29results about How to "Improve capture accuracy" patented technology

The invention relates to the technical field of model construction, and discloses a neural network-based frozen soil unfrozen water content detection model construction method and system. The method comprises the following steps: carrying out segmented sampling test on a clay sample in a temperature interval of 0 DEG C to-20 DEG C to obtain a multi-dimensional original data set, and carrying out standardization and principal component analysis on the data set to obtain a standardized feature vector; and performing three-stage classification marking on the feature vectors to obtain a training data matrix. A deep neural network is adopted to carry out five-fold cross validation training, and a prediction model of three hidden layers is constructed. And performing correction calculation on model output according to soil mineral composition and porosity parameters, and finally identifying an abnormal value through root-mean-square error analysis and a three-time standard deviation principle to obtain a standardized prediction result. According to the method, the mapping relation between the thermal parameters and the unfrozen water content is established, correction is carried out in combination with the soil body characteristic parameters, and the technical problem that the prediction precision of the unfrozen water content is not high in the prior art can be solved.

The invention relates to an artificial intelligence technology, and discloses an image-based tea leaf pest detection method and device, equipment and a storage medium. The method comprises the following steps: acquiring a pest image; performing multi-level feature extraction on the pest image by using a regular enhanced backbone network in a pre-trained tea pest detection model to obtain multi-scale features; performing feature fusion operation based on a space pooling pyramid and a path aggregation pyramid on the multi-scale features by using a lightweight neck network in the tea pest detection model to obtain a multi-scale fusion feature map; and carrying out target detection operation on a pest part in the pest image by utilizing a pest box selection head network in the tea leaf pest detection model according to the multi-scale fusion feature map to obtain a multi-scale detection box set, and carrying out non-maximum suppression processing on the multi-scale detection box set to obtain a final detection box. According to the invention, the portability of the tea leaf pest detection model and the pest image capturing accuracy can be improved.

This invention relates to the field of power distribution system resilience enhancement technology, specifically a method for enhancing the resilience of a mobile hydrogen energy storage power distribution system. This method includes data acquisition and parameter initialization; constructing physical constraints for the power-transportation coupled system based on a multi-microgrid reconfiguration strategy; expanding the original typhoon scenario using an enhancement strategy to generate an expanded sample set including wind and solar power output prediction errors; constructing a fuzzy uncertainty set covering multi-source uncertainties; establishing a sub-Bruker optimal scheduling model with the objective of minimizing the overall system operating cost; and performing dual transformation and linearization on the sub-Bruker optimal scheduling model to obtain the trajectory of the mobile hydrogen energy storage vehicle, charging and discharging commands, and the action sequences of distribution network segment switches and tie switches. This invention effectively solves the problems of high difficulty in multi-grid resource coordination and difficulty in accurately obtaining data distribution characteristics under extreme disasters, significantly improving the economic efficiency of post-disaster system recovery and achieving an effective balance between robustness and economy.

This application provides a software defect prediction method based on a dynamic path adaptive graph convolutional network. The method includes: constructing graph structure data of software modules; inputting the graph structure data into a graph convolutional network to learn the dependencies between modules; adaptively assigning weights to multi-hop paths through a dynamic path weighted convolutional layer and suppressing long path noise interference based on a path length decay coefficient; optimizing the adjacency matrix in real time based on a multi-head attention mechanism, enhancing key dependency edges and pruning redundant connections; embedding dynamic path scoring and an adaptive graph update algorithm into a classification objective function, updating training weights with a balancing strategy, and outputting defect prediction results through the optimized model. This application can effectively solve the performance degradation problems of traditional graph convolutional networks in scenarios with insufficient dependency modeling, noise accumulation, and class imbalance under the static graph assumption.

The invention discloses an event-driven adaptive sampling and hierarchical transmission method based on a data change rate. The event-driven adaptive sampling and hierarchical transmission method specifically comprises the steps that S1, an equipment end collects industrial equipment operation data at a fixed frequency and generates a data frame; s2, the server side calculates the data change rate of adjacent data frames from the same equipment, and generates an abnormal score based on the change rate; s3, the server side divides the data frame into event levels at least including a heartbeat level, a conventional level, an abnormal level and an accident snapshot level according to the abnormal score Score; s4, the server performs adaptive adjustment on an issuing strategy to the client according to the event level; s5, the server triggers an accident snapshot level event, and reliably transmits and issues the accident snapshot level event to the client through a TCP; and S6, the server side enables the data of different event levels to enter different priority queues and dispatches and issues the data according to the priorities. According to the invention, through event grading, sampling self-adaption and grading transmission, priority arrival of key abnormal data and complete reservation of accident data are realized.

