151 results about "Intervention effect" patented technology

The invention relates to an infection risk assessment method and system for nursing, and the method comprises the steps: collecting multi-source heterogeneous data of a patient, and fusing the multi-source heterogeneous data into a unified original data set; obtaining a standardized risk feature vector based on the original data set; inputting the risk feature vectors into a multi-modal risk assessment model, and outputting risk probabilities of individual infection and group infection; forming a personalized risk index based on the risk probability; according to the personalized risk indexes, the decision support system outputs hierarchical intervention measures matched with the risk levels, and records and feeds back execution conditions and intervention effect data of the hierarchical intervention measures for dynamically updating parameters of the risk assessment model; and integrating the intervention effect data and the early warning signal into feedback information to form a dynamic infection risk assessment system adapted to specific hospital characteristics. The accuracy of infection risk assessment of nursing can be improved.

The invention belongs to the field of student psychological health management, and particularly relates to a student psychological evaluation system based on AI reinforcement learning optimization. According to the system, the psychological state of the student is dynamically evaluated through multi-modal data acquisition, deep data processing and reinforcement learning optimization, and the problems of singleness and hysteresis in a traditional psychological evaluation method are solved. The system comprises a data acquisition layer, a data processing layer, a reinforcement learning optimization layer, a gamification evaluation module, a feedback and intervention layer and a user interface layer. The data acquisition layer acquires behavior, physiological and social data in multiple channels; the data processing layer performs cleaning, feature extraction and fusion on the data; the reinforcement learning optimization layer adjusts the psychological evaluation strategy in real time; the gamification evaluation module enhances the student participation degree through a task and reward mechanism; and the feedback and intervention layer provides real-time feedback and personalized intervention suggestions. The psychological evaluation accuracy, participation and intervention effect can be improved, and the method is suitable for psychological health management scenes of schools and related educational institutions.

The invention discloses an infection risk assessment method and system for burn nursing, and the system comprises a long-term trend analysis module and a short-term anomaly detection module, the long-term trend analysis module employs an improved Transform-XL model, and captures the chronic cumulative effect of inflammation indexes within 72 hours through a relative position coding and memory multiplexing mechanism; the short-term anomaly module is based on a bidirectional LSTM reconstruction network, combines a dynamic threshold algorithm to identify sudden physiological anomaly in a window of 6 hours in real time, generates a final infection risk index through a confidence-driven adaptive fusion algorithm and dynamic weighted dual-channel output, triggers clinical response in combination with a hierarchical early warning mechanism, and introduces closed-loop feedback optimization, so as to achieve the early warning of the physiological anomaly. The actual intervention effect feedback model is iterated, the infection recognition time is shortened to 3.2 hours, the ROC-AUC reaches 0.94, and the reasonable utilization rate of antibiotics is increased to 89%.

The invention relates to the technical field of artificial intelligence and nutrition health management, and discloses an intelligent nutrition correction tracking early warning method and system.The intelligent nutrition correction tracking early warning method comprises the steps that a hierarchical heterogeneity causal effect model is constructed, and individual features are mapped to a potential type space; representing a causal relationship between nutrition intervention and result variables by using a structural causal model, and evaluating an intervention effect by using a dual robust estimator; personalized grouping is realized by identifying individual subgroups; a reinforcement learning algorithm based on context awareness is combined with an anti-fact reward function, and a personalized nutrition intervention strategy is optimized; key feature combinations are extracted through a differentiable decision tree and SHAP value analysis, and multi-level causal interpretation is generated. According to the method, anti-factual reasoning, reinforcement learning and causal relationship extraction technologies are fused, accurate prediction, evaluation and early warning of the individual nutrition intervention effect are realized, and the personalized precision of nutrition intervention and the user compliance are remarkably improved.

The invention discloses a task processing method and device based on causal agent cooperation, equipment and a medium, and the method comprises the steps: constructing a dual-agent architecture comprising a general agent and a causal agent, and determining a chain-type solution of a to-be-processed task through the general agent; performing intervention type analysis, intervention effect analysis and chained causal reasoning analysis on the chained solution through the causal agent to judge whether the chained solution is matched with the to-be-processed task, and if not, updating the chained solution through the universal agent until the chained solution is matched with the to-be-processed task; generating a thinking chain of each sub-task in the chain solution through the universal agent, verifying the process logic rationality of each thinking chain through the causal agent, and updating the thinking chain which does not pass the verification through the universal agent until the verification is passed; a subsequent task sequence of a current subtask in the chain solution is determined through a causal agent, and anti-factual reasoning of a result effect is carried out through a general agent according to the subsequent task sequence. And the task processing stability is improved.

