5855 results about "Computer device" patented technology

The invention relates to the technical field of tooth modeling, and discloses a three-dimensional tooth modeling system based on computer vision, computer equipment and a readable storage medium. According to the method, mirror reflection, diffuse reflection and subsurface scattering components in an original image are separated, mirror reflection intensity is normalized in combination with a dynamic truncation algorithm, pixel saturation is eliminated, groove and nest textures are reserved, a complete point cloud is obtained based on a two-dimensional texture image and cubic spline repair, and a multi-exposure point cloud sequence is obtained through bimodal calibration. The method comprises the following steps: solving the problem of data dislocation, carrying out weight assignment and data fusion on three-dimensional points in a plurality of exposure point cloud sequences to obtain three-dimensional fusion feature data, combining layered optical modeling and photon tracking compensation deviation, fusing clinical constraints, finally dynamically adjusting parameters, feeding back and optimizing, and outputting a micron-sized precision model. The modeling defect caused by difficulty in effectively coordinating feature contribution degrees under different exposure conditions is overcome, and high-precision modeling is realized.

The invention discloses a computing power analysis method used in a cloud computing environment, a matching method and a computer device. The computing power analysis method used in the cloud computing environment comprises the steps that historical task operation data of different types of computing power nodes in a cloud computing platform are acquired, associated computing power portraits corresponding to the computing power nodes are generated through preprocessing, and then a target model is trained in combination with the historical task operation data to obtain a task-computing power mapping model; performing multi-dimensional dynamic and static analysis on a to-be-executed user calculation task to obtain task feature information; and analyzing the task feature information based on the task-computing power mapping model to obtain analysis information of each type of computing power nodes. According to the method, the required computing power resources and the analysis information of each type of computing power nodes can be accurately predicted according to the dynamic and static analysis feature information of the task before the task is executed, so that accurate computing power identification and intelligent matching are realized, the reasonability and efficiency of task allocation are improved, the resource waste rate is effectively reduced, and the adaptation accuracy in a multi-tenant environment is improved.

The embodiment of the invention relates to the field of artificial intelligence, and provides a model context protocol injection attack protection method and device based on dynamic semantic analysis, computer equipment and a storage medium, and the method comprises the steps: obtaining request data corresponding to a call request initiated by a user through a model context protocol, carrying out the preprocessing of the request data, obtaining the preprocessed request data; performing semantic vectorization on request text and context historical information in the request data through a lightweight bidirectional encoder representation model to output an initial risk score; correcting the initial risk score according to context historical information carried in a model context protocol to obtain a corrected final risk score; and performing hierarchical defense decision according to the final risk score, and determining risk grading information corresponding to each piece of request data so as to execute a protection action corresponding to each piece of risk grading information. By adopting the method, the accuracy of identifying the protocol injection attack can be improved.

The invention discloses a heterogeneous computing power resource dynamic cooperative scheduling method and system and a computer device. The method comprises the steps of obtaining multi-dimensional feature information of a to-be-scheduled task, current state information of each node in a heterogeneous computing power resource pool and scheduling environment state information; training a time sequence model by adopting historical associated data, inputting the multi-dimensional feature information, the current state information and the scheduling environment state information into the trained model to obtain associated trend information in a future preset time window, and then combining the multi-dimensional feature information and the current state information of all the nodes to obtain the scheduling environment state information. A multi-objective optimization function fusing task delay, cost consumption and energy consumption is constructed, the function is optimized based on a preset strategy, a target decision strategy is obtained, a scheduling instruction set is generated after analysis, allocation information and target nodes are determined according to the scheduling instruction set, and tasks are issued. According to the method, dynamic collaborative scheduling of heterogeneous computing power resources can be realized, and the overall utilization efficiency of the resources is accurately, efficiently and effectively improved.

Embodiments of the present application relate to the technical field of defect detection, and provide a defect detection method and apparatus, a computer device, and a storage medium. The method comprises: acquiring training sample data; performing extraction on a sample defect image on the basis of a defect region extraction model to obtain a sample defect positioning image; performing detection on the sample defect positioning image on the basis of an image defect detection model to obtain a sample detection result; calculating a first loss value on the basis of sample defect mask data and predicted defect mask data, and calculating a second loss value on the basis of a sample defect category, a predicted defect category, a sample bounding box and a predicted bounding box, so as to determine a model loss value; updating model parameters of the defect region extraction model and the image defect detection model on the basis of the model loss value, so as to construct a target defect detection model; and performing detection on a target detection image on the basis of the target defect detection model to obtain a target detection result. The embodiments of the present application can improve the defect detection efficiency and accuracy of objects to be detected.

