85results about How to "Reduce computing needs" patented technology

The invention relates to the technical field of prostate MRI (Magnetic Resonance Imaging) image segmentation, in particular to a prostate MRI image segmentation method based on Mamb-Unet, which realizes effective fusion of features of different scales between an encoder and a decoder, and fuses multi-scale information on a space axis by using a space attention bridge segment to fuse fusion features of different levels from the encoder, so that the segmentation accuracy of the prostate MRI image is improved. And then global and local information is captured through 1D convolution and full connection in the channel attention bridge segment, so that a channel attention map is generated, channel information is better integrated, and multi-level different-scale feature fusion is realized.

The invention provides a smoothed particle fluid dynamics simulation wave impact structure boundary force processing method, which comprises the following steps: S1, initializing the speed, density and position of particles by establishing a numerical model; s2, dividing fluid particles and boundary particles; s3, calculating and correcting the density and speed variation of the particles according to the hydrodynamic equation and the boundary rejection effect; s4, calculating the pressure and the speed of boundary particles through a Schelerd interpolation method, and reflecting the interaction between the fluid and the solid; s5, based on the pressure and the position of the boundary particles, calculating the pressure intensity, obtaining the stress and the torque of the structure, and updating the degree of freedom; and S6, updating the particle state and entering the solution of the next time step. According to the method, when the influence of the boundary force is determined, the influence of the relative speed of fluid particles and wall surface particles is considered, meanwhile, the maximum speed in a flow field is approximately replaced by the artificial sound velocity, and the purposes of accurately simulating the interaction process of the ocean wave-ocean engineering structure and ensuring the stability of numerical simulation calculation are achieved.

The invention provides a rapid forecasting method for environmental response of a ship sailing in an ice region. The rapid forecasting method comprises the following steps: S1, establishing a motion response mathematical model of the ship on the sea under interference of the environment with ice, wind and waves; s2, establishing a ship navigation sea area ice condition and sea condition forecasting model; s3, enriching the environmental motion and structural response database of ship navigation in the ice area or performing data assimilation according to actual measurement information; and S4, according to the planned ship route of the ship and the forecasting result of the regional ice area environmental elements, carrying out refined real-time forecasting of the ice area environmental elements on the ship route region per hour within 24 hours, adopting multi-level decomposition and forecasting in the forecasting process, decomposing the forecasting into different time scales for forecasting, and finally, carrying out the forecasting of the ice area environmental elements in the ship route region. And summing different forecasts to obtain a final forecast result. According to the invention, the motion and structural response of the ship can be quickly and accurately obtained under the influence of a complex ice region environment.

The invention belongs to the field of oil-gas exploration seismic data processing, and relates to a first-motion wave travel time chromatography velocity inversion method, system and device and a medium. According to the method, the first arrival travel time and the grid unit travel time are acquired, and the minimum grid unit travel time is selected; an initial velocity field closer to a real structure is established through micro-logging constraint so as to replace a conventionally used gradient velocity field. The initial velocity field and the first arrival travel time are combined to construct the target function, the slowness information and the regularization information are added in the target function, the multi-information constrained target function is obtained, and the inversion stability and the calculation precision are improved. And adopting a Gaussian-Newton method to minimize the target function of the multi-information constraint, solving the minimized target function of the multi-information constraint, and carrying out iterative updating on a solving result to obtain an inversion velocity field. Explicit calculation and sensitive kernel function storage are not needed, the method is more suitable for large-scale data processing, and the calculation time of speed inversion and the requirement for storage space are reduced.

The invention provides a ship ocean route optimization forecasting method, which comprises the following steps of S1, establishing an initial route according to a voyage number task, a steering point and a route type; s2, establishing a motion response mathematical model of the ship on the sea to the environment; s3, obtaining a marine environment forecasting result of a sea area through which the ship passes; s4, calculating the motion response of the ship passing through the sea area; s5, collecting other requirement conditions in the current voyage task of the ship as other constraints; s6, performing optimization calculation to obtain an optimized route and an optimized propeller rotating speed which should be adopted by the ship under the optimization target; and S8, repeating the steps S3-S7, and executing the newly obtained route and speed optimization scheme. According to the method, the ship navigation plan elements including the ship route and the propeller rotating speed are optimized, and a navigation scheme meeting the optimization requirement of a user can be obtained on the premise of avoiding severe sea conditions to guarantee safety. The method is beneficial to intelligent construction of ship navigation.

