107 results about "Layered model" patented technology

The invention discloses a geothermal field parameter inversion calculation method, device and system, and a storage medium. The method comprises the following steps: establishing a geophysical stratified model; through Monte Carlo sampling of preset parameter spaces of the crustal heat generation rate and the heat conductivity, a temperature value is calculated in combination with a heat conduction equation, and a Gaussian likelihood function evaluation model is constructed to predict the matching degree of the temperature and the actually measured temperature; obtaining the optimal estimation of the thermal parameters, a confidence interval and a correlation matrix among the parameters based on the posterior probability distribution; and according to the correlation matrix, taking a high-confidence result generated by Monte Carlo inversion as priori knowledge of a physical guidance neural network PINNs, and finally outputting a thermal parameter spatial distribution prediction result of the target area through parameter initialization constraint, output layer range limitation and a physical regularization loss item. By adopting the technical scheme of the invention, the defects that the existing actually measured geothermal field parameters are rare and the regional characteristics cannot be described and the geophysical joint inversion of the geothermal field parameters cannot be realized in the prior art are overcome.

The systems and methods disclosed herein orchestrate task execution among autonomous (or semi-autonomous) AI agentic models (“agents”) responsive to a received query by using a hierarchical model cascade to classify queries into agent domains. Queries are processed iteratively by a series of hierarchical levels containing one or more AI models, where each layer is more complex and imposes fewer resource constraints. Each level generates a classification and a confidence score pertaining to the classification. A dynamic bypass mechanism analyzes the classifications and confidence scores at each level to dynamically determine if one or more levels of the hierarchy can be bypassed while resulting in an accurate classification. The final classifications are matched to one or more agents that process the query. Responses from the candidate agents are aggregated into an output that is responsive to the input.

The invention discloses a health management service system and method based on AI optimization. The method comprises the following steps: S1, collecting multi-source health data of a user and constructing a structured health data set; s2, constructing a health state graph containing node attributes, edge connection weights and time indexes; s3, inputting the health state atlas into a linear graph neural network to generate a health state embedded vector; s4, collecting context information of a user, and fusing to generate personalized state perception representation and a health target embedding vector; s5, inputting the improved general value layering model to generate a structured intervention action candidate set; s6, screening and outputting a personalized health intervention scheme based on the matching score; s7, constructing a state evolution sequence, and inputting the state evolution sequence into a semi-supervised anomaly detection algorithm for monitoring and recognition; and S8, when the risk threshold is exceeded, triggering early warning and dynamically adjusting the intervention scheme. According to the method, collaborative optimization of personalized modeling and intelligent intervention strategies is realized, and the method is suitable for health management service scenes.

The invention provides a functionally graded material modeling and path planning method based on additive manufacturing, and relates to the technical field of functionally graded material additive manufacturing. The method comprises the following steps: firstly, slicing a three-dimensional space model of a part to obtain geometric contour information of a model slice layer; taking the geometric contour of the layered model slice as a constraint, and carrying out triangulation processing on the two-dimensional polygon slice layer to obtain a two-dimensional triangular mesh model; correlating the material information of the customized workpiece with the geometric information by using a curved surface grid thermal diffusion model algorithm, namely, designing a single or multiple heat source points or heat source lines, and performing gradient diffusion in a grid to generate a scalar field containing material gradient distribution information; extracting a contour line of a specific scalar value to perform region division on the slice model to form a plurality of gradient feature sub-regions; and finally, according to the contour features of different sub-regions, a corresponding path planning strategy is adopted to optimize the path, an optimal path is generated, and accurate forming of the functionally graded material is achieved.

The invention relates to the technical field of computer programming languages, and particularly discloses a computer language programming method based on business logic. Aiming at the defects of deep coupling of business logic and technology implementation, high cross-domain adaptation cost, hardware operation code redundancy, high asynchronous programming complexity and the like in the prior art, the invention provides the following core schemes: a modularized development framework, a cross-platform adaptive mechanism, an event hierarchical model, a hardware intention analysis engine and a code, namely a document system. According to the method, the development complexity of a multi-field system is remarkably reduced, the business logic expression efficiency and maintainability are improved, the method is suitable for cloud micro-services, embedded equipment and mixed language scenes, and a technical basis is provided for intelligent programming.

