Method and system for solving medical teaching training business problems based on a knowledge base management system

Abstract

The application relates to the technical field of data interaction, and discloses a medical teaching training business problem solving method and system based on a knowledge base management system. The method comprises the following steps: collecting multi-source heterogeneous data and cleaning, constructing a knowledge graph containing problem-regulation-solution association based on a BERT model and a graph database, monitoring policies and business indexes in real time to trigger dynamic updating of the graph, generating an individualized training scheme in combination with student characteristics, monitoring a teaching process through multi-modal data and dynamically adjusting, and constructing a multi-dimensional index system to optimize the knowledge graph. The system comprises data access and cleaning, knowledge graph construction, dynamic updating, individualized scheme generation, intelligent monitoring and effect evaluation modules. The application solves the problems of special population teaching adaptation, real-time policy response and business process optimization, realizes a closed loop of 'data collection-knowledge construction-application verification' through the knowledge graph and AI algorithms, improves teaching quality and efficiency, and reduces training cost and compliance risk.

Description

Technical Field

[0001] This invention relates to the field of data interaction technology, specifically to a solution and system for medical teaching and training business problems based on a knowledge base management system. Background Technology

[0002] The current healthcare training system faces multiple challenges in its intelligent transformation, with both traditional training models and existing intelligent systems exhibiting significant shortcomings: Traditional healthcare training relies on fixed textbooks and general lesson plans, lacking targeted training programs for specific positions such as emergency medicine, pediatrics, and geriatric nursing, as well as for special populations such as resident trainees and those transitioning from other specialties; while existing intelligent training systems incorporate AI, they have not built personalized models adapted to the clinical operating habits, emergency response capabilities, and psychological stress characteristics of different healthcare professionals. Medical policies, regulations, and clinical technologies are frequently updated, and traditional training relies on manual interpretation and integration into courses, resulting in long cycles and a tendency for training content to become disconnected from clinical requirements and assessment standards; existing medical knowledge management systems are mostly static storage systems, lacking the ability to automatically analyze policy and guideline changes, failing to accumulate senior healthcare professionals' practical experience in real time, and unable to intelligently connect with changes in training needs brought about by technological iterations, thus failing to meet the requirements of real-time, scenario-based, and compliant clinical teaching.

[0003] The multi-source data, including operational data of medical staff, emergency response status, patient feedback, and assessment indicators, has not formed an effective knowledge accumulation. The knowledge connection across departments and diseases relies on the experience of the supervising teachers and lacks the support of a systematic knowledge graph. As a result, medical staff find it difficult to establish a scenario-based mapping of "theory-practice-emergency response", and the construction of a unified clinical competency system of "knowledge-action-emergency response" is hindered.

[0004] The enrollment, training, assessment, and onboarding processes are fragmented, resource allocation is inefficient, and mentor assignment relies on manual scheduling, making it impossible to intelligently match mentors based on their areas of expertise with trainees' skill gaps. Furthermore, the system lacks closed-loop management of the entire business process, making it difficult to optimize training cycles and control training costs through data-driven approaches. It also cannot effectively respond to dynamic changes in the regulatory requirements of the National Health Commission, posing compliance risks. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a solution and system for medical teaching and training business problems based on a knowledge base management system. This system solves the problems of untimely personalized training for medical staff in special positions, real-time adaptation to medical policies and regulations, systematic improvement of clinical practical skills, and optimization of the entire business process.

[0006] To achieve the above objectives, the present invention provides the following technical solution: The solution to operational problems in healthcare staff training based on a knowledge base management system includes the following steps: Step 1: Multi-source heterogeneous data access and cleaning Obtain heterogeneous data from multiple sources, including the National Health Commission's API, hospital operation systems, clinical equipment sensors, and third-party medical data platforms, covering policies, regulations, treatment guidelines, operation records, and clinical practice data. We obtain real-time data on medical policies, treatment guidelines, and assessment standards through the official interface of the National Health Commission. Collect business data such as medical records, theoretical exam scores, practical assessment records, and patient complaints / satisfaction through the hospital's HIS / LIS / EMR system; Clinical simulation equipment and wearable devices are used to collect data on the accuracy of medical and nursing operations, reaction speed, and physiological characteristics. Connect with medical literature databases and clinical case databases to obtain data on typical cases and emergency response plans.

