constructed by the corresponding point connection line of any subject to be learned matching embodiment diagram in the rich supply subject corresponding demand dimension matching diagram and the maximum deviation value of the distance of any subject to be learned point connection line in the same dimension of the single supply subject corresponding demand dimension matching diagram as rich matching data and single matching data, respectively, and compare them with the maximum area deviation value threshold and the maximum distance deviation value threshold respectively: If the maximum deviation value of the area of ​​the area constructed by the corresponding point connection line of any subject to be learned in the matching diagram of the demand dimension corresponding to the enriched supply subject does not exceed the maximum area deviation value threshold, or the maximum distance deviation value of the connection line of the same dimension point of any subject to be learned in the matching diagram of the demand dimension corresponding to the single supply subject exceeds the maximum distance deviation value threshold, it is inferred that the current online teaching resource allocation is unreasonable, and an intelligent allocation regulation signal is generated and sent to the teaching resource allocation management platform. After receiving the intelligent allocation regulation signal, the teaching resource allocation management platform increases the range of the subjects to be learned matched by the enriched supply subject to avoid the single type of subjects to be learned corresponding to the enriched supply subject during real-time allocation, that is, to avoid all dimensions of the allocated subjects to be learned being very good or very poor; at the same time, the subjects to be learned with low values ​​in each dimension are given priority in enriching the supply subject matching; If the maximum deviation value of the area of ​​the area constructed by the corresponding point connection line of any subject to be learned in the matching diagram of the demand dimension corresponding to the rich supply subject exceeds the maximum area deviation value threshold, and the maximum deviation value of the distance connection line of any subject to be learned in the same dimension of the matching diagram of the demand dimension corresponding to the single supply subject does not exceed the maximum distance deviation value threshold, it is inferred that the current online teaching resource allocation is reasonable, and an allocation non-regulation signal is generated and sent to the teaching resource allocation management platform; The above formulas are obtained by collecting a large amount of data and performing software simulation, and a formula close to the actual value is selected. The coefficients in the formula are set by technicians in this field according to actual conditions; When the present invention is in use, the demand dimension evaluation unit performs demand dimension evaluation on students participating in online teaching, collects and marks students participating in online teaching as subjects to be learned based on online real-name sign-in; divides the demand dimension of the subject to be learned into learning level dimension, learning style dimension and learning preference dimension according to the data standard of the subject to be learned, collects demand dimension information, substitutes it into the formula to obtain the real-time demand dimension evaluation coefficient of the subject to be learned, and divides the subject to be learned into complex allocation subject and simple allocation subject according to the coefficient threshold comparison; the supply dimension evaluation unit performs supply dimension evaluation on real-time online teaching resources, marks online teaching resources that have completed registration and obtained online display rights as supply subjects, and divides the supply dimension of the supply subject into textbook type dimension and textbook modality dimension; collects supply dimension information, substitutes it into the formula to obtain the supply dimension evaluation coefficient of the real-time online teaching resources, and divides the supply subject into rich supply subject and single supply subject according to the coefficient threshold comparison; the matching degree analysis unit accurately matches the subject to be learned and the online teaching resources through dimensional analysis.

[0024] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An online teaching resource allocation and management system based on digital twin technology, characterized in that: It includes a demand dimension evaluation unit, a supply dimension evaluation unit and a matching degree analysis unit; The demand dimension assessment unit conducts demand dimension assessment on students participating in online teaching, collects and marks students participating in online teaching as subjects to be learned based on online real-name sign-in; divides the demand dimension of the subjects to be learned into learning level dimension, learning style dimension and learning preference dimension according to the data standard of the subjects to be learned, collects demand dimension information, substitutes it into the demand dimension formula to obtain the real-time demand dimension assessment coefficient of the subjects to be learned, and divides the subjects to be learned into complex allocation subjects and simple allocation subjects according to the comparison of coefficient thresholds; The supply dimension evaluation unit evaluates the supply dimension of real-time online teaching resources, marks the online teaching resources that have completed the real-time online registration and obtained the online display permission as the learning subject, and divides the supply dimension of the learning subject into the textbook type dimension and the textbook modality dimension; collects the supply dimension information, and substitutes it into the supply dimension formula to obtain the supply dimension evaluation coefficient of the online teaching resources, and divides the learning subject into a rich supply subject and a single supply subject according to the coefficient threshold comparison; The matching analysis unit accurately matches the learners and online teaching resources through dimensional analysis.

