152 results about "Temporal models" patented technology

Techniques and systems for the dynamic generation of a map interface include generating temporal activity models by representing individual social media postings as having respective density distributions in time. Each posting's temporal density distribution spans multiple sequential time windows centered on the posting's timestamp, with density contributions decreasing in value for time windows further from the timestamp. The temporal models may be combined with spatial density distributions to generate comprehensive geo-temporal representations of social media activity. A graphical user interface displays an interactive map with overlay elements determined based on calculated activity attributes, including detected temporal patterns and anomalies identified by comparing current activity models against historical baselines. The modeling approach enables improved visualization of activity patterns while providing inherent privacy protection through probabilistic representation of individual posts.

The invention relates to the technical field of natural resource monitoring and spatial information processing, in particular to a method and a system for feeding back land utilization change based on a land spatio-temporal model. The method comprises the following steps: deploying multi-source land monitoring equipment, carrying out collaborative data acquisition and standardization processing, and constructing a land space-time reference data set; performing triple mapping on the land space-time reference data set to obtain a land semantic association graph; constructing a land utilization knowledge graph based on the land semantic association graph; constructing a land change detection initial model by using the land utilization knowledge graph; meanwhile, in a high-frequency change scene, such as an urban and rural ecologic zone or an ecological sensitive area, a traditional model is slow in response to short-term land utilization disturbance, automatic adjustment cannot be carried out through deviation feedback between historical errors and model output, and the reliability of the model in actual application scenes such as resource regulation and control is limited.

The invention relates to the technical field of Internet of Things anomaly identification, in particular to an Internet of Things anomaly traffic identification method and system based on a deep neural network. The method comprises the following steps: collecting communication data of each piece of IoT equipment in real time from an edge gateway of the Internet of Things; preprocessing the collected communication data, and constructing a multi-dimensional feature vector; based on a convolutional neural network and a bidirectional long-short-term memory network, performing time sequence feature extraction and anomaly discrimination on the multi-dimensional feature vector to output a traffic anomaly probability; and comparing the abnormal probability output by the depth time sequence modeling neural network with a dynamic threshold value, and if the abnormal probability exceeds a preset threshold value, determining that the traffic is abnormal. A gating mechanism is introduced into a bidirectional long-short-term memory layer, a gating coefficient is calculated at a time step level, the influence weight of time step information on final output is dynamically adjusted, feature expression of key time steps is strengthened, noise or irrelevant information is suppressed, and the sensitivity of a model to time sequence data is improved.

The embodiment of the invention relates to the technical field of ground surveying and mapping data modeling, in particular to a ground surveying and mapping driven space-time evolution modeling method and system.The method comprises the steps that firstly, multi-dimensional correlation features of elevations, coverage types and deformation data in at least three continuous periods are integrated; the limitation of single time point or single parameter analysis is broken through, and the nonlinear space-time coupling rule in earth surface evolution can be accurately captured; secondly, a spatial-temporal model construction method based on dynamic parameter optimization effectively fuses geographic spatial correlation features and coverage evolution trends, so that the model has adaptive characterization capability for a complex geographic process; in addition, by establishing a closed-loop feedback mechanism of prediction results and model parameters, real-time iterative optimization of the model parameters is realized, and long-term prediction stability in different geographical scenes is remarkably improved.

The invention discloses an artificial intelligence-driven industrial Internet of Things equipment fault prediction system, and relates to the technical field of industrial Internet of Things and predictive maintenance, an edge-cloud collaborative architecture is adopted, real-time acquisition, preprocessing and online fault prediction of industrial equipment sensing data are realized, the system uses a Transform neural network to construct a hierarchical spatio-temporal model, and the fault prediction of the industrial equipment sensing data is realized. Time sequence data are processed in a segmented mode through a sliding window method, a causal reasoning enhancement mechanism is integrated, an industrial equipment causal atlas is constructed, attention masks are generated, a model is focused on key features, and therefore prediction accuracy and interpretability are improved, meanwhile, a closed-loop continuous optimization mechanism is established by the system, and prediction efficiency is improved. And an edge fault prediction result and actual operation feedback are uploaded to a cloud, a causal atlas and model parameters are updated, adaptive optimization of the model is realized, and the system provides efficient, accurate and explainable decision support for preventive maintenance of industrial equipment.

