106 results about "Integration algorithm" patented technology

The invention discloses an underground engineering geological safety dynamic risk assessment method based on multi-source data fusion, which relates to the technical field of risk assessment, and comprises the following steps: collecting multi-source heterogeneous data related to underground engineering, extracting implicit information, modeling underground engineering geological safety risk factors into a risk network, and establishing a risk network model; calculating the comprehensive importance of the nodes based on a Stacking integration algorithm, and identifying key risk factors; acquiring characteristic parameters of key risk factors by using spatio-temporal characteristics of implicit information, introducing a random walk mechanism to acquire a dynamic accident prediction chain, and performing learning representation by using a graph attention network to acquire probability distribution of an accident evolution path; and assessing the vulnerability of the connection edge in the risk network, establishing a dynamic risk assessment model based on the node importance and the edge vulnerability, and obtaining a dynamic risk value corresponding to the accident according to the accident occurrence probability and the risk mitigation factor. According to the invention, intelligent identification, dynamic evaluation and accurate early warning of risk factors are realized, and the accuracy and real-time performance of risk identification and evaluation are improved.

The invention relates to an AI server cooling water pump vibration quantity self-adaptive calibration test method and system, an intelligent test platform integrates a six-dimensional force sensor and a temperature compensation vibration exciter, the platform rigidity is automatically calibrated, a water pump-pipeline system transfer function is obtained, and a rotating speed-lift-modal frequency three-dimensional mathematical model is established; a three-axis MEMS accelerometer array is arranged at sensitive parts such as a water pump bearing seat and a motor shell, and vibration, current and pressure signals are synchronously collected; carrying out time-frequency domain signal processing by adopting variational mode decomposition in combination with self-adaptive S transformation, and extracting a 128-dimensional full-frequency domain feature vector containing a modulation side frequency band; effective values of vibration acceleration, speed and displacement are calculated through a frequency domain integration algorithm, and current harmonic interference is corrected through an electromagnetic vibration compensation model; and finally, a vibration health degree evaluation model containing 18 characteristic parameters is established based on a support vector machine, the test system comprises an intelligent test platform unit, a multi-source sensing unit, an edge calculation unit and a data management unit, and microsecond-level synchronous acquisition and GB-level data throughput are realized through a time sensitive network. And online incremental learning and automatic generation of a test report are supported. The method has the effect of improving the water pump vibration quantity test precision.

The invention discloses a GNSS (Global Navigation Satellite System) signal quality adaptive integrated navigation method, and relates to the technical field of vehicle navigation. IMU and GNSS positioning information and positioning states are read, integrated navigation is realized through an ESKF loose combination algorithm, a measurement covariance matrix is changed in real time according to the GNSS positioning state and a DOP factor, and finally, an error state value is estimated and fed back to an integrated navigation system. Position and speed information errors and attitude Euler angle errors under a navigation coordinate system are fed back to INS mechanical arrangement, angular acceleration zero offset errors of the IMU are fed back to the IMU, and updating of the state quantity is completed. A measurement covariance matrix is changed in real time according to a GNSS positioning state and a DOP factor by aiming at IMU and GNSS data through a loosely combined ESKF fusion algorithm, an error state is estimated and fed back to an integrated navigation system, and the positioning precision can be effectively improved.

The invention discloses a flexible finger friction contact collision dynamics simulation method based on an absolute node coordinate method, which comprises the following steps: simplifying a finger of a flexible manipulator into a connecting rod system, taking a tail end knuckle as a flexible beam for consideration, and carrying out unit division on the flexible beam system based on the absolute node coordinate method; a contact collision process is described by using an additional constraint method, meanwhile, switching of a viscous state and a sliding state is considered, a friction-containing flexible finger contact collision dynamic model is established, and a dynamic equation of a system is solved by adopting a double-circulation implicit integral algorithm; and obtaining a curve graph that the flexible finger tail end displacement changes along with time and the finger tail end contact force changes along with time. The invention provides a new frictional contact collision dynamic model for a flexible manipulator system, and provides more perfect data information and image information for researchers in the field.

