967 results about "Data simulation" patented technology

An apparatus comprises a cyber security restoration engine configured to simulate an asset of a computing network that is involved in a simulated cyberattack. The cyber security restoration engine is configured to generate data representative of a simulated cyber security scenario involving the asset of the computing network. The simulated cyber security scenario is derived from a real world cyber security scenario mapped to the asset.

The present invention provides a privacy-enhanced structured data simulation and generation method and system. The method comprises: step 1, a data conversion phase: performing normalization preprocessing on data; step 2, a probabilistic graphical model construction phase: on the basis of a Bayesian form, constructing the posterior distribution for variational inference from the data having undergone normalization preprocessing in step 1, obtaining an association relationship between the features of structured data by using a Stein variational gradient descent method, and when introducing differential privacy noise, using a Monte Carlo estimation algorithm to automatically obtain the amount of noise that needs to be added for each update step; and step 3: a data generation phase: using the association relationship obtained in step 2 as a metric set to generate more accurate simulation data than real data. The beneficial effect of the present invention is that: the method of the present invention avoids gradient clipping when applying DP-SGD, thereby avoiding the selection of clipping parameters and reducing the adverse effects of gradient clipping on an inference process.

A method of modeling interactive intelligent three-dimensional implicit structure based on deep learning is provided, including: generating a plurality of geological simulation structural models by using a data simulation technology, to construct a geological simulation structural model library, where the geological simulation structural model has a diversified fold and a fault feature; acquiring, for each, model, geological fault data and unevenly distributed geological horizon data to obtain a training sample data set; training a neural network by using the training sample data set; inputting multi-source heterogeneous data of a target region into a trained neural network, so as to output a geological structural model corresponding to the multi-source heterogeneous data, where the multi-source heterogeneous data includes fault interpretation data and horizon interpretation data, and the multi-source heterogeneous data includes at least one selected from: geological outcrop observation data, well logging data, various geophysical data or empirical knowledge data.

The invention discloses a control strategy model construction method based on big data, and relates to the technical field of intelligent control, a traffic situation deduction and emergency strategy generation module uses a multi-agent traffic simulation technology, simulates a traffic situation in combination with real-time traffic data, evaluates risks and early warns potential crisis by integrating multiple factors, and provides a control strategy model for the traffic situation deduction and emergency strategy generation module. And after an early warning is received, an emergency strategy is generated by using an intelligent algorithm, the strategy is evaluated and optimized by using virtual rehearsal, a result is fed back to a management department, and a synergistic effect with other modules is achieved. Traditional traffic detection equipment data is integrated with multi-source heterogeneous data such as mobile phone signaling, shared bicycle and online hailed bicycle GPS, intelligent vehicle-mounted equipment and public traffic operation, more comprehensive and accurate information such as traffic flow, flow direction, travel behavior track and the like is provided, and rich and accurate basis is provided for traffic management decisions.

The invention discloses a power transmission line icing real-time monitoring method and system based on multi-parameter fusion, and relates to the field of data processing, and the method comprises the steps: receiving conductor physical parameters at the same moment, and simulating an icing track of a target power transmission line section in combination with current environment data; determining a windward side of the icing track based on the current wind direction, and simulating a windward icing layer of the icing track by taking the windward side as a starting surface, taking the current wind speed as a reference wind speed and adopting an icing layer simulation strategy; based on the topographic data of the target power transmission line section, determining a high-risk icing area in the icing track, and determining icing layer data of the high-risk icing area; determining an icing galloping area in the icing track based on the vibration parameter and the strain parameter; and fitting the windward icing layer, the high-risk icing area and the icing galloping area with the icing track, and optimizing the fitted icing track according to the icing layer data to generate a visual interface. According to the invention, the accuracy of icing monitoring of the power transmission line can be effectively improved.

Simulation data of the autonomous vehicle is processed by executing a simulation based on the simulation data to generate a simulation result. Then a perception scenario is generated from the simulation result. The generated perception scenario is validated by verifying whether a constraint is satisfied to produce a validated perception scenario. The validated perception scenario can be used to create or refine a perception model used for controlling the operation of autonomous vehicles.

