A political and ideological teaching management platform based on linkage of blocks

Abstract

The present application relates to the technical field of ideological and political teaching management.The present application relates to an ideological and political teaching management platform based on linkage of a slice area.The platform comprises a resource acquisition module, a node setting module, a resource screening module, a resource comparison module and a teaching management module;the resource acquisition module is used for acquiring ideological and political teaching resources, performing teaching resource splitting on the ideological and political teaching resources, simultaneously performing teaching attribute analysis on the teaching resources, and acquiring teaching attributes corresponding to the teaching resources;by constructing a full-process digital and collaborative management system, the shortcomings of the prior art are effectively made up, and the present application has a significant application value;by standardized resource splitting, attribute analysis and slice area clustering, the present application realizes standardized integration and efficient linkage of ideological and political resources, breaks the barrier of inter-school resources, and enables high-quality resources in a slice area to flow accurately and be reused fully, thereby avoiding waste caused by repeated construction of resources and solving the predicament of a single school lacking resources.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ideological and political teaching management, in particular to an ideological and political teaching management platform based on district linkage. BACKGROUND

[0002] As the core carrier of implementing the fundamental task of educating people with morality, the core goal of ideological and political teaching is to cultivate students' correct world outlook, outlook on life and values, and to improve students' comprehensive ideological and political qualities such as patriotism, legal literacy and social responsibility.

[0003] The existing ideological and political teaching management technology focuses on the partial optimization of a single school or a single teaching link, and is difficult to meet the needs of collaborative, precise and efficient ideological and political teaching. The resources of different schools and different districts are self-sufficient, and there is a lack of unified classification standard and attribute analysis system, which leads to the difficulty of high-quality resources flowing across regions, the coexistence of resource waste and resource scarcity, and the inability to effectively support the differentiated needs of different school stages and different teaching goals within the district. In order to reduce this situation, an ideological and political teaching management platform based on district linkage is proposed. SUMMARY

[0004] The purpose of the present application is to provide an ideological and political teaching management platform based on district linkage to solve the problems raised in the background technology.

[0005] To achieve the above purpose, an ideological and political teaching management platform based on district linkage is provided, which includes a resource acquisition module, a node setting module, a resource screening module, a resource comparison module and a teaching management module.

[0006] The resource acquisition module is used to acquire ideological and political teaching resources, and to perform teaching resource splitting on the ideological and political teaching resources, while performing teaching attribute analysis on the teaching resources, to acquire the teaching attributes corresponding to the teaching resources.

[0007] The node setting module is used to establish resource districts according to the teaching attributes, and to match the teaching resources to the corresponding resource districts, while collecting teaching targets and distribution schemes, and setting target nodes according to the teaching targets.

[0008] The resource screening module is used to set the number of resource types for the target nodes in combination with the distribution scheme, and then perform attribute matching on the resource districts in combination with the target nodes, and screen and retain the matching results in combination with the number of resource types, to form a candidate resource set.

[0009] The resource comparison module is used to set a similarity threshold, to perform same-type similarity analysis on the candidate resource set and the teaching resources of the distribution scheme, and to compare the analysis results with the similarity threshold, to update the candidate resource set to retain only the teaching resources greater than the similarity threshold.

[0010] The teaching management module is used for randomly adjusting the updated candidate resource set, performing cross analysis of the same type of resources in combination with the allocation scheme, obtaining a resource scheme of the adaptive target node according to a cross result, and combining the resource scheme with the allocation scheme to perform teaching management according to the resource quantity.

[0011] As a further improvement of the technical solution, in the resource acquisition module, the stored ideological and political teaching resources are acquired from the ideological and political teaching management end through the connection of the ideological and political teaching management end.

[0012] The ideological and political teaching resources are split according to ideological and political themes, and the ideological and political teaching resources are split into multiple individual teaching resources, and the teaching attributes of the teaching resources are analyzed to obtain the corresponding teaching attributes of the teaching resources.

[0013] The teaching attributes are obtained by performing semantic analysis on the teaching resources.

[0014] As a further improvement of the technical solution, in the node setting module, the upper limit of the resource quantity and the attribute difference threshold of the resource area are set, then the teaching resources are randomly selected as the representative resources of the resource area, and then the other teaching resources are combined with the representative resources to perform attribute difference comparison, and the teaching resources within the upper limit of the resource quantity and meeting the attribute difference threshold are matched into the resource area.