This application provides a multimodal zoned induction and precise trapping method based on airflow field reconstruction, relating to the field of pest control technology. The method includes constructing a peripheral light-odor composite attraction zone and pre-optimizing the odor concentration using AI; forming a circumferential swirling locking zone using an annular guide hood and monitoring the residence time in real time; increasing the fan speed to form an axial laminar flow trapping zone after target detection and using a trajectory prediction algorithm; periodically and intelligently executing reverse micro-blowing to break up the stacked structure of the sticky traps; dynamically adjusting the light-attracting and airflow modes according to environmental parameters and handling external interference. This application achieves high trapping accuracy and low energy consumption.

This invention discloses a method, apparatus, storage medium, and terminal for capturing facial expressions. The method includes: acquiring a face image to be recognized and extracting a target implicit code from the face image; inputting the face image to be recognized and the target implicit code into a pre-trained facial expression capture model, and outputting facial expression information corresponding to the face image to be recognized; wherein the facial expression information is generated based on facial expression features, and the facial expression features are generated by fusing multiple extracted features. Because this application extracts multiple features and fuses them into new features for model training, it can effectively improve the model's accuracy in capturing facial expression information.

This invention discloses a dynamic start-stop and pressure stabilization control method for water pumps based on operating condition identification, relating to the fields of mechatronics and automation control technology. The method includes the following steps: S1, outputting the predicted arrival time node of the reflected wave; S2, outputting operating condition attribute labels; S3, parsing the operating condition attribute labels; S4, inputting the dynamic fluctuation characteristics and graph topology space into an improved DCRNN model, introducing a bandgap filter based on acoustic impedance constraints and a resonant coupling diffusion mechanism to perform frequency domain filtering suppression and impedance matching coupling; S5, outputting a dynamic start-stop delay reference; S6, outputting the primary water hammer wave; S7, outputting the error compensation coefficient; S8, performing adaptive linkage correction on the dynamic start-stop delay reference and wave amplitude evolution reference. This invention overcomes the limitations of traditional methods, such as static threshold fixation, semantic simplicity, and neglect of spatiotemporal coupling topological constraints, providing an efficient solution for proactive defense against transient flow in fluid pipeline networks.

The invention relates to the field of small target detection, in particular to an improved unmanned aerial vehicle small target detection method based on YOLO26s, and the method comprises the steps: constructing a YOLO26s-SOD model for the small target detection of an unmanned aerial vehicle, replacing a first C3k2 module and a second C3k2 module of an original backbone network with a context anchor point residual attention feature fusion module, replacing a third C3k2 module with an A2C2f module, and carrying out the detection of the small target of the unmanned aerial vehicle. Deleting modules behind the windmill-shaped wavelet transform convolution module and connecting the modules with a first convolution module; a first Upsample module in an original neck network is replaced by a first connection module, a context anchor point residual attention feature fusion module and a DySample module which are connected in sequence, a second Upsample module is replaced by the DySample module, and a C3k2 module in the neck network is replaced by the context anchor point residual attention feature fusion module. A DySample module, a second connection module and a context anchor point residual attention feature fusion module which are connected in sequence are added behind the second C3k2 module; and the space focusing capability on a tiny target is obviously improved.

The present invention discloses a multi-objective rapid iteration optimization design method for IGBT modules of wind turbines. First, based on the actual processing, manufacturing and use requirements of IGBT modules of large wind turbines, a multi-objective performance improvement optimization design model is established, and the rapid iteration of the performance response model under different design variables is realized by combining parametric modeling; then, based on the efficient proxy modeling method of self-learning adaptive sampling strategy, the performance response of the multi-objective optimization design model is accurately evaluated, and a high-precision and low-cost design proxy model is constructed; then, based on the genetic algorithm of adaptive bandwidth kernel density estimation, an adaptive bandwidth kernel density estimation genetic algorithm assisted by a proxy model is introduced to quickly and accurately iterate and solve the Pareto solution set of the multi-objective optimization design model. The present invention significantly improves the use performance of the IGBT module of the wind turbine, ensures its reliability and economy, and effectively improves the efficiency and accuracy of the multi-objective optimization design.