The invention discloses a motion intervention multi-dimensional decision support system and method for CIPN remission, and relates to the technical field of decision support, and the system comprises an intervention effect prediction unit which is used for collecting the multi-dimensional data of a patient, constructing a patient simulation model, and carrying out the simulation of different motion intervention schemes through the Markov decision process theory, establishing an intervention effect prediction model based on the simulation result; an exercise intervention recommendation unit; an encouraging and exciting unit; the optimal motion recommendation unit is used for updating a patient simulation model according to the constructed encouraging and incentive mechanism, analyzing a causal relationship between a motion effect and neural restoration through a hypergraph gradient causal discovery algorithm, and screening out an optimal motion recommendation scheme; and a visualization unit. According to the invention, a personalized exercise intervention scheme can be customized according to the individual characteristics and physiological status of the patient, and the effects of different intervention strategies are predicted, so that the pertinence and effectiveness of exercise treatment are improved.

The invention provides an intelligent virtuality and reality combined psychological health service device based on digital elements, and aims to improve the accuracy and individuation level of psychological health management. The device firstly obtains the physiological indexes, behavior data, environmental factor data and social economic data of an individual, and carries out multi-modal fusion to form comprehensive feature data. Based on this, a psychological health environment factor model is constructed to quantify the influence of the external environment on the individual psychological state, and the calculation weight and prediction logic of the negative emotion large model are optimized. And the optimized negative emotion large model is used for identifying an individual emotion state, analyzing factors such as social environment, economic pressure and life events in combination with the mental health environment factor model, and generating an individual mental health assessment result. And according to an evaluation result, the virtual digital doctor provides intelligent pre-inquiry and other services. Through data-driven intelligent analysis and virtual-real combined intervention means, the accessibility, accuracy and intervention effect of psychological health services are improved.

The invention discloses a child autism adaptive brain-computer fusion intervention system based on a large model, and the system comprises a multi-modal data collection module which is used for collecting the neural data, behavior data and clinical scale data of a user; the feature extraction and user portrait construction module is used for performing feature extraction and subtype recognition on the multi-modal data to generate a personalized portrait; the multi-defect intervention normal form generation module is used for dynamically generating a personalized intervention task on the basis of a large language model in combination with the personalized portrait and the historical state of the task; the self-adaptive regulation and control module is used for dynamically adjusting an intervention strategy and task difficulty according to the feedback of the real-time neural data and the dynamic change of the behavior data; the user interaction interface module is used for providing a multi-modal human-computer interaction interface; and the effect evaluation and long-term tracking module is used for generating an individual and group intervention effect report. By utilizing the system and the method, accurate, personalized, multi-dimensional and long-term intervention on the autism children can be realized.

The invention discloses a psychological health education auxiliary system based on big data, and relates to the technical field of psychological health education, the psychological health education auxiliary system uses a camera to collect eye movement data, combines CNN to extract static features and LSTM to capture time sequence changes, improves anxiety detection objectivity, combines embedded online learning, short video watching or classroom scenes, and improves anxiety detection accuracy. Daily data collection is carried out, the anxiety trend is continuously tracked by adopting a dynamic analysis method, and the change of the psychological state is monitored and evaluated in real time; the psychological intervention content can be dynamically adjusted based on anxiety scores and personalized feedback strategies, for example, relaxation training, psychological adjustment videos or emotion evaluation questionnaires are pushed, so that psychological health education is more personalized, and invalid or generalized psychological education information is reduced; in addition, a reinforcement learning algorithm is introduced, recommendation content is continuously optimized through interaction data of a tested object, the intervention effect is improved, and psychological education resources can adapt to dynamic changes of individual psychological states.

The invention relates to a weight loss intervention method and device for chronic renal failure and obesity patients based on an individualized prescription. The method comprises the following steps: constructing a multi-modal dynamic time sequence feature vector of a patient; identifying and quantifying potential causal effects of different weight loss intervention measures on physiological outcomes of renal functions, electrolyte balance, anemia states, bone mineral metabolism and weight indexes of the CKD patient according to a dual robust estimator; dynamically constructing a weight loss intervention measure causal model according to the potential causal effect of the physiological outcome of each patient and the physiological and pathological background; and inputting the multi-modal dynamic time sequence feature vector of the patient into a weight loss intervention measure causal model, and generating a daily diet scheme, an exercise scheme and corresponding high-confidence risk early warning and causal explanation of the patient. The method overcomes the limitation that the traditional method only pays attention to correlation and cannot clearly determine the intervention effect, ensures that the causal atlas and the intervention scheme are optimized according to the real-time change state of the patient, and realizes a real dynamic prescription.

