Traditional Chinese medicine nursing management system and method based on cloud follow-up visit management platform

Abstract

The invention discloses a traditional Chinese medicine nursing management system and method based on a cloud follow-up visit management platform, and belongs to the technical field of intelligent nursing. The management system comprises the following modules: a dynamic data acquisition module for acquiring physiological parameters, living habits and environmental data of a patient in real time and performing primary processing; the dynamic health archive construction module is used for constructing and continuously updating an individualized health archive of the patient based on the data collected in real time; the traditional Chinese medicine knowledge map and reasoning module depends on the traditional Chinese medicine nursing knowledge map and combines an intelligent reasoning technology to realize accurate traditional Chinese medicine syndrome differentiation and treatment analysis and automatically generate a personalized nursing scheme; the multi-dimensional patient feedback module is used for constructing a feedback mechanism fusing objective data, patient subjective feeling and medical care evaluation so as to extract key nursing effect information; and the nursing scheme iterative optimization module dynamically adjusts and optimizes the traditional Chinese medicine nursing scheme based on the feedback data, and realizes personalized and self-adaptive continuous improved nursing.

Description

Technical Field

[0001] This application relates to the field of intelligent nursing technology, and more specifically, to a traditional Chinese medicine nursing management system and method based on a cloud follow-up management platform. Background Art

[0002] As an important part of traditional medicine, traditional Chinese medicine nursing has unique advantages in the fields of chronic disease management, postoperative rehabilitation, sub-health conditioning, etc. However, traditional Chinese medicine nursing relies on offline diagnosis and treatment and empirical judgment, making it difficult to achieve standardization, personalization, and dynamic optimization. As a result, the adaptability of nursing plans is low, patient compliance is poor, and the nursing effect is difficult to quantitatively evaluate. In addition, with the intensification of social aging and the growth of the number of patients with chronic diseases, the demand for efficient and accurate traditional Chinese medicine nursing is increasing day by day, and the traditional model has been difficult to meet the development requirements of modern medical services.

[0003] In recent years, the rapid development of information technology has provided new opportunities for the intelligent transformation of traditional Chinese medicine nursing. The application of technologies such as cloud computing, big data, and artificial intelligence has made it possible to remotely collect, analyze, and feedback patient health data. Some medical institutions have begun to try to use cloud follow-up management platforms to collect patients' physiological data through intelligent devices and provide personalized nursing suggestions in combination with traditional Chinese medicine theory. However, existing solutions still have problems such as limited data collection dimensions, insufficient intelligent analysis capabilities, and inaccurate optimization of nursing plans, making it difficult to achieve truly intelligent, dynamic, and personalized nursing.

[0004] In summary, how to build a traditional Chinese medicine nursing management system with accurate data collection, intelligent analysis, and personalized nursing optimization capabilities based on a cloud follow-up management platform has become a technical problem that urgently needs to be solved. Summary of the Invention

[0005] In order to overcome a series of defects existing in the prior art, the purpose of this application is to provide a traditional Chinese medicine nursing management system based on a cloud follow-up management platform for the above problems, including the following modules:

[0006] A dynamic data collection module that real-time collects patients' physiological parameters, living habits, and environmental data and performs preliminary processing;

[0007] A dynamic health record construction module that constructs and continuously updates patients' individualized health records based on the real-time collected data;

[0008] A traditional Chinese medicine knowledge graph and reasoning module that relies on the traditional Chinese medicine nursing knowledge graph and combines intelligent reasoning technology to achieve accurate traditional Chinese medicine syndrome differentiation and treatment analysis and automatically generate personalized nursing plans;

[0009] A multi-dimensional patient feedback module that constructs a feedback mechanism integrating objective data, patients' subjective feelings, and medical staff evaluations to extract key nursing effect information;

[0010] The nursing plan iterative optimization module dynamically adjusts and optimizes the traditional Chinese medicine nursing plan based on feedback data to achieve personalized and adaptive continuous improvement of nursing.

[0011] The remote collaboration module enables remote data sharing and collaborative consultation among patients, medical staff, and institutions.

[0012] Furthermore, the dynamic data acquisition module includes the following components:

[0013] The physiological parameter acquisition unit relies on intelligent wearable devices to continuously monitor key physiological indicators and provide high-quality basic data for the dynamic monitoring of individual health.

[0014] The behavior habit tracking unit records and analyzes the user's daily living habits based on a mobile application to discover healthy behavior patterns.

[0015] The environmental factor monitoring unit uses Internet of Things sensors to collect indoor and outdoor environmental parameters, evaluate the potential impact of the external environment on individual health, and provide environmental adaptability suggestions.

[0016] The data preprocessing and calibration unit standardizes, denoises, and synchronizes the time of the collected raw data, and dynamically calibrates the data deviation of various sensors and devices to ensure the accuracy, consistency, and quality of the data.

[0017] Furthermore, the dynamic health record construction module includes the following components:

[0018] The multi-dimensional feature extraction unit automatically identifies and extracts key health features from dynamically collected multi-source heterogeneous data based on deep learning algorithms to construct an individual health feature vector space.

[0019] The time series dynamic modeling unit uses time series analysis techniques to construct a dynamic probability model of an individual's health status to capture the long-term change trends and potential laws of health indicators.

[0020] The personalized health portrait construction unit combines the extracted health features with the dynamic probability model to generate a highly individualized dynamic health record, comprehensively depicting an individual's physiological, psychological, and behavioral characteristics.

[0021] The health risk assessment and early warning unit uses machine learning algorithms to conduct risk stratification and trend prediction on the health record, identify potential health abnormalities and development directions, and provide personalized health early warning and intervention suggestions.

[0022] The cross-dimensional correlation analysis unit deeply explores the internal correlations of different dimensional indicators in the health record to reveal the complex dynamic change mechanism of the health status.

[0023] The health record continuous update unit constructs a real-time dynamic update mechanism to ensure that the health record can accurately reflect an individual's current health status and continuously optimize future health predictions.

[0024] Furthermore, machine learning algorithms are used to perform risk stratification and trend prediction on the health record, identify potential health abnormalities and development directions, and provide personalized health warnings and intervention suggestions, including the following steps:

[0025] Based on historical health data and verified risk marker samples, patients are accurately classified into high, medium, and low risk levels to form a dynamically adjustable risk stratification framework;

[0026] The degree and change rate of the patient's health indicators deviating from the baseline value are monitored in real time, and the disease development path is generated based on knowledge graph reasoning, and the occurrence probability and time window of different health risk events are quantified;

[0027] According to the risk stratification results and trend prediction data, personalized multi-level warning thresholds and trigger logics are configured for the patient, and a differentiated warning scheme is constructed by combining the patient's characteristic portrait and behavior pattern;

[0028] For the identified risk types, personalized intervention suggestions based on evidence-based medicine are automatically generated, including lifestyle optimization, traditional Chinese medicine nursing plans, mental health management strategies, and environmental factor control measures.

[0029] Furthermore, the traditional Chinese medicine knowledge graph and reasoning module includes the following components:

[0030] The traditional Chinese medicine knowledge ontology and terminology standardization unit constructs a standardized knowledge ontology covering traditional Chinese medicine theory concepts, syndrome differentiation elements, and disease relationships, standardizes traditional Chinese medicine professional terms and performs multi-dimensional mapping to eliminate semantic ambiguity and provide a structured basis for the knowledge graph;

[0031] The semantic association reasoning unit uses natural language processing and semantic analysis technologies to mine the deep semantic associations between traditional Chinese medicine concepts in the knowledge graph and realize cross-concept intelligent reasoning and knowledge connection;

[0032] The syndrome differentiation and treatment intelligent matching unit accurately identifies the most suitable traditional Chinese medicine syndrome differentiation type and personalized treatment plan based on the individual health record, combined with rule matching in the knowledge graph and machine learning algorithms;

[0033] The initial nursing plan generation unit dynamically constructs a personalized traditional Chinese medicine nursing plan by integrating the knowledge graph, individual health data, and syndrome differentiation reasoning results, as the basis for plan iteration and optimization;

[0034] The knowledge graph dynamic learning unit establishes a closed-loop learning mechanism and continuously optimizes and expands the semantic network and reasoning ability of the knowledge graph through clinical feedback and practice data;

[0035] Multimodal Knowledge Integration Unit, which integrates multimodal information covering text, images, and clinical data to enrich the semantic depth and expressive power of the knowledge graph.

