39 results about "Generalized additive model" patented technology

The invention discloses a driving mechanism analysis method of reservoir group scheduling for flood non-consistent evolution, which comprises the following steps: acquiring a smooth reservoir entering flood sequence by utilizing a topologically coupled physical manifold constraint denoising model, and constructing dynamic topological characteristics representing propagation time lag and sensitivity through a hydraulic propagation mechanism-based Figure ordinary differential equation; constructing a structural causal model comprising a scheduling capability index, ectogenic hydrological driving and topological characteristics, and fitting a nonlinear dependency relationship by using a causal generalized additive model; executing anti-fact inference, and calculating a local causal driving index and a global causal cumulative effect index through a dry budget; and generating an optimal scheduling strategy for suppressing variation based on the causal index. According to the method, systematic analysis from data physical restoration to causal mechanism decoupling can be realized, the driving contribution of a scheduling behavior to flood inconsistency is accurately quantified, and decision support is provided for scientific flood control of a drainage basin.

The invention discloses a pond multi-objective optimization management method oriented to agricultural watershed non-point source pollution regulation and control, and relates to the technical field of agricultural watershed non-point source pollution abatement. Multi-dimensional attributes such as pond application types, morphological characteristics and sediment nutrient states are integrated into a unified quantitative framework for the first time, and a generalized additive model is constructed to obtain a multi-objective optimization model; nonlinearity and interaction of pond attributes on river water quality influence can be accurately captured, and the accuracy of river nitrogen and phosphorus concentration prediction is improved. According to the method, pond management measures are quantified into controllable decision variables, a multi-target dynamic optimization model for balancing environmental benefits and economic benefits is established, a Pareto optimal solution is used for replacing a traditional experience decision, a scientific, efficient and quantitatively-implemented pond management scheme is provided, the one-sidedness of a single target is avoided, and the method is suitable for large-scale popularization and application. The cost minimization is realized while the non-point source pollution reduction is maximized, and the accurate configuration and scientific quantitative decision of pond management resources are realized.

The invention provides a leather production line energy consumption optimization control method and system, and relates to the technical field of industrial control, and the method comprises the steps: obtaining a multi-source data set of a leather production line; dividing the multi-source data set into a plurality of working condition fragment data; performing feature extraction on each working condition fragment data to obtain a feature vector sequence; according to the feature vector sequence, through a semi-parameter generalized additive model, carrying out load prediction on the leather production line; based on the load prediction result, constructing a multi-target energy consumption optimization model of the leather production line; solving the multi-target energy consumption optimization model to obtain an optimal production strategy; according to the optimal production strategy, constructing a normalized performance index; performing performance robustness judgment on the optimal production strategy according to the normalized performance index; according to the normalized performance index, the process parameters of the optimal production strategy are adjusted in real time; and executing the adjusted optimal production strategy to complete the energy consumption optimization control of the leather production line.

The present invention relates to device for estimating a probability of a health state of a patient, particularly a probability of an infection of the patient, the device comprising an input unit configured to obtain physiological data of the patient, the physiological data comprising information about a plurality of time series of physiological parameters of the patient, a processor configured to extract the plurality of time series from the physiological data and to apply a machine learning algorithm on the plurality of time series to estimate the probability of the health state of the patient, and an output unit configured to provide the probability of the health state, wherein the machine learning algorithm is based on a generalized additive model, in which results of an application of a learned non-linear univariate function on the parameter time series are summed up and the probability of the health state is obtained by applying the standard sigmoid function on said sum.

The invention belongs to the technical field of ecological restoration, and provides a mountainous river-oriented fish diversified habitat natural shaping restoration method, which comprises the following steps of: determining fishes with a relative important index greater than 5% in a river as target fishes; the habitat factors of the river are screened, the habitat factors with colinearity are divided into a factor group, and only one habitat factor is reserved in each factor group to serve as a representative habitat factor; carrying out modeling on the resource distribution and representative habitat factors of the target fishes by adopting a generalized additive model; screening to obtain a generalized additive model with a minimum akaike information criterion, and determining an optimal habitat factor combination; and determining a restoration scheme according to the habitat factor combination, and adjusting the habitat of the target fish. According to the restoration method, the natural process is simulated to remodel the diversity of the mountain river habitat, the key problems of hydrological connectivity fracture, habitat simplification, engineering and ecological contradiction and the like are solved, and fish population restoration and ecological system self-maintenance are achieved.

