80 results about "Intervention effect" patented technology

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 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 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 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 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 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 a correlation analysis method for sleep disorder and cardiovascular adverse events of an MINOCA patient, relates to the field of clinical decision of cardiovascular diseases, and aims to perform anti-factual reasoning on data of a specific patient based on a structural causal model and simulate the correlation between sleep disorder and cardiovascular adverse events of the patient under the condition of changing one or more sleep characteristic variables. The theoretical change value of the cardiovascular adverse event risk of the patient is calculated, and the expected risk reduction benefit of intervention is calculated; and generating a decision support report, and performing visual display. According to the method, by means of time sequence causal discovery and a structural causal model, the causal relationship and effect quantification of sleep features, intermediary indexes and adverse events are defined, and a core action mechanism is revealed; a personalized intervention effect is simulated through anti-factual reasoning, and a precise risk prediction and intervention basis is provided for clinic; the visual decision support report reduces the clinical application threshold, assists in optimizing the personalized treatment scheme of the MINOCA patient, and effectively reduces the risk of cardiovascular adverse events.

The invention discloses a method and a system for training a dialogue intervention large model for children with autism, and the system comprises a data processing module which is used for converting original dialogue data from theme dialogue intervention records of children with autism and doctors into a high-quality multi-round dialogue data set; the model training module is used for finely adjusting an existing large language model on the basis of the high-quality multi-round dialogue data set so as to learn dialogue styles and intervention strategies in the high-quality multi-round dialogue data set, and an intervention large model which can follow an ABA application behavior analysis principle to perform theme dialogues with autistic children is obtained; and the model evaluation module is used for comprehensively evaluating the intervention effect of the intervention large model from the sentence dimension and the dialogue dimension so as to feed back the model fine adjustment process. According to the method, the large model following the ABA application behavior analysis principle can be trained to perform dialogue intervention with the autism children, the intervention effect is effectively improved, and accessible, efficient and low-cost social communication rehabilitation support is provided for the autism children.

The invention discloses an emotion intervention system and device based on a reminiscence therapy, and belongs to the technical field of mental health intervention and intelligent medical equipment. The device comprises a stimulation presentation subsystem, a physiological signal collection subsystem, a behavior response recognition subsystem, a neural response modeling subsystem, an intervention effect evaluation subsystem and a central control and feedback regulation subsystem, a closed-loop intervention framework is formed through real-time communication bus interconnection, and by presenting an individualized life event image set, multi-modal physiological signals and behavior responses are synchronously collected, so that the intervention effect is evaluated. A neural response model is constructed based on a brain region abnormal activation mode disclosed by functional magnetic resonance imaging, the activation level of a target brain region is predicted, the intervention effect is evaluated by integrating multiple indexes, and a central control system dynamically adjusts a stimulation sequence according to an evaluation result and a neural response predicted value by applying a hierarchical reinforcement learning algorithm. Personalized and precise emotion intervention aiming at the subclinical depression state is realized, and the intervention effect and the nerve regulation pertinence are effectively improved.

The application belongs to the technical field of disease risk prediction, and discloses a computer network-based risk prediction system for senile dementia, which comprises the following steps: collecting multi-modal data of a target user to obtain social interaction data, physiological parameter data and cognitive behavior data; extracting cognitive fluctuation nodes, social degradation nodes and physiological disorder nodes through dynamic fluctuation feature analysis to form a potential key node feature set; constructing an individualized dementia conversion risk map based on time sequence correlation and cross-modal coupling characteristics of the potential key node feature set to identify key risk nodes; determining a risk inflection point and predicting a senile dementia risk grade by analyzing the topological structure change and time evolution mode of the dementia conversion risk map; and providing a personalized risk intervention scheme and monitoring intervention effects in real time; the application realizes early and accurate prediction and active intervention of dementia risk, and significantly improves prevention efficiency and clinical decision value.

The present application relates to the technical field of postoperative rehabilitation, in particular to a postoperative rehabilitation intervention system for patients with neurological diseases based on evidence-based medical evidence. The steps implemented by the system include: obtaining characteristic data of a patient with a neurological disease to be analyzed and reference patients, a rehabilitation scheme of the reference patients and a postoperative Barthel index, calculating a reference factor of the reference patients corresponding to each rehabilitation scheme under each preoperative state; obtaining a comprehensive Barthel index of each rehabilitation scheme under each preoperative state by using the reference factor and the postoperative Barthel index, determining a correction factor under each preoperative state according to the comprehensive Barthel index, and correcting the comprehensive Barthel index to obtain a target Barthel index; determining a reference rehabilitation scheme by combining the similarity of the characteristic data of the patient with a neurological disease to be analyzed and different reference patients and the target Barthel index. The present application can improve the postoperative rehabilitation intervention effect for patients with neurological diseases.

