87 results about "Nursing care quality" patented technology

The invention relates to the field of medical informatization, in particular to a nursing risk assessment system and method based on multi-modal data. According to the nursing risk assessment system, data acquisition, preprocessing, multi-modal fusion, risk assessment and early warning are integrated, and comprehensive, accurate, timely and personalized nursing assessment is realized. The system collects physiological signals, nursing behaviors and environment data, the data are preprocessed and fused to generate feature vectors, a risk assessment matrix is constructed, and risk indexes are calculated. The risk early warning module sends early warning information to the nurse work station in real time, and nursing safety is ensured. Innovative technologies such as multi-modal data fusion, real-time dynamic evaluation, personalized adjustment and the like greatly improve the nursing quality, reduce the occurrence rate of adverse events, relieve the burden of medical staff and improve the utilization efficiency of medical resources. Long-term application is hopeful to significantly improve patient prognosis and reduce medical cost, and makes an important contribution to development of intelligent medical treatment and precise nursing.

The invention discloses a pediatric nursing quality prediction and optimization method based on transfer learning, and the method comprises the following steps: S1, collecting target pediatric nursing data, and carrying out the preprocessing; s2, selecting source data, and extracting source data features; s3, constructing and training a nursing quality prediction model; s4, carrying out migration and fine tuning on the nursing quality prediction model by adopting an improved fine tuning method; s5, inputting target pediatric nursing data to the fine-adjusted nursing quality prediction model, calculating a nursing quality index, and evaluating a prediction result; s6, generating a nursing scheme based on the prediction result, and optimizing the nursing scheme according to the execution effect; and S7, applying the optimized nursing scheme to actual nursing work, and establishing an updating mechanism, so that the nursing quality prediction model is combined with new data to carry out iterative optimization. The pediatric nursing quality prediction and optimization are realized by adopting transfer learning and deep learning methods, and the pediatric nursing quality prediction and optimization method has the advantages of high data adaptability, high prediction accuracy and intelligent optimization of the nursing scheme.

The embodiment of the invention provides a nurse station ward information collection method and system based on a wireless network. A flexible electronic skin network is integrated in a patient mattress, pressure and temperature data are captured through distributed piezoresistive units, a multi-modal thermodynamic diagram is generated, and data synchronization is achieved through a wireless ad hoc network protocol. And driving an air cushion bed actuator to perform gradient compensation according to a matching result of the pressure gradient track and the physiological parameters, transmitting a compressed hierarchical data packet to a nurse station gateway by adopting a remote wide area network protocol, reconstructing a three-dimensional pressure model and generating an early warning instruction. According to the technical scheme provided by the embodiment of the invention, the occurrence probability of health problems such as bedsore caused by long-time bed lying is remarkably reduced, and the nursing quality of the patient is improved.

The invention relates to the technical field of medical care, in particular to a pediatric care quality evaluation method based on multi-modal data interaction. The method comprises the steps of multi-modal data acquisition, data preprocessing, nursing behavior feature analysis, infant state evaluation, and nursing quality scoring and feedback. According to the scheme, the multi-source heterogeneous data in the nursing process is collected, the ResNet50 model, the Glove model and the Mel-frequency cepstral coefficient are used for preprocessing and feature extraction of the multi-modal data of the image, the audio and the text, and the integrity and the analysis efficiency of feature fusion are improved; according to the scheme, a multi-granularity feature interactive fusion method is adopted, key features in the nursing process are extracted and fused from multiple dimensions, and the nursing behavior features and the infant state features are comprehensively obtained, so that the accuracy and the intelligent level of nursing quality evaluation are remarkably improved, and real-time monitoring and scientific feedback support of the nursing process are achieved.

The invention discloses a medical care data intelligent evaluation system based on multi-modal data fusion, and belongs to the technical field of medical care. The problems that subjective deviation exists in the prior art, evaluation results of different medical workers on the same patient may be different, the medical data source is single, and patient information cannot be comprehensively reflected are solved. Through real-time feedback of nursing personnel, the frequency of abnormal data in a feedback result within a period of time is analyzed, so that the updating frequency of the model is dynamically adjusted, and misjudgment caused by model deviation is reduced; according to the method, the abnormal data in the multi-modal data are identified by calculating Z-score, the time sequence data are set to be high priority, and the abnormal data at the high priority are preferentially fed back to the nursing personnel, so that the response speed of the nursing personnel to abnormal conditions is increased, the distribution of nursing resources is optimized, and the nursing efficiency is improved. Therefore, nursing personnel can deal with emergencies in time, and nursing quality and patient safety are improved.

