Knowledge base-based urinary tumor perioperative period propaganda and education system

Abstract

The invention belongs to the technical field of knowledge bases, and discloses a urinary tumor perioperative period propaganda and education system based on a knowledge base, and the system comprises the knowledge base which comprises content collection and screening, content arrangement and structuring, and updating and maintenance; an intelligent question answering and personalized recommendation system comprises a natural language processing (NLP) module, a knowledge retrieval and reasoning module and a personalized recommendation system. The interactive learning and feedback system comprises an interactive interface, a learning process tracking mechanism and a feedback mechanism; and the multi-platform support and offline access module comprises a multi-platform adaptation function and an offline access function. According to the method, a comprehensive, intelligent and personalized knowledge base is constructed, and the advanced natural language processing technology and the data analysis method are combined, so that perioperative period propaganda and education service is provided for urinary tumor patients.

Description

Technical Field

[0001] The present invention belongs to but is not limited to the technical field of knowledge bases, and in particular relates to a perioperative education system for urological tumors based on a knowledge base. Background Art

[0002] Existing technology 1: Traditional document or video education

[0003] Currently, perioperative education for urological tumors mainly relies on traditional paper documents or video materials. Patients learn about precautions before and after surgery, rehabilitation guidance, and other information by reading manuals or watching videos.

[0004] Existing technical problems:

[0005] Inconvenient information acquisition: Paper documents are easy to lose and difficult to access at any time; video materials may not be able to be viewed smoothly due to device limitations or network problems.

[0006] Lack of personalization: Traditional educational materials usually have fixed content and cannot provide personalized educational content based on the patient's specific circumstances (such as age, condition, psychological state, etc.).

[0007] Lack of interactivity: Patients can only passively receive information and cannot ask questions or get feedback immediately, which affects the effectiveness of education.

[0008] Existing technology 2: Education system based on simple questions and answers

[0009] Some medical institutions have developed education systems based on simple questions and answers, where patients can obtain relevant information by entering keywords or selecting preset questions.

[0010] Existing technical problems:

[0011] Limited knowledge base: The system knowledge base may not be comprehensive enough to cover all patients’ questions and needs.

[0012] Low intelligence: The system can usually only handle simple questions and answers, and it is difficult to give accurate answers to complex situations or personalized questions.

[0013] Lack of tracking and evaluation: The system cannot record the patient's education process and learning effects, making it difficult to conduct subsequent tracking and evaluation. Summary of the invention

[0014] In view of the problems existing in the prior art, the present invention provides a perioperative education system for urological tumors based on a knowledge base.

[0015] The present invention is implemented as follows: a knowledge base-based urological tumor perioperative education system, the system comprising:

[0016] 1) Knowledge base, including:

[0017] Content collection and screening: Collect relevant content through channels such as medical literature, professional guidelines, and clinical expert opinions, and screen the information for accuracy and relevance.

[0018] Content organization and structuring: The collected content is classified and organized, and structured according to the different stages before, during and after the operation to facilitate user query and use.

[0019] Update and Maintenance: Regularly update the knowledge base content to ensure the timeliness and accuracy of the information, and delete or correct outdated or inaccurate information.

[0020] 2) Intelligent question-answering and personalized recommendation system, including:

[0021] Natural Language Processing (NLP) module: Use NLP technology to parse questions input by users and understand their intentions and needs.

[0022] Knowledge retrieval and reasoning: Based on user questions, relevant content in the knowledge base is retrieved and logical reasoning is performed to provide accurate answers.

[0023] Personalized recommendation system: Based on the user's condition, surgery type, personal preferences and other information, personalized educational content is recommended to help patients better understand surgery-related information.

[0024] 3) Interactive learning and feedback system, including:

[0025] Interactive interface: Provides a friendly user interface that supports content display in various forms such as text, images, and videos, making it convenient for users to interact and learn.

[0026] Learning process tracking: record the user's learning process and progress, analyze learning effects, and provide personalized learning suggestions.

[0027] Feedback mechanism: Collect user feedback, analyze common problems and needs, and optimize education content and system functions.

[0028] 4) Multi-platform support and offline access modules, including:

[0029] Multi-platform adaptation: supports multiple devices such as PC, mobile phones, tablets, etc., ensuring that users can easily access the system on different platforms.

[0030] Offline access function: Supports offline download function, users can view downloaded missionary content in an offline environment to ensure the accessibility of information.

[0031] 5) Contents of perioperative education for urinary tumors, including:

[0032] Preoperative education: including preoperative preparation, precautions, preoperative examinations, etc., to help patients understand the various preparations before surgery.

[0033] Intraoperative education: Introduce information such as surgical procedures, anesthesia methods, and surgical risks to give patients a comprehensive understanding of the surgical process.

[0034] Postoperative education: covers postoperative care, rehabilitation training, dietary recommendations, review plans, etc., to guide patients' postoperative recovery and care.

[0035] 6) System security and privacy protection, including:

[0036] Data encryption: Encrypt users’ personal information and health data to ensure data security.

[0037] Privacy protection: Strictly abide by relevant laws and regulations, protect user privacy, and do not disclose any personal information to third parties without the user's consent.

[0038] Through the collaborative work of the above modules, the present invention provides a knowledge base-based perioperative education system for urological tumors, which can effectively improve patients' understanding of surgery-related information and enhance patients' preoperative preparation and postoperative rehabilitation effects.

[0039] Furthermore, the content collection and screening specifically includes:

[0040] (1) Expert interview process:

[0041] Design an interview outline to cover the key points of perioperative education;

[0042] Invite well-known experts in the field to conduct one-on-one interviews, record and organize the interview content;

[0043] Conduct quality assessment on interview content and filter out valuable information;

[0044] (2) Literature search strategy:

[0045] Determine the search keywords, such as "urological tumors", "perioperative period", "education", etc.;

[0046] Conduct advanced searches in professional databases and set reasonable screening conditions, such as time range, document type, etc.;

[0047] Screen and download relevant literature, read, summarize and generalize;

[0048] (3) Patient survey methods:

[0049] Design survey questionnaires, including open-ended and closed-ended questions, to cover patients' main questions and needs;

[0050] Distribute questionnaires in hospitals or online platforms to collect patient feedback;

[0051] Conduct statistical analysis on the questionnaire data to extract key information.

[0052] Furthermore, the content arrangement and structuring specifically include:

[0053] (1) Establishment of classification system:

[0054] Establish a clear classification system based on different perioperative stages and patient needs;

[0055] Assign a unique identifier and description to each category;

[0056] (2) Knowledge graph construction:

[0057] Use graph databases (such as Neo4j) to store knowledge graph data;

[0058] Define entities, relationships, and attributes, and convert the collected content into nodes and edges in the graph;

[0059] Use graph query languages ​​(such as Cypher) for knowledge retrieval and reasoning;

[0060] The knowledge base update and maintenance specifically includes:

[0061] (1) Regular update mechanism:

[0062] Set a fixed update cycle (such as quarterly or semi-annual);

[0063] During the update cycle, new research results, clinical guidelines and patient feedback are collected;

[0064] Update the knowledge base to ensure the timeliness and accuracy of the content;

[0065] (2) Feedback processing flow:

[0066] Set up dedicated feedback channels (e.g. email, online forms, etc.);

[0067] Categorize, organize and prioritize collected feedback;

[0068] Update the knowledge base or adjust the education strategy based on the feedback.

[0069] Furthermore, the natural language processing (NLP) module specifically includes:

[0070] (1) Text preprocessing tools:

[0071] Use open source libraries (such as NLTK, spaCy) for preprocessing operations such as word segmentation, stop word removal, and part-of-speech tagging.

[0072] Customize thesaurus and rules to improve the accuracy and efficiency of preprocessing;

[0073] (2) Intent Recognition Model:

[0074] Choose a suitable pre-trained model (such as BERT, RoBERTa, etc.);

[0075] Fine-tune the model and train it using annotated question-answering datasets;

[0076] Evaluate model performance and optimize model parameters and structure;

[0077] (3) Entity Recognition Module:

[0078] Named Entity Recognition (NER) technology is used to identify key entities in user input.

[0079] Combining domain knowledge base and contextual information to improve the accuracy of entity recognition.

[0080] Furthermore, the knowledge retrieval and reasoning specifically include:

[0081] (1) Exact matching algorithm:

[0082] Implement an accurate matching algorithm based on keywords;

[0083] Calculate the similarity between the user's question and the answer in the knowledge base, and select the answer with the highest similarity to return;

[0084] (2) Fuzzy matching and reasoning:

[0085] Use fuzzy query techniques (such as wildcards, regular expressions) for fuzzy matching.

[0086] Combined with knowledge graphs for semantic reasoning, it generates answers relevant to user questions.

[0087] Furthermore, the personalized recommendation system specifically includes:

[0088] (1) User portrait construction:

[0089] Design a user portrait model, including basic information, medical condition information, learning preferences and other dimensions;

[0090] Use historical user data and real-time interaction data to build user portraits;

[0091] (2) Recommendation algorithm implementation:

[0092] Select appropriate recommendation algorithms (such as collaborative filtering, content-based recommendation, etc.);

[0093] Generate a recommendation list based on the user profile and the current question context;

[0094] Sort and display recommendation results to improve user experience.

[0095] Furthermore, the interactive interface design specifically includes:

[0096] (1) Interface prototype design:

[0097] Use design tools (such as Sketch, Figma) to draw interface prototypes;

[0098] Design a concise and clear navigation bar, Q&A area, recommendation area, and learning progress display area;

[0099] (2) Interaction design:

[0100] Design a smooth user interaction process to ensure that users can easily complete learning tasks;

[0101] Add animation effects and prompt information to improve the friendliness and ease of use of the user interface;

[0102] The learning process tracking specifically includes:

[0103] (1) Learning record database:

[0104] Design the learning record database table structure to store the user's learning time, learning content, learning progress and other information;

[0105] Use database management systems (such as MySQL, MongoDB) for data storage and management;

[0106] (2) Learning Assessment Mechanism:

[0107] Design quizzes and questionnaires to assess users’ learning outcomes;

[0108] Automatically score and provide feedback on user answers;

[0109] (3) Feedback mechanism

[0110] Instant feedback system:

[0111] Provide immediate feedback (such as correct / incorrect) when users answer questions;

[0112] Adjust the difficulty and type of follow-up questions based on the user’s responses;

[0113] (4) Comprehensive feedback report:

[0114] Generate a comprehensive feedback report after the learning process, including learning outcomes, areas for improvement, etc.

[0115] Provide download and share functions to facilitate users to save and share learning results.

[0116] Furthermore, the multi-platform adaptation specifically includes:

[0117] (1) Responsive design:

[0118] Use CSS media queries and fluid layout techniques to achieve responsive design;

[0119] Ensure that the system can display and interact well on different devices and screen sizes;

[0120] (2) Mobile application development:

[0121] Develop mobile apps using cross-platform frameworks (such as Flutter and React Native);

[0122] Customize the UI / UX design of the mobile app to ensure a consistent user experience with the web version.

[0123] Furthermore, the offline access function specifically includes:

[0124] (1) Data caching mechanism:

[0125] Implement data caching technology (such as HTML5 Cache Manifest, Service Workers, etc.) to cache the data required by users locally;

[0126] Load data from the local cache when the user is offline to ensure system availability;

[0127] (2) Offline reminder function:

[0128] Display a reminder message when the user is offline to inform the user that the online resource is currently unavailable;

[0129] It provides the recommendation and download functions of offline learning content, making it convenient for users to study without an Internet connection.

[0130] Another object of the present invention is to provide an information data processing terminal, which is used to implement the knowledge base-based urological tumor perioperative education system.

[0131] In combination with the above technical solutions and the technical problems solved, the advantages and positive effects of the technical solutions to be protected by the present invention are as follows:

[0132] First, build a comprehensive knowledge base:

[0133] Integrate the latest research results, clinical guidelines, expert experience, etc. in the field of urological tumors at home and abroad to build a comprehensive and authoritative knowledge base.

[0134] The knowledge base is structured to facilitate rapid retrieval and reasoning of the system.

[0135] Intelligent Q&A and personalized recommendations:

[0136] Natural language processing technology is used to realize intelligent question and answer between patients and the system. The system can understand the patient's natural language input and give accurate and professional answers.

[0137] Based on the patient's personal information (such as age, gender, condition, etc.) and historical question and answer records, personalized recommendations are made through machine learning algorithms to provide patients with customized educational content.

[0138] Interactive learning and feedback:

[0139] An interactive learning interface is designed so that patients can interact with the system by clicking, selecting, etc., thereby increasing the fun and participation of learning.

[0140] The system can record the patient's learning process and results, evaluate the effectiveness of education through data analysis, and provide suggestions for improvement.

[0141] Continuous updates and optimizations:

[0142] Update the knowledge base regularly to ensure the timeliness and accuracy of the education content.

[0143] Collect patient feedback and continuously optimize system functions and user experience.

[0144] Multi-platform support:

[0145] The system supports a variety of terminal devices (such as mobile phones, tablets, computers, etc.) and operating systems, making it convenient for patients to study anytime and anywhere.

[0146] Provide offline download function to ensure normal use in no network environment.

[0147] Through the above solutions, this system can effectively solve the problems of inconvenient information acquisition, lack of personalization, and lack of interactivity in traditional education methods, and provide more comprehensive, intelligent, and personalized perioperative education services for patients with urological tumors.

[0148] Second, the technical solution of the present invention solves the following existing technical problems:

[0149] 1. Information is incomplete and difficult to update:

[0150] Existing problems: Traditional perioperative education for urological tumors mainly relies on paper manuals or single digital materials, with untimely information updates and incomplete content.

[0151] Solution: The present invention constructs a knowledge base module and utilizes web crawler technology and natural language processing technology to automatically collect, filter and update the latest medical information, ensuring that the content is comprehensive and updated in real time.

[0152] 2. Lack of personalization:

[0153] Existing problem: The existing education system is unable to provide personalized advice based on the patient's specific situation, resulting in the information obtained by the patient not matching their own needs.

[0154] Solution: Through intelligent question-answering and personalized recommendation systems, the present invention can parse user input and provide personalized education content based on the patient's specific condition and surgery type, thereby improving the pertinence and effectiveness of education.

[0155] 3. Poor interactivity and lack of feedback mechanism:

[0156] Existing problem: Traditional education methods lack interactivity, and patients are unable to ask questions and receive feedback in a timely manner.

[0157] Solution: The interactive learning and feedback system of the present invention improves patient participation through a user-friendly interface and learning process tracking function, and collects user opinions and needs through a feedback mechanism to further optimize the content of education.

[0158] 4. Insufficient cross-platform support:

[0159] Existing problems: Existing systems usually only support a single platform and cannot meet the needs of patients on different devices.

[0160] Solution: The multi-platform support and offline access module of the present invention utilizes responsive design and mobile application framework to achieve seamless adaptation of multiple devices such as PCs, mobile phones, and tablets, and through the offline access function, ensures that patients can obtain information even in an off-line environment.

[0161] 5. Insufficient data security and privacy protection:

[0162] Existing problems: Insufficient patient data security and privacy protection measures, posing a risk of information leakage.

[0163] Solution: This invention uses data encryption and privacy protection mechanisms, adopts the AES encryption algorithm to encrypt and store user data, and implements a user consent mechanism to ensure data security and user privacy.

[0164] Significant technological advances achieved:

[0165] 1. Comprehensiveness and real-time nature of information:

[0166] The knowledge base module ensures the comprehensiveness and timeliness of educational content through automated collection and screening, providing the latest and most complete medical information to help patients better understand pre- and post-operative precautions and rehabilitation methods.

[0167] 2. Personalization and intelligence:

[0168] The intelligent question-answering and personalized recommendation system uses NLP technology and recommendation algorithms to achieve intelligent retrieval and personalized recommendation of education content, ensuring that the information obtained by patients is highly matched with their own needs, thereby improving the effectiveness of education.

[0169] 3. Interactivity and feedback mechanism:

[0170] The interactive learning and feedback system improves patient participation and interactivity. Through the learning process tracking and feedback mechanism, it continuously optimizes and improves the education content, enhancing the patient's learning experience and effect.

[0171] 4. Cross-platform support and convenience:

[0172] Multi-platform support and offline access modules ensure seamless adaptation of the system on a variety of devices and provide offline access functions, which improves the convenience and usability of the system and meets the needs of patients in different environments.

[0173] 5. Data security and privacy protection:

[0174] The system uses AES encryption algorithm and privacy protection mechanism to ensure the security and privacy of user data, comply with relevant laws and regulations, and enhance patients' trust and willingness to use.

[0175] By solving the above-mentioned problems in the prior art and achieving significant technological progress, the present invention provides a more comprehensive, personalized, interactive, and cross-platform perioperative education system for urological tumors, which effectively improves the patients' preoperative preparation and postoperative rehabilitation effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0176] Figure 1 It is a structural diagram of a perioperative education system for urological tumors based on a knowledge base provided by an embodiment of the present invention;

[0177] Figure 2 is a content collection and screening structure diagram provided by an embodiment of the present invention;

[0178] Figure 3 It is a structural diagram of a natural language processing (NLP) module provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0179] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0180] like Figure 1 As shown, an embodiment of the present invention provides a perioperative education system for urological tumors based on a knowledge base, the system comprising:

[0181] Knowledge base, including content collection and screening, content organization and structuring, updating and maintenance;

[0182] Intelligent question answering and personalized recommendation system, including natural language processing (NLP) module, knowledge retrieval and reasoning, and personalized recommendation system;

[0183] Interactive learning and feedback system, including interactive interface, learning process tracking, and feedback mechanism;

[0184] Multi-platform support and offline access module, including multi-platform adaptation and offline access functions.

[0185] like Figure 2 As shown, the content collection and screening specifically includes:

[0186] (1) Expert interview process:

[0187] Design an interview outline to cover the key points of perioperative education;

[0188] Invite well-known experts in the field to conduct one-on-one interviews, record and organize the interview content;

[0189] Conduct quality assessment on interview content and filter out valuable information;

[0190] (2) Literature search strategy:

[0191] Determine the search keywords, such as "urological tumors", "perioperative period", "education", etc.;

[0192] Conduct advanced searches in professional databases and set reasonable screening conditions, such as time range, document type, etc.;

[0193] Screen and download relevant literature, read, summarize and generalize;

[0194] (3) Patient survey methods:

[0195] Design survey questionnaires, including open-ended and closed-ended questions, to cover patients' main questions and needs;

[0196] Distribute questionnaires in hospitals or online platforms to collect patient feedback;

[0197] Conduct statistical analysis on the questionnaire data to extract key information.

[0198] The content arrangement and structuring specifically include:

[0199] (1) Establishment of classification system:

[0200] Establish a clear classification system based on different perioperative stages and patient needs;

[0201] Assign a unique identifier and description to each category;

[0202] (2) Knowledge graph construction:

[0203] Use graph databases (such as Neo4j) to store knowledge graph data;

[0204] Define entities, relationships, and attributes, and convert the collected content into nodes and edges in the graph;

[0205] Use graph query languages ​​(such as Cypher) for knowledge retrieval and reasoning;

[0206] The knowledge base update and maintenance specifically includes:

[0207] (1) Regular update mechanism:

[0208] Set a fixed update cycle (such as quarterly or semi-annual);

[0209] During the update cycle, new research results, clinical guidelines and patient feedback are collected;

[0210] Update the knowledge base to ensure the timeliness and accuracy of the content;

[0211] (2) Feedback processing flow:

[0212] Set up dedicated feedback channels (e.g. email, online forms, etc.);

[0213] Categorize, organize and prioritize collected feedback;

[0214] Update the knowledge base or adjust the education strategy based on the feedback.

[0215] like Figure 3 As shown, the natural language processing (NLP) module specifically includes:

[0216] (1) Text preprocessing tools:

[0217] Use open source libraries (such as NLTK, spaCy) for preprocessing operations such as word segmentation, stop word removal, and part-of-speech tagging;

[0218] Customize thesaurus and rules to improve the accuracy and efficiency of preprocessing;

[0219] (2) Intent Recognition Model:

[0220] Choose a suitable pre-trained model (such as BERT, RoBERTa, etc.);

[0221] Fine-tune the model and train it using annotated question-answering datasets;

[0222] Evaluate model performance and optimize model parameters and structure;

[0223] (3) Entity Recognition Module:

[0224] Named Entity Recognition (NER) technology is used to identify key entities in user input.

[0225] Combining domain knowledge base and contextual information to improve the accuracy of entity recognition.

[0226] The knowledge retrieval and reasoning specifically include:

[0227] (1) Exact matching algorithm:

[0228] Implement an accurate matching algorithm based on keywords;

[0229] Calculate the similarity between the user's question and the answer in the knowledge base, and select the answer with the highest similarity to return;

[0230] (2) Fuzzy matching and reasoning:

[0231] Use fuzzy query techniques (such as wildcards, regular expressions) for fuzzy matching.

[0232] Combined with knowledge graphs for semantic reasoning, it generates answers relevant to user questions.

[0233] The personalized recommendation system specifically includes:

[0234] (1) User portrait construction:

[0235] Design a user portrait model, including basic information, medical condition information, learning preferences and other dimensions;

[0236] Use historical user data and real-time interaction data to build user portraits;

[0237] (2) Recommendation algorithm implementation:

[0238] Select appropriate recommendation algorithms (such as collaborative filtering, content-based recommendation, etc.);

[0239] Generate a recommendation list based on the user profile and the current question context;

[0240] Sort and display recommendation results to improve user experience.

[0241] The interactive interface design specifically includes:

[0242] (1) Interface prototype design:

[0243] Use design tools (such as Sketch, Figma) to draw interface prototypes;

[0244] Design a concise and clear navigation bar, Q&A area, recommendation area, and learning progress display area;

[0245] (2) Interaction design:

[0246] Design a smooth user interaction process to ensure that users can easily complete learning tasks;

[0247] Add animation effects and prompt information to improve the friendliness and ease of use of the user interface;

[0248] The learning process tracking specifically includes:

[0249] (1) Learning record database:

[0250] Design the learning record database table structure to store the user's learning time, learning content, learning progress and other information;

[0251] Use database management systems (such as MySQL, MongoDB) for data storage and management;

[0252] (2) Learning Assessment Mechanism:

[0253] Design quizzes and questionnaires to assess users’ learning outcomes;

[0254] Automatically score and provide feedback on user answers;

[0255] (3) Feedback mechanism

[0256] Instant feedback system:

[0257] Provide immediate feedback (such as correct / incorrect) when users answer questions;

[0258] Adjust the difficulty and type of follow-up questions based on the user’s responses;

[0259] (4) Comprehensive feedback report:

[0260] Generate a comprehensive feedback report after the learning process, including learning outcomes, areas for improvement, etc.

[0261] Provide download and share functions to facilitate users to save and share learning results.

[0262] The multi-platform adaptation specifically includes:

[0263] (1) Responsive design:

[0264] Use CSS media queries and fluid layout techniques to achieve responsive design;

[0265] Ensure that the system can display and interact well on different devices and screen sizes;

[0266] (2) Mobile application development:

[0267] Develop mobile apps using cross-platform frameworks (such as Flutter and React Native);

[0268] Customize the UI / UX design of the mobile app to ensure a consistent user experience with the web version.

[0269] The offline access function specifically includes:

[0270] (1) Data caching mechanism:

[0271] Implement data caching technology (such as HTML5 Cache Manifest, Service Workers, etc.) to cache the data required by users locally;

[0272] Load data from the local cache when the user is offline to ensure system availability;

[0273] (2) Offline reminder function:

[0274] Display a reminder message when the user is offline to inform the user that the online resource is currently unavailable;

[0275] It provides the recommendation and download functions of offline learning content, making it convenient for users to study without an Internet connection.

[0276] An embodiment of the present invention provides an information data processing terminal, which is used to implement the knowledge base-based urological tumor perioperative education system.

[0277] The following is a specific implementation plan of the knowledge base-based perioperative education system for urological tumors, including the detailed functions and technical implementation of each module:

[0278] System architecture:

[0279] 1. Knowledge base module

[0280] 2. Intelligent question answering and personalized recommendation system

[0281] 3.Interactive learning and feedback system

[0282] 4.Multi-platform support and offline access module

[0283] 5. System security and privacy protection

[0284] Technical implementation:

[0285] 1. Knowledge base module

[0286] Functional description:

[0287] -Collect, screen, organize and maintain perioperative education content for urological tumors.

[0288] Technical implementation:

[0289] -Content collection and screening

[0290] Use web crawler technology to automatically collect relevant content from medical literature, professional guides and other websites.

[0291] The collected content is screened through human review and machine learning algorithms.

[0292] -Content organization and structuring

[0293] Natural language processing (NLP) technology is used to parse text, classify information and store it in a structured database.

[0294] Use a NoSQL database like MongoDB to store structured content for fast retrieval and scalability.

[0295] -Update and maintenance

[0296] Run crawlers regularly to update content.

[0297] Implement automated scripts to regularly check the content in the database and mark and delete outdated information.

[0298] ```Python

[0299] #Example: Using Scrapy to collect content

[0300] import scrapy

[0301]

[0302] 2. Intelligent question-answering and personalized recommendation system

[0303] Functional description:

[0304] - Parse user input, search the knowledge base, provide answers and personalized recommendations.

[0305] Technical implementation:

[0306] -Natural Language Processing (NLP) module

[0307] Use SpaCy or NLTK for text parsing and intent identification.

[0308] -Knowledge retrieval and reasoning

[0309] Use Elasticsearch for full-text retrieval and combine it with logical reasoning algorithms to generate answers.

[0310] -Personalized recommendation system

[0311] Based on information such as the user's condition and surgery type, recommendation algorithms (such as collaborative filtering or content-based recommendation) are used to provide personalized education content.

[0312] ```Python

[0313] import spacy

[0314] from elasticsearch import Elasticsearch

[0315] #NLP analysis

[0316] nlp=spacy.load('en_core_web_sm')

[0317] defparse_question(question):

[0318] doc=nlp(question)

[0319] return[token.lemma_for token in doc ifnot token.is_stop]

[0320] #Full text search

[0321] es=Elasticsearch()

[0322] defsearch_knowledge_base(query):

[0323] response=es.search(index="knowledge_base",body={"query":{"match":{"content":query}}})

[0324] return response['hits']['hits']

[0325] #Example: Personalized Recommendations

[0326] defrecommend_content(user_profile):

[0327] #Generate recommendations based on user information

[0328] recommendations=[]

[0329] ifuser_profile['condition']=='pre-surgery':

[0330] recommendations=search_knowledge_base('pre-surgery care')

[0331] #Add more recommendation logic

[0332] return recommendations

[0333] 3.Interactive learning and feedback system

[0334] Functional description:

[0335] - Provide a user-friendly interface, track the learning process, and collect user feedback.

[0336] Technical implementation:

[0337] -Interactive interface

[0338] Build user interfaces using front-end frameworks like React or Vue.js.

[0339] The backend uses Flask or Django to provide API services.

[0340] -Learning process tracking

[0341] Implement the user activity recording function and use MySQL or PostgreSQL database to store user learning data.

[0342] -Feedback mechanism

[0343] Provide user feedback forms and use form data to analyze common problems and needs.

[0344]

[0345] ```Python

[0346] #Example: Using Flask to provide API services

[0347] from flask import Flask,request,jsonify app=Flask(__name__)

[0348] @app.route(' / api / learning',methods=['GET'])

[0349] defget_learning_content():

[0350] user_id=request.args.get('user_id')

[0351] #Get learning content based on user ID

[0352] return jsonify({'content':'Learning content for user'})

[0353] if __name__ == '__main__':

[0354] app.run(debug=True)

[0355] 4.Multi-platform support and offline access module

[0356] Functional description:

[0357] -Supports access from multiple devices and provides offline download function.

[0358] Technical implementation:

[0359] -Multi-platform adaptation

[0360] Use responsive design (such as Bootstrap) to adapt to multiple devices such as PC, mobile phones, tablets, etc. Build mobile applications (iOS / Android) using React Native or Flutter framework.

[0361] - Offline access function

[0362] Use Service Worker to enable offline access to web applications.

[0363] Implement offline storage in mobile apps, using SQLite or the built-in database.

[0364] ```javascript

[0365] / / Example: Using Service Worker to implement offline access

[0366]

[0367] 5. Description of system security and privacy protection functions:

[0368] -Protect user data security and privacy. Technical implementation:

[0369] -Data encryption

[0370] The user data is encrypted and stored using the AES encryption algorithm.

[0371] -Privacy protection

[0372] Implement a user consent mechanism to ensure that users give their informed consent to data collection and usage.

[0373] Comply with GDPR and other relevant laws and regulations.

[0374] ```Python

[0375] from Crypto.Cipher importAES

[0376] import base64

[0377] #AES encryption

[0378] defencrypt_data(data,key):

[0379] cipher=AES.new(key,AES.MODE_EAX)

[0380] ciphertext,tag=cipher.encrypt_and_digest(data.encode('utf-8'))

[0381] return base64.b64encode(cipher.nonce+tag+ciphertext).decode('utf-8')

[0382] #AES decryption

[0383] defdecrypt_data(encrypted_data,key):

[0384] encrypted_data=base64.b64decode(encrypted_data.encode('utf-8'))

[0385] nonce=encrypted_data[:16]

[0386] tag=encrypted_data[16:32]

[0387] ciphertext=encrypted_data[32:]

[0388] cipher=AES.new(key,AES.MODE_EAX,nonce=nonce)

[0389] return cipher.decrypt_and_verify(ciphertext,tag).decode('utf-8')

[0390] ```

[0391] Through the technical implementation of the above modules, the present invention provides a knowledge base-based perioperative education system for urological tumors, which can effectively improve patients' understanding of surgery-related information and enhance patients' preoperative preparation and postoperative rehabilitation effects.

[0392] It should be noted that the embodiments of the present invention can be implemented by hardware, software, or a combination of software and hardware. The hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. It can be understood by a person of ordinary skill in the art that the above-mentioned devices and methods can be implemented using computer executable instructions and / or contained in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the carrier medium. The device and its modules of the present invention can be implemented by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software, such as firmware.

[0393] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principle of the present invention should be covered by the protection scope of the present invention.

Claims

1. A knowledge base-based perioperative education system for urological tumors, characterized in that: The system includes: 1) Knowledge base, including: Content collection and screening: Collect relevant content through medical literature, professional guidelines, and clinical expert opinion channels, and screen the information for accuracy and relevance; Content organization and structuring: The collected content is classified and organized according to the different stages before, during and after the operation to facilitate user query and use; Update and maintenance: regularly update the knowledge base content to ensure the timeliness and accuracy of the information, and delete or correct outdated or inaccurate information; 2) Intelligent question-answering and personalized recommendation system, including: Natural Language Processing (NLP) module: uses NLP technology to parse user input questions and understand their intentions and needs; Knowledge retrieval and reasoning: Retrieve relevant content from the knowledge base based on user questions and perform logical reasoning to provide accurate answers; Personalized recommendation system: Based on the user's condition, surgery type, and personal preference information, personalized education content is recommended to help patients better understand surgery-related information; 3) Interactive learning and feedback system, including: Interactive interface: Provides a friendly user interface and supports content display in multiple forms such as text, images, and videos, making it convenient for users to interact and learn; Learning process tracking: record the user's learning process and progress, analyze learning results, and provide personalized learning suggestions; Feedback mechanism: collect user feedback, analyze common problems and needs, and optimize education content and system functions; 4) Multi-platform support and offline access modules, including: Multi-platform adaptation: supports multiple devices such as PC, mobile phones, and tablets, ensuring that users can easily access the system on different platforms; Offline access function: Supports offline download function, users can view the downloaded teaching content in an offline environment to ensure the accessibility of information; 5) Contents of perioperative education for urinary tumors, including: Preoperative education: including preoperative preparation, precautions, and preoperative examination contents, to help patients understand the various preparations before surgery; Intraoperative education: Introduce the surgical process, anesthesia methods, and surgical risk information to give patients a comprehensive understanding of the surgical process; Postoperative education: covers postoperative care, rehabilitation training, dietary advice, and review plan content, guiding patients’ postoperative recovery and care; 6) System security and privacy protection, including: Data encryption: Encrypt users’ personal information and health data to ensure data security; Privacy protection: Strictly abide by relevant laws and regulations, protect user privacy, and do not disclose any personal information to third parties without the user's consent.

2. The knowledge base-based perioperative education system for urological tumors according to claim 1 is characterized in that: The content collection and screening specifically include: (1) Expert interview process: Design an interview outline to cover the key points of perioperative education; Invite well-known experts in the field to conduct one-on-one interviews, record and organize the interview content; Conduct quality assessment on interview content and filter out valuable information; (2) Literature search strategy: Determine the search keywords, such as "urological tumors", "perioperative period", and "education"; Conduct advanced searches in professional databases and set reasonable screening conditions, such as time range and document type; Screen and download relevant literature, read, summarize and generalize; (3) Patient survey methods: Design survey questionnaires, including open-ended and closed-ended questions, to cover patients' main questions and needs; Distribute questionnaires in hospitals or online platforms to collect patient feedback; Conduct statistical analysis on the questionnaire data to extract key information.

3. The knowledge-based perioperative education system for urological tumors according to claim 1 is characterized in that: The content arrangement and structuring specifically include: (1) Establishment of classification system: Establish a clear classification system based on different perioperative stages and patient needs; Assign a unique identifier and description to each category; (2) Knowledge graph construction: Use graph databases (such as Neo4j) to store knowledge graph data; Define entities, relationships, and attributes, and convert the collected content into nodes and edges in the graph; Use graph query languages ​​(such as Cypher) for knowledge retrieval and reasoning; The knowledge base update and maintenance specifically includes: (1) Regular update mechanism: Set a fixed update cycle (such as quarterly or semi-annual); During the update cycle, new research results, clinical guidelines and patient feedback are collected; Update the knowledge base to ensure the timeliness and accuracy of the content; (2) Feedback processing flow: Set up a dedicated feedback channel (e.g. email, online form); Categorize, organize and prioritize collected feedback; Update the knowledge base or adjust the education strategy based on the feedback.

4. The knowledge base-based perioperative education system for urological tumors according to claim 1, characterized in that: The natural language processing (NLP) module specifically includes: (1) Text preprocessing tools: Use open source libraries (such as NLTK, spaCy) to perform word segmentation, stop word removal, and part-of-speech tagging preprocessing operations. Customize thesaurus and rules to improve the accuracy and efficiency of preprocessing; (2) Intent Recognition Model: Choose a suitable pre-trained model (such as BERT, RoBERTa); Fine-tune the model and train it using annotated question-answering datasets; Evaluate model performance and optimize model parameters and structure; (3) Entity Recognition Module: Named Entity Recognition (NER) technology is used to identify key entities in user input. Combining domain knowledge base and contextual information to improve the accuracy of entity recognition.

5. The knowledge base-based perioperative education system for urological tumors according to claim 1, characterized in that: The knowledge retrieval and reasoning specifically include: (1) Exact matching algorithm: Implement an accurate matching algorithm based on keywords; Calculate the similarity between the user's question and the answer in the knowledge base, and select the answer with the highest similarity to return; (2) Fuzzy matching and reasoning: Use fuzzy query techniques (such as wildcards, regular expressions) for fuzzy matching. Combined with knowledge graphs for semantic reasoning, it generates answers relevant to user questions.

6. The knowledge base-based perioperative education system for urological tumors according to claim 1, characterized in that: The personalized recommendation system specifically includes: (1) User portrait construction: Design a user portrait model, including basic information, disease information, and learning preference dimensions; Use historical user data and real-time interaction data to build user portraits; (2) Recommendation algorithm implementation: Choose appropriate recommendation algorithms (such as collaborative filtering, content-based recommendation); Generate a recommendation list based on the user profile and the current question context; Sort and display recommendation results to improve user experience.

7. The knowledge base-based perioperative education system for urological tumors according to claim 1, characterized in that: The interactive interface design specifically includes: (1) Interface prototype design: Use design tools (such as Sketch, Figma) to draw interface prototypes; Design a concise and clear navigation bar, Q&A area, recommendation area, and learning progress display area; (2) Interaction design: Design a smooth user interaction process to ensure that users can easily complete learning tasks; Add animation effects and prompt information to improve the friendliness and ease of use of the user interface; The learning process tracking specifically includes: (1) Learning record database: Design the learning record database table structure to store the user's learning time, learning content, and learning progress information; Use database management systems (such as MySQL, MongoDB) for data storage and management; (2) Learning Assessment Mechanism: Design quizzes and questionnaires to assess users’ learning outcomes; Automatically score and provide feedback on user answers; (3) Feedback mechanism Instant feedback system: Provide immediate feedback (such as correct / incorrect) when users answer questions; Adjust the difficulty and type of follow-up questions based on the user’s responses; (4) Comprehensive feedback report: Generate a comprehensive feedback report after the learning process, including learning outcomes and areas for improvement; Provide download and share functions to facilitate users to save and share learning results.

8. The knowledge base-based perioperative education system for urological tumors according to claim 1, characterized in that: The multi-platform adaptation specifically includes: (1) Responsive design: Use CSS media queries and fluid layout techniques to achieve responsive design; Ensure that the system can display and interact well on different devices and screen sizes; (2) Mobile application development: Develop mobile apps using cross-platform frameworks (such as Flutter and React Native); Customize the UI / UX design of the mobile app to ensure a consistent user experience with the web version.

9. The knowledge base-based perioperative education system for urological tumors according to claim 1, characterized in that: The offline access function specifically includes: (1) Data caching mechanism: Implement data caching technology (such as HTML5 Cache Manifest, Service Workers) to cache the data required by users locally; Load data from the local cache when the user is offline to ensure system availability; (2) Offline reminder function: Display a reminder message when the user is offline to inform the user that the online resource is currently unavailable; It provides the recommendation and download functions of offline learning content, making it convenient for users to study without an Internet connection.

10. An information data processing terminal, characterized in that: The information data processing terminal is used to implement the knowledge base-based perioperative education system for urological tumors as described in any one of claims 1-9.

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