The invention discloses a light energy power station fault prediction system based on deep learning. The system comprises a data acquisition module used for reading equipment operation data from a sensor; the data preprocessing module is used for denoising, interpolating and standardizing the equipment data; the graph convolutional network construction module is used for constructing an equipment data graph structure and extracting features; the Lemap dimension reduction module is used for mapping the high-dimensional equipment features to a low-dimensional space; the time sequence modeling module is used for constructing a time sequence prediction model based on the low-dimensional features; the hyper-parameter optimization module is used for optimizing hyper-parameters of the time sequence model; the model verification module is used for evaluating the precision and response time of the fault prediction model; the model deployment module is used for deploying the prediction model to a monitoring system; the fault prediction and early warning module is used for monitoring in real time and generating fault early warning; and the continuous optimization module is used for regularly optimizing and retraining the fault prediction model. The method achieves the high efficiency of fault prediction of the light energy power station, remarkably improves the prediction precision and the reliability of equipment operation, and is widely suitable for equipment monitoring and early warning.

The present invention is directed to a Spatiotemporal scene-graph embedding methodology that models scene-graphs and resolves safety-focused tasks for autonomous vehicles. The present invention features a computing system comprising instructions for accepting the one or more images, extracting one or more objects from each image, computing an inverse-perspective mapping transformation of the image to generate a bird's-eye view (BEV) representation of each image, calculating relations between each object for each image, and generating a scene-graph for each image based on the aforementioned calculations. The system may further comprise instructions for calculating a confidence value for whether or not a collision will occur through the generation of a spatio-temporal graph embedding based on a spatial graph embedding and a temporal model.

The invention relates to a multi-dimensional and multi-level capability modeling method for production plan scheduling. The method comprises the following steps: defining process basic data based on a time model of each process stage in a production process; defining product basic data based on the material archive and the bill of material; defining resource basic data based on the device capability and the personnel capability; classifying the products based on the characteristics of the product families and the raw materials to obtain each product group; classifying processes based on product processes and process requirements to obtain each process group; classifying the resources based on the grouping of the equipment and the personnel to obtain each resource group; a production plan target is determined, capability modeling is carried out based on the production plan target and constraint conditions to obtain a production plan scheduling model, and the constraint conditions comprise constraints of basic data of products / product groups, working procedures / working procedure groups and products / product groups, so that the data processing efficiency is improved, and formulation of a production plan is optimized.

The invention discloses a deep learning model for picking up a seismic phase from a seismic signal with a low signal-to-noise ratio. The deep learning model comprises a feature extraction trunk, a bidirectional time sequence-channel attention module (BTCA) and a multi-scale dilated convolutional layer module (DSCN). According to the feature extraction trunk, a cascaded LiteMobileBlock module is used, shallow high-resolution features are extracted from an original waveform step by step, and an initial feature map is generated; the bidirectional time sequence-channel attention module integrates the time sequence modeling capability of the bidirectional LSTM and the spectrum sensing capability (emphasizing importance of different sensors / directions) of a channel attention mechanism, and outputs fusion features; and the multi-scale cavity convolution layer module utilizes parallel cavity convolution modules with different expansion coefficients to synchronously capture a local mutation and global oscillation mode of a waveform to generate multi-scale enhancement features, further enhance the capture capability of the model on long-range time dependence in seismic signals through LSTM, and output time sequence enhancement features. According to the model, a multi-scale cavity convolution module, a bidirectional time-frequency attention mechanism module and an LSTM enhanced sequence modeling module are fused, so that the feature extraction and time sequence modeling capability of a seismic signal with a low signal-to-noise ratio is improved, and robust pickup of a seismic phase is realized.