[0007] The data cleaning method is as follows: First, the Dedupe library is used to eliminate duplicate student / case data, and invalid operation logs are filtered by regular expressions. Then, Scikit-learn's Imputer combined with the random forest regression algorithm is used to fill in the missing physiological / operational data. Finally, IsolationForest is used to detect abnormal operation data (such as a single simulation training time exceeding 10 hours or an abnormally high operation error rate) and a data cleaning report is generated.

[0008] Step Two: Knowledge Graph Construction and Cross-Domain Association Based on the BERT+BiLSTM model, entities and relationships are extracted: entities in the medical field such as emergency cardiopulmonary resuscitation, medication contraindications for elderly patients, and operational errors by resident trainees are identified, and semantic relationships such as cause, applicability, optimization, and association are extracted. A multi-dimensional knowledge network containing clinical problems, policies and regulations, treatment guidelines, solutions, and equipment adaptation is constructed through a graph database (such as Neo4j) to complete the knowledge graph construction.

[0009] The knowledge graph construction method is as follows: key clauses of policy documents and treatment guidelines are extracted through TextRank to establish a regulatory knowledge subgraph, and a time dimension is introduced to record the effective period of knowledge nodes, supporting historical version retrospective and impact analysis; Jaccard similarity is used to merge cross-source entities, and the conflict problem of clinical knowledge from different sources is solved through DS evidence theory to ensure the logical consistency of the graph.

[0010] Step 3: Dynamic Knowledge Updates and Intelligent Decision-Making Real-time monitoring of changes in healthcare policies and fluctuations in business indicators triggers full or partial updates to the knowledge graph. The RotatE algorithm is used to embed the knowledge graph into the complex space, calculate the embedding vector distance between the new knowledge and the existing network, generate an influence heatmap, and mark key association paths; The update proposal will be transformed into a blockchain smart contract, and a multi-weighted node vote will be held, inviting hospital management (40% weight), senior clinical instructors (40% weight), and health commission regulators (20% weight). When the number of votes in favor exceeds 67%, the update will be automatically executed, and the voting records will be encrypted and stored in the medical consortium blockchain. An XGBoost prediction model is built based on historical data. The input is knowledge update characteristics, and the output is a prediction of the impact on indicators such as training cost, cycle, and assessment pass rate. The contribution of each factor is analyzed by the SHAP value. Delta coding technology is used to perform incremental updates while retaining historical versions. After the update, the AI ​​teaching module, simulator scoring logic and other related systems are adjusted synchronously. The prediction accuracy is verified by A / B testing. When the deviation exceeds 15%, the model is automatically optimized and fed back to the knowledge graph to form a closed loop.

[0011] Step 4: Personalized Solution Generation and Path Optimization By combining the job attributes, clinical experience, operational behaviors, physiological characteristics, and knowledge graph association rules of medical and nursing trainees, differentiated training programs are generated: We designed a tiered training path for resident trainees, including basic operations, case observation, and simulated practical training, and customized specialized adaptation tutorials for personnel changing jobs from other disciplines. Strengthen communication skills with elderly patients and fall emergency response modules for medical staff in elderly care positions; increase the frequency of emergency rescue scenarios and high-intensity stress coping training for medical staff in emergency positions. By utilizing transfer learning technology, emergency response experience in critical care medicine can be transferred to emergency training scenarios to optimize training content for complex cases (such as the treatment of multiple injuries). By dynamically optimizing the training path through reinforcement learning (PPO algorithm), the training efficiency is improved by shortening the duration of a single high-difficulty simulation training for trainees with weak psychological stress tolerance, increasing the frequency of repeated practice of basic operations, and designing a reward function that integrates trainees' mastery of knowledge points, types of operational errors, and emotional states.

