1176results about How to "Fit closely" patented technology

According to the goaf detection method based on transient electromagnetic and seismic wave field joint inversion, data acquisition and joint inversion are carried out on the goaf through the combination of the transient electromagnetic detection system and the reflection seismic detection system, the complementary advantages of the two detection methods are played, the bottlenecks of a single method in the aspects of resolution and signal-to-noise ratio are broken through, and the goaf detection method based on transient electromagnetic and seismic wave field joint inversion is realized. Fine characterization of the structure, the physical property and the stability of the goaf is realized; a machine learning classification algorithm is introduced to analyze a goaf multi-attribute feature data set, and intelligent discrimination of the type, scale and stability of the goaf under complex geological conditions is realized; establishing a quantitative response relationship between goaf physical property parameters and goaf electrical and elastic difference characteristic parameters, constructing a goaf transient electromagnetic and seismic wave field joint forward modeling numerical model, and constructing an electromagnetic-seismic joint inversion objective function; and calculating and solving to obtain goaf resistivity, seismic wave velocity three-dimensional distribution and uncertainty quantitative evaluation results, and reducing blind areas of goaf judgment.

The invention relates to a steel structure component virtual pre-assembly method based on three-dimensional laser scanning. Designing a self-adaptive hierarchical scanning strategy, and dynamically adjusting parameters of scanning equipment to obtain data of each region of the component; abnormal points are detected and eliminated by fusing scanning data of different levels; datum points are extracted, coordinate calibration is carried out, data of all levels are spliced, and a full-component three-dimensional point cloud model is formed; classifying the point clouds by using a multi-dimensional data analysis algorithm; establishing a closed-loop data verification system; designing a multi-feature point matching algorithm; performing iterative docking and position calibration through an algorithm; quantitative analysis is carried out on spatial deviation between the components in the assembling process; after the deviation is detected, a fine adjustment scheme is generated, and the position of the component is adjusted; a virtual pre-assembly path is generated according to the shapes, the connection modes and the installation sequence of the components, and the assembly sequence and the layout of spatial positions are controlled; and the assembly state of each component is monitored in real time, and the path is optimized and adjusted through a feedback mechanism.

The invention relates to the technical field of clothing data processing, in particular to a digital design method of three-dimensional prototype clothing. The method comprises the following steps: obtaining human body three-dimensional scanning data and carrying out feature point labeling, reconstructing a three-dimensional human body model as basic data of costume design, carrying out contour looseness calculation on the human body model, generating a looseness data set, carrying out sample plate plane analysis to obtain a plane sample plate data set, and after collecting costume fabric data, carrying out data processing on the plane sample plate data set. The method comprises the following steps: performing physical characteristic mapping on a human body model and a planar template, reconstructing a three-dimensional garment model conforming to fabric characteristics, performing spatial positioning on the template, performing virtual sewing simulation, generating a shape data set of the garment, performing dynamic motion analysis on the shape data of the garment according to the human body three-dimensional model, and evaluating the comfort level of the garment. The garment form is locally adjusted to reconstruct a planar template. According to the invention, a more comprehensive and more reliable digital design method of the three-dimensional prototype clothing is realized.

The invention discloses a pallet protection corner automatic putting machine. The pallet protection corner automatic putting machine comprises a top protection corner putting combination machine and aside face protection corner putting combination machine; the top protection corner putting combination machine comprises a bottom circulation base and a pair of top protection corner single machines,the side face protection corner putting combination machine comprises a bottom fixed base and four side face protection corner single machines, the top face protection corner single machines are provided with electrical control parts, top protection corner containing grooves, limiting mechanisms, and top protection corner mounting claws, the top protection corner mounting claws grab protection corners in the top protection corner containing grooves, and put the protection corners on four edges of the top of a packaging box, each side face protection corner single machine comprises a second electrical control part, a side protection corner containing groove, and a side protection corner mounting claw, and the side protection corner mounting claws grab the protection corners in the side protection corner mounting grooves and put the protection corners on the four sides of the side face of the packaging box. The pallet protection corner automatic putting machine is reasonable in layout,smooth in operation, high in adaptation, automatic in full process, free of artificial duty and capable of greatly improving protection corner putting and threading packaging capacity.

