Network information technology consultation communication platform application method and system

Abstract

The invention discloses a network information technology consultation and communication platform application method and system, relates to the technical field of network information, and realizes personalized consultation and communication of new and old users by constructing a multi-technology database connection platform. When a new user registers, initial simulation consultation is carried out, data mining is deeply carried out to construct a similar technology database, and a matching degree is calculated to push related contents. And the old user constructs a demand model according to the historical record and pushes customized information. And all alternating-current recorded data are protected and prevented from being leaked. The system analyzes the IP and the geographic position in real time, implements risk area management and dual identity verification, and ensures data security. According to the method, the information pushing precision and the user experience are improved, the data protection is enhanced, and the security and the service quality of the platform are improved.

Description

Technical Field

[0001] The present invention relates to the field of network information technology, and particularly relates to an application method and system for a network information technology consulting and communication platform. Background Art

[0002] In today's era of rapid development of information technology, the network information technology consulting and communication platform has become an important carrier for promoting technological progress and innovation. In recent years, with the continuous maturity of technologies such as big data, cloud computing, and artificial intelligence, the traditional technology consulting and communication model has gradually transformed into an online platform. These platforms integrate various technical resources to provide users with convenient consulting and communication services, greatly improving the efficiency of technology dissemination and knowledge sharing. However, the existing network information technology consulting and communication platforms still have certain deficiencies in aspects such as user experience, depth of data mining, personalized recommendation, and data protection.

[0003] Firstly, in the process of user registration and consultation on existing platforms, there is often a lack of in-depth exploration of user interests and personalized recommendations. Although new users can select topics of interest during registration, the platform fails to effectively utilize this information during subsequent consultations, resulting in a deviation between the pushed technical data and expert recommendations and the actual needs of users. Secondly, the data mining models of existing platforms are relatively single, making it difficult to accurately calculate the comprehensive similarity between user interest vectors and technical feature vectors, thus affecting the accurate grasp of user needs. In addition, for old users, existing platforms also have deficiencies in calculating the professionalism and interest matching degree, and fail to fully utilize the historical behavior data of users to optimize the recommendation results. Finally, in terms of data protection, the protection measures for user communication and consultation records on existing platforms are not perfect, and there is a risk of data leakage. The application method and system of the network information technology consulting and communication platform invented by our side have conducted in-depth research and innovation in these aspects in order to provide better services and a safer user experience. Summary of the Invention

[0004] In view of the above existing problems, the present invention aims to provide an application method and system for a network information technology consulting and communication platform, aiming to solve problems such as inaccurate user interest recognition, inaccurate personalized service push, imperfect data security protection measures, and poor user communication experience in the prior art. By constructing a personalized consulting and communication platform based on a multi-technology database, in-depth exploration and accurate matching of user interests are realized, a user demand model is dynamically constructed to provide personalized content recommendations, and at the same time, a comprehensive data protection mechanism is designed to ensure user privacy and data security, thereby improving the efficiency and satisfaction of users in the process of information acquisition and communication, and ensuring the stable operation and information security of the platform.

[0005] To solve the above technical problems, a method for applying a network information technology consulting and communication platform is proposed, including

[0006] Construct a network information technology consulting and communication platform to connect multiple technology databases. Users select the login or registration button. Users who select the login button are defined as old users, and users who select the registration button are defined as new users. When the new user registers, they select the topics of interest. After registering and logging in, a corresponding consulting assistant is assigned for preliminary simulation consulting. Under the condition of data protection, in-depth data mining is carried out through a computer multi-data model, and the mined data is stored in the similar technology database of the current user. Combining the results of data mining, calculate the matching degree between the data in the multiple technology databases and the content of interest of the new user, push the corresponding technology data, recommend experts and star users. After the new user selects the technology data or communication object, enter the communication and consulting mode, and construct a type-I user demand model in real time. After the old user logs in, obtain the user's professional nature according to the historical records, construct a type-II user demand model in real time, calculate the professional similarity and interest matching degree between the data in the multiple technology databases and the old user, and push the corresponding technology data, experts, star users or technical problems according to the calculation results. After selecting the content, enter the communication and consulting mode. Save the communication and consulting records of all users to the data protection library for data protection to prevent data leakage.

