Method and system for promoting efficient communication and cooperation in industry-education integration community

Abstract

The present application relates to the technical field of industry-education integration, and relates in particular to a method and system for promoting efficient communication and cooperation in an industry-education integration community. The method comprises: constructing a dynamic cooperation platform based on multi-dimensional feature fusion, and generating a cooperation feature matrix by means of collecting user behavior data, task requirement data, and resource distribution data; using the cooperation feature matrix to perform real-time matching on a user and generate an optimal cooperation path, and dynamically adjusting a cooperation policy by means of a path optimization algorithm; establishing a real-time interaction channel on the basis of the cooperation path, analyzing user interaction content by means of semantic analysis and affective computing techniques, and generating a cooperation feedback report; and integrating education resources and industry resources in the platform to construct a resource map, and implementing accurate pushing and sharing of resources by means of a resource recommendation algorithm. The present method and system significantly improve the overall cooperation efficiency and quality of the industry-education integration community.

Description

A method and system for promoting efficient communication and collaboration within an industry-education integration community. Technical Field

[0001] This invention belongs to the field of educational technology and communication technology, specifically a method and system for promoting efficient communication and collaboration within an industry-education integration community. Background Technology

[0002] With the deepening of industry-education integration, promoting efficient communication and collaboration among industry-education integration communities has become a key research focus. Existing technical solutions mainly focus on task allocation, information dissemination, and teaching management, but they still have some shortcomings in practical applications, especially in multi-party collaboration, real-time interaction, and resource integration.

[0003] A search revealed a patent, CN113656686B, titled "A Method and Service System for Generating Task Reports Based on Industry-Education Integration," published on September 6, 2024. This patent utilizes a matching recommendation algorithm based on task feature parameters and user behavior feature parameters to achieve efficient matching between tasks and users and generate task management reports. However, this technical solution primarily focuses on task allocation and execution record generation, lacking support for real-time multi-party interaction and failing to meet the needs of industry-education integration communities in dynamic collaborative scenarios. Furthermore, its matching mechanism relies heavily on preset recommendation thresholds, which may lead to insufficient matching accuracy in complex scenarios, impacting overall collaboration efficiency.

[0004] A search revealed a method and service system for information push based on industry-education integration, with publication number CN113254833B, published on October 29, 2021. This patent achieves accurate information push by analyzing user group characteristic parameters and target object characteristic parameters. However, this technical solution focuses on the accuracy of information push and fails to fully consider the real-time communication and collaboration needs among members of the industry-education integration community. Its push mechanism is mainly based on static data, making it difficult to adapt to dynamically changing collaborative environments, which may lead to information lag or mismatch. In addition, this solution lacks support for the integration and sharing of multi-party resources, limiting its application effectiveness in complex industry-education integration scenarios. Technical issues

[0005] The aforementioned problems indicate that existing industry-education integration technologies still have certain shortcomings in areas such as real-time multi-party interaction, dynamic collaborative support, and resource integration and sharing. Therefore, this invention provides a method and system for promoting efficient communication and collaboration within an industry-education integration community. The aim is to improve the overall collaborative efficiency and quality of the industry-education integration community by constructing an efficient communication platform, achieving real-time interaction and dynamic collaboration, and optimizing resource integration and sharing mechanisms, thereby meeting the needs of deep integration between modern education and industry. Technical solutions

[0006] To address the aforementioned problems in the prior art, this invention provides a method and system for promoting efficient communication and collaboration within an industry-education integration community. This system enables real-time multi-party interaction, dynamic collaborative support, and resource integration and sharing, thereby improving the overall collaborative efficiency and quality of the industry-education integration community.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0008] Firstly, this invention provides a method for promoting efficient communication and collaboration within an industry-education integration community, comprising the following steps:

[0009] A dynamic collaboration platform based on multi-dimensional feature fusion is constructed, which generates a collaboration feature matrix by collecting user behavior data, task requirement data, and resource distribution data;

[0010] The system uses a collaboration feature matrix to match users in real time, generates the optimal collaboration path, and dynamically adjusts the collaboration strategy through a path optimization algorithm.