The invention discloses a credit risk prediction method based on data and dynamic feature optimization and Bagging integration, and the method comprises the following steps: S1, obtaining an initial data set, carrying out the data preprocessing, balancing the data set through an ATGA algorithm, and reducing the data noise; s2, using LightGBM as an agent model, calculating importance scores of all features, gradually screening key features, adaptively adjusting feature screening standards according to data distribution through a dynamic threshold algorithm, gradually screening feature subsets, and inputting the screened features into a subsequent model; and S3, constructing a TabPFN model by using a Bagging integration algorithm, introducing a dynamic weight distribution mechanism, adaptively adjusting the weight of each base model according to sample features, and finally realizing credit risk prediction through the TabPFN model. According to the method, the credit risk prediction process is comprehensively optimized in three aspects of data set optimization, feature screening optimization and model performance optimization, so that indexes such as the accuracy rate and the rate of return are improved.

The invention discloses a retrieval enhancement generation method based on dynamic document block segment optimization, which comprises the following steps: S1, acquiring an input document, and preprocessing the input document; s2, the document is segmented into a plurality of paragraphs through a natural language processing technology, each paragraph comprises a plurality of smaller blocks, and an index is constructed according to the paragraphs and the blocks; s3, after a query request is received, related paragraphs and blocks are retrieved through indexes; s4, according to the requirement of the query request, the size of the blocks is dynamically adjusted, segments are constructed through the adjusted blocks, and each segment comprises a plurality of blocks; s5, fusing the block obtained by retrieval with the corresponding context information, and promoting the organic combination of the segment, the block content and the context information through a fusion algorithm; and S6, generating answers based on the fused segments through a language model, and performing inspection confirmation and content optimization on the generated answers.

An online estimation method and system for state of charge (SOC) errors. The method comprises: acquiring an initial SOC error value at a power-on moment of a battery management system, and updating the initial SOC error value by means of an OCV table lookup algorithm when a correction condition is satisfied; calculating a real-time SOC error of a battery by means of an ampere-hour integration algorithm; detecting a real-time calculation process of the ampere-hour integration algorithm, and when a Rint model correction condition is triggered, correcting the real-time SOC error by means of a Rint model algorithm; and when the battery reaches a preset condition, zeroing out the SOC error and then performing updating by means of the ampere-hour integration algorithm until a power-off moment of the battery management system is reached, saving a corresponding real-time SOC error and outputting same. A theoretical basis for SOC error estimation is provided to realize online SOC error estimation; algorithms having higher precision are selected during estimation, so as to reduce SOC errors; and the calculation of a current SOC error can be calibrated, such that the method and system are adapted to scenarios involving different battery packs and different aging conditions.

The invention discloses a wavefront restoration method based on multi-path integration, and relates to the technical field of wavefront measurement and restoration, and the method comprises the following specific steps: data collection and initialization: carrying out the preliminary detection of wavefront through a Hartmann sensor microlens array, dividing the space where the wavefront is located into a plurality of rectangular small regions, according to the method, a multi-path calculation phase evaluation value set is introduced, all shortest paths from an initial point to each to-be-estimated point are found by using a shortest path search algorithm, it is ensured that multiple possibilities of wavefront propagation can be considered in the calculation process, and by introducing a path weight distribution mechanism, the estimation accuracy of the wavefront propagation is improved. According to the method, geometric characteristics, optical characteristics and correlation with surrounding paths of paths are comprehensively considered, each path is endowed with different weights, forward integration is carried out on discrete data results by adopting an innovative mixed integration algorithm, local detail loss or error amplification is effectively avoided, and thus a more accurate wavefront phase value is obtained.