[0007] As a preferred solution of the method for applying a network information technology consulting and communication platform according to the present invention, wherein: the data mining includes that the network information technology consulting and communication platform connects multiple technology databases, and the multiple technology databases crawl the latest published technical articles and white papers through technology forums, periodicals and websites; establish an expert group to review new technologies in line with the current technology trend and decide to update the database; allow users to submit new technologies or new articles that the users think on the platform, and update them to the database after being reviewed by the expert group;

[0008] In the process of preliminary simulation consulting, the system extracts the keywords selected, questions and feedback of the new user, and records them as the user interest vector U = [u1, u2,..., u n , uses the multi-data model for data mining, and based on the technical feature vector P = [p1, p2... p m , calculates the comprehensive similarity S1 between the user interest vector U and the technical feature vector P through an improved data mining model:

[0009]

[0010] Among them, U represents the interest vector selected by the user in the preliminary simulation consulting, n is the number of keywords, u i is the i-th interest technology keyword expressed by the user, P represents the technical feature vector currently stored in the multiple technology databases, m is the number of technical feature vectors, pj is the j-th technical feature vector, S1 represents the similarity between the user interest vector U and the technical feature vector P, k is a constant adjustment term, b is a small positive number to prevent the denominator from being zero, and i and j are variable indices;

[0011] Judge whether the similarity S1 is higher than the preset threshold θ. When S1 > θ, it is considered that the current technology meets the user's interest, and the mining result is stored in the similar technology database.

[0012] As a preferred solution of the application method of a network information technology consulting and communication platform described in the present invention, wherein: calculating the matching degree between the data in the multi-technology database and the content of interest to the new user includes extracting the technical data vector T related to the new user's interest from the multi-technology database with data mining content added, and calculating the similarity S2 between the user interest vector U and each technical data vector T g :

[0013]

[0014] wherein, S2 represents the similarity between the new user interest vector U and the g-th technical data vector T g After the new user browses and selects the multi-technical data, calculate the weighted average of all similarities to form a comprehensive matching degree:

[0015]

[0016] wherein, M represents the overall matching degree between the new user and the technical data in the multi-technology database, w i represents the weight of the technical data selected by the new user, and is referenced by the selection frequency;

[0017] After the new user obtains the matching degree value, push the technical data, experts, and star users with high matching degrees to the past selections according to the matching degree value.

[0018] As a preferred solution of the application method of a network information technology consulting and communication platform described in the present invention, wherein: the type I user demand model includes, during the consultation process, the system records the user's feedback information according to the user's questions, comments, and selection of technical data, and converts the user feedback information into a demand signal D to form the type I user demand model state:

[0019] R t = R t-1 +β·((D t -R t-1 )·log(1 + M avg ))

[0020] wherein, R t-1Denote the status of the Type-I demand model at time t-1, and β represents the learning rate, which controls the impact of new feedback on model update; M avg Denote the average matching degree, R t Denote the status of the Type-I user demand model at time t, D t Denote the demand signal converted from the user feedback information at time t;

[0021] And the platform adjusts the demand model to meet user requirements by continuously monitoring the subsequent participation and satisfaction of users.

[0022] As a preferred solution of an application method of a network information technology consulting and communication platform according to the present invention, wherein: the Type-II user demand model includes that when an old user logs in, the system automatically extracts the historical consultation records of the user, and obtains the professional nature of the old user through data analysis and converts it into a feature vector P nature , and the behavior data of the user on the platform includes the topics consulted by the user, the frequency and interaction depth of the consultation, the technical questions and experts responded by the user;

[0023] Construct the Type-II user demand model R old =f(P nature , history, Y context ), where R old Denote the user demand representation, f represents the dynamic function that combines the input parameters to generate the model, P nature Denote the feature vector corresponding to the professional nature of the user; history represents the historical behavior data of the user, including past activity records and selections; Y context Denote the context weight, which contains context-related information.

[0024] As a preferred solution of an application method of a network information technology consulting and communication platform according to the present invention, wherein: calculating the data of the multi-technology database and the professional similarity and interest matching degree of the old user includes analyzing the mutual relationship between multiple technical fields and calculating the professional similarity in combination with the professional nature of the old user:

[0025]

[0026] Wherein, S professional Denote the professional similarity, that is, the similarity degree between the professional nature of the user and the technology, P is the technical feature vector, c represents a constant controlling the relationship strength, which is used to balance the score; E relation Denote the connection strength between the user feature and the technical feature;

[0027] Sort according to the professional similarity from large to small, select the top 50% of the technical data, and calculate the interest matching degree by fusing the interaction parameters of the user and using the probability model:

[0028]

[0029] Among them, S interest represents the interest matching degree, that is, the matching degree of the technical data selected by the user and the professional similarity. I user,l is the interaction frequency of the user on any technical data topic l. Q interest,l is the interest intensity weight on any technical data topic l, and L is the total amount of the top 50% of the selected technical data;

[0030] Allocate weights to calculate the fusion degree S combined = 0.6·S professional + 0.4·S interest , and push the corresponding technical data, experts, star users or technical problems according to the calculation results. After selecting the content, enter the communication and consultation mode.