[0011] Establish a real-time interactive channel based on the collaboration path, and analyze user interaction content through semantic analysis and sentiment computing technologies to generate collaboration feedback reports;

[0012] By integrating teaching and industry resources within the platform, a resource map is constructed, and resource recommendation algorithms are used to achieve precise resource delivery and sharing. Beneficial effects

[0013] By constructing a dynamic collaboration platform using multi-dimensional feature fusion technology, comprehensive perception and intelligent matching of user behavior, task requirements, and resource distribution are achieved, significantly improving the accuracy and efficiency of collaboration. Simultaneously, real-time interactive channels based on semantic analysis and sentiment computing effectively capture user intent and emotional state, enhancing the depth and quality of communication. Furthermore, the introduction of resource graphs enables the efficient integration and sharing of teaching and industry resources, meeting the dynamic collaboration needs in complex scenarios and promoting the efficient operation of the industry-education integration community as a whole.

[0014] Optionally, the step of using the collaboration feature matrix to perform real-time matching of users and generate the optimal collaboration path includes:

[0015] User behavior data, task requirement data, and resource distribution data are mapped to a high-dimensional feature space to form user feature vector U, task feature vector T, and resource feature vector R.

[0016] The matching degree between the three is calculated using the similarity calculation formula S(U,T,R)=α·cos(U,T)+β·cos(T,R)+γ·cos(U,R), where α,β,γ are weight coefficients and satisfy α+β+γ=1, and cos(X,Y) represents the cosine similarity between vectors X and Y.

[0017] An initial collaboration path is generated based on the matching results, and then a path optimization algorithm is applied. Dynamically adjust the path, where w i The path weight d(v) i ,v i+1 ) represents the distance function between nodes, and finally obtains the optimal cooperative path.

[0018] Optionally, establishing a real-time interactive channel based on a collaborative path includes:

[0019] Semantic analysis of user interaction content is performed using natural language processing technology to extract key intent information;

[0020] The emotional computing model E = σ(W1·X + b1) is used to analyze the user's emotional state, where X is the input text vector, W1 and b1 are model parameters, and σ is the activation function.

[0021] By combining semantic analysis results with emotional states, a collaborative feedback report is generated, and the collaborative strategy is dynamically adjusted through a feedback mechanism to ensure efficient and accurate communication. Attached Figure Description

[0022] Figure 1 is a flowchart illustrating the method for promoting efficient communication and collaboration within an industry-education integration community provided in an embodiment of the present invention.

[0023] Figure 2 is a schematic diagram of the construction of the dynamic collaboration platform and the generation process of the collaboration feature matrix in an embodiment of the present invention.

[0024] Figure 3 is a schematic diagram of the process of generating the optimal cooperative path based on the path optimization algorithm in an embodiment of the present invention.

[0025] Figure 4 is a schematic diagram of the establishment of the real-time interactive channel and the process of semantic analysis and sentiment calculation in an embodiment of the present invention.

[0026] Figure 5 is a schematic diagram illustrating the process of constructing the resource map and implementing accurate resource recommendation algorithm in an embodiment of the present invention. Embodiments of the present invention

[0027] This invention provides a method and system for promoting efficient communication and collaboration within an industry-education integration community. Its core lies in constructing a dynamic collaboration platform through multi-dimensional feature fusion technology, and combining key technologies such as semantic analysis, sentiment computing, and resource mapping to achieve real-time multi-party interaction, dynamic collaboration support, and resource integration and sharing. The specific embodiments of this invention will be described in detail below with reference to Figures 1 to 6.