The invention relates to an icing power transmission line galloping simulation method and simulation system based on a composite time integral. The method comprises the following steps: S1, establishing a nonlinear finite element model of an icing power transmission line; s2, carrying out time domain solution by adopting a composite time integration algorithm; calculating the solution of the moment in two sub-steps according to the solution of the moment, namely from the first sub-step to the second sub-step; the first sub-step adopts a trapezoidal rule for integration, and the second sub-step adopts a three-point backward difference format based on information of three moments; s3, coupling and solving a nonlinear aerodynamic load related to the speed; s4, obtaining the galloping response of the power transmission line; the problems that in a transmission line galloping simulation technology based on a traditional Newmark method, contradiction between calculation efficiency and numerical precision exists in the strong nonlinear coupling problem, accurate tracking of low-frequency galloping and efficient suppression of high-frequency noise are difficult to achieve at the same time under the large time step length, simulation calculation efficiency is low, and convergence is poor are solved.

The invention discloses a method for predicting the lignin separation and extraction effect of a deep-eutectic solvent based on machine learning. The method comprises the following steps: constructing a sample database containing characteristic parameters such as hydrogen bond acidity, hydrogen bond alkalinity and polar surface area of the deep-eutectic solvent and a lignin removal rate; based on a solvent molecular structure, key characteristic variables influencing the lignin removal rate are extracted; a Stacking integration algorithm is adopted, Xgboost, RF and SVM are taken as base learners, CNN is taken as a meta learner, and a machine learning prediction model taking the removal rate as a target variable is constructed; performing hyper-parameter tuning by adopting five-fold cross validation, and constructing a machine learning prediction model; explaining the model by using an SHAP method, generating a feature importance ranking map and an SHAP value distribution map, and intuitively displaying the influence of key variables on the lignin extraction removal rate; finally, the lignin removal effects of different eutectic solvent systems are predicted through a machine learning model, and the solvent composition proportion is adjusted in combination with feature analysis so as to optimize the separation process.

The invention discloses a moon leap trajectory planning method, system and device and a medium. The method comprises the steps of obtaining target landing point data; based on the acquired data, establishing a moon surface leap device kinetic model by defining a specific coordinate system and a state variable; based on the kinetic model, discretizing the kinetic equation through a numerical integration algorithm, constructing and solving a leap trajectory convex optimization problem with optimal burnup, and obtaining an obstacle avoidance-free optimal trajectory; based on the obstacle avoidance-free optimal trajectory, performing obstacle avoidance iteration through an iteration obstacle increase strategy to obtain a static obstacle avoidance trajectory; and on the basis of the static obstacle avoidance trajectory, the flight trajectory is adjusted again by calculating the trajectory error band width and the maximum extension distance, and a dynamic safety enhanced trajectory is obtained. According to the method, safe, robust and efficient moon surface leap trajectory planning in a complex and uncertain environment is realized.

The invention relates to the technical field of data processing, and discloses an engineering construction whole process digital management method and system. The method comprises the following steps: collecting and standardizing engineering construction full-period multi-source data; constructing a stacked integrated prediction system by using the standardized data to obtain a three-dimensional index prediction value; allocating resources based on the predicted value in combination with the ergonomics risk index to obtain a configuration scheme; uncertainty factors are quantified for the configuration scheme, an optimal scheme set is calculated through a multi-target symbiotic algorithm, and a management strategy is formed. According to the method, high-precision prediction of the engineering three-dimensional indexes is realized through the stacking integration algorithm, ergonomics risk assessment and resource allocation optimization are combined, and the technical defect that human factor engineering is neglected in a traditional method is overcome.