The application discloses a mindfulness prediction-intervention-feedback system and a control method, relates to the technical field of mindfulness training systems, and comprises a wearable user interaction device and sequentially connected user management module, mindfulness intervention training module, dynamic prediction module, intervention effect evaluation module and feedback interaction module; the initial mindfulness level, depression level and anxiety level of a user are determined; based on the depression level and anxiety level of the user, a target training scheme of the user is determined, and the user is prompted to perform mindfulness training according to the target training scheme; the target training scheme is adjusted based on an emotional state factor; the real-time mindfulness level of the user and the completion progress of the target training scheme by the user are determined according to a preset time interval, a mindfulness training report is generated in combination with the initial mindfulness level, and visual processing is performed; the mindfulness training method is selected by evaluating the mindfulness level of the user, and the rationality of the mindfulness training scheme can be improved.

The invention relates to the technical field of virtual reality medical treatment, and discloses a head and neck cancer chemoradiotherapy related symptom remote monitoring and multi-sensory intervention method based on VR, and the method comprises the following steps: S1, a patient carries out preliminary evaluation through a first input module of a VR terminal, and transmits monitoring data to a data processing module through the VR terminal; s2, performing secondary evaluation on the average score or the symptom of which any score is in a second interval in a targeted manner through a first input module of the VR end, and sending a multi-sensory first-level intervention request or a multi-sensory second-level intervention request or a multi-sensory third-level intervention request to the VR end by a data processing module according to the score condition of the symptom; and S3, after the patient completes corresponding intervention, performing post-intervention evaluation through the first input module of the VR end, and comparing the evaluation conditions before and after intervention by the data processing module to generate an intervention effect evaluation report. Related symptoms of a patient can be dynamically monitored in real time, symptom changes can be found in time, corresponding intervention is given, treatment interruption is prevented, and the lifetime is prolonged.

This invention discloses a verifiable application attack detection method based on artificial intelligence causal inference, including data collection and structuring, causal graph construction, intervention effect calculation, abnormal causal determination, verifiable evidence chain generation, detection result output, and false positive adaptive correction steps. By constructing a structured causal graph model between attack behavior and abnormal system states, this invention can effectively distinguish between "spurious correlations" and "true causality," significantly reducing the false positive rate caused by business fluctuations or system changes. The verifiable mechanism designed in this invention can generate a complete and tamper-proof causal evidence chain from the original attack payload to the final abnormal state for each detection conclusion, making the detection results not only accurate but also transparent, auditable, and reproducible. This greatly improves the judgment efficiency of security operations teams and provides a reliable technical foundation for security forensics scenarios based on blockchain or third-party arbitration.

The invention discloses an early diagnosis and intervention curative effect evaluation method for children with autism, and belongs to the technical field of medical diagnosis. The method specifically comprises the following steps: S1, obtaining historical case big data: collecting the historical case big data of an autistic truly diagnosed child and a normally developed child and corresponding diagnosis results and intervention diagnosis and treatment records, and associating to form a historical database; s2, historical data preprocessing: performing cleaning, standardization and missing value processing on historical case big data, and associating and integrating the historical case big data into a structured sample set; by collecting behavior, physiology, environment and family multi-dimensional data, mining an internal mode of the data in combination with depth features, and then utilizing a multi-modal fusion diagnosis model for analysis, dependence on subjective experience of doctors and a single scale is reduced; the classification accuracy of the model is optimized through cross validation, a diagnosis report contains core abnormal features and a judgment basis, manual recheck is supported, and the risks of missed diagnosis and misdiagnosis are further reduced.

The invention discloses a dynamic trajectory construction and visual mapping method for association of mental disorder brain network damage and systemic system diseases, and belongs to the field of artificial intelligence medical application. According to the method, high-resolution MRI images, biomarkers and clinical information of major mental disorder patients are collected, and the influence of factors such as age, gender, medication and diagnosis on the braingut axis and the cardio-cerebral axis is evaluated through multi-modal data fusion. By constructing a disease dynamic trajectory model, brain structures and function change modes corresponding to different mental disorders are identified. Large-scale samples are analyzed through machine learning, potential risks and protection factors are extracted, and a visual tool is developed to visually display changes of the brain under different disease systems. A closed-loop feedback mechanism is established through follow-up visit, the disease progress and the intervention effect are dynamically tracked, and key evaluation indexes are identified. According to the invention, theoretical basis and practical guidance are provided for early screening, precise intervention and personalized treatment of mental disorders, and the diagnosis and treatment accuracy and efficiency are remarkably improved.