The invention provides a multi-stage structure topological optimization method based on a potential diffusion model, and relates to the technical field of engineering design and structure design optimization. Creating a structure topology optimization image data set by using a traditional SIMP method and performing preprocessing; mapping the structure topological optimization image to a low-dimensional potential space for representation so as to reduce the data dimension and improve the calculation efficiency; a forward diffusion process and a reverse diffusion process are carried out on the initial potential variable in the low-dimensional potential pair, a denoising network is added in the reverse diffusion process, and a cross attention mechanism and a condition mechanism are introduced, so that a topological structure is adjusted and designed according to physical and functional requirements, and the structural fidelity is ensured; after training is completed, potential variables of a low-dimensional space are mapped back to a high-dimensional space, a topological structure is generated, few-step SIMP direct optimization is carried out on the topological structure, the structure is rapidly refined, physical information is directly injected into the design so as to adapt to given boundary conditions, and generation of the optimal topological structure can be completed through few-step iteration.

The invention relates to the technical field of automatic control, in particular to an intelligent parking lot access control system and method, and the system comprises a reservation information processing module, a vehicle entrance detection module, a parking space intelligent distribution module, a navigation path calculation module, an indicator light control module and a parking duration calculation module. According to the invention, through automatic identity verification and parking space matching, manual intervention is reduced, the efficiency and accuracy of the reservation process are optimized, the magnetic field inductor is used for identifying vehicle entrance and starting the image acquisition equipment, and vehicle information is identified and recorded, so that the calculation demand and equipment energy consumption of image analysis are reduced, and the vehicle entrance speed and safety are optimized; through parking space distribution and navigation path calculation, in combination with indication lamp control, an optimal navigation route is provided for a driver, a vehicle is guided to accurately arrive at a designated parking space, the time for finding the parking space by the vehicle is shortened, and through a mode of automatically calculating parking fees and controlling an exit barrier gate, payment and departure processes are optimized, and the convenience of the parking process is improved.

The invention relates to a multi-robot unknown environment collaborative exploration method based on a dynamic layering and zoning strategy, which comprises the steps that a plurality of robots are distributed in different areas of an unknown environment for collaborative exploration, each robot constructs a local map in real time through an SLAM algorithm, and a global map is constructed based on information sharing among the robots; based on the exploration area in the global map, segmenting the two-dimensional exploration area corresponding to the global map into a plurality of sub-areas through a dynamic layering and partitioning strategy to obtain a layering and partitioning result; wherein the layering and partitioning result is a sub-region set when the two-dimensional exploration region reaches the optimal hierarchy; based on the starting point position of each robot, distributing the layering and partitioning results among the robots, and balancing the task load between any two adjacent robots in the multiple robots to obtain a task unit set of each robot; and a minimum coverage path is planned on the optimal level based on the task unit set of each robot, and collaborative exploration is carried out based on the minimum coverage path.

The invention relates to a question and answer method and device, computer equipment and a readable storage medium. The method comprises the following steps: inputting an initial question text of a user into a quantized language model to obtain word slot parameters corresponding to the initial question text; the word slot parameter comprises description information related to the target question and answer scene; the quantized language model is a language model obtained by compressing model parameters of the large language model; inputting the word slot parameters and the initial question text into the quantized language model to obtain a standardized question text; the standardized question text is a text which conforms to a preset text structure and clearly reflects the intention of the user; the standardized problem text is input into a vector model, and a plurality of documents with the highest correlation degree with the standardized problem text are obtained; and inputting the standardized question text and the plurality of documents into the quantized language model to obtain an answer text generated by answering the initial question text. The method can improve the reasoning speed of the question answering system.

The invention discloses a rapid assessment method for seawater intrusion and sustainable freshwater exploitation in an phreatic aquifer considering hydraulic boundary changes. The method comprises the following steps: constructing a conceptual model based on specific working conditions; potential energy distribution in the phreatic aquifer is calculated; the seawater intrusion range is determined by searching an equipotential line equal to potential energy at a salt toe position in a potential energy field through interpolation and the height of a salt-fresh water interface; and calculating the sustainable fresh water exploitation rate: calculating the stagnation point position, wherein the water pumping rate when the stagnation point coincides with the salt toe position is the sustainable fresh water exploitation rate. According to the method, the evaluation precision and calculation efficiency of the seawater invasion range and the sustainable fresh water exploitation rate can be remarkably improved while the result accuracy is ensured; by adopting the conceptual model based on the working condition, the research area range and parameter setting can be flexibly adjusted according to actual requirements, and by optimizing the calculation model, the requirements of calculation resources and manpower and material resources are remarkably reduced under the condition that additional equipment or resource investment is not increased.