The invention provides a medical image segmentation method and system, computer equipment and a storage medium, and belongs to the field of image processing, and the method comprises the steps: extracting preliminary features of a medical image through depth separable convolution, and splicing the preliminary features with original image residuals to obtain a preliminary feature map; after an encoder performs average pooling dimension reduction, local details and global contour features of a dimension reduction feature map are extracted by using left and right branches of a lightweight convolution module LDB, then a downsampling feature map is obtained through channel attention CA weighted fusion, and attention is calculated in combination with a self-attention mechanism module EMHA to obtain a depth feature map and a bottleneck feature map; the decoder weights the depth feature map by means of a channel and space attention to obtain a CBAM enhanced feature map, upsamples the bottleneck feature map and then splices the bottleneck feature map with the CBAM enhanced feature map, features are extracted through an LDB module, and finally a pixel-level segmentation result is output through upsampling and deconvolution, so that image segmentation achieves the effects of high quality, low complexity and low operand.

The invention is suitable for the technical field of intelligent customer service, and provides an intelligent question-answering method and system for public rental housing and computer equipment, and the method comprises the steps: receiving a query request input by a user, recognizing a query intention and key parameters through analyzing the query request, and generating an action framework containing the query intention, the key parameters and a preset retrieval strategy; at least one retrieval path is selected and executed according to the action framework, information is retrieved in parallel from heterogeneous data sources at least comprising a vector database, a search engine and a knowledge graph database, and all recalled source information is integrated into an evidence chain capable of being verified; and constraining a generation process of the large language model based on an evidence chain to generate a preliminary answer, calling a rule engine to perform policy compliance verification on the preliminary answer, and after the verification is passed, outputting a final answer with a traceable source label. According to the method and the system, the problem that defects exist when business scenes which are complex, dynamic and high in compliance requirement in the public rental housing field are processed in the prior art is solved.

The invention relates to the field of power networks, and provides an intelligent generation method for power equipment accident recovery. The method comprises the following steps: converting key semantic elements into a structured query instruction; acquiring operation state data of the power grid equipment from a power grid monitoring system in real time, and dynamically updating the operation state data to a pre-constructed power knowledge graph; on the basis of a structured query instruction, performing graph traversal and reasoning on the updated electric power knowledge graph, and searching all reachable electric paths from an available power supply point to a target recovery object; carrying out feasibility evaluation on the reachable electrical path in combination with an operation constraint condition, and generating a candidate recovery path; performing multi-objective optimization sorting on the candidate recovery paths to generate an operation instruction sequence; and converting the operation instruction sequence into at least one form of output scheme. According to the invention, automatic generation and optimization from the natural language instruction to the intelligent recovery scheme are realized.

The invention relates to the technical field of multi-modal large language model training, in particular to a prompt construction method and system for a multi-modal large language model, computer equipment and a medium. The method comprises the following steps: extracting a key frame set from an input video stream; executing a motion reconstruction process on the video stream to generate motion track information; and performing visualization processing on the motion track information to generate a track visualization graph. Performing space-time correlation coding on the key frame set and the motion track information to generate an enhanced key frame; a multi-modal prompt is constructed in a mode of integrating visual input and text input, and the multi-modal prompt is input into a preset multi-modal large language model for spatial reasoning. Through the mode, the technical problem that an existing prompting method is difficult to give consideration to the spatial reasoning precision and the calculation efficiency is solved, efficient and accurate spatial reasoning of the multi-modal large language model is achieved, and the calculation efficiency, the reasoning precision and the environmental adaptability of the model are improved.

The invention discloses a drainage basin water quality pollution source flux identification method based on an SWAT model, computer equipment and a storage medium, and the method comprises the steps: obtaining topographic data, land utilization data, soil type data and meteorological data, and enabling the topographic data, the land utilization data, the soil type data and the meteorological data to form a drainage basin spatial database; based on the preprocessed digital elevation model, determining a water flow direction and setting a reasonable water collection area threshold value, and dividing sub-basins to construct a basin calculation unit network topology structure; obtaining pollution source data, and constructing an SWAT-CUP model according to the pollution source data; performing system calibration and verification on the watershed hydrological process by adopting an SUFI-2 algorithm in the SWAT-CUP model; carrying out system calibration and verification on the total nitrogen load of the drainage basin by adopting an SWAT-CUP model; and based on the calibrated SWAT hydrological model, the input condition of the pollution source is changed. The method can cover the whole watershed and quantify the contribution rate of different pollution sources; cost is high, operation is simple, and long-term change can be dynamically simulated.