[0015] The resource area with the lowest attribute difference is preferentially selected, and for the teaching resources that are not matched into the resource area, a new resource area is newly built.

[0016] As a further improvement of the technical solution, in the node setting module, the teaching target and the allocation scheme are acquired from the ideological and political teaching management end.

[0017] The teaching target is a constraint condition for the screening of the teaching resources.

[0018] The allocation scheme is a teaching resource collocation scheme before the teaching management by the teaching management module.

[0019] The teaching target is set as a target node, the semantics of the teaching target are obtained by performing semantic analysis on the teaching target, then the semantics are split according to the difference, and the target node is set according to the split result.

[0020] As a further improvement of the technical solution, in the resource screening module, resource type demand analysis is performed on each target node to determine the resource type demand of each target node on the teaching resources.

[0021] The initial resource type quantity is set according to the resource type demand, then the teaching resource quantity corresponding to each target node in the allocation scheme is determined, and the initial resource type quantity is adjusted according to the corresponding teaching resource quantity.

[0022] The more teaching resources there are, the more the initial resource type quantity can be adjusted upwards;

[0023] The fewer the corresponding teaching resources, the less the initial resource type quantity will be adjusted upwards.

[0024] As a further improvement to this technical solution, the resource filtering module extracts teaching resources of the same type from the resource area according to the resource type requirements of the target node, and then performs attribute matching by combining the teaching resources of the same type with the semantics of the target node to obtain the matching degree between the teaching resources in the resource area and the semantics of the target node.

[0025] The teaching resources are sorted according to their matching degree, and then filtered and retained based on the resource type data. The teaching resources retained for each target node are summarized to form a candidate resource set.

[0026] Among them, teaching resources with high matching degree will be given priority.

[0027] As a further improvement to this technical solution, the resource comparison module sets a similarity threshold from the ideological and political education management end, performs a similarity analysis between the candidate resource set and the teaching resources in the allocation scheme, and obtains the similarity between the teaching resources in the candidate resource set and the teaching resources in the allocation scheme; wherein, the similarity analysis is completed through teaching attributes.

[0028] Compare the similarity score with a similarity threshold;

[0029] If the similarity between the teaching resources in the candidate resource set and the teaching resources in the allocation scheme is greater than the similarity threshold, then the resource is retained.

[0030] If the similarity between the teaching resources in the candidate resource set and the teaching resources in the allocation scheme is less than the similarity threshold, then the candidate resource set will not be retained, and the update of the candidate resource set will be completed.

[0031] As a further improvement to this technical solution, in the teaching management module, the teaching resources in the updated candidate resource set are randomly adjusted, and then the randomly adjusted teaching resources are combined with the allocation scheme to perform cross-analysis of resources of the same type, and multiple resource schemes are obtained based on the analysis results.

[0032] In this process, random adjustments only compress teaching resources without expanding them;

[0033] The resource plan is adapted to each target node, and only the adapted resource plan is retained. In particular, the teaching resource attributes of the resource plan are combined with the target nodes for coverage analysis to ensure that the teaching resource attributes of the resource plan cover the target nodes; otherwise, it is considered unsuitable.

[0034] As a further improvement to this technical solution, the teaching management module will retain the appropriate resource scheme and compare the resource quantity with the allocation scheme.

[0035] If the resource data of the resource plan is greater than that of the allocation plan, then no teaching management will be carried out;

[0036] If the resource data of the resource plan is less than that of the allocation plan, the resource plan with the fewest resources is selected to replace the allocation plan, thus completing the teaching management.

[0037] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0038] 1. This ideological and political education teaching management platform based on regional linkage effectively compensates for the shortcomings of existing technologies by constructing a fully digital and collaborative management system, and has significant application value. Through standardized resource decomposition, attribute analysis and regional clustering, it realizes the standardized integration and efficient linkage of ideological and political resources, breaks down the resource barriers between schools, and allows high-quality resources within the region to flow accurately and be fully reused. It avoids the waste caused by the duplication of resources and solves the dilemma of resource scarcity in a single school. It provides rich and suitable resource support for ideological and political education of different grades and different teaching needs within the region.

