632results 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 the field of particle size distribution measurement, in particular to a particle size distribution measurement method and system based on machine learning and a light scattering method.The method comprises the steps that a to-be-measured particle sample is irradiated through a light source, and meanwhile light scattering intensity data of different scattering angles and different wavelengths are collected through a multi-angle scattered light detector; constructing a multi-modal deep learning model, and forming an integrated learning architecture in combination with a support vector machine model and a random forest model; inputting the preprocessed multi-wavelength light scattering intensity data into the trained integrated learning architecture to obtain a preliminary particle size distribution result of the to-be-detected particle sample; based on the improved Mie scattering theory, a high-precision theoretical model considering the particle shape, the refractive index, the surface roughness and the internal structure is constructed; an improved Bayesian optimization algorithm is adopted, and the initial particle size distribution result is optimized and adjusted by minimizing the error between the theoretical light scattering intensity and the actually collected light scattering intensity, so that a corrected particle size distribution result is obtained.

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 discloses a text generation method, electronic equipment and a storage medium, and relates to the field of large model technology and text processing. The method comprises the following steps: acquiring to-be-processed data; based on the data structure information of the to-be-processed data, a target map of the to-be-processed data is constructed, and the target map is used for describing different entities in the to-be-processed data and the incidence relation between the different entities; generating a target problem of the to-be-processed data based on the target map, and identifying a problem type of the target problem; and generating a reply text of the target question based on the target question and the question type. The technical problem that the text generation effect is poor in the prior art is solved.

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 seamless steel tube defect detection method and device based on machine vision and a storage medium, and belongs to the technical field of image analysis. According to the method, the potential defect area can be comprehensively positioned by using data of multiple detection modes (image, ultrasonic, eddy current and ray detection), the possible limitation of a single detection mode is overcome, and the detection accuracy is improved. The key detection information block is determined from the multi-mode detection data, the key detection attribute vector is constructed, and the like, so that effective information of the data can be deeply mined, and the state of the potential defect area can be more accurately described. Defect detection discrimination vectors are constructed through a plurality of detection attention levels and defect labels, and detection viewpoints are output, so that the defects can be classified and evaluated in detail, an accurate basis can be provided for quality control of seamless steel pipes, the safety and reliability of the seamless steel pipes in use in various industries are guaranteed, the detection efficiency is improved, and the detection cost is reduced. And the cost risk caused by misjudgment or missed judgment defects is reduced.

The invention relates to the technical field of light source driving control, and discloses an area array type LED light source driving control method, device and equipment. The method comprises the following steps: constructing an upper-layer display control subsystem and a lower-layer drive control subsystem of the area array type LED display system, and collecting a multi-dimensional state data set; performing N-step prediction optimization calculation and brightness uniformity constraint calculation to obtain an optimal control sequence of the LED unit; carrying out extended state observer processing to obtain an LED unit compensation control quantity; compensation area construction and area brightness compensation analysis are carried out, and LED unit area compensation parameters are obtained; performing multi-stage PWM period division and modulation calculation to obtain an LED unit driving control signal; and performing consistency constraint calculation and gradient optimization calculation to obtain global display control parameters of the area array type LED display system, so that smooth transition between the sub-area control units is realized, and the overall display effect of the large-size display system is ensured.

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 belongs to the technical field of meteorological disaster prediction, and discloses a flood early warning method based on rainfall multilayer perception. Comprising the steps of collecting meteorological parameter data, terrain parameter data, hydrological soil data and human activity data; preprocessing the collected meteorological parameter data, topographic parameter data, hydrological soil data and artificial activity data to obtain a meteorological feature data set, a topographic feature data set, a hydrological soil feature data set and an artificial activity feature data set; fusing the meteorological feature data set, the topographic feature data set, the hydrological soil feature data set and the human activity feature data set to obtain a comprehensive feature data set; training according to the comprehensive feature data set to obtain a flood risk prediction model, and predicting through the flood risk prediction model to obtain a flood risk index; the predicted flood risk index is compared with a preset flood risk index threshold value, and whether flood early warning needs to be carried out or not is judged; and a worker is helped to accurately make flood control measures.

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 relates to the technical field of railway power supply and distribution, particularly provides an operation and maintenance management method and system for railway power supply and distribution, and is used for solving the problem of low operation and maintenance management efficiency caused by poor accuracy of fault identification and positioning. The method comprises the following steps: receiving equipment operation data of a railway power supply and distribution system collected by a sensor network through the Internet of Things; inputting the data into a pre-trained graph neural network model, and identifying a fault equipment identifier and a fault mode; determining a target fault propagation path according to the fault equipment identifier, the fault mode and the multi-level power supply and distribution knowledge graph; wherein the multi-level power supply and distribution mapping knowledge domain comprises an equipment level mapping knowledge domain, a region level mapping knowledge domain and a system level mapping knowledge domain which are associated with one another; according to the target fault propagation path, a fault severity evaluation model is adopted to predict the fault severity, a corresponding operation and maintenance management report is determined, and corresponding processing is carried out according to the operation and maintenance management report. According to the technical scheme provided by the invention, the operation and maintenance management efficiency is improved.