The application relates to the field of power system data acquisition and edge computing, and discloses a multi-source heterogeneous data pre-acquisition and edge intelligent processing method, which comprises the following steps: using a modal division mechanism, combining the operation state of power equipment, and performing condition correction on an adaptive sampling frequency to obtain a corrected sampling frequency, so as to acquire multi-source heterogeneous data; extracting fusion core features from multi-dimensional feature vectors; substituting the fusion core features into a general fault judgment model and a time sequence prediction model to respectively output equipment fault prediction results and time sequence prediction results, and obtaining early warning information; based on the fusion core features, the early warning information and the equipment fault prediction results of each acquisition node, and combined with the Lymerzi complexity theory, the spatio-temporal coupling law is identified; and based on the spatio-temporal coupling law and the time sequence prediction results, edge end decision suggestions are provided for source-grid-load-storage collaborative regulation. The application effectively improves the power system data processing efficiency and the power grid operation and maintenance regulation level.

This invention discloses a method for constructing a vegetation drought index that integrates soil information and multi-source remote sensing data. The method includes: constructing an index dataset of multi-source remote sensing data for a given historical year, including a precipitation condition index dataset, a temperature condition index dataset, a vegetation condition index dataset, a water body index dataset, a temperature-vegetation drought index dataset, and a soil moisture drought index dataset; performing Z-score centering on the index dataset, and classifying pixels with Z-scores greater than a threshold as outliers, thereby removing outliers to obtain a preprocessed dataset; performing principal component analysis on the preprocessed dataset, extracting the first principal component as a comprehensive drought index, and filling the result with outliers from the original image to obtain a regional vegetation comprehensive drought index; and assessing the drought risk of a target area based on the regional vegetation comprehensive drought index. This invention improves the predictive ability and adaptability for drought occurrence.

The application discloses a high-fidelity single-sample DOM particle-fluorescent component synchronous characterization system and method, and belongs to the field of water environment monitoring.The system comprises a gradient ultrafiltration enrichment unit, an intelligent FlFFF separation unit, a time sequence-particle size linkage collection unit, a multi-parameter collaborative correction EEM detection unit and a multi-dimensional data fusion analysis module, and realizes synchronous characterization through the steps of gradient ultrafiltration enrichment, intelligent separation, differential collection, collaborative correction detection and multi-dimensional analysis.The application has the advantages of high fidelity and high precision through the technical improvements of double-stage ultrafiltration pressure control and concentration self-adaptive flow field regulation, is suitable for various water environment samples, can output a DOM biological activity score, and provides support for ecological function research.

The present application relates to the technical field of weather forecast, especially a strong convective rainfall short-term forecast method and system based on multi-band radar observation, the method comprising the following steps: obtaining multi-band radar observation data, and performing feature extraction on the multi-band radar observation data to construct a multi-band radar differentiated feature set; performing convective system development stage identification based on the multi-band radar differentiated feature set to obtain a convective evolution stage identification result; dynamically and differentially fusing the multi-band radar differentiated feature set according to the convective evolution stage identification result to generate a multi-band radar fusion feature set; constructing an adaptive short-term forecast model, and inputting the multi-band radar fusion feature set into the adaptive short-term forecast model to output a strong convective rainfall short-term forecast result. The present application supports regional emergency scheduling, and matches the fine and time-sensitive business needs of the short-term warning of strong convective rainfall in coastal urban clusters.

This paper presents a method and system for correcting and predicting supersonic internal flow fields by integrating topology consistency evaluation and physical constraint latent space mapping, belonging to the interdisciplinary fields of fluid aerodynamics design and artificial intelligence. The method first employs a topology consistency evaluation based on a self-organizing mapping grid to realize the nonlinear coupling evolution of parameter sets and spatial errors under supersonic conditions in a hexagonal topological space. Gaussian smoothing and dot product operations are used to quantify the topology consistency between parameters and spatial errors, automatically selecting core parameters. Next, a physical constraint latent space mapping architecture is constructed, introducing a composite physical loss function to reduce the dimensionality of high-dimensional flow field features and establish a mapping model from core parameters to the latent space. Decoder weights are frozen to ensure the continuity of physical laws and derivatives. Finally, an error-driven correction mechanism is used to statistically analyze residuals and generate an error feedback matrix to complete the implicit core parameters, achieving closed-loop reconstruction and corrective prediction of the surrogate model. This method can significantly improve the physical fidelity of supersonic internal flow field reconstruction and effectively solve the problem of large prediction deviations in the flow field behind the gate.