The invention relates to the technical field of language barrier evaluation, and discloses a deep learning-based language barrier executive intervention effect evaluation method. The method comprises the following steps: acquiring real-time voice data and facial expression data of a language barrier patient in intervention training through a multi-modal data acquisition device to form an original behavior feature set; performing acoustic feature hierarchical analysis on the voice data by adopting a time sequence feature extraction network to generate a voice time sequence feature vector; performing micro-expression dynamic capture on the expression data through a three-dimensional convolutional neural network to generate an expression state feature vector; inputting the two types of vectors into a multi-modal feature fusion layer to carry out cross-modal correlation analysis, and generating a comprehensive behavior evaluation matrix; on the basis of the matrix, an intervention effect analysis model driven by an attention mechanism is adopted, a behavior improvement degree index of the current intervention stage is calculated, accurate evaluation of the intervention effect is achieved, and support is provided for dynamic adjustment of language barrier rehabilitation intervention.

The invention relates to the technical field of artificial intelligence and data processing, in particular to an execution type intervention effect evaluation method based on deep learning, and the method specifically comprises the following steps: recording a heart rate signal of an autistic child during intervention task training through wearable heart rate monitoring equipment; the collected heart rate signals are preprocessed; constructing a data set and dividing the data set into a training set, a verification set and a test set; performing dynamic time segmentation and adaptive noise suppression on each segment of sample data in the data set; constructing an executive intervention effect evaluation model based on deep learning, inputting the denoised training set into the model for training, and verifying performance indexes of the model through a verification set; and inputting data in a test set into the trained model to obtain a final evaluation result. The heart rate signals reflecting psychological load, cognitive participation and emotional fluctuation are analyzed and processed, the situation of the intervention effect can be objectively estimated, and manual judgment is assisted.

The invention discloses a big data analysis-based occupational health management system optimization method. The method comprises the following steps of 1, collecting and preprocessing multi-source health information data of employees; 2, mapping the multi-modal data set into a unified-dimension employee health feature tensor; 3, constructing a health risk isomerism map based on the employee health feature tensor; 4, inputting the health risk heterogeneous atlas into the improved GATv2 model to obtain a health risk embedded representation vector; 5, performing risk assessment on the health risk embedded representation vector, and identifying a high-risk employee node; step 6, constructing an intervention action candidate set for the high-risk employee nodes, and generating an optimal intervention strategy by adopting an MOPSO algorithm; and step 7, executing the optimal intervention strategy and generating a score of an intervention effect. According to the invention, the improved GATv2 model and the MOPSO algorithm are fused, and dynamic assessment and accurate intervention of occupational health are realized.

The invention relates to the technical field of psychological health intelligent evaluation and collaborative intervention, and discloses a family-school psychological health collaborative method and system.The method comprises the steps that a multi-dimensional data model is built based on the psychological theory, and family-school double-end data collection is achieved through standardized indexes; constructing a multi-dimensional psychological state diagram structure, and realizing home-school data deep fusion by adopting a time sequence dynamic coding method; a time sequence modeling and multi-level risk assessment method is adopted, and a dynamic mental health risk assessment result and an intelligent early warning signal are output; a cross-platform collaborative intervention management method is adopted, and a personalized intervention strategy and a collaborative task allocation scheme are output; and a multi-dimensional effect evaluation and reinforcement learning optimization method is adopted to realize continuous optimization of an intervention strategy. Through artificial intelligence technologies such as the dynamic graph neural network and deep reinforcement learning, intelligent fusion and cooperative intervention of family-school psychological health data are realized, and the early recognition accuracy and intervention effect of psychological health problems are improved.

The invention discloses a cognitive behavior training system, method and device and a program product, relates to the field of medical rehabilitation, and is used for solving at least one problem of single training target, single task difficulty and single task element. According to the method, main training tasks of five dimensions are carried out in stages, cognitive competence corresponding to task training of each dimension is progressively carried out step by step, and centralized, continuous, selective, alternate and dispersive attention cognitive competence intervention is respectively carried out. The main training task of each dimension sequentially comprises multiple levels of sub-training tasks with gradually increased difficulty, and each level of sub-training task comprises multiple groups of segmented tasks. And respectively evaluating the training effect under each dimension. According to the method, the difference and compliance of cognitive behavior training are improved, the intervention efficiency is improved, and the long-term intervention effect is ensured.