The invention provides a converter valve fault identification method based on voiceprint small sample learning, and belongs to the technical field of artificial intelligence, and the method comprises the steps: obtaining a to-be-identified target audio comprising converter valve environment noise, and carrying out the noise reduction processing based on FRCRN, and obtaining a noise-reduced audio signal; carrying out feature extraction based on the Mel frequency cepstrum coefficient and the Gammatone filter bank cepstrum coefficient, and carrying out dimension reduction on features based on a principal component analysis method to obtain feature vectors; inputting the feature vector into a voiceprint recognition model which is completely trained based on a voiceprint small sample for recognition, and obtaining a recognition result; according to the method, the to-be-recognized audio is denoised based on the FRCRN, most noise in the converter valve environment can be filtered out, and the technical effect of accurately recognizing the to-be-recognized audio is achieved; by adjusting the structure of the voiceprint recognition network, useful information of the features can be highlighted, redundancy of repeated information is reduced, and therefore the recognition accuracy of the voiceprint recognition model in the converter valve noisy environment is improved.

The invention relates to a mechanism data collaboratively driven dam safety state intelligent diagnosis method and system. The method comprises the steps of collecting environmental quantity data, dam deformation data and corresponding monitoring point coordinates; a PINN model is constructed, the PINN model comprises an input layer, a hidden layer and an output layer, the input layer receives the collected data, and the output layer outputs a predicted value of a deformation component; the PINN model is trained, the weight and bias of the PINN model are adjusted by minimizing a comprehensive loss function, and the comprehensive loss function comprises a data driving loss function, a physical constraint loss function and a boundary condition loss function; using the trained PINN model to predict a deformation field; and comparing the prediction result with the monitoring index to obtain an abnormal region, and performing abnormality diagnosis on the abnormal region. Through the mechanism data synergistic effect, it is guaranteed that model prediction follows the physical law, actual monitoring data can be better fitted, and high-precision dam safety state prediction and diagnosis are achieved.

The invention discloses a functional connection and dynamic model-based anatomical connection prediction method, which comprises the following steps of: firstly, constructing a neurodynamic model, taking a random matrix as initial input, combining functional connection data and the neurodynamic model, and generating a coupling matrix which is more in line with neurodynamic characteristics by iteratively optimizing the random matrix; and the coupling matrix is used for predicting anatomical connection and replacing anatomical connection data to construct a high-precision brain model. According to the method, the initial random matrix is iteratively optimized, so that the generated coupling matrix gradually approaches real anatomical connection, non-intrusive anatomical connection prediction is realized, dependence on intrusive structure connection data is remarkably reduced, and compared with an existing neurodynamics modeling method depending on dMRI data, the method has the advantages that the modeling efficiency is improved, and the modeling cost is reduced. The used DMF model achieves higher fitting degree in structure and function, has higher flexibility and adaptability, is beneficial to revealing deep connection between functional connection and anatomical connection, and provides more efficient technical support for brain network research.

The invention discloses a project management system and method based on a generative adversarial network. The system comprises a data acquisition unit, a feature extraction unit, a progress prediction unit and a scheme generation unit. Wherein the feature extraction unit is used for respectively extracting time sequence features and space features by adopting a long-short term memory network and a multi-head image attention network; the progress prediction unit generates a prediction sequence through a generator based on a generative adversarial network architecture, and evaluates the rationality of the prediction sequence through a discriminator; the scheme generation unit generates a progress adjustment scheme based on key path analysis, resource optimization and a buffer time allocation method; according to the invention, the accuracy and reliability of project progress prediction can be effectively improved, and a scientific basis is provided for project management decision.