[0036] Furthermore, the multi-dimensional patient feedback module includes the following components:

[0037] Objective Index Comprehensive Evaluation Unit, which integrates clinical monitoring data, physiological index changes, and objective treatment effect evaluations to construct a multi-dimensional treatment effect index system;

[0038] Patient Subjective Feeling Analysis Unit, which collects patients' subjective feedback on the treatment process and effects, and uses natural language processing technology for in-depth analysis to mine personalized experience information;

[0039] Emotional Semantic Mining Unit, which, based on emotion computing technology, identifies emotional intensity, emotional types, and potential psychological states from patients' feedback texts, extracts deep-level emotional information to assist in precise nursing decision-making;

[0040] Medical Staff Professional Evaluation Unit, which collects and integrates medical staff's professional evaluations of patients' treatment processes to provide authoritative and professional support for feedback analysis;

[0041] Multi-source Feedback Intelligent Fusion Unit, which uses intelligent fusion algorithms to deeply correlate and comprehensively analyze objective data, patients' subjective feelings, and medical staff's professional evaluations;

[0042] Key Nursing Effect Extraction Unit, which uses machine learning and text analysis technologies to automatically identify and refine key nursing effect indicators and optimization directions from multi-dimensional feedback information;

[0043] Feedback Quality Evaluation Unit, which constructs an evaluation mechanism for the credibility and effectiveness of feedback information to ensure the scientific nature, representativeness, and clinical value of the extracted information.

[0044] Furthermore, the nursing plan iteration and optimization module includes the following components:

[0045] Nursing Plan Deviation Analysis Unit, which, based on the comparative analysis of traditional Chinese medicine nursing plans and actual implementation effects, accurately identifies potential deviations and optimization spaces, and constructs a quantitative plan deviation evaluation index system;

[0046] Personalized Plan Dynamic Adjustment Unit, which combines patients' dynamic health records and multi-dimensional feedback data to achieve precise optimization and personalized adjustment of nursing plans;

[0047] Machine Learning Iterative Algorithm Unit, which uses reinforcement learning and Bayesian optimization algorithms to continuously optimize nursing plan adjustment strategies to achieve autonomous learning and intelligent evolution;

[0048] Cross - professional collaborative optimization unit, which builds a collaborative optimization mechanism among medical staff, traditional Chinese medicine experts and nursing management systems, integrates professional insights and intelligent algorithms, and jointly promotes the continuous improvement of nursing plans;

[0049] Scheme iteration risk management unit, which establishes a safety assessment and risk control mechanism during the optimization process to ensure that the optimization of nursing plans meets the requirements of clinical safety and controllability.

[0050] Furthermore, the remote collaboration module includes the following components:

[0051] Distributed data storage unit, which constructs a highly available and scalable distributed storage system based on the cloud computing architecture to achieve efficient storage, fast retrieval and highly reliable access of massive medical data;

[0052] Comprehensive data security and governance unit, which uses blockchain technology to establish a data verification and traceability mechanism, and formulates unified data security standards and multi - level access control frameworks to ensure the integrity, traceability and compliance of medical data;

[0053] Cross - institutional data interaction unit, which constructs a standardized medical data exchange protocol to standardize data sharing standards and interfaces between different medical institutions;

[0054] Real - time communication and collaboration unit, which constructs a real - time communication system based on the cloud - native architecture to support multi - party secure and efficient instant collaboration;

[0055] Collaboration process orchestration unit, which designs a standard workflow for cross - institutional collaboration, provides flexible collaboration scenario configuration, realizes the traceability and manageability of the collaboration process, and supports real - time monitoring and warning of collaboration status;

[0056] Multi - terminal collaborative access unit, which provides a unified multi - terminal access solution for patients, medical staff and managers, and supports secure and convenient collaboration on multiple device terminals.

[0057] Traditional Chinese medicine nursing management method based on the cloud follow - up management platform, including the following steps:

[0058] Real - time collect patients' physiological parameters, behavior habit data and environmental factors, and perform standardized processing, denoising and spatio - temporal synchronization on the collected raw data;

[0059] Extract health feature vectors from the collected data, reveal the internal relationships between different indicators through multi - dimensional correlation analysis, form personalized health records, and establish a real - time update mechanism;

[0060] Intelligently match the patients' health records with the knowledge graph, accurately identify traditional Chinese medicine syndrome differentiation types, and automatically generate targeted traditional Chinese medicine nursing plans based on the reasoning results of the graph, individual characteristics and clinical experience;

[0061] Establish a three-in-one system that integrates objective indicators, patient feedback, and medical staff evaluation. Use sentiment analysis and intelligent fusion algorithms to extract key indicators, comprehensively evaluate the nursing effect, and guide the optimization direction;

[0062] Utilize reinforcement learning and Bayesian optimization to adjust the nursing strategy according to the difference between the actual effect and the expectation. Combine expert experience to construct a collaborative decision-making mechanism, forming a closed-loop mode of "evaluation - adjustment - verification - optimization";

[0063] Build a cloud-native distributed medical data platform. Ensure data security through blockchain, establish a standardized data exchange protocol, support remote consultation and unified access of multiple terminals, and achieve precise, information-based, and intelligent traditional Chinese medicine nursing management.

[0064] Compared with the prior art, the present application has the following beneficial effects:

[0065] Through modules such as dynamic data collection, personalized health records, traditional Chinese medicine knowledge graph reasoning, multi-dimensional patient feedback, intelligent nursing plan optimization, and cloud-based remote collaboration, the present application realizes a precise, personalized, and dynamically optimized traditional Chinese medicine nursing plan, providing intelligent health management for patients throughout their life cycle. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 It is a schematic structural diagram of a traditional Chinese medicine nursing management system based on a cloud follow-up management platform disclosed in an embodiment of the present application.

[0067] Figure 2 It is a schematic flowchart of a traditional Chinese medicine nursing management method based on a cloud follow-up management platform disclosed in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0068] To make the objectives, technical solutions, and advantages of the implementation of the present invention clearer, the technical solutions in the embodiments of the present invention will be described in more detail below with reference to the accompanying drawings in the embodiments of the present invention. In the drawings, the same or similar reference numerals denote the same or similar elements or elements with the same or similar functions throughout. The described embodiments are some, but not all, of the embodiments of the present invention.

[0069] All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0070] The embodiments described below with reference to the accompanying drawings and directional terms are exemplary only and are intended to explain the present invention and should not be construed as limiting the present invention.

[0071] Such as Figure 1As shown in the figure, the TCM nursing management system based on the cloud follow-up management platform includes the following modules:

[0072] Dynamic data collection module collects the patient's physiological parameters, living habits and environmental data in real time and performs preliminary processing;

[0073] Dynamic health record building module, which builds and continuously updates the patient's individual health record based on real-time collected data;

[0074] The TCM knowledge graph and reasoning module relies on the TCM nursing knowledge graph and combines intelligent reasoning technology to achieve accurate TCM syndrome differentiation and treatment analysis and automatically generate personalized nursing plans;

[0075] Multi-dimensional patient feedback module, building a feedback mechanism that integrates objective data, patient subjective feelings and medical evaluation to extract key nursing effect information;

[0076] The nursing plan iterative optimization module dynamically adjusts and optimizes the TCM nursing plan based on feedback data to achieve personalized, adaptive and continuous improvement of nursing care;

[0077] The remote collaboration module enables remote data sharing and collaborative consultation between patients, medical staff and institutions.

