219results about How to "Accurate description" patented technology

The invention relates to the technical field of numerical control machine tools, and discloses a five-axis high-precision numerical control machine tool control method, device and equipment and a storage medium, and the method comprises the steps: collecting temperature data and displacement data of a five-axis high-precision numerical control machine tool, and carrying out state space modeling to obtain a machine tool motion state model; stress data and thermal error influence factors of the feed shaft are calculated; feeding shaft stress data and the thermal error influence factors are input into the compensation model, real-time compensation parameters are calculated, compensation control is conducted on the machine tool feeding shaft, and a compensation control result is obtained; according to the compensation control result, an initial machining track optimization model is constructed, and optimized machining parameters are generated; performing segmented machining control based on the optimized machining parameters to obtain real-time machining data; the initial machining track optimization model is optimized based on the real-time machining data, a target machining track optimization model is obtained, and the dynamic response performance and the control precision of the five-axis high-precision numerical control machine tool are improved through the method.

The invention relates to a high-precision multi-channel phased array radio measurement calibration method and a high-precision multi-channel phased array radio measurement calibration system. The method comprises the following steps: carrying out electromagnetic characteristic modeling on N array element channels and an antenna housing of the multi-channel phased-array antenna to obtain a channel measurement model; carrying out electromagnetic field numerical calculation on the multi-channel phased-array antenna to obtain an initial error data matrix; mapping the initial error data matrix to a two-dimensional weight matrix, and performing optimal matching solution on the two-dimensional weight matrix to obtain a theoretical calibration coefficient matrix; connecting the standard gain antenna and the multi-channel phased-array antenna by adopting a vector network analyzer, and performing far-field scanning measurement on the multi-channel phased-array antenna to obtain actually measured amplitude-phase characteristic data; and performing weighted least square iterative calculation on the actually measured amplitude-phase characteristic data and the theoretical calibration coefficient matrix to obtain a target calibration compensation coefficient. According to the invention, accurate measurement of the amplitude-phase characteristics of the multi-channel phased-array antenna is realized, and the stability of the calibration process is improved.

The invention relates to a shared energy storage virtual inertia compensation control method for wind power cluster collection, and belongs to the technical field of power system control. In order to solve the problem of insufficient cooperative control of a wind power system and an energy storage system in the prior art, a dynamic model is established by obtaining operation parameters, and the dynamic model comprises a wind turbine generator active power control model and an energy storage system charging and discharging model; designing a virtual inertia control strategy, and introducing virtual inertia control to simulate inertia response characteristics of the synchronous generator; shared energy storage coordination control is carried out, and a strategy is designed by using an MPC method according to an inertia demand and an SOC state; establishing a frequency dynamic response model and calculating inertia response time as a virtual inertia control start-stop condition; and optimizing controller parameters and designing locking logic by adopting an intelligent algorithm. The method can improve the system performance and efficiency, enhance the frequency stability, improve the energy utilization efficiency of the energy storage system, optimize the system performance, and improve the system stability and reliability.

The invention relates to the technical field of optical signal processing, in particular to an optical signal processing algorithm based on biological agent fluorescence detection, which comprises the following steps: S1, collecting fluorescence signals with different wavelengths emitted by each probe, and generating a multi-channel fluorescence spectrum data set; s2, carrying out spectral de-mixing on the fluorescence signals in the fluorescence spectrum data set, separating a plurality of overlapped fluorescence signals, and decoupling the fluorescence signals with different wavelengths; s3, for highly overlapped fluorescence signals, carrying out secondary overlapping separation treatment on the fluorescence signals by using a nonlinear optimization method; s4, extracting the intensity, wavelength and time change characteristics of each fluorescent probe signal, and constructing a multi-dimensional fluorescent signal characteristic vector; and S5, by amplifying the characteristic peak value of the weak fluorescence signal, it is ensured that the signal is not covered by strong signals of other probes in multi-probe detection. According to the invention, the resolution of fluorescence detection is improved, so that the high-precision detection effect can be maintained under complex experimental conditions.

