1303results about How to "Calculation speed" patented technology

The invention provides a construction site collision risk assessment method based on a digital twinborn scene, and relates to the technical field of building simulation and collision detection. According to the method, after collision risk evaluation factors are screened out by constructing a construction site digital twinborn scene, correlation analysis is carried out by using a weighted Heraonian average operator, and a determined weight is obtained; constructing a membership function of each collision risk evaluation factor, performing fuzzy reasoning, establishing a construction site collision risk evaluation mechanism, and outputting an evaluation result; dynamically generating and updating the working space of the digital twinborn scene according to the evaluation result, reconstructing the digital twinborn scene, and executing voxelization collision detection to obtain an accurate collision detection result; and according to an accurate collision detection result, in combination with the generated dynamic working space, calculating a collision time-space amount to obtain a final risk value. The method is high in collision detection speed and accurate, is applied to a dynamic complex environment of a construction site, and can achieve objective and accurate evaluation of space-time collision risks.

The invention belongs to the field of geophysical exploration, and particularly relates to an aviation natural source electromagnetic method complex medium parallel three-dimensional forward modeling method. Comprising the following steps: S1, constructing a three-dimensional geological geometric model of a research area; s2, generating a model unstructured tetrahedral mesh; s3, setting anisotropic conductivity tensor and magnetic conductivity of the model unstructured tetrahedral mesh; s4, constructing a frequency domain electric field control equation; s5, adopting a vector finite element discrete electric field control equation; s6, setting boundary conditions; s7, solving a finite element linear equation; s8, calculating three components of an electric field and a magnetic field at the measuring point and the base station; and S9, calculating impedance, apparent resistivity, phase and tipper data at the measuring point. According to the method, a three-dimensional forward modeling algorithm is provided for the aviation natural field source electromagnetic method, a basis is provided for research of the aviation natural field source electromagnetic method theory and inversion interpretation of actual observation data, and then the aviation natural field source electromagnetic method can better serve the application fields of mineral exploration, engineering exploration and the like.

The invention discloses an indoor unmanned aerial vehicle flight path planning processing method and system, and the method comprises the steps: scanning an indoor environment through a laser radar, and obtaining real-time obstacle information; the indoor environment is scanned through laser radar equipment, so that point cloud information and obstacle position information of a plurality of obstacles in the environment can be obtained; dividing the grid map into a plurality of levels of subareas according to the density of the obstacles in the grids and the probability value of the occupied area; generating an initial flight path by adopting an improved fast expansion random tree algorithm based on the partition regions of the multiple levels; smoothing the initial flight path to obtain an optimized flight path; an improved fast expansion random tree algorithm is adopted to generate an initial path, and compared with a traditional algorithm, the algorithm has higher calculation speed and better path quality.

The invention provides a high-resolution positioning method for noise source imaging. The high-resolution positioning method comprises the following steps: designing a sensor array layout and selecting sensors; a multi-channel acquisition synchronous acquisition sensor and records signals to obtain acquisition data; performing signal processing on the acquired data by using adaptive filtering, machine learning calibration, time-frequency analysis and intelligent beam forming technologies to obtain standard data; based on the standard data, performing high-resolution direction estimation by using an optimized multi-signal classification algorithm, and removing a sidelobe effect by using an optimized CLEAN algorithm to complete processing of the standard data; and based on the processed standard data, reconstructing a spatial distribution image of the noise source by using an inversion technology, and dynamically adjusting the resolution of the reconstructed spatial distribution image by using an adaptive algorithm to realize high-resolution positioning of noise source imaging. According to the invention, high-resolution positioning can be realized, the reliability and precision of noise detection are improved, and noise control and management are more effective.