[0078] In summary, the TCM nursing management system based on the cloud follow-up management platform disclosed in this embodiment realizes the intelligent management of the whole process from data collection, health record construction, intelligent reasoning, nursing feedback to program optimization and remote collaboration through the collaborative work of multiple modules. It not only improves the scientificity and accuracy of TCM nursing, but also greatly improves the personalization and adaptability of nursing. Through dynamic data-driven and intelligent optimization, it can continuously learn and evolve to provide patients with more accurate and effective nursing services.

[0079] Furthermore, the dynamic data acquisition module includes the following components:

[0080] The physiological parameter collection unit relies on smart wearable devices to monitor key physiological indicators in real time and provide high-quality basic data for dynamic monitoring of individual health;

[0081] Behavior tracking unit, which records and analyzes users’ daily habits based on mobile applications to explore healthy behavior patterns;

[0082] Environmental factor monitoring unit, which uses IoT sensors to collect indoor and outdoor environmental parameters, evaluate the potential impact of the external environment on individual health, and provide environmental adaptability recommendations;

[0083] The data preprocessing and calibration unit standardizes, denoises, and synchronizes the time of the collected raw data, and dynamically calibrates the data deviation of various sensors and devices to ensure the accuracy, consistency, and quality of the data.

[0084] In summary, the dynamic data acquisition module realizes comprehensive and accurate monitoring of individual health conditions through the collaborative work of multiple efficient components. The physiological parameter acquisition unit uses intelligent wearable devices to continuously monitor key physiological indicators such as heart rate, blood pressure, and body temperature, providing high-quality health data, laying a solid foundation for dynamic monitoring, being able to promptly reflect the health change trend, and providing data support for disease early warning. The behavior habit tracking unit relies on mobile applications to record and analyze users' daily behavior habits, and explores healthy behavior patterns, such as exercise volume, diet regularity, and rest conditions, helps identify potential health risk factors, and provides personalized suggestions for health management. The environmental factor monitoring unit uses Internet of Things sensors to collect indoor and outdoor environmental data (such as temperature, humidity, air quality, etc.), evaluates the impact of the external environment on individual health, especially for patients with chronic diseases or special needs, provides corresponding environmental adaptability suggestions, and optimizes the health environment. Finally, the data preprocessing and calibration unit ensures the accuracy and consistency of multi-source data through standardization, denoising, and time synchronization processing, and eliminates sensor errors through dynamic calibration, making the data more accurate and reliable, and providing stable basic data for subsequent analysis and decision-making. The coordinated action of this series of components can provide all-round health monitoring and management in real time, improving the accuracy and reliability of individual health monitoring.

[0085] Furthermore, based on the mobile application to record and analyze users' daily life habits, and explore healthy behavior patterns, including the following steps:

[0086] Construct a multi-source behavior data acquisition framework to integrate mobile device sensors, user-initiated records, and environmental perception system data;

[0087] Design a behavior label system with traditional Chinese medicine characteristics to associate behavior data with the living, diet, and emotional dimensions in traditional Chinese medicine theory;

[0088] Use time series clustering algorithms to identify stable behavior patterns and unconventional behavior events from continuous behavior data streams;

[0089] Apply periodic analysis techniques to explore the daily, weekly, monthly, and seasonal patterns of users' behaviors, and establish an individualized behavior prediction model;

[0090] Evaluate the impact of behavior patterns on health based on traditional Chinese medicine health preservation theory, and generate behavior optimization suggestions that conform to traditional Chinese medicine concepts;

[0091] Construct a progressive behavior guidance mechanism through micro-intervention techniques to smoothly adjust bad living habits and reduce the resistance to adjustment.

[0092] In summary, through multi-level data integration and intelligent analysis, the accurate identification and optimized intervention of individual health behavior patterns are realized. First, the construction of the multi-source behavior data collection framework integrates mobile device sensors (such as accelerometers, GPS, heart rate sensors), user-initiated input data (such as diet and rest records), and environmental perception systems (such as air quality monitoring) to ensure the comprehensiveness and high timeliness of behavior data. Second, by designing a behavior label system with traditional Chinese medicine characteristics, the user's behavior data can be corresponded to the core dimensions of daily life, diet regulation, and emotional regulation in traditional Chinese medicine theory, thus establishing a personalized behavior portrait that conforms to the concept of traditional Chinese medicine health management. The application of the time series clustering algorithm can extract stable daily behavior patterns from continuous data streams and identify abnormal behavior events, such as sleep disorders and irregular diets, providing data support for health risk early warning. The periodic analysis technology further explores the user's behavior rhythm, reveals the changing trends of individual living habits in different time cycles (such as daily, weekly, monthly, and seasonal), and constructs an individualized behavior prediction model, providing a scientific basis for long-term health management. The evaluation mechanism based on traditional Chinese medicine health preservation theory can quantify the impact of behavior patterns on health, such as determining whether eating habits meet the individual's physical needs and whether rest affects the balance of yin and yang, and accordingly provide personalized traditional Chinese medicine behavior optimization suggestions. Finally, the micro-intervention technology guides users to improve bad habits in small steps and progressively, reducing the psychological resistance to behavior adjustment and making health interventions more acceptable and sustainable. This complete set of technical systems realizes the integration of traditional Chinese medicine theory and modern data analysis technology, providing accurate and efficient support for personalized and intelligent traditional Chinese medicine health management.

[0093] Furthermore, the dynamic health record construction module includes the following components:

[0094] The multi-dimensional feature extraction unit, based on deep learning algorithms, automatically identifies and extracts key health features from dynamically collected multi-source heterogeneous data to construct an individual health feature vector space;

[0095] The time series dynamic modeling unit uses time series analysis technology to construct a dynamic probability model of the individual's health status, capturing the long-term change trends and potential laws of health indicators;

[0096] The personalized health portrait construction unit combines the extracted health features with the dynamic probability model to generate a highly individualized dynamic health record, comprehensively depicting personal physiological, psychological, and behavioral characteristics;

[0097] The health risk assessment and early warning unit uses machine learning algorithms to perform risk stratification and trend prediction on the health record, identify potential health abnormalities and development directions, and provide personalized health early warning and intervention suggestions;

[0098] A cross - dimensional correlation analysis unit deeply explores the internal correlations of indicators in different dimensions of the health record, revealing the complex dynamic change mechanism of the health status;

[0099] A file continuous update unit constructs a real - time dynamic update mechanism to ensure that the health record can accurately reflect an individual's current health status and continuously optimize future health predictions.

[0100] In summary, the dynamic health record construction module comprehensively improves the accuracy and real - time performance of individual health management through a series of advanced technical components. First, the multi - dimensional feature extraction unit, based on deep learning algorithms, can automatically identify and extract key health features from multi - source heterogeneous data, such as physiological data, behavior habits, and environmental parameters, etc., thus constructing a unique health feature vector space for each individual. This process greatly improves the integration ability and intelligent level of health data. Then, the time - series dynamic modeling unit uses time - series analysis technology to accurately capture the long - term change trends and potential laws of health indicators, providing strong support for constructing a dynamic probability model of an individual's health status. It can identify the long - cycle laws of health changes, predict possible future health events, and enhance the forward - looking nature of health management. The personalized health portrait construction unit combines the extracted health features with the dynamic health status model to generate a highly individualized health record, comprehensively depicting an individual's physiological, psychological, and behavioral characteristics. Thus, each health record is not only a collection of data but also an accurate mapping of each user's health information. The health risk assessment and early warning unit conducts risk stratification and trend prediction on the health record through machine - learning algorithms. It can give early warnings in a timely manner when an individual's health status is abnormal and provide personalized intervention suggestions to ensure the health and safety of users. The cross - dimensional correlation analysis unit deeply explores the internal correlations between different health dimensions, revealing the complex dynamic change mechanism of the health status, providing a theoretical basis for deeper health understanding and intervention. Finally, the file continuous update unit establishes a real - time dynamic update mechanism to ensure that the health record always reflects an individual's latest health status and continuously optimizes the accuracy of future health predictions through data feedback. Overall, the components of this module cooperate closely, not only improving the accuracy of health record construction but also ensuring its dynamic adaptability and forward - looking nature in health management, making personalized health management more intelligent, efficient, and accurate.