The invention discloses a hand joint motion capture device, comprising a hand surface fixing block (1), five finger assemblies, each of the finger assemblies comprising at least one finger joint (2), each adjacent finger joint (2) and the finger joint (2) and the hand surface fixing block (1) are hinged via a hinged connector (3), each of the hinged connectors (3) is provided with an angle sensor (4), and the five finger assemblies and the hand surface fixing block (1) are respectively arranged at a ratio of 1:1 to the human palm. The angle sensor is used to directly capture the bending angle of the joint in real time and continuously at a ratio of 1:1, with high capture accuracy, while taking into account low cost, simple operation, and low environmental requirements.

The invention belongs to the technical field of optical fibers, and particularly relates to single-mode circular truncated cone optical fiber tweezers for capturing nanoparticles based on an LP11 mode, the single-mode circular truncated cone optical fiber tweezers comprise a first single-mode optical fiber, a second single-mode optical fiber is fixed at one end of the first single-mode optical fiber, the first single-mode optical fiber is connected with the second single-mode optical fiber in a core staggering manner, and the second single-mode optical fiber is fixed at the other end of the first single-mode optical fiber. A tapered optical fiber pigtail is arranged at one end, far away from the first single-mode optical fiber, of the second single-mode optical fiber; the preparation method comprises the following steps: S1, cutting a section of first single-mode fiber and a section of second single-mode fiber for later use, S2, stripping a coating layer from one end of the second single-mode fiber, S3, grinding one end of the second single-mode fiber, and S4, carrying out core staggering connection on the first single-mode fiber and the second single-mode fiber. The capturing radius is further improved, so that the capturing precision and the capturing radius of the single-fiber optical tweezers are improved, and the manufacturing difficulty of the single-fiber optical tweezers is reduced.

The application relates to the technical field of industrial control and artificial intelligence, and discloses an industrial intelligent full-stack domestication multivariate time sequence prediction and intelligent diagnosis system, which comprises the following steps: accessing heterogeneous operation data of equipment in a data source layer; shunting the operation data through a measuring point prefix routing mechanism in a backend service layer, and triggering a degradation fallback logic to return a simulation sequence when an interface is abnormal, and constructing a sliding window sample; extracting features from the sliding window sample through a multivariate model in an intelligent agent layer, outputting a prediction deviation vector, generating a dynamic abnormal threshold value based on historical residual errors; performing online abnormal detection on the dynamic abnormal threshold value in the backend service layer, injecting a prompt word into the prediction deviation vector when an abnormality occurs, outputting a diagnosis result by combining knowledge retrieval and fine-tuning of a large language model in the intelligent agent layer; and performing elastic computing scheduling and rendering in a front-end display layer. The application improves the abnormality capturing accuracy and the fault diagnosis professionalism.

The application discloses a kind of based on big data analysis's radiological medical equipment workload prediction method, it is related to medical equipment internet of things technical field, including the following steps: S1, constructs multidimensional feature portrait tensor;S2, by improving TabTransformer model, introduce the SEIR state evolution mechanism based on space epidemiology modeling, generate time-consuming interference coefficient mapping relationship;S3, output individual time-consuming interference coefficient;S4, output each patient's microcosmic real occupancy time;S5, output single-node net occupancy time after timing compensation;S6, generate the real timing occupancy sequence of radiological medical equipment;S7, output workload prediction distribution atlas.The application overcomes the limitation that traditional method isocoupled simple, ignores queuing ripple effect and static mapping deviation, provides efficient solution for radiological medical equipment workload prediction.