The invention belongs to the technical field of fuel cells, and particularly relates to a fuel cell centrifugal air compressor irreversible loss prediction method and system, and the method comprises the steps: collecting performance data based on a fuel cell centrifugal air compressor comprehensive performance test platform, and building and verifying a multi-physics coupling CFD simulation model; defining an irreversible loss type and related structure parameters, constructing an integrated CFD simulation framework, generating a parameter combination table through an orthogonal test, and performing simulation calculation; an RL-GHEM method is provided, the weight and combination strategy of a base model are optimized, linear and nonlinear combination modes are dynamically switched, and irreversible loss prediction models under different working conditions are constructed. The hybrid integrated model based on reinforcement learning guidance is provided, the model combination strategy is dynamically optimized through the reward mechanism, the base model with the optimal performance is optimized, the prediction precision, stability and robustness are improved, precise prediction of the irreversible loss of the air compressor is achieved, and the problem that in the prior art, the prediction precision is insufficient under variable working conditions is solved.

The invention provides a thermal runaway battery ejection device and a using method thereof.The thermal runaway battery ejection device comprises an ejection mechanism, and the ejection mechanism is close to a battery and used for ejecting the battery; the ejection mechanism is provided with a driving mechanism, and the driving mechanism is used for driving the ejection mechanism to work; a protection plate is arranged on the side, close to the ejection mechanism, of the battery. The device is applied to battery bins and a partition plate, the partition plate is provided with a plurality of battery bins, batteries are located in the battery bins, and sensors are arranged in the battery bins. Two partition plates are arranged, a side plate is arranged between the two partition plates, and a top plate is arranged at the tops of the partition plates. The ejection mechanism is connected with the partition plate. A spring is not used as ejection power, the ejection force cannot be reduced due to long-time use, and it is guaranteed that when the battery is in thermal runaway, enough ejection force is provided for ejecting the thermal runaway battery; the battery thermal runaway can be quickly responded, the battery can be timely popped out, the thermal runaway diffusion risk is reduced, fire explosion is prevented, and peripheral equipment is protected.

The invention provides cosmetic loose powder and a preparation method thereof, the cosmetic loose powder provided by the invention at least contains a filler, an emollient and a solvent, and does not contain silica silylate; the bulk density of the cosmetic loose powder is 0.250 to 0.464 g / cm < 3 >; the mass percent of the solvent in the loose powder for making up is 2-20%. In the preparation process of the loose powder for making up, the solvent is dried by adopting a vacuum freeze drying process. According to the preparation method, the traditional concept that loose powder basically does not contain water is abandoned, a method for realizing a water-in-powder system by adopting an organic silicon compound is not imitated, and the powder with certain solvent content and bulk density is prepared on the basis of a vacuum freeze-drying process; the powder has enough bulkiness, and the loose powder for make-up has good fitness after being used for make-up.

The invention relates to a server test environment automatic monitoring method and system. The method comprises the following steps: acquiring various data in a test environment of a plurality of servers in a specified period, and dividing the data into a training set, a test set and a verification set; the method comprises the following steps of: acquiring various monitored server test environments, inputting the monitored server test environments into a server test environment monitoring model for training, testing and verifying, acquiring various data in the monitored server test environments in real time, inputting the data into the server test environment monitoring model, and outputting monitoring results of the monitored server test environments in real time through the server test environment monitoring model. And the server obtains abnormal information according to the monitoring result, obtains an abnormal position and an abnormal severity degree of the monitoring test environment based on the abnormal information according to whether the abnormal information needs to be processed or not, analyzes an abnormal solution according to the abnormal position and abnormal data, and processes the abnormality based on the obtained abnormal solution. The server test environment monitoring efficiency is effectively improved, and the missed test rate is reduced.