[0101] Furthermore, using time - series analysis technology to construct a dynamic probability model of an individual's health status and capture the long - term change trends and potential laws of health indicators includes the following steps:

[0102] Discretely represent the individual's health status as a time series {X t}, and set the initial state probability P(X1 = x i ) = πi , where X t is the health status at time t; π i is the probability distribution of the initial health status, where i represents a specific health status;

[0103] Construct a state transition probability matrix A based on the hidden Markov model, where each element A ij = P(X t = x j |X t-1 = x i ) represents the transition probability from state x i to state x j . Among them, P(X t = x j |X t-1 = x i ) is the transition probability from the health status X t-1 = x i at time t - 1 to the health status X t = x j at time t;

[0104] Define the relationship between the health status and the health indicators, and associate the observed data with the health status through . Among them, is the conditional probability of observing the data Y t under the given health status X t ;

[0105] Use the forward algorithm to gradually calculate the probability distribution P(X 1:t |Y t 1:t ) of the current health status under the condition of the given historical observed data Y. It is expressed by the formula as: . Among them, P(Y t |X t ) is the probability of observing the data Y t under the given health status X t ; P(X t |X t-1 ) is the transition probability of the health status X t-1 under the given previous health status X t ; P(X t-1 |Y 1:t-1 ) is the probability distribution of inferring the previous health status X 1:t-1 from the historical observed data Y t-1 ; P(Y t |Y 1:t-1 ) is the probability of observing the data Y 1:t-1 when the given historical observed data is Yt Total probability;

[0106] Perform trend smoothing on the time series of health indicators and calculate the long-term trend Combine the smoothed trend information with the dynamic probability model to further identify potential patterns in the evolution of health states, where is the predicted value of the health indicator obtained by smoothing; w t-k is the weight function used for smoothing, representing the influence degree of historical observation data on the current predicted value; T is the total time length of the time series; Y t-k is the observed data at time t - k.

[0107] Furthermore, use machine learning algorithms to perform risk stratification and trend prediction on the health records, identify potential health abnormalities and development directions, and provide personalized health warnings and intervention suggestions, including the following steps:

[0108] Based on historical health data and verified risk marker samples, accurately classify patients into high, medium, and low risk levels to form a dynamically adjustable risk stratification framework;

[0109] Real-time monitor the degree and change rate of the patient's health indicators deviating from the baseline value, and generate the disease development path based on knowledge graph reasoning, and quantify the occurrence probability and time window of different health risk events;

[0110] According to the risk stratification results and trend prediction data, configure personalized multi-level warning thresholds and trigger logics for patients, and construct a differentiated warning plan in combination with the patient's characteristic portrait and behavior pattern;

[0111] For the identified risk types, automatically generate personalized intervention suggestions based on evidence-based medicine, including lifestyle optimization, traditional Chinese medicine care plans, mental health management strategies, and environmental factor regulation measures.

[0112] Furthermore, the traditional Chinese medicine knowledge graph and reasoning module includes the following components:

[0113] Traditional Chinese medicine knowledge ontology and terminology specification unit, construct a standardized knowledge ontology covering traditional Chinese medicine theory concepts, syndrome differentiation elements, and disease relationships, perform standardized processing and multi-dimensional mapping on traditional Chinese medicine professional terms, eliminate semantic ambiguity, and provide a structured basis for the knowledge graph;

[0114] Semantic association reasoning unit, adopt natural language processing and semantic analysis technologies to mine the deep semantic associations between traditional Chinese medicine concepts in the knowledge graph, and realize cross-concept intelligent reasoning and knowledge connection;

[0115] The syndrome differentiation and treatment intelligent matching unit, based on the personal health record, combines rule matching in the knowledge graph and machine learning algorithms to accurately identify the most suitable traditional Chinese medicine syndrome differentiation type and personalized treatment plan;

[0116] The initial nursing plan generation unit comprehensively combines the knowledge graph, personal health data, and syndrome differentiation reasoning results to dynamically construct a personalized traditional Chinese medicine nursing plan, which serves as the basis for iterative optimization of the plan;

[0117] The knowledge graph dynamic learning unit establishes a closed-loop learning mechanism to continuously optimize and expand the semantic network and reasoning ability of the knowledge graph through clinical feedback and practice data;

[0118] The multi-modal knowledge integration unit integrates multi-modal information covering text, images, and clinical data to enrich the semantic depth and expression ability of the knowledge graph.

[0119] In summary, the Traditional Chinese Medicine Knowledge Graph and Reasoning Module combines Traditional Chinese Medicine theory with modern data analysis technology through a highly intelligent technical architecture, providing strong support for the generation and optimization of personalized nursing plans. First, the Traditional Chinese Medicine Knowledge Ontology and Terminology Specification Unit constructs a standardized knowledge framework by standardizing Traditional Chinese Medicine theory concepts, syndrome differentiation elements, and disease-syndrome relationships, conducts multi-dimensional mapping and standardization processing on Traditional Chinese Medicine professional terms, eliminates semantic ambiguities, and lays a solid structural foundation for subsequent reasoning and analysis. This part ensures the accuracy and usability of the knowledge graph. Next, the Semantic Association Reasoning Unit uses natural language processing and semantic analysis technologies to deeply explore the internal semantic associations between Traditional Chinese Medicine concepts, realizes cross-concept intelligent reasoning and knowledge connection, can understand and apply the complex theories of Traditional Chinese Medicine, and achieves higher-level knowledge discovery and reasoning capabilities. The Syndrome Differentiation and Treatment Intelligent Matching Unit, based on the rules in the personal health record and the knowledge graph, accurately identifies the most suitable Traditional Chinese Medicine syndrome differentiation type through machine learning algorithms and provides personalized treatment plans for users. This process combines the physiological and psychological data of individuals to ensure that the treatment plan better meets the actual needs. The Initial Generation Unit of the Nursing Plan comprehensively considers the knowledge graph, health data, and syndrome differentiation reasoning results, dynamically generates personalized Traditional Chinese Medicine nursing plans, and provides basic data for the iterative optimization of subsequent nursing plans. This dynamic generation mechanism enables each nursing plan to adapt to the changes in the patient's health status in real time. The Knowledge Graph Dynamic Learning Unit, through a closed-loop learning mechanism, continuously obtains new knowledge from clinical feedback and practice data, continuously optimizes and expands the semantic network and reasoning capabilities of the graph, and ensures that the Traditional Chinese Medicine knowledge graph becomes more accurate and comprehensive over time. Finally, the Multi-modal Knowledge Integration Unit enriches the semantic depth and expression ability of the knowledge graph by integrating various information forms such as text, images, and clinical data, and can understand the Traditional Chinese Medicine nursing plan and its actual application effect more comprehensively and accurately. Overall, this module can not only accurately identify the health needs of patients, provide personalized nursing and treatment plans based on Traditional Chinese Medicine theory, but also ensure the continuous optimization and adaptability of nursing plans through continuous learning and the support of multi-modal data.

[0120] Furthermore, by integrating the knowledge graph, personal health data, and syndrome differentiation reasoning results, a personalized Traditional Chinese Medicine nursing plan is dynamically constructed, including the following steps:

[0121] Based on the syndrome differentiation reasoning results, accurately match the patient's Traditional Chinese Medicine syndrome type, and extract the key nursing intervention points and treatment principles of the corresponding syndrome type from the knowledge graph;

[0122] Combined with the individual differences of the patient and the disease stage, identify the current core problems and nursing priorities, and establish the main framework and priority of the plan;

[0123] Intelligently screen the intervention combinations that match the patient's syndrome types from the knowledge graph nursing measure library, perform conflict detection and synergy effect analysis, eliminate potential measure contradictions, optimize the overall nursing effect, and ensure the internal consistency of the plan;

[0124] Based on the patient's age, constitution, living environment, and personal preferences, finely adjust the specific parameters of the nursing plan, transform the theoretical plan into an implementable plan, set clear stage goals and quantitative indicators, and formulate a detailed nursing schedule and implementation path;

[0125] For possible special situations and acute reactions, preset response plans and adjustment strategies, and construct an all-round nursing safety guarantee system integrating traditional Chinese and Western medicine to ensure that the risks in the nursing process are controllable and the emergency response is timely.