The invention discloses a dynamic control method for a complex curved surface five-axis numerical control machining tool vector, which comprises the following steps of: scanning the surface of a target workpiece by using a high-precision three-dimensional scanner to obtain point cloud data; based on the point cloud data, establishing a mathematical model S (u, v) of the target complex curved surface; a mathematical model based on the target complex curved surface; establishing a comprehensive dynamic model of the five-axis numerical control system; optimizing the initial tool posture in combination with the kinetic model; collecting dynamic operation data of the cutter and the workpiece; on the basis of the dynamic operation data, a model predictive control (MPC) algorithm is used for adjusting the machining path and the cutter direction in real time; according to the method, a comprehensive dynamic model of a five-axis numerical control system is established, dynamic coupling effects of a machine tool kinematic chain, cutter flexibility, cutting force, a temperature field and a stress field are brought into a unified framework, and complex dynamic behaviors are accurately described and controlled through multi-body dynamic modeling and an optimization algorithm.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a video analysis processing system and method based on a multi-modal large model, and the method comprises the steps: enabling the multi-modal control large model to receive a task which is input by a user and needs to be completed through natural language description, carrying out the semantic analysis of the input of the user, extracting task information, and carrying out the analysis of the task information; a task related to video understanding is decomposed into sub-tasks and the sub-tasks are sent to a video understanding module; the video understanding module receives a video or a video clip required by a task, executes a subtask to analyze the video, and generates embedding and description of video content to be input into a multi-modal control large model; when the multi-modal control large model receives a query request of a user, a query task of the user is analyzed into executable subtasks and mapped to a video understanding task, and a response result based on the query request is generated based on embedding and description of video content generated by the video understanding large model. Therefore, effective fusion and mutual enhancement of cross-modal information are realized, and the video understanding precision is improved.

The invention relates to the field of aramid fiber band defect detection, and discloses an X-ray-based aramid fiber band defect online detection method and device. The method comprises the following steps: carrying out potential defect analysis on a physical characteristic image and a material parameter distribution diagram to obtain spatial distribution data of a potential defect region; physical characteristic quantitative analysis is carried out on the spatial distribution data of the potential defect area, a defect physical descriptor is obtained, and the defect physical descriptor comprises a density anomaly feature, a molecular chain orientation disorder degree and a thermal anomaly feature; and performing multi-dimensional feature extraction and weight distribution on the defect physical descriptor according to a preset material performance standard to obtain a quality score and a physical performance parameter of the aramid fiber band, and generating a defect detection report according to the quality score and the physical performance parameter. According to the invention, the comprehensiveness, accuracy and reliability of aramid fiber band defect detection are improved.

The invention discloses a data prediction method based on a kernel regression primary function Bayesian dynamic linear model, and belongs to the technical field of bridge structure health prediction. A to-be-analyzed bridge is detected by using a structural health monitoring system (SHM), and a time series dynamic model KR-BDLM of a Bayesian dynamic linear model is established to describe a bridge structure strain response changing along with time, so that bridge structure health monitoring data prediction and missing value filling are realized; performing parameter optimization reasoning on parameters of the KR-BDLM model by adopting a maximum likelihood algorithm; providing a time-varying state parameter and an observation prediction value of a bridge structure through the optimized KR-BDLM model, and further realizing SHM monitoring data single-step prediction; and multi-step prediction is carried out on the basis of single-step prediction, so that longer-time prediction of bridge data is realized. The prediction result is high in precision, the engineering structure safety can be effectively guaranteed, and the leading-edge development of the structure health monitoring field is promoted.

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 belongs to the technical field of parametric modeling, and discloses a dynamic parametric modeling method for a tunnel operation area. Comprising the steps of collecting actual measurement data of a tunnel operation area; based on the collected actual measurement data, an initial model of the tunnel operation area is established; dividing the initial model of the tunnel operation area into a plurality of model areas, and constructing an operation area undirected graph by taking the model areas as nodes; colors are distributed to nodes in the operation area undirected graph; for each model area, determining a corresponding parameter group according to the allocated color, and updating based on the initial model of the tunnel operation area and the parameter group corresponding to each model area to obtain an overall parameterized model; the dynamic parametric modeling result of the whole tunnel operation area provides a scientific decision basis for design optimization, construction scheme formulation, risk assessment, operation management and the like of tunnel engineering.

The invention provides a low-energy-consumption three-dimensional point cloud single-target tracking method based on a spiking neural network, and the method comprises the steps: obtaining the three-dimensional point cloud data of a previous frame and a current frame of a target, designing a voxel grid module, and dividing the point cloud into 3D voxels; a feature extraction module is designed, a three-stage pulse convolution block is used as a feature extractor, voxelization point cloud features are extracted, a Transform encoder layer is added, and global context information of the point cloud is captured through a self-attention mechanism; a part-to-part motion modeling module is designed, continuous point cloud features on a time sequence are modeled and coded part by part through a two-dimensional pulse convolution block, and feature information changes of a previous frame and a current frame in corresponding space parts are obtained; and designing a regression prediction module, and inferring the relative motion of the target through a pulse regression prediction module. According to the method, energy consumption is reduced, efficiency is improved, and meanwhile it is guaranteed that the network accurately tracks the target in diversified scenes and motion states.