The invention relates to the field of intelligent scene rehabilitation, and particularly discloses a cognition-swallowing integrated intelligent scene rehabilitation system based on AI and AR, which accurately identifies compensation behaviors and confidence thereof by collecting IMU kinematics data and physiological signal flow in real time and calculating an interaction relationship between motion and physiological features, and improves rehabilitation accuracy. And the generation and dynamic rendering of the AR visual correction instruction vector are driven. Through the mode, millisecond-level self-adaptive adjustment based on the nerve-muscle coupling index is realized, namely, when a compensatory behavior or swallowing ability decline is detected, the complexity of AR content is automatically reduced to unload cognitive load, implicit safety protection in a training stream is realized, and the training efficiency is improved. Therefore, the system can maximize the cognitive training intensity on the premise of ensuring swallowing safety, and avoids strengthening of wrong movement habits, so that the clinical practicability and the double intervention effect of the rehabilitation system are remarkably improved.

The invention relates to the technical field of medical data processing, in particular to a VTE intelligent monitoring system supporting a multi-role demand, and provides the following scheme: a doctor role end constructs an evidence hypergraph based on multi-source clinical data, generates an evidence association vector through partial sequence conflict lattice merging, and stores the evidence association vector into a database; and searching in the guide version dictionary tree to obtain a current effective node, and outputting prevention and treatment suggestions and version pointing information to form a traceable evidence chain. The nursing role end receives and analyzes the tracing chain, screens matched prevention and treatment schemes from an intervention measure set in combination with real-time monitoring data, generates a nursing execution instruction and performs dynamic adjustment and feedback according to an intervention effect, so that closed-loop cooperation of risk assessment, intervention execution and result certification among multiple roles is realized, and the risk assessment efficiency is improved. And the timeliness, the traceability and the execution consistency of the prevention and treatment suggestions are ensured.

The embodiment of the invention provides a self-adaptive multi-scene anxiety intervention method and a related device. The method comprises the following steps: acquiring an electroencephalogram signal of a target user; preprocessing the electroencephalogram signal to obtain a target electroencephalogram signal; the target electroencephalogram signals are input into a deep learning model for anxiety grade judgment, an anxiety grade judgment result is obtained, and the deep learning model comprises a multi-scale separable convolution unit, a space attention unit, a Transform-Mama unit and a classifier unit; and according to the anxiety grade determination result, matching a corresponding virtual interaction scene to perform dynamic intervention on the target user. Therefore, according to the embodiment of the invention, the matched virtual interaction scene can be dynamically adjusted according to the real-time anxiety grade judgment result, continuous and personalized intervention is realized, and the sense of participation and the intervention effect of the target user are improved.

The invention belongs to the technical field of exercise psychology and cognitive behavior intervention, and particularly discloses a shadowboxing cognitive intervention method and system based on exercise psychology. The method comprises the steps that historical cognition and emotion data of an intervention object are collected, multi-dimensional state evaluation and grouping are carried out in combination with action execution performance, and then a personalized shadowboxing training scheme and a psychological intervention strategy are formulated. The system dynamically adjusts an intervention path through multi-source data acquisition, action recognition, emotion perception and an intervention effect feedback mechanism, and realizes continuous optimization of a cognitive state. The method and the system can be widely applied to psychological rehabilitation, educational intervention and emotion regulation scenes, and have the characteristics of high intelligent degree, high intervention precision and good adaptability.

The invention discloses an attribution analysis method and device, equipment, a storage medium and a computer program product, and relates to the technical field of data analysis, and the method comprises the steps: carrying out the causal structure learning of a target data set, obtaining a target causal relationship in the target data set, and constructing a causal knowledge graph based on the target causal relationship; in response to a selection instruction of a main analysis index of a target user, querying a node set having causal association with the main analysis index from the causal knowledge graph, and determining a recommended association node based on causal strength; performing intervention effect calculation on the main analysis index based on the main analysis index and the recommended association node to generate anti-fact deduction result data; and performing multi-dimensional confidence assessment on the anti-fact deduction result data to obtain an attribution path credibility score. And through causal structure learning and multi-dimensional confidence assessment, the problem that attribution analysis depends on experience hypothesis is solved, and the analysis reliability is improved.

The application relates to the technical field of medical data processing, and particularly relates to a VTE intelligent monitoring system supporting multi-role requirements. The following scheme is provided: a doctor role end constructs an evidence hypergraph based on multi-source clinical data, generates an evidence correlation vector through partial order conflict grid merging, searches in a guideline version dictionary tree to obtain a current effective node, outputs prevention and treatment suggestions and version pointing information to form a traceable evidence chain. A nursing role end receives and analyzes the traceable chain, screens matched prevention and treatment schemes from an intervention measure set in combination with real-time monitoring data, generates nursing execution instructions and dynamically adjusts and feeds back according to intervention effects, and realizes closed-loop cooperation of risk assessment, intervention execution and result back-checking among the multi-roles, so that the timeliness, traceability and execution consistency of the prevention and treatment suggestions are guaranteed.

The invention discloses an intelligent monitoring and intervening system for postoperative pain of children, and belongs to the technical field of medical monitoring. The system comprises a state perception and discretization module, an atomization intervention decision module, an intervention execution and effect monitoring module and a value table asynchronous updating module. The system obtains and discretizes the child patient state in real time through a state perception and discretization module so as to generate a state index; the atomization intervention decision module determines an optimal intervention action through one-time atomization query operation on the basis of the state index; the intervention execution and effect monitoring module is responsible for executing the action and monitoring the effect; and finally, the value table asynchronous updating module asynchronously updates the intervention strategy according to the intervention effect. According to the method, the decision delay is reduced by compressing the complex decision process into the atomized table look-up operation, meanwhile, adaptive optimization of the intervention strategy is achieved through an asynchronous value updating mechanism, and the problems that in the prior art, intervention measures are poor in adaptability and high in response delay are solved.

The invention discloses a health status assessment method and device, electronic equipment and a storage medium, and through the method and the device, traditional physiological index data and neurological function defect symptom data of a user can be collected at the same time by means of mobile terminal equipment, so that the blank of neglecting of digital collection of early symptoms of neurological function defect in an existing method is filled; then the two types of data are analyzed through a multi-dimensional health data fusion analysis model, internal association between the data and cerebrovascular disease outcome and relapse is fully mined, and the problems that only traditional physiological index monitoring is relied on, neglect is given to digital acquisition of early symptoms of neurological impairment, multi-modal data fusion analysis is lacked, and the accuracy of data analysis is poor are solved. The technical effects that early warning signals of the cerebrovascular diseases are accurately captured, the accuracy rate of health state evaluation and early warning is improved, a health management scheme meeting individual requirements is generated, the out-of-hospital rehabilitation monitoring and intervention effect is enhanced, and long-term scientific management of the cerebrovascular diseases is assisted are achieved.

The application provides a dynamic modeling method and system for multi-field skill AI automatic semantic labeling, and relates to information retrieval, medical health and cross-field skill fusion scene. Through nonlinear dynamic modeling and biomedical stability control, the semantic label automatic generation and real-time matching of talent skill and health intervention effectiveness are realized. The application innovatively integrates the sound wave frequency of fetal brain promoting music method, maternal action instruction and blood type nutrition scheme into the "skill-time" frequency domain space, combines the Gevrey smoothing operator and fractional derivative evolution equation, accurately separates the temporary fetal movement fluctuation and low-frequency core characteristics, and breaks through the characterization limitation of traditional models on nonlinear biological coupling effect. By introducing the "intervention effect-time" frequency domain component and local dependency constraint, the short-term effectiveness and long-term value of music intervention on fetal neurodevelopment are quantified, and the model distortion problem caused by extreme data or cross-field parameter resonance in traditional methods is solved.

This application relates to the field of computer science and discloses a method, device, medium, and product for simulating clinical trials based on molecular perturbation spectra. The method includes: determining the molecular perturbation spectrum of a target drug; determining a predictive model from genomics to high-dimensional omics based on genomic and high-dimensional omics data in a first population dataset; determining the predicted omics data for an individual based on the predictive model and the genomic data of an individual in a second population dataset; wherein the second population dataset also includes clinical outcome data; determining a grouping scheme for the simulated clinical trial by optimizing an individual allocation model based on the molecular perturbation spectrum and the predicted omics data of all individuals, maximizing the similarity between the predicted omics differences and the molecular perturbation spectrum between the simulated experimental group and the simulated control group; constructing a simulated clinical trial based on the grouping scheme; and determining an estimated value of the intervention effect of the target drug based on the differences in clinical outcomes between the simulated experimental group and the simulated control group in the simulated clinical trial.