The invention provides a 3D small sample segmentation method and system based on registration alignment and interlayer consistency. The method comprises the following steps: S1, supporting memory extraction; and S2, carrying out sequential segmentation. According to the invention, a 3D medical image is regarded as a series of 2D slice sequences, and sequential processing is carried out by fully using the time sequence modeling capability of SAM2. In this way, the FSMIS-SAM2 can maintain good space consistency between the slices, and the segmentation precision is remarkably improved. The core technology of the FSMI-SAM2 framework is to process a 3D medical image sequence by using a memory enhancement converter of the SAM2. In addition, the invention further provides an innovative inference initial position estimation method, the most appropriate initial slice can be determined for inference through accurate registration according to changes of the anatomical structure of a patient, it is ensured that a high-confidence-coefficient segmentation result can be obtained in the initial stage of sequence inference, and high-quality initialization is provided for subsequent slice segmentation.

The invention provides an intelligent baking temperature control system and method based on the Internet of Things. The system comprises a data sensing module, a dynamic modeling module, an intelligent decision-making module and an interactive monitoring module. The method comprises the following steps: firstly, collecting multi-source heterogeneous data, performing data cleaning and feature alignment, and outputting a standardized baking data set; thirdly, constructing a temperature field space-time model based on the standardized baking data set, and generating a digital twinborn body; then, on the basis of the digital twin, a control instruction set is generated through conjoint analysis of a temperature control strategy and energy consumption optimization, equipment is driven to execute temperature control operation, and execution state data is fed back in real time to form closed-loop control; and finally, the interactive monitoring module provides a visual interface, displays temperature field evolution, an equipment state and material response information, and supports a user to adjust a control instruction. Through multi-source data fusion, temperature modeling, intelligent optimization analysis and user interaction, the temperature control precision, the energy efficiency and the user operation experience are improved.

The invention provides a children practice situation evaluation method, medium and system, and belongs to the technical field of deep learning models.The children practice situation evaluation method includes the steps that children and standard playing audio signals are collected and preprocessed into time-frequency representation, a time window sequence is constructed to calculate window similarity, beat mark points are extracted to form a rhythm sequence, and the rhythm sequence is obtained; a rhythm perception analysis model is input to generate a rhythm feature representation vector, a multi-dimensional deviation vector is formed by applying multi-level time model analysis, a rhythm deviation tolerance range matrix is constructed, deviation similarity is calculated, and finally a rhythm performance score is output through a rhythm evaluation neural network model. According to the method, a convolutional neural network, a long-short term memory network and an attention mechanism are fused, and comprehensive quantitative evaluation of the playing rhythm performance of the children is realized through large-scale data set training and expert scoring verification.

The invention relates to the technical field of video analysis, in particular to a time sequence action positioning method and device based on bidirectional interaction and dynamic feature enhancement. The method comprises the following steps: constructing a self-adaptive feature enhancement strategy, and carrying out layered network architecture integrated dynamic modeling and local and global time sequence interactive modeling through the self-adaptive feature enhancement strategy; the adaptive feature enhancement method comprises a multi-scale dynamic time sequence modeling module and a global and local adaptive bidirectional interaction module. Introducing a self-adaptive feature enhancement strategy into the encoder; and performing feature enhancement on the initial feature through an encoder to generate an enhanced time sequence feature. The invention provides an effective self-adaptive two-way interactive dynamic time sequence enhancement framework, which uses learnable local and global affine matrixes to carry out time sequence modeling in parallel. The parallel structure supports iterative bidirectional information transmission between characteristics and adaptive balance of short-term and long-term dependencies.