The invention discloses an off-line and on-line combined unmanned aerial vehicle simulation flight path planning method, which belongs to the field of unmanned aerial vehicle flight path planning, and specifically comprises the following steps: obtaining a series of insertion waypoint longitudes and latitudes through a waypoint interpolation algorithm according to a selected route before a flight task is executed in an off-line mode; then altitude information stored in an elevation database is obtained in an elevation query mode, the target height of each waypoint of the unmanned aerial vehicle is calculated, and then route planning in the whole task process is completed; in the online mode, in the flight mission execution process, a real-time to-ground laser ranging system is adopted to obtain real-time to-ground height information of the unmanned aerial vehicle, after a measurement error is calibrated by combining flight attitude information of the unmanned aerial vehicle, the measurement error is compared with the actual flight height of the unmanned aerial vehicle, and the to-ground height of the unmanned aerial vehicle is calibrated on the basis of a pre-planned route; and the ground-imitating flight precision of the unmanned aerial vehicle is improved. According to the invention, high-precision ground-imitating equal-altitude flight route planning of the long-endurance unmanned aerial vehicle in a complex environment is realized.

The invention discloses an overlapping response separation and peak positioning method for thickness measurement of an ultrathin multilayer transparent material in spectral confocal. The method comprises the following steps: 1, acquiring a spectral confocal calibration spectrum and a transparent material overlapping response spectrum; 2, evaluating random noise distribution, and obtaining an intensity detection threshold value and a shape coefficient; 3, obtaining an overlapping response initial peak value position by using the second derivative of the overlapping response spectrum; 4, calculating a generalized Gaussian function fitting initial parameter of overlapping response; 5, iterating the overlapping response parameters until the fitting residual change before and after iteration meets the precision requirement; 6, discarding overlapping responses which do not meet the measurement constraint; 7, the step 5 and the step 6 are executed again until all the overlapping responses meet the measurement constraint, and the peak value position of each overlapping response is obtained. According to the invention, the separation of spectrum confocal overlapping response is realized, the peak value positioning precision is improved, and the thickness of the ultrathin multilayer transparent material can be measured.

The invention discloses a three-dimensional geological implicit modeling method based on global constraint features, a medium and equipment, and relates to the technical field of geological modelling, the three-dimensional geological implicit modeling method based on global constraint features mainly comprises the following steps: carrying out vertical dimension sampling and horizontal dimension sampling on original geological data; a profile data set and a drilling data set are obtained, global feature extraction is carried out on the profile data set and the drilling data set to obtain global vertical features and global horizontal features, global feature fusion and coordinate matching are carried out on the global vertical features and the global horizontal features to obtain a machine learning data set, and a Stacking machine learning model is trained to obtain a trained Stacking machine learning model; and predicting a research area by using the trained Stacking machine learning model to obtain a three-dimensional geologic model. According to the three-dimensional geological implicit modeling method based on the global constraint features, the medium and the equipment provided by the invention, the global expression of the underground space in the three-dimensional geological modeling process can be enhanced.

The invention belongs to the technical field of geological prospecting, and particularly discloses an exploration method for constructing an altered rock type gold mine in a shallow coverage area, which comprises the following steps of: firstly, optimizing a working area by using regional geochemical prospecting abnormity, and gradually reducing a prospecting target area by using 1: 25,000 geochemical measurement, 1: 10,000 soil profile measurement and 1: 10,000 geological grass survey; identifying a linear negative topographic development section by using an unmanned aerial vehicle, quickly positioning a gold-bearing structure alteration zone by using a manner of stripping soil and adopting a detection sample, and performing ground surface control by using a groove exploration project; and a target position is predicted by combining the ore body grade, the ore body thickness and the product contour map of the ore body grade thickness by utilizing a structure superposition halo method, drilling engineering deep verification is carried out, and finally the topography and the landform are recovered to find out the shape, the occurrence, the scale and the resource potential condition of the gold mineralized body. The method is simple and convenient, small in environmental disturbance, low in cost investment and good in coverage area effect, the purpose of rapid prospecting is achieved, and an excellent gold ore prospecting effect is achieved in the Heidong region of the Qieemun Tagean, the cliff and the Qieemun Tagean.