The invention belongs to the field of computational medicine, particularly relates to a method and a system for constructing full-parameter digital twinning of a patient and performing treatment simulation, and aims to solve the problems that an existing digital twinning model cannot fuse multi-scale data, lacks dynamic optimization capability and is disjointed with a clinical decision process. The method comprises the following steps: acquiring and integrating multi-source heterogeneous data of a patient; performing cross-scale fusion modeling based on the data, and constructing a personalized digital twinborn initial parameter set containing genetic background correction; driving a multi-physics field coupling engine to simulate an intervention effect, constructing a reverse optimization problem taking real-time monitoring data as a dynamic constraint, and solving an optimal intervention scheme parameter; and carrying out verification simulation on the optimization scheme and generating a treatment report containing quantitative evaluation and risk early warning. According to the invention, by constructing a full-parameter and mechanism model and introducing a dynamic closed-loop optimization verification process, the personalized precision, safety and clinical decision support value of treatment scheme simulation are significantly improved.

The invention relates to the technical field of mental health management, in particular to a mental health intervention effect assessment method and system. Collecting demographic basic information, psychological health problem types, psychological state improvement evaluation values before and after intervention and intervention feature information of each intervention object; for any intervention object, screening a historical intervention object matched with the intervention object from a historical intervention database as a reference object according to the demographic basic information, the psychological health problem type and the intervention feature information of the intervention object; calculating a deviation difference value between the psychological state improvement evaluation value of the intervention object and the psychological state improvement evaluation value of the reference object, comparing and judging the deviation difference value based on a preset deviation threshold value, and judging whether the intervention object is a suspected effect abnormal object or not; and for any suspected effect abnormal object, obtaining intervention behavior data during the intervention period.

The invention relates to the technical field of health management, in particular to an obese patient remote dynamic intervention system based on AI early warning, which comprises a multi-dimensional data acquisition module, an AI risk layering and early warning module, a personalized intervention scheme generation module, a remote collaborative management module and an effect evaluation and optimization module, the multi-dimensional data acquisition module is used for acquiring patient related data; the AI risk layering and early warning module constructs a multi-dimensional dynamic score card model to carry out risk scoring and layering, and obesity health risk early warning is generated; a personalized intervention scheme generation module constructs a multidisciplinary intervention strategy matrix, and dynamically generates an intervention scheme according to an early warning result and individual features of a patient; the remote collaborative management module realizes information interaction and collaborative intervention of multi-party resources; and the effect evaluation and optimization module tracks the intervention effect and optimizes the model and the intervention scheme. Therefore, the problems of data lag, intervention scheme solidification, untimely early warning and the like in obese patient management are solved.

The invention discloses an AI-based atrial fibrillation patient health risk prediction method, and belongs to the technical field of medical information, and the method specifically comprises the steps: receiving a physiological parameter record and an intervention treatment record of an atrial fibrillation patient, carrying out the time sequence alignment operation, and forming structured patient data; extracting risk-related features from the structured patient data, wherein the risk-related features are divided into basic risk features and intervention response features; inputting the basic risk features into a basic risk prediction network, and outputting a basic risk score; inputting the intervention response characteristics and the basic risk score into an intervention effect separation network together to generate a post-intervention risk score; calculating a net effect value of treatment intervention according to the difference between the basic risk score and the post-intervention risk score; combining the basic risk score, the post-intervention risk score and the net effect value to generate a patient individualized long-term risk prediction trajectory; and accurate prediction of the long-term health risk of the atrial fibrillation patient is realized.

The invention discloses an early diagnosis and treatment system for children with autism based on facial representation multi-modal fusion. A system collects a face video when a child watches a specific video (7 minutes, 08 seconds), first 2000 frames of images are extracted as input, expression data are extracted by adopting an improved contrast language-image pre-training model, sight line data are obtained in combination with GazeMobileNet, and the expression data are displayed on a display screen. Time sequence feature extraction and classification are carried out through a multi-mode fusion network (including a preliminary memory module, a key extraction module and a hidden learning module) of a multi-valve memory mechanism. In the intervention stage, the system provides intervention games such as whack-a-mole, pattern recognition and the like to train reaction, cognition, coordination and language abilities, and an evaluation report is generated according to training data such as reaction time and the like. The system integrates data acquisition, feature learning, multi-modal classification and intervention training, improves the diagnosis accuracy and the intervention effect, and has the characteristics of simplicity and convenience in operation and high universality.