The invention belongs to the technical field of computer vision three-dimensional scene repair, and provides an automatic driving three-dimensional scene repair method based on color point cloud prior guidance, which comprises the following steps: acquiring multi-source heterogeneous data; constructing an incomplete semantic two-dimensional Gaussian field according to the multi-source heterogeneous data, and generating an incomplete image sequence, an incomplete depth image sequence and an opacity image sequence; obtaining a color point cloud based on the instance segmentation image, the opacity image, the incomplete image and the incomplete depth image of the reference image; generating a texture pseudo view angle sequence based on the color point cloud; generating a repaired image sequence by taking the texture pseudo view angle sequence as a condition signal of the fine-tuning video diffusion model; generating a repair depth sequence based on the color point cloud; calculating a repair Gaussian loss function based on the repair image sequence and the repair depth sequence, and performing iterative repair optimization on the incomplete semantic two-dimensional Gaussian field to obtain a complete repair two-dimensional Gaussian field; the invention further discloses a computer device. And texture-geometry collaborative automatic driving scene three-dimensional repair is realized.

The invention discloses a fault identification method and device for a rotating mechanism, computer equipment and the rotating mechanism. The method comprises the following steps: acquiring rotating speed information and a time domain vibration signal of the rotating mechanism; converting the time domain vibration signal into an angular domain vibration signal based on the rotating speed information; performing feature extraction on the angular domain vibration signal based on the autocorrelation matrix of the angular domain vibration signal and the filter coefficient to update the filter coefficient and obtain a target filter coefficient; and determining fault characteristics of the rotating mechanism based on a target vibration signal obtained by processing the angular domain vibration signal based on the target filtering coefficient. According to the method provided by the invention, the fault features of the unsteady vibration signals of the rotating mechanism can be effectively extracted, so that reliable fault diagnosis of the rotating mechanism is realized.

The invention relates to a vehicle infrastructure collaborative perception enhancement method and device, computer equipment and a storage medium. According to the method, the structured occupied main space constructed by the roadside nodes is analyzed, aligned and injected into the vehicle side occupied space in real time, so that the environment states of a blind area, a shielding area and a remote area which cannot be directly observed by the vehicle side are obtained. Time extrapolation is carried out on the dynamic traffic participants and the dynamic traffic participants are mapped to a vehicle side coordinate system, so that position deviation caused by communication delay and sensing time difference can be effectively compensated; spatial resampling and coordinate transformation are executed based on the grid specification of the vehicle-mounted model, and it can be guaranteed that roadside occupation information is consistent with the structure of the vehicle-mounted model; meanwhile, through a fusion mechanism of uncertainty propagation and gating adjustment, reliable weighted absorption of external occupancy priori can be realized in the presence of sensing noise, model errors and positioning errors. Uniform expression of the occupied space of the vehicle side and the road side, time-space consistency alignment and controllable and credible fusion are achieved.

The invention provides a drug-disease association prediction method and system, computer equipment and a medium, and belongs to the technical field of computers. The method comprises the following steps: constructing a drug-protein-disease heterogeneous network, and extracting a plurality of element path sub-graphs; inputting the meta-path sub-graph into a multi-scale diffusion graph convolution module, executing learnable multi-step graph diffusion on the basis of graph convolution, synchronously capturing local adjacency and high-order topological information, and generating node embedding; and performing dynamic weighted fusion by utilizing meta-path attention to obtain unified representation. In order to relieve imbalance of positive and negative samples, implementing difficult negative sampling in the embedding space, and constructing a balance training set with the positive samples; medicine-disease features are spliced, a regularization XGBoost classifier is trained, and unknown correlation accurate prediction is achieved. By adopting the method, the drug-disease association prediction precision and efficiency are improved, multi-scale topology and priori knowledge are fused, and a powerful calculation tool is provided for drug relocation.

The invention provides a video abnormal behavior positioning method and device based on a multi-modal large model, and a medium, and relates to the field of computer vision. The method is applied to computer equipment and comprises the following steps: acquiring a to-be-processed video, and determining a plurality of query videos and a plurality of support videos; obtaining a thinking chain text according to the visual language model; obtaining a first robust feature representation and a second robust feature representation according to a semantic time sequence pyramid encoder; obtaining subtitle text fusion features according to a text encoder; and further performing alignment processing to obtain an alignment similarity matrix so as to determine a prediction interval of the query video, determine an abnormal behavior positioning result of the to-be-processed video and determine a dynamic model fine tuning sample. The video abnormal behavior positioning method and device solve the technical problems that in the prior art, visual information is excessively depended on, deep semantic understanding of the action context and the target relation is lacked, misjudgment is easily generated in the alignment process, the positioning precision is insufficient, and the video abnormal behavior positioning efficiency is low.

The invention is suitable for the technical field of sponge city monitoring and evaluation, and provides a sponge city evaluation method and system, computer equipment and a computer program product, and the method comprises the steps: obtaining multi-source time sequence data of a target sponge city; performing three-stage dynamic optimization on a basic rainstorm flood management model based on the multi-source time sequence data to obtain a target rainstorm flood management model; and performing performance evaluation on the target sponge city through the target rainstorm flood management model, and generating an evaluation result. According to the method provided by the invention, an accurate rainstorm flood management model can be dynamically constructed based on real-time multi-source time sequence data, and real-time, accurate and comprehensive evaluation is carried out on sponge city construction.

The embodiment of the invention discloses a multi-dimensional intention recognition method and device, computer equipment and a readable storage medium. The method comprises the steps of obtaining domain knowledge data, RAG data of user history retrieval and user portrait data, obtaining an original data set, converting the original data set into structured fields, and constructing a domain knowledge base based on all the structured fields; receiving a current question, preprocessing the current question to obtain a standard text, and performing semantic deep analysis on the standard text through a preset large language model to obtain a semantic analysis result; multi-dimensional scoring is carried out based on the semantic analysis result, weighted summation is carried out based on the multi-dimensional scoring result, and a plurality of candidate intentions and corresponding confidence coefficients are obtained; and performing descending sorting according to the confidence, and selecting the candidate intention with the highest confidence as an intention recognition result. According to the method, the recognition accuracy of the core appeal in the user question is improved.

The invention relates to a wind power cluster short-term power prediction method and device of a space-time diagram neural network fused with physical information and computer equipment, and the method comprises the steps: obtaining related information data of each wind power plant in a wind power cluster, and carrying out the preprocessing; forming a physical prior data set through an engineering analysis model fusing the wake flow analysis model and the blocking effect model; taking each wind power plant as a node of the graph, constructing graph structure data for predicting the power of the wind power plant, and forming a dynamic adjacent matrix; constructing a space-time diagram neural network WB-STGNN model architecture comprising a diagram convolutional neural network module, a gating time convolutional network and a multi-layer perceptron; the method comprises the following steps: pre-training by using a physical prior data set, and then performing formal training based on historical power data and a dynamic adjacency matrix to obtain a space-time diagram neural network WB-STGNN model; inputting the wind speed of the prediction day, and predicting the active power of the whole wind power cluster in 24 hours of the prediction day. By adopting the method, the precision and efficiency of wind power cluster power prediction can be effectively improved.

A ship sub-assembly weld seam identification method, comprising: creating a point cloud weld seam identification training dataset by means of point cloud discretization, virtual sampling, weld-zone semi-automatic interactive annotation, and style transfer-based augmentation of ship sub-assembly CAD data; training a point cloud weld seam identification model, and exporting a weld seam identification model data file; using a 3D point cloud camera array as a sampling camera to collect workpiece 3D point cloud data of a sub-assembly workpiece to be welded at the production site; by means of the weld seam identification model data file, obtaining weld seam annotation position data and positioning a weld seam; and issuing a welding process plan and instruction to control a welding robotic arm and a control device therefor to complete sub-assembly workpiece welding. Also provided are a ship sub-assembly weld seam identification system, a computer device, and a computer-readable storage medium. The method overcomes the shortcomings in conventional 3D weld seam identification methods such as point cloud incompleteness and identification failures caused by steel types and the imaging angles of 3D cameras, thereby greatly improving the adaptability of automatic weld seam identification methods to complex workpieces.