The invention discloses an isogeometry-based composite material thin shell dynamics prediction method, and belongs to the field of composite material thin shell dynamics behavior prediction. The implementation method comprises the steps that geometric modeling is conducted on the composite thin shell based on an isogeometric analysis method, unification of geometric modeling and numerical analysis is achieved through a non-uniform B spline (NURBS), and a composite thin shell geometric model is obtained; calculating a tensile stiffness matrix, a tensile-bending coupling stiffness matrix and a bending stiffness matrix of the composite material thin shell through the composite material parameters; establishing a kinetic model of the composite material thin shell unit through continuous medium mechanics, assembling the kinetic model to obtain an overall kinetic model of the composite material thin shell, and establishing a kinetic equation of the composite material thin shell based on the overall kinetic model of the composite material thin shell and the stiffness matrix; a kinetic equation is solved based on a generalized alpha implicit time integration algorithm, the kinetic behavior of the composite thin shell under the action of an external force F is predicted, and the stability and precision of thin shell kinetic behavior prediction are improved.

The invention relates to the technical field of indoor layout design, and discloses an indoor layout design system based on user behavior data analysis. The system comprises a user behavior data acquisition module which captures and transmits a user movement track coordinate sequence, residence time distribution data and equipment interaction event records in real time through a multi-sensor network; the layout analysis module constructs a dynamic layout model by using a decision tree integration algorithm, and outputs theoretical layout parameters including a space occupancy rate and functional region division; the layout difference detection module performs multi-dimensional comparison of space overlapping degree, path conflict index and regional utilization efficiency difference on theoretical and actual layout parameters to generate a difference index vector; the problem region positioning module identifies a layout abnormal region through a spectral clustering algorithm based on the difference index vector and predefined spatial topology information; and the adaptive design strategy generation module automatically generates a layout adjustment strategy instruction according to the abnormal region position and the attribute data.

The invention discloses a three-dimensional design model conformity analysis method and system based on a large language model, and the method comprises the steps: obtaining the related data and design specification constraint conditions of a three-dimensional design model, and building a data interaction channel through a collaborative review platform; calling a semantic geometry collaborative verification algorithm to process the data and extracting feature parameters; generating a rule adaptation weight matrix through a space rule learning adaptation model; constructing a mapping relation between the feature vector and the design specification through a CAD point cloud feature coding mapping model; carrying out multi-dimensional conformity verification on the basis of the result; and outputting the comprehensive analysis data set. The system realizes deep cooperative processing of geometric and semantic data of a three-dimensional design model through multi-unit cooperative linkage and integration of an algorithm and a platform function, improves the accuracy of rule adaptation and feature mapping, comprehensively covers a conformity verification dimension, and meets a high-precision analysis requirement in a complex scene.

The invention discloses a bucket dividing regulation and control method and device for a financial data warehouse and electronic equipment, and relates to the technical field of artificial intelligence or other related fields, and the method comprises the steps: collecting a time sequence performance set of the financial data warehouse through a distributed collection system; multi-level feature extraction is carried out on the time sequence performance set by adopting a sliding window mechanism to obtain a time sequence feature set, and the categories of time sequence features comprise instantaneous fluctuation features, periodic trend features and steady state baseline features; performing association analysis on the time sequence feature set based on a decision tree integration algorithm by taking an expected query performance requirement as a target to obtain a bucket-dividing adjustment strategy; and bucket index reconstruction is carried out on the financial data warehouse based on the bucket division adjustment strategy, and the financial data warehouse after index updating is obtained. Through the method and the device, the technical problem that the system performance is degraded due to the fact that a static bucket dividing mechanism of a real-time data warehouse cannot dynamically adapt to space-time imbalance of data loads in a financial service scene in the related technology is solved.

The invention relates to the technical field of neural signal processing, in particular to a behavior neural signal optimal weighted integration algorithm applied to language brain diseases, which comprises the following steps of: 1, acquiring a basic activity mode of a brain of a user through electroencephalogram data, and analyzing characteristics of low-frequency oscillation and a functional connection network; 2, in task state electroencephalogram data collection, a user completes a language processing task; step 3, identifying two sets of electroencephalogram indexes closely related to language brain diseases through feature extraction and screening; 4, through an iterative optimization method, different weights are given to different indexes, and the reasonability of weight distribution is verified through a machine learning technology; and 5, generating a comprehensive language brain health score for clinical evaluation. According to the method, multi-dimensional data are integrated into index weight distribution of neural signals, and brain multi-dimensional electrical data and behavioral data are combined, so that a set of efficient language brain function evaluation system is constructed.