The invention discloses a hospital nursing quality evaluation system based on hospital information terminal data, and relates to the technical field of medical information, and the system comprises a data acquisition module which extracts index data based on a hospital terminal, and the index data comprises patient satisfaction subjective index data, nursing personnel working life data, performance data and adverse event occurrence probability; after preprocessing the data, constructing a data set; and the analysis module performs correlation analysis on the data of the data set based on the Pearson's correlation coefficient, constructs a nursing quality evaluation model through a neural network, and performs training optimization on the quality evaluation model. The comprehensive evaluation level is efficiently and objectively output through the evaluation module, the result is presented through a visual chart, managers can conveniently master the comprehensive evaluation level, hospitals accurately position nursing service short boards according to the evaluation result, targeted optimization is implemented, the nursing system is continuously improved, the requirements of patients are met, and it is ensured that the nursing quality is kept at a high level; and data in a hospital can be fully utilized.

The invention discloses an anesthesia awakening state prediction method and system, and the method comprises the steps: firstly constructing a standard prediction model, then obtaining an actual physiological parameter set, generating an actual recovery state parameter set according to the actual physiological parameter set, and generating an initial recovery curve in the standard prediction model; generating a minimum verification circle, and performing primary correction on the initial recovery curve through the minimum verification circle to obtain a corrected recovery curve; according to the correction recovery curve and a standard prediction model, initial prediction waking time is calculated, according to the actual recovery state parameter set and the correction recovery curve, a composite deviation rate is generated, according to the composite deviation rate, secondary correction is carried out on the initial prediction waking time, and prediction waking time is obtained. According to the method, accurate positioning of the wake-up point is realized by correcting the intersection point of the recovery curve and the standard prediction model, and early warning is performed in time, so that the work pressure of medical staff is reduced, the orderly development of medical work can be ensured, and the nursing quality and the operation efficiency of a wake-up ward can be improved.

The invention discloses an emergency rescue room nursing resource dynamic scheduling method and system based on demand prediction, and relates to the technical field of resource dynamic scheduling. The method comprises the following steps: collecting and preprocessing historical emergency room data, and constructing a multi-dimensional feature data set; respectively training an improved GRU model and a Transform model for high-precision prediction of patient inflow and triage level distribution in a future time period, and quantifying a prediction result as standard nursing time; based on real-time nurse data, a multi-target optimization model with the purposes of minimizing patient waiting time, balancing nurse loads, controlling cost and meeting preferences is built, a Pareto optimal scheduling scheme set is solved and generated through a genetic algorithm, and a manager selects a final scheme according to the actual situation; according to the invention, the conversion from passive response to active accurate scheduling is realized, and the operation efficiency, the resource utilization rate and the nursing quality of the emergency rescue room are effectively improved.

The invention relates to a clinical symptom nursing strategy intelligent recommendation system and method based on artificial intelligence, and the method combines historical clinical symptoms and nursing big data to construct a corresponding nursing strategy intelligent recommendation model, thereby enabling the nursing strategy intelligent recommendation model to recognize the feature relationship between different symptom features and corresponding nursing strategies. And enabling the model to recognize the symptom characteristics of a clinical patient and intelligently recommend a corresponding nursing strategy. The system can also combine model output and nurse feedback, fuse and recommend a clinical symptom nursing strategy most related to patient symptoms, so that nurses can make preliminary assessment and deal with the appearing symptoms to confirm nursing problems, sort critical and urgent conditions, develop nursing measures, highlight nursing key points and improve the nursing efficiency. And potential complications can be prevented or treated in time. In clinical nursing work, clinical nurses can be effectively guided to perform judgment thinking and carry out predictive nursing, so that the nursing work is homogenized, and the nursing quality and safety are improved.

A system and method for facilitating a member journey through healthcare service by augmenting stewardship workflow across multiple service areas is disclosed. The system and method includes augmenting the healthcare stewardship workflow by standardizing the healthcare service practices, increasing the quality of care for patients, improving the workflow efficiency of healthcare personnel, reducing the costs associated with providing healthcare services, and enabling the ease of regulatory compliance using machine learning techniques. The system and method provides the healthcare personnel with access to healthcare-relevant and fact-based artificial intelligence powered by machine learning for decision support opportunities to drive recommended care pathways.