[0031] As a preferred solution of an application method of a network information technology consultation and communication platform according to the present invention, wherein: the data protection includes saving the communication and consultation records of all users to a data protection library for data protection, and setting data protection rules for the platform and users respectively;

[0032] The data protection rules for the platform are as follows:

[0033] Rule 1: Adopt a hierarchical storage strategy, and different storage and backup strategies are adopted for data with different sensitivities, which are divided into high, medium, and low security level storage areas. Among them, the low security level storage area is shared data and no data protection is performed;

[0034] Rule 2: Implement an AI-driven dynamic data protection library architecture, and the risk indicators of access frequency and data sensitivity are evaluated in real time through AI. When the risk score of the risk indicator is higher than 70% of the specified range, the data is transferred to the high security level storage area;

[0035] Rule 3: The platform constructs an environment monitoring unit, and biometric authentication is performed simultaneously when a storage request is received. The biometric failure rate is guaranteed to be less than 0.1%. Biometric authentication includes fingerprint, voice, and pupil;

[0036] The data protection rules for users are as follows:

[0037] When the content consulted by the user contains sensitive information, the system automatically marks the current content and stores the data in an isolation area for waiting for review. When the marking rate of the system relative to the total consultation records exceeds 30%, an automatic alarm is issued and a risk review is carried out;

[0038] When a user accesses the data protection library, the IP address and geographical location are analyzed in real time. When the IP address accessed by the user's request is classified into the medium-risk area, and when the user starts the normal communication and consultation mode, the access in the medium-risk area is immediately transferred to the low-risk area for processing. When the IP address requested by the user has not been registered or has not been used normally in the past, the access in the medium-risk area is immediately transferred to the high-risk area for processing. When the request frequency of the unrecognized new connection IP exceeds 5 times, two-factor authentication is enabled.

[0039] Another object of the present invention is to provide a network information technology consultation and communication platform application system. The present invention solves the matching problem between user needs and professional information in the process of network information technology consultation and communication. By constructing a personalized user demand model and a precise data push mechanism, efficient and secure information consultation and communication are realized, while protecting user data privacy and enhancing the user experience.

[0040] As a preferred embodiment of a network information technology consultation and communication platform application system according to the present invention, it includes a user management module, a data analysis and recommendation module, and a data protection module;

[0041] In the user management module, when a new user registers, they select the topics they are interested in. After registration and login, a corresponding consultation assistant is assigned for initial simulation consultation; when an old user logs in, the system extracts the historical consultation records, analyzes the professional nature, and constructs a user demand model;

[0042] In the data analysis and recommendation module, the system performs data mining through multiple data models, extracts the user interest vector, calculates the similarity with the technology data vector in the multi-technology database, and based on the similarity and the user's historical behavior data, pushes the matching technology data, experts, and star users, constructs a user demand model in real time, and adjusts the model according to the user's feedback to meet the user's needs;

[0043] In the data protection module, all users' communication and consultation records are saved in the data protection library, and different data protection rules are implemented. The platform adopts a hierarchical storage strategy, an AI-driven dynamic data protection library architecture, and an environmental monitoring unit to ensure data security; the user data protection rules perform sensitive information marking, IP address and geographical location analysis, and risk review.

[0044] The beneficial effects of the present invention: By constructing a network information technology consultation and communication platform and connecting to a multi-technology database, the present invention realizes the initial classification of user information and the starting point of personalized services, provides an efficient information acquisition and communication environment for users, and at the same time, by differentiating user identities, different service paths are provided for new and old users, thereby improving the user experience and the personalized service level of the platform.

[0045] During the registration process, in-depth identification of user interests and construction of a similar technology database are achieved. The platform can more accurately grasp the user's interest points, provide more accurate technical data and expert recommendations for users, thereby improving the communication quality and efficiency of users on the platform.

[0046] By calculating the matching degree based on the data mining results and pushing corresponding content, rapid response to the interests of new users and provision of personalized content are realized. The platform can dynamically adjust the pushed content to ensure that the information received by users is highly relevant to their interests, thereby improving user satisfaction and platform stickiness.

[0047] For old users, in-depth understanding of the professional nature and preferences of old users is achieved, and more customized services can be provided based on the historical behavior data of users, enabling old users to obtain information and resources on the platform that better meet their professional needs, thereby enhancing user loyalty and platform attractiveness.

[0048] The platform not only guarantees the privacy and security of users, but also maintains the reputation of the platform and the trust of users in the platform. By implementing a hierarchical storage strategy and an artificial intelligence-driven dynamic data protection library architecture, the platform can effectively address data security challenges and ensure that sensitive information is properly processed. Description of the Drawings

[0049] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for description in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0050] Figure 1 It is the overall flowchart of an application method for a network information technology consulting and communication platform provided by an embodiment of the present invention.

[0051] Figure 2 It is the system scheme module diagram of an application system for a network information technology consulting and communication platform provided by an embodiment of the present invention. Detailed Embodiments

[0052] To make the above objects, features, and advantages of the present invention more obvious and understandable, the detailed embodiments of the present invention will be described in detail below with reference to the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0053] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, the present invention may be practiced in other ways different from those described herein. Persons skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0054] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation manner of the present invention. The appearances of "in one embodiment" in different places in this specification do not all refer to the same embodiment, nor are they embodiments that are mutually exclusive of other embodiments individually or selectively.

[0055] The present invention is described in detail in conjunction with schematic diagrams. When elaborating on the embodiments of the present invention, for the convenience of explanation, the cross-sectional views showing the device structure will be locally enlarged out of the general proportion, and the schematic diagrams are only examples and should not limit the scope of protection of the present invention herein. In addition, in actual production, three-dimensional spatial dimensions including length, width, and depth should be included.

[0056] Meanwhile, in the description of the present invention, it should be noted that the orientation or positional relationship indicated by terms such as "upper, lower, inner, and outer" is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention. In addition, the terms "first, second, or third" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0057] Unless otherwise clearly defined and limited in the present invention, the terms "mounted, connected, and coupled" shall be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may also be a mechanical connection, an electrical connection, or a direct connection, or may be indirectly connected through an intermediate medium, or may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0058] Example 1, referring to Figure 1 , which is the first embodiment of the present invention. This embodiment provides a method for applying a network information technology consulting and communication platform, including:

[0059] S1: Build an online information technology consulting and communication platform to connect multiple technology databases. Users can choose the login or registration button. Define users who choose the login button as old users and those who choose the registration button as new users. The purpose is to provide technical consulting and communication services for new users, promoting knowledge sharing and technological progress. The multiple technology databases gather the latest information, research results, and technical literature from various technical fields, obtaining data through multiple channels such as technical forums and periodicals to ensure the accuracy and timeliness of information.

[0060] When new users register, they choose topics they are interested in. After registration and login, corresponding consulting assistants are assigned for initial simulation consulting. Under the condition of data protection, in-depth data mining is carried out through computer multi-data models, and the mined data is stored in the similar technology database of the current user. By guiding new users to choose topics they are interested in, the platform can effectively capture user preferences, laying a foundation for personalized consulting. The data selected by users during registration can provide initial data for subsequent recommendation algorithms, effectively improving the accuracy of matching. Among them, the initial simulation consulting stage can not only sort out the needs of new users but also, within the framework of data protection, effectively mine data through multi-data models, ensuring the privacy of new users while mining valuable information, enhancing the trust of new users, and helping to improve the stickiness of new users to the platform.

[0061] Furthermore, the online information technology consulting and communication platform connects multiple technology databases. The multiple technology databases crawl the latest published technical articles and white papers through technical forums, periodicals, and websites. An expert group is set up to review new technologies in line with the current technological trend and decide on database updates. Users are allowed to submit new technologies or new articles they think on the platform, and after being reviewed by the expert group, they are updated to the database. Through the review of the expert group, the platform can continuously update and optimize the technology database to keep up with the current pace of technological development, enabling users' suggestions and new technologies to be incorporated into the database in a timely manner, providing the latest and most comprehensive information resources for all users.

[0062] During the initial simulation consulting process, the system extracts the keywords selected, questions, and feedback of new users, records them as the user interest vector U = [u1, u2, …, u n , and uses multi-data models for data mining to accurately reflect users' technical interests and provide strong data support for their subsequent knowledge recommendations. Based on the technical feature vector P = [p1, p2…p m , the comprehensive similarity S1 between the user interest vector U and the technical feature vector P is calculated by improving the data mining model:

[0063]

[0064] Among them, U represents the interest vector selected by the user in the initial simulation consultation, n is the number of keywords, and u i is the interest technology keyword expressed by the i-th user. P represents the technology feature vector currently stored in the multi-technology database, m is the number of technology feature vectors, and p j is the j-th technology feature vector. S1 represents the similarity between the user interest vector U and the technology feature vector P. k is a constant adjustment term, b is a small positive number to prevent the denominator from being zero, and i and j are variable indices;

[0065] Judge whether the similarity S1 is higher than the preset threshold θ. When S1 > θ, it is considered that the current technology meets the user's interest, and the mining result is stored in the similar technology database. By calculating the similarity, the preference degree of the user for a specific technology can be accurately judged, and then the relevant technology information is stored in the similar technology database to form a personalized technology recommendation.

[0066] S2: Combine the results of data mining, calculate the matching degree between the data in the multi-technology database and the content of interest to the new user, push the corresponding technology data and recommend experts and star users. After the new user selects the technology data or communication object, enter the communication consultation mode and build a type-I user demand model in real time.

[0067] Furthermore, extract the technology data vector T related to the user's interest from the multi-technology database with data mining content added, and calculate the similarity S2 between the user interest vector U and each technology data vector T g :

[0068]

[0069] Among them, S2 represents the similarity between the new user interest vector U and the g-th technology data vector T g . g is a variable index. After the new user browses and selects the multi-technology data, calculate the weighted average of all similarities to form a comprehensive matching degree:

[0070]

[0071] Among them, M represents the overall matching degree between the new user and the technology data in the multi-technology database, and w i represents the weight of the new user's selection of technology data, which is referenced by the selection frequency;

[0072] After the new user obtains the matching degree value, push the technology data, experts, and star users with a high matching degree with the past selection according to the matching degree value; by calculating the matching degree between the user interest and the technology data, it is possible to push the technology data and expert recommendations highly relevant to the user's needs, thereby improving the user experience. For example, the new user can quickly find the required information and avoid information redundancy under the traditional search method.

[0073] It should also be noted that the star users include personal quality scores and interaction scores for each user, and the thresholds for personal quality scores and interaction scores are set at 80 (on a 100-point scale) and 4 (on a 10-point scale) respectively;

[0074] The personal quality score includes the quality of the uploaded technology, the quality of answering technical questions, etc.;

[0075] According to the proportion fusion of the two scores, the total score is obtained. According to the size ranking of the total score, the top 10% of the users are rated as star users. If the user does not choose an expert for communication and consultation, star users can be recommended for communication and consultation; Star users enable new users to obtain high-quality resources and information exchanges during the consultation process, increasing user trust, promoting knowledge sharing and experience exchanges, and improving the activity of the entire platform.

[0076] It should be noted that during the consultation process, the system records the user's feedback information based on the user's questions, comments, and selection of technical data, and converts the user feedback information into a demand signal D, forming a type-I user demand model state:

[0077] R t = R t-1 + β·((D t - R t-1 )·log(1 + M avg ))

[0078] Among them, R t-1 represents the type-I demand model state at time t-1, β represents the learning rate, controlling the impact of new feedback on model update; M avg represents the average matching degree, R t represents the type-I user demand model state at time t, D t represents the demand signal converted from the user feedback information at time t;

[0079] And the platform adjusts the demand model to meet user requirements by continuously monitoring the user's subsequent participation and satisfaction. By continuously monitoring the user's participation and satisfaction, it can quickly identify and respond to changes in user needs. The real-time update of the demand model ensures the flexibility of the recommendation system, helping users obtain technical information that better meets their current needs.

[0080] S3: After the old user logs in, obtain the user's professional nature according to the historical record, and construct a type-II user demand model in real time. Calculate the data of the multi-technology database and the professional similarity and interest matching degree of the old user, and push the corresponding technical data, experts, star users, or technical questions according to the calculation results. After selecting the content, enter the communication and consultation mode.

[0081] Furthermore, when an old user logs in, the system automatically extracts the user's historical consultation records, obtains the professional nature of the old user through data analysis, and converts it into a feature vector P nature , the user's behavioral data on the platform includes the topics of user consultations, the frequency and depth of interactions, the technical questions and experts that the user responds to; it reduces the user's active input, improves the system's response efficiency, and reduces redundant information that may appear in traditional interactions.

[0082] Construct the type II user demand model R old = f(P nature , history, Y context ), where R old represents the user demand representation, f represents the dynamic function that combines the input parameters to generate the model, and P nature represents the feature vector corresponding to the user's professional nature; history represents the user's historical behavioral data, including past activity records and selections; Y context represents the context weight, which contains context-related information.

[0083] Combining the user's historical behavioral data and context information to construct a dynamic model representing the user's needs to cope with changes in user needs, ensuring the timeliness and relevance of the recommended content, providing decision support based on real data for experts and technology providers, and helping to focus on the user's real needs.

[0084] It should be noted that analyze the mutual relationship between multiple technical fields, and calculate the professional similarity in combination with the professional nature of old users:

[0085]

[0086] Among them, S professional represents the professional similarity, that is, the degree of similarity between the user's professional nature and the technology, P is the technical feature vector, c represents a constant controlling the relationship strength, used to balance the score; E relation represents the connection strength between the user feature and the technical feature; sort the technical data according to professional relevance, which is convenient for users to quickly obtain the technical information most suitable for their needs, improve the efficiency of information filtering, reduce the time for users to grope in the massive data, and enable users to find solutions in the shortest time.

[0087] Sort according to the professional similarity from large to small, select the top 50% of the technical data, and calculate the interest matching degree by integrating the user's interaction parameters and using a probability model:

[0088]

[0089] Among them, S interestRepresents the interest matching degree, that is, the matching degree of the technical data selected by the user and the professional similarity, I user,l Is the interaction frequency of the user on any technical data topic l, Q interest,l Is the interest intensity weight on any technical data topic l, and L is the total amount of the top 50% of the selected technical data; Precise push improves the possibility for users to obtain information in a timely manner, promotes the interaction between experts and users, forms a virtuous cycle, and then improves the activity of the overall technical consultation platform.

[0090] Allocate the weight to calculate the fusion degree S combined = 0.6·S professional + 0.4·S interest , and push the corresponding technical data, experts, star users or technical problems according to the calculation results. After selecting the content, enter the communication and consultation mode.

[0091] S4: Save all users' communication and consultation records in the data protection library for data protection to prevent data leakage.

[0092] Furthermore, save all users' communication and consultation records in the data protection library for data protection, and set data protection rules for the platform and users respectively;

[0093] The data protection rules for the platform are as follows:

[0094] Rule 1: Adopt a hierarchical storage strategy. Different sensitivity data adopts different storage and backup strategies, which are divided into high, medium, and low security level storage areas. Among them, the low security level storage area is shared data and no data protection is carried out; By dividing the data into high, medium, and low three security levels, the system can apply different protection measures according to the specific sensitivity of the data. This differential management not only improves the efficiency of data storage, but also ensures the maximum protection of sensitive data and prevents unnecessary leakage risks.

[0095] Rule 2: Implement an AI-driven dynamic data protection library architecture. Through AI, real-time evaluate the risk indicators of access frequency and data sensitivity. When the risk score of the risk indicator is higher than 70% of the specified range, the data will be transferred to the high security level storage area; The platform can real-time evaluate the data access frequency and its risks. Especially when the risk indicator exceeds 70%, it can quickly transfer the data to the high security level storage area. This provides a timely response mechanism in case of potential leakage risks. The dynamic processing ability significantly enhances the data security protection, effectively resists unauthorized access, and other external threats such as hacker attacks.

[0096] Rule 3: The platform constructs an environmental monitoring unit that conducts biometric authentication simultaneously when a storage request is incoming. The biometric failure rate is guaranteed to be less than 0.1%. Biometric authentication includes fingerprint, voice, and pupil. Biometric authentication is conducted during data storage requests to ensure that only authenticated users can access sensitive data. Due to the high accuracy of biometric technology, the risk of unauthorized access can be effectively reduced. In addition, the low failure rate (less than 0.1%) of biometric authentication ensures the continuity and smoothness of the user experience and is suitable for application in multiple scenarios.

[0097] It should be noted that the data protection rules for users are as follows:

[0098] When the content of a user's consultation contains sensitive information, the system automatically marks the current content and stores the data in an isolation area for review. When the marking rate of the system relative to the total consultation records exceeds 30%, an automatic alarm is triggered and a risk review is conducted;

[0099] When a user accesses the data protection library, the IP address and geographical location are analyzed in real time. When the IP accessed by the user is requested, it is classified into the medium-risk area. When the user starts the normal communication and consultation mode, the access in the medium-risk area is immediately converted to the low-risk area for processing. When the IP requested by the user has not been registered or has not been used normally in the past, the access in the medium-risk area is immediately converted to the high-risk area for processing. When the request frequency of the newly connected IP that cannot be identified exceeds 5 times, two-factor authentication is enabled.

[0100] When the content of a user's consultation involves sensitive information, the system automatically marks and isolates this content, forming a proactive protection mechanism to safeguard personal privacy. When the marking rate exceeds 30%, a risk review is initiated to effectively prevent potential compliance issues. In addition, the real-time analysis of the IP address and geographical location, combined with two-factor authentication, provides a two-layer guarantee for data access control.

[0101] Example 2, the second example of the present invention, which is different from Example 1 in that:

[0102] When the above-mentioned functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs.

[0103] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch instructions from the instruction execution system, apparatus, or device and execute the instructions), or in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in combination with an instruction execution system, apparatus, or device.

[0104] More specific examples (non-exhaustive list) of computer-readable media include the following: electrical connection parts with one or more wirings (electronic devices), portable computer disk cartridges (magnetic devices), random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memories), fiber optic devices, and portable compact disc read-only memories (CDROM). Additionally, a computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, then editing, interpreting, or processing it in other suitable ways if necessary, and then storing it in a computer memory.

[0105] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application specific integrated circuits with appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0106] Example 3, referring to Figure 2 , which is the third embodiment of the present invention. This embodiment provides a network information technology consulting and communication platform application system, including a user management module, a data analysis and recommendation module, and a data protection module;

[0107] User management module: When a new user registers, they select topics of interest. After registering and logging in, a corresponding consulting assistant is assigned for initial simulation consulting. When an old user logs in, the system extracts historical consulting records, analyzes the professional nature, and constructs a user demand model;

[0108] Data analysis and recommendation module: The system performs data mining through multiple data models, extracts user interest vectors, calculates the similarity with technology data vectors in the multi-technology database, and based on the similarity and the user's historical behavior data, pushes matching technology data, experts, and star users, constructs a user demand model in real time, and adjusts the model according to user feedback to meet user needs;

[0109] The data protection module saves all users' communication and consulting records in the data protection library, implements different data protection rules, and the platform adopts a hierarchical storage strategy, an AI-driven dynamic data protection library architecture, and an environmental monitoring unit to ensure data security; the user data protection rules perform sensitive information marking, IP address and geographical location analysis, and risk review.

[0110] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A method for applying a network information technology consulting and communication platform, characterized in that: including Construct a network information technology consulting and communication platform to connect multiple technology databases. Users select the login or registration button. Define users who select the login button as old users and users who select the registration button as new users; When the new user registers, they select topics of interest. After registering and logging in, a corresponding consulting assistant is assigned for initial simulated consultation. Under the condition of data protection, in-depth data mining is carried out through computer multi-data models, and the mined data is stored in the similar technology database of the current new user; Combined with the results of data mining, calculate the matching degree between the data in the multi-technology database and the content of interest to the new user, push the corresponding technology data, recommend experts and star users. After the new user selects the technology data or communication object, enter the communication and consultation mode, and construct a type-I user demand model in real time; After the old user logs in, obtain the professional nature of the old user according to the historical record, construct a type-II user demand model in real time, calculate the professional similarity and interest matching degree between the data in the multi-technology database and the old user, and push the corresponding technology data, experts, star users or technical questions according to the calculation results. After selecting the content, enter the communication and consultation mode; Save the communication and consultation records of all users to the data protection library for data protection to prevent data leakage.

2. The application method of a network information technology consulting and communication platform according to claim 1, wherein: The data mining includes: the network information technology consulting and communication platform connects multiple technology databases. The multi-technology database crawls the latest published technical articles and white papers through technology forums, periodicals and websites; establish an expert group to review new technologies in line with the current technology trend and decide to update the database; allow users to submit new technologies or new articles they think on the platform, and update them to the database after being reviewed by the expert group; During the initial simulation consultation process, the system extracts the keywords selected, questions, and feedback of new users, and records them as the user interest vector U = [u1, u2, …, u n , uses a multi-data model for data mining, and based on the technical feature vector P = [p1, p2…p m , calculates the comprehensive similarity S1 between the user interest vector U and the technical feature vector P through an improved data mining model: Among them, U represents the interest vector selected by the user in the initial simulation consultation, n is the number of keywords, and u i is the interest technology keyword expressed by the i-th user, P represents the technology feature vector currently stored in the multi-technology database, m is the number of technology feature vectors, and p j is the j-th technology feature vector, S1 represents the similarity between the user interest vector U and the technology feature vector P, k is a constant adjustment term, b is a small positive number to prevent the denominator from being zero, and i and j are variable indices; Judge whether the similarity S1 is higher than the preset threshold θ. When S1>θ, it is considered that the current technology meets the user's interest, and the mining result is stored in the similar technology database.

3. The application method of a network information technology consulting and communication platform according to claim 2, characterized in that: Calculating the matching degree between the data in the multi-technology database and the content of interest to the new user includes extracting the technical data vector T related to the new user's interest from the multi-technology database with data mining content added, and calculating the similarity S2 between the user interest vector U and each technical data vector T g : Among them, S2 represents the similarity between the new user interest vector U and the g-th technical data vector T g where g is a variable index. After the new user browses and selects multiple technical data, the weighted average of all similarities is calculated to form a comprehensive matching degree: Among them, M represents the overall matching degree between the new user and the technical data in the multi-technology database, and w i represents the weight of the technical data selected by the new user, which is referenced by the selection frequency; After the new user obtains the matching degree value, push the technology data, experts and star users with high matching degree with the past selections according to the matching degree value.

4. The application method of a network information technology consulting and communication platform according to claim 3, characterized in that: The type-I user demand model includes: during the consultation process, the system records the user's feedback information according to the user's questions, comments and selections of technology data, and converts the user's feedback information into a demand signal D to form the state of the type-I user demand model: R t = R t-1 + β·((D t - R t-1 )·log(1 + M avg )) Among them, R t-1 represents the state of the type-I demand model at time t-1, β represents the learning rate, which controls the impact of new feedback on model update; M avg represents the average matching degree, and R t represents the state of the type-I user demand model at time t, and D t represents the demand signal converted from the user feedback information at time t; And the platform adjusts the demand model by continuously monitoring the user's subsequent participation and satisfaction to meet the user's requirements.