The invention discloses a method and a system for adjusting the massage frequency and force of a seat, the seat, a vehicle and a medium. The method comprises the following steps: acquiring a currently played music signal in real time; analyzing the obtained music signal, and extracting the frequency, rhythm speed and volume of the music; calculating corresponding massage frequency and massage strength according to the extracted frequency, rhythm speed and volume; the calculated massage frequency and massage force are converted into control signals; according to the received control signal, the seat massage device is adjusted, and the massage frequency and the massage strength are adjusted. According to the invention, music and seat massage are combined, and the frequency and strength of seat massage are dynamically adjusted through music melody, so that the comfort and pleasure of passengers are improved.

The invention relates to a blue film depoling linkage synchronization method, device and equipment and a storage medium, and the method comprises the steps: obtaining the ejection height of an ejector pin, the target position of the ejector pin, the force of the ejector pin and the target position of a suction nozzle based on all attributes of a chip and the characteristics of a blue film; acquiring image information to obtain initial position coordinates of the ejector pin and the suction nozzle; planning a moving route and a time driving sequence in the same preset time, driving the ejector pin and the suction nozzle to move, and when it is monitored that the ejector pin moves to the ejector pin target position, deviation exists between the current position of the suction nozzle and the planned position in the moving route of the suction nozzle, adjusting the speed curve of the suction nozzle according to the deviation of the suction nozzle; and when it is detected that the ejector pin reaches the ejector pin jacking height and the suction nozzle moves to the suction nozzle target position, a suction instruction operation is sent to the vacuum generator, and the suction force is adjusted until the chip is separated from the blue film, so that the purposes of achieving accurate, efficient and linkage synchronous operation in the blue film core removal process and improving the overall production efficiency of chip core removal are achieved.

The embodiment of the invention provides an e-commerce content intelligent recommendation method and device based on cross-platform user characteristics, equipment and a computer readable storage medium. The method comprises the following steps: acquiring feature information of a current platform target user; matching the feature information with a constructed database to obtain portrait information corresponding to the feature information; the database comprises portrait information of the user acquired based on social media, an e-commerce platform and / or a forum; calculating the similarity between the feature information and the portrait information; on the basis of the similarity, e-commerce content with high integrating degree with the target user is obtained; and pushing the e-commerce content to the target user based on the promotion mode of the current platform. In this way, the user experience is greatly improved while the e-commerce content recommendation accuracy is improved.

The invention relates to the technical field of transportation scheduling, and particularly discloses an unmanned Internet of Things workshop cargo transportation robot scheduling method and system, and the method comprises the steps: receiving demand point locations inputted by an administrator, calculating the virtual distance between the demand point locations, and creating a point location distribution map; obtaining task parameters of each demand point location based on a preset matrix to obtain a task parameter table, and calculating a delivery level of each demand point location based on the task parameter table; selecting a distribution path in a point location distribution diagram according to the distribution level, and feeding back the distribution path to an administrator; and correcting the distribution path based on the feedback information of the administrator, and generating a scheduling instruction according to the corrected distribution path. According to the method, the task parameter table is obtained by counting the task parameters of each demand point location based on the matrix, the task parameter table is subjected to self-identification, the dynamic weight of each index is determined, and then the path selection process is adjusted, so that the integrating degree of the path and the actual situation is greatly improved.

The invention discloses a medical data processing method and apparatus, and an electronic device. The method comprises the steps of constructing a plurality of single-mode prediction models and a plurality of single-mode training data sets in one-to-one correspondence with the single-mode prediction models; based on the single-mode training data set, training a single-mode feature extractor in the corresponding single-mode prediction model; constructing a multi-modal prediction model and a multi-modal training data set; and based on the multi-modal training data set, training a multi-modal classifier in the multi-modal prediction model to obtain a trained multi-modal prediction model. The trained multi-modal prediction model can perform accurate feature representation on the medical data of the multiple modals, and on the basis, the risk of the coronary heart disease can be accurately predicted based on the medical data of the single modal or the medical data of the multiple modals through the multi-modal prediction model.