The invention relates to a three-stage tooth intelligent accurate segmentation method based on a shape distance field, and the method comprises the following steps: S1, obtaining a tooth image, and carrying out the preprocessing; s2, extracting a region of interest, and determining a foreground region; s3, calculating the distance and direction from each foreground pixel in the foreground region to the centroid of the corresponding tooth to form a centroid offset vector; s4, generating a tooth centroid density map, and performing clustering identification on centroids of the teeth; s5, designing a segmentation mask loss function and a segmentation boundary loss function of the model; s6, designing a shape loss function based on a distance field; and the trained tooth segmentation model comprises a multi-label mask segmentation sub-network, a multi-label boundary segmentation sub-network and a shape sub-network based on a distance field, which are respectively used for executing a mask image generation task, a boundary detection task and a boundary fine description task, and outputting a final segmentation result. Compared with the prior art, the method has the advantages of improving the accuracy of tooth segmentation and the like.

The invention provides a ship dynamic stability modeling method and system based on multi-external force coupling, and the method comprises the steps: building a short-peak wave model based on wind wave characteristic parameters under different sea conditions and motion state parameters of a ship, and carrying out the calculation under ground coordinates to obtain a first wave surface equation; a first wave surface equation located in a ground coordinate system is converted into a ship body stable coordinate system through a coordinate and frequency conversion method, a second wave surface equation suitable for the surrounding environment of the ship is obtained, then a fluctuation pressure model caused by sea waves is established, the fluctuation pressure model is corrected through a Smith correction term method, and the ship body stable coordinate system is obtained. A multi-wave superposition fluctuation pressure model is obtained through a harmonic superposition analysis method; then a generalized disturbance force matrix is constructed based on a finite volume method, Archimedes restoring force, inertia hydrodynamic force and viscosity hydrodynamic force are calculated by adopting a specific calculation method, and then a ship motion model is established, so that the accuracy of the model is effectively improved, and the dynamic behavior of the ship can be more accurately predicted under the complex sea condition.

The invention discloses a construction method of a TiH binary system machine learning potential. The construction method comprises the following steps: constructing a reference electronic structure data set by adopting first principle calculation software VASP; training and testing an artificial neural network potential-ANN potential model on the reference data set by using the artificial neural network model; vASP and ANN potential obtained through training are used for calculating and comparing a Dimer curve, a lattice constant, an elastic modulus and the like for performance testing; and performing targeted improvement on data with large comparison error between the ANN model and the DFT result in model test and performance test results by increasing related configuration weights until an ANN potential model conforming to the precision is obtained. The machine learning potential model constructed by the invention can better fit the interatomic interaction potential energy surface of the TiH binary system, solves the problems of small time-space scale and low precision of the traditional empirical potential in the calculation of the first principle, and provides a technical basis for the high-precision and large-size simulation and research of the TiH binary system.

The invention discloses a fracture diagnosis method and device, and belongs to the technical field of oil and gas reservoir fracture exploitation, and the diagnosis method comprises the steps: building a water hammer equation set through a water hammer continuity equation, a water hammer motion equation and a liquid elasticity equation; solving the water hammer equation set to obtain a distribution equation of the water head, the flow velocity and the density of the middle well section; initial conditions in the studied wellbore are determined, and expressions of water head, flow and density distribution are obtained in combination with the distribution equation; coupling the water hammer equation set with a wellhead flow boundary condition and a well bottom crack boundary condition, and calculating to obtain a water hammer pressure curve; and crack parameters are determined by adjusting crack related parameters in the well bottom crack boundary conditions. The device comprises a memory for storing a computer program and a processor, wherein the program comprises the steps corresponding to the method. According to the method, the related parameters of the fractures after fracturing are rapidly diagnosed based on the water hammer pressure in the fracturing process, a basis is provided for design and modification of an oil and gas resource reservoir transformation scheme, and therefore the fracturing transformation effect is improved.