The invention discloses a method for rapidly evaluating seawater intrusion and sustainable freshwater exploitation in a confined aquifer by considering hydraulic boundary change. The method comprises the following steps: constructing a conceptual model based on specific working conditions; potential energy distribution in the confined aquifer is calculated; the seawater intrusion range is determined by searching an equipotential line equal to potential energy at a salt toe position in a potential energy field through interpolation and the height of a salt-fresh water interface; and calculating the sustainable fresh water exploitation rate: calculating the stagnation point position, wherein the water pumping rate when the stagnation point coincides with the salt toe position is the sustainable fresh water exploitation rate. According to the method, the evaluation precision and calculation efficiency of the seawater invasion range and the sustainable fresh water exploitation rate can be remarkably improved while the result accuracy is ensured; by adopting the conceptual model based on the working condition, the research area range and parameter setting can be flexibly adjusted according to actual requirements, and by optimizing the calculation model, the requirements of calculation resources and manpower and material resources are remarkably reduced under the condition that additional equipment or resource investment is not increased.

The invention discloses a tense knowledge graph completion method based on a relational graph convolutional neural network, and relates to the technical field of knowledge graph completion. The method comprises the following steps: a sub-graph sampler for global and local information perception creates a sub-graph after acquiring a key neighbor of a query node; an encoder generates time-aware representation of the key neighbors from the sub-graphs, and graph convolution is used for carrying out feature aggregation to obtain embedded representation of query; a knowledge graph scoring function, as a decoder, scores the likelihood of temporal facts using a query embedded representation. The problem of insufficient global and local information capture in temporal knowledge graph completion is solved, and the reasoning ability for the event evolution law is improved, so that the accuracy of the completion task is enhanced. According to the method, the problems of high model complexity, large parameter quantity and large memory occupation in an existing tense knowledge graph completion method based on deep learning are solved, the model structure is simplified, the calculation and memory requirements are reduced, meanwhile, the model performance is kept, and the practicability and efficiency of the model are improved.

The invention discloses a multi-modal medical image classification method based on an improved DenseNet, a medical image classification network is constructed, the network comprises a preliminary feature extraction module, a feature refining module and a classification module, the feature refining module fuses the interlayer dense connection characteristic of the DenseNet and the efficient feature processing capability of a Mamba module, and the classification module is used for classifying the Mamba module. Extraction and fusion of complex image features are realized; and the classification module adopts a full connection layer and a Softmax function and is responsible for converting rich image feature representation into a specific image classification result. According to the invention, through the transition layer with the Mama module, the capturing capability of the model on the key features of the image is enhanced, so that the classification precision of the 3D medical image is further improved on the basis of maintaining the original advantages of the DenseNet, and powerful technical support is provided for the application of deep learning in medical image processing.

The invention provides a student classroom behavior detection method based on Transform and task dynamic alignment. On one hand, the GLTB is added into the backbone network to enhance the feature extraction capability. Wherein the CGLU is used for enhancing the non-linear feature expression capability, and the global feature extraction capability of the MHSA is combined, so that the method has higher feature extraction capability. And on the other hand, a neck structure network improved by GLSA is adopted, global features can be effectively extracted, fine-grained local features can be captured, and interaction between global information and local information can be promoted. According to the method, when a complex task is processed, key information in the image can be identified and understood more accurately. In addition, a novel detection head ETADH is adopted, interactive features of classification and positioning tasks are learned through a feature extractor, and task decomposition is performed through a task decomposition module. In this way, the interactivity between the two tasks is enhanced, and the detection precision is improved by effectively utilizing the associated information between the two tasks.