[0126] In summary, the technical effects of dynamically constructing a personalized traditional Chinese medicine nursing plan based on the knowledge graph, personal health data, and syndrome differentiation reasoning results are reflected in its accuracy, personalization, and dynamic adaptability. First of all, based on the results of syndrome differentiation reasoning, it can accurately match the patient's traditional Chinese medicine syndrome types, and extract key nursing intervention points and treatment principles related to the syndrome types from the knowledge graph to ensure that the nursing plan has a theoretical basis and pertinence. Next, combined with the patient's individual differences and different stages of the disease, it can identify the current core problems and nursing priorities, establish the main framework and priority of the plan, and ensure that each nursing plan can focus on the patient's most urgent health needs. The knowledge graph nursing measure library provides a rich variety of intervention combinations. By intelligently screening the measures that match the patient's syndrome types and performing conflict detection and synergy effect analysis, potential nursing measure contradictions are eliminated, the overall nursing effect is optimized, the internal consistency of the plan is ensured, conflicts between multiple intervention measures are avoided, and the safety and effectiveness of the nursing plan are improved. Further, based on the patient's age, constitution, living environment, and personal preferences, the specific parameters of the nursing plan can be finely adjusted, the theoretical plan can be transformed into an implementable nursing plan, clear stage goals and quantitative indicators can be set, and a detailed nursing schedule and implementation path can be formulated to ensure the operability of the nursing process and the evaluability of the effect. Finally, considering the possibility of special situations and acute reactions, response plans and adjustment strategies are preset, and an all-round nursing safety guarantee system integrating traditional Chinese and Western medicine is constructed to ensure that the risks in the nursing process are controllable, the emergency response is timely, and the safety and reliability of the nursing process are greatly improved. Overall, through precise matching, dynamic adjustment, conflict elimination, and all-round guarantee mechanisms, the efficient construction and implementation of a personalized traditional Chinese medicine nursing plan are realized.

[0127] Furthermore, the intelligent screening of intervention combinations that match the patient's syndrome types from the knowledge graph nursing measure library and the implementation of conflict detection and synergy effect analysis include the following steps:

[0128] Based on semantic similarity calculation, match the patient's syndrome type with the nursing intervention measures in the knowledge graph to generate candidate intervention combinations;

[0129] Construct an interaction model of intervention measures to evaluate the enhancing, antagonistic or neutralizing relationships among the intervention measures in the candidate combinations;

[0130] Use graph theory algorithms to analyze the dependence relationships among the intervention measures, and identify the optimal order and time intervals for measure implementation;

[0131] Calculate the synergy effect score of the intervention combination based on historical case data to predict the overall nursing effect;

[0132] Apply multi-objective optimization algorithms to balance the implementation difficulty, resource consumption and patient acceptance on the premise of ensuring the nursing effect;

[0133] Generate an intervention combination conflict report and a synergy gain analysis to provide a basis for optimizing the plan for medical staff.

[0134] Furthermore, the process of integrating multi-modal information covering text, images and clinical data includes the following steps:

[0135] Establish a unified multi-modal knowledge representation framework to convert traditional Chinese medicine knowledge of different modalities into computable vector representations;

[0136] Use deep learning models to extract visual features from image data and identify traditional Chinese medicine diagnosis information including tongue images, facial images and pulse conditions;

[0137] Construct a cross-modal knowledge mapping mechanism to achieve two-way conversion and verification between text descriptions and visual representations;

[0138] Design a multi-modal knowledge fusion algorithm to integrate complementary information from different sources and modalities at the semantic level;

[0139] Implement knowledge consistency checks to identify and reconcile potential contradictions and conflicts in different modality knowledge;

[0140] Through an adaptive weight mechanism, dynamically adjust the importance weights of each modality knowledge according to different clinical scenarios to improve the accuracy of knowledge application.

[0141] Furthermore, the multi-dimensional patient feedback module includes the following components:

[0142] Objective index comprehensive evaluation unit, which integrates clinical monitoring data, physiological index changes and objective treatment effect evaluations to construct a multi-dimensional treatment effect index system;

[0143] Patient subjective feeling analysis unit, which collects the patient's subjective feedback on the treatment process and effect, and uses natural language processing technology for in-depth analysis to mine personalized experience information;

[0144] An emotional semantic mining unit, based on emotion computing technology, identifies the emotional intensity, emotional type and potential psychological state from the patient feedback text, extracts deep-level emotional information to assist in accurate nursing decisions;

[0145] A medical and nursing professional evaluation unit, which collects and integrates the professional evaluations of medical and nursing staff on the patient's treatment process, providing authoritative and professional support for feedback analysis;

[0146] A multi-source feedback intelligent fusion unit, which uses intelligent fusion algorithms to deeply correlate and comprehensively analyze objective data, patients' subjective feelings and medical and nursing professional evaluations;

[0147] A key nursing effect extraction unit, which uses machine learning and text analysis technologies to automatically identify and refine key nursing effect indicators and optimization directions from multi-dimensional feedback information;

[0148] A feedback quality evaluation unit, which constructs an evaluation mechanism for the credibility and effectiveness of feedback information to ensure the scientificity, representativeness and clinical value of the extracted information.

[0149] In summary, the technical effect of the multi-dimensional patient feedback module lies in its all-round and multi-level feedback analysis mechanism, which can comprehensively and accurately evaluate and optimize the nursing effect. First of all, the objective index comprehensive evaluation unit constructs a multi-dimensional treatment effect index system by integrating clinical monitoring data, physiological index changes and objective treatment effect evaluation, providing an objective basis for the evaluation of the nursing plan. The patient subjective feeling analysis unit uses natural language processing technology to deeply analyze the patient's subjective feedback, excavate personalized experience information, and can capture the needs and expectations that may not be fully expressed by the patient during the treatment process, so as to enhance the pertinence and humanization of the treatment. The emotional semantic mining unit analyzes the emotional intensity and type in the patient feedback through emotional computing technology, further reveals the potential psychological state, provides more abundant emotional level support for nursing decision-making, and helps nurses understand the emotional needs and psychological changes of patients. The medical and nursing professional evaluation unit collects the professional evaluations of medical and nursing staff on the patient's treatment process, providing authority and professionalism guarantee for feedback analysis, making the overall nursing plan more scientific and accurate. The multi-source feedback intelligent fusion unit uses intelligent fusion algorithms to deeply correlate and comprehensively analyze data from different sources - objective data, patient subjective feelings and medical and nursing evaluations - ensuring the comprehensiveness and multi-dimensionality of feedback information. The key nursing effect extraction unit uses machine learning and text analysis technologies to automatically identify and refine key nursing effect indicators, providing directional guidance for the optimization of subsequent nursing plans. Finally, the feedback quality evaluation unit ensures the reliability of the extracted information in terms of scientificity, representativeness and clinical value by establishing an evaluation mechanism for the credibility and effectiveness of feedback information, thus ensuring the effectiveness and accuracy of the entire feedback analysis. Overall, through the comprehensive and intelligent processing of multi-dimensional feedback information, this module can optimize the nursing plan in real time and improve the quality and effect of patient treatment.

[0150] Furthermore, based on emotional computing technology, to identify emotional intensity, emotional type and potential psychological state from patient feedback text and extract deep-level emotional information, the following steps are included:

[0151] Construct a TCM-featured emotional semantic library, including specific semantic markers for symptom expression, TCM experience description and treatment feelings;

[0152] Use a deep learning model to perform multi-level semantic decomposition on patient feedback text to identify explicit and implicit emotions;

[0153] Establish an emotional intensity quantification model to convert qualitative emotional expressions into computable quantitative indicators and construct an emotional change trajectory;

[0154] Combined with the TCM theoretical framework, map emotional semantics to the theory related to the five emotions in TCM, and establish an emotion-viscera correlation analysis model;

[0155] By comparing and analyzing the emotional change trends in patients' continuous feedback, identify the impact of the nursing plan on the patients' psychological state.