[0012] Step 5: Intelligent Monitoring and Intervention of the Teaching Process Real-time data collection from sensors in clinical simulation equipment, wearable devices, and operational recordings, combined with affective computing and behavioral analysis models, dynamically adjusts teaching content and triggers risk warnings. Edge computing nodes are deployed to process real-time operation video streams. Federated learning is used to protect the privacy of trainees across different hospitals. Visual, tactile, and auditory multimodal fusion technology is used to perceive the trainees' status in all aspects: facial expressions and voice tone are analyzed based on the ResNet-50+LSTM model to identify negative emotions such as tension and anxiety, and to automatically push reassuring prompts and operation reminders. When an operational violation is detected, a vibration warning is triggered on the simulation equipment. If a trainee fails to perform the "emergency treatment for anaphylactic shock" correctly three times in a row, the corresponding theoretical micro-lesson and VR-specific training will be automatically inserted. Distraction detection is based on the YOLOv8 target detection algorithm, combined with head pose estimation technology. When the gaze deviates from the operating area for more than 2 seconds, a level 3 warning is triggered, and the risk level is simultaneously transmitted to the teaching device.

[0013] Step Six: Business Performance Evaluation and Knowledge Feedback Construct a multi-dimensional indicator system that includes training quality, operational efficiency, and clinical suitability, and feed the evaluation results back to the knowledge graph for continuous optimization: Design a counterfactual analysis experiment to quantify the effect of blockchain evidence storage on improving training quality; By analyzing community interaction data of medical trainees through social networks, we evaluated the effect of peer-to-peer case discussions on learning motivation; By using the association rules between training methods and clinical outcomes as a new knowledge input graph, a closed loop of data collection, knowledge construction, application verification, and rule optimization is formed.

[0014] A problem-solving system for healthcare worker training based on a knowledge base management system, including: Data access and cleaning module: The hardware layer deploys a clinical simulation equipment data acquisition device, a smart wearable device interface, and a medical video acquisition module to support real-time access of multi-source data; the software layer integrates a data cleaning engine, configured with the Dedupe deduplication algorithm, a random forest missing value imputation model, and an IsolationForest anomaly detection module to output a standardized dataset; it obtains medical records and assessment records through the hospital HIS system interface, and obtains simulation operation equipment data through a CAN bus interface.

[0015] The knowledge graph construction module includes a knowledge extraction unit that uses a BERT+BiLSTM model to dynamically extract entities and relationships, supporting the parsing of multimodal data such as policy documents, clinical cases, and practical videos; a knowledge fusion unit that merges cross-source entities using the Jaccard similarity algorithm and resolves knowledge conflicts using DS evidence theory to ensure the logical consistency of the graph; and storage in the Neo4j graph database, supporting millisecond-level cross-departmental and cross-disease related queries (such as quickly locating the corresponding policies, regulations, procedures, and practical correction schemes for "pediatric medication errors").

[0016] Dynamic update module: Includes a change detection submodule, which compares the similarity of policy documents and treatment guidelines in real time, and sets thresholds for business indicators such as pass rate, operation error rate, and number of complaints; a version control submodule, which records the timestamp, summary of updated content, and scope of impact for each update, and supports one-click rollback to a historical stable version; and connects to the National Health Commission's API to synchronize policy updates daily and automatically trigger the update process.

[0017] Personalized solution generation module: including trainee profile engine, which integrates medical and nursing positions, clinical experience, operational behavior, and psychological assessment data to generate composite tags such as "emergency room - resident trainee - weak emergency response ability - anxious type"; solution configuration unit predefines "elderly care exclusive rules" and "emergency room position specific requirements", supports dynamic adjustment and combination of parameters, and automatically generates training paths adapted to different trainees.