2. According to claim 1, an online teaching resource allocation and management system based on digital twin technology is characterized in that: The demand dimension information includes the growth rate of the mean error rate of teaching course test assignments when the teaching course video duration in the learning level dimension of the learner increases cumulatively, the numerical ratio of the proportion of real-time classroom participation time of teaching course videos of different tutors and the increase span of error rate of teaching course test assignments in the learning style dimension of the learner, and the maximum deviation value of the control speed of the error rate of teaching course test assignments of different subjects of the learner when the proportion of classroom participation time in teaching course videos of different subjects in the learning preference dimension of the learner are the same, and they are labeled XW-sp, XW-fg and XW-xh respectively.

3. According to claim 2, an online teaching resource allocation and management system based on digital twin technology is characterized in that: The demand dimension formula is: , where bn1, bn2 and bn3 are preset proportional coefficients, and S is the contingency correction coefficient of data collection corresponding to each type of demand dimension, and α is used as the error correction factor.

4. According to claim 3, an online teaching resource allocation and management system based on digital twin technology is characterized in that: If the real-time demand dimension evaluation coefficient of the subject to be learned exceeds the demand dimension evaluation coefficient threshold, the corresponding subject to be learned will be marked as a complex allocation subject; if the real-time demand dimension evaluation coefficient of the subject to be learned does not exceed the demand dimension evaluation coefficient threshold, the corresponding subject to be learned will be marked as a simple allocation subject.

5. The online teaching resource allocation and management system based on digital twin technology according to claim 1, characterized in that: The supply dimension information includes the proportion of knowledge points of non-single type in the content presentation type of each knowledge point in the textbook type dimension of real-time online teaching resources, and the number of overlapping knowledge points between the number of dynamically displayed knowledge points and the number of statically displayed knowledge points in the textbook modality dimension of real-time online teaching resources, which are marked as GW-zb and GW-sl respectively.

6. The online teaching resource allocation and management system based on digital twin technology according to claim 5 is characterized in that: The supply dimension formula is: , where vg1 and vg2 are preset proportional coefficients, and e is a natural constant.

7. The online teaching resource allocation and management system based on digital twin technology according to claim 6 is characterized in that: If the supply dimension evaluation coefficient of the real-time online teaching resources exceeds the supply dimension evaluation coefficient threshold, the online teaching resources are judged to be a rich supply entity; if the supply dimension evaluation coefficient of the real-time online teaching resources does not exceed the supply dimension evaluation coefficient threshold, the online teaching resources are judged to be a single supply entity.

8. The online teaching resource allocation and management system based on digital twin technology according to claim 1, characterized in that: The operation process of the matching analysis unit is as follows: The number of complex learning subjects and simple allocation subject selections corresponding to the rich supply subjects and single supply subjects in the online teaching resources is obtained, and they are marked as the proportion of complex rich matching combination resources, the proportion of complex single matching combination resources, the proportion of simple rich matching combination resources and the proportion of simple single matching combination resources respectively; the proportion of complex single matching combination resources is marked as the abnormal matching proportion, and the proportion of simple rich matching combination resources is marked as the inefficient matching proportion; the proportion of complex rich matching combination resources and the proportion of simple single matching combination resources are marked as the normal matching proportion.

9. The online teaching resource allocation and management system based on digital twin technology according to claim 8, characterized in that: Obtain the numerical growth span of the normal matching ratio when real-time online teaching resources are allocated to learning entities, the control speed of the real-time numerical increase span of the abnormal matching ratio, and the corresponding speed numerical ratio of the normal matching ratio numerical growth speed and the inefficient matching ratio numerical reduction speed at any allocation moment; If any of the numerical growth span of the normal matching ratio when real-time online teaching resources are allocated to the learning subject, the control speed of the real-time numerical increase span of the abnormal matching ratio, and the corresponding speed numerical ratio of the normal matching ratio numerical growth speed and the inefficient matching ratio numerical reduction speed at any allocation moment does not exceed the corresponding set threshold, then it is determined that the matching degree analysis of the real-time online teaching resources and the learning subject is abnormal; If any of the numerical growth span of the normal matching ratio when real-time online teaching resources are allocated to learning entities, the control speed of the real-time numerical increase span of the abnormal matching ratio, and the corresponding speed ratio of the normal matching ratio growth rate and the inefficient matching ratio decrease rate at any allocation moment exceeds the corresponding set threshold, then the allocation matching analysis of real-time online teaching resources and learning entities is judged to be normal.

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