[0039] 2. This ideological and political education teaching management platform based on regional linkage establishes a full-link precise matching mechanism for teaching objectives, target nodes, resource regions, and precise selection by relying on a semantic analysis model and quantitative matching algorithm specifically for the ideological and political field. Through semantic feature extraction, attribute difference calculation, similarity verification and other technical means, it ensures a high degree of fit between resources and teaching objectives, effectively solving the problems of blind resource matching and low adaptability in existing technologies, making ideological and political education more targeted, and improving the accuracy of teaching implementation and the educational effect.

[0040] 3. This ideological and political education teaching management platform based on regional linkage constructs a linkage management architecture with regional linkage as the core, integrating the entire process functions such as resource scheduling, teaching collaboration, and quality control. It realizes the unified planning, dynamic allocation, and closed-loop management of inter-school teaching resources within the region, reduces manual coordination costs, and improves the overall efficiency of regional ideological and political education teaching management. At the same time, through resource compression and optimization, cross-analysis, and quantity comparison, the most streamlined and efficient resource solutions are selected, minimizing teaching implementation costs while meeting teaching needs, and realizing the intensive utilization of ideological and political education teaching resources. Attached Figure Description

[0041] Figure 1 This is a flowchart illustrating a regional linkage-based ideological and political education management platform according to the present invention. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] Please see Figure 1 As shown, the purpose of this embodiment is to provide a political education teaching management platform based on regional linkage, including a resource acquisition module, a node setting module, a resource filtering module, a resource comparison module, and a teaching management module;

[0044] The resource acquisition module is used to acquire ideological and political education teaching resources, break down these resources into teaching resource segments, analyze the teaching attributes of the resources, and obtain the corresponding teaching attributes. The integrated ideological and political education teaching resources are then broken down into standardized units and assigned quantifiable teaching attributes to provide a basis for subsequent area division and target matching.

[0045] In the resource acquisition module, the stored ideological and political teaching resources are obtained from the ideological and political teaching management terminal by connecting to the ideological and political teaching management terminal.

[0046] Establish a secure data connection with the ideological and political education management terminal, and initiate resource retrieval requests through the API interface. The request parameters include resource type filtering conditions (such as text and video) and ideological and political theme keywords (such as red heritage and legal literacy).

[0047] The ideological and political education teaching resources are broken down according to the ideological and political education theme, and then broken down into multiple separate teaching resources. At the same time, the teaching attributes of the teaching resources are analyzed to obtain the corresponding teaching attributes of the teaching resources.

[0048] Extract the core content of the acquired resources (text resources: extract the main text; audio and video resources: extract subtitles or scripts). Based on the preset ideological and political theme classification system (such as five major categories: national sentiment, red heritage, rule of law awareness, technological innovation, and ecological protection), identify the theme of the core content of the resources, and split them independently according to a single theme. Resources that integrate multiple themes (such as composite resources of red stories and rule of law cases) are split into independent resource units corresponding to a single theme, and each resource unit is assigned a unique split ID (original resource ID + split sequence number).

[0049] Teaching attributes are obtained through semantic analysis of teaching resources. A pre-trained BERT model for ideological and political education (fine-tuned based on a Chinese ideological and political education corpus) is loaded. Standardized text is input into the model, and semantic feature vectors are extracted. Based on a pre-defined teaching attribute labeling system (e.g., matching grade level), the attribute category of each resource unit is determined by similarity matching between semantic feature vectors and attribute label vectors. Simultaneously, the attribute categories are quantified and encoded (e.g., matching grade level: primary school = 1, junior high school = 2, senior high school = 3; implementation difficulty: easy = 1, medium = 2, difficult = 3), generating a teaching attribute data table for the resource unit and completing the attribute analysis.

[0050] The node setting module is used to establish resource zones based on teaching attributes and match teaching resources to corresponding resource zones. At the same time, it collects teaching objectives and allocation plans, and sets target nodes according to teaching objectives. Zones are formed by clustering resources according to their attributes, and teaching objectives are broken down into actionable target nodes to lay the groundwork for the correspondence between resource zones and target nodes.

[0051] In the node setting module, the upper limit of the number of resources and the attribute difference threshold of the resource area are set. Then, teaching resources are randomly selected as representative resources of the resource area. Then, other teaching resources are compared with the representative resources for attribute differences. Teaching resources within the upper limit of the number of resources and meeting the attribute difference threshold are matched into the resource area.