The invention provides a learning performance precise teaching management method based on time sequence behavior modeling. The learning performance precise teaching management method comprises the following steps: S1, carrying out multi-modal learning behavior data acquisition; s2, aligning and segmenting the time sequence data; s3, performing knowledge retention rate differential modeling; s4, carrying out time sequence feature extraction; s5, constructing a mixed time sequence model; s6, performing dynamic learning ability evaluation; s7, carrying out adaptive resource recommendation; s8, carrying out cross-correction knowledge diffusion optimization; the method has the following advantages: the method is real-time and accurate; the data is acquired to acquire the intervened closed-loop delay lt; compared with the traditional method, the time is increased by 40 times. And cost optimization: the workload of teachers is reduced by 58% and the resource purchase cost is reduced by 42% through an automation strategy. The scale effect is that federal learning supports ten-thousand-person-level concurrence, and the model updating period is shortened to the hour level from the quarter level. Education fairness: cross-school knowledge diffusion enables the superior rate of weak schools to be improved by 29%.

The invention provides a weak supervision video anomaly detection method and system based on prompt learning, and belongs to the technical field of abnormal event detection based on computer vision, and the method comprises the steps: obtaining to-be-processed video data; and processing the acquired to-be-processed video data by using a pre-trained anomaly detection model to obtain a specific classification result of the abnormal events in the video. According to the invention, a video local and global adaptive time modeling module is introduced to capture local and global dependency relationships at the same time, and the relationship between the demand of detailed time modeling and the calculation efficiency is balanced; by utilizing an external knowledge base, the distinguishing capability of the model on different categories is improved; according to the method, a text-video comparison loss function is designed, the similarity of a correctly matched text-video pair is enhanced, the similarity of wrong matching is reduced, and too high similarity of a negative sample is effectively inhibited, so that the distinguishing capability of the model is improved, the matching of the text and the video is more accurate, and the video and text alignment capability of the model is enhanced.

The invention relates to the technical field of large model reasoning, in particular to a reasoning performance optimization method and system based on a large model, and the method comprises the following steps: initializing a reasoning performance optimization agent; collecting hardware environment indexes in real time, wherein the hardware environment indexes comprise a video memory utilization rate, a CPU (Central Processing Unit) exchange number, residual video card resources, storage IOPS (Input / Output Per Second) and network throughput; the method has the beneficial effects that related statistical indexes, including model types, model weight file total volume, model average sequence length, the number of tokens per second output by the model, first token time of the model, a display card list occupied by the model, the size of a KVcache block, the size of a KVcache sliding window and the like, of each model in a service system are comprehensively collected and analyzed; the system performance is comprehensively evaluated, and the defects that in the prior art, performance evaluation is not comprehensive, and a real-time monitoring mechanism for key indexes such as memory occupation and network bandwidth is lacked are overcome.

The invention belongs to the technical field of universal time Transform models, and particularly relates to a universal time Transform model zero sample prediction training method which comprises the following specific steps: S1, data preprocessing and enhancement: firstly collecting and integrating time series data of multiple fields, then performing feature engineering on the time series data, and then enhancing the data; s2, improving a model structure and a training strategy: firstly, adding an adaptive weight adjustment mechanism in an attention module of a universal time Transform model; through the multi-field data fusion and data enhancement technology, types and features of training data are enriched, so that the model can learn wider data modes and laws; therefore, the model can be better generalized when facing zero sample prediction tasks in different fields, the prediction accuracy and stability are improved, and the overfitting problem caused by single data is reduced.

The invention relates to the technical field of face recognition, in particular to a safety management system based on face capture, which comprises a multispectral acquisition module, a biological feature extraction module and a space-time modeling module, comprising face image data of a visible light wave band and a near-infrared wave band; the biological feature extraction module performs subcutaneous biological tissue chromatographic analysis based on spectral absorption difference according to the facial image data of the near-infrared band; according to the face image data of the visible light wave band and the near-infrared wave band, surface material reflection characteristic identification is carried out; according to the face image data of the visible light wave band, dynamic micro-expression relevance feature extraction is carried out; the space-time modeling module receives a processing result of the biological feature extraction module and constructs a feature model including a short-term dynamic mode and a long-term evolution trend.