[0018] Intelligent monitoring module: including edge computing nodes, which realize lightweight processing such as video frame extraction and operation feature extraction on the clinical simulation terminal, reducing the transmission pressure on the cloud; risk assessment model, which identifies violations and high-risk behaviors based on random forest algorithm, outputs "operation risk level" and synchronizes it to the teaching terminal; and integrates multimodal perception unit to identify trainees' emotions and distraction status in real time and trigger corresponding intervention mechanisms.

[0019] The effectiveness evaluation module includes an indicator calculation engine that summarizes data such as training cycle, cost, assessment pass rate, and clinical operation error rate in real time to generate a multi-dimensional dashboard; and a predictive analysis unit that predicts business trends based on an LSTM model to provide data support for teaching assignment and resource allocation.

[0020] This invention provides a solution and system for medical teaching and training business problems based on a knowledge base management system. It has the following beneficial effects: 1. A "tiered teaching + scenario adaptation" program is designed for resident trainees, those changing jobs from other specialties, and medical staff in special positions. Based on their clinical skills and psychological stress data, the program automatically adjusts the training difficulty and prompt intensity to shorten the job adaptation cycle. The system generates composite tags by integrating multi-dimensional features to achieve dynamic adaptation of teaching parameters, creating a precise training model that is "personalized for every individual."

[0021] 2. The knowledge graph establishes a multi-dimensional network of connections between "clinical problems, policies and regulations, treatment guidelines, solutions, and equipment adaptation." When medical staff encounter problems such as "non-standard aseptic operation" during simulation training, the system automatically links to corresponding micro-lessons on treatment guidelines, typical cases, and VR-specific training resources, realizing intelligent mapping between theoretical knowledge and clinical practice, and helping to build a systematic clinical capability system.

[0022] 3. By accessing the National Health Commission's policies and treatment guidelines in real time via API, and using natural language processing technology to automatically parse documents, the hospital-wide training program can be adjusted within 24 hours (including knowledge node updates, AI learning assistant strategy optimization, and simulator parameter calibration). This ensures that the training content is updated in sync with clinical requirements and assessment standards, completely changing the lagging situation of traditional medical and nursing training that relies on manual interpretation.

[0023] 4. Knowledge graphs optimize training paths, accurately eliminate invalid content, and dynamically adjust training plans using reinforcement learning algorithms, thereby reducing the cost of medical and nursing training. Blockchain-based evidence storage technology encrypts and uploads training hours and assessment data to the blockchain, automatically verifying completion status and significantly reducing compliance risks and management costs. Simultaneously, a visual dashboard containing 32 core indicators is constructed, presenting key data such as training cycle, cost, pass rate, and instructor efficiency ratio in real time. LSTM models are used to predict business trends and trigger intelligent interventions.

[0024] 5. The intelligent monitoring module collects medical and nursing operation data and status in real time through simulated equipment sensors, cameras and smart wearable devices. It uses multimodal fusion technology to identify risks such as operation violations, distraction and insufficient emergency response capabilities, triggers early warnings in real time and dynamically adjusts teaching content. It strengthens standardized operation and safety awareness from the training stage and reduces the risk of medical errors in clinical practice. Attached Figure Description

[0025] Figure 1 This is a flowchart of the entire invention. Detailed Implementation

[0026] 1. Hardware deployment and data acquisition environment setup Deploy the following equipment in the hospital's clinical skills training center: Clinical simulation equipment: Deploys the LapSim surgical simulator, Mimic humanoid simulator, and CareSim nursing operation model, and outputs operation data in real time through RS232 / 485 interface. The operation data includes instrument movement trajectory, force, speed, angle, operation time, etc. Wearable devices: Trainees wear smart bracelets and TobiiProGlasses3 eye trackers, which are synchronized to the local edge gateway via Bluetooth; the smart bracelets are used to collect heart rate variability and skin conductance; the TobiiProGlasses3 eye trackers track gaze distribution and gaze duration.