The invention discloses a water pollution event situation analysis inversion method, and relates to the field of remote sensing technology and environment model technology, the method comprises the following steps: carrying out feature fusion on an obtained infrared image and an ultraviolet fluorescence image, building an oil film thickness calculation model based on the fused comprehensive features, and obtaining the observation concentration of a detection area by using the model; establishing a convective diffusion model of the target water area, introducing a loss item related to time and flow velocity, estimating the predicted concentration of the detection area by using the convective diffusion model, establishing an inversion model based on the observed concentration and the predicted concentration, and speculating the leakage position and leakage time in the target water area through an optimization iteration mode; according to the technical scheme, the infrared image and the ultraviolet fluorescence image are subjected to feature fusion, the oil concentration of the detection area can be accurately calculated, the loss item related to the time and the flow velocity is introduced into the convection diffusion model, and the inversion precision of the leakage position and the leakage time is improved.

The invention relates to a PPT file content extraction method and system based on a large language model, electronic equipment and a storage medium. The PPT file content extraction method comprises the steps that a PPT file is obtained through a crawler, and a video processing tool extracts PPT key frames and audio-to-text in a video; pPT image processing relates to enhancement, segmentation, OCR and object detection; the large language model application covers a multi-modal input layer, a vision-language fusion network and model fine tuning; and converting the PPT and video PPT data into HTML (Hypertext Markup Language) files. Compared with a PPT file processing method in the related technology, the method can automatically generate data required by large model training in batches. A large number of high-quality structured data sets are provided, the large model can better learn features and rules of PPT content, detailed text description can be generated according to visual information of PPT, PPT description is improved to be more accurate, and the quality of the data sets is higher.

The invention discloses a multi-level feature fusion medical image segmentation method and system based on a diffusion model, and belongs to the technical field of medical image processing. The method comprises the following steps: acquiring an original medical skin lesion image and preprocessing the image, wherein the preprocessing comprises size adjustment and normalization; gaussian noise is added to the input image through the forward diffusion process, and the image with the noise added is obtained; constructing a denoising network encoder, and inputting the preprocessed original input image and the noise image into the denoising network encoder; and constructing a de-noising network decoder, sending the output features of the encoder into the decoder through jump connection and a multi-level feature fusion module, and outputting a segmentation result graph. According to the invention, through a boundary perception loss function and a multi-level feature fusion mechanism, the segmentation precision is improved, and the robustness of the model to noise is enhanced.

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 embodiment of the invention provides an inspection report generation method and device for a cloud network networking scene, a medium and a product, and relates to the technical field of computers, and the method comprises the steps: retrieving a document related to the semantics of abnormal item information from a predetermined knowledge base based on the abnormal item information, and taking the document as the context information of the abnormal item information; the abnormal item information is used for indicating an abnormal detection result of any inspection item of a detection object, and the detection object is one or more products used for cloud network networking; based on the context information and the exception item information, performing exception summary on the inspection items by using a predetermined large language model to obtain an exception item summary text; and generating an inspection report by using the abnormal item summary text. In the scheme, the context information can enhance the semantic understanding of the large language model for the abnormal item information, the abnormal item summary text generated by the large language model can more accurately describe the abnormal item information, the accuracy and readability of the inspection report are improved, and the interpretation difficulty of the inspection report is reduced.

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 provides a pedestrian image enhancement method and device based on a multi-kernel feature fusion convolutional neural network, and a medium, and belongs to the technical field of computer vision. The method comprises the steps that pedestrian images shot under the severe weather condition and the shielding condition are collected to serve as an original image data set, and the original image data set is marked to serve as a training data set; establishing a pedestrian image enhancement model based on a multi-kernel feature fusion convolutional neural network; training a pedestrian image enhancement model through a training set, inputting the training data set into the neural network model, and training the network by adopting a supervised learning mode until a training termination condition is met; and deploying the trained pedestrian image enhancement model to the front end of the target detection network, and enhancing the input image of the target detection network. According to the invention, the definition and recognition accuracy of pedestrian imaging are improved, and the generalization ability and robustness of the model are improved.

The invention relates to the technical field of communication, and provides a base station dormancy method based on flow prediction, and the method comprises the steps: carrying out the feature extraction of historical network flow data, obtaining a flow feature vector, and calculating the time sequence similarity between space units based on the flow feature vector; constructing a space-time dependency graph based on the time sequence similarity; obtaining static environment characteristics and dynamic environment characteristics of the target area according to the spatial environment data, and calculating model parameters based on the static environment characteristics and the dynamic environment characteristics; based on the space-time dependency graph and the model parameters, performing traffic prediction on the target area by using a traffic prediction model to obtain predicted network traffic of all space units; and according to the predicted network traffic, constructing an optimization objective function used for describing all base station states of the target area, and carrying out minimization solution on the optimization objective function to obtain a base station dormancy control scheme of the target area. The method provided by the invention can improve the accuracy of dormancy control on the base station.

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 multi-element aggregation regulation and control method suitable for user-side flexible resources, and belongs to the field of power system operation and control. The multi-aggregation regulation and control method is designed for aggregating the large-scale user side flexibility resource regulation capability to participate in centralized management and control and coordinated scheduling, meanwhile, the system flexibility requirement is considered, and in order to meet the flexible supply and demand balance and weaken the conservative property of inner approximation feasible region calculation under the condition of avoiding an infeasible solution, the method has a good application prospect. According to the method, the flexible resource aggregation feasible region can be accurately described, the optimization effect is not affected while the solving time is remarkably shortened, and the method can be used for solving the flexible resource aggregation feasible region under the constraints of three aspects of the disposable computing power, the storage space and the computing time. And centralized control and coordinated scheduling of large-scale flexible resources are realized.