[0052] Prioritize resources with the lowest attribute differences to enter resource zones. For teaching resources that do not match any resource zones, create new resource zones for them, following these steps:

[0053] Based on the total amount of ideological and political education resources in the area and the actual teaching needs, the upper limit of the number of resources in a single resource area is set to 10-20. Based on the teaching attribute tag system, an attribute difference threshold is set. The smaller the threshold, the more similar the resource attributes in the area, and the higher the matching accuracy. This is verified through a large number of ideological and political education resource clustering.

[0054] Then, from the teaching resource database with completed attribute annotation, a stratified random sampling method is used to select candidate representative resources according to the proportion weight of resource types. The attribute coverage rate of the candidate representative resources (the number of ideological and political topics covered and the total number of ideological and political topics) is calculated, and the resource with the highest attribute coverage rate is selected as the representative resource of the first resource area.

[0055] Extract the quantitative vector of teaching attributes of representative resources, and extract the quantitative vector of attributes of other resources one by one. Calculate the attribute difference degree between each resource and the representative resource, sort them from smallest to largest difference degree, prioritize the selection of teaching resources with difference degree less than the attribute difference threshold, and then include the sorted teaching resources into the area in turn until the number of resources in the area reaches N or there are no resources that meet the difference threshold to be included, thus completing the construction of the current area, and adding other resources to the area.

[0056] For teaching resources that failed to be matched, a new resource zone will be created for them, and they will be matched again with the remaining unmatched teaching resources until all teaching resources have a corresponding resource zone.

[0057] In the node setting module, teaching objectives and allocation plans are obtained through the ideological and political education teaching management terminal;

[0058] The teaching objectives are the constraints for selecting teaching resources;

[0059] The allocation scheme is the teaching resource allocation scheme before teaching management is carried out through the teaching management module;

[0060] To set target nodes for teaching objectives, semantic analysis is performed on the teaching objectives to obtain their semantic meaning. Then, the semantic meaning is broken down into differences, and target nodes are set based on the results of the difference breakdown. The steps are as follows:

[0061] Through the dedicated data interface of the ideological and political education teaching management terminal, a request to retrieve objectives and plans is initiated. The request parameters include the teaching scenario identifier, teaching stage, and teaching cycle. Then, the structured data returned by the management terminal is received, in which the teaching objectives and allocation plans are stored in text form.

[0062] Then, the fine-tuned BERT model for ideological and political education (trained based on the corpus of the "Ideological and Political Education Curriculum Standards") is loaded. The teaching objective text is input into the model, and the output is a semantic feature vector with a dimension of 512. The core connotation of the objective is captured, and the similarity between the semantic feature vector and the preset core competency label vectors of ideological and political education (such as cognitive ability, emotional attitude, and behavioral practice) is calculated. The top 3 core competency labels with the highest similarity are selected as the core semantic orientation of the teaching objective.

[0063] Based on the core semantic orientation, the semantics of the teaching objectives are hierarchically decomposed. The first level is decomposed into core competency dimensions (such as cognition and emotion), and the second level is decomposed into specific competency requirements. Then, the semantic difference of each specific competency requirement after the second level is calculated. When the difference is ≥0.4, it is determined as an independent semantic unit and set as a target node separately; when the difference is <0.4, it is merged into the same target node, thereby avoiding node redundancy.

[0064] The resource filtering module is used to set the number of resource types for target nodes in conjunction with the allocation scheme, then perform attribute matching between resource regions and target nodes, and filter and retain the matching results in conjunction with the number of resource types to form a candidate resource set; according to the resource requirements of the target nodes, suitable resources are selected from the corresponding regions to form a preliminary candidate set;

[0065] In the resource screening module, a resource type demand analysis is performed on each target node to determine the resource type demand of each target node for teaching resources;

[0066] Extract the core semantic keywords and core capability tags (such as cognitive ability and concept understanding) of each target node, and match the resource type corresponding to each target node based on the preset resource type and capability tag mapping table (such as cognitive ability corresponding to text and audio / video resources).

[0067] Set the initial number of resource types for resource type requirements, then determine the number of teaching resources corresponding to each target node in the allocation plan, and adjust the initial number of resource types based on the corresponding number of teaching resources;

[0068] The more teaching resources there are, the more the initial resource type quantity can be adjusted upwards;

[0069] The fewer the corresponding teaching resources, the less the initial resource type quantity will be adjusted upwards.