The invention discloses a postoperative chest cold and hot compress integrated nursing pad for a thoracic surgery patient. The postoperative chest cold and hot compress integrated nursing pad comprises a heating layer, a refrigerating layer, a nursing pad sensor, a physiological sensor, an intelligent control unit and a man-machine interaction system. The heating layer is composed of a liquid circulation heating cushion layer and a small heating pump, the refrigerating layer is composed of a liquid circulation refrigerating cushion layer and a miniature refrigerating pump, the nursing pad sensor layer is located between the heating layer and the refrigerating layer, and the physiological sensor is used for collecting the physiological state of a patient. The intelligent control unit integrates an artificial intelligence algorithm, intelligently regulates and controls work of the heating layer and the refrigerating layer according to feedback signals of the sensor, and achieves automatic switching and precise adjustment of the cold and hot compress modes. And the man-machine interaction system is convenient for medical personnel and patients to operate and feed back. Through multi-sensor data acquisition and intelligent algorithm analysis and in combination with a remote monitoring function, a personalized and intelligent chest cold and hot compress nursing scheme is provided for a patient after the thoracic surgery, recovery of the patient is effectively promoted, and the nursing quality is improved.

The invention provides a multi-mode intelligent analysis internal fistula postoperative rehabilitation nursing method and system, and belongs to the technical field of medical nursing and artificial intelligence crossing. The application aims to solve the problems of single monitoring dimension, subjective risk judgment and static homogenization of the nursing scheme in the existing internal arteriovenous fistula postoperative nursing. The method comprises the following steps: acquiring multi-modal data related to a wound state and a hemodynamic state of an internal arteriovenous fistula; processing the multi-modal data to generate standardized feature data which can be analyzed; generating a risk assessment result about the health condition of the internal arteriovenous fistula; and a preset nursing scheme is dynamically adjusted based on a risk assessment result. According to the method and the device, multi-modal data are fused and an intelligent algorithm is used for collaborative analysis, so that the accuracy and early stage of risk identification can be realized, the individuation and high efficiency of a nursing scheme are realized, and the nursing quality is improved.

The invention relates to a nursing home priority service response method, system and device based on digital twinning, and the method comprises the following steps: collecting health data of old people and state data of nursing personnel, and carrying out the preprocessing of the data; constructing an old people health state dynamic model, a nursing resource dynamic model and an environment path model; coupling the three models to form a digital twin model; calculating a service response priority based on the health score, the waiting time attenuation and the medical history characteristics of the elderly; according to the service response priority, scheduling the matched nursing personnel to carry out service response; and collecting a service execution result after the nursing personnel completes the task, updating the digital twinborn model based on the service execution result, and dynamically adjusting a scheduling strategy. According to the invention, real-time mapping and dynamic optimization of the health state of the elderly, the position of the nursing personnel and the environment path information in the nursing home are realized, and the service response efficiency, the resource scheduling accuracy and the nursing quality are remarkably improved.

The invention relates to the technical field of extubation action recognition and early warning systems, and discloses a patient unplanned extubation action recognition and early warning system based on wrist strap equipment, the system comprises the wrist strap equipment worn on the wrist of a patient, and the equipment is internally provided with an action sensor, a processor and a communication module. The action sensor is used for detecting hand actions of a patient, the processor analyzes data of the sensor to recognize non-planned extubation actions, and once potential extubation actions are recognized, the system immediately gives out early warning to medical staff in a sound-light alarm and wireless communication mode. The method has the advantages of high recognition accuracy, real-time early warning, protection of patient privacy, reduction of burden of medical staff, improvement of medical safety and the like, is suitable for various patients in hospitals, particularly patients in intensive care units, is beneficial to reduction of the incidence rate of non-planned tube drawing events, reduction of adverse nursing events, reduction of harm to the patients, and is beneficial to popularization and application. The nursing quality is improved and the satisfaction degree is improved.

The invention provides a nursing quality management and control system based on multi-modal data, relates to the field of intelligent nursing, and adopts a data processing technology based on artificial intelligence to perform comprehensive analysis on a set described by acquired nursing feedback texts and an environment state image of a target inpatient area. Therefore, nursing feedback text association semantics related to a plurality of target patient objects and environment state characteristics of the target inpatient area are captured, and nursing quality management suggestions are intelligently generated based on remarkable interaction representation information between two cross-modal data including the nursing feedback semantics and the environment state of the inpatient area. Therefore, more accurate and effective nursing quality management suggestions can be provided, and more scientific and reasonable nursing quality management can be realized.

