428results about How to "Break through limitations" patented technology

The invention provides a battery film production automation control method and system based on visual inspection, and relates to the technical field of production control, and the method comprises the steps: collecting a film image, carrying out the fusion, generating a fused film image, carrying out the noise reduction, constructing a feature point matching relation, removing wrong point pairs, carrying out the geometric correction, carrying out the sub-pixel-level alignment, and obtaining a registration image; edge enhancement is carried out based on an improved Frangian filtering algorithm, an edge region is repaired and segmented to obtain a film edge contour, three-dimensional reconstruction is carried out, three-dimensional point cloud coordinates are solved, a film three-dimensional contour model is reconstructed, a film defect detection model is constructed, and a film defect distribution diagram and defect confidence are obtained through forward propagation; performing association analysis with a production process, constructing a feature mapping model, generating a defect knowledge base and matching a process parameter optimization strategy, generating a dynamic instruction and sending the dynamic instruction to a station control unit, monitoring operation data, performing closed-loop feedback control, and obtaining a current optimal control scheme.

The invention provides a cross-modal similarity-based text mining data query method and system, relates to the technical field of data mining, comprises a cross-modal similarity-based text mining data query method and system, and belongs to the technical field of natural language processing. The method comprises the following steps: constructing a multi-level feature extraction network to respectively obtain text, image and time sequence feature vectors; feature fusion is realized by using an adaptive cross-modal contrast learning network, and a cross-modal similarity matrix is constructed; performing hierarchical screening on the target text by adopting a multi-level screening model to obtain a candidate text set; a text relation reasoning module is constructed based on a graph neural network, and text clustering and relation prediction are completed through a multi-task learning framework. According to the method, the accuracy of cross-modal text data query is improved, the adaptivity of feature fusion is enhanced, and efficient text relationship mining is realized.

The invention discloses a data exception traceability tracking and positioning method, system and device and a storage medium. Relates to the technical field of power system data management. At present, the problems of insufficient data anomaly detection accuracy, low traceability positioning efficiency, incomplete knowledge association and the like in analysis and application links of power marketing data are solved. The method comprises the following steps: carrying out preprocessing and feature extraction on power marketing data, constructing a data association relationship network through a clustering analysis and statistical method, analyzing the difference of data features, analyzing the propagation path of an abnormal point identified in a data stream by adopting a path reasoning technology, and constructing a data abnormity tracing positioning model. A semi-supervised learning method is adopted to improve the accuracy of the model, result optimization and hyper-parameter adjustment are carried out, and multi-condition factors influencing business data information are introduced into the model as hyper-parameters. According to the technical scheme, the anomaly detection and traceability positioning capability of the power marketing data is greatly improved.

The invention discloses a medical form data identification method and system based on OCR and MLLM, and the method comprises the steps: receiving original image data, and generating an optimized image sequence through adaptive contrast enhancement, regional importance weighting and multi-resolution optimization; feature extraction is carried out based on the optimized image sequence, a text-space attention map is constructed, and text recognition is realized; performing visual coding and text coding on the optimized image sequence and the text recognition result, and obtaining uniform feature representation through feature alignment; constructing an information level diagram based on unified feature representation, performing relation reasoning, and combining professional knowledge verification to obtain structured feature information; and performing data verification on the structured feature information to obtain normalized data. According to the invention, efficient identification and accurate structuring of the medical form are realized, and the digitization efficiency of the medical data is improved.

The invention discloses an e-commerce platform high-risk user identification method based on time series data analysis. The method comprises the following steps: S1, constructing a user behavior data set; s2, organizing the collected original user behavior data into a behavior sequence according to a time sequence based on the collected original user behavior data set; s3, performing data cleaning on the generated user behavior sequence; s4, carrying out clustering analysis on the preprocessed behavior sequence by adopting a time sequence clustering algorithm in Scikit-learning, and identifying a user group with similar behavior characteristics; s5, dividing the user behavior modes into different risk categories based on a clustering analysis result; s6, automatically labeling new potential high-risk users on the basis of labeled high-risk user data by utilizing a semi-supervised clustering algorithm, and expanding a risk identification range; s7, generating a corresponding risk score; and S8, performing collective management and prevention for high-risk user groups. According to the method, the user groups with similar high-risk characteristics can be identified, and the flexibility and effectiveness of the system in coping with different risk scenes are improved.