The present invention relates to a motorcycle engine control system. The control system collects motorcycle engine driving data in real time, including operating condition data and air-fuel ratio; inputs the driving data into a preset exhaust emission analysis model to predict the current concentration of harmful gases in the exhaust; constructs a generalized additive model to quantify the response relationship between the driving data and the concentration of harmful gases in the exhaust, and controls the air-fuel ratio of the current engine operating condition based on the response relationship. The present invention solves the problem in the prior art of difficulty in accurately controlling the air-fuel ratio according to different engine operating conditions, thereby increasing the concentration of harmful gases and polluting the environment. By constructing a random forest model as an exhaust emission analysis model, the concentration of harmful gases in exhaust emissions can be accurately predicted using engine operating conditions. By establishing a generalized additive model, the problem of the difficulty in quantifying the nonlinear relationship between driving data and harmful gas concentration is resolved, thereby improving the control accuracy of the air-fuel ratio under different engine operating conditions.

A method for generating a prediction system for a machine, the machine being subjected to cyclically variable ambient conditions, the prediction system being configured to predict at least one process variable of the machine. The method comprises a first step, in which a set of training data is provided, the set of training data comprising a plurality of ambient parameters of the machine, a plurality of performance parameters of the machine, and at least one process variable of the machine. Furthermore, the method comprises that a correlation value between the at least one process variable and each of the plurality of ambient parameters and each of the performance parameters of the machine is determined for a first time interval. In a further step, at least one model relevant ambient parameter and at least one model relevant performance parameter are determined based on the corresponding correlation values. Still further, the at least one model relevant ambient parameter, the at least one model relevant performance parameter, and the process variable are being fed into a model generating algorithm and generating a prediction model for the first time interval, the prediction model for the first time interval being a part of the prediction system. In the disclosed method, the model generating algorithm is a Generalized Additive Model (GAM).

The invention relates to the technical field of ecological assessment, and discloses a coastal wetland ecosystem condition assessment method and device, a medium and a product, different smooth regression functions are obtained through double generalized additive model processing, and the influence of an environmental single factor on vegetation and the cooperative influence of the environment and the vegetation on benthic organisms are accurately quantified. Furthermore, the index value is determined by taking historical data as a reference and combining a nonlinear rule of a smooth function, so that subjective weighting deviation is avoided. Furthermore, the weight is calculated based on the association between the real-time index value and the biological index, so that human subjective intervention is avoided, and the weight is ensured to reflect the actual contribution of the index to the ecological system. Meanwhile, the influence of single data deviation is reduced by using the multi-tree integration characteristic of the random forest model, so that the weight result is more stable. Furthermore, the complex ecosystem condition of the coastal wetland can be comprehensively evaluated in combination with the index value and the weight, the biodiversity is protected, and the stability of the ecosystem is maintained.

The present invention discloses a sand body seismic intelligent prediction method based on RF-GAM, comprising the following steps: S1: collecting drilling data and seismic data of a target block, extracting sand-to-formation ratio information from the drilling data, and extracting seismic attribute information from the seismic data; S2: analyzing the correlation between the two types of information and screening relevant factors of the sand-to-formation ratio; S3: using a multivariate linear regression analysis method to predict sand bodies and obtain preliminary sand body prediction results; S4: based on the preliminary sand body prediction results, screening locations where the relevant factors match the sand-to-formation ratio, and using the seismic attribute information at these locations as influencing factors of the sand-to-formation ratio; S5: using a random forest to screen and obtain important influencing factors; S6: using a generalized additive model to perform attribute fusion, and visualizing the results of the attribute fusion to obtain the final sand body prediction results. The present invention can predict more accurate sand body prediction results, providing technical support for oil and gas field exploration and development.

This invention provides a method and system for analyzing the impact of driver following behavior on vehicle emissions, relating to the technical fields of traffic engineering and environmental science. The method includes: S1, extracting driver characteristic indicators from vehicle trajectory data of a following vehicle convoy, and visualizing the spatiotemporal trajectory of vehicles based on the vehicle trajectory data; S2, measuring the driver's heterogeneity parameters using the vehicle spatiotemporal trajectory map, and statistically determining the number of times the driver follows another vehicle using an improved Newell model combined with a dynamic time warping algorithm; S3, collecting driving environment parameters, vehicle technical condition parameters, and vehicle operating condition parameters from the vehicle trajectory data of the following vehicle convoy, inputting them into a localized MOVES model, and estimating the emission factors of road traffic pollutants; S4, constructing a semi-parametric generalized additive model to analyze the nonlinear correlation between driver following behavior and the emission factors of road traffic pollutants.