The invention discloses a remote supervision long-tail relation extraction method based on a constraint graph. The method comprises the following steps: constructing the constraint graph; performing vector representation on the relationship in the constraint graph and the constraint relationship of the entity type; obtaining a sentence set and an entity pair of a to-be-extracted relationship; splicing each word in a word embedding-position embedding-part-of-speech embedding manner to obtain embedded representation of the sentence; obtaining a final feature extraction vector representation of the sentence according to the embedded representation of the sentence; splicing the feature extraction vector representation of the sentence and the two entity type representations to obtain a first vector representation of the sentence; obtaining the entity specific attention of the i word relative to the j entity according to the sentence set of the relation to be extracted and the entity pair; and according to the first vector representation of the sentence, the entity specific attention, the relationship of the fusion constraint graph and the constraint relationship of the entity type, performing relationship prediction on the sentence set of which the relationship is to be extracted. The prediction accuracy of the model can be effectively improved.

The application discloses a short-term real-time electricity price prediction method based on LSTM-GRU and cross-time-domain multi-head attention mechanism. First, historical multi-source characteristic measured sequences and future multi-source characteristic prediction sequences of a target region are collected; a future temperature prediction sequence of a key region is obtained; a thermal power climbing amount prediction value of a future time step is calculated to obtain a future thermal power climbing amount prediction sequence; a marginal cost tendency coefficient of a historical time step is calculated to obtain a historical marginal cost tendency coefficient measured sequence; each sequence is normalized, and each time step is time-encoded; then, a sliding window method is used for sample reconstruction, each sample comprising a historical characteristic matrix, a future characteristic matrix and a label; an electricity price prediction model is constructed, comprising a historical encoder, a future decoder, a cross-time-domain multi-head attention module and a full connection layer; and the electricity price prediction model is trained. The method realizes complementation and cooperation of historical operation states and future scene information, and improves prediction accuracy.

A camera module (1) is configured to capture an optical image (3) of a target area and includes a lens member (10), an imager (20), a light transmission member (40) and a base (60). The lens member is configured to receive light from the target area. The imager has a curved portion (203) protruding in a direction away from the lens member and is configured to capture an optical image formed on the curved portion by light passing through the lens member. The light transmission member optically couples the lens member and the imager and allows light from the lens member to be transmitted toward the imager. The base has a support portion (601) supporting an outer edge of the imager and a fluid space (602) defined inside the support portion. A heat dissipation fluid (603, 2603) undergoes convection in the fluid space. The support portion and the fluid space of the base are located on a side of the curved portion opposite to the light transmission member.

The invention relates to the technical field of machine tool spindles, in particular to a multifunctional combined machining spindle of an electric drive axle shell. A hydraulic steering machine rotates to drive a second lower rotating support to rotate around the axis of a motor, and on the premise that a second upper rotating support is fixed, a whole planet wheel assembly is subjected to deformation transmission; the lower rotating bracket II drives the piece Latin mechanism and the knife handle II to rotate by a specified angle respectively; after the upper portion of a hydraulic steering machine is stressed to rotate, the middle portion of the hydraulic steering machine drives a side shaft driving mechanism to conduct bending machining, the lower portion of the hydraulic steering machine drives a driving gear to rotate, and the driving gear sequentially drives a driven gear and a lower rotating support to rotate. The lower rotating support drives the hydraulic cylinder, the supporting plate, the hydraulic rod, the second lower gear, the knife handle support, the Latin barrel, the Latin clamping jaw and the first knife handle to rotate by a specified angle. The multi-angle machining requirement for the new energy reducer shell oil duct is met through rotation of the two split shaft mechanisms of the lower shaft driving mechanism and the side shaft driving mechanism.

The invention relates to the technical field of power distribution area inverter cooperative control, and discloses a distributed cooperative control method, system and device for a power distribution area and a medium, and the method comprises the steps: calculating the real-time risk values of a plurality of risk indexes corresponding to the multi-dimensional operation data of the power distribution area; a risk adjustment mechanism based on operation scene features is introduced for correction, and a comprehensive risk index and a dominant risk type of the power distribution area are obtained; when the comprehensive risk index exceeds a preset high-risk threshold value, controlling each inverter node in the power distribution area to exchange state information so as to determine a master node and a plurality of slave nodes through a distributed election algorithm; then, the master node is controlled to calculate a total adjustment demand according to the dominant risk type, and an optimal resource allocation scheme is solved and issued to the corresponding slave nodes in combination with the maximum safety adjustment amount and the expected contribution degree received from the slave nodes so as to control execution of the slave nodes; quick response to real-time risks is realized, and the distributed cooperative control efficiency is improved.