The invention discloses a gas pipeline and subway safety distance optimization method based on numerical simulation, and relates to the technical field of subway tunnel safety, and the method comprises the steps: simulating gas pipeline explosion data through a KFX Exsim gas explosion model; establishing a fluid-solid coupling model, a soil dynamic model and a thermodynamic coupling model, and coupling the three models to obtain a multi-physics coupling model; based on a multi-physics field coupling model, performing decomposition and region division on a numerical simulation task in combination with a parallel computing framework, and simulating the influence of gas pipe explosion on the subway; based on a numerical simulation result, evaluating the safety of the tunnel under the action of the explosion load; based on a safety evaluation result, optimizing a safety distance between the gas pipeline and the tunnel; according to the method, the fluid-solid coupling model, the soil dynamic model and the thermodynamic coupling model are established and coupled to form a multi-physical field coupling model comprehensively covering the explosion environment, and accurate simulation of complex behaviors of the subway tunnel in the explosion environment is achieved.

The invention discloses a sand-blown saline-alkali area cultivated land quality evaluation method, relates to the technical field of computer agriculture, and aims to solve the problem that an existing cultivated land quality evaluation method is inaccurate in a sand-blown saline-alkali area cultivated land evaluation result. The method comprises the following steps: 1, collecting research area data of cultivated land in a sand-blown saline-alkali area, wherein the data comprises field survey data, remote sensing image data and other auxiliary data; 2, calculating a remote sensing index; 3, constructing a farmland quality evaluation system in the sandstorm saline-alkali area; 4, determining the weight of each evaluation index of the index layer by adopting a consistent matrix method; 5, carrying out membership degree assignment on each evaluation index based on a membership function and a Delphi method; 6, adopting an accumulation method to obtain a cultivated land quality comprehensive index P; and 7, dividing the distribution interval of the cultivated land quality comprehensive index P into ten grades by adopting an equidistant method. The method is high in speed, good in stability, low in cost and capable of achieving large-area and long-time-sequence cultivated land quality evaluation in the sandstorm and saline-alkali area.

The invention discloses an optimized molecular dynamics simulation method for a monocrystalline silicon cutting process. Comprising the following steps: initializing and setting parameters, creating a workpiece area, creating an abrasive particle area, creating an atomic box, setting atomic mass, fixing a boundary layer, selecting a potential function, carrying out energy minimization processing and relaxation processing, setting cutting parameters, and carrying out operation simulation and result visualization processing. According to the method, interaction between silicon atoms and interaction between silicon and carbon can be described more accurately by using Tersoft potential; the simulation stability is improved by setting time step length, temperature control and energy minimization; by defining different temperature control layers such as a constant temperature layer and a Newton layer, complex dynamic behaviors in the material cutting process can be better simulated; by optimizing a calculation model and an algorithm, the speed and the precision of atomic motion trail and stress distribution in the process of simulating the cutting of the monocrystalline silicon are remarkably improved, and the stability of the simulation process is ensured.

The invention discloses an assembly geometric digital twin modeling and pose calculation method based on deep learning, and belongs to the technical field of precision mechanical system modeling. Comprising the following steps: firstly, selecting a small number of product key contact surfaces for point cloud measurement; secondly, performing redundancy removal, expansion and arrangement on the data by adopting an up-sampling / down-sampling and data enhancement method; then, an assembly data set is established through assembly pose parameter calculation and data enhancement; then training a neural network by using a deep learning technology, and establishing a geometric distribution error and assembly pose mapping model; according to the method, part surface geometric distribution errors and assembly pose errors in an assembly body are considered, a geometric digital twin model with complete and accurate error information is established, and a model basis is provided for product precision prediction, product assembly process optimization, product assembly visualization and product quality improvement; and the method is suitable for precision mechanical products with high precision and large-batch production requirements.

The invention relates to the technical field of ammonia-nitrogen wastewater degradation, in particular to a deamination reactor optimization method based on CFD and BP neural networks. The method comprises the following steps: performing three-dimensional modeling according to geometric parameters of a deamination reactor, and obtaining a sample set; training a neural network through the sample set; and predicting the cavitation volume fraction and length in the deamination reactor by using a neural network. According to the deamination reactor optimization method based on the CFD and the BP neural network, the cavitation intensity of the deamination reactor is predicted based on a CFD numerical simulation result in combination with the neural network. CFD numerical simulation has the defects of low simulation speed, tedious operation steps and large workload, the cavitation intensity in the device with different parameters is calculated by constructing a neural network model, the amount of information to be processed is large, the calculation speed is high, complex problems can be rapidly processed, and the calculation efficiency is improved.