[0070] Set the initial quantity of resource types according to the resource type requirement list (initial quantity of text type = 3, initial quantity of audio and video type = 2, which can be adjusted according to the conventional allocation ratio of ideological and political teaching). Then, extract the teaching resource quantity corresponding to each target node from the allocation plan, calculate the proportion of the resource quantity of that node to the total resource quantity in the allocation plan, and dynamically adjust the initial quantity according to the proportion. The larger the R, the larger the adjustment. Generate the final quantity of resource types to ensure that the quantity matches the teaching needs and avoid resource redundancy or insufficiency. The formula is as follows:

[0071] ;

[0072] Among them, Q z Q0 is the final number of resource types, k is the initial number of resource types, k is the adjustment coefficient (ranging from 0.5 to 1.5, set according to the teaching scenario), and R is the resource quantity ratio. When R is greater than 50%, k is 1.5, and when R is less than 20%, k is 0.5.

[0073] In the resource filtering module, based on the resource type requirements of the target node, similar teaching resources are extracted from the resource area. Then, the similar teaching resources are combined with the semantics of the target node for attribute matching to obtain the matching degree between the teaching resources in the resource area and the semantics of the target node.

[0074] The teaching resources are sorted according to their matching degree, and then filtered and retained based on the resource type data. The teaching resources retained for each target node are summarized to form a candidate resource set.

[0075] Among these steps, priority is given to retaining teaching resources with high matching rates. The steps are as follows:

[0076] Based on the resource type requirements of the target node, extract similar teaching resources from the corresponding resource area (e.g., extract text resources from the text resource area). Load the BERT model used in the ideological and political education domain mentioned above to generate semantic feature vectors for the target node and attribute semantic vectors for the extracted resources. Calculate the matching degree between the two, sort the resources of the same type from high to low matching degree, and, considering the final number of resource types, prioritize retaining the top-ranked resources. Finally, summarize all the resources filtered from the target nodes to generate a candidate resource set. Label each resource with a matching node ID and matching degree score to complete the filtering. The formula is as follows:

[0077] ;

[0078] Where P(M) represents the semantic matching degree between the target node and the teaching resource. This is the semantic feature vector of the target node. P(M) is the semantic vector of the teaching resources. The closer P(M) is to 1, the stronger the semantic adaptability between the resources and the nodes.

[0079] The resource comparison module is used to set a similarity threshold, perform a similarity analysis between the candidate resource set and the teaching resources in the allocation scheme, and compare the analysis results with the similarity threshold to update the candidate resource set, retaining only teaching resources with a similarity greater than the threshold; by comparing with the initial allocation scheme, resources with high similarity are selected to ensure that the candidate resources are consistent with the original scheme.

[0080] In the resource comparison module, a similarity threshold is set from the ideological and political education teaching management end (set independently by the management user, based on the teaching scenario and resource type). The candidate resource set and the teaching resources in the allocation scheme are then analyzed for similarity based on their type to obtain the similarity between the teaching resources in the candidate resource set and those in the allocation scheme. The similarity analysis is performed using teaching attributes, and the steps are as follows:

[0081] Extract resource type tags from the candidate resource set, group them by type, and then extract corresponding teaching resources of the same type from the allocation scheme to form a benchmark set for comparison. This ensures that the candidate resources and benchmark resources are completely consistent in type, avoiding invalid cross-type comparisons. Then, extract the teaching attributes (resource form, appropriate grade level, ideological and political goals, implementation difficulty, and teaching duration) of the candidate resources and benchmark resources, obtain the quantitative coding value of each attribute, calculate the similarity of individual attributes, and then obtain the comprehensive similarity through weighted summation to quantify the attribute fit between the two. Finally, calculate the comprehensive similarity of each resource in the candidate resource set with the same type of resource in the benchmark set (taking the highest similarity with the benchmark resource as the final similarity of the candidate resource), as shown in the following formula:

[0082] ;

[0083] Where T is the final similarity threshold, T0 is the baseline threshold for the corresponding teaching scenario (classroom teaching = 0.7), and w t Weights for resource types (text = 1.0, audio / video = 0.9);

[0084] ;

[0085] Among them, S zh To assess overall similarity, k corresponds to five core teaching attributes (resource type, appropriate grade level, ideological and political goals, implementation difficulty, and teaching duration), w k Assign weights to each attribute, such as resource type 0.15, appropriate educational stage 0.3, ideological and political goals 0.3, implementation difficulty 0.15, and teaching duration 0.1, S k Let be the similarity of the k-th attribute;

[0086] Compare the similarity score with a similarity threshold;

[0087] If the similarity between the teaching resources in the candidate resource set and the teaching resources in the allocation scheme is greater than the similarity threshold, then the resource is retained.