[0028] First, as shown in Figure 1, an embodiment of the present invention provides a schematic diagram of the overall process for promoting efficient communication and collaboration within an industry-education integration community. This method mainly includes four key steps: constructing a dynamic collaboration platform based on multi-dimensional feature fusion, generating optimal collaboration paths using a collaboration feature matrix, establishing real-time interactive channels, and integrating teaching and industry resources to construct a resource map. These steps together constitute a complete collaboration system that can significantly improve the collaboration efficiency and quality of the industry-education integration community.

[0029] In the first step, constructing a dynamic collaboration platform based on multi-dimensional feature fusion is the foundation of the entire method. As shown in Figure 2, this platform generates a collaboration feature matrix by collecting user behavior data, task requirement data, and resource distribution data. Specifically, user behavior data includes user operation records, historical interaction information, and preference settings; task requirement data covers task goal descriptions, priority settings, and completion deadlines; and resource distribution data involves the types, quantities, and availability of teaching and industry resources. These data are mapped to a high-dimensional feature space, forming user feature vector U, task feature vector T, and resource feature vector R. To ensure the accuracy of the feature vectors, this embodiment uses principal component analysis (PCA) to reduce the dimensionality of the original data, thereby extracting the most representative feature dimensions. Subsequently, the matching degree between the three is calculated using the similarity calculation formula S(U,T,R)=α·cos(U,T)+β·cos(T,R)+γ·cos(U,R), where α, β, and γ are weight coefficients and satisfy α+β+γ=1, and cos(X,Y) represents the cosine similarity between vectors X and Y. The introduction of this formula enables the platform to comprehensively consider the correlation between users, tasks, and resources, thereby generating a more accurate collaboration feature matrix. For example, in a practical application scenario, a university teacher wants to collaborate with corporate engineers to develop a practical course. The platform generates corresponding feature vectors by collecting behavioral data, task requirement data, and resource distribution data from both parties, and calculates a matching degree of 0.85 using the above formula, indicating that both parties have high collaboration potential.

[0030] In the second step, generating the optimal collaborative path using the collaborative feature matrix is ​​a crucial step in achieving efficient collaboration. As shown in Figure 3, this embodiment first generates an initial collaborative path based on the matching degree results, and then dynamically adjusts the path using a path optimization algorithm to obtain the optimal solution. Specifically, the path optimization algorithm employs... Dynamically adjust the path, where w i The path weight d(v) i ,v i+1 The path weights represent the distance function between nodes, ultimately yielding the optimal collaboration path. Here, nodes can be users, tasks, or resources, while path weights reflect the importance or priority of different nodes. For example, in a scenario where a company needs to collaborate with multiple universities on a technology research and development project, the platform generates an initial collaboration path by calculating the matching degree between each university and the company. Subsequently, the path optimization algorithm dynamically adjusts the path weights based on factors such as the geographical location, research and development capabilities, and resource availability of each university, ultimately determining the optimal collaboration path. This process not only improves the accuracy of collaboration but also significantly shortens the task completion time.

[0031] In the third step, establishing a real-time interactive channel based on the collaborative path is the core means to ensure the efficiency and accuracy of communication. As shown in Figure 4, this embodiment uses natural language processing technology to semantically analyze user interaction content and extract key intent information. Specifically, the platform uses a bidirectional long short-term memory network (BiLSTM) model to encode the user's input text, generating an input text vector X. Subsequently, the sentiment computing model E = σ(W1·X + b1) is used to analyze the user's emotional state, where X is the input text vector, W1 and b1 are model parameters, and σ is the activation function E. This model can effectively capture the user's emotional tendency. For example, in a real-world case, an engineer from a company expressed dissatisfaction when discussing project progress with a university teacher. The platform identified his emotional state as negative through the sentiment computing model and promptly generated a collaborative feedback report. The report not only includes the semantic analysis results but also proposes specific improvement suggestions, such as adjusting task allocation or increasing resource support. Through this feedback mechanism, the platform can dynamically adjust its collaborative strategy to ensure the efficiency and accuracy of communication.