The invention discloses a multi-source data fusion low-altitude unmanned aerial vehicle trajectory tracking method and system. The method comprises the steps of flight path data collection, wherein the flight path data collection is divided into cooperative flight path data collection and non-cooperative data collection; flight path data and flight path fusion: performing cooperative flight path data and cooperative flight path fusion processing and radar flight path data and radar flight path fusion processing; the flight path data and flight path fusion comprises data preprocessing, flight path data filtering updating and flight path data and flight path association; the cooperation track and radar track fusion is used for carrying out association fusion on the cooperation track and a radar mark track, and comprises a track filtering sub-process and track-to-track association; the flight path management is used for maintaining and managing all current flight paths, including temporary flight path maintenance and management and stable flight path maintenance and management, the temporary flight path is mainly a radar temporary flight path, and the stable flight path comprises a stable cooperative target flight path and a radar stable flight path. Fusion tracking of the flight path of the unmanned aerial vehicle detected by various heterogeneous sensing devices can be realized.

The invention discloses a multi-scale digital rock core rapid reconstruction method based on an agent model, and belongs to the field of digital rock core rapid reconstruction. According to the method, through combination of multiple steps and the model, all-around support is provided for construction of a multi-scale digital core, key links of data acquisition, feature extraction, model construction and multi-scale information fusion are covered, a basis is provided for reservoir physical property evaluation through accurate prediction of permeability, and the reservoir physical property evaluation accuracy is improved through combination of microscopic images and macroscopic attributes. Effective connection of cross-scale information is realized, finally, a digital core with a real microstructure is generated, and a high-fidelity data model is provided for oil-gas exploration.

The invention discloses a battery remaining life prediction method, device, equipment and medium, and relates to the technical field of new energy, the method comprises the following steps: obtaining charge-discharge cycle data of a lithium battery to be detected, preprocessing the charge-discharge cycle data, and extracting capacity sequence characteristics in the charge-discharge cycle data; inputting the capacity sequence characteristics into a trained battery residual life prediction model to obtain the residual battery capacity of the lithium battery to be detected after each charge-discharge cycle output by the battery residual life prediction model; determining the number of charge-discharge cycles required when the residual battery capacity of the lithium battery to be detected reaches the preset capacity; and determining the residual battery cycle life of the current charge-discharge cycle according to the charge-discharge cycle times and the charge-discharge cycle times corresponding to the current charge-discharge cycle. According to the method, the key feature information of the data is effectively enhanced, so that the battery residual life prediction model has robustness and higher prediction precision on the prediction of the battery residual cycle life.

The invention discloses a method for predicting the heavy metal adsorption capacity of micro-plastics, which comprises the following steps of: screening heavy metal and micro-plastic adsorption data from literature and professional databases, and arranging the data into a sample data set; performing preprocessing including data cleaning, missing value processing and data standardization on the sample data set; constructing an adsorption capacity prediction model, and performing training and evaluation to determine an optimal prediction model; using random search, Bayesian optimization or grid search to adjust hyper-parameters of the model; and carrying out adsorption capacity prediction on microplastics and heavy metals with unknown adsorption capacities by using the adsorption prediction model. The method can be practically applied to research on adsorption of the heavy metal ions to the micro-plastics, interaction between the heavy metal ions and the micro-plastics can be visually understood, and ecological risks caused by coexistence of the heavy metal ions and the micro-plastics can be evaluated.