The invention discloses an automatic station soil humidity observation data layer-by-layer correction method based on meteorological environment factors and multi-model integration, which considers that the influence of evaporation and rainfall on soil humidity has a hysteresis effect and the change of humidity of different layers of soil is influenced by the change of humidity of an upper layer at the same time. The method comprises the following steps of: performing layer-by-layer deviation correction on automatic station soil humidity observation data by taking manually observed soil humidity as a reference, combining meteorological and environmental factors such as air temperature, wind speed, rainfall and vegetation index and comprehensively adopting a generalized additive model (GAM) to integrate various machine learning algorithms such as Cubist, random forest, XGBoost and CatBoost; the observation precision of the corrected soil humidity data is remarkably improved, particularly, more obvious improvement is shown in deep soil, construction of a high-precision and long-term stable soil humidity observation network is facilitated, and a reliable data basis is provided for verification of remote sensing and numerical simulation soil humidity products.

The present application provides a kind of based on multi-model coupling sound scene ecological red line quantitative demarcation method and system.The present application constructs the cascade detection mechanism of generalized additive model and segmented regression combination, can accurately identify the ecosystem cliff type degradation node;Supporting scale response curve and random forest-variance inflation factor joint dimensionality reduction strategy, on the basis of eliminating multicollinearity interference, lock core driving factor, avoid the blindness of artificial specified spatial scale;And introduce non-parametric conditional inference tree to build multi-model cross-validation system, by calculating the deviation rate intelligent discrimination environmental heterogeneity, adaptive output rigid control boundary point or elastic early warning buffer zone, with multi-level dynamic backtracking mechanism realizes different habitat and data characteristics under multi-scene coverage, fully safeguards the algorithm coherence, result robustness and dynamic fault tolerance ability, for sound scene ecological red line scientific demarcation, accurate control and dynamic supervision provides stable and reliable technical support.

The invention relates to an intelligent decomposition and optimization method for an annual power generation plan of a power plant, belongs to the technical field of power plan scheduling, and solves the problems of low granularity and low evaluation dimension of an existing power plan scheduling model. The method comprises the following steps: dividing a target year into a plurality of basic optimization time periods based on the operation mode difference between a heat supply season and a non-heat supply season; taking the minimum annual total gas consumption predicted by the generalized additive model as an optimization target, and coupling a smooth penalty term to construct a basic optimization target function; generating a feasible operation interval of each basic optimization period; generating a plurality of feasible initial solutions in the feasible operation interval, performing iterative solution on each feasible initial solution by adopting a constraint optimization algorithm to obtain an optimal solution, and selecting a solution with the lowest basic optimization objective function value from all the optimal solutions as a final optimal solution; integer correction is carried out on the power generation amount and the heat supply amount in the final optimal solution, and a prediction interval of the gas consumption amount is generated based on the corrected power generation amount and heat supply amount.

The present invention relates to the technical field of female reproductive aging assessment, and discloses a method for female reproductive aging assessment. The method comprises the following steps: collecting female infertility-related data to obtain the cumulative live birth situation after a single controlled ovarian stimulation; using a random forest model to select age, anti-Müllerian hormone, antral follicle count, and basal follicle-stimulating hormone as the main predictive indicators for cumulative live birth, and confirming the non-linearizable curve characteristics of their correlations; based on the generalized additive model, using the above indicators to better predict cumulative live birth and mapping it to the corresponding age to obtain the reproductive aging assessment indicator - reproductive age. The prediction model still has a relatively high accuracy in the external dataset, and the reproductive age has universality and popularization for the differentiation of the population and the assessment of fertility. The beneficial effects of the present invention are: the constructed reproductive age model breaks through the linear framework of the traditional chronological age and effectively improves the accuracy of the assessment of reproductive aging and female fertility.