The invention discloses a double-domain combined narrow-band radar moving target classification and identification method and related products. Processing a target echo signal received by the radar to obtain a distance Doppler spectrum; obtaining a micro-Doppler time-frequency spectrum based on a target echo signal received by a radar; respectively cutting the distance Doppler spectrum and the micro Doppler time-frequency spectrum to obtain distance Doppler domain characteristic data and micro Doppler domain characteristic data; and inputting the distance Doppler domain feature data and the micro Doppler domain feature data into a pre-constructed RFICNet to obtain a target classification result. According to the method, dual-domain data features of the distance Doppler domain and the micro-Doppler domain are combined, the method does not depend on complex echo data preprocessing operation and excessive prior physical assumptions, dual-domain joint feature information can make up for the deficiency of feature basis during classification, and the classification recognition precision of the narrow-band radar moving target is improved.

The invention discloses a grid-connected wind power plant operation performance evaluation method and system based on correlation analysis, and the method comprises the steps: collecting a grid-connected performance evaluation index evaluation data source, and forming historical sampling data; a test data set is obtained after preprocessing; performing correlation analysis on the test data set, selecting feature column data with high correlation degree to form a new test data set, and constructing a wind power plant operation performance evaluation model; mining the correlation between a preset grid-connected wind power plant operation performance evaluation index and each influence factor in the new test data set, and converting the correlation into a quantitative evaluation index; substituting the actual operation data of the wind power plant into the model, calculating the numerical value of each performance index of the wind power plant, and evaluating the performance of each performance index of the wind power plant in combination with the quantitative evaluation index to obtain the operation performance evaluation result of the grid-connected wind power plant. According to the method, the problem that the index coverage is small in an existing evaluation method is solved, and the operation performance index evaluation system of the wind turbine generator can be established more comprehensively and deeply.

The invention discloses a modeling method and a turbulence prediction method of a wind turbine airfoil turbulence model. The modeling method comprises the following steps: acquiring an incoming flow working condition and a geometric shape of a wind turbine airfoil, performing grid division, and calculating a wall surface distance; constructing an RANS turbulence model, and calculating a flow field data prediction value of each grid point; correcting the flow field data predicted value by adopting a data assimilation algorithm to obtain a flow field data high confidence value; calculating a vortex viscosity correction factor and turbulent flow characteristic data of each grid point, and forming a characteristic set of the grid points by using the wall surface distances of the grid points and the turbulent flow characteristic data; a symbolic regression algorithm is adopted to extract a vortex viscosity correction factor function expression, and a data assimilation algorithm is adopted to correct the vortex viscosity correction factor function expression; and correcting the RANS turbulence model by adopting the corrected vortex viscosity correction factor function expression to obtain the wind turbine airfoil turbulence model. The wind turbine airfoil turbulence model constructed by the method can describe the wind turbine airfoil turbulence more accurately, and the prediction precision is improved.

The invention relates to a zero-sample open vocabulary scene understanding method based on semantic instance generation, and the method comprises the steps: obtaining multi-frame image data including depth information, obtaining 2D mask information through segmentation for each frame of image data, carrying out the semantic feature extraction of a 2D mask, projecting the semantic feature to a 3D space, obtaining a 3D point cloud segment, and carrying out the segmentation of the 3D point cloud segment; combining the 3D point cloud fragments into 3D instances through time sequence grouping combination by adopting a semantic-guided under-segmentation filtering and combination threshold linear attenuation strategy to obtain a 3D instance set; for semantic features of each instance in the 3D instance set, performing feature aggregation processing on the premise of considering intra-class similarity and inter-class difference; and based on the 3D instance set after feature aggregation, constructing a hybrid octree-graph structure to realize zero-sample open vocabulary scene understanding. Compared with the prior art, the problems of inaccurate instance segmentation, inaccurate feature aggregation and low spatial representation efficiency are effectively relieved.

The invention belongs to the technical field of watershed hydrological process simulation research, particularly relates to a distributed storage and hypersonic dynamic combination model construction method based on water and soil conservation measures, and solves the technical problems that an existing loess plateau region hydrological model is low in simulation precision and poor in applicability. Firstly, a research area is subdivided into a plurality of uniform grids through uniform grid division, and each grid represents different underlying surface features; the method comprises the following steps: carrying out an observation test on a typical field slope of a loess plateau to obtain observation data under different water and soil conservation measures, and determining a model parameter range in each grid under the water and soil conservation measures. According to the method, the rainfall-runoff process in each grid is linked with each other by combining a one-dimensional motion wave equation to form a complete distributed storage hyperdynamic combined model, the model structure can dynamically reflect runoff production mechanisms of different grid units, and the prediction capability of the model is improved.