[0027] Video capture: Three high-definition cameras are installed inside the simulator at a frame rate of 30fps to capture the trainees' facial expressions and the operation area. Edge computing nodes: NVIDIA Jetson XavierNX is deployed in the simulation chamber to be responsible for video frame extraction (5 frames per second), and the MobileNetV3 lightweight model is used for feature extraction. The raw video data is encrypted with AES-256 and stored locally for 7 days. Cloud server: Configure an AWSEC2g4dn.xlarge instance and deploy Neo4j4.4 graph database, Hyperledger Fabric consortium blockchain, and TensorFlowServing model inference service; Data Interface: A scheduled task calls the API of the National Health Commission's official website at 2:00 AM every day to obtain the latest policies, regulations and treatment guidelines, and connects to the hospital's HIS system via REST API to obtain trainee rotation plans and past assessment records.

[0028] 2. Step 1: Multi-source heterogeneous data access and cleaning The data cleaning engine is developed based on Python 3.9, and its implementation is as follows: Dedupe: Using the Dedupe library, hash matching of student name, ID number, and employee ID is performed to eliminate duplicate files across systems and merge records with a similarity greater than 0.85. Missing value imputation: For missing data such as trainees' reaction speed and heart rate, Scikit-learn's SimpleImputer is used to imput the missing values ​​with the mean, and random forest regression (n_estimators=200, max_depth=10) is used for secondary prediction calibration. The missing value imputation error is controlled within ±5%. Anomaly detection: Based on IsolationForest (contamination=0.01), abnormal operation records are marked, such as a single simulation training session lasting more than 8 hours, or the same operation error occurring more than 20 times consecutively, generating an anomaly data report for manual review; Output: A standardized dataset is generated, with each trainee having over 200 fields including basic profile, rotation department, theoretical exam score, practical assessment results, physiological time series data, and policy mastery.

[0029] 3. Step Two: Knowledge Graph Construction and Cross-Domain Association Entity relation extraction is performed using a BERT+BiLSTM+Attention architecture. Entity recognition: Using a fine-tuned BERT-base-chinese model, trained on 50,000 labeled medical training corpora (including policy documents, case reports, and teaching logs), it can accurately extract entities such as "cardiopulmonary resuscitation operation specifications", "thrombolytic therapy for acute myocardial infarction", and "resident trainee - operational error - hand stability", with a total of over 120,000 entities; Relation extraction: The BiLSTM hidden layer has a dimension of 256 and the Attention layer output has a dimension of 128. Relationships such as "cause", "applicable", "optimize", and "associate" are extracted from teaching logs and case discussion records, with an accuracy of 87%. Knowledge fusion: Using Jaccard similarity with a threshold of 0.8, the same entity from different hospitals or different guidelines is merged, such as “CPR” and “cardiopulmonary resuscitation”; when knowledge conflicts are encountered (such as two guidelines having different treatments for allergic reactions), the credibility is calculated using the DS evidence theory, and the version with support > 0.7 is retained and the conflict is marked and recorded. Storage: The graph database uses Neo4j4.4, with approximately 500,000 nodes and 2 million relationships, supporting millisecond-level cross-department queries; for example, the query "Standardized Trainee - Pediatrics - Medication Error" automatically associates with policies and regulations, standardized operation videos, relevant case studies, and intensive training modules.

[0030] Regulatory Subgraph: Key clauses of the treatment guidelines are extracted using TextRank to create a "regulatory knowledge subgraph". A time attribute is added to each knowledge node, including the effective start date, expiration date, and version number. Historical versions can be retrieved with one click.