The invention discloses a soil texture remote sensing mapping method based on time window screening. The method comprises the following steps: step 1, collecting and testing a soil sample; 2, screening a remote sensing image time window; step 3, acquiring and preprocessing an environment covariable; 4, constructing a multi-source data set; 5, constructing a soil texture prediction model; 6, multi-temporal remote sensing covariable combination optimization modeling is carried out; and step 7, soil texture mapping based on the optimal multi-temporal remote sensing combination. And fusing the multi-source environment covariable and the multi-temporal remote sensing data, and constructing a soil texture prediction model based on the optimal multi-temporal model combination by adopting an extreme gradient lifting algorithm. The problems of insufficient representativeness and weak generalization ability of single-time-phase remote sensing data are solved. On one hand, the generalization ability of the model is improved, and on the other hand, quantitative analysis of the key environmental factors is realized in combination with the SHAP technology, the interpretability of the model is enhanced, and meanwhile, the soil texture prediction precision is effectively improved.

The invention provides an aerospace craft discrete trajectory precision reconstruction method based on a variable order, and the method comprises the following steps: calculating the order of a reconstruction model based on a given highest-order dynamic parameter of an aerospace craft discrete trajectory; calculating a coefficient of the reconstruction model; based on a given aerospace craft discrete trajectory, constructing a continuous time model of a flight trajectory; and substituting any moment into the continuous time model of the flight trajectory to obtain trajectory data of the aerospace craft at any moment. According to the method, reconstruction can be completed with higher precision based on given discrete trajectory data, and trajectory continuity is considered; order configuration can be flexibly carried out, and the adaptability to discrete trajectories with different dynamic change rules is improved.

In various examples, machine learning models that benefit from temporal context while being computationally efficient to train and use are described herein. For instance, the disclosed systems and methods may apply a temporal series of images to a model and use intermediate features output from one or more backbone layers of the model as training data. In some examples, one or more recursive layers and / or one or more head layers of the model—or another model—may be trained using the training data by applying the intermediate features to the recursive layer(s). The recursive layer(s) may output a state representative of a temporal combination of the intermediate features, and the state may be applied to the head layer(s) to make one or more predictions. During inference, the recursive layer(s) may, in some examples, continuously update the state based on previous states of the recursive layer(s).

The invention discloses a human behavior recognition method and device based on multi-modal knowledge graph reasoning enhancement, and relates to the technical field of image processing, and the method comprises the steps: obtaining to-be-recognized video data; uniformly sampling to-be-identified video data to obtain a plurality of key frames; processing the plurality of key frames by adopting a trained human body behavior recognition network, and obtaining a category result of the video data to be recognized by utilizing complementarity between the visual information and the text information; wherein the trained human body behavior recognition network is obtained by training the initial human body behavior recognition network by taking data of a preset category as a training set. According to the method, the semantic comprehension capability and the space-time modeling capability of the model can be improved.

In various examples, to improve path perception in machine learning implementations, a temporal model includes a backbone model trained to predict one or more path perception outputs, such as, path geometry, path class, path uncertainty and / or other path attributes, for a current input frame. To create temporal context, the temporal model enables the backbone model to separately operate (in parallel or otherwise) on a set of frames that are temporally related to the current input frame. The outputs of the separate executions of the backbone model are then concatenated and processed via one or more convolution operations to generate a set of features that will be fed to the final output layer of the pipeline that encapsulates one or more path perception outputs that are generated based on temporal context.

The invention relates to the field of petroleum and petrochemical fire fighting, in particular to an SIS-based interlocked control device for petroleum and petrochemical fire fighting, which comprises a sensing fusion unit for acquiring optical fiber, radar, acoustics, an event camera and dual-band images, and generating a feature tensor and an attitude matrix through multi-scale synchronization and gating sparse fusion. And the prediction and risk assessment unit outputs disaster prediction data, a propagation potential energy matrix and a risk entropy by using the graph neural network spatial-temporal model and the generative adversarial correction. The interlocking control unit constructs a binary optimization model by taking the minimum action number and the residual potential energy as targets, obtains a minimum action set by means of quantum annealing and a minimum-maximum game, and writes the minimum action set into the memristive programmable logic array to generate an interlocking instruction stream. And the execution and feedback unit controls the two-phase flow jetting device, the robot unit and the heat shielding device to complete treatment, and feedback event flow is used for model incremental learning and action deviation correction. According to the invention, millisecond interlocking triggering is realized, the action redundancy is reduced, and the fire spreading radius is obviously reduced.

The invention relates to a method for generating a spatio-temporal model (4DLVM) of a part of a patient's heart, comprising the following steps: (E1) Receiving a plurality of ultrasound images (EG) of a patient's heart; (E2) Generating a spatial model (3DLVM) of at least a given part of the heart from the plurality of ultrasound images received; Receiving a photoplethysmographic (PPG) signal from said patient; Determining a function of change of volume of the heart (LVVC), during all or part of a cardiac cycle of the patient's heart, from the photoplethysmographic signal; Generating a spatio-temporal model (3DLVM) of said part of the heart by means of the spatial model of said part of the heart and said function of change of volume of the heart.

The invention discloses an airport operation situation dynamic prediction method and device, equipment, a storage medium and a program product, and relates to the technical field of airport management.The airport operation situation dynamic prediction method comprises the steps that multi-source heterogeneous data of airport operation is acquired; performing multi-modal fusion based on the multi-source heterogeneous data to obtain multi-modal spatio-temporal data; and inputting the multi-modal spatio-temporal data into a pre-trained airport operation situation spatio-temporal model for situation dynamic prediction to obtain a situation prediction result. Due to the fact that multi-modal fusion is carried out based on the multi-source heterogeneous data, complementarity and collaboration among the data can be mined, and therefore all-dimensional dynamic prediction of the airport operation situation is achieved. The situation dynamic prediction is carried out through the pre-trained airport operation situation spatial-temporal model, so that the instantaneity of processing the abnormal time is improved, and the application scene is enlarged.

The invention belongs to the technical field of control theories, and provides a ZNN model design method for solving a time-varying Sylvester equation based on a filter, and the method comprises the steps: building an error function based on the basic knowledge of the time-varying Sylvester equation; designing a predefined time function based on a high-gain method, introducing the predefined time function into a ZNN model, and considering unknown noise interference in a ZNN model solving process; for unknown noise interference, a filter containing a predefined time function is introduced, and a subsystem of a neural network model is constructed; a Lyapunov function is constructed, a ZNN is obtained through stability analysis, noise can be effectively suppressed, and a time-varying Sylvester equation is solved at predefined time; and solving a time-varying Sylvester equation by using the predefined time ZNN model based on the filter, and verifying the performance of the predefined time ZNN model based on the filter. The method is used for solving a time-varying Sylvester equation, and the influence of noise can be effectively suppressed.

A computer-implemented system for predicting financial behavior and adaptive budget optimization based on machine learning, consisting of: a multitude of distributed processing nodes to enable low-latency communication between the nodes; a transaction data ingestion processor configured to establish authenticated connections with a plurality of financial data sources, wherein the ingestion module is further configured to normalize received transaction records into a standardized schema comprising at least a merchant identifier, a transaction category, a timestamp, a transaction amount, and optional geolocation metadata; a preprocessing engine comprising a classification sub-module trained through supervised learning to assign transaction categories based on merchant identifiers and context attributes, and a feature extraction sub-module configured to compute temporal, statistical, and behavioral feature vectors from the normalized transaction data; a prediction control unit comprising a plurality of lightweight neