Information technology service consultation system based on big data

Abstract

The invention discloses an information technology service consultation system based on big data. The system comprises a data acquisition module, a big data storage and management module, a data analysis and mining module, an intelligent consultation module and a user management and feedback module. The invention relates to the technical field of information technology service consultation, in particular to an information technology service consultation system based on big data, which can deeply mine and analyze knowledge and experience in the field of information technology through a big data technology to realize intelligence, high efficiency and individuation of consultation service. Omnibearing, multi-level and high-precision information technology consultation services are provided for users, and enterprises and individuals are assisted in accurate decision making and quick action inquiry in information technology waves.

Description

Technical Field

[0001] The present invention relates to the field of information technology service consulting technology, and specifically to an information technology service consulting system based on big data. Background Art

[0002] In today's digital age, information technology is developing explosively, new technical concepts and application solutions are emerging in an endless stream, and enterprises and individuals are increasingly relying on professional consulting services when facing information technology decisions. However, the traditional information technology service consulting model faces many difficulties.

[0003] Traditional consulting is highly dependent on the consultants’ personal professionalism and experience. However, even experienced experts have very limited knowledge compared to the vast amount of information technology information. It is difficult to ensure a comprehensive and in-depth understanding and answer to complex and ever-changing technical problems. For example, in the emerging blockchain technology application consulting, which involves encryption algorithms, distributed ledger architecture, smart contract development and other aspects of knowledge, it is difficult for a single consultant to master all aspects, which may lead to one-sided consulting opinions.

[0004] Furthermore, the traditional consulting process is cumbersome and time-consuming. From raising questions to collecting and analyzing information, and finally forming a consulting solution, it often takes a long period of time. In a fast-paced business environment, this may cause companies to miss the best time to apply technology or the decision-making window. For example, when planning a digital transformation strategy, if a company delays decision-making due to a lengthy consulting process, it may be at a disadvantage in market competition.

[0005] In addition, traditional consulting lacks effective integration and in-depth mining of historical data. Past consulting cases and technical knowledge have not been fully utilized, and a systematic experience inheritance and intelligent optimization mechanism cannot be formed. Each consultation is like starting from scratch, and it is difficult to quickly locate the core of the problem and provide solutions with the help of historical experience, resulting in a huge waste of resources. Summary of the invention

[0006] In view of the above situation, in order to remedy the above-mentioned existing defects, the present invention provides an information technology service consulting system based on big data. This system can deeply mine and analyze the knowledge and experience in the field of information technology through big data technology, realize the intelligence, efficiency and personalization of consulting services, and provide users with all-round, multi-level and high-precision information technology consulting services, helping enterprises and individuals to make accurate decisions and take quick actions in the wave of information technology.

[0007] The present invention proposes an information technology service consulting system based on big data, which includes a data acquisition module, a big data storage and management module, a data analysis and mining module, an intelligent consulting module and a user management and feedback module;

[0008] The data acquisition module transmits the collected and cleaned information technology data to the big data storage and management module through a network interface or a data transmission channel;

[0009] The data analysis and mining module is connected to the big data storage and management module through a data access interface. When a data analysis task is required, the data analysis and mining module sends a data query request to the big data storage and management module. The big data storage and management module retrieves relevant data from the distributed storage system according to the request and provides it to the data analysis and mining module. This connection is based on a standard data query language and a corresponding database connection driver to ensure accurate data acquisition and interaction;

[0010] The data analysis and mining module transmits the analysis results to the intelligent consulting module in a specific data format. After receiving the user's consultation request, the intelligent consulting module transmits the key information of the user's question to the data analysis and mining module. The data analysis and mining module performs mining analysis based on this information and returns the results.

[0011] The intelligent consulting module interacts with the user management and feedback module during the user consultation process. The user management and feedback module provides the intelligent consulting module with relevant information of the user so that the intelligent consulting module can provide personalized services according to the user's characteristics. At the same time, the intelligent consulting module transmits the user's feedback information to the user management and feedback module after the consultation. The user management and feedback module collects, organizes and stores the feedback information, and uses it for subsequent system optimization and personalized service adjustment. They share and transmit information through internal function calls or message queues to ensure continuous optimization of user experience and intelligent evolution of the system.

[0012] Further, the data acquisition module includes a multi-source data acquisition submodule and a data cleaning and conversion submodule;

[0013] Multi-source data collection submodule: Using web crawler technology, we crawl data from major information technology professional websites, forums, social media platforms, etc., covering technical articles, industry trends, user discussions and other information; at the same time, we establish data cooperation interfaces with major information technology companies and research institutions to obtain exclusive data such as internal technical reports and research results;

[0014] Data cleaning and conversion submodule: A cleaning method based on rules and machine learning is used to remove duplicate, erroneous, and irrelevant information from the collected data. At the same time, unstructured data (such as text and images) is converted into structured data.

[0015] Furthermore, the big data storage and management module includes a distributed storage submodule, a data indexing and retrieval submodule, and a data security and authority management submodule;

[0016] Distributed storage submodule: Build a large-scale data storage cluster based on Hadoop Distributed File System (HDFS), store the cleaned and converted massive information technology data in multiple nodes, and use data redundancy strategies to ensure data reliability and availability. Even if some nodes fail, the data can still be fully restored.

[0017] Data indexing and retrieval submodule: Use indexing tools such as Apache Solr to create multi-dimensional indexes for stored data, including technical keyword indexes, time indexes, data source indexes, etc. When users query, the system can quickly locate relevant data based on the index, greatly improving retrieval efficiency;

[0018] Data security and rights management submodule: Encryption technology is used to encrypt stored data to ensure the security of data during storage and transmission. At the same time, a complete rights management system is established to assign different data access rights according to user roles (such as ordinary users, enterprise administrators, system administrators) to prevent data leakage and illegal access.

[0019] Furthermore, the data analysis and mining module includes a data mining algorithm library submodule, an intelligent analysis engine submodule, and a model training and optimization submodule;

[0020] Data mining algorithm library submodule: Integrates multiple advanced data mining algorithms to classify and group similar information technology problems or solutions;

[0021] Intelligent analysis engine submodule: As the core driver of data analysis and mining, the engine automatically calls appropriate data mining algorithms to analyze data according to user consultation requirements;

[0022] Model training and optimization submodule: Based on a large amount of historical data and real-time collected data, the data mining algorithm model is continuously trained and optimized.

[0023] Furthermore, the intelligent consultation module includes a natural language processing submodule, an intelligent matching and recommendation submodule, and an interactive interface submodule;

[0024] Natural language processing submodule: Use natural language processing technology (NLP), including lexical analysis, syntactic analysis, semantic understanding and other components, to deeply analyze the natural language consultation questions input by users;

[0025] Intelligent matching and recommendation submodule: Based on the results of natural language processing, with the support of the big data storage and management module and the data analysis and mining module, the most relevant information technology knowledge, cases and solutions are intelligently matched. Personalized recommendation technologies such as collaborative filtering algorithms are used to combine the user's historical consultation records, industry, enterprise size and other information to recommend the most suitable content for the user's needs;

[0026] Interactive interface submodule: It provides a user-friendly interactive interface and supports multiple consultation methods, such as text input, voice input, file upload, etc. The interface displays the consultation results in an intuitive and clear way. At the same time, the interface has a real-time feedback mechanism, and users can evaluate and ask questions about the consultation results at any time. The system further optimizes the consultation service based on user feedback.

[0027] Further, the user management and feedback module includes a user information management submodule and a feedback collection and processing submodule;

[0028] User information management submodule: Comprehensively manage user registration information, including basic personal information, company information, consultation history, favorites, etc. Through the analysis of user information, build user portraits, deeply understand the user's needs and behavior habits, and provide a basis for personalized consulting services;

[0029] Feedback collection and processing submodule: After the user consultation is completed, the user's feedback is actively collected, including satisfaction with the consultation results, suggestions for system functions, etc. Sentiment analysis technology is used to classify user feedback. For positive feedback, users are given points, honorary titles and other rewards to encourage users to continue using the system; for negative feedback, the reasons are deeply analyzed and the problems are fed back to the data analysis and mining module and the intelligent consulting module to promote targeted optimization and improvement of the system.

[0030] The beneficial effects achieved by the present invention using the above structure are as follows: The information technology service consulting system based on big data has the following advantages:

[0031] 1. Through in-depth mining and analysis of big data, the system can accurately locate the core of users' information technology needs and provide consulting solutions that are highly in line with actual conditions. Whether it is complex enterprise information technology architecture planning or the solution of specific technical problems, it can give accurate answers based on massive data and intelligent algorithms, greatly improving the accuracy and effectiveness of consulting.

[0032] 2. Abandoning the cumbersome process of traditional consulting, based on the efficient collaboration of the system architecture and the fast calculation of intelligent algorithms, it can respond to user consulting needs in a very short time. Users do not need to wait for a long time to obtain the required information technology consulting services. Especially when dealing with emergency technical decision-making scenarios, it can win valuable time for users and improve decision-making efficiency.

[0033] 3. The application of user portraits and personalized recommendation technologies constructed by the user management and feedback module enables the system to deeply understand the unique needs and preferences of each user. Whether it is technical learning consultation for individual users or business transformation consultation for corporate users, they can enjoy exclusive personalized services, enhancing users' sense of identification and dependence on the system.

[0034] 4. The rich information technology knowledge and case experience accumulated by the big data storage and management module have become a treasure trove of industry knowledge inheritance. It not only provides consulting services to current users, but also provides data support for information technology researchers and educators to promote the dissemination and innovation of knowledge. At the same time, the new technical connections and trends constantly discovered by the data analysis and mining module also provide inspiration and direction for the innovative development of information technology.

[0035] 5. For consulting service providers, the big data-based system reduces excessive reliance on a large number of high-end professional consulting talents and reduces labor costs. At the same time, the efficient operation and precise services of the system can attract more users, increase market share and economic benefits. For corporate users, precise consulting services can help avoid waste of resources and cost increases caused by technical decision-making errors, and maximize cost-effectiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0037] Figure 1 This is a workflow diagram of the information technology service consulting system based on big data of the present invention. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0039] It should be noted that the words "front", "rear", "left", "right", "up" and "down" used in the following description refer to directions in the drawings, and the words "inside" and "outside" refer to directions toward or away from the geometric center of a specific component, respectively.

[0040] like Figure 1As shown, the technical solution adopted by the present invention is as follows: an energy storage vehicle charging pile proposed by the present invention includes a data acquisition module, a big data storage and management module, a data analysis and mining module, an intelligent consulting module and a user management and feedback module;

[0041] The data acquisition module transmits the collected and cleaned information technology data to the big data storage and management module through a network interface or a data transmission channel;

[0042] The data analysis and mining module is connected to the big data storage and management module through the data access interface. When data analysis tasks are required, the data analysis and mining module sends data query requests to the big data storage and management module. The big data storage and management module retrieves relevant data from the distributed storage system according to the request and provides it to the data analysis and mining module. This connection is based on standard data query languages ​​(such as SQL or HiveQL designed specifically for big data) and corresponding database connection drivers to ensure accurate data acquisition and interaction;

[0043] The data analysis and mining module transmits the analysis results to the intelligent consulting module in a specific data format (such as JSON or a custom data structure). After receiving the user's consultation request, the intelligent consulting module will pass the key information of the user's problem to the data analysis and mining module, and the data analysis and mining module will perform mining and analysis based on this information and return the results. The communication between them can be based on the RESTful architecture network service interface or the internal message transmission mechanism to achieve two-way flow of data and analysis results, so that the intelligent consulting module can generate accurate and personalized consulting reports or solutions for users based on the analysis results;

[0044] The intelligent consultation module interacts with the user management and feedback module during the user consultation process. The user management and feedback module provides the intelligent consultation module with relevant user information (such as historical consultation records, user preferences, permission information, etc.) so that the intelligent consultation module can provide personalized services based on user characteristics. At the same time, the intelligent consultation module passes the user's feedback information to the user management and feedback module after the consultation. The user management and feedback module collects, organizes and stores the feedback information and uses it for subsequent system optimization and personalized service adjustments. They share and transmit information through internal function calls or message queues to ensure continuous optimization of user experience and intelligent evolution of the system.

[0045] The data acquisition module includes a multi-source data acquisition submodule and a data cleaning and conversion submodule;

[0046] Multi-source data collection submodule: Using web crawler technology, data is captured from major information technology professional websites, forums, social media platforms, etc., covering technical articles, industry trends, user discussions and other information; at the same time, data cooperation interfaces are established with major information technology companies and research institutions to obtain exclusive data such as internal technical reports and research results; it can also collect internal information technology system operation data of enterprises through sensors and other equipment, such as server load, network traffic and other real-time information;

[0047] Data cleaning and conversion submodule: A cleaning method based on rules and machine learning is used to remove duplicate, erroneous, and irrelevant information from the collected data. For example, machine learning models are used to identify and filter out advertising content, low-quality technical discussion posts, etc. At the same time, unstructured data (such as text and images) are converted into structured data, such as extracting key technical points, application scenarios, and other information from technical articles and storing them as fields in the database table for subsequent analysis and processing.

[0048] The big data storage and management module includes a distributed storage submodule, a data indexing and retrieval submodule, and a data security and authority management submodule;

[0049] Distributed storage submodule: Build a large-scale data storage cluster based on Hadoop Distributed File System (HDFS), and distribute and store the cleaned and converted massive information technology data in multiple nodes. Use data redundancy strategies to ensure data reliability and availability, so that even if some nodes fail, the data can still be fully restored;

[0050] Data indexing and retrieval submodule: Use indexing tools such as Apache Solr to create multi-dimensional indexes for stored data, including technical keyword indexes, time indexes, data source indexes, etc. When users query, the system can quickly locate relevant data based on the index, greatly improving retrieval efficiency. For example, when a user queries "the application of artificial intelligence in medical imaging diagnosis", the system can quickly filter out relevant technical documents, case analysis and other data through technical keyword indexes;

[0051] Data security and rights management submodule: Encryption technology is used to encrypt stored data to ensure the security of data during storage and transmission. At the same time, a complete rights management system is established to assign different data access rights according to user roles (such as ordinary users, enterprise administrators, system administrators) to prevent data leakage and illegal access.

[0052] The data analysis and mining module includes a data mining algorithm library submodule, an intelligent analysis engine submodule, and a model training and optimization submodule;

[0053] Data mining algorithm library submodule: It integrates a variety of advanced data mining algorithms, such as cluster analysis algorithms (K-Means, DBSCAN, etc.) for classifying and grouping similar information technology problems or solutions; association rule mining algorithms (Apriori, FP-Growth, etc.) for discovering potential associations between different information technology elements, such as the association between a certain software configuration and specific network performance; classification algorithms (decision tree, support vector machine, etc.) for classifying and predicting the risk level and feasibility of information technology projects;

[0054] Intelligent analysis engine submodule: As the core driver of data analysis and mining, the engine automatically calls appropriate data mining algorithms to analyze data according to user consultation needs. For example, when a user consults about the solution for upgrading the enterprise information technology system, the intelligent analysis engine calls the clustering analysis algorithm to classify the upgrade cases of similar enterprises in the past, and then uses the association rule mining algorithm to find the key technical factors related to the upgrade. Finally, the classification algorithm is used to predict the feasibility and risks of different upgrade solutions, and a comprehensive analysis report is provided to the user;

[0055] Model training and optimization submodule: Based on a large amount of historical data and real-time collected data, the data mining algorithm model is continuously trained and optimized. The model performance is evaluated by cross-validation, A / B testing and other methods, and the model parameters and structure are adjusted according to the evaluation results to improve the accuracy and generalization ability of the model. For example, when training a decision tree model to predict the cost of information technology projects, by continuously adjusting parameters such as the depth of the tree and the branch node selection criteria, the model can accurately predict the cost range on different types of project data.

[0056] The intelligent consulting module includes a natural language processing submodule, an intelligent matching and recommendation submodule, and an interactive interface submodule;

[0057] Natural language processing submodule: Use natural language processing technology (NLP), including lexical analysis, syntactic analysis, semantic understanding and other components, to deeply analyze the natural language consultation questions input by users. For example, convert the user's question "I want to know how to optimize the database performance of the enterprise?" into a structured query statement, extract the key information "enterprise database performance optimization", and understand that the user's intention is to seek an optimization solution;

[0058] Intelligent matching and recommendation submodule: Based on the results of natural language processing, with the support of the big data storage and management module and the data analysis and mining module, the most relevant information technology knowledge, cases and solutions are intelligently matched. Personalized recommendation technologies such as collaborative filtering algorithms are used to recommend the most suitable content for users based on their historical consultation records, industry, enterprise size and other information. For example, if the user's company is in the e-commerce industry and has consulted on network security issues before, when consulting on database performance optimization, the system will give priority to recommending cases and solutions in the e-commerce industry that optimize database performance and take network security into consideration;

[0059] Interactive interface submodule: Provides a user-friendly interactive interface and supports multiple consultation methods, such as text input, voice input, file upload, etc. The interface displays the consultation results in an intuitive and clear way, such as generating reports with pictures and texts, operation demonstration videos, etc., to facilitate user understanding and application. At the same time, the interface has a real-time feedback mechanism, and users can evaluate and ask questions about the consultation results at any time. The system further optimizes the consultation service based on user feedback.