A straight line fitting method for image detection in a complex interference environment comprises the following steps: firstly, carrying out interference point deletion on a feature point set of an environment image, then carrying out multiple times of random sampling, forming a total point set by using associated points through distance operation, obtaining a plurality of straight line models through initial fitting, and carrying out linear fitting on the linear models; and then calculating an interior point proportion in the total point set according to the linear model, finally selecting the total point set with the maximum number of interior points, and continuously reducing a residual threshold in multiple iterative calculations to improve an interior point proportion threshold by dynamically adjusting a feedback mechanism of the residual threshold when the interior points are calculated. In the initial stage of iteration, more points can enter the model to ensure the diversity and robustness of the model, and the threshold value is gradually tightened along with the convergence of the model, so that the interior points conforming to the model are screened out more accurately. And finally, performing secondary sampling on the inner points and performing straight line fitting on an inner point set subjected to secondary sampling again to eliminate potential error inner points and further improve the fitting precision.

The invention discloses an interface internal morphology reconstruction method, which comprises the following steps of: firstly, obtaining an interface morphology and interface friction force experiment result through a rough interface friction experiment; then, dividing the direction angle of the surface topography into different feature directions, analyzing the interface topography by using a deep learning technology based on computer vision to obtain a distribution proportion of the interface topography corresponding to the feature direction, and obtaining a topography orientation proportion identification result according to the distribution proportion; combining with an interface friction force experiment result to construct a friction force-orientation ratio scale rate relational expression, and calculating to obtain the morphology orientation ratio of the target sample by utilizing the relational expression and combining with the relation between the spring mass block model and the friction force; and finally, obtaining a reconstructed morphology according to the morphology orientation proportion and an interface internal morphology reconstruction formula based on the feature direction. According to the method, the morphology reconstruction and prediction at any moment in the friction process of the target sample can be realized, and the power spectrum density of the reconstructed interface morphology and the actual morphology is well matched.

The invention relates to the technical field of cables, in particular to an airtight cold-resistant low-smoke flame-retardant instrument cable used in an explosive environment, which comprises a protective sleeve, a cable body mechanism is arranged in the protective sleeve, the cable body mechanism comprises inner sheaths, and a plurality of inner sheaths are uniformly mounted in the protective sleeve at equal intervals. According to the invention, heat dissipated in the use process of the cable can be utilized to preheat the main body structure of the cable, so that the cold resistance of the cable is improved while internal heat dissipation is promoted; according to the invention, the structure of the cable is improved, the connection stability among the structures of the cable can be improved, the air tightness of the cable is effectively improved, the throttling effect of the heat insulation shell and the guide ring can be fully utilized, the flame retardance of the cable is improved, the cable can be better matched with the external environment, and the application range of the cable can be effectively expanded while the working stability of the cable is improved.

The invention provides a dynamic driving risk assessment method and system based on DNC and NGBoost-PA-WOE, and the system comprises a database which is used for storing an urban road traffic map, historical traffic accident data and historical traffic violation data, and can automatically update the data; the data acquisition module is used for acquiring vehicle dynamic data and environment characteristics; the data preprocessing and cleaning module is used for preprocessing and cleaning the data; the degree centrality factor (DNC) analysis module is used for calculating a DNC value of a target road node; the evidence weight module is used for calculating WOE values corresponding to the accident rate, the violation rate, the vehicle dynamic data and the environment characteristics of the nodes; and the risk assessment module is used for predicting the risk score of the road node according to the DNC value, the WOE value, the accident rate and the violation rate. According to the method, the NGBoost-PA-WOE model is adopted to predict the risk score of the road node, the model combines the NGBoost algorithm, the FPA and the WOE, and the prediction precision of the risk score can be effectively improved.