The invention discloses a customer service event dynamic scheduling method based on time sequence modeling, and the method comprises the following steps: S1, collecting heterogeneous original data, and constructing a service event sequence; s2, inputting the service event sequence into an improved Transform model, and outputting an event semantic feature vector; s3, constructing a multi-scale time window, and generating a time feature vector array; s4, splicing the event semantic feature vector and the time feature vector to generate a joint feature vector; s5, inputting the joint feature vector into an improved decision tree integration algorithm, generating a scheduling priority score value, and recording a decision path and a corresponding feature node index; s6, executing a service event queue operation, and generating a service event scheduling list; and S7, acquiring service event execution feedback information, and updating parameters. According to the method, time sequence modeling, feature scoring and feedback optimization mechanisms are fused, and intelligent recognition, efficient sorting, interpretable scheduling and dynamic evolution of customer service tasks are achieved.

The invention provides a training method for aero-engine modeling driven by mechanism and data fusion, and relates to the technical field of aero-engine modeling simulation, and the method comprises the steps: decomposing a simulation model of an aero-engine into a plurality of assemblies containing to-be-identified unknown parameters, and employing an assembly integration algorithm to associate the plurality of assemblies, an end-to-end differentiable complete simulation model is formed; based on hierarchical division of the simulation model, performing parameter pre-estimation by adopting a top-down layer-by-layer progressive strategy to obtain an initial estimation value of the unknown parameter; the strategy comprises the following steps: firstly, estimating unknown parameters of components in a highest level according to actually measured output data, and generating a training target of a next lower level based on a calculation result of an estimated level so as to complete estimation of unknown parameters of the lower level in sequence; and performing gradient back propagation in the complete simulation model by taking the initial estimation value as a starting point to optimize and update unknown parameters.

The present invention discloses a method for identifying spatial micro-motion targets based on the Stacking integration algorithm, which mainly solves the problems of single feature extraction in existing spatial micro-motion target identification methods and the inability of traditional single classifiers to fully exploit the classification potential of features. The implementation scheme is as follows: 1) Use the multi-transform domain feature extraction method to extract the target time domain, frequency domain, and time-frequency domain features, and generate a training sample set and a test sample set; 2) Construct a Stacking integration classifier model in which four primary classifiers are connected in parallel and then cascaded with a secondary classifier; 3) Use the training sample set and the cross-validation method to train the Stacking integration classifier; 4) Input the test sample set into the trained Stacking integration classifier to obtain the classification result. The present invention can fully characterize the target characteristics, exploit the classification potential of features, improve the recognition rate of spatial micro-motion targets, and can be used for ballistic target recognition.

The invention provides an online model updating hybrid test method based on a dual adaptive UKF algorithm, and the method comprises the steps: carrying out the initialization of a to-be-recognized constitutive model parameter, determining a parameter initial value seismic oscillation according to a genetic algorithm, inputting the seismic oscillation into a step-by-step integration algorithm, and obtaining the displacement response dk of an overall structure; obtaining the boundary displacement of the test substructure based on the displacement response dk and the constitutive model parameter estimation value of the previous step, carrying out real loading according to the boundary displacement, obtaining the real counterforce and real displacement of the test substructure, and carrying out parameter estimation based on a dual adaptive UKF algorithm; and obtaining a new constitutive model parameter value according to the new constitutive model parameter value and the displacement response dk of the overall structure, obtaining the counter-force Rk of the overall structure on each dynamic degree of freedom, realizing online identification and model updating of the constitutive model parameter, and performing loop iteration to the process of ending the test, thereby improving the precision and stability of parameter identification, and improving the test efficiency. The problem that the loading boundary of the test substructure is inaccurate is effectively solved.

The invention belongs to the field of aerospace electronic measurement, and provides an inductance type proximity sensor resolving method and system based on a time domain integration algorithm, and the method comprises the steps: applying a generated sine excitation voltage to a measurement loop; synchronously acquiring sine excitation voltage and response voltage of an inductive proximity sensor in a measurement loop; based on a time domain integration algorithm, respectively carrying out orthogonal integration on the sine excitation voltage and the response voltage, and calculating respective amplitudes and phases; according to the amplitude, the phase and the resistance value of the reference resistor, an inductance value and a parasitic resistance value of the inductive proximity sensor are calculated through a trigonometric function relationship; and establishing a piecewise linear fitting model through a least square method, and calibrating the inductance value to obtain a calibrated inductance value. The method has the advantages of high reliability, strong anti-noise capability, no need of complex electronic hardware, and reduction of the development cost at a lower development guarantee level.

The invention belongs to the technical field of machine learning, and particularly relates to an algorithm research based on GNN multi-mode lightweight and edge computing fusion integration, which comprises the following steps: step 1, acquiring sensor data from at least two different modes; and 2, constructing the multi-modal data into a uniform heterogeneous graph, wherein the data of different modals are expressed as nodes or edges of different types. According to the method, a unified heterogeneous graph is constructed, and data from different sources are mapped into different types of nodes or edges in the graph. Then, through a cross-modal attention mechanism, dynamically learning association strength and an interaction mode between different modal nodes, and realizing end-to-end multi-modal information deep fusion in a GNN message transmission process, thereby avoiding information loss caused by simple splicing or later fusion in a traditional method; the cross-modal attention mechanism can dynamically calculate and allocate importance weights of different modals according to specific input samples and contexts.

The invention belongs to the technical field of filling type field-induced phase-change materials, and discloses a method for regulating and controlling the performance of a filling type field-induced phase-change material. The method comprises the following steps: (1) data set acquisition: acquiring previous experimental data from publicly published literatures, tables and graphs and forming a data set; (2) data preprocessing: executing all preprocessing by using a Python language in the Juputer Notebook, wherein the Python language is used in the Juputer Notebook; standard Scaler is used to carry out standardization on the data set; (3) constructing a machine learning model: constructing a prediction model of the nonlinear conductivity of the filled polymer composite material by adopting support vector regression, kernel ridge regression, random forest regression, XGBoost regression and gradient enhanced regression; and (4) integrating prediction results by adopting a Stacking integration algorithm. According to the method, the SEL strategy is adopted, the performance of the Eb and alpha models is improved by integrating the five basic models, the prediction performance of the models can be improved, the generalization ability of the models can be enhanced, and the models can well express new and unseen data.

The invention relates to the technical field of airborne visual perception, and discloses an airborne visual perception robustness design method and device based on deep ensemble learning, and the method comprises the steps: collecting a data set which comprises a flight visual perception image; designing a plurality of base learners with different advantages; each base learner carries out training by using the data set to obtain a plurality of sub-models as a plurality of models; and performing grouping matching on the prediction frames of the multiple models according to the geometric center point position and the overlapping degree, performing adaptive weighting on each group of matching frames through confidence, generating a final bounding box, and obtaining an integration algorithm. According to the method, multiple base learners with different feature extraction advantages are designed, and prediction results are integrated by using an adaptive weighted fusion strategy, so that misjudgment and missed judgment are effectively reduced, the target detection robustness is improved, and a powerful guarantee is provided for flight safety.