[0060] The user management and feedback module includes a user information management submodule and a feedback collection and processing submodule;

[0061] User information management submodule: Comprehensively manage user registration information, including basic personal information, company information, consulting history, favorites, etc. By analyzing user information, we can build user portraits, gain an in-depth understanding of user needs and behavior habits, and provide a basis for personalized consulting services. For example, we can analyze the hot topics in the field of information technology that users are concerned about based on their consulting history, and push relevant technical information and consulting services to users in advance;

[0062] Feedback collection and processing submodule: After the user consultation is completed, the user's feedback is actively collected, including satisfaction with the consultation results, suggestions for system functions, etc. Sentiment analysis technology is used to classify and process user feedback. For positive feedback, users are given points, honorary titles and other rewards to encourage them to continue using the system; for negative feedback, the reasons are analyzed in depth, and the problems are fed back to the data analysis and mining module and the intelligent consultation module to promote targeted optimization and improvement of the system. For example, if the user feedback consultation results are too complex and difficult to understand, the system will adjust the output strategy of the intelligent consultation module, simplify the report language, and add examples.

[0063] When in use, the data collection module is started, and the multi-source data collection submodule collects information technology data from multiple channels such as the Internet, internal enterprise systems, and cooperative institutions according to the preset collection rules and frequencies. The collected data enters the data cleaning and conversion submodule, and after cleaning and conversion, it forms structured data and is transmitted to the big data storage and management module;

[0064] The distributed storage submodule of the big data storage and management module receives data and stores it in the HDFS cluster. The data indexing and retrieval submodule creates indexes for the stored data. Meanwhile, the data security and permission management submodule encrypts the data and sets access permissions.

[0065] Users raise consulting questions through the interactive interface submodule of the intelligent consulting module, which can be in the form of text, voice or uploaded files. The natural language processing submodule parses the questions, extracts key information and understands the user's intention. Then, the intelligent matching and recommendation submodule matches relevant knowledge, cases and solutions based on user information and key information of the questions, under the synergy of the big data storage and management module and the data analysis and mining module, and presents them to the user through the interactive interface submodule;

[0066] The user management and feedback module records the user's consultation process information and collects user feedback after the consultation. The feedback collection and processing sub-module analyzes and processes the feedback and feeds back the relevant information to other modules to promote continuous optimization and improvement of the system.

[0067] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, material or apparatus including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, material or apparatus.

[0068] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An information technology service consulting system based on big data, including a data acquisition module, a big data storage and management module, a data analysis and mining module, an intelligent consulting module, and a user management and feedback module; The data acquisition module transmits the collected and cleaned information technology data to the big data storage and management module through a network interface or a data transmission channel; The data analysis and mining module is connected to the big data storage and management module through a data access interface. When a data analysis task is required, the data analysis and mining module sends a data query request to the big data storage and management module. The big data storage and management module retrieves relevant data from the distributed storage system according to the request and provides it to the data analysis and mining module. This connection is based on a standard data query language and a corresponding database connection driver to ensure accurate data acquisition and interaction; The data analysis and mining module transmits the analysis results to the intelligent consulting module in a specific data format. After receiving the user's consultation request, the intelligent consulting module transmits the key information of the user's question to the data analysis and mining module. The data analysis and mining module performs mining analysis based on this information and returns the results. The intelligent consulting module interacts with the user management and feedback module during the user consultation process. The user management and feedback module provides the intelligent consulting module with relevant information of the user so that the intelligent consulting module can provide personalized services according to the user's characteristics. At the same time, the intelligent consulting module transmits the user's feedback information to the user management and feedback module after the consultation. The user management and feedback module collects, organizes and stores the feedback information, and uses it for subsequent system optimization and personalized service adjustment. They share and transmit information through internal function calls or message queues to ensure continuous optimization of user experience and intelligent evolution of the system.