The invention discloses an intelligent interaction system for recognizing voiceprint information through environmental sound, and relates to the field of man-machine interaction. Comprising an environment sound pickup module which picks up environment sound in a vehicle by using a microphone array and carries out filtering and denoising processing on the sound; the voiceprint feature extraction module is used for converting the sound signal into a spectrogram by using short-time Fourier transform, distinguishing different passengers through different waveform features, and then extracting MDCC features in the sound signal as sound feature vectors; according to the invention, through picking up the environment sound, then extracting the voice data of the passenger, carrying out feature extraction on the voice data again, obtaining the voice content of the passenger, carrying out emotion and intention analysis on the voice content of the passenger, and then carrying out recommendation and related control, the active degree and the automation degree of man-machine interaction are increased, and the experience of the passenger is improved.

The invention relates to a power distribution network intelligent monitoring method based on a data middle station and edge calculation, and the method comprises the following steps: S1, arranging a sensor in a power distribution network, collecting key data in real time, and carrying out the denoising processing and normalization processing of the collected data; s2, performing preliminary anomaly detection on the processed data at the edge end by using a lightweight GMM model; s3, storing the preprocessed data from the edge nodes in the data center set, and performing secondary detection by adopting a variational auto-encoder based on a preliminary anomaly detection result to obtain a final detection result; s4, constructing a causal relationship graph of the power distribution network, and performing anomaly analysis based on a final detection result; and S5, generating a maintenance report according to the final detection result and the abnormal analysis result. According to the invention, by integrating the edge calculation and the data center, and through multi-stage detection and comprehensive analysis, the abnormity in the network is found and predicted in time, and a reliable guarantee is provided for intelligent management and optimization of the power distribution network.

The invention belongs to the field of rockfill material grading detection, and relates to a rockfill material grading detection method and device based on an image segmentation algorithm, and the method comprises the steps: 1) obtaining an image after paving of stone particles; 2) preprocessing the stone image; 3) inputting the preprocessed image into an instance segmentation model to obtain mask information of stone particles; 4) extracting mask quantitative indexes and shape characteristic parameters in the mask information of the stone particles, and performing particle size and stone total volume conversion in each particle size range on the mask quantitative indexes and the shape characteristic parameters; (5) converting the obtained stone particle size and volume into three-dimensional stone particle gradation, and drawing a stone particle apparent gradation curve; 6, the obtained stone particle apparent grading curve is corrected through the data fusion model.The final stone particle three-dimensional overall grading curve is obtained.The method has the advantages of being high in adaptability, free of a special detection environment and high in detection precision.

The invention belongs to the technical field of computer-aided diagnosis, and particularly relates to a dynamic brain network feature learning method based on Mamba, which comprises the following steps of: performing data processing, including data collection, data preprocessing, dynamic function network sequence construction and state conversion sequence construction; performing sample enhancement by taking the processed data as sample data; constructing a Mama model comprising a linear embedding layer, a Mama layer, a linear mapping layer I, an activation function and a loss function as a classification model; training the Mama model by using the state conversion sequence, and optimizing hyper-parameters of the Mama model; and based on the trained Mama model, predicting the enhanced sample data, and determining a classification result of the sample data through a label output by the Mama model. According to the method, the relation hidden in the dynamic brain network state transition sequence can be efficiently captured, and high-precision classification of each brain network sequence is realized.

The invention discloses a processing method for rounding internal and external spline molded surfaces, which comprises the following steps of: S1, adopting a clamping mode of one clamping and one supporting or direct clamping, and clamping by designing an elastic bushing or a spline bushing; s2, aligning an external spline by designing a spline positioning ring, and machining by adopting a step-by-step method; s3, a spline bushing is designed based on the same principle in the step S2, and an internal spline is aligned; s4, designing a forming column milling cutter; s5, constructing a two-dimensional or three-dimensional spline model of the corresponding size; according to the method, the machining quality is high, and the reliability and stability of spline connection can be effectively improved.