The invention discloses a purchase demand prediction method and system based on big data analysis, and relates to the technical field of supply chain management and intelligent prediction, and the method comprises the steps: collecting multi-source heterogeneous data, and carrying out the data fusion and preprocessing; based on a causal inference technology, key influence factors of purchase demand changes are analyzed, and a purchase demand prediction model is constructed; dynamically adjusting the prediction model by using real-time data, and generating a real-time or periodic purchase demand prediction result; carrying out data simulation and model optimization aiming at an extreme scene of a purchase demand; and optimizing a supply and demand matching strategy based on a prediction result, and carrying out iterative improvement on the prediction model through a feedback mechanism. Through multi-source heterogeneous data fusion, causal inference, dynamic adjustment and feedback optimization mechanisms, high-precision purchase demand prediction is realized, a supply and demand matching strategy is optimized, the adaptability of the model to market fluctuation and extreme scenes is enhanced, the purchase cost is effectively reduced, and the supply chain management efficiency and stability are improved.

The invention discloses an immersive space virtual-real interaction method and system, and relates to the technical field of virtual reality interaction, and the method comprises the steps: collecting a full-view scene picture and illumination data through a camera, carrying out the preprocessing, simulating the real optical characteristics according to the scene picture and illumination data, and building a virtual scene; the virtual scene is evaluated and optimized; acquiring a user gesture image for analysis, identifying a user interaction command for virtual scene interaction, and displaying an interaction result in real time; and recording and storing the user interaction command and the interaction result. According to the method, the trueness and quality of the virtual scene are effectively improved, meanwhile, the user interaction command is recognized and analyzed by collecting the user gesture image and converting the user gesture image into the three-dimensional coordinate for virtual scene interaction, the gesture recognition accuracy is improved, and the experience effect of user virtual scene interaction is greatly improved.

The invention discloses a refrigerator equipment data simulation and instruction interactive simulation system which comprises the following steps: S1, monitoring the working state of refrigerator equipment in real time, and generating equipment working state data; s2, transmitting the working state data of the equipment to a data processing center through a network; s3, receiving and storing the working state data of the equipment, and generating a working data report; s4, performing deep analysis on the working data report, and outputting an optimized optimization control instruction; s5, generating a control instruction according to the optimization control instruction, and transmitting the control instruction to the refrigerator equipment through a data transmission module; s6, virtual simulation of the working state of the refrigerator equipment is carried out through the working data report of the data processing center; and S7, the operation state of the refrigerator equipment is monitored in real time, potential faults are recognized, and early warning information is generated according to the recognition result. The method has the advantages of intelligent prediction, efficient optimization, real-time feedback, simulation verification and the like.

The invention relates to the field of real-time visualization of road conditions, in particular to a data simulation visualization rendering method and system for urban traffic road conditions. The method comprises the following steps: extracting a real-time satellite streetscape image based on urban satellite remote sensing scanning, and performing scene pixel-level segmentation to obtain scene texture rendering parameters; scene illumination visual identification is carried out according to the real-time satellite streetscape image, traffic scene background modeling is carried out based on scene texture rendering parameters, and a real-time scene background model is constructed; the method comprises the following steps: acquiring urban-level multi-source traffic monitoring data flow, performing vehicle state sensing, and constructing a multi-dimensional particle feature matrix; road network topological correlation analysis and global traffic network state perception are carried out according to the real-time satellite streetscape images, and a road network state perception model is constructed. According to the invention, a real real-time traffic environment is visualized, scene effects in different traffic states are presented, the current road condition can be rapidly evaluated, and the traffic control decision efficiency is improved.

The invention discloses a multi-unmanned aerial vehicle cooperative positioning method and application, and relates to the technical field of unmanned aerial vehicle control and navigation, and the method comprises the steps: constructing a three-dimensional visual simulation platform, including dynamic environment modeling, sensor data simulation and virtual-real combination verification; identifying and tracking a target through a multi-sensor data fusion and deep learning algorithm of the three-dimensional visual simulation platform; based on a multi-unmanned aerial vehicle cooperative positioning algorithm, realizing real-time estimation of the position and state of the target through geometric modeling and interactive multi-model unscented Kalman filtering; information fusion and collaborative decision among multiple unmanned aerial vehicles are realized through a wireless communication network; and optimizing the flight path, task allocation and target identification strategy of the unmanned aerial vehicle based on simulation feedback. The problems that a traditional unmanned aerial vehicle cluster is low in positioning precision and poor in target recognition and tracking capacity in a complex environment can be solved.