[0156] Furthermore, adopt an intelligent fusion algorithm to deeply correlate and comprehensively analyze objective data, patients' subjective feelings, and medical and nursing professional evaluations, including the following steps:

[0157] Construct a multi-level weighted fusion model, and dynamically allocate fusion weights according to the reliability, timeliness, and relevance of different feedback sources;

[0158] Apply a deep learning network to identify potential correlation patterns among feedback data and mine hidden information across data sources;

[0159] Design a conflict identification and coordination mechanism. When there are obvious differences in feedback from different sources, initiate the conflict coordination process, and solve the data inconsistency problem through expert rules and statistical analysis.

[0160] Furthermore, the nursing plan iterative optimization module includes the following components:

[0161] Nursing plan deviation analysis unit. Based on the comparative analysis of the traditional Chinese medicine nursing plan and the actual implementation effect, accurately identify potential deviations and optimization spaces, and construct a quantitative plan deviation evaluation index system;

[0162] Personalized plan dynamic adjustment unit. Combine the patient's dynamic health record and multi-dimensional feedback data to achieve precise optimization and personalized adjustment of the nursing plan;

[0163] Machine learning iterative algorithm unit. Adopt reinforcement learning and Bayesian optimization algorithms to continuously optimize the nursing plan adjustment strategy and achieve autonomous learning and intelligent evolution;

[0164] Cross-professional collaborative optimization unit. Build a collaborative optimization mechanism for medical staff, traditional Chinese medicine experts, and nursing management systems, integrate professional insights and intelligent algorithms, and jointly promote the continuous improvement of the nursing plan;

[0165] Plan iteration risk management unit. Establish a safety assessment and risk control mechanism during the optimization process to ensure that the optimization of the nursing plan meets the requirements of clinical safety and controllability.

[0166] In summary, the technical effect of the nursing plan iterative optimization module is that it can dynamically and intelligently adjust and optimize the nursing plan, so as to achieve personalized continuous improvement and improve the quality and effect of nursing. The nursing plan deviation analysis unit accurately identifies potential deviations and optimization spaces by comparing the traditional Chinese medicine nursing plan with the actual implementation effect, constructs a quantitative plan deviation evaluation index system, and provides a clear evaluation standard and direction for the subsequent optimization work. The personalized plan dynamic adjustment unit can adjust the nursing plan in real time according to the patient's health status and feedback by combining the patient's dynamic health record and multi-dimensional feedback data, ensuring that each patient can receive customized nursing services, thereby improving the nursing effect. The machine learning iterative algorithm unit continuously adjusts the nursing plan by using reinforcement learning and Bayesian optimization algorithms, continuously optimizes the adjustment strategy of the nursing plan, and continuously improves the accuracy and efficiency of plan optimization through autonomous learning and intelligent evolution. The cross-professional collaborative optimization unit forms a cross-professional collaborative optimization mechanism by integrating the professional insights and intelligent algorithms of medical staff, traditional Chinese medicine experts and nursing management systems, jointly promotes the continuous improvement of the nursing plan, and ensures the multi-dimensional optimization and scientific nature of the plan. The plan iterative risk management unit establishes a safety assessment and risk control mechanism during the optimization process to ensure that the adjustment of the nursing plan always meets the clinical safety and controllability requirements and avoids potential risks and adverse consequences. Generally speaking, through the integration of multiple advanced algorithms and cross-professional collaboration, the nursing plan iterative optimization module can not only achieve fine-tuning of the nursing plan, but also ensure its safety and effectiveness in the clinical environment.

[0167] Furthermore, by combining the patient's dynamic health record and multi-dimensional feedback data, the accurate optimization and personalized adjustment of the nursing plan are realized, including the following steps:

[0168] Using the health record H(t) as the basis, construct the optimization objective function of the nursing plan, that is, by minimizing to balance the improvement of various health indicators and clinical constraints, ensuring that the nursing plan is both accurate and safe. Among them, N represents the total number of nursing goals; w r represents the weight of the rth nursing goal; L r represents the loss function of the rth nursing goal; L r (C, H(t)) represents the loss of the rth nursing goal of the nursing plan C at time t based on the patient's dynamic health record H(t); λ represents the regularization parameter, which is used to balance the loss term and risk term in the nursing plan optimization objective, ensuring that the optimization of the nursing plan not only effectively achieves the goal, but also takes into account the safety and controllability of the nursing plan; R(C) represents the risk term of the nursing plan C;

[0169] Adopt the gradient descent method, according to the formula Perform iterative updates, continuously adjust the nursing plan, and gradually approach the optimal solution. Among them, C(t + 1) represents the updated nursing plan at time t + 1; η represents the learning rate; represents the gradient operation with respect to the nursing plan C;

[0170] Combine the nursing plan with patient data, extract multi-dimensional feedback information, and evaluate the nursing effect to provide a basis for subsequent adjustments;

[0171] Integrate the gradients of the health status and feedback information, and through achieve personalized adjustment and continuous optimization of the nursing plan, ensuring that the treatment plan fits the actual needs of the patient. Among them, is the updated personalized nursing plan; γ is the adjustment factor, which controls the adjustment strength of the nursing plan during comprehensive optimization; γ is the adjustment factor, which controls the adjustment strength of the nursing plan during comprehensive optimization; α is the weight coefficient used to balance the relationship between the health status and the nursing effect; represents the gradient with respect to the feedback effect F; F effect (t) represents the nursing effect at time t.

[0172] Furthermore, adopt reinforcement learning and Bayesian optimization algorithms to continuously optimize the nursing plan adjustment strategy, including the following steps:

[0173] Construct a mapping between the nursing plan state space and the action space, and parameterize the nursing intervention measures into a set of quantifiable adjustment actions;

[0174] Design a multi-objective reward function based on patient health improvement indicators to balance short-term efficacy and long-term health benefits;

[0175] Use the Monte Carlo tree search algorithm to explore potential nursing plan adjustment paths and predict the long-term effects of different adjustment strategies;

[0176] Apply the Bayesian optimization framework to finely tune the key parameters of the nursing plan and construct a parameter-effect probability distribution model;

[0177] Implement an adaptive exploration-exploitation balance strategy based on Thompson sampling to continuously optimize the nursing plan on the premise of ensuring patient safety;

[0178] Establish a memory mechanism for plan iteration, accumulate effective experience, avoid repeated ineffective attempts, and accelerate the learning convergence speed.

[0179] Furthermore, the remote collaboration module includes the following components:

[0180] Distributed data storage unit, which constructs a highly available and scalable distributed storage system based on the cloud computing architecture to achieve efficient storage, fast retrieval, and highly reliable access of massive medical data;

[0181] Comprehensive data security and governance unit, which uses blockchain technology to establish a data verification and traceability mechanism, and formulates unified data security standards and multi-level access control frameworks to ensure the integrity, traceability, and compliance of medical data;

[0182] Cross-institutional data interaction unit, which constructs a standardized medical data exchange protocol to standardize data sharing standards and interfaces between different medical institutions;

[0183] Real-time communication and collaboration unit, which constructs a real-time communication system based on the cloud native architecture to support secure and efficient instant collaboration among multiple parties;

[0184] Collaboration process orchestration unit, which designs standard workflows for cross-institutional collaboration, provides flexible collaboration scenario configuration, realizes traceability and manageability of the collaboration process, and supports real-time monitoring and early warning of collaboration status;

[0185] Multi-terminal collaborative access unit, which provides a unified access solution for multiple terminals for patients, medical staff, and managers, and supports secure and convenient collaboration on multiple device terminals.