[0031] 4. Step Three: Dynamic Knowledge Updates and Intelligent Decision-Making Change detection: Every day at midnight, the old and new policy documents are compared using the DiffMatchPatch library. If the text similarity is less than 90%, the update process is automatically triggered. Impact Assessment: The RotatE algorithm is used to embed the knowledge graph into a complex space (embedding dimension 200), calculate the Euclidean distance between the embedding vectors of new knowledge nodes and existing networks, generate an impact heatmap, and mark key association paths (such as the impact of the "2025 version of the CPR guidelines" on training modules such as compression depth and compression frequency). Blockchain voting: The updated proposal code is encoded as a Solidity smart contract, deployed on the Hyperledger Fabric consortium blockchain. Voting nodes are weighted as follows: hospital management 40%, senior clinical instructors 40%, and representatives from the Health Commission 20%. An update is automatically executed when ≥67% of the votes are in favor, and voting records are encrypted and stored. Impact Prediction: Based on XGBoost (tree depth 6, learning rate 0.1, feature dimension 20), the impact of updates on training costs and cycle is predicted (e.g., adding a VR craniocerebral surgery module requires an equipment investment of 2 million yuan, and the estimated single-period training cost will increase by 15%). SHAP analysis shows that "equipment procurement needs" contribute 68% and "guideline complexity" contributes 22%. Incremental execution: Delta encoding is used to store only the newly added / modified parts, and the version number is incremented. After the update, the recommended order of AI teaching modules and the weight of simulator scores are automatically adjusted (e.g., the pressing depth score is adjusted from 50% to 60%), and A / B testing is conducted (the control group uses old knowledge, and the experimental group uses new knowledge, with a cycle of 2 weeks). If the pass rate deviation exceeds 15%, the prediction model is automatically optimized.

[0032] 5. Step Four: Personalized Solution Generation and Path Optimization Trainee profile: Integrating clinical position (emergency / pediatrics / nursing / internal medicine, etc.), operational behavior, physiological characteristics, and psychological assessment data to generate composite tags such as "emergency department - resident trainee - weak emergency response ability - anxious type"; Adaptation solutions: Automatically reduce the complexity of simulated scenarios for anxious trainees (e.g., change emergency treatment for multiple injuries to a single disease), play alpha wave soothing music, and provide real-time voice prompts through bone conduction headphones; customize "Geriatric Nursing Specialized Tutorial" for nurses transitioning from other specialties, including modules on "Fall Emergency Treatment + Communication with Patients with Cognitive Impairment"; Transfer learning: The experience of senior doctors in cardiovascular interventional surgery is transferred to the VR training module for new trainees through knowledge distillation, thereby optimizing the operation path; Reinforcement learning: The PPO algorithm (learning rate 3e-4, update step size 2048, clip parameter 0.2, discount factor 0.99) is used to dynamically adjust the training plan. The state space includes: knowledge mastery (10 dimensions), operational error types (8 categories), and emotional state (positive / negative / neutral); the action space includes: changing training subjects, adjusting difficulty, inserting theoretical micro-lessons, rest reminders, and 12 other teaching actions; the reward function is: correct operation +1, incorrect operation -2, correct theoretical answer +0.5, timeout -0.2, and emotional improvement +0.1. After 5000 training iterations, the pass rate for the resident trainees' first aid assessment increased by 23%.

[0033] 6. Step Five: Intelligent Monitoring and Intervention of the Teaching Process Emotion recognition: Facial video streams are captured by an in-vehicle camera, cropped and normalized by edge nodes, and then input into a ResNet-50 (pre-trained on the FER2013 dataset) to extract visual features; the speech signal (sampled at 16kHz) is converted into 40-dimensional features by MFCC and input into an LSTM (128 hidden layers). Through early fusion (concatenating feature vectors), the signal enters a fully connected layer to output seven basic emotions. When "tension" or "anxiety" is detected for more than 30 seconds consecutively, mindfulness guidance or an encouraging message is automatically triggered. Distraction warning: Based on YOLOv8n (input 640×640, COCO pre-training) to detect the trainee's head posture, combined with eye tracker gaze point data, when the gaze is out of the operating area (such as the simulated endoscope display) for more than 2 seconds, the operating handle motor triggers low-frequency vibration (frequency 50Hz, amplitude 0.5mm); if it exceeds 5 seconds, high-frequency vibration (100Hz) + audible and visual alarm is triggered, and the information is recorded in the assessment log. Violation identification: Based on a random forest (10 trees, maximum depth 8), operational risks are assessed in real time. Input features include the standard deviation of operational effort, distance from the standard path, and correctness of tool use. When the risk level > 0.8, the simulator is automatically locked and a guidance video pops up, while the risk information is synchronized to the instructor's tablet terminal. Privacy Protection: A federated learning framework is adopted, with each hospital (equipment side) training its own "Operational Risk Recognition Model" (EfficientNet-B0) locally. After each training round, encrypted gradients are uploaded to the cloud, and the global model is aggregated using the FedAvg algorithm. Original operation videos are stored locally for 7 days and are not transmitted outside the hospital.