The invention relates to a fuel cell system operation scheme prediction method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring a preset operation parameter item of a to-be-predicted fuel cell system, and acquiring a preset value range and preset value precision of the preset operation parameter item; randomly generating a plurality of groups of operation parameter values of the preset operation parameter item according to a preset value range and preset value precision; respectively converting each operation parameter value into gene information of individuals in a genetic algorithm to obtain an initial population; and obtaining preset constraint condition information and a plurality of preset objective functions, and obtaining a predicted operation scheme of the to-be-predicted fuel cell system according to a preset constraint condition, each preset objective function and the initial population. By adopting the method, the running scheme prediction efficiency of the fuel cell system can be improved.

The invention relates to a light guide plate defect image data enhancement method and device and a medium, and relates to the technical field of image data enhancement, and the method comprises the steps: obtaining a defect image and a corresponding defect type label, and constructing an original defect image data set; training the defect type prediction initial model through the original defect image data set to obtain a defect type prediction model after preliminary training; generating a potential defect image, and inputting the potential defect image into the preliminarily trained defect type prediction model; comparing the potential defect image with the defect image to obtain the similarity between the potential defect image and the defect image; selecting potential defect images of which the similarity is within a similarity threshold value and corresponding potential defect type labels, and constructing a potential defect image data set; and training the preliminarily trained defect type prediction model through the potential defect image data set to obtain a finally trained defect type prediction model. The light guide plate defect detection method has the effect of improving the light guide plate defect detection accuracy.

The invention discloses an Android malicious application detection method based on a multi-view deep neural network, and relates to the technical field of information. The invention provides an Android malicious application detection method based on a multi-view deep neural network in view of the technical problems of numerous attack means, complex and changeable semantics, difficulty in ensuring the coverage rate of attack behaviors by executing detection only from a single view and the like of the current Android malicious application. According to the method, multi-view static feature information is organically fused, the synergistic effect of static features under all views is fully exerted, a target object is comprehensively described, limitation and fuzziness caused by application analysis from a single view are avoided, and finally accurate detection of malicious applications is achieved.

The invention discloses a quantitative laser speckle blood flow imaging method based on a neuromorphic visual sensor, and relates to the technical field of biomedical engineering. The method solves the problem that static scattered light caused by physiological behaviors and electronic noise of imaging equipment have adverse effects on blood flow imaging quality in a traditional laser speckle contrast imaging method, and comprises the following steps: step 1, using a helium-neon laser as a light source, and irradiating a scattering medium with an expanded laser beam to form a speckle pattern; 2, a microfluidic injector is adopted, two parameters of the inner diameter and the flow of the injector are set, and the flow speed range is controlled; 3, asynchronously sensing the change of the light intensity of the speckle pattern by using an event camera, and shooting the speckle pattern by using a frame camera; and 4, acquiring event stream data by the event camera, and processing the data by using an algorithm. Real-time and high-resolution blood flow imaging and flow velocity calculation are achieved, and the inherent limitation of a traditional method is overcome.

The invention relates to the technical field of data processing, and particularly discloses a data processing method of cloud computing based on artificial intelligence, comprising the following steps: S1, calculating the average number of data packets on the same time node in different calibration periods, generating a first coordinate point, and fitting to obtain a first curve; s2, dividing the judgment period to obtain sub-periods, acquiring the number of data packets transmitted by a port in real time in the sub-periods, and drawing a curve; s3, real-time flow and comparison flow are calculated, and flow points in the coordinate system are determined; connecting the flow points to obtain a flow line, determining a flow angle, and determining a radian value of the flow angle; obtaining a flow triangle based on the flow point, and determining the area of the flow triangle; and S4, calculating an abnormal coefficient, screening the to-be-determined port according to the abnormal coefficient, and determining whether the to-be-determined port is abnormal or not. According to the method, the to-be-determined ports are screened, so that the consumption of computing resources required for carrying out abnormity judgment on all the ports is reduced.

The invention discloses a pre-training model fine tuning method for data protection, and an application method and device. The method comprises the following steps: creating an agent model for data protection; according to a preset face image set and the description text thereof, performing iteration fine tuning of a preset time step on the proxy model to obtain a proxy personalized generation model; attacking the proxy personalized generation model by using a preset adversarial sample set and the description text to obtain a disturbance value; adding the disturbance value to a preset confrontation sample set, and continuing to execute the step of attacking the proxy personalized generation model until the number of attacks reaches a preset number threshold, and obtaining a target confrontation sample; and using the target adversarial sample to carry out iteration fine tuning of a preset time step on the proxy model again, and obtaining a final proxy personalized generation model under the condition that the current fine tuning frequency reaches a preset disturbance threshold. By adopting the embodiment of the invention, the calculation demand in an actual training scene is less, and the waste of calculation resources is reduced.

The invention relates to the technical field of target detection and tracking, and provides a lightweight dual-dynamic vehicle detection and classification method and system in a complex road environment, and the method comprises the steps: carrying out the preprocessing of a vehicle image through an image collection module, and constructing a target data set; inputting the new vehicle image into a vehicle detection classification model for analysis so as to automatically output the type and position information of the vehicle; wherein the vehicle detection classification model comprises a trunk feature extractor, a multi-scale adaptive feature fusion device and a classification locator; the trunk feature extractor extracts multiple pieces of feature layer information from the vehicle image; and the multi-scale adaptive feature fusion device extracts and fuses the information of a plurality of feature maps and automatically outputs the category and position information of the vehicle through the classification locator. Compared with an existing advanced deep learning vehicle detection algorithm, the method has the advantages that the parameter quantity and the calculated quantity are smaller, the detection precision is higher, and the light-weight vehicle detection problem under the complex road background can be solved.

The invention relates to a system for sharing map matching results, which comprises a map matching module (30), the invention relates to a map matching system (10) comprising:-a map matching module (30) adapted to-receive digital map data (28, 28 ') from a map provider (24),-receive location information,-perform map matching on the received location information using the digital map data (28, 28'), and-generate map-matched location information, and-a map matching result sharing module (34) connected to the map matching module (30), the map matching result sharing module (34) is adapted to-receive map matching location information from the map matching module (30) and-aggregate messages (36, 36 ') comprising the map matching location information as map matching results to be shared. The invention also provides a method of receiving shared map matching location information by a receiving entity (44) and matching the shared map matching location information to a local map. The invention also relates to a computer program product and a computer readable medium for performing the method.

The invention discloses an encrypted traffic classification method based on feature fusion and a packet dilated convolutional network, which identifies network traffic with high precision and ensures privacy and computational efficiency at the same time, and comprises the following steps: data preprocessing: only retaining 26 bytes to protect privacy through segmentation, filtering and truncation; feature extraction: adopting a double-sliding window mechanism and a designed hash function, generating concrete and abstract features in cooperation with an embedded layer, and realizing joint feature representation; in the model design, firstly, lightweight convolutional network design is carried out, a receptive field is expanded through a lightweight grouping dilated convolutional structure, and channel mixing and point-by-point convolution are added to increase interchannel communication; and in a classification output stage, a cosine annealing mechanism is used to dynamically adjust a learning rate so as to improve a training effect and a convergence speed. The method is superior to an existing lightweight method in the aspects of accuracy, stability, processing speed and resource occupation, is suitable for efficient traffic classification application of resource-constrained equipment, and has practical and promotional values.

The invention discloses an intelligent robot end-to-end motion planning method. The method comprises the following steps: constructing a simulation environment for extraterrestrial detection; the simulation environment is used for simulating motion characteristics of the robot in the extraterrestrial detection environment and wheel soil contact dynamic characteristics, and outputting sensing information of the robot based on a received action instruction; an end-to-end motion planning model is constructed, and the motion planning model takes perception information of the robot as input and takes the action instruction as output; the motion planning model is provided with a reward function, and the reward function is fused with artificial priori knowledge and actual engineering constraints; training the motion planning model based on a simulation environment to obtain a trained model; and in response to deployment of the trained model to an upper computer of the extraterrestrial exploration robot, enabling the robot to output a motion planning result based on real sensing information measured by a sensor of the robot. According to the invention, computing resources are saved, and the end-to-end motion planning capability of the robot under the constraint of limited resources can be improved.

The invention discloses an attention learning mechanism-based inverter model control prediction method, system and device, and a storage medium, and the method comprises the steps: pre-constructing a three-phase inverter simulation model, carrying out the real-time sampling of a state signal through employing an MPC controller, and obtaining a signal characteristic set; performing feature selection on the signal feature set, and extracting feature data; setting a screening threshold to screen the feature data, and taking the screened feature data as optimal features; inputting the optimal feature into a pre-trained machine learning model module to obtain a prediction result of the control signal of the three-phase inverter; the control performance can be improved while more calculation amount and communication traffic are reduced.