The invention relates to an intelligent nursing and monitoring device used after glaucoma filtration, and belongs to the technical field of medical instruments, the intelligent nursing and monitoring device comprises a rigid main frame, the inner side of the rigid main frame is provided with a flexible sealing gasket, the upper portion of the rigid main frame is integrated with a self-adaptive positioning system, and the left side and the right side of the rigid main frame are connected with directional physical nursing execution systems; a microenvironment humidity monitoring system is embedded in the ball head, and the rear side of the frame is connected with an intelligent central control system. The self-adaptive positioning system accurately recognizes eyeball and filtered bubble areas, the directional physical nursing execution system reproduces standardized nursing operation, the microenvironment humidity monitoring system verifies the nursing effect in real time, and the intelligent central control system overall plans all the systems to cooperate and supports remote data transmission. The device can solve the problems of inaccurate postoperative nursing, lagged effect feedback and poor patient compliance, improves the nursing quality, assists in improving the success rate of an operation, and is suitable for families and clinical nursing scenes.

The application discloses a medical care data intelligent evaluation system based on multi-modal data fusion and belongs to the technical field of medical care. The application solves the problems that the existing technology has subjective bias, the evaluation results of different medical staffs for the same patient can be different, and the medical data source is single and cannot comprehensively reflect the patient information. Through the real-time feedback of nursing staff, the frequency of model update is dynamically adjusted by combining the frequency of abnormal data in the feedback results in a period of time, so as to reduce the misjudgment caused by model bias; the abnormal data in the multi-modal data is identified by calculating Z-score, the time series data is set as high priority, and the abnormal data in the high priority is preferentially fed back to the nursing staff, which not only improves the response speed of the nursing staff in the face of abnormal conditions, but also optimizes the allocation of nursing resources, so that the nursing staff can more timely handle the emergency, thereby improving the nursing quality and patient safety.

The invention discloses a nursing service data intelligent management system for a doctor seeing patient, and relates to the technical field of nursing service data management. The nursing service data intelligent management system for the doctor seeing patient comprises a first data acquisition and analysis module, a second data acquisition and analysis module, a comprehensive nursing evaluation module and a nursing optimization module, according to the method, acquisition of medical record data and nursing service data, calculation of a nursing demand index and a nursing quality response index and comprehensive evaluation of a nursing fit index are sequentially realized, nursing measures are driven to be optimized accordingly, and intelligent and self-adaptive regulation and control of nursing services are realized. The intelligent nursing sensing network is used for intelligently recognizing the nursing requirement of a patient, quantifying the nursing quality and the physiological response state, introducing a nursing fit index to measure deviation and correlation among multiple indexes, driving a nursing optimization module to adaptively output adjustment suggestions, and achieving conversion from static management to dynamic sensing.

The present application provides a nursing machine. The nursing machine includes a housing (1), the housing (1) includes a clothing treatment area, the clothing treatment area includes a nursing area (402) and a storage area (403), a partition door (5) is provided between the nursing area (402) and the storage area (403), the partition door (5) is closed when the nursing area (402) is in the state of nursing the clothing (13), and is opened when the clothing (13) moves from the nursing area (402) to the storage area (403) after the nursing of the clothing (13) is completed. According to the nursing machine of the present application, the nursing area and the storage area of the nursing machine can be separated, so as to avoid affecting other materials of clothing during the nursing process, and ensure the nursing quality and nursing effect.