The invention belongs to the technical field of field effect transistor performance prediction, and particularly relates to a reliability prediction method, device and system for a field effect transistor, and the method comprises the steps: determining the correlation between the time sequence data of environment parameters, electrical parameters and performance parameters in historical operation records and the change of the performance parameters through a correlation analysis method when the time sequence data changes; key performance parameters are screened out, then a virtual operation model of the field effect transistor is constructed before life prediction is conducted through the screened key performance parameters, and historical data are used for conducting validity verification on the virtual operation model; finally, dynamic changes of environment parameters and electrical parameters are simulated through historical data when the successfully verified virtual operation model is utilized, the degradation trend of key performance parameters is recorded, residual life prediction is carried out based on the degradation trend, prediction efficiency is improved, life prediction can more accurately reflect actual working conditions, and the service life prediction efficiency is improved. And the accuracy of residual life prediction is improved.

The invention discloses a water body remote sensing recognition method based on hyperspectral data simulation and U-net. The water body remote sensing recognition method comprises the following steps: A, data collection and preprocessing; b, selecting and manufacturing a sample area; c, data simulation; d, derived parameters are calculated; e, manufacturing a training sample and a verification sample; f, training a U-net network model; g, multispectral water body detection; and H, precision evaluation. According to the method, a high-quality training sample is obtained through a hyperspectral remote sensing image, multispectral data of different sensors are simulated, the problem that the reusability of water body classification samples among different multispectral satellites and at different geographic distances and time is not sufficient is solved, the multispectral data Landsat8 and Sentinel2 are taken as examples, and the reusability of the water body classification samples is improved. It is proved that by means of the method, easily-confused ground objects such as building shadows and cloud shadows in the water body recognition process can be effectively removed, and on the basis of accurately extracting large-area rivers and lakes, the recognition precision of small lakes and ponds is improved.

The invention provides a tunnel deformation early warning method and system based on digital twinborn and data monitoring, and belongs to the technical field of computer data simulation.The method comprises the steps that firstly, entity rock stratum dynamic monitoring data, real-time construction disturbance data and initial digital twinborn model configuration parameters of a tunnel construction area are obtained; and then virtual-real dynamic coupling and feedback iteration processing is carried out on the virtual-real dynamic coupling model to generate an initial virtual-real coupling digital twinborn model, the initial virtual-real coupling digital twinborn model is iteratively optimized based on real-time construction disturbance data to obtain a final virtual-real coupling digital twinborn model, and deduction of the rock stratum deformation linkage effect is carried out according to the final virtual-real coupling digital twinborn model. The method comprises the following steps: generating a rock stratum deformation linkage path set, performing risk linkage mode identification on the rock stratum deformation linkage path set, determining a tunnel rock stratum large deformation risk linkage mode, and finally generating construction early warning information including a risk linkage diffusion area, a risk linkage evolution period and an emergency response priority according to the risk linkage mode, and transmitting the construction early warning information to a tunnel construction management system. And the tunnel construction safety is effectively guaranteed.

The invention provides a simulation design method applied to a simulation surface acoustic wave filter, and the method comprises the steps: measuring an actual surface acoustic wave filter model, obtaining a Y parameter of the filter, and carrying out the de-embedding processing of an admittance characteristic curve; according to the admittance characteristic curve, COM model parameters are extracted, and static capacitance is extracted; coupling mode model data simulation is carried out according to the extracted COM model parameters, and comparison with actually measured data is carried out; carrying out geometric modeling on the SAW filter to obtain a multi-physical field model and a grid thereof; adding corresponding boundary conditions and excitation conditions to the geometric model, and generating a grid file; importing the grid file into a spectral element algorithm, and saving calculation time by adopting distributed operation; and comparing the generated simulation curve of the COM model with a simulation curve obtained by a spectral element algorithm and actually measured data. And the problem of overlong simulation time of an existing acoustic wave filter is greatly solved while the simulation precision is not influenced.

The invention relates to the technical field of tunnel engineering safety, in particular to a tunnel fire multi-stage damage collaborative prediction method and system, and the method comprises the steps: constructing a data simulation fusion model, and carrying out the processing of fire and tunnel structure parameter data, and obtaining a multi-working-condition parameter database; constructing a heat conduction equation, a fire plume and wall surface heat exchange relational expression, a boundary layer correction model and a comprehensive damage evaluation model, and analyzing the equation, the relational expression and the model by utilizing a multi-physical field coupling unified boundary condition to obtain a multi-physical boundary constraint condition; obtaining a multi-physical constraint loss function based on a network architecture and a multi-physical boundary constraint condition, and further obtaining a comprehensive constraint loss function of a temperature field-stress field-damage index; constructing a PINN-LSTM network architecture and a damage evolution analysis model based on the fire ending initial state coding vector, the fire course feature vector and the cooling process feature vector; and performing linkage analysis on the function, the frame and the model to predict and monitor the transient damage and the long-term creep damage of the tunnel.