The embodiment of the invention discloses a multi-unmanned aerial vehicle cooperative traffic state monitoring method, device and equipment and a medium, and relates to the technical field of multi-unmanned aerial vehicle cooperative control, and the method comprises the steps: carrying out the routing inspection in a preset routing inspection region through a plurality of unmanned aerial vehicles; identifying whether the traffic state is abnormal or not through the trained deep learning model; when it is recognized that the traffic state is abnormal, cooperative unmanned aerial vehicles are selected, the cooperative unmanned aerial vehicles are distributed to the traffic state abnormal areas, and traffic state data are cooperatively collected through the cooperative unmanned aerial vehicles; each cooperative unmanned aerial vehicle fuses the traffic state data of other cooperative unmanned aerial vehicles and then outputs fused traffic state data; and obtaining a real-time traffic state monitoring result based on the fused traffic state data of the cooperative unmanned aerial vehicle. According to the method, the same scene is perceived from different angles through the multiple unmanned aerial vehicles, data of multiple view angles can be fused, the accuracy of accident detection is improved, and data support can be provided for related departments to deal with traffic anomalous events.

The invention belongs to the technical field of artificial influence weather, and discloses an artificial rain and snow enhancement effect evaluation method based on a layer-by-layer iteration mode, and the method comprises the steps: determining a catalyst diffusion track and an affected region through employing a high-resolution numerical mode, the airplane observation data comprises the background ice crystal number concentration of the affected area and the ice crystal number concentration under the condition that the catalyst is used; reading the ice crystal number concentration of the sowing layer from the observation data, and calculating the correlation between the L0 ice crystal number concentration of the sowing layer and the L1 radar reflectivity factor of the first layer; calculating correlation coefficients of radar reflectivity factors between every two adjacent layers from the sowing layer to the ground layer by layer; a high-resolution numerical mode is adopted to simulate diffusion of a catalyst, the ice crystal number concentration of an affected area in airplane observation data is read and substituted into the relational expression, radar reflectivity factors of all layers are calculated layer by layer in an iterative mode, and the precipitation rate is calculated according to the radar reflectivity factors of all layers; and calculating the rainfall rate under the background ice crystal number concentration, comparing, and quantitatively evaluating the artificial rain and snow increasing effect.

The invention discloses an open source threat intelligence acquisition method and system based on a large language model, and the method comprises the steps: receiving a problem proposed by a user through an Agent module, analyzing the problem based on the large language model, and selecting a tool provided by a Toolkits module based on an analysis result; the Toolkits module collects and processes the open source threat intelligence information according to the tool selected by the Agent module; the memory module records an interaction process between the user and the Agent module and an interaction process between the Agent module and the Toolkits module, when a question proposed by the user is not recorded, operation is performed according to the steps 1-2, and when the question proposed by the user is recorded, the memory module participates in interaction between the Agent module and the Toolkits module by providing a historical context. According to the method, threat intelligence can be autonomously obtained based on user requirements without manually scanning tools or querying related websites and platforms, and a large amount of time and labor cost are saved.

The invention discloses a thrombolysis prediction method based on deep learning and multi-modal fusion, and belongs to the technical field of thrombolysis prediction, and the method comprises the steps: S1, collecting an MRI image of a patient with ischemic cerebral apoplexy, and constructing a cerebral apoplexy automatic segmentation model; s2, carrying out image registration; s3, lesion segmentation; and S4, thrombolysis prediction. According to the cerebral apoplexy automatic segmentation model based on the gating parallel Mama network, the long-distance dependency relationship in the image can be effectively captured, ischemic cerebral apoplexy lesions can be rapidly and accurately segmented, and the lesion identification efficiency and accuracy are greatly improved; according to the algorithm provided by the invention, the linear time complexity is realized, the computing resource consumption is greatly reduced, and the bottleneck of the prior art is broken through; according to the cerebral apoplexy thrombolysis prediction model based on the multi-modal data, scientific automatic decision support is provided for judging whether the patient is suitable for thrombolysis treatment or not, and the quality and safety of medical decision are improved.

The invention provides a single cell transcription reaction prediction algorithm based on cross-domain feature cross migration. Drug screening is carried out by using a deep learning technology. The algorithm combines feature decoupling, cross-domain transfer learning and linear modeling methods, and specifically comprises encoder feature decoupling, feature cross transfer, disturbance state modeling and transcriptional spectrum prediction. According to the method, cross-domain transfer learning is applied to the field of drug or gene disturbance-induced cellular response prediction for the first time, and the model is constructed by using gene transcriptional profile data. In the embodiment, the whole genome determination coefficient between the predicted drug perturbation transcriptional profile and the real transcriptional profile reaches 0.81, the differential genome determination coefficient reaches 0.62, efficient prediction of the unicellular external perturbation reaction is successfully achieved, a new thought and method are provided for the field of tumor treatment, and beneficial reference is provided for future drug research and development.

The invention belongs to the technical field of ship and ocean engineering, and discloses a ship seakeeping intelligent forecasting method and system based on a physical information neural network. According to the method, a CFD-RAO method is combined with PINNs, and rapid and high-precision prediction of ship seakeeping is realized by constructing a coupling model of a physical equation and a neural network loss function. According to the invention, rapid and high-precision prediction of ship seakeeping is realized. The method not only can effectively cope with multi-degree-of-freedom coupling motion under complex sea conditions, but also has high real-time performance and adaptability, and provides more scientific and reliable technical support for ship design and navigation safety.

The invention discloses an ore pillar stability in-situ monitoring system which comprises a top plate grouting anchoring mechanism, a top plate settlement and weighting monitoring mechanism and an ore pillar circumferential deformation monitoring mechanism, and the system effectively fixes an upper structure on an ore body top plate through an air inflation pipeline, a grouting pipeline, an exhaust pipeline and a serial alarm circuit which are arranged in the system. Furthermore, real-time measurement of the stress variation and the self-degradation deformation of the pillar is realized through comprehensive cooperation of multiple sensors, a jack and a monitoring line, and a foundation is laid for the stability evaluation of the pillar. Based on the system, the invention further provides an in-situ assessment method for the stability of the jamb, the stable state of the jamb is divided into four grades of extremely high, high, medium and low risks according to a deformation-time curve and a stress-time curve of the jamb, multi-index comprehensive grading characterization of the stability of the jamb is realized, and a foundation is laid for differentiated treatment of a goaf jamb group. Reference and guidance are provided for avoiding goaf collapse accidents.

The invention discloses a sewage treatment fault diagnosis method and system based on data analysis, and the method comprises the steps: continuously optimizing a diagnosis strategy in the input and training process of a large amount of data through introducing a fault diagnosis model, and improving the accurate recognition capability of a fault under a complex working condition; therefore, the system not only has strong learning ability, but also can perform self-optimization through a reward mechanism, thereby reducing the occurrence rate of false report and missing report. An adaptive weight adjustment mechanism is introduced, so that the model can automatically adjust weight distribution and increase response to a new fault when the system has a novel fault or few faults; by dynamically adjusting the weight vector, the diagnosis model of the invention has strong self-learning ability and can continuously adapt to new fault modes and working condition changes, and the weight adjustment mechanism greatly improves the generalization ability and adaptability of the model, and especially has significant advantages in the face of fault scenes with strong uncertainty.

The invention discloses a circuit level optimization method based on NAND and XOR majority logic, which comprises the following steps of: analyzing target design circuit information to obtain an NAND graph AIG; logic optimization with the optimization node number as the target is executed on the AIG; the AIG after node number optimization is logically converted into an XOR majority logic diagram XMG; logic optimization with the optimization logic hierarchy as the target is executed on the XMG obtained through logic conversion; the XMG after logic hierarchy optimization is logically converted into an AIG; logic optimization with the optimization logic hierarchy as the target is executed on the AIG obtained through logic conversion; and executing time delay-oriented process mapping on the AIG after logic hierarchy optimization, and generating and outputting a process gate-level netlist. According to the method, the logic optimization processes of the AIG and the XMG are effectively combined, the local optimization problem in optimization is improved, the adaptability to a specific design target is enhanced, the optimization efficiency and effect of large-scale circuit design are improved, and a better circuit after logic optimization is obtained.

The invention belongs to the technical field of data acquisition and transmission, and discloses an intelligent acquisition system and method using a Beidou communication technology. The method comprises the following steps: collecting an operation data set of power equipment; preprocessing parameters in the operation data set to obtain primary processing data; performing feature extraction and dimension reduction processing on the preliminarily processed data to obtain key feature vectors; encrypting the key feature vector to obtain encrypted data; calculating to obtain a data importance score and a real-time requirement score based on the key feature vector; the position information of the power equipment is obtained through Beidou satellite positioning; dynamically selecting a Beidou satellite communication mode according to the position information, the data importance score and the real-time requirement score of the power equipment; sending the encrypted data to a ground data center through a Beidou communication network in a Beidou satellite communication mode; full coverage, wide connection and easy access of electric power data acquisition are realized, and the application of the electric power Internet of Things is promoted.

The invention relates to a throat reflux disease detection method and system based on a laryngoscope image and an RFS-Net algorithm, and the method comprises the steps: collecting the laryngoscope image, screening a disease type by a professional worker according to the laryngoscope image, and carrying out the RFS evaluation of the collected laryngoscope image, and obtaining a first image; the first image is sent to an FCN-ResNet network for pixel-level region prediction, and a second image is obtained; continuously sending the second image into a DSE-ResNet network for feature quantization to obtain a quantization score of the first image; sending the obtained quantized score of the first image into an RFS-Net model, and performing LPR prediction to output a score; and the throat reflux disease is evaluated according to the score, so that the accuracy of diagnosing the throat reflux disease by machine recognition is improved, and automatic evaluation is realized.

The invention belongs to the technical field of unmanned aerial vehicle attack and defense, and discloses an intelligent defense and attack control system for an unmanned aerial vehicle. The method comprises the following steps: acquiring environment sensing data of an unmanned aerial vehicle, wherein the environment sensing data comprises state data of the unmanned aerial vehicle and image data of a surrounding environment; preprocessing the image data of the surrounding environment to obtain model input image data; performing target detection on the model input image data by using a variant Poolform algorithm to obtain comprehensive information of a target object; according to the state data of the unmanned aerial vehicle and the comprehensive information of the target object, solving an optimal attack defense strategy by using a perception integrated attack algorithm; the optimal attack defense strategy is sent to an unmanned aerial vehicle execution terminal, and the unmanned aerial vehicle is controlled to execute the optimal attack defense strategy; the optimal attack and defense strategy can be dynamically generated according to the real-time change of the environment situation, and the survivability and the attack efficiency of the unmanned aerial vehicle in the confrontation environment are remarkably improved.

The invention discloses a proton exchange membrane fuel cell state monitoring method, equipment and a medium, belongs to the technical field of fuel cells, and aims to solve the problem that the voltage response of a fuel cell is analyzed as a one-dimensional signal in the conventional method for monitoring the state of the fuel cell based on an electrochemical impedance spectroscopy. And the accurate recognition capability of the PEMFC performance state is limited. The method comprises the following steps: acquiring a voltage response signal after a current interference signal is injected into a fuel cell; decomposing the voltage response signal into a plurality of voltage response components; performing feature extraction on any voltage response component in the plurality of voltage response components to obtain a target feature corresponding to the voltage response signal; and inputting the target features into a classifier model, and determining the state of the fuel cell. According to the method, the multi-scale information of the voltage response signal in the EIS is obtained, and the accuracy of fuel cell state recognition is improved.

The invention relates to the field of tensegrity metamaterials, in particular to an internal stress self-loading two-rod two-cable tensegrity metamaterial cell element structure which comprises a rigid shell component, a T-shaped rod piece, an elastic assembly and a hydrogel driving assembly. The T-shaped rod piece is driven by the hydrogel to slide in the channel direction of the shell component, and finally self-balancing is achieved under the joint constraint of the tension elastic assembly and the tension elastic assembly. The self-stabilization, self-recovery and self-adaption mechanical characteristics of the tensegrity structure are utilized, the overall stability and durability of a traditional structure are improved, and the limitation of local stress concentration and single-point faults is overcome. A method for generating preloading stress according to the swelling characteristic of the hydrogel is provided, the limitation that external loading is needed for the whole conventional tensioning is broken through, and the breakthrough from passive bearing to active response is achieved. The method is suitable for various fields of intelligent materials, flexible sensors, deformable structures, underwater detection equipment and the like.

The invention provides a raindrop spectrum inversion determination method and device based on a cloud radar and a dual-polarization radar, and relates to the technical field of atmospheric sciences, and the method comprises the steps: obtaining a plurality of first raindrop spectrums corresponding to a target region through the cloud radar; acquiring a second raindrop spectrum corresponding to the target area and a second horizontal reflectivity factor corresponding to the second raindrop spectrum through the dual-polarization radar; and for each first raindrop spectrum, determining a target raindrop spectrum corresponding to the first raindrop spectrum based on all the first raindrop spectrums, the second raindrop spectrum and the second horizontal reflectivity factor. According to the technical scheme, the advantages of the cloud radar and the dual-polarization radar are combined, the limitation of adopting a single radar is broken through, and the accurate target raindrop spectrum can be obtained.

The invention relates to the technical field of electric power internet of things, in particular to a distributed networking temperature monitoring system based on a swan gap system. Comprising an acquisition program and a networking program, the acquisition program requests data of an acquisition end sensor through http, then the data is processed and converted into a json format and stored in a database, the requested data is displayed through arkui, and the data is circularly rendered and displayed on a screen; the networking program is based on an OpenHarmony distributed soft bus, and carries a mobile phone and other color screens to carry out distributed networking. The system integrates the advantages of real-time data processing, flexible distributed networking capability, cross-device data sharing and cooperative work, high safety and reliability, easy expansion and maintenance and the like, and provides an efficient and intelligent solution for the fields of environment monitoring, industrial control and the like.

The invention discloses a sand storm weather prediction method, system and device based on generative AI and a medium, and relates to the technical field of sand storm prediction. The method comprises the steps that an MM-DDPM model is constructed, the MM-DDPM model is utilized to jointly encode the relevance between meteorological elements and sand storm labels in a sand storm data set, and a sand storm enhanced sample is generated; performing parameter adjustment and optimization on the pre-trained meteorological large model by using the sand storm enhanced sample to obtain a trained sand storm prediction model, and performing weather prediction by using the sand storm prediction model to obtain a sand storm prediction result; the sand storm prediction result comprises occurrence time, duration time and spatial distribution of a sand storm event; the pre-trained meteorological large model is constructed based on a Fengwu model and a sand storm fitting layer. According to the method, the limitation of the prior art on data scarcity and imbalance can be broken through by generating the reliable sand storm data sample, and the precision and stability of sand storm prediction are improved.

The invention discloses an electroencephalogram multi-index network standardized modeling method which comprises the following steps: S1, adopting a multi-country electroencephalogram cross-spectrum data set based on a large-scale full life cycle as original electroencephalogram data, calculating a cross-spectrum matrix of the original electroencephalogram data, and preprocessing the original electroencephalogram cross-spectrum matrix; s2, constructing a weighted undirected network by using the preprocessed original electroencephalogram cross spectrum matrix to serve as a brain function network; s3, calculating network features of three dimensions of brain network function integration, function separation and centrality of the brain function network, and establishing an evolution trajectory of the network features; and S4, designing an interpretable lightweight neural network regression model, and reconstructing upper triangular elements of the brain function network to obtain a complete brain function network, namely a normalized brain function network corresponding to the age. The invention further discloses an electroencephalogram multi-index network standardized modeling system. The principle of the brain function network can be reflected more accurately.

The invention provides a multi-adapter fine-tuning automobile question and answer method, computer equipment and a storage medium. The method comprises the following steps: establishing and pre-training a base large model; performing fine tuning on the base large model based on the maintenance fault problem data set to obtain a maintenance fault adapter; performing fine adjustment on the base large model based on the vehicle use problem data set to obtain a vehicle use problem adapter; performing fine tuning on the base large model based on the general field problem data set to obtain a general field problem adapter; and performing fine adjustment on the base large model based on the intention recognition task data set to obtain an intention recognition adapter. According to the invention, fine adjustment can be carried out according to problems in different fields, and the specificity of each field is ensured, so that the precision of field adaptation is improved, and consistent performance is ensured. The method is an important improvement on an existing single fine adjustment method, and the problem of field deviation can be effectively solved.

The invention relates to the field of composite materials, in particular to a complex structural part forming method of a dynamic cross-linked polymer-based composite material. The method specifically comprises the following steps: loading a dynamic cross-linked polymer prepolymer on fiber cloth to obtain dynamic cross-linked polymer pre-impregnated fiber cloth, laying the dynamic cross-linked polymer pre-impregnated fiber cloth layer by layer, and curing to obtain a flat-plate-shaped dynamic cross-linked polymer-based composite material; the method comprises the following steps: taking a dynamic cross-linked polymer-based composite material as a simple flat plate unit, putting the dynamic cross-linked polymer-based composite material into a mold, and carrying out hot-pressing, remolding and shaping to obtain parts with different shapes; and the parts in different shapes are assembled according to the complex structural part and connected at a connecting interface through a hot pressing welding method or a stock solution bonding method, and the complex structural part is obtained. The invention provides a simple and efficient new method for preparing the complex structural member of the dynamic cross-linked polymer-based composite material, and solves the problems of complicated preparation, low error-tolerant rate and poor flexibility of the complex structural member in the prior art.

The invention relates to the technical field of building materials, and particularly discloses a method for quantitatively evaluating concrete surface characteristics, which comprises the following steps: (1) carrying out three-dimensional scanning on the concrete surface to obtain a three-dimensional dot matrix coordinate measurement space; (2) dividing the measurement space obtained in the step (1) into a plurality of subunits, namely a box with the side length of r, r being a real number, and obtaining the distribution probability of the height of the concrete surface profile in each subunit; (3) establishing an evaluation formula of concrete surface characteristics; and (4) obtaining the following analysis parameters: delta f (q, r) and delta alpha (q, r) which are used for describing the surface characteristics of the concrete. According to the method, the roughness, uniformity and complexity of the concrete surface can be accurately described, and the method can be used for guiding improvement of the bonding performance of the concrete interface in a combined component or maintenance and reinforcement scene.

The embodiment of the invention relates to a log structured processing method and device, equipment and a medium, and the method comprises the steps: determining a sample log matched with a current log in a plurality of historical logs, and obtaining a sample regular expression corresponding to the sample log; inputting the current log, the sample log, the sample regular expression and the generated prompt word into an expression generation model, and outputting to obtain a current regular expression corresponding to the current log; performing field data extraction on the associated log through the current regular expression to obtain target structured data of the associated log; wherein the associated log is the same as the current log in type. According to the embodiment of the invention, the regular expression of the current log is automatically and accurately generated by referring to the sample log and the sample regular expression through the model, the development efficiency is improved, the log of the same type as the current log is subjected to log analysis processing through the regular expression, and the log analysis and maintenance cost is reduced.

The invention discloses an arbitrary curvature container pressure measurement system and method based on acoustic impedance analysis, and the system comprises a measurement assembly, a power unit, a control unit, a signal source, a data processing unit, a data output unit, and a calibration assembly. The measuring assembly comprises an ultrasonic transducer for emitting ultrasonic waves and an ultrasonic transducer for receiving ultrasonic signals, the ultrasonic transducers are installed on the outer surface of the container, structural damage to the container is not needed, the control unit is responsible for exciting the ultrasonic transducers and regulating and controlling working parameters of the ultrasonic transducers, and the data processing unit receives and processes the ultrasonic signals. Measurement data are analyzed and calculated through a mathematical model, a container pressure measurement result is finally output, and the power supply unit and the data output unit respectively supply power to the system and provide a real-time display or remote transmission function of the measurement result. According to the invention, based on acoustic impedance analysis, accurate measurement of the pressure of the container with any curvature can be realized, and the reliability and precision of ultrasonic measurement of the pressure of the container can be improved.

The invention provides a method and a system for predicting a poultry growth model based on environmental monitoring. The method comprises the following steps: acquiring a historical image and a current image of each breeding cage in which poultry is bred; training a support vector machine model according to the historical images to obtain a trained support vector machine model, obtaining first future growth index data corresponding to each current image according to the trained support vector machine model, and calculating second future growth index data corresponding to each current image; and performing weighted summation on the first future growth index data and the second future growth index data to obtain final future growth index data corresponding to each current image. According to the method, environmental monitoring data is introduced into the poultry breeding field, a prediction model is constructed by using a machine learning algorithm, the limitation of a traditional breeding method is broken through, a novel and scientific growth index prediction method is provided, and subjectivity and inaccuracy of traditional manual judgment are reduced.

The invention relates to a manufacturing method of a three-dimensional spiral directional active ventricular structure, which comprises the following steps of: inducing cells and hydrogel fibers to generate directional arrangement by utilizing internal stress generated by a three-dimensional driving bracket encapsulated in hydrogel; a spiral directional ventricle structure consistent with the natural ventricle in arrangement direction is designed and constructed through the macroscopic shape and the structural unit of the three-dimensional driving stent, active ventricle tissue manufacturing with systolic and diastolic, blood pumping and electrical conduction functions is achieved through in-vitro dynamic culture, and the requirements of tissue repair and in-vitro drug testing are met.