[0032] In the fourth step, integrating teaching and industry resources to construct a resource graph is a crucial guarantee for achieving resource sharing and precise delivery. As shown in Figure 5, this embodiment first categorizes and organizes the teaching and industry resources in the platform, forming the basic framework of the resource graph. Subsequently, a resource recommendation algorithm is used to achieve precise delivery and sharing of resources. Specifically, the resource recommendation algorithm employs collaborative filtering technology, combining users' historical behavioral data and current task requirements to calculate the relevance score of resources. For example, in a certain scenario, a university teacher wants to obtain teaching resources related to artificial intelligence. The platform uses the resource recommendation algorithm to filter out course materials, experimental equipment, and enterprise cases that meet the teacher's needs and pushes them to the teacher. In addition, the resource graph also supports cross-institutional resource sharing. For example, after completing a project, a company donates its remaining R&D equipment to a university. The platform uses the resource graph to achieve rapid matching and allocation of the equipment. This process not only improves resource utilization but also promotes deep cooperation between teaching and industry.

[0033] In summary, this invention, by constructing a dynamic collaboration platform based on multi-dimensional feature fusion and combining key technologies such as semantic analysis, sentiment computing, and resource graphs, achieves efficient communication and collaboration within an industry-education integration community. In practical applications, this method has been widely used in joint R&D projects between universities and enterprises, practical course development, and talent training programs, achieving significant results. For example, in a smart manufacturing course jointly developed by a university and an enterprise, the platform generated a precise collaboration feature matrix by collecting behavioral data, task requirement data, and resource distribution data from both parties, and determined the optimal collaboration path using a path optimization algorithm. Simultaneously, the platform captured the communication intentions and emotional states of both parties through a real-time interactive channel, generating detailed collaboration feedback reports. Furthermore, the introduction of resource graphs enabled the efficient integration and sharing of teaching and industry resources, ultimately successfully completing the course development task and achieving positive social feedback.

[0034] The embodiments of this invention fully demonstrate the feasibility and superiority of its technical solution, not only solving the problems existing in the prior art, but also providing strong support for the efficient operation of the industry-education integration community. Through the above detailed description, those skilled in the art can clearly understand the technical principles and implementation steps of this invention, and can flexibly apply them in practical applications.

Claims

1. A method for promoting efficient communication and collaboration in a production-education integration community, characterized in that, The method comprises the steps of: A dynamic collaboration platform based on multi-dimensional feature fusion is constructed, and a collaboration feature matrix (4) is generated by collecting user behavior data (1), task demand data (2), and resource distribution data (3); The optimal collaboration path (5) is generated by using the collaboration feature matrix (4) to match the users in real time, and the collaboration strategy is dynamically adjusted by the path optimization algorithm; Based on the collaboration path (5), a real-time interaction channel is established, the user interaction content is analyzed by semantic analysis and sentiment calculation technology, and a collaboration feedback report (6) is generated; The teaching resources and industrial resources in the platform are integrated to construct a resource graph (7), and the resource recommendation algorithm is used to realize the accurate pushing and sharing of resources.

2. The method of claim 1, wherein, The dynamic collaboration platform based on multi-dimensional feature fusion comprises: The user behavior data (1), task demand data (2), and resource distribution data (3) are respectively mapped to a high-dimensional feature space to form a user feature vector U, a task feature vector T, and a resource feature vector R; The matching degree between the three is calculated by a similarity calculation formula S(U,T,R)=α·cos(U,T)+β·cos(T,R)+γ·cos(U,R), wherein α, β, and γ are weight coefficients, and α+β+γ=1, and cos(X,Y) represents the cosine similarity of vectors X and Y. 3.The method of facilitating efficient communication and collaboration of the production-teaching integration community according to claim 1, wherein, The optimal collaboration path (5) is generated by using the collaboration feature matrix, which comprises: According to the matching degree result, an initial cooperative path is generated, and a path optimization algorithm is used Dynamic adjustment path, wherein w i is the path weight d(v i ,v i+1 ) represents the distance function between nodes, and finally the optimal cooperative path is obtained.