The invention discloses a humanoid robot adaptive anti-interference recovery method based on reinforcement learning enhanced hierarchical model prediction control, and the method comprises the steps: receiving a user instruction, a robot state and a model prediction control parameter through a reinforcement learning decision module, and outputting a first-layer MPC parameter and a second-layer MPC parameter which are subjected to strategy enhancement after training; the first-layer MPC is combined with dynamic constraints, the multi-step foothold sequence is optimized and updated through the first-layer MPC parameters with the enhanced output strategy and event triggering signals, the second-layer MPC is combined with the first-layer model to predict and control the optimized robot state, the updated multi-step foothold sequence and the second-layer MPC parameters with the enhanced output strategy are optimized, and the multi-step foothold sequence is optimized. The invention relates to a contact wrench for optimizing a multi-step foothold sequence. According to the method, the walking disturbance recovery agility and the self-adaptive balance capability of the humanoid robot in complex terrains and external disturbance can be improved.

The invention relates to an energy storage electric power spot market simulation clearing method based on hierarchical model control prediction, and the method comprises the following steps: S1, building an electric power spot market global simplified clearing model containing energy storage, and taking the model as an upper prediction control model; s2, establishing an electric power spot market detailed clearing model containing energy storage as a lower execution model; s3, dividing long-time simulation into M solving periods; in each solving period, global simplification clearing optimization is firstly utilized to obtain an energy storage final state SOC constraint value of each remaining period; on the basis of the energy storage final state SOC constraint value of the current period, market simulation clearing of the current period is completed by using a detailed clearing model; and finally, updating the system state according to the current clearing result, and rolling to advance to the next cycle until clearing of all cycles is completed. According to the method, dynamic global optimization of the energy storage SOC constraint is realized through a hierarchical model prediction control mechanism, and the calculation efficiency and the market simulation clearing effect are effectively balanced.

The invention relates to an airport pavement dynamics simulation method and device under airplane dynamic load excitation, equipment and a medium. The method comprises the following steps: constructing a target pavement hierarchical model based on a void position and a void area which are obtained in advance; inputting taxiing load time history data generated according to aircraft taxiing working conditions into the target pavement layering model to obtain dynamic response characteristics; the dynamic response characteristics comprise displacement time history data, stress time history data and strain time history data of each monitoring node of the pavement surface layer. By adopting the method, the coupling relationship between the dynamic load and the pavement structure of the aircraft can be truly reflected.

The invention relates to the field of seabed sediment classification mapping, and provides a seabed sediment classification mapping method based on shallow stratum profile data, which comprises the following steps: acquiring sensor data of a target seabed, including multi-beam topographic data, side-scan image data, seismic data and shallow stratum profile data; multi-dimensional acoustic-terrain-seismic features are constructed, acoustic feature matching is carried out on multi-beam terrain data and side-scan image data, seismic data are introduced, and an extended hierarchical model is constructed; constructing a preliminary submarine topography model diagram by using a spatial interpolation algorithm, repairing a preliminary missing region by using a generative adversarial network, generating a complete submarine topography model diagram, embedding multi-dimensional acoustic-topography-seismic features, and predicting a sediment migration trend; and generating a seabed substrate classification planar graph and a three-dimensional geological profile map, and performing seabed substrate classification mapping in combination with a sediment migration trend. According to the invention, the resolution and integrity of the submarine topography model can be significantly improved, and the dynamic classification map reflecting the current and future patterns is generated.

The invention provides a large-scale complex group consensus decision-making method, terminal equipment and a medium. The method comprises the following steps: acquiring a probability language evaluation matrix of a plurality of agents participating in decision-making on a plurality of candidate schemes on a plurality of scheme attributes; adjusting the probabilistic language evaluation matrix based on a reinforcement learning algorithm, and driving a plurality of agents to reach a consensus to obtain a new probabilistic language evaluation matrix; for the new probabilistic language evaluation matrix, utilizing a Bayesian hierarchical model to calculate an attribute weight corresponding to each scheme attribute; according to the hesitation degree and the consensus degree of each agent in the new probability language decision matrix, determining an agent weight corresponding to the agent; converting the new probability language evaluation matrix into a numerical evaluation matrix through a cloud model; fusing the numerical evaluation matrix, the attribute weight and the agent weight to obtain a scheme evaluation cloud model; and determining a final scheme for achieving the large-scale complex group consensus from the plurality of candidate schemes according to the scheme evaluation cloud model. According to the invention, the precision of large-scale complex group consensus decision can be improved.