The invention discloses a self-adaptive closed-loop neural feedback system for improving anxiety based on individual specificity and an implementation method, and the system comprises a data collection and processing module, an open-loop control module and a self-adaptive negative feedback module, a complete treatment closed loop from signal acquisition and processing to open-loop control to adaptive negative feedback to signal acquisition and processing is realized; an individual fuzzy controller unit is combined with a traditional electroencephalogram neural feedback framework, the anxiety state of an individual is monitored and adjusted in real time, the task difficulty is accurately changed, and therefore the optimal anxiety intervention effect is rapidly achieved. According to the method, the self-adjusting ability of each individual within a period of time is reflected through iterative evaluation of the anxiety state of the individual, and the task difficulty is adjusted according to different individual characteristics, so that the individual is stimulated to play a higher potential; neural feedback training is carried out by applying the self-adaptive task difficulty, individualized and accurate multi-course nerve regulation training can be made in an auxiliary mode, and the anxiety emotion of an individual is improved.

The invention provides a tumor patient group psychotherapy method and system based on artificial intelligence, and relates to the technical field of artificial intelligence. The method comprises the following steps: firstly, collecting demographic and disease course information, psychological scale scores, physiological signals and voice-text-face multi-modal data of a patient; emotional features are jointly recognized through a deep model, psychological needs are evaluated, clustering is carried out in combination with disease course stages, and patients are automatically distributed to homogeneous treatment groups. In the implementation process, the speaking balance degree, the topic dominant rate and the intra-group cohesion are calculated in real time, and if indexes cross the boundary, a guide instruction is generated to adjust discussion. And the system adaptively selects and dynamically adjusts a cognitive behavior therapy according to the emotion and interaction state, accepts a commitment therapy or a positive pressure reduction script until the emotion returns to a safety interval, carries out closed-loop summarization on intervention effect data, and outputs a report containing an emotion trend, interaction quality and an intervention effect. According to the method, real-time accurate evaluation, immediate intervention adjustment and continuous optimization can be realized.

The invention discloses a healing landscape intervention method and device based on user feedback, and the method comprises the steps: carrying out the intervention of a to-be-intervened user according to a preset intervention scheme, and obtaining the feedback data of the to-be-intervened user after the intervention; wherein the preset intervention scheme comprises a preset healing landscape video; collecting user multi-dimensional data of the to-be-intervened user, and preprocessing the feedback data and the user multi-dimensional data to obtain a plurality of personal emotion indexes corresponding to the to-be-intervened user; and according to the plurality of personal emotion indexes, constructing a healing isomerism map, and according to the healing isomerism map, adjusting the preset intervention scheme. According to the invention, the intervention effect of psychological intervention based on the healing landscape can be improved.

The invention provides a psychological intervention system based on virtual reality cognitive behavior emotion quantitative analysis. The psychological intervention system comprises a multi-modal data acquisition module, an emotion quantitative analysis module, a virtual scene generation module and an intervention effect feedback module. Through a closed-loop mechanism of real-time monitoring, dynamic modeling, scene intervention and effect feedback, digitization, individuation and scientization of mental health intervention are realized, and core pain points of a traditional method in efficiency, compliance and accuracy are solved.

The invention relates to the technical field of limb rehabilitation training, in particular to an artificial intelligence-based cognitive impairment assessment and intervention training system, which comprises a path construction module, an action acquisition module, a matching recognition module, a rhythm regulation and control module and a cognitive intervention module. According to the method, the standard path is constructed in the virtual task and combined with the three-dimensional action data of the user to form the path section response result, so that the spatial integrating degree of the action of the user and the target path can be identified, and quantitative description of the action execution stability and continuity is realized through coordinate difference analysis; a cooperative path record of task execution is obtained in combination with a trajectory coverage rate and a matching relationship, and a trend analysis and parameter regulation mechanism is introduced, so that a task pushing rhythm and a user response rhythm are dynamically matched, and tracking correction of an intervention effect in a training stage and fine evaluation of cognitive limb disorder performance are realized. The analysis granularity of cognitive state and action collaboration is improved, and the mapping precision between the intervention content and the user execution characteristics is enhanced.

The invention discloses an optimization method for senile cognitive impairment intervention. The optimization method comprises the following steps: S1, a multi-modal evaluation stage: acquiring neurophysiology, behavior and language data of a patient through a multi-source sensing module; s2, personalized prescription generation: calculating a comprehensive cognitive impairment index (CCI), and generating a personalized cognitive training scheme; s3, the intervention module executes the steps that a virtual training scene is generated in the AR environment; and S4, effect verification and iteration: generating a neuroplasticity evaluation report after training, and quantifying the parahippocampal gyrus-prefrontal cortex function connection enhancement degree. Neurophysiology, behaviors and language data of a patient are synchronously collected by adopting a multi-source sensing module, and a personalized cognitive training scheme is generated based on a comprehensive cognitive disorder index. Meanwhile, a virtual scene is constructed to execute an intervention module, and the parahippocampal gyrus-prefrontal lobe function connection enhancement degree is quantitatively analyzed after intervention, so that an evaluation-decision-intervention-verification closed-loop system is constructed, and the intervention effect of the senile cognitive impairment is remarkably improved.

The invention discloses a quantitative evaluation method for an interference effect of trombone playing training on suppression control ability, which comprises the following steps of: acquiring pulse wave signals in various states when a subject performs trombone playing training, denoising, and extracting physiological features from the pulse wave signals; the method comprises the following steps: collecting behavior data when a subject executes playing training, including a playing airflow signal and position control data, and extracting behavior characteristics from the behavior data; when the correct rate and the response of executing the inhibition control test task by the subject are recorded, calculating the score of the test task through probability distribution fitting; and constructing a data set by taking the physiological features and behavior features of the subject as explanatory variables and taking the test task score as a target variable, training a regression model and screening an optimal feature subset to construct a quantitative formula, and obtaining a quantitative evaluation result of the intervention effect. According to the invention, a quantitative analysis method is provided for research on the intervention effect of the music therapy on the suppression control ability by relying on trombone playing training.

The invention provides a psychological crisis intervention system after a flight accident, and relates to the technical field of psychological crisis intervention. The data acquisition module is used for acquiring a psychological data set of a to-be-tested crowd, the first evaluation module is used for determining a target crowd according to the psychological data set, the storage module is used for storing a psychological intervention method, and the first intervention module is used for generating a corresponding intervention scheme for the target crowd according to the psychological intervention method. The second evaluation module is used for setting a time threshold value and evaluating the target crowd which passes through the time threshold value and accepts intervention again to obtain an evaluation result of each target person in the target crowd, and the second intervention module is used for performing intervention effect judgment according to the evaluation result of each target person to obtain a judgment result. The problem that accurate and timely psychological crisis intervention is difficult to realize when the psychological crisis intervention after the flight accident is handled in the prior art is solved.

The invention discloses an intelligent evaluation and intervention system for child neurodevelopmental disorders, relates to the technical field of child neurodevelopmental disorder evaluation, and aims to provide comprehensive and accurate behavior, language and cognitive ability evaluation for children through advanced artificial intelligence, big data and multi-modal technologies and generate a personalized intervention scheme according to an evaluation result. The system comprises an evaluation module, an intervention module and a data analysis and feedback module, the evaluation module is combined with computer vision, voice recognition, natural language processing and eye movement tracking technologies, behavior, language and cognitive data of children are automatically collected, and a behavior score, a language score and a cognitive score are generated; the intervention module automatically adjusts the intervention intensity according to the scores; and the data analysis and feedback module analyzes the evaluation result and the intervention effect in real time, generates a personalized evaluation report and optimizes the intervention scheme. According to the invention, through an intelligent algorithm and an automatic process, the evaluation efficiency and accuracy are greatly improved.

The invention relates to the technical field of intelligent nursing, in particular to an intelligent nursing intervention system and method for nausea and vomiting after a pulmonary operation. The analysis of nausea and vomiting performance is established on the basis of continuous and quantifiable indexes, the sensitivity of judging a risk trend is improved, a risk change path is identified through a long-short-term memory neural network, an influence rule of intervention intensity on a nausea and vomiting evolution trend is accurately described, stage prediction of a nursing intervention effect is realized, and the risk prediction accuracy is improved. Structural optimization is performed on an intervention time sequence and nursing task allocation and execution rhythm through a convolutional neural network, an intervention rhythm path is recombined in combination with resource adaptation information, the coverage density of intervention measures for risk periods is enhanced, integrated response of nursing resources in a high-sensitivity interval is promoted, and synchronous optimization of task allocation and time sequence matching is realized.