5. The application method of a network information technology consulting and communication platform according to claim 4, characterized in that: The Class II user demand model includes that when an old user logs in, the system automatically extracts the user's historical consultation records, and obtains the professional nature of the old user through data analysis and converts it into a feature vector P nature , the user's behavior data on the platform includes the topics of user consultations, the frequency and depth of interactions, the technical questions and experts that the user responds to; Construct the Class II user requirement model R old = f(P nature , history, Y context ), where R old represents the user requirement representation, f represents the dynamic function that combines the input parameters to generate the model, and P nature represents the feature vector corresponding to the user's professional nature; history represents the user's historical behavior data, including past activity records and selections; Y context represents the context weight, which contains context-related information.

6. A method for applying a network information technology consulting and communication platform according to claim 5, characterized in that: The calculation of the professional similarity and interest matching degree between the data in the multi-technology database and the old user includes analyzing the mutual relationship between multiple technology fields and calculating the professional similarity in combination with the professional nature of the old user: Among them, S professional represents the professional similarity, that is, the similarity between the professional nature of the user and the technology. P is the technical feature vector, and c represents a constant for controlling the relationship strength, which is used to balance the score; E relation represents the connection strength between the user feature and the technical feature; Sort according to the professional similarity from large to small, select the top 50% of the technology data, and calculate the interest matching degree by integrating the user's interaction parameters and using a probability model: Among them, S interest represents the interest matching degree, that is, the matching degree of the technical data selected by the user and the professional similarity. I user,l is the interaction frequency of the user on any technical data topic l, Q interest,l is the interest intensity weight on any technical data topic l, and L is the total amount of the top 50% of the selected technical data; Calculate the integration degree S by allocating weights combined = 0.6·S professional + 0.4·S interest , and push the corresponding technical data, experts, star users or technical questions according to the calculation results. After selecting the content, enter the communication and consultation mode.

7. A method for applying a network information technology consulting and communication platform as described in claim 6, characterized in that: The data protection includes saving the communication and consultation records of all users to the data protection library for data protection, and setting data protection rules for the platform and users respectively; The data protection rules for the platform are as follows: Rule 1: Adopt a hierarchical storage strategy. Different levels of sensitive data adopt different storage and backup strategies, which are divided into high, medium, and low security-level storage areas. Among them, the low security-level storage area is for shared data and no data protection is performed. Rule 2: Implement an AI-driven dynamic data protection library architecture. Through AI, risk indicators of access frequency and data sensitivity are evaluated in real time. When the risk score of the risk indicator is higher than 70% of the specified range, the data is transferred to the high security-level storage area. Rule 3: The platform constructs an environment monitoring unit to conduct biometric authentication simultaneously when a storage request is incoming. The biometric failure rate is guaranteed to be lower than 0.1%. Biometric authentication includes fingerprint, voice, and pupil. The data protection rules for users are as follows: When the content of the user's consultation contains sensitive information, the system automatically marks the current content and stores the data in the isolation area for review. When the marking rate of the system relative to the total consultation records exceeds 30%, an automatic alarm is issued and a risk review is conducted. When the user accesses the data protection library, the IP address and geographical location are analyzed in real time. When the user requests an IP access, it is classified into the medium-risk area. When the user starts the normal communication and consultation mode, the access to the medium-risk area is immediately changed to the low-risk area for processing. When the IP requested by the user has not been registered or has not been used normally after registration in the past, the access to the medium-risk area is immediately changed to the high-risk area for processing. When the request frequency of the new connection IP is not recognized more than 5 times, two-factor authentication is enabled.

8. A network information technology consulting and communication platform application system, which is applied to the network information technology consulting and communication platform application method described in any one of claims 1 to 7, and is characterized in that: It includes a user management module, a data analysis and recommendation module, and a data protection module. For the user management module, when a new user registers, they select the topics of interest. After registration and login, a corresponding consultation assistant is assigned for initial simulation consultation. After an old user logs in, the system extracts the historical consultation records, analyzes the professional nature, and constructs a user demand model. For the data analysis and recommendation module, the system conducts data mining through multiple data models, extracts the user interest vector, calculates the similarity with the technology data vectors in the multi-technology database, and based on the similarity and the user's historical behavior data, pushes the matching technology data, experts, and star users, constructs a user demand model in real time, and adjusts the model according to the user feedback to meet the user's needs. For the data protection module, all the communication and consultation records of users are saved in the data protection library, and different data protection rules are implemented. The platform adopts a hierarchical storage strategy, an AI-driven dynamic data protection library architecture, and an environment monitoring unit to ensure data security; the user data protection rules perform sensitive information marking, IP address and geographical location analysis, and risk review.

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