[0034] 7. Step Six: Business Performance Evaluation and Knowledge Feedback Multi-dimensional indicator system: Construct 32 core indicators, including training cycle (total duration of standardized training), cost (cost per trainee using simulator), pass rate (theory and practice), medical error simulation rate, instructor efficiency ratio, trainee satisfaction, etc., which are displayed in real time through Grafana; Counterfactual analysis: The experimental group used blockchain-based evidence storage for training hours, while the control group used traditional paper records. The authenticity of the assessment data and the pass rate of the two groups were compared. The results showed that the evidence storage group had a 0% rate of training hour forgery and a 4.2% higher pass rate than the control group, which was attributed to the increased effective training time. Social network analysis: Interaction data (posts, replies, likes) from online case discussion communities were extracted from trainees, and NetworkX was used to calculate centrality and community structure. It was found that trainees in tightly knit communities achieved 11.3% higher final assessment scores than those in isolated communities. The system automatically recommends highly active groups to new trainees and pushes two trending cases for discussion each week. Closed-loop feedback: The evaluation results are added to the knowledge graph as a new relationship "training method-effect", and the weights of the association rules are updated. For example, the recommendation weight of "VR simulation + group discussion" is increased from 0.7 to 0.85, so as to recommend better strategies to trainees in the future.

[0035] 8. System Implementation Based on the above methods, a problem-solving system for medical staff training is constructed, and the deployment and interaction of each module are as follows: Data access and cleaning module: The software layer integrates the Dedupe deduplication engine, random forest filling model, and IsolationForest anomaly detection module. The hardware layer collects data from simulated devices through a CAN bus interface and receives wearable data through Bluetooth protocol. Knowledge graph construction module: a knowledge extraction unit based on BERT+BiLSTM, a knowledge fusion unit based on Jaccard+DS evidence theory, stored in the Neo4j graph database; Dynamic update module: The change detection submodule periodically scans the policy API; the blockchain voting submodule is based on Hyperledger Fabric; and the version control submodule supports one-click rollback. Personalized solution generation module: The student profile engine generates composite tags, and the solution configuration unit supports dynamic rule adjustment; Intelligent monitoring module: Edge computing nodes (Jetson) perform lightweight processing, and the risk assessment model integrates random forest; Performance evaluation module: Indicator calculation engine (Grafana), predictive analysis unit (LSTM).

[0036] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A solution to problems in medical teaching and training operations based on a knowledge base management system, characterized in that: Includes the following steps: Step 1: Obtain multi-source heterogeneous data, including policies and regulations, teaching records, and practical data, from health management department APIs, hospital operation systems, ward sensors, and third-party data platforms; perform data cleaning on the data to form a standardized dataset. Step 2: Extract entities and relationships based on the BERT model, and construct a business knowledge network containing the association between "problem-regulation-solution" through a graph database to complete the construction of the knowledge graph; Step 3: Monitor policy changes and business indicator fluctuations in real time to trigger full or partial updates to the knowledge graph, and retain historical versions through incremental learning algorithms; Step 4: Combine trainees' physiological characteristics, operational behaviors, and knowledge graph association rules to generate training programs tailored to specific groups, and optimize training paths through reinforcement learning; Step 5: Collect real-time data from ward sensors and the environment, combine it with affective computing and behavioral analysis models, dynamically adjust the teaching content and trigger risk warnings; Step Six: Construct a multi-dimensional indicator system that includes teaching quality, business efficiency, and user experience, and feed the evaluation results back to the knowledge graph for continuous optimization.