The invention relates to a load quantification method for a floating fan in a typhoon scene, and the method comprises the steps: constructing an environment data simulation frame which comprises an external nesting and an internal nesting, the internal nesting is used for achieving wave flow coupling and generating wave flow scene data, and the external nesting is used for carrying out wave flow coupling and obtaining external environment data of the floating fan; the method comprises the following steps: constructing a multi-time-scale load prediction framework, inputting environmental data into the multi-time-scale load prediction framework, and outputting load distribution of typhoon to a floating fan under a long time scale, the multi-time-scale load prediction framework comprising a short-time-scale dynamic coupling analysis model and a long-time-scale load prediction model, and the long-time-scale dynamic coupling analysis model comprises a short-time-scale dynamic coupling analysis model and a long-time-scale dynamic coupling analysis model. The short-time-scale dynamic coupling analysis model is used for obtaining a load prediction sample, and the long-time-scale load prediction model is used for predicting the load prediction sample and outputting load distribution under a long time scale. Compared with the prior art, the method has the advantages of being more practical in environment simulation, accurate and efficient in load prediction and the like.

The embodiment of the invention provides an unconventional oil reservoir oil extraction contribution degree determination method and device, equipment and a medium. The method comprises the steps that the relation between the saturation degree of wet-phase fluid and capillary pressure in an oil reservoir is determined according to a plurality of reference capillary pressure curves; performing simulation analysis on the crack propagation direction and the seepage pressure in the oil reservoir volume fracturing process, and determining a three-dimensional propagation rule of the crack and artificial crack parameters; performing coupling processing on the three-dimensional fracture model and the matrix attribute model to obtain an oil reservoir geological engineering integrated model; establishing a numerical simulation model according to the relationship between the wet-phase fluid saturation and the capillary pressure and the reservoir geological engineering integrated model; performing numerical simulation on the oil reservoir development process through the numerical simulation model to obtain data simulation results of the oil reservoir in different development stages; the oil production contribution degrees of displacement and imbibition in different development stages of the oil reservoir are determined according to a data simulation result, and a theoretical basis and technical support are provided for oil reservoir exploitation.

The invention relates to the technical field of data simulation, in particular to a bearing fault simulation method and system. The method comprises the following steps: collecting multi-modal bearing data so as to construct a bearing distributed edge data set; extracting contact power data of the bearing distributed edge data set, and calculating a bearing rigidity change curve according to the contact power data; carrying out random parameter modeling based on the bearing distributed edge data set, and carrying out physical modeling benchmark reference on the bearing rigidity change curve to obtain a bearing fault physical-data hybrid model; therefore, by integrating multi-modal data acquisition, physical-data hybrid modeling and a dynamic hyper-parameter adjustment mechanism, the defects of a traditional bearing fault diagnosis method in the aspects of accuracy and real-time performance are overcome, and the fault prediction and early warning precision and the response speed are improved.

The invention belongs to the technical field of thermal power plant safety analysis, and relates to a risk assessment method, system and device for a hydrogen storage system of a thermal power plant and a storage medium. Comprising the following steps: collecting operation data of the hydrogen storage system in real time, and preprocessing the data; based on the preprocessed data, utilizing a trained Bayesian LSTM model to predict the hydrogen leakage probability; if the hydrogen leakage probability exceeds a preset threshold value, CFD is combined with the preprocessed data to simulate a diffusion behavior after hydrogen leakage, and the severity of a leakage event is calculated based on a simulation result; and inputting the hydrogen leakage probability and the severity of the leakage event into a Bayesian network model, calculating a risk value, and triggering a grading early warning mechanism according to the risk value. According to the method, the sensitivity and accuracy of risk identification are improved, the scientificity of early warning decision making is enhanced, and intelligent guarantee is provided for safe operation of the hydrogen storage system of the thermal power plant.