The invention relates to the technical field of data analysis, and discloses a multi-dimensional feature-based connection passenger flow prediction method and device, and the method comprises the steps: obtaining user riding data corresponding to a public transport network and passenger flow influence data associated with the user riding data; preprocessing the user riding data to obtain target riding data; counting historical connection passenger flow data based on a connection passenger flow prediction formula and according to the target riding data; extracting features of the passenger flow influence data and the historical connection passenger flow data based on a multi-dimensional feature processing model to obtain multi-dimensional features associated with the historical connection passenger flow data; and on the basis of a dynamic generalized additive model, according to the multi-dimensional features, calculating the predicted connection passenger flow of any connection station. Therefore, the dynamic prediction of the bus and subway connection passenger flow can be realized on the basis of improving the analysis comprehensiveness of the multi-dimensional characteristics of the bus and subway connection, so that the prediction accuracy of the bus and subway connection passenger flow is improved.

The invention discloses a health effect oriented atmospheric particulate pollution source management and control scheme design method based on an isotope ratio. The method comprises the following specific implementation steps: 1, integrating an isotope ratio, a meteorological parameter, an ecological environment factor, a social economic parameter and health data, performing one-to-one matching of space-time positions, and establishing an isotope ratio prediction model through a machine learning algorithm; 2, establishing a pseudo-Poisson distribution generalized addition model of the isotope ratio and the disease burden and death rate of the crowd, and analyzing a health condition change condition corresponding to a unit isotope change; and a third step, performing health risk traceability analysis by adopting a Bayesian algorithm coupled with a Monte Carlo algorithm, quantifying contribution degrees of different pollution sources to health influences, and further formulating a health effect-oriented accurate management and control scheme. According to the method, the traceability advantage of the isotope fingerprints is combined with the crowd health effect, the nonlinear relation between the isotope ratio and the crowd health effect is quantitatively analyzed, and technical support is provided for scientifically formulating a health effect oriented atmospheric pollution prevention and treatment strategy.

A computer-implemented method of predicting disease severity in a subject includes receiving, at a generalized additive model, input data corresponding to a set of one or more features of the subject, and generating a predicted disease severity score for the subject as an output of the generalized additive model, wherein generating the predicted disease severity score for the subject includes processing the received input data using the generalized additive model.

The invention discloses a sucker rod corrosion fatigue influence factor analysis method based on double-model fusion, and belongs to the field of safety assessment of oil exploitation equipment. In order to solve the problems that in the prior art, analysis dimensions are limited, and prediction precision and model interpretability are difficult to consider at the same time, the method comprises the steps that firstly, a high-dimensional parameterized data set is generated through finite element simulation; secondly, analyzing and visualizing the independent nonlinear influence trend of each factor by adopting a generalized additive model (GAM); meanwhile, a high-precision XGBoost prediction model is constructed, and the global importance, local dependence and complex interaction of each factor are quantified in combination with an SHAP method; and finally, fusing double-model results to carry out cross validation and comprehensive evaluation. According to the method, high-precision prediction and high interpretability are combined, comprehensive insight of a multi-factor coupling effect is realized, and the robustness and credibility of an evaluation conclusion are remarkably improved through a built-in verification mechanism.

The application discloses a kind of pond multi-objective optimization management methods for agricultural watershed non-point source pollution regulation, it is related to agricultural watershed non-point source pollution control technical field, for the first time, the multi-dimensional attributes such as pond use type, form feature and sediment nutrient state are integrated into unified quantification framework, by constructing generalized additive model, the nonlinearity and interaction of the influence of pond attribute on river water quality can be accurately captured, the accuracy of river nitrogen and phosphorus concentration prediction is improved. Pond management measures are quantified as controllable decision variables, a multi-objective dynamic optimization model is established to balance environmental benefits and economic benefits, the traditional experience decision is replaced by Pareto optimal solution, a scientific and efficient pond management scheme is proposed, which can be quantitatively implemented, avoiding the one-sidedness of single target, maximizing non-point source pollution reduction while minimizing cost, achieving precise allocation and scientific and quantitative decision of pond management resources.

The application relates to a combined electricity sales quantity prediction method, device, equipment and medium, wherein the method comprises the following steps: collecting power technology parameters, associated parameters and environment adaptation parameters in real time and preprocessing; taking the preprocessed power technology parameters, associated parameters and environment adaptation parameters as inputs, outputting a first sub-period electricity sales quantity prediction value by using an LSTM neural network, and introducing a power error penalty factor to impose an additional penalty on the peak-valley period prediction error in the LSTM neural network training process; taking the sub-period electricity sales quantity as an explained variable, the preprocessed associated parameters as core explained variables, and introducing a power grid power transmission efficiency, a transformer loss coefficient and an extreme weather influence coefficient as power regulation factors to construct a generalized additive model and output a second sub-period electricity sales quantity prediction value; and calculating a fusion weight, fusing the first and second sub-period electricity sales quantity prediction values, and obtaining a final electricity sales quantity prediction result. Compared with the prior art, the application has the advantages of high prediction accuracy.