The invention relates to a three-dimensional grid model processing method, computer equipment, a readable storage medium and a program product. The method comprises the steps of performing voxelization on a three-dimensional grid model of a target object to obtain a plurality of voxels corresponding to the target object in a preset reference space; determining the visibility value of the voxel according to the projection information of the voxel under the preset camera visual angle; determining a position relation label of the voxel relative to a reference point according to a communication relation between the voxel and the reference point in a preset reference space; screening out a target voxel from the voxels according to the visibility values and the position relation labels of the voxels; and determining model characterization parameters of the three-dimensional grid model according to the spatial information of the target voxel in the three-dimensional grid model. By adopting the method, the generation precision of the three-dimensional model can be improved.

The invention relates to a partial discharge type identification method and device, computer equipment, a readable storage medium and a program product. The method comprises the steps that discharge signals including a current signal, an ultrasonic signal and an ultrahigh frequency signal are obtained and subjected to feature extraction, feature data corresponding to the discharge signals are obtained, the feature data are input into a graph convolutional network model, feature fusion can be conducted on the feature data, and a fused multi-modal feature vector is obtained; and inputting the fused multi-modal feature vector into a preset depth transfer learning model to obtain a predicted partial discharge identification type corresponding to the discharge signal. The method can improve the identification accuracy and reliability of the partial discharge type, and is of great significance to guarantee the stable operation of a power system.

Disclosed are a speech encoder training method performed by a computer device. The method includes: masking a first sub-feature representation at a first feature position in a first text feature representation to obtain a first masked feature representation; performing feature prediction on a masked first feature position in the first masked feature representation based on a first speech feature representation to obtain a first predicted feature representation; and training a first speech encoder based on a difference between the first predicted feature representation and the first sub-feature representation to obtain a second speech encoder. The first speech encoder is trained by combining data in a speech modality with data in a text modality, and information included in the data in the text modality is adopted so that the first speech encoder can learn relatively high-level semantic representations of speech, thereby improving the prediction accuracy of representations.

The invention relates to the technical field of artificial intelligence and computer vision, and discloses a visual perception task processing method and device, a storage medium and computer equipment, and the method comprises the steps: generating a plurality of corresponding candidate visual inputs in response to a received visual perception task request comprising visual inputs and user query; inputting the candidate visual input and the user query into a multi-modal large language model, calculating an uncertainty score, and screening out at least one optimal candidate visual input; and inputting the optimal candidate visual input and the user query into the multi-modal large language model, and generating and outputting a response result for the visual perception task request. According to the method, through a unified framework which does not need training, only utilizes internal uncertainty signals of the model and keeps end-to-end processing, the fine-grained sensing ability of the model is improved while resource consumption is reduced, and the practicability and reliability in high-difficulty fine-grained sensing tasks such as tiny focus areas in medical images are enhanced.

The invention relates to a myocardial resection area determination method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a heart scanning image, and determining a heart three-dimensional model according to the heart scanning image; determining a heart short-axis section and a heart long-axis section according to the heart three-dimensional model; the clock direction angle of the scalpel is determined on the short axis section of the heart; based on the clock direction angle, the excision depth of the scalpel is determined on the long-axis section of the heart; and performing myocardial resection on the heart three-dimensional model according to the clock direction angle and the resection depth through a scalpel model, determining an updated three-dimensional model after myocardial resection, and marking a resection area on the updated three-dimensional model. According to the scheme, accurate preoperative planning guidance and comprehensive heart structure information presentation are provided for the myocardial rotary cutting operation, and therefore the accuracy and safety of the myocardial rotary cutting operation are improved.

The invention relates to a rolling bearing embedded lubrication state evaluation method and a computer device. The rolling bearing embedded lubrication state evaluation method comprises the steps that a temperature signal of a rolling bearing and a frequency domain spectrum amplitude sequence are spliced to generate a multi-source spectrum fusion feature vector; a random forest model is adopted to screen mean value features extracted from the temperature signals and time domain features and frequency domain features extracted from the vibration signals, the sound signals and the sound emission signals to obtain a sensitive feature set; inputting the sensitive feature set into a support vector machine model to output a first lubrication state evaluation result; inputting the multi-source spectrum fusion feature vector into a MobileNet V2 model to output a second lubrication state evaluation result; and fusing the first lubrication state evaluation result and the second lubrication state evaluation result through a D-S evidence theory to obtain a rolling bearing lubrication state evaluation result. The multi-source fusion evaluation method considering accuracy, robustness and engineering practicability is constructed, so that the problems of high limitation, insufficient fusion layers, high model complexity and the like of an existing single signal are solved.