[0186] In summary, the technical effects of the remote collaboration module are reflected in its integration of various advanced technologies, which realizes the efficient storage, reliable access, and optimized cross-institutional collaboration of medical data, thereby enhancing the synergy and efficiency of medical services. The distributed data storage unit constructs a highly available and scalable storage system through a cloud computing architecture, which can not only efficiently store a large amount of medical data but also support fast retrieval and highly reliable access, ensuring that data can be obtained in a timely manner at any time and place. The comprehensive data security and governance unit uses blockchain technology to ensure the integrity, traceability, and compliance of medical data, while ensuring data security through a multi-level access control framework, effectively preventing data tampering and leakage. The cross-institutional data interaction unit promotes data sharing and seamless connection between different medical institutions through a standardized medical data exchange protocol, eliminating the problem of data silos. The real-time communication and collaboration unit supports multi-party instant and secure remote collaboration based on a cloud-native architecture, improving the communication efficiency between teams. The collaboration process orchestration unit ensures the traceability and manageability of the collaboration process by designing flexible cross-institutional workflows, and guarantees the smooth operation of the collaboration through real-time monitoring and warning functions. The multi-terminal collaborative access unit provides a unified multi-terminal access solution for patients, medical staff, and managers, making secure cross-device collaboration possible and enhancing universality and convenience. Overall, the remote collaboration module provides an efficient, secure, and flexible remote collaboration solution for the medical industry by optimizing data storage, security, collaboration processes, and multi-terminal access, greatly enhancing the collaborative effect and operational efficiency of medical services.

[0187] Furthermore, a data verification and traceability mechanism is established using blockchain technology, and a unified data security standard and multi-level access control framework are formulated to ensure the integrity, traceability, and compliance of medical data, including the following steps:

[0188] Build a permission-based consortium blockchain network and set differentiated data access and operation permissions for different roles;

[0189] Use smart contracts to automatically execute data authorization, usage auditing, and privacy protection processes, realizing automated management of the entire process of data sharing;

[0190] Deploy a zero-knowledge proof protocol to verify the authenticity of patients' health information without exposing the original data;

[0191] Implement multiple encryption and distributed storage strategies to ensure the security and integrity of sensitive medical data;

[0192] Establish a two-way synchronization mechanism between blockchain records and traditional medical systems to ensure data consistency and traceability;

[0193] Design an automatic audit system for data compliance, continuously monitor data operation behaviors, and ensure compliance with medical data privacy protection regulations.

[0194] As Figure 2 shown, this embodiment also provides a traditional Chinese medicine nursing management method based on a cloud follow-up management platform, which is implemented based on the above traditional Chinese medicine nursing management system, and includes the following steps:

[0195] Collect patients' physiological parameters, behavior habit data, and environmental factors in real time, and perform standardization processing, denoising, and spatio-temporal synchronization on the collected raw data;

[0196] Extract health feature vectors from the collected data, reveal the internal relationships between different indicators through multi-dimensional correlation analysis, form personalized health records, and establish a real-time update mechanism;

[0197] Intelligently match the patients' health records with the knowledge graph, accurately identify the traditional Chinese medicine syndrome differentiation types, and automatically generate targeted traditional Chinese medicine nursing plans based on the reasoning results of the graph, individual characteristics, and clinical experience;

[0198] Establish a three-in-one system that integrates objective indicators, patient feedback, and medical staff evaluation, use sentiment analysis and intelligent fusion algorithms to refine key indicators, comprehensively evaluate the nursing effect, and guide the optimization direction;

[0199] Utilize reinforcement learning and Bayesian optimization to adjust the nursing strategy according to the difference between the actual effect and the expectation, combine expert experience to construct a collaborative decision-making mechanism, and form a closed-loop mode of "evaluation - adjustment - verification - optimization";

[0200] Build a cloud-native distributed medical data platform, ensure data security through blockchain, establish a standardized data exchange protocol, support remote consultations and unified access of multiple terminals, and achieve accurate, information-based, and intelligent traditional Chinese medicine nursing management.

[0201] In summary, the traditional Chinese medicine nursing management method based on the cloud follow-up management platform improves the accuracy, real-time performance, and personalization of traditional Chinese medicine nursing services by integrating advanced technologies and intelligent algorithms. First, by collecting patients' physiological parameters, behavior habits, and environmental factors in real time, and performing standardized processing, denoising, and spatio-temporal synchronization on these raw data, a high-quality data foundation is provided for subsequent analysis. Second, multi-dimensional correlation analysis is used to extract health feature vectors, revealing the internal relationships between different health indicators, helping to construct personalized health records, and ensuring the continuous accuracy and adaptability of the records through a real-time update mechanism. Then, through intelligent matching with the knowledge graph, combined with traditional Chinese medicine syndrome differentiation and individual characteristics, a personalized nursing plan is automatically generated, enhancing the pertinence and scientific nature of nursing. In addition, combined with patient feedback, medical staff evaluation, and sentiment analysis, the nursing effect is comprehensively evaluated and the optimization direction is guided to ensure the comprehensiveness and flexibility of nursing services. Using reinforcement learning and Bayesian optimization algorithms, the nursing strategy is continuously adjusted, and through the closed-loop "evaluation - adjustment - verification - optimization" mode, the accuracy and effect of the nursing plan are further improved. At the same time, by constructing a cloud-native distributed medical data platform, combined with blockchain technology to ensure data security, and supporting remote consultation and multi-terminal access through a standardized data exchange protocol, precise, information-based, and intelligent traditional Chinese medicine nursing management is achieved. Overall, this method improves the overall efficiency and service quality of traditional Chinese medicine nursing management by integrating technologies such as data collection, intelligent analysis, real-time optimization, and data security, and promotes the intelligent development of the nursing field.

[0202] Finally, it should be pointed out that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A traditional Chinese medicine nursing management system based on a cloud follow-up management platform, characterized in that Includes the following modules: Dynamic data collection module collects the patient's physiological parameters, living habits and environmental data in real time and performs preliminary processing; Dynamic health record building module, which builds and continuously updates the patient's individual health record based on real-time collected data; The TCM knowledge graph and reasoning module relies on the TCM nursing knowledge graph and combines intelligent reasoning technology to achieve accurate TCM syndrome differentiation and treatment analysis and automatically generate personalized nursing plans; Multi-dimensional patient feedback module, building a feedback mechanism that integrates objective data, patient subjective feelings and medical evaluation to extract key nursing effect information; The nursing plan iterative optimization module dynamically adjusts and optimizes the TCM nursing plan based on feedback data to achieve personalized, adaptive and continuous improvement of nursing care; The remote collaboration module enables remote data sharing and collaborative consultation between patients, medical staff and institutions.

2. The traditional Chinese medicine nursing management system based on the cloud follow-up management platform according to claim 1, wherein The dynamic data acquisition module includes the following components: The physiological parameter collection unit relies on smart wearable devices to monitor key physiological indicators in real time and provide high-quality basic data for dynamic monitoring of individual health; Behavior tracking unit, which records and analyzes users' daily habits based on mobile applications to explore healthy behavior patterns; Environmental factor monitoring unit, which uses IoT sensors to collect indoor and outdoor environmental parameters, evaluate the potential impact of the external environment on individual health, and provide environmental adaptability recommendations; The data preprocessing and calibration unit standardizes, denoises, and time-synchronizes the collected raw data, and dynamically calibrates the data deviations of various sensors and devices to ensure the accuracy, consistency, and quality of the data.

3. The traditional Chinese medicine nursing management system based on the cloud follow-up management platform according to claim 1, wherein The dynamic health record building block includes the following components: The multi-dimensional feature extraction unit, based on deep learning algorithms, automatically identifies and extracts key health features from dynamically collected multi-source heterogeneous data and constructs a personal health feature vector space; The time series dynamic modeling unit uses time series analysis technology to build a dynamic probability model of individual health status and capture the long-term change trends and potential laws of health indicators; The personalized health profile construction unit combines the extracted health characteristics with the dynamic probability model to generate a highly individualized dynamic health profile that comprehensively describes the individual's physiological, psychological and behavioral characteristics; The health risk assessment and early warning unit uses machine learning algorithms to stratify risks and predict trends in health records, identify potential health abnormalities and development directions, and provide personalized health warnings and intervention recommendations; Cross-dimensional correlation analysis unit, which deeply explores the intrinsic correlation of indicators of different dimensions in health records and reveals the complex dynamic change mechanism of health status; The file continuous updating unit builds a real-time dynamic update mechanism to ensure that the health record can accurately reflect the individual's current health status and continuously optimize future health predictions.