The invention discloses an electric power meteorological evaluation method based on a hybrid strategy generalized additive model. The method comprises the following steps: obtaining transformer area distribution transformer historical load data and meteorological data; on the basis of the preprocessed data, feature analysis is carried out on meteorological factors through a maximum mutual information coefficient method, a maximum mutual information coefficient matrix is formed, and key meteorological factors having significant influence on loads are screened out; establishing a generalized additive model, and fitting the load data and the key meteorological factors to obtain a nonlinear fitting curve between the power load and the meteorological factors; introducing a hybrid strategy evaluation index, and identifying a critical meteorological index corresponding to a load sudden change point through a maximum curvature method or a maximum slope method; and fine evaluation and regional risk early warning of the power load are realized by using the critical meteorological indexes. The method can quantify the influence of different meteorological factors on the load, automatically identifies the load sensitive critical point, improves the accuracy and scientificity of power grid risk early warning, and is suitable for load management and intelligent scheduling of each transformer area.

The application discloses a kind of reservoir group scheduling to the driving mechanism analysis method of non-consistent evolution of flood, comprising: using topological coupling physical manifold constraint denoising model to obtain smooth reservoir flood sequence, and the dynamic topological feature representing propagation time lag and sensitivity is constructed by graph neural ordinary differential equation based on hydraulic propagation mechanism;Structural causal model containing scheduling ability index, exogenous hydrology driving and topological feature is constructed, and nonlinear dependence relationship is fitted using causal generalized additive model;Perform counterfactual inference, calculate local causal driving index and global causal cumulative effect index by intervention operator;Based on causal index, the optimization scheduling strategy of inhibiting variation is generated.The application can realize the systematic analysis from data physics reduction to causal mechanism decoupling, accurately quantify the driving contribution of scheduling behavior to non-consistency of flood, and provide decision support for scientific flood control of river basin.

The invention provides an accurate regulation and control method and system for a water treatment biological membrane flora, and belongs to the technical field of water treatment biological membranes. The method comprises the following steps: a data acquisition step: acquiring microbial community data and environment operation parameters from a plurality of water treatment biological membrane systems; an index calculation step: calculating microbial diversity and functional characteristic indexes by using a 16S rRNA high-throughput sequencing result; a model construction step: constructing a neural network based on a structure interaction generalized additive model; an influence analysis step: obtaining the influence rate of each input variable and the variable pair on the microbial diversity and functional characteristic indexes through interpretability analysis; and a regulation and control output step: outputting a visual regulation and control suggestion based on a modeling result. The system comprises a data acquisition module, an index calculation module, an interpretation modeling module, an influence analysis module and a parameter regulation and control module. The method has the advantages of being high in interpretability, good in generalization performance, high in engineering applicability and the like, and a scientific basis and a universal method are provided for intelligent regulation and control of flora.

The invention relates to an intelligent self-adaptive environment perception sensor. The method comprises the following steps: detecting and reading initial environment parameters of a current sensor; analyzing the working state of the sensor according to the preliminary environmental parameters; and according to the working state, sensor detection parameters are adjusted, and corrected environment parameters are obtained. The method solves the problem of low detection precision caused by incapability of adapting to an extreme environment in the prior art. A Bayesian network model of environmental parameters and working states of the sensor is constructed to quantify the relation between the extreme environment and the anomaly, so that accurate analysis of detection anomaly in the extreme environment is realized, and a foundation is laid for correcting a detection result. And a generalized additive model between the probability that the working state is abnormal and the detection deviation is established, so that the accuracy of correction of the detection deviation is improved.

The present invention discloses a three-stage high-precision near-surface temperature remote sensing estimation method, comprising: performing temporal normalization on remotely sensed surface temperature data to eliminate imaging time differences; clustering a study area into multiple sub-areas based on differences in natural conditions; and using four machine learning models to estimate the temperature in each sub-area based on the temporally normalized surface temperature data and spatial auxiliary variables to obtain spatially determined temperature estimates; and integrating the temperature estimation results of the four machine learning models using a generalized additive model to obtain a more accurate near-surface temperature. This method not only effectively eliminates the uncertainty caused by temporal differences in surface temperature but also considers the differences in model adaptability in different geographical environments. By integrating multiple single machine learning models based on an integrated approach, the accuracy of remote sensing temperature estimation is improved.