The invention belongs to the technical field of machine learning, and aims to provide a post-training quantification method based on symbology metrics, which comprises the following steps: randomly generating a symbology expression tree species group; evaluating each individual in the symbolic expression tree species, and calculating to obtain a performance index of each individual; based on the performance index of each individual, selecting two individuals with optimal performance indexes from the symbolic expression tree species as parent individuals; generating new individuals according to the parent individuals, and adding the new individuals into the symbolic expression tree species; performing optimization processing on individuals in the symbolic expression tree species group to obtain an optimized symbolic expression tree species group so as to obtain a final symbolic expression tree species group; and performing quantification processing on the specified model based on the final symbolic expression tree species group. According to the method, the burden of quantitative index calculation in the model quantification process can be relieved, the calculation and storage requirements of the large language model are remarkably reduced, and the large language model can be efficiently operated on resource-limited equipment.

The invention discloses a shipping stowage method, device and equipment, a storage medium and a program product, and relates to the technical field of shipping intelligent stowage, and the shipping stowage method comprises the following steps: reading a container shipping period of a target ship in a container wharf; according to the container shipping period, shipping containers in a storage yard are combined, and a shipping container string to be loaded is obtained; and carrying out shipping stowage on the target ship based on the shipping container string to be stowage to obtain a shipping stowage plan. According to the shipping stowage method, shipping stowage is carried out through the container shipping period combination shipping container string, shipping stowage of the whole container wharf ship is achieved, the problem that in the prior art, the whole ship shipping stowage convenient to adjust cannot be achieved is solved, manual intervention on the wharf ship shipping stowage is reduced, and the shipping stowage convenience and usability are improved.

The embodiments of the present application disclose a speech signal enhancement method, device, storage medium and electronic device, which can be applied to the cloud field, artificial intelligence field or speech application field. The above method includes: performing feature extraction processing on the speech signal to obtain speech feature information; inputting the above speech feature information into the target model to extract the gain signal to obtain the gain signal corresponding to the above speech signal; enhancing the above speech signal based on the above gain signal to obtain an enhancement result; wherein the above target model includes at least one speech processing module, and the above speech processing module is composed of three gate loop units, and the above gate loop unit is the only speech signal processing unit used in the above target model. The present application not only reduces the degree of heterogeneity and computing requirements, but also improves the denoising and enhancement effect.

The invention relates to the technical field of myopia prediction, in particular to a myopia prediction method and system based on an eye fundus image and electronic equipment. Comprising the following steps: acquiring a pathological myopia medical data set, performing data enhancement and standardization processing on the pathological myopia medical data set to generate a fundus image data set, and dividing the fundus image data set into a training set and a verification set; selecting a baseline model, and performing structured pruning on the baseline model to obtain a pruning model; performing self-distillation training on the pruning model by using the fundus image data set to generate a myopia prediction auxiliary model; obtaining a to-be-detected eye fundus image; and inputting a to-be-detected fundus image into the myopia prediction auxiliary model to generate a myopia prediction result of the to-be-detected fundus image. The myopia prediction is carried out through the method, which pathologic myopia the fundus image to be detected belongs to can be quickly and accurately identified and classified, personal errors are reduced, and a more consistent diagnosis result is provided.

The invention discloses a transcranial magnetic stimulation waveform design method, a transcranial magnetic stimulation waveform design device and electronic equipment, and belongs to the technical field of medical instrument design, and compared with a traditional commercial discharge circuit, the transcranial magnetic stimulation waveform design method selects a novel discharge circuit with controllable waveform parameters, and has higher flexibility. Taking a first preset circuit parameter and a second preset circuit parameter in the waveform parameter controllable transcranial magnetic discharge circuit as optimization indexes, taking a minimum selectivity index as an optimization target, and utilizing an optimization algorithm to find out an optimal first preset circuit parameter and a corresponding second preset circuit parameter; taking a waveform corresponding to the optimal first preset circuit parameter and the corresponding second preset circuit parameter as a target design waveform; the method can improve the selectivity of transcranial magnetic stimulation, the determined target design waveform can improve the controllable transcranial magnetic stimulation effect to a large extent, and meanwhile, the potential of waveform parameter controllable transcranial magnetic stimulation is exerted to the maximum extent.