The invention discloses a multi-level voltage collaborative optimization method for a power distribution network and a related device. The method comprises the following steps: acquiring day-ahead prediction data, intra-day multi-period rolling prediction data and a multi-target cross-layer voltage optimization model which takes electric energy quality and economic operation into consideration and comprises a transformer substation layer, a feeder layer and an equivalent transformer area layer; the day-ahead prediction data are input into a multi-target cross-layer voltage optimization model, a first regulation and control instruction used for regulating a transformer substation layer-feeder layer-equivalent transformer area voltage layer is output, and the first regulation and control instruction comprises a switch action control instruction and an active-reactive power control instruction; reducing decision variables and constraint conditions of the multi-target cross-layer voltage optimization model in a time domain by using the time scale of the intra-day multi-cycle rolling prediction data to obtain an intra-day multi-cycle rolling optimization model; the intra-day multi-cycle rolling optimization model is solved by adopting a distributed primitive dual gradient method, a second regulation and control instruction used for adjusting a transformer substation layer-feeder layer-equivalent transformer area voltage layer is obtained, and the second regulation and control instruction comprises an active power-reactive power control instruction; and performing power distribution network multi-level voltage collaborative optimization according to the first regulation and control instruction and / or the second regulation and control instruction. The method aims at giving consideration to the power quality and economic benefits of the power distribution network, avoiding voltage out-of-limit, better utilizing various adjusting resources to achieve the highest efficiency, reducing the calculation amount by adopting a distributed algorithm, and improving the calculation speed.

The invention relates to the technical field of heat exchangers, in particular to an integral three-dimensional numerical simulation method for an intercooler in a local non-thermal balance state, which comprises the following steps: S1, extracting a periodic repeated infinitesimal structure of the intercooler as a simulation research object; s2, under the given inlet and outlet conditions, the heat exchange amount qr of the infinitesimal structure, the viscous resistance coefficient Dh and the inertial resistance coefficient Ch of the hot side fins and the viscous resistance coefficient Dc and the inertial resistance coefficient Cc of the cold side fins are calculated; s3, establishing a periodic infinitesimal model for removing hot side fins, and solving the equivalent porosity epsilon e, h of a hot side channel; s4, establishing a periodic infinitesimal model without a cold side fin, replacing the periodic infinitesimal model with a porous medium local heat balance model, and solving the equivalent porosity epsilon e, c of a cold side channel; and S5, an overall three-dimensional simplified model is established, and the overall pressure loss and heat exchange amount of the heat exchanger under the specified inlet and outlet conditions are solved. The simulation method provided by the invention can accurately simulate the performance of the complex heat exchanger in the non-heat-balance state.

The invention provides an active load shedding optimization control method, system and device of a wind power plant and a storage medium, and belongs to the technical field of active power control of the wind power plant, and the method comprises the steps: constructing and training an SAC intelligent agent through an SAC algorithm; acquiring a wind power plant state quantity; inputting the state quantity of the wind power plant into the SAC intelligent agent to obtain a vector # imgabs0 # composed of active power initial reference values of all wind turbine generators; inputting the # imgabs 1 # into a constraint controller for secondary calculation to obtain a vector # imgabs 2 # composed of active power optimization reference values of the wind turbine generators; the wind power plant executes # imgabs3 #; and repeating the steps, and adjusting the active power of each wind turbine generator in real time. The model is high in generalization, stability, reliability and learning efficiency, real-time online calculation of the equivalent fatigue load can be achieved, reasonable distribution of the active power of the wind power plant can be achieved under the condition that the active power constraint of the wind power plant is met, the equivalent fatigue load of the whole wind power plant can be reduced, and the maintenance cost can be reduced.