[0088] If the similarity between the teaching resources in the candidate resource set and the teaching resources in the allocation scheme is less than the similarity threshold, then the candidate resource set will not be retained, and the update of the candidate resource set will be completed.

[0089] The teaching management module is used to randomly adjust the updated candidate resource set and perform cross-analysis of similar resources in conjunction with the allocation scheme. Based on the cross-analysis results, it obtains resource schemes that are suitable for the target nodes and combines the resource schemes with the allocation scheme to manage teaching based on the number of resources. It generates resource schemes that are suitable for the target nodes from the precise candidate resource set, verifies the compatibility between the schemes and the nodes, and compares the adapted schemes with the initial allocation schemes to determine the final execution scheme, thereby realizing the implementation and management of resources.

[0090] In the teaching management module, the teaching resources in the updated candidate resource set are randomly adjusted, and then the randomly adjusted teaching resources are combined with the allocation scheme to conduct cross-analysis of similar resources, and multiple resource schemes are obtained based on the analysis results.

[0091] The random adjustment process involves compressing teaching resources without expanding them, and the steps are as follows:

[0092] Set a compression ratio range, such as 20%-50%, which is dynamically determined based on resource redundancy. When the total resource amount is greater than 30% of the allocation plan, the compression ratio is 50%; when the total resource amount is less than or equal to 30% of the allocation plan, the compression ratio is 20%. Use random sampling to compress each group of resources. For example, if there are 10 text resources and the compression ratio is 30%, then 7 will be randomly retained to generate a compressed and adjusted subset of resources.

[0093] Then, the teaching attributes of similar resources in the allocation scheme are extracted as the basis for cross-analysis. The compressed resource subset is cross-compared with the similar resources in the allocation scheme to analyze the complementarity of resources (avoiding duplication of knowledge points) and logical connection (such as the process adaptation between pre-class resources and classroom resources). Based on the cross-analysis results, multiple resource schemes are combined (number of schemes = number of resource type groups × 2 to ensure diversity). Each scheme is labeled with the resource type composition and teaching attribute list.

[0094] The resource schemes are adapted to each target node, and only adapted resource schemes are retained. Specifically, the teaching resource attributes of the resource schemes are combined with the target nodes for coverage analysis to ensure that the teaching resource attributes of the resource schemes cover the target nodes; otherwise, they are considered incompatible. For each resource scheme, attribute coverage analysis is performed to verify whether the attributes of the resources in the scheme completely cover the attribute requirements of the corresponding target node. Only resource schemes whose attributes completely cover the target nodes are retained (incompatible schemes are directly eliminated), generating a set of adapted resource schemes.

[0095] In the teaching management module, the resource quantity will be compared by combining the retained and adapted resource schemes with the allocation schemes;

[0096] If the resource data of the resource plan is greater than that of the allocation plan, then no teaching management will be carried out;

[0097] If the resource data of the resource plan is less than that of the allocation plan, the resource plan with the fewest resources is selected as the optimal plan, synchronized to the ideological and political teaching management terminal to replace the allocation plan, update the resource scheduling list and teaching implementation plan, and complete the teaching management.