4. The method of claim 1, wherein the method further comprises: The real-time interaction channel based on the collaboration path comprises: The semantic analysis of the user interaction content is performed by natural language processing technology to extract key intent information; The sentiment state of the user is analyzed by using a sentiment calculation model E=σ(W1·X+b1), wherein X is an input text vector, W1 and b1 are model parameters, and σ is an activation function; The collaboration feedback report (6) is generated by combining the semantic analysis result and the sentiment state, and the collaboration strategy is dynamically adjusted by the feedback mechanism.

5. The method of claim 1, wherein the method further comprises: The integration of teaching resources and industrial resources in the platform to construct a resource graph (7) comprises: The teaching resources and industrial resources in the platform are classified and arranged to form the basic framework of the resource graph; The accurate pushing and sharing of resources are realized by the resource recommendation algorithm.

6. A system for promoting efficient communication and collaboration within an industry-education integration community, characterized in that: It comprises: A dynamic collaboration platform construction module is used to construct a dynamic collaboration platform based on multi-dimensional feature fusion, and a collaboration feature matrix (4) is generated by collecting user behavior data (1), task demand data (2), and resource distribution data (3); A user matching and path optimization module is used to match the users in real time by using the collaboration feature matrix (4) to generate the optimal collaboration path (5), and the collaboration strategy is dynamically adjusted by the path optimization algorithm; A real-time interaction channel module is used to establish a real-time interaction channel based on the collaboration path (5), analyze the user interaction content by semantic analysis and sentiment calculation technology, and generate a collaboration feedback report (6); A resource integration and recommendation module is used to integrate the teaching resources and industrial resources in the platform to construct a resource graph (7), and the accurate pushing and sharing of resources are realized by the resource recommendation algorithm.

7. The system of claim 6, wherein, The dynamic collaboration platform construction module comprises: A data collection unit is configured to collect user behavior data (1), task demand data (2), and resource distribution data (3); A feature mapping unit is configured to map the user behavior data (1), the task demand data (2), and the resource distribution data (3) to high-dimensional feature spaces respectively, to form a user feature vector U, a task feature vector T, and a resource feature vector R; A similarity calculation unit is configured to calculate the matching degree among the three through a similarity calculation formula S(U, T, R) = α·cos(U, T) + β·cos(T, R) + γ·cos(U, R). 8.The system of facilitating efficient communication and collaboration of the production-teaching integration community according to claim 6, wherein, The user matching and path optimization module comprises: An initial path generation unit is configured to generate an initial collaborative path according to the matching degree result; path optimization unit for applying a path optimization algorithm A dynamic adjustment path, and finally an optimal collaborative path (5) is obtained. 9.The system of facilitating efficient communication and collaboration of the production-teaching integration community according to claim 6, wherein, The real-time interaction channel module comprises: A semantic analysis unit is configured to perform semantic analysis on user interaction content through natural language processing technology, and extract key intent information; An emotion analysis unit is configured to analyze the user emotional state by using an emotion calculation model E = σ(W1·X+b1); A feedback generation unit is configured to generate a collaborative feedback report (6) in combination with the semantic analysis result and the emotional state, and dynamically adjust the collaboration strategy through a feedback mechanism. 10.The system of facilitating efficient communication and collaboration of the production-teaching integration community according to claim 6, wherein, The resource integration and recommendation module comprises: A resource classification and arrangement unit is configured to classify and arrange the teaching resources and industrial resources in the platform, to form a basic framework of a resource atlas; A resource recommendation unit is configured to realize accurate pushing and sharing of resources through a resource recommendation algorithm.

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