4. The traditional Chinese medicine nursing management system based on the cloud follow-up management platform according to claim 3, wherein, Use machine learning algorithms to stratify health records and predict trends, identify potential health abnormalities and development directions, and provide personalized health warnings and intervention recommendations, including the following steps: Based on historical health data and verified risk marker samples, patients are accurately divided into high, medium and low risk levels to form a dynamically adjustable risk stratification framework; Real-time monitor the degree and change rate of the patient's health indicators deviating from the baseline value, infer and generate the disease development path based on the knowledge graph, and quantify the occurrence probability and time window of different health risk events; According to the risk stratification results and trend prediction data, configure personalized multi-level warning thresholds and triggering logics for patients, and construct a differentiated warning scheme by combining the patient's characteristic portrait and behavior pattern; For the identified risk types, automatically generate personalized intervention suggestions based on evidence-based medicine, including lifestyle optimization, traditional Chinese medicine nursing plan, mental health management strategy, and environmental factor regulation measures.

5. The traditional Chinese medicine nursing management system based on the cloud follow-up management platform according to claim 1, characterized in that The traditional Chinese medicine knowledge graph and reasoning module includes the following components: Traditional Chinese medicine knowledge ontology and terminology standardization unit, construct a standardized knowledge ontology covering traditional Chinese medicine theory concepts, syndrome differentiation elements and disease relationships, perform standardized processing and multi-dimensional mapping on traditional Chinese medicine professional terms, eliminate semantic ambiguity, and provide a structured basis for the knowledge graph; Semantic association reasoning unit, adopt natural language processing and semantic analysis technologies to mine the deep semantic associations between traditional Chinese medicine concepts in the knowledge graph, and realize intelligent reasoning and knowledge connection across concepts; Syndrome differentiation and treatment intelligent matching unit, based on the personal health record, combine the rule matching in the knowledge graph and machine learning algorithms to accurately identify the most suitable traditional Chinese medicine syndrome differentiation type and personalized treatment plan; Initial nursing plan generation unit, comprehensively combine the knowledge graph, personal health data and syndrome differentiation reasoning results to dynamically construct a personalized traditional Chinese medicine nursing plan as the basis for plan iteration and optimization; Knowledge graph dynamic learning unit, establish a closed-loop learning mechanism, and continuously optimize and expand the semantic network and reasoning ability of the knowledge graph through clinical feedback and practice data; Multi-modal knowledge integration unit, integrate multi-modal information covering text, images and clinical data to enrich the semantic depth and expression ability of the knowledge graph.

6. The traditional Chinese medicine nursing management system based on the cloud follow-up management platform according to claim 1, characterized in that, The multi-dimensional patient feedback module includes the following components: Objective index comprehensive evaluation unit, integrate clinical monitoring data, physiological index changes and objective treatment effect evaluation, and construct a multi-dimensional treatment effect index system; Patient subjective feeling analysis unit, collect the patient's subjective feedback on the treatment process and effect, and use natural language processing technology for in-depth analysis to mine personalized experience information; Emotional semantic mining unit, based on emotional computing technology, identify the emotional intensity, emotional type and potential psychological state from the patient feedback text, extract deep emotional information to assist in accurate nursing decision-making; Medical staff professional evaluation unit, collect and integrate the professional evaluations of medical staff on the patient's treatment process, and provide authoritative and professional support for feedback analysis; Multi-source feedback intelligent fusion unit, adopt intelligent fusion algorithms to deeply associate and comprehensively analyze objective data, patient subjective feelings and medical staff professional evaluations; Key nursing effect extraction unit, use machine learning and text analysis technologies to automatically identify and refine key nursing effect indicators and optimization directions from multi-dimensional feedback information; Feedback quality evaluation unit, construct a credibility and effectiveness evaluation mechanism for feedback information to ensure the scientificity, representativeness and clinical value of the extracted information.

7. The traditional Chinese medicine nursing management system based on the cloud follow-up management platform according to claim 1, characterized in that, The nursing plan iteration and optimization module includes the following components: The nursing plan deviation analysis unit accurately identifies potential deviations and optimization spaces based on the comparison and analysis between traditional Chinese medicine nursing plans and actual implementation effects, and constructs a quantitative plan deviation evaluation index system; The personalized plan dynamic adjustment unit combines the patient's dynamic health record and multi-dimensional feedback data to achieve the precise optimization and personalized adjustment of the nursing plan; The machine learning iterative algorithm unit adopts reinforcement learning and Bayesian optimization algorithms to continuously optimize the nursing plan adjustment strategy and achieve autonomous learning and intelligent evolution; The cross-professional collaborative optimization unit builds a collaborative optimization mechanism for medical staff, traditional Chinese medicine experts, and the nursing management system, integrates professional insights and intelligent algorithms, and jointly promotes the continuous improvement of the nursing plan; The plan iteration risk management unit establishes a safety assessment and risk control mechanism during the optimization process to ensure that the optimization of the nursing plan meets the requirements of clinical safety and controllability.

8. The traditional Chinese medicine nursing management system based on the cloud follow-up management platform according to claim 1, characterized in that, The remote collaboration module includes the following components: The distributed data storage unit constructs a highly available and scalable distributed storage system based on the cloud computing architecture to achieve the efficient storage, rapid retrieval, and highly reliable access of massive medical data; The comprehensive data security and governance unit uses blockchain technology to establish a data verification and traceability mechanism, and formulates unified data security standards and a multi-level access control framework to ensure the integrity, traceability, and compliance of medical data; The cross-institutional data interaction unit constructs a standardized medical data exchange protocol to standardize the data sharing standards and interfaces between different medical institutions; The real-time communication and collaboration unit constructs a real-time communication system based on the cloud-native architecture to support secure and efficient instant collaboration among multiple parties; The collaboration process orchestration unit designs a standard workflow for cross-institutional collaboration, provides flexible collaboration scenario configuration, realizes the traceability and management of the collaboration process, and supports the real-time monitoring and early warning of the collaboration status; The multi-terminal collaborative access unit provides a unified access solution for multiple terminals for patients, medical staff, and managers, and supports secure and convenient collaboration on multiple device terminals.

9. The traditional Chinese medicine nursing management method based on the cloud follow-up management platform is implemented based on the traditional Chinese medicine nursing management system described in any one of claims 1-8, and includes the following modules: Real-time collect the patient's physiological parameters, behavior habit data, and environmental factors, and perform standardized processing, denoising, and spatio-temporal synchronization on the collected raw data; Extract health feature vectors from the collected data, reveal the internal relationships between different indicators through multi-dimensional correlation analysis, form a personalized health record, and establish a real-time update mechanism; Intelligently match the patient's health record with the knowledge graph, accurately identify the traditional Chinese medicine syndrome differentiation types, and automatically generate a targeted traditional Chinese medicine nursing plan based on the reasoning results of the graph, individual characteristics, and clinical experience; Establish a three-in-one system integrating objective indicators, patient feedback, and medical staff evaluation, use sentiment analysis and intelligent fusion algorithms to refine key indicators, comprehensively evaluate the nursing effect, and guide the optimization direction; Use reinforcement learning and Bayesian optimization to adjust the nursing strategy according to the difference between the actual effect and the expectation, combine expert experience to construct a collaborative decision-making mechanism, and form a "evaluation - adjustment - verification - optimization" closed-loop mode; Build a cloud-native distributed medical data platform, ensure data security through blockchain, establish a standardized data exchange protocol, support remote consultations and unified access of multiple terminals, and achieve precise, information-based and intelligent traditional Chinese medicine nursing management.

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