The invention discloses a power distribution network risk assessment method based on a photovoltaic output prediction error, and the method comprises the steps: constructing an isolated tree of an isolated Sessen algorithm, carrying out the fusion processing of the historical data of mass distributed photovoltaic output, and correcting the abnormal value of the photovoltaic output; a probability distribution model of a photovoltaic output prediction error is constructed based on a photovoltaic output typical scene, a power distribution network photovoltaic typical output scene is divided based on an adaptive K-means clustering algorithm, and distributed photovoltaic output is subjected to ultra-short-term prediction by adopting BiLSTM according to meteorological conditions to obtain a photovoltaic output prediction curve. Carrying out modeling on the photovoltaic output prediction error by adopting KDE to obtain a probability distribution curve of the photovoltaic output prediction error; in combination with a semi-invariant method and a Gram-Charger expansion series method, probability distribution of voltage of each node of the power distribution network is obtained through probabilistic load flow calculation; and calculating the node voltage out-of-limit probability of the power distribution network, and evaluating the voltage out-of-limit risk of the power distribution network. According to the method, the key uncertainty factors in the change of the running state of the power distribution network can be determined.

The invention discloses an amorphous forming ability prediction method based on Transform and table data conversion. The amorphous forming ability prediction method comprises the following steps: acquiring basic attributes and amorphous forming ability attributes of amorphous alloy composition elements in a metal alloy sample; the basic attributes refer to element properties simulated based on an experiment or a density functional theory and comprise atomic basic properties, chemical properties and physical properties, and the amorphous forming ability attributes comprise a critical casting diameter Dmax; generating an amorphous alloy data set; wherein the data set comprises an independent variable and a response variable, the independent variable comprises element composition and mixing enthalpy Hmix, and the response variable is a critical casting diameter Dmax; defining a relation model, wherein the relation model is an integrated model formed by three types of models; and training the integrated model by using an amorphous alloy data set, and constructing an amorphous forming ability evaluation model. According to the technical scheme, various relation models for predicting the critical casting diameter can be trained, and the various relation models are combined to construct the optimal amorphous forming ability evaluation model.

The invention belongs to the technical field of data processing, and discloses a multi-source data information deep mining and fusion processing method oriented to performance maintenance, which comprises the following steps of: acquiring multi-source heterogeneous data representing the running state and the performance state of terminal equipment by using different data acquisition equipment, and performing data fusion processing on the multi-source heterogeneous data, and carrying out deep mining of an internal relationship on the fused data, determining key elements influencing the performance state of the terminal equipment, carrying out comprehensive diagnosis on the running state of the terminal equipment, and judging the change trend of the running state of the terminal equipment. According to the technical scheme, reliable evaluation can be carried out on equipment performance maintenance, so that the efficiency and the accuracy of equipment management are improved.

A hybrid gesture tracking method comprises the following steps: S1, acquiring a first frame of image, performing hand recognition on the first frame of image, and combining with a preset depth range to obtain an initial hand region through fusion; s2, performing hand recognition on the initial hand region to obtain a first hand region; s3, recognizing a hand key point and a hand posture of the first hand region; s4, determining the position of a central hand key point of the first hand region; s5, calculating a motion included angle theta between the central hand key point and the previous frame of image in the three-dimensional space; and S6, determining a complete movement track of the hand according to the sum of the movement included angles theta, the position of the central hand key point and the movement amplitude. The method has the characteristics of good stability, high accuracy, rapidness and high efficiency.

The invention discloses a coal rock fracture image recognition method and system based on deep learning and post-processing, a coal rock fracture image recognition model is adopted to recognize and classify spliced coal rock fracture images, the model comprises an improved YOLOv5 target detection network, an image segmentation network and a classifier which are connected in sequence, after the image segmentation network outputs the binary image, parallax depth calculation is carried out on cracks and non-cracks in the crack images at different angles, and the cracks are determined based on the parallax depth; after the fracture is determined, carrying out image feature numeralization on the binarized image, and extracting fracture image feature parameters; the fractal dimension of the fracture is calculated, whether the fracture can be represented or not is preliminarily judged based on the fractal dimension, if yes, the fracture image feature parameters are input into a classifier, the type of the coal rock fracture is output, and if not, the specific type of the fracture is output. And the parallax depth, the fractal dimension and the image features are integrated for recognition and classification, so that the problems of misrecognition and missed recognition are effectively reduced.