The invention relates to a segmentation model-based costal cartilage assessment method and application thereof, and the method comprises the following steps: obtaining to-be-assessed costal cartilage 3D CT scanning image data, and carrying out the preprocessing, and obtaining the preprocessed image data; inputting the preprocessed image data into a pre-trained segmentation model for segmentation to obtain a segmentation result of each costal cartilage; and based on the segmentation result of each costal cartilage, evaluating by using a costal cartilage measurement model constructed based on a generalized additive model to obtain costal cartilage measurement indexes including the volume, length, cross sectional area, CT average value, calcification rate and calcification CT average value of each costal cartilage. Compared with the prior art, the method has the advantages of comprehensively and accurately evaluating various indexes of the costal cartilage and the like.

The application relates to an intelligent self-adaptive environment sensing sensor, which detects and reads preliminary environment parameters of a current sensor; analyzes the working state of the sensor according to the preliminary environment parameters; adjusts the sensor detection parameters according to the working state to obtain corrected environment parameters. The application solves the problem that the prior art cannot adapt to extreme environments, thereby leading to low detection precision. A Bayesian network model of sensor environment parameters and working states is constructed to quantify the relationship between extreme environments and abnormalities, so that accurate analysis of detection abnormalities in extreme environments is realized, and a foundation is laid for correction of detection results. A generalized additive model between the probability of the working state being abnormal and detection deviation is established, and the accuracy of detection deviation correction is improved.

The present invention discloses a method and device for deducing the composition of variable-frequency flood areas in a watershed under the influence of reservoir regulation. The method comprises: considering the impact of reservoir regulation on the inconsistency of floods, applying a reservoir index to characterize the reservoir storage capacity, adopting a generalized additive model with the reservoir index as a covariate to characterize the inconsistency of floods entering a hydrological station or a downstream reservoir under the influence of reservoir regulation, and estimating time-varying distribution parameters by combining a maximum likelihood method and a quantile map fitting method; constructing a watershed flood area composition model by using a vine-structured Copula joint distribution function for the nonlinear, asymmetric, and high-dimensional dependent structure of the composition of the watershed flood area under the influence of reservoir regulation, and analyzing the variable-frequency characteristics of the composition of the watershed flood area under the influence of reservoir regulation; and calibrating the parameters of the vine-structured Copula joint distribution function by combining a sequential estimation method and a particle swarm optimization algorithm based on water balance constraints to deduce the composition of the variable-frequency flood areas in the watershed, thereby improving the adaptability of the watershed flood area composition model under a changing environment.

A method for generating a prediction system for a machine, the machine being subjected to cyclically variable ambient conditions, the prediction system being configured to predict at least one process variable of the machine. The method comprises a first step, in which a set of training data is provided, the set of training data comprising a plurality of ambient parameters of the machine, a plurality of performance parameters of the machine, and at least one process variable of the machine. Furthermore, the method comprises that a correlation value between the at least one process variable and each of the plurality of ambient parameters and each of the performance parameters of the machine is determined for a first time interval. In a further step, at least one model relevant ambient parameter and at least one model relevant performance parameter are determined based on the corresponding correlation values. Still further, the at least one model relevant ambient parameter, the at least one model relevant performance parameter, and the process variable are being fed into a model generating algorithm and generating a prediction model for the first time interval, the prediction model for the first time interval being a part of the prediction system. In the disclosed method, the model generating algorithm is a Generalized Additive Model (GAM).

The application provides a multi-sensor fusion vital sign monitoring method and system. Real-time data streams from multiple heterogeneous vital sign sensors are received and optimized to obtain optimized real-time vital sign data streams. A dynamic weight adjustment mechanism is used for automatic adjustment to generate weighted vital sign data reflecting the true physiological condition. A generalized additive model is used in combination with a nonlinear fusion strategy and an anomaly detection algorithm for comprehensive analysis and processing to obtain an overall health score of the current vital sign. A Bayesian network intelligent early warning system is used for potential health risk prediction processing to generate personalized health advice. The application is sent to pre-set contacts or medical service institutions, and a user interaction interface is provided to allow the user to view and confirm the received information and the measures taken. The technical solution provided by the application improves the accuracy of health assessment and the level of personalized service.