The invention relates to the technical field of digital twinborn and drainage basin environment monitoring simulation, in particular to a drainage basin environment data simulation deduction system based on a digital twinborn technology, which comprises a digital twinborn management center used for calling multi-source heterogeneous monitoring data of a drainage basin environment to obtain an aligned environment data sequence; the causal coupling analysis unit is used for obtaining a steady-state logic signal or a dynamic decoupling signal; when a dynamic decoupling signal is generated, the topology adaptive reconstruction unit is used for performing structural mutation recognition feedback analysis on a calculation map of the digital twin model to obtain a reconstructed topology map; when the steady-state logic signal is generated, the simulation deduction execution unit is used for performing environment evolution deduction analysis on the aligned environment data sequence based on the current calculation map to obtain a simulation deduction result; according to the method, model calculation divergence or misleading output caused by sensor faults is effectively avoided, and the calculation problem caused by multi-source data heterogeneity is solved.

The invention discloses a modeling method for an IGBT switch transient state of an electric drive system of a new energy vehicle. The modeling method comprises the steps that S1, original waveform data and simulation waveform data are collected; s2, calculating the original waveform data and the simulation waveform data by using a linear interpolation method to obtain pre-processed original waveform data and pre-processed simulation waveform data; s3, determining a training data set of a neural network; S4, establishing an artificial neural network model of a multi-layer perceptron structure, and determining the number of neurons of each layer according to the original waveform data and the simulation waveform data; s5, dividing the training data set into a training data set and a verification data set, performing training, and determining model parameters of optimal verification loss according to the verification data set; and S6, carrying out cycle training on each training time point, storing the weight and bias of the neural network corresponding to each training time point, and forming an IGBT switch transient model of the electric drive system. The transient waveform simulation device can accurately simulate the transient waveform when the IGBT is switched on and switched off, and is suitable for simulation of a large amount of data.

The invention discloses a method and device for dynamically adjusting the clamping force of a thin-wall part, equipment and a storage medium, and relates to the field of data simulation. The method comprises the following steps: determining the clamping form and size of a pneumatic clamp tool according to the size of a thin-wall part, simulating a clamping stress process through finite element simulation, and establishing a mapping model between clamping force and deformation; initial machining process parameters are set according to the size and precision requirements of the thin-wall part, and the maximum allowable clamping force under different material allowances is obtained based on the mapping model; stability qualitative analysis is conducted on the clamping stability, and milling parameters are optimized according to the analysis result; and the milling process and the milling parameters are obtained in real time, and the clamping force in the milling process is dynamically adjusted at fixed time by controlling the pneumatic clamp tool through the PLC. Dynamic adjustment of the clamping force is achieved through the established mapping model of the clamping force and deformation, and the problem of deformation caused by rigidity change of the thin-wall part in the machining process is solved by combining layered milling optimization and PLC timing control.

The invention discloses a surrounding rock tunnel blasting dynamic simulation method, and belongs to the technical field of data simulation. The method comprises the following steps: acquiring three-dimensional geological data; constructing an initial mechanical parameter field of the surrounding rock based on the three-dimensional geological data; based on the initial mechanical parameter field, discretizing the surrounding rock area by adopting a finite element method, dividing grids and initializing mechanical attributes of each unit; based on the mechanical properties, blasting simulation is conducted, in the simulation process, the stress distribution state of the surrounding rock is monitored in real time, and whether the stress concentration phenomenon exists or not is judged; calculating a propagation path and an attenuation rate of a stress wave generated by explosive explosion in the surrounding rock through numerical simulation based on a judgment result; based on the propagation path and attenuation rate of the stress wave, the fracture mode and fragment distribution characteristics of the surrounding rock are analyzed; and obtaining a final blasting simulation result based on the stress distribution state, the fracture mode and the fragment distribution characteristics of the surrounding rock. The method can reflect the dynamic response of the surrounding rock and predict the blasting effect.

The invention relates to the technical field of data simulation processing, in particular to a thermal simulation method and system for a circuit board and a medium. The method comprises the following steps: acquiring circuit board layer data; extracting board layer material thermal resistance parameters of the circuit board layer data, wherein the board layer material thermal resistance parameters comprise copper foil thermal resistance parameters and insulating medium thermal resistance parameters; identifying the layered structure of the circuit board layer data, and recording as layered structure data; component layout characteristics are determined according to the layered structure data, interlayer thermal resistance distribution calculation is carried out on the board layer material thermal resistance parameters based on the component layout characteristics, and layered structure thermal resistance distribution data are generated. Through a data processing technology and an analogue simulation technology, heat transfer interaction analysis is carried out on the copper foil layer and the insulating medium layer of the circuit board, and the heat transfer direction and the heat accumulation effect of the circuit board are evaluated so as to construct a heat simulation model of the circuit board.