The invention discloses a self-adaptive Monte Carlo macro particle simulation method and system based on parallel computing, and relates to the technical field of particle simulation, and the method comprises the steps: initializing parameters, if the current step number is not the maximum step number, storing node data, balancing particle loading, and calculating charge density and battery density; using macro particles to represent a group of real particles, updating the positions and speeds of all the particles in parallel, and calculating new charge distribution; setting adaptive grid configuration, and dynamically adjusting grid density according to electric field intensity and local density of charge distribution; and based on the updated particle position and speed, evaluating and processing a collision event, storing collision data, re-calculating surface charge density data, adding 1 to the current step number, and circularly executing to finish simulation when the maximum step number is reached. According to the method, the accuracy of a simulation result is improved through the improved collision model, electrical discharge behaviors under different conditions are better predicted, and more reliable data support is provided for design and safety evaluation of high-voltage equipment.

The invention discloses a method and device for detecting the center position in a laser spot image, and the method comprises the steps: obtaining a laser spot image which is a fuzzy circle / circular ring light spot, and solving the center-of-gravity coordinate of the fuzzy circle / circular ring light spot as an initial center position; calculating radial distances from the initial circle center position along a plurality of different directions of the fuzzy circle / circular ring light spot, and calculating an average value of the radial distances in the different directions as an average radius; based on the average radius, utilizing a least square method to fit a new circle center position; and taking the new circle center position as an initial circle center position, repeating iterative calculation until the difference between the current fitting new circle center position and the previous fitting new circle center position is smaller than a preset threshold value, and determining the current fitting new circle center position as a final circle center position. The method is more accurate in circle center position detection and higher in calculation speed.

The invention discloses a computer and large model interaction system based on an artificial dictation command, and the system comprises a control unit which is used for controlling and managing the whole information collection and conversion process; the acquisition module is responsible for capturing voice signals, recognizing the voice signals and converting the voice signals into readable text data for a computer; the analysis module is used for analyzing the text data and converting the text data into a readable instruction format for a large model; the interface module is used for receiving and processing an instruction format and realizing a corresponding function; and the feedback module is used for adjusting and optimizing the accuracy of the voice recognition system and the instruction analysis module according to the feedback result of the user. According to the method, through a deep learning algorithm based on a large model technology, commands dictated by a user are accurately recognized, texts are analyzed and key information is extracted through a large model, and the texts are converted into executable instruction formats, so that seamless joint of man-machine interaction is realized.

The invention belongs to the field of artificial intelligence and digital medical treatment, and relates to a drug recommendation method, which comprises the following steps: preprocessing medical record data and gene data according to a preset data standard specification to obtain standard medical record data and standard gene data, and fusing the standard medical record data and the standard gene data through a health analysis model for prediction to obtain a drug recommendation result. And analyzing the health analysis result, the standard medical record data and the standard gene data through a drug recommendation model to obtain a drug recommendation scheme. The invention further provides a drug recommendation device, computer equipment and a storage medium. In addition, the invention also relates to a block chain technology, and the medical record data and the gene data can be stored in a block chain. According to the method, the efficiency and accuracy of drug recommendation can be improved, the personalized level of drug recommendation is remarkably improved, drug treatment is more targeted and effective, and the drug use safety of patients is improved.

The invention discloses a wireless power transmission electromagnetic environment prediction method based on a PSO-RBF neural network, and the method comprises the steps: arranging a plurality of sensors in a target region of a wireless power transmission system, collecting space coordinate data (xi, yi, zi) and electromagnetic field intensity data Bi, and carrying out the preprocessing of the collected data, including data cleaning and normalization; constructing a radial basis function neural network model for predicting the electromagnetic field intensity by taking the space coordinates as input and the electromagnetic field intensity as output; the method comprises the following steps: constructing a PSO-RBF neural network wireless power transmission electromagnetic environment prediction model, optimizing RBF neural network parameters by using a PSO algorithm, training the PSO-optimized RBF neural network, and performing multiple iterations and cross validation to obtain a trained RBF neural network model to predict the electromagnetic field intensity; in the to-be-measured area, the electromagnetic field intensity of the corresponding position is predicted according to the space coordinates of the to-be-measured position. According to the invention, the electromagnetic environment of the wireless power transmission system can be monitored and evaluated in real time, and potential electromagnetic environment risks can be found in time.