This invention provides a method for evaluating the uniformity of the flow field in a wind tunnel test section, belonging to the field of wind tunnel construction technology. This invention acquires three-dimensional full-field instantaneous velocity data and wall shear stress distribution, and uses the Stoer-Wagner minimum cut algorithm based on the edge weight function of velocity gradient and turbulence intensity to divide the flow field into a high-uniformity core region and a low-uniformity boundary region. A transition prediction model based on the Monte Carlo method is established to quantify the random influence of wall roughness. The uniformity of the flow field is determined based on velocity uniformity indices, turbulence indices, and flow deflection angle indices, and the contraction section profile parameters and rectifying grid configuration parameters are iteratively optimized. This solves the technical problem of inaccurate flow field boundary identification caused by the lack of a three-dimensional full-field real-time measurement and numerical simulation collaborative verification mechanism in the evaluation of the flow field uniformity in wind tunnel test sections.

The invention discloses a Qinghai-Tibet Plateau global climate mode rainfall downscaling method based on double attention, and relates to the technical field of rainfall downscaling, and the method comprises the following steps: obtaining coarse resolution global climate mode CMIP6 rainfall data, high resolution reference rainfall data and multi-source auxiliary data, and carrying out the standardization preprocessing of all data; a generative adversarial network model is constructed, and a generator of the generative adversarial network model adopts a dual attention mechanism embedded with a channel attention module and a space attention module and is connected in series with a dense block to form a feature extraction main body; training the generative adversarial network model by adopting a two-stage sequential downscaling strategy; and inputting to-be-downscaled CMIP6 rainfall data into the final model, and outputting to obtain a high-resolution downscaled rainfall product. According to the method, the generative adversarial network fusing dense connection and a double attention mechanism is combined with the multi-scale discriminator, so that high-precision downscaling of the CMIP6 rainfall data is realized.

This invention discloses an adaptive optimization method for video encoding and decoding based on generative adversarial networks, comprising: S1: acquiring video stream data and preprocessing it; S2: extracting basic image features and encoding features from the video stream; then calculating cross-modal fusion features and region enhancement features, and finally calculating the optimized encoding parameter set; S3: encoding the preprocessed video stream data to obtain a continuous bitstream conforming to the H.264 standard; S4: calculating the initial decoded video frame sequence and defect region features, inputting them into a timing correction generator to obtain the repaired image, and then outputting the repair optimization signal; S5: optimizing the generator parameters based on the repair optimization signal, and outputting the repaired video frame sequence. This invention can solve the problems of insufficient accuracy in motion vector prediction and blurring / ghosting after decoding in traditional methods for dynamic image encoding and decoding.

The invention discloses a gynaecology and obstetrics information processing method and system based on the Internet, relates to the technical field of the Internet, and enables a nursing scheme to be more personalized and dynamic by performing extraction and data processing on a data set D and integrating to form a feature data set F. By establishing a time sequence analysis algorithm, the system can calculate an adjustment index C reflecting the fluctuation of the health state of the puerpera, and the adjustment index C is matched with a health fluctuation evaluation threshold TZ to generate a nursing regulation and control suggestion and a nursing scheme which are consistent with the health state of the puerpera, so that the defect that the nursing scheme cannot follow the health change of the puerpera in real time is overcome. Besides, by introducing an effect monitoring mechanism, calculating and adjusting the change trend of an index C, generating an effect execution index R and continuously evaluating the execution effect of the nursing scheme, the effect of the nursing scheme is ensured to always meet the health requirement of the puerpera. The nursing plan is matched with the actual recovery state of the puerpera, so that the recovery effect of the puerpera is improved.

The present invention relates to the technical field of cell detection, and specifically to a microfluidic chip rapid detection system, comprising a sample pretreatment and fluid control module, which is used to perform preliminary treatment on input sample cells and ensure that the samples flow accurately in the microfluidic chip; a sensor detection and signal processing module, which is used to perform real-time detection on the sample cells after pretreatment, and send the detected data signals to a circulating tumor cell capture module; the circulating tumor cell capture module is used to capture circulating tumor cells according to the data signals; the present invention integrates optical sensors with microfluidic channels to realize real-time detection of optical signals and fluorescence signals of sample cells, and can more accurately extract circulating tumor cell characteristics; by introducing magnetic field manipulation technology and multi-channel confined microstructure design, combined with fluid dynamic regulation of shear stress, directional capture of circulating tumor cells is achieved.