The invention discloses a power transmission line corona discharge risk analysis method, device and equipment, and relates to the field of power transmission line safety analysis, and the key points of the technical scheme are as follows: establishing a dielectric model for representing the polarization characteristics of different types of bird nests on a power transmission tower, and calculating the power transmission line corona discharge risk based on the weighted sum of the dielectric model; obtaining a first mathematical model used for describing bird nest polarization characteristics, and introducing an environment correction factor of the power transmission line to correct the first mathematical model to obtain a second mathematical model; determining the polarization intensity of different types of bird nests on the power transmission line according to a second mathematical model; correcting the initial corona field intensity of the power transmission line containing the bird nest according to the electric field distortion rate and the polarization intensity to obtain the corrected initial corona field intensity; and constructing a corona discharge probability model according to the corrected initial corona field intensity, the electric field distortion rate and the polarization intensity, and analyzing the corona discharge risk of the power transmission line based on the corona discharge probability model.

The invention discloses a diffusion capacitance-based reverse recovery model turn-on loss calculation method and system, and the method comprises the steps: extending the junction capacitance range of a SiC MOSFET through combining the diffusion capacitance and a three-capacitor model, and can more accurately describe the reverse recovery characteristic of a medium-voltage SiC MOSFET, and achieve the simulation precision of the model on the actual device behavior. And a solid foundation is provided for subsequent analysis and optimization. By solving the linearized reverse recovery model, the method can obtain voltage and current waveforms in the reverse recovery process of the SiC MOSFET. The waveforms are key data for evaluating the turn-on loss and directly reflect energy conversion and loss conditions of a device in the turn-on and turn-off process, and the method can further calculate the heat loss of the medium-voltage SiC MOSFET in the turn-on process according to the reversely recovered voltage and current waveforms.

The invention provides a multi-scale hybrid photovoltaic power generation power prediction method and related equipment, and relates to the technical field of time sequence prediction. The method comprises the steps of 1, acquiring power data of a target photovoltaic power station and at least one other photovoltaic power station in an area where the target photovoltaic power station is located in the same historical time period; and step 2, inputting the obtained power data of the plurality of photovoltaic power stations in the same historical time period into a preset power prediction model to obtain the power data of the target photovoltaic power station in the future time period. The power prediction model in the invention can fully model the cross-latitude dependency relationship between the target photovoltaic power station and other photovoltaic power stations in the area range where the target photovoltaic power station is located.

The invention discloses a microseismic signal analysis sensor arrangement method and system and a computer device. The method comprises the following steps of: constructing a Port-Hamiltonian neural network model containing a Hamilton quantity and a dissipation item, wherein the Port-Hamiltonian neural network model comprises the Hamilton quantity and the dissipation item; historical micro-seismic data of the micro-seismic monitoring area are collected and preprocessed; based on the neural network model, the geological parameters and the preprocessed historical micro-seismic data, learning the dynamic characteristics of each monitoring unit of the micro-seismic monitoring system, and outputting dynamic parameters such as the inherent frequency, the damping ratio and the modal shape of the system; constructing a data-driven Min-Max MPC controller based on the kinetic parameters, wherein the controller generates a sensor arrangement strategy according to the monitoring precision and the resource consumption; and calculating the optimal arrangement position of the sensor according to the arrangement strategy. The invention further provides a system for implementing the method. According to the method, the Port-Hamiltonian neural network model is introduced, and the Hamilton quantity and the dissipation characteristics of the system are combined, so that the dynamic characteristics of the micro-seismic monitoring system are accurately described.

The invention discloses a method and a device for identifying electrothermal coupling parameters. The identification method comprises the following steps: constructing an equivalent circuit model of a battery, and carrying out preliminary identification on electrical performance parameters corresponding to a plurality of charge state points; according to the heat production mechanism of the battery, a heat production model of the battery cell is constructed to be coupled into the battery equivalent circuit model, an electrothermal coupling model is obtained, and the heat production model comprises heat parameters; and jointly identifying the electrical performance parameters and the thermal parameters in the electrothermal coupling model to obtain optimized electrothermal coupling parameters. Through the identification method, the identification accuracy of the electrical performance parameters in the battery model can be improved, the heat production condition of the battery can be described more accurately, and the accuracy and the reliability of identification results of the electrical performance parameters and the heat parameters in the battery can be ensured at the same time.