The invention discloses a low-brightness image security enhancement method and device based on deep learning. The method comprises the following steps: firstly, collecting a large number of low-brightness images covering different scenes, illumination conditions and object contents, and preprocessing the low-brightness images; then a deep learning model is constructed, the model comprises an image light simulation module and an image enhancement module, and the image light simulation module is used for simulating the influence of a light source on an image based on low-brightness image data, calculating scattering, reflection and shadow effects after interaction of light and the image, generating light simulation data and enhancing the light simulation data; the image enhancement module is used for adjusting related parameters for enhancement processing according to the light simulation data; then training the constructed deep learning model by using the preprocessed low-light image data; and finally, enhancing a to-be-processed low-light image by using the trained deep learning model. According to the method, the identification accuracy and the application efficiency of the model can be remarkably improved, the image enhancement quality is improved, and the enhanced image shows loyalty physical rationality and detail authenticity.

The invention provides a training method and device of a body simulation model, electronic equipment and a storage medium, and relates to the technical field of model training. The method comprises the steps that first training data and second training data are obtained, the first training data are multi-modal data collected in a real environment and subjected to time-space synchronization processing, and the second training data are simulation data generated through a digital twin model; the digital twin model is a data simulation environment constructed based on the first training data; and training an initial body simulation model according to the first training data and the second training data to obtain a trained body simulation model. According to the method, the high-fidelity digital twin model is constructed, and the vision and depth of simulation data are ensured to be consistent with physical attributes of physical data in a simulation environment, so that the training precision of model training and the robustness of a control strategy are improved.

The invention relates to a simulation system for operation and maintenance work of an underwater oil extraction structure. The simulation system comprises a semi-physical interaction device, a control module and a visual simulation module, the semi-physical interaction device is used for a diver to install, operate and maintain an underwater Christmas tree, and collecting the body movement posture, the hand position and posture of the diver and the state data of the underwater Christmas tree in the operation process. The control module is used for carrying out control and data monitoring on the semi-physical interaction equipment and sending control and monitoring data to the visual simulation module; and the visual simulation module is used for simulating a three-dimensional scene according to all data. Based on a semi-physical interaction technology, interactive simulation of a diver using a virtual or semi-physical tool and an underwater production facility in a limited space is realized, and the technical problem of diver movement and virtual reality interactive simulation operation is solved. Therefore, the invention can be widely applied to the technical field of underwater operation.

The invention discloses a photovoltaic support installation data simulation management system and method, and relates to the technical field of virtual installation, and the method comprises the steps: taking each participant as a data node, distributing a storage block for each data node, and connecting all storage blocks to construct a block chain network; a common verification is set by using the common information, and a user of each data node in the block chain network can read data of all nodes according to the common verification; extracting an independent feature of each data node, setting an independent verification, and reading and writing installation data generated by each data node according to the independent verification by each data node; selecting a 3d modeling tool, and constructing a virtual installation model by using the collected operation data; and the user inputs the installation data stored in the block chain network in real time into the virtual installation model for virtual installation.

The invention discloses a multi-axis linkage numerical control machine tool control system, which comprises a cutter adjusting module, which is used for carrying out parameter adjustment according to first state data, second state data and third state data to obtain fourth state data of a cutter; the trajectory planning module is used for simulating motion trajectories of a plurality of shafts according to the fourth state data by utilizing simulation software, and performing precision compensation on the motion trajectories of the shafts; and the risk prevention module is used for predicting the first risk and the second risk and implementing prevention measures. The angle and the cutting speed of the cutter are adjusted in a self-adaptive mode, the cutter is matched with the curvature of the surface of the workpiece, so that the flatness of the machining face is improved, the situation that the extrusion force of the cutter on the workpiece is too large is avoided, deformation, except for cutting, of the workpiece is prevented, and the machining precision is improved. A plurality of main shafts and slave shafts are subjected to analog simulation, the cutting angle and the cutting speed of the cutter are further adjusted according to the simulation result, and safe operation of equipment is guaranteed on the premise that the machining precision is guaranteed.