The invention relates to a multi-modal data fusion method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: respectively carrying out feature extraction on image input data from at least two modes to obtain an image feature vector of each mode; on the basis of the image feature vector of each mode, Dirichlet distribution parameters corresponding to each mode are generated, the distribution difference between the modes is evaluated, and the Dirichlet distribution parameters are used for representing the evidence intensity of each category in the classification or clustering task; according to the distribution difference, fusing the Dirichlet distribution parameters of each mode to obtain a basic probability distribution result after fusion; and dynamically adjusting the basic probability distribution result by using a Kalman filter, and outputting a final classification or clustering result. By adopting the method, the fusion weight of each modal data can be dynamically adjusted, and the accuracy and reliability of a multi-modal fusion system are improved.

The invention belongs to the technical field of asphalt mixed material experiments, and particularly relates to test equipment for measuring the high-temperature stability performance of an asphalt mixed material, the test equipment comprises a test platform, an environment control module and a data acquisition module, the test platform comprises a test bracket, samples to be tested are uniformly arranged on an inclined adjusting plate on the test bracket, and the environment control module is connected with the data acquisition module; the data acquisition module comprises a laser displacement sensor, and the laser displacement sensor is arranged at the position, corresponding to the to-be-tested sample, of the adjusting plate; according to the invention, the laser displacement sensor located in the protection box is started at regular time, and the relative displacement change between the laser displacement sensor and the to-be-tested sample is calculated according to the change of the reflected laser, so that the actual deformation displacement of the to-be-tested sample is accurately measured and recorded and is transmitted to external computer equipment; and real-time and accurate data acquisition of the deformation degree of the to-be-tested sample is realized.

A method and apparatus for correcting the thickness of a film layer, and a computer device. The method comprises: acquiring parameter values of controllable parameters and time information corresponding to the parameter values, and generating a first vector set and a second vector set; inputting the first vector set and the second vector set into a pre-trained prediction model to output a predicted value of a thickness correction value, the pre-trained prediction model comprising a convolutional neural network and a gated recurrent unit arranged in parallel, the convolutional neural network being configured to process the first vector set, and the gated recurrent unit being configured to process the second vector set; and adjusting the parameter values of the controllable parameters on the basis of the predicted value of the thickness correction value.

The invention relates to a defect detection device and method, computer equipment and a computer readable storage medium. The device comprises an imaging subsystem, a structured light illumination subsystem and a data fusion processing unit. The structured light illumination subsystem comprises a coupling device, and the coupling device is used for coupling an illumination light path of the structured light illumination subsystem into an imaging light path of the imaging subsystem; the imaging subsystem is used for synchronously acquiring a light field image of the tested sample under the condition that the structured light illumination subsystem projects the structured light to the tested sample, and the light field image comprises light field depth data and a structured light image; and the data fusion processing unit is used for fusing the light field depth data and the structured light image to reconstruct the three-dimensional morphology of the detected sample, and generating a defect detection result of the detected sample based on the three-dimensional morphology. By adopting the device, the reconstruction precision of the three-dimensional morphology can be improved, and tiny pit defects on the surface of a transparent / weak texture sample can be identified.

The invention is suitable for the technical field of ultrasonic imaging, and provides a three-dimensional ultrasonic imaging method and system, computer equipment and a computer program product, and the method comprises the steps: transmitting a multi-angle composite plane wave sequence to a target region through a one-dimensional ultrasonic array with the frequency gradual change characteristic in the elevation direction, and receiving a reflected echo signal sequence, generating a target gray-scale image sequence corresponding to the echo signal sequence, acquiring coordinates of each ultrasonic contrast agent microbubble in each frame of gray-scale image, generating a two-dimensional coordinate set, calculating the axial half-peak full width of a point spread function region of each ultrasonic contrast agent microbubble, and calculating the axial half-peak full width of the point spread function region of each ultrasonic contrast agent microbubble; determining the coordinate of each ultrasonic contrast agent microbubble in the elevation angle direction based on the axial half-peak full width, generating an elevation angle coordinate set, and generating a three-dimensional contrast image of the target area based on the two-dimensional coordinate set and the elevation angle coordinate set. According to the method provided by the invention, the problem that three-dimensional ultrasonic imaging is limited by the surface ultrasonic array can be solved, and low-cost, simple and efficient three-dimensional ultrasonic imaging based on the one-dimensional ultrasonic array is realized.