2. The information technology service consulting system based on big data according to claim 1 is characterized by: The data acquisition module includes a multi-source data acquisition submodule and a data cleaning and conversion submodule; Multi-source data collection submodule: Using web crawler technology, we crawl data from major information technology professional websites, forums, social media platforms, etc., covering technical articles, industry trends, user discussions and other information; at the same time, we establish data cooperation interfaces with major information technology companies and research institutions to obtain exclusive data such as internal technical reports and research results; Data cleaning and conversion submodule: A cleaning method based on rules and machine learning is used to remove duplicate, erroneous, and irrelevant information from the collected data. At the same time, unstructured data (such as text and images) is converted into structured data.

3. The information technology service consulting system based on big data according to claim 2 is characterized by: The big data storage and management module includes a distributed storage submodule, a data indexing and retrieval submodule, and a data security and authority management submodule; Distributed storage submodule: Build a large-scale data storage cluster based on Hadoop Distributed File System (HDFS), store the cleaned and converted massive information technology data in multiple nodes, and use data redundancy strategies to ensure data reliability and availability. Even if some nodes fail, the data can still be fully restored. Data indexing and retrieval submodule: Use indexing tools such as Apache Solr to create multi-dimensional indexes for stored data, including technical keyword indexes, time indexes, data source indexes, etc. When users query, the system can quickly locate relevant data based on the index, greatly improving retrieval efficiency; Data security and rights management submodule: Encryption technology is used to encrypt stored data to ensure the security of data during storage and transmission. At the same time, a complete rights management system is established to assign different data access rights according to user roles (such as ordinary users, enterprise administrators, system administrators) to prevent data leakage and illegal access.

4. The information technology service consulting system based on big data according to claim 3 is characterized by: The data analysis and mining module includes a data mining algorithm library submodule, an intelligent analysis engine submodule and a model training and optimization submodule; Data mining algorithm library submodule: Integrates multiple advanced data mining algorithms to classify and group similar information technology problems or solutions; Intelligent analysis engine submodule: As the core driver of data analysis and mining, the engine automatically calls appropriate data mining algorithms to analyze data according to user consultation requirements; Model training and optimization submodule: Based on a large amount of historical data and real-time collected data, the data mining algorithm model is continuously trained and optimized.

5. The information technology service consulting system based on big data according to claim 4 is characterized in that: The intelligent consultation module includes a natural language processing submodule, an intelligent matching and recommendation submodule, and an interactive interface submodule; Natural language processing submodule: Use natural language processing technology (NLP), including lexical analysis, syntactic analysis, semantic understanding and other components, to deeply analyze the natural language consultation questions input by users; Intelligent matching and recommendation submodule: Based on the results of natural language processing, with the support of the big data storage and management module and the data analysis and mining module, the most relevant information technology knowledge, cases and solutions are intelligently matched. Personalized recommendation technologies such as collaborative filtering algorithms are used to combine the user's historical consultation records, industry, enterprise size and other information to recommend the most suitable content for the user's needs; Interactive interface submodule: provides a user-friendly interactive interface and supports multiple consultation methods, such as text input, voice input, file upload, etc. The interface displays the consultation results in an intuitive and clear manner. At the same time, the interface has a real-time feedback mechanism, and users can evaluate and ask questions about the consulting results at any time. The system further optimizes the consulting service based on user feedback.

6. The information technology service consulting system based on big data according to claim 5 is characterized by: The user management and feedback module includes a user information management submodule and a feedback collection and processing submodule; User information management submodule: Comprehensively manage user registration information, including basic personal information, company information, consultation history, favorites, etc. Through the analysis of user information, build user portraits, deeply understand the user's needs and behavior habits, and provide a basis for personalized consulting services; Feedback collection and processing submodule: After the user consultation is completed, the user's feedback is actively collected, including satisfaction with the consultation results, suggestions for system functions, etc. Sentiment analysis technology is used to classify user feedback. For positive feedback, users are given points, honorary titles and other rewards to encourage users to continue using the system; for negative feedback, the reasons are deeply analyzed and the problems are fed back to the data analysis and mining module and the intelligent consulting module to promote targeted optimization and improvement of the system.

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