22 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 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 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 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 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 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.

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 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 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.

The invention discloses an atmospheric pollution source tracking method and system based on big data, and relates to the technical field of atmospheric pollution prevention and control, and the method comprises the steps: collecting multi-source data, carrying out the optimization of the collected data through a generalized additive model, and generating a fusion data set; obtaining a corresponding feature map through a CNN model and a positive definite matrix factorization method based on the fusion data set, generating a fusion tensor, obtaining a concentration contribution coefficient through a Gaussian plume model, constructing a joint likelihood function, constructing Bayesian prior based on the fusion tensor and the joint likelihood function, and generating a visual map layer; and identifying and sorting emission hotspots based on the visual layer to obtain an emission hotspot list, obtaining a key area pollution report through the emission hotspot list, generating a pollution source intervention strategy report based on the key area pollution report, and performing strategy execution effect evaluation. According to the invention, the source positioning precision of atmospheric pollution source tracking is improved.

The application discloses a land use data downscaling method based on a generalized additive model and related equipment, and the method comprises the following steps: acquiring a LUH2 data set and downscaling requirements, wherein the LUH2 data set comprises data of various land class types; performing fishing net construction on a preset area based on the LUH2 data set, the downscaling requirements and preset driving factor data to obtain a coarse fishing net and a fine fishing net; inputting the coarse fishing net and the fine fishing net into a preset initial generalized additive model for downscaling processing to obtain an intermediate land class area prediction result for the preset area; optimizing the initial generalized additive model based on the intermediate land class area prediction result to obtain a target generalized additive model; and outputting a target land class area prediction result for the preset area based on the target generalized additive model. The problem that the resolution of the LUH2 data set is low is solved.

The invention relates to a pump station dispatching system based on an AI intelligent agent. The system comprises a water load module and a pump set scheduling module, the water load module comprises an enhanced generalized additive model and a DQN reinforcement learning agent; the enhanced generalized additive model comprises a generalized additive model and a gradient boosting tree, the generalized additive model constructs a nonlinear response function of each feature according to historical data, and the gradient boosting tree optimizes the nonlinear response functions; the enhanced generalized additive model calculates the predicted water supply amount according to the nonlinear response function of each feature and the weight of the nonlinear response function; the DQN optimizes the weight according to the supply and demand deviation; after the enhanced generalized additive model obtains the predicted water supply amount, key boundary conditions of water supply demands are generated after confidence interval evaluation; and the pump set scheduling module outputs the optimal scheme of pump set scheduling in each time period under the condition of meeting the key boundary conditions of the water supply requirements in different time periods. The system has the advantages of accurate prediction, good robustness and excellent energy efficiency.

The invention provides a long-distance intelligent ear tag system with a machine learning ear temperature compensation function for cattle. The system comprises a plurality of long-distance intelligent ear tags for cattle; the Internet of Things platform comprises a trained cattle rectum temperature prediction model, and the trained cattle rectum temperature prediction model is used for determining rectum temperature data of multiple cattle according to the ear temperature measurement data, the environment temperature and the data measurement time transmitted by the multiple intelligent ear tag devices for the cattle. The trained cattle rectum temperature prediction model comprises a generalized additive model algorithm and a gradient boosting tree model algorithm. According to the invention, remote data transmission with a plurality of remote intelligent ear tag devices for cattle can be carried out at least through an Internet of Things platform, collection of ear temperature data of cattle in a large-area farm is realized, consumption of manpower and material resources is avoided, and the rectum temperature of cattle in the farm can be predicted more accurately through a generalized additive model (GAM) of the Internet of Things platform.

The invention discloses a non-monitoring-data-dependent multi-source data fusion urban wastewater treatment capacity dynamic prediction method, and belongs to the crossing field of environmental engineering, drainage engineering, computer science and artificial intelligence. The problems that an existing urban wastewater treatment capacity prediction method is poor in monitoring data integrity and monitoring results have lag deviation are solved. The method comprises the following steps: collecting historical multi-dimensional dynamic data of a city to be subjected to wastewater treatment capacity prediction; constructing an input data set; based on the input data set, generating a comprehensive index representing the soil moisture degree; constructing a wastewater treatment share prediction model based on a generalized additive model framework, and training the wastewater treatment share prediction model by using the input data set, the generated comprehensive index and the corresponding daily wastewater treatment share; and predicting a daily share predicted value of the day by using the trained prediction model, and multiplying the daily share predicted value of the day by the annual total wastewater treatment capacity predicted value to obtain a final daily wastewater treatment capacity predicted value. The method is suitable for sewage prediction.