The invention discloses a transformer design method and system based on a hierarchical genetic algorithm and a medium, and belongs to the field of transformer design. The method comprises the steps that a structural hierarchical model of an amorphous alloy three-dimensional roll iron core transformer is constructed based on transformer parameters; inputting the structure layering model into a preset multi-physics field coupling simulation model, calculating iron core no-load loss power and winding load loss power, and determining a simulation temperature field in combination with the amorphous alloy iron core thermal conductivity and a dynamically calibrated boundary convective heat transfer coefficient; according to the sequence of the three-dimensional roll iron core layer, the winding layer and the oil duct layer, hierarchical genetic algorithm optimization is executed to obtain an optimization scheme, iterative simulation is conducted on the optimization scheme until an obtained simulation result meets a first preset condition, and the amorphous alloy three-dimensional roll iron core transformer is constructed based on the determined target structure parameters. And the design rationality and the operation safety of the amorphous alloy three-dimensional roll iron core transformer can be improved.

The invention discloses an image recognition method based on end, edge and cloud layered federated learning of knowledge distillation, which is characterized in that original data or full-quantity model parameters are replaced by transmitting embedded vectors, a synthetic sample is combined to serve as a knowledge carrier, the communication traffic is reduced by more than 60% compared with the traditional federated learning, and the image recognition method is adaptive to mass equipment concurrent scenes. In the training process, original data are only locally converted into vectors and never leave end equipment, so that the risk of data leakage is avoided from the source, and the requirements of privacy protection laws and regulations are met. By introducing a knowledge distillation method and hierarchical model deployment (end side light weight and cloud side high performance), equipment with different computing power can participate in cooperative training, and the problem of computing power resource mismatching is solved. Meanwhile, a Logs-based sample screening mechanism and multi-dimensional structured loss (and) are innovatively combined, and compared with existing schemes such as FedAvg and FedAgg, the method has the advantages that the test accuracy is remarkably improved, and particularly, the advantages are more outstanding in a high data heterogeneous scene.

The application relates to an Internet of Things abnormal data detection method based on improved feature selection and a hierarchical model, mainly comprising the following steps: collecting Internet of Things data, performing data missing value, abnormal value and Z-score standardization data preprocessing; removing redundant features from the preprocessed data by using an improved correlation feature selection algorithm, screening important features, and performing data dimension reduction; dividing the feature-selected data into a training set and a test set; further dividing the training set into a sub-training set and two validation sets; inputting the sub-training set into a multilayer hierarchical model for training, saving the trained model; and obtaining a final training model. Finally, the test set is used to test the effect of the overall model, and a detection result is obtained. The application effectively selects features of Internet of Things data, reasonably divides data and fuses hierarchical models, and the detection effect of the Internet of Things abnormal data is obviously improved.

The application provides a logging data layering method based on a T-KAN structure, relates to the geological field, and comprises the following steps: obtaining logging curve data of multiple wells and performing pretreatment; performing windowing on the logging curve data and constructing corresponding labels to obtain training samples; constructing a layering model based on a Transformer encoder; training the layering model by using supervised learning and in combination with the training samples; inputting the pretreated and windowed logging curve data of a new well into the trained layering model to output a point-by-point prediction sequence; performing smoothing filtering and merging layer boundary candidate points on the point-by-point prediction sequence; alternately determining layer tops and layer bottoms in a depth sequence to divide continuous stratum intervals and output top boundary depths and bottom boundary depths of the intervals. The application realizes high automation and intelligence of a logging layering process and greatly reduces the dependence on artificial experience.

Techniques for adaptive multi-level recommendation include processing input features to generate pre-processed input features, processing the input features and the pre-processed input features using a plurality of expert models to generate expert outputs, mixing the input features and the expert outputs to generate mixed expert outputs, processing the mixed expert outputs using a first model of the hierarchical model to generate intermediate outputs, and processing the mixed expert outputs and the intermediate outputs using a second model of the hierarchical model to generate a final output.