The invention provides a system frequency response estimation method and system based on a fault position, and belongs to the technical field of new energy power grids, and the method comprises the steps: constructing a new energy multi-operation-state model; solving the node voltage of the state model after the fault based on the superposition principle and iterative calculation, establishing a mapping model of the fault position and the low-cross cluster, obtaining the initial value of the active power vacancy, and comprehensively considering the difference between the initial value of the active power vacancy and the active recovery rate between stations at different fault positions. Calculating the system-level total active power vacancy considering the station low-voltage crossing characteristic difference; and constructing a system frequency response model based on the frequency modulation capability difference caused by the fault position, and estimating the system frequency response by adopting the response model. According to the invention, quantitative description of active power vacancy and system equivalent frequency modulation capability loss after disturbance at different fault positions is realized, so that the accuracy of frequency response analysis is improved.

The invention relates to the technical field of space target monitoring, and discloses an RCS information and Doppler information-assisted adaptive PHD filtering method, which comprises the following steps of: 1, establishing a Gaussian mixture logarithmic normal distribution model; 2, on the basis of the Gaussian mixture logarithmic normal distribution model, improving the Gaussian component weight of the Gaussian mixture logarithmic normal distribution model, and establishing an adaptive PHD filter assisted by RCS information and Doppler information; according to the method, target RCS information and Doppler information are introduced into a multi-target filtering updating process, and target feature information is fully utilized to perform space multi-target tracking in a space-based scene; meanwhile, according to the method, the difference between the target and the clutter is enlarged by using the target RCS information and the Doppler information; the problems that an existing filtering method cannot provide target amplitude and RCS information, the accuracy cannot be effectively guaranteed, and different target tracks cannot be effectively distinguished are solved.

The present invention discloses a nonlinear calibration method and device for a line structured light 3D camera laser surface, including: performing reverse projection according to the camera's intrinsic parameter matrix, lens distortion parameters and extrinsic parameter matrix to obtain the three-dimensional coordinates of the center point of the laser line in the two-dimensional image captured by the line structured light 3D camera in the camera coordinate system; fitting the surface using a trained neural network model according to the three-dimensional coordinates of the center point of the laser line to obtain the surface model of the line structured light 3D camera laser surface in the camera coordinate system; obtaining the ray corresponding to each pixel according to the three-dimensional coordinates of each pixel on the center contour of the laser line in the two-dimensional image captured by the line structured light 3D camera; calculating the intersection of the ray corresponding to each pixel and the surface model of the line structured light 3D camera laser surface to complete the three-dimensional reconstruction of the line structured light 3D camera laser surface. The present invention can effectively improve the accuracy of laser surface calibration and the accuracy of three-dimensional reconstruction results.

The invention discloses a water hammer analogue simulation method and device for fracturing, and belongs to the technical field of oil and gas reservoir fracturing exploiting.The simulation method comprises the steps that a simulated shaft is determined; establishing a water hammer continuity equation and a water hammer motion equation based on the water head, the flow, the fluid density and the like; establishing a water hammer equation set by combining a liquid elastic equation combination, and solving to obtain a distribution equation of the water head, the flow velocity and the density; establishing a relational expression of fluid pressure, density and flow, and establishing a water hammer equation set in parallel to obtain a water hammer simulation model; initial conditions and boundary conditions are set, and a curve of water hammer pressure changing along with time is obtained through fitting. The device comprises a memory for storing a computer program and a processor, wherein the program comprises the steps corresponding to the method. According to the method, the water hammer pressure in the fracturing process can be more accurately simulated, developers are helped to analyze the fracturing effect by using the water hammer pressure in the fracturing process, so that fracturing parameters are optimized, a better fracturing effect is obtained, and efficient development of unconventional oil and gas resources is promoted.

The invention relates to the technical field of odor recognition, and discloses an odor feature recognition method, which comprises the following steps of: acquiring an odor data set by using an odor sensor array consisting of a plurality of odor sensors, generating corresponding odor feature sequences through feature extraction, and combining odor features of the odor feature sequences in the same unit time, so as to obtain an odor feature recognition result. According to the method, the dynamic change of the smell is accurately captured and described through the smell sensor array and the feature extraction technology, the dynamic change of the smell is accurately captured and described, the detection accuracy is improved, and the detection accuracy is improved. The accuracy and reliability of smell recognition are improved by combining time sequence analysis, the adaptive capacity of the system in a complex smell environment is enhanced through feature combination and time sequence correlation analysis, and the problem that in the prior art, the data analysis depth of a smell sensor array on complex smells is insufficient is solved.