[0098] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A political education teaching management platform based on regional linkage, characterized in that: It includes a resource acquisition module, a node setting module, a resource filtering module, a resource comparison module, and a teaching management module; The resource acquisition module is used to acquire ideological and political education teaching resources, break down the teaching resources into teaching resource segments, analyze the teaching attributes of the teaching resources, and acquire the teaching attributes corresponding to the teaching resources. In the node setting module, the upper limit of the number of resources and the attribute difference threshold of the resource area are set. Then, teaching resources are randomly selected as representative resources of the resource area. Then, other teaching resources are compared with the representative resources for attribute differences. Teaching resources within the upper limit of the number of resources and meeting the attribute difference threshold are matched into the resource area. Prioritize selecting resources with the lowest attribute differences for inclusion in resource zones. For teaching resources that do not match resource zones, create new resource zones for them. In the node setting module, teaching objectives and allocation schemes are obtained through the ideological and political education management terminal; The teaching objectives are the constraints for selecting teaching resources; The allocation scheme is the teaching resource allocation scheme before teaching management is carried out through the teaching management module; The teaching objectives are set as target nodes. The semantics of the teaching objectives are obtained by performing semantic analysis. Then, the semantics are differentiated and the target nodes are set according to the results of the differentiation. The resource filtering module is used to set the number of resource types for target nodes in conjunction with the allocation scheme, then perform attribute matching between resource areas and target nodes, and filter and retain the matching results in conjunction with the number of resource types to form a candidate resource set; The resource comparison module is used to set a similarity threshold, perform a similarity analysis between the candidate resource set and the teaching resources of the allocation scheme, and compare the analysis results with the similarity threshold to update the candidate resource set and retain teaching resources with a similarity threshold. In the teaching management module, the teaching resources in the updated candidate resource set are randomly adjusted, and then the randomly adjusted teaching resources are combined with the allocation scheme to perform cross-analysis of similar resources, and multiple resource schemes are obtained based on the analysis results. In this process, random adjustments will compress teaching resources without expanding them; The resource plan is adapted to each target node, and the adapted resource plan is retained. In particular, the teaching resource attributes of the resource plan are combined with the target nodes for coverage analysis to ensure that the teaching resource attributes of the resource plan cover the target nodes; otherwise, it is considered unsuitable. In the teaching management module, the resource quantity will be compared by combining the retained and adapted resource schemes with the allocation schemes. If the resource data of the resource plan is greater than that of the allocation plan, then no teaching management will be carried out; If the resource data of the resource plan is less than that of the allocation plan, the resource plan with the fewest resources is selected to replace the allocation plan, thus completing the teaching management.

2. The ideological and political education management platform based on regional linkage according to claim 1, characterized in that: In the resource acquisition module, stored ideological and political teaching resources are obtained from the ideological and political teaching management terminal by connecting to the ideological and political teaching management terminal. The ideological and political education teaching resources are broken down according to the ideological and political education theme, and then broken down into multiple separate teaching resources. At the same time, the teaching attributes of the teaching resources are analyzed to obtain the corresponding teaching attributes of the teaching resources. The teaching attributes are obtained through semantic analysis of the teaching resources.

3. The ideological and political education management platform based on regional linkage as described in claim 1, characterized in that: In the resource screening module, a resource type demand analysis is performed on each target node to determine the resource type demand of each target node for teaching resources. Set the initial number of resource types for resource type requirements, then determine the number of teaching resources corresponding to each target node in the allocation plan, and adjust the initial number of resource types based on the corresponding number of teaching resources; The more teaching resources there are, the more the initial resource type quantity can be adjusted upwards; The fewer the corresponding teaching resources, the less the initial resource type quantity will be adjusted upwards.

4. The ideological and political education management platform based on regional linkage according to claim 3, characterized in that: In the resource filtering module, based on the resource type requirements of the target node, similar teaching resources are extracted from the resource area. Then, the similar teaching resources are combined with the semantics of the target node for attribute matching to obtain the matching degree between the teaching resources in the resource area and the semantics of the target node. The teaching resources are sorted according to their matching degree, and then filtered and retained based on the resource type data. The teaching resources retained for each target node are summarized to form a candidate resource set. Among them, teaching resources with high matching degree will be given priority.

5. The ideological and political education management platform based on regional linkage according to claim 1, characterized in that: In the resource comparison module, a similarity threshold is set from the ideological and political education management end, and the candidate resource set and the teaching resources in the allocation scheme are analyzed for similarity of the same type to obtain the similarity between the teaching resources in the candidate resource set and the teaching resources in the allocation scheme; among which, the similarity analysis is completed through teaching attributes; Compare the similarity score with a similarity threshold; If the similarity between the teaching resources in the candidate resource set and the teaching resources in the allocation scheme is greater than the similarity threshold, then the resource is retained. If the similarity between the teaching resources in the candidate resource set and the teaching resources in the allocation scheme is less than the similarity threshold, then the candidate resource set will not be retained, and the update of the candidate resource set will be completed.

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