The invention relates to the technical field of soil improvement, and discloses a soil improvement effect real-time monitoring method and system and a storage medium. The method comprises the following steps: collecting multiple depth data samples in the vertical direction of soil, and constructing a layered soil characteristic data set; identifying the position of the high-heterogeneous section based on the data set; injecting a tracer agent at the position and imaging in real time to obtain a permeation path image sequence; predicting a diffusion velocity and a retardation point position by using a random forest model, dynamically adjusting injection parameters to eliminate retardation, and obtaining permeation path data; tracking and quantifying the action change of the modifier in each soil layer through time sequence analysis to obtain an action effect quantitative index; the index is compared with an initial data set, a hierarchical model is optimized, and a hierarchical monitoring framework is constructed; and integrating multiple depth data samples to verify the framework, and generating an evaluation report of the soil improvement effect. According to the invention, the accurate characterization of the vertical layering characteristic of the soil and the scientific and quantitative evaluation of the improvement effect are realized, and the use efficiency of the modifier is improved.

The application discloses a CTC system localization hardware performance monitoring data visualization implementation method, solves the problems of poor adaptability of existing general monitoring schemes to localization hardware and railway business scenes, disconnection of data and business, weak visualization analysis and difficult special network deployment. The method ensures the improvement of data accuracy through automatic matching and multi-dimensional verification of hardware types, differentiated packet loss completion and business correlation abnormal value filtering; constructs a device-line-business three-dimensional layered model, combines railway operation diagrams for differentiated down-sampling and weighted statistics, and automatically associates business event labels to realize deep binding of performance data and business logic; through multi-index linkage visualization, pixel density detection intelligent labeling and multi-dimensional interaction filtering, the performance bottleneck positioning efficiency is improved; through three-level dependency list management, resource path mapping and static link encapsulation, an executable file that can completely run offline is generated, and the stable operation rate of the localization terminal is ensured.

The application discloses a kind of highway survey radar measurement systems based on electromagnetic wave, through quantum magnetic force gradient tensor analysis and space coordinate calibration, synthetic aperture radar echo feature extraction module cooperation, using quantum magnetic force navigation model and synthetic aperture radar algorithm, realize multi-source data space-time registration and weighted fusion, construct highway geology layered model and inversion geological parameter, after grid processing of fusion data, generate three-dimensional visual model.The system overcomes the defects of traditional technology multi-source data fusion deficiency and low precision of geological parameter inversion, improves the precision, efficiency and reliability of highway survey, provides accurate geological information and visual survey results for highway engineering design and construction, has important application value in the field of highway survey.

An Artificial Intelligence (AI) based computational system is used to non-invasively estimate the presence of a range of aneuploidies and mosaicism in an image of embryo prior to implantation. Aneuploidies and mosaicism with similar risks of adverse outcomes are grouped and training images are labelled with their group. Separate AI models are trained for each group using the same training dataset and the separate models are then combined, such as by using an Ensemble or Distillation approach to develop a model that can identify a wide range of aneuploidy and mosaicism risks. The AI model for a group is generated by training multiple models including binary models, hierarchical layered models and a multi-class model. In particular the hierarchical layered models are generated by assigning quality labels to images. At each layer the training set is partitioned in the best quality images and other images. The model at that layer is trained on the best quality images, and the other images are passed down to the next layer and the process repeated (so the remaining images are separated into next best quality images and other images). The final model can then be used to non-invasively identify aneuploidy and mosaicism and associated risk of adverse outcomes from an image of an embryo prior to implantation.

The present application relates to the technical field of electromagnetic sounding, in particular to a multi-step variation AB-Hy encoding electromagnetic sounding one-dimensional inversion method and device, for the measured AB-Hy encoding electromagnetic sounding full area apparent resistivity observation value, the method presets the feasible solution space of one-dimensional horizontal layered model, randomly generates a population, forward calculation of the full area apparent resistivity of each individual in the population, calculates the fitness value of each individual in the population according to the full area apparent resistivity calculation value and observation value of each individual;Select the individual with the highest current fitness value as the optimal individual, and perform iterative calculation of multi-step differential evolution with the optimal individual participating in the population individual;The individual with the highest fitness value obtained is taken as the inversion result of the observation data. AB-Hy electromagnetic sounding one-dimensional inversion based on multi-step differential evolution algorithm is realized, the geoelectric model parameters corresponding to the observation data are obtained, and the basis for geological interpretation is provided. The method has fast optimization convergence speed and high accuracy of inversion result.