The invention relates to the technical field of motors, in particular to a vehicle motor torque ripple suppression method and device.The method comprises the steps that the current torque, the current rotating speed and the current electrical angle of at least one motor of a vehicle are obtained, a relation table of the torque, the rotating speed and the electrical angle and iq compensation values is inquired, and a target iq compensation value for suppressing torque ripple is obtained, wherein the relation table is calculated by a pre-trained neural network model, and the output of the at least one motor is controlled according to the target iq compensation value, so that the actual rotating speed output by the at least one motor is consistent with the target rotating speed. According to the embodiment of the invention, the iq compensation value relation table of the vehicle motor can be obtained based on the neural network model, and the relation table is inquired according to the current torque, the current rotating speed and the current electrical angle so as to obtain the iq compensation value capable of suppressing the torque ripple, so that comprehensive suppression of the torque ripple is realized, the suppression precision of the vehicle motor to the torque ripple is improved, and the suppression efficiency of the vehicle motor is improved. And stability and practicability are realized.

The invention discloses a runoff prediction method based on a bidirectional long-short-term memory recurrent neural network, and belongs to the technical field of runoff prediction. The method comprises the following steps: preprocessing original data to obtain a data set, and dividing the data set into a training set and a verification set; constructing a runoff prediction network model, wherein the runoff prediction network model comprises a forward LSTM module, a reverse LSTM module, an Adam optimizer, a Dropout regularization layer and a Dense full connection layer which are connected in sequence; setting a hyper-parameter and a loss function, and training the constructed model by adopting the training set to obtain a trained runoff prediction model; and inputting verification set data into the trained runoff prediction model to obtain predicted runoff. According to the method, the association between the past and future runoff time series data can be mined, the problem of poor feature extraction and memory effect of early learning part data caused by unidirectional learning is effectively avoided, and the prediction precision is improved while better applicability is achieved.

The invention discloses a multi-story building mapping method and system based on laser radar and IMU coupling and a medium, and the method comprises the steps: scanning and collecting environment information through a laser radar sensor to obtain point cloud data, carrying out the motion distortion removal of a laser radar point cloud through IMU input data, and carrying out the down-sampling of the point cloud data through a grid down-sampling algorithm; carrying out ground point cloud removal on the single-frame point cloud based on a Markov random field; constructing an optimized pose map based on a multi-plane ground constraint pose constraint algorithm; calculating to obtain a fusion error value by using an error fusion algorithm based on Kalman filtering, and compensating motion estimation; registering and fusing the point cloud data of the current key frame and the global map obtained by accumulation at the preorder moment through a point iteration ICP algorithm; according to the mapping method, the phenomena of mapping drifting and scene degradation are effectively restrained for corridor scenes, upstairs and downstairs and use scenes of multiple similar floors with similar indoor mapping.

The invention belongs to the technical field of data analysis, and particularly relates to an information analysis method for bidding documents. The information analysis method comprises the following steps: S1, obtaining a newly added bidding and tendering file, and sending the file into a preprocessing module; s2, the preprocessing module outputs corresponding preprocessing data to a classification module; s3, the classification module divides the content in the current preprocessed data into valid tables, core blocks and independent blocks, and then the valid tables, the core blocks and the independent blocks are sent into a table analysis module, a core analysis module and an independent analysis module respectively; and S4, the table analysis module, the core analysis module and the independent analysis module respectively output the first analysis data, the second analysis data and the third analysis data to an integration module for integration to obtain core information and output the core information. According to the method, the core information of various bidding and tendering files can be accurately and efficiently analyzed.