The invention relates to a nursing device for hemodialysis patients, and belongs to the technical field of medical nursing devices. In order to solve the problems that in the prior art, cost is high, and numbness cannot be improved through active movement, an arm arc-shaped bearing piece, an auxiliary massage mechanism, a hand training piece, an angle adjusting mechanism and a debugging mechanism are arranged, the arm arc-shaped bearing piece is used for supporting the arm of a patient, and the auxiliary massage mechanism is arranged on one side of the arm arc-shaped bearing piece and communicates with the arm arc-shaped bearing piece; the hand training piece is connected to one end of the arc-shaped arm bearing piece, the angle adjusting mechanism is arranged at the bottom of the arc-shaped arm bearing piece and connected with the hand training piece, and the angle adjusting mechanism is matched with the hand training piece to adjust the angle of the arc-shaped arm bearing piece. The debugging mechanism is arranged on the auxiliary massage mechanism and connected with the hand training piece through an air guide pipe. According to the device, the massage strength can be adjusted according to individual requirements of a patient, the arm of the patient can bend and stretch at a micro angle during massage, numbness of the arm during dialysis is avoided, and the nursing quality is improved.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a dynamic body position adjusting and nursing method for elderly disabled people, which comprises the following steps: S1, acquiring multi-dimensional health data including physiological data, skin data and clinical data; s2, establishing a correlation model of the body position combination strategy and the occurrence rate of the pressure sores, and mining the correlation of the body position combination strategy and the health risk; s3, constructing a dynamic body position strategy library, and associating a body position combination strategy with the corresponding multi-dimensional health data by associating a fuzzy weight function; s4, performing comprehensive analysis by adopting a CNN-RNN-Attention fusion model, and accurately evaluating the occurrence state grade of the pressure sores; and S5, performing intelligent execution of the body position combination. According to the invention, the timeliness and accuracy of nursing are greatly improved, the occurrence rate of pressure sores is effectively reduced by 60%, and the nursing quality is remarkably improved.

This invention provides an intelligent anti-extubation vest and its application control method. Through sensor fusion and intelligent control technologies, it upgrades traditional passive fixation to an active protection system, shifting from "post-event handling" to "pre-event warning." Clinical trials show that it can reduce unplanned extubation rates, shorten nurse response times, and significantly improve the safety and quality of care for thoracic surgery patients. This invention achieves active protection through a closed-loop control system of "real-time monitoring - automatic clamping - multi-level alarms," ​​realizing an active anti-extubation mechanism and improving fixation reliability.

The invention discloses an intelligent monitoring and tracing system for intravenous medication allocation nursing quality, and relates to the technical field of intelligent nursing management, and the system comprises a time anchoring module, a conflict sensing module, a number fusing module, a dual-factor code forming module and an entropy gate regulation and control module. The time anchoring module is used for constructing a unified time anchoring chain and establishing a number pool monitoring system; and recording traceability code generation request frequency and request node distribution in a time slice corresponding to the unified time anchoring chain, and generating a high-concurrency interval list according to a recording result. Dynamic hierarchical management of traceability code generation is realized through time anchoring and a conflict perception mechanism, a high-concurrency area is identified in advance, dispersed scheduling is carried out, and code uniqueness and traceability chain continuity are ensured. In combination with serial number fusing, two-factor coding and entropy gate regulation and control mechanisms, cross-node non-repeated coding and adaptive load adjustment are realized, the collision risk is reduced, and the safety and stability of medication tracing are improved.

The invention relates to an optimal evidence-based laparoscopic postoperative shoulder pain standardized nursing process optimization method and device, and the method comprises the steps: collecting the baseline data of a patient before the laparoscopic operation, and the baseline data comprises the demographic information, the medical history information and the operation information; inputting the baseline data of the patient into the trained preoperative individualized shoulder pain risk prediction and grading model, and predicting a risk grading result of the patient; inputting the risk grading result into a trained optimal evidence-based medical evidence rule engine model, and outputting a perioperative period nursing intervention scheme; the optimal evidence-based medical evidence rule engine model comprises a rule engine tree, an intervention strategy library, an intra-operative optimization module, a postoperative analgesia module and a postoperative rehabilitation module. Through the optimal evidence-based medical evidence rule engine model, the nursing intervention scheme in the perioperative period is intelligently output according to the risk level of the patient, and the nursing quality is remarkably improved.

An integrated labor management system is used to monitor laboring patients and their fetus or fetuses during labor and delivery. The system is intended to reduce complications, improve quality labor care and, ultimately, reduce adverse maternal and neonatal outcomes. The system is designed for labor monitoring by caregivers such as obstetricians, nurses, and midwives. By integrating the technologies described, the integrated care delivery system provides a low-cost, fit-for-purpose, highly interoperable and customizable labor management technology package for LMIC settings.

This invention discloses an intelligent fluid intake and output management tool, relating to the field of medical device technology. The platform includes: a fluid intake and output measurement module for real-time collection of patient intake and output data; a data transmission module for transmitting the intake and output data to a mobile management terminal; a fluid intake and output conversion module for acquiring intake and output fluid data; and a data entry module for recording the intake and output fluid data and generating a fluid intake and output report. This invention solves the technical problem of inaccurate patient fluid intake and output records in clinical nursing due to subjective confusion in measurement methods and duplicate or missed recordings. It achieves the technical effect of improving the accuracy and convenience of recording, reducing human error, and ensuring the quality of basic nursing care by automatically converting fluid data and generating standardized fluid intake and output reports.

The invention relates to an ICU bedside pressure sore risk image identification and early warning method. The method comprises the following steps: continuously acquiring images at a preset frame rate before and after the same skin area of a patient is pressed and decompressed to obtain a sequence; extracting color, brightness or texture features of the region from the sequence, comparing the color, brightness or texture features with baseline features before decompression, if a recovery threshold is met, judging that the region is reversible, otherwise, judging that the region is limited in recovery when continuous deviation exceeds a first time length; calculating regional displacement and deformation within a second duration under the condition that the limited state is recovered, and judging that the compression is hidden and continuous when the displacement is smaller than a threshold value; and comparing the characteristics of the region before and after body position adjustment or nursing intervention, and if the characteristics are not improved or deteriorated, outputting a pressure sore high-risk early warning. The method does not need an additional contact type sensor, is suitable for continuous and non-invasive monitoring beside an ICU bedside, can provide early warning in the early stage of formation of pressure sores and even before macroscopic injuries occur, and helps nurses to take targeted intervention measures in time, so that the occurrence rate of the pressure sores is reduced, and the nursing quality and the working efficiency of the ICU are improved.

The invention discloses an intelligent lying and defecating nursing system and method, and relates to the technical field of medical nursing, and the system comprises a lying toilet unit, a camera unit, a washing and nursing unit, a flushing unit, a suction unit and a drying unit; the camera unit is used for acquiring images of a dirt bearing base of the lying toilet unit in real time, acquiring cleaning management data in the lying excrement nursing process through a sensor array arranged on the lying toilet unit, and calculating and analyzing to obtain a flushing index and a cleaning index of the excrement flushing unit; according to the method, a cleaning index multi-dimensional evaluation mechanism is introduced, so that quantitative judgment on the cleaning degree is realized; the flushing amount threshold value and the cleaning upper limit threshold value are set, cleaning is automatically stopped when the cleaning degree reaches the standard after single-time flushing, and when the cleaning degree does not reach the standard, the amount of clean water extracted again is dynamically adjusted based on the first cleaning coefficient and the second cleaning coefficient, the dirt receiving base is cleaned again, and the lying defecation nursing quality of a user is greatly improved.

The invention discloses a grading intelligent evaluation system and method for patients in an anesthesia recovery period, belongs to the technical field of anesthesia, and solves the problems that subtle changes of numerous physiological parameters of the patients are difficult to comprehensively and continuously monitor by adopting a traditional evaluation mode and depending on self-evaluation of indexes of a monitor and the like at present; the method comprises the following steps: collecting physical sign monitoring data of a patient in real time based on a sensor and an Internet of Things technology, carrying out multi-dimensional parallel evaluation analysis on the physical sign monitoring data of the patient by an anesthesia recovery period patient evaluation model, and outputting an evaluation analysis result of the patient. Judging whether the real-time state evaluation level exceeds a preset patient risk threshold value or not; according to the method, the anesthesia recovery period patient evaluation model is used for carrying out multi-dimensional parallel evaluation analysis on the physical sign monitoring data of the patient, the nursing grading requirement of the patient is judged in an intelligent mode, objectivity and scientificity of nursing grading decision making are ensured, nursing quality is improved, complication risks are reduced, and meanwhile the comfort degree and satisfaction degree of the patient are improved.

The present invention relates to a nursing home priority service response method, system and device based on digital twins, comprising the following steps: collecting health data of the elderly and status data of caregivers, and preprocessing the data; constructing a dynamic model of the elderly's health status, a dynamic model of care resources and an environmental path model; coupling the above three models to form a digital twin model; calculating the service response priority based on the elderly's health score, waiting time decay and medical history characteristics; scheduling matching caregivers to respond to the service according to the service response priority; collecting the service execution results after the caregivers complete the task, and based on the service execution results, updating the digital twin model and dynamically adjusting the scheduling strategy. The present invention realizes real-time mapping and dynamic optimization of the health status of the elderly, the location of caregivers and the environmental path information in the nursing home, significantly improving the service response efficiency, resource scheduling accuracy and care quality.