The embodiment of the invention provides a robot control method, a robot control device and a robot, which are applied to an application scene in which a robot needs to be in rigid contact with a target, such as fine surgical operations such as orthopedic joint replacement and dental implantation of a surgical robot. The control method comprises the steps of obtaining contact force data of the robot; filtering the contact force data according to the change rate of the contact force at the current moment so as to determine preprocessed contact force data; servo control parameters of the robot are determined according to the preprocessed contact force data, a self-adaptive admittance control model and a speed Weil method numerical integration algorithm; and controlling the robot to work according to the servo control parameters of the robot. In the filtering processing process of the contact force data, the filtering intensity can be dynamically adjusted according to the change rate of the contact force at the current moment, tool bounce or tremor is avoided when the robot makes contact with a rigid target, and the working stability, precision and safety of the robot are improved.

The application discloses a kind of composite heavy metal pollution site three-dimensional identification and earthwork volume estimation method and system, and it is related to computer data processing technical field.Target site survey data is obtained and engineering voxel space resolution is set, and the chemical action parameter between heavy metals is extracted to construct composite spatial variation function.Three-dimensional distribution model is constructed using three-dimensional space interpolation, and preliminary pollution spatial grid set is extracted according to risk control threshold discretization.Boole logic operation is introduced to cross screen out single and composite heavy metal pollution area, and three-dimensional engineering boundary is obtained by boundary topology reconstruction with minimum mechanical construction envelope as constraint, and then the corresponding pollution total volume is estimated using spatial volume integration algorithm.The application can output three-dimensional boundary with engineering excavation continuity and realize accurate estimation of various pollution quantities.

The invention discloses an electric spark flicker real-time visual detection method based on a regional integration algorithm. The method comprises the following steps: S1, collecting a video data stream, and carrying out preprocessing and parameter setting; s2, performing real-time target detection based on a YOLO deep learning network, and positioning an ROI region; s3, performing image enhancement on the ROI by using a multi-parameter combination optimization technology; s4, spark feature extraction is carried out based on a regional brightness integral algorithm; s5, performing data processing and analysis by adopting a double-thread parallel architecture; s6, spark feature recognition is realized in combination with a dynamic threshold mechanism; and S7, real-time output and exception handling of a detection result are realized. According to the electric spark flicker real-time visual detection method based on the regional integration algorithm, environmental interference is effectively restrained, the processing efficiency is remarkably improved, and the method can be applied to scenes such as quality detection in the industrial production process, equipment operation state monitoring and online detection of an automatic production line.

The present invention relates to the fields of water conservancy and hydropower engineering, and shipping engineering technology, and discloses a method for underwater geological interpretation based on a multi-source data integration algorithm, comprising the following steps: acquiring underwater geological data, resistivity data, topographic data, and water level data; constructing a relationship between relative resistivity and formation threshold; calculating water depth data and distance from the abyssal line; selecting an integrated learning algorithm to predict relative resistivity to achieve formation prediction; calculating the underwater terrain slope to determine the formation condition; and integrating the results of the integrated learning algorithm's formation prediction and the results of the underwater terrain slope determination to achieve underwater geological interpretation. The present invention not only efficiently achieves underwater geological interpretation and reduces the safety risks of geological workers working above water, but also provides more reliable results and saves costs, providing a new approach to underwater geological interpretation.

The invention discloses an efficient simulation method for collision between long ellipsoid particles and a wall surface based on discrete elements, and belongs to the technical field of gas-solid two-phase flow numerical simulation. The method comprises the steps that a universal long ellipsoid particle-wall surface collision elastic force model is established, and the model represents a universal function relation between elastic force and particle geometry, material attributes and kinematics state parameters; based on the elastic force model and the momentum theorem, constructing a particle-wall surface collision dynamic model; based on the kinetic equation, a discrete time method and a numerical integration algorithm are adopted, and force, speed and energy parameters, changing along with time, of the long ellipsoid particles and the wall face in the whole collision period are solved. According to the method, the analytic model is embedded into a discrete element simulation framework to replace calculation-intensive finite element solution, so that second-level efficient solution is realized while the calculation precision is ensured, and the calculation efficiency is remarkably improved.