This invention provides a method, apparatus, and electronic device for modeling maritime rescue processes. The method includes: constructing a hierarchical model architecture driven by discrete event system specifications for the maritime rescue process, dividing the maritime rescue system model into a discrete event system layer and an object model layer; constructing a generalized rescue platform model and modular sub-models; the modular sub-models include a controller model, a maneuvering model, and a sensor model; constructing a complete rescue system simulation architecture based on the generalized rescue platform model; and performing repeated simulations and performance analysis on the complete rescue system simulation architecture to obtain a performance analysis report. This invention achieves accurate representation of the rescue system and solves the problems of poor accuracy and completeness in existing related technologies for rescue modeling.

The invention relates to the technical field of hand office modeling, and discloses a preparation scene modeling method and system for realizing a tidal play hand office based on digitization, and the method comprises the steps: obtaining a two-dimensional design drawing, an associated semantic text and a preparation material of the tidal play hand office, and constructing an initial three-dimensional model of the tidal play hand office; querying the material attribute of the preparation material, performing stress simulation on the tide play hand board by using the initial three-dimensional model to obtain a stress distribution cloud picture, and performing internal structure optimization on the initial three-dimensional model to obtain an optimized model; carrying out non-manufacturability verification on the optimization model to obtain an irregular topological region, and carrying out node coordinate optimization and boundary constraint processing on the irregular topological region to obtain a target manual model; and performing hierarchical slicing on the target hand-made model to obtain a hierarchical model, constructing printing parameters of the target hand-made model, and performing preparation deployment on the hierarchical model to obtain a damp play hand-made preparation scene model. According to the invention, the precision of the scene modeling of the tide playing handwork can be improved.

The invention belongs to the technical field of saturated sandy soil dynamic analysis, and particularly relates to a saturated sandy soil pore pressure evaluation method based on weak permeable interlayer permeability. Establishing a layered model composed of saturated sand and the weak permeable interlayer; selecting a dynamic constitutive model suitable for cyclic loading conditions; applying a self-weight load to the dynamic constitutive model to generate an initial stress field of the dynamic constitutive model; based on a finite difference theory, constructing a dynamic fluid-solid coupling analysis model of a saturated sand dynamic response control equation and a pore water seepage control equation; simulating pore water pressure response of the saturated sandy soil under different power working conditions; and quantitatively evaluating the influence of the permeability of the weakly permeable interlayer on the pore pressure accumulation, dissipation and liquefaction response of the saturated sand. According to the method, the dynamic numerical model considering the interlayer permeation difference is established, and the influence of the weakly permeable interlayer on the pore water pressure generation, diffusion and dissipation process is systematically analyzed, so that the pore pressure response of the saturated sand is accurately evaluated.

The application relates to a kind of robot end effector multiscale clamping force control device and method based on DIC, wherein the method comprises the following steps: S1, obtaining multiscale signal, collecting multiscale speckle image: S2, processing multiscale data and extracting features, image is processed by power platform multi-thread;S3, mapping multiscale physical quantity and constructing model, power platform is associated with multiscale feature and parameter and builds layered model;S4, control multiscale closed loop and adjust real-time, power platform uses layered adaptive algorithm to adjust PWM parameter.The application can consider non-contact sensing, multiscale modeling and real-time control, and break the force control bottleneck of robot soft body end effector.

The invention discloses a power distribution network planning standard file knowledge graph construction method and system based on an ALBERT-Transformer-Attention-CRF model, and the method comprises the steps: constructing a three-layer structure knowledge unit model for a standard file, defining the composition, entity, relation and attribute of each layer of knowledge unit, and giving an instance indication of the knowledge unit; the method comprises the following steps: firstly, dividing knowledge units, extracting knowledge of a standard core element layer, extracting data of a standard file layer by adopting a rule-based method, finally designing an ALBERT-hierarchical Transform-hierarchical Attention-CRF model to realize an entity recognition task of a standard article, and extracting an article relationship; and performing knowledge storage and application, and storing the constructed knowledge graph. According to the method, efficient management and effective utilization of power distribution network planning standard file knowledge are realized, and a new thought is provided for power distribution network planning standardized intelligent construction.