2. The solution to the problem of medical teaching and training business based on a knowledge base management system according to claim 1, characterized in that, The data cleaning method in step one is as follows: First, the Dedupe library is used to eliminate duplicate data, and invalid records are filtered by regular expressions. Then, Scikit-learn's Imputer is used to fill missing values. Finally, IsolationForest is used to detect abnormal operation data.

3. The method according to claim 1, characterized in that, In step two, the knowledge graph is constructed as follows: key clauses of policy documents are extracted using TextRank, a legal knowledge subgraph is established, and a time dimension is introduced to record the effective period of knowledge nodes.

4. The solution to the problem of medical teaching and training business based on a knowledge base management system according to claim 1, characterized in that, In step three, the dynamic update mechanism works as follows: when policy changes or abnormal business indicators are detected, the impact of knowledge updates on the knowledge graph network is first quantified using the RotatE algorithm to generate a heatmap. Then, the update proposal is converted into a blockchain smart contract for voting by multi-weighted nodes. Once the threshold is exceeded, the update is automatically executed and encrypted for notarization. Next, an association model is built based on historical data to predict the impact of knowledge updates on business indicators such as training costs. The contribution is analyzed using SHAP values. Subsequently, Delta encoding technology is used to execute incremental updates and trigger adjustments to the association modules. Finally, A / B testing is used to verify the accuracy of the predictions. When the deviation exceeds the threshold, the model is automatically optimized and fed back to the knowledge graph to form a closed loop.

5. The solution to the problem of medical teaching and training business based on a knowledge base management system according to claim 1, characterized in that, In step four, a tiered training module is designed for resident trainees, and a customized tutorial is provided for cross-disciplinary medical staff transitioning to new roles. At the same time, transfer learning is used to optimize VR clinical training content.

6. The solution to the problem of medical teaching and training business based on a knowledge base management system according to claim 1, characterized in that, In step five, edge computing nodes are deployed to process real-time video streams, federated learning is used to protect student privacy, and a multimodal fusion technology combining vision, touch, and hearing is employed to comprehensively perceive the student's status.

7. The solution to the problem of medical teaching and training business based on a knowledge base management system according to claim 1, characterized in that, In step six, a counterfactual analysis experiment is designed to quantify the impact of blockchain-based evidence storage on teaching quality, and the role of student communities in promoting learning motivation is evaluated through social network analysis.

8. A medical teaching and training business problem-solving system based on a knowledge base management system, characterized in that: include: The data access and cleaning module performs data access and cleaning; the knowledge graph construction module performs knowledge graph construction; the dynamic update module performs dynamic knowledge updates; the personalized solution generation module performs personalized solution generation; the intelligent monitoring module performs teaching process monitoring; and the effect evaluation module performs effect evaluation and feedback.

9. The medical teaching and training business problem-solving system based on a knowledge base management system according to claim 8, characterized in that, The data access and cleaning module acquires surgical simulator and nursing operation model data through the clinical simulation equipment interface, and integrates smart bracelets and eye trackers to collect physiological data of trainees. The data preprocessing unit fills missing values ​​based on random forest regression and uses Min-Max normalization to process sensor data.

10. The medical teaching and training business problem-solving system based on a knowledge base management system according to claim 8, characterized in that, The knowledge graph construction module is based on the BERT+BiLSTM model to extract entities and relations, and uses Jaccard similarity to merge entities and resolves knowledge conflicts through DS evidence theory.

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