The invention discloses an event-driven data hierarchical model statistical method, which relates to the technical field of data processing, and adopts a self-adaptive processing mechanism capable of sensing context characteristics of a data stream, and a system performs data hierarchical model statistical analysis according to real-time throughput and data attributes. According to the method, an event-driven mode, a timed task scheduling mode and a micro-batch processing mode are flexibly and automatically switched, so that the high throughput demand of historical data backtracking is effectively considered while low-delay real-time calculation is ensured, and the optimal balance between the resource utilization rate and the calculation efficiency is realized. And the user retention and active indexes are processed by introducing a bitmap-based combined storage structure and displacement arithmetic logic, so that the storage space occupation is greatly compressed, the complex set operation is converted into efficient binary bit operation, and the query performance of cross-cycle statistics is remarkably improved. In addition, a multi-level cache mechanism is utilized to optimize a streaming association process, and the access pressure on an external dimension database is effectively reduced.

The invention belongs to the technical field of artificial intelligence, and particularly relates to an artificial intelligence model service method based on edge computing, which comprises the following steps: S1, edge-cloud collaborative task allocation; s2, hierarchical model deployment and parallel acceleration; s3, adaptive processing of multi-modal feature driving is carried out; s4, dynamic resource scheduling and priority control; s5, credibility evaluation and result fusion; s6, hardware-level acceleration and energy efficiency optimization are carried out; according to the method, edge-cloud cooperative task allocation is adopted, a lightweight model is executed at an edge end according to data features, a complex model is processed at the cloud end, a large amount of data is prevented from being transmitted to the cloud end, meanwhile, communication between edge nodes adopts a lightweight protocol, the overhead is reduced, data layer transmission is processed through compressed sensing, and 5GURLLC slice rapid transmission is combined, so that the data transmission efficiency is improved. Therefore, the network bandwidth pressure is reduced, and the transmission delay is reduced.

The invention discloses a firepower attack target distribution method and system based on a multi-constraint dynamic pointer network, and the method comprises the steps: firstly building a hierarchical model which comprises a platform layer, a target layer and a rule layer, carrying out the modeling of a data relation in collected data through employing a hyperedge based on the hierarchical model, and forming a time-varying hypergraph which is updated along with time steps; according to the method, the functions of integrally representing the capacity, the time window, the collaborative grouping, the area limitation and the fair coverage constraint in the same graph space by adopting the time-varying hypergraph and the advancement rule and timely distributing are realized; a primitive-dual online adjustment mechanism is introduced in the aspect of multi-target tradeoff to automatically allocate constraint budget for a Lagrange multiplier according to real-time violation degree, and load variance fairness and regularization are superposed, so that interpretable dynamic balance can be obtained for resource utilization, time window satisfaction, collaborative consistency and coverage balance, a security boundary can be provided, and the real-time performance of the multi-target tradeoff can be improved. And secondary selection of upper-layer strategies or players in the set can be facilitated, and the method is suitable for being widely popularized and used.

The invention discloses a photovoltaic power generation prediction method based on the combination of regional features and weather features, and relates to the technical field of photovoltaic power generation prediction, and the method comprises the following steps: constructing a regional feature hierarchical model which comprises a regional feature model, a seasonal feature model and a weather feature model, respectively quantifying the influence of regional parameters, seasonal changes and subdivided weather types on photovoltaic output; according to the method, the integrity, the accuracy and the consistency of the data are improved through hierarchical acquisition and preprocessing of the regional data, and a high-quality data basis is provided for model construction; according to the regional characteristic hierarchical modeling, influence factors are deeply analyzed from three dimensions of region, season and weather, and the accuracy and reliability of photovoltaic output prediction under different geographical conditions, season changes and weather conditions are remarkably improved; typical weather scene setting and simulation ensure that the model keeps high prediction precision under complex and changeable weather conditions.

The invention provides a logging data layering method based on a T-KAN structure, and relates to the field of geology, and the method comprises the steps: obtaining logging curve data of a plurality of wells, and carrying out the preprocessing; windowing the logging curve data and constructing a corresponding label to obtain a training sample; a hierarchical model is constructed on the basis of a Transform encoder; training the hierarchical model by adopting supervised learning in combination with a training sample; preprocessing and windowing logging curve data of a new well, inputting the logging curve data into the trained layered model, and outputting a point-by-point prediction sequence; carrying out smooth filtering on the point-by-point prediction sequence and merging layer boundary candidate points; and alternately determining the layer top and the layer bottom according to a depth sequence, dividing continuous stratum sections, and outputting the top boundary depth and the bottom boundary depth of each stratum section. According to the invention, high automation and intelligentization of the logging layering process are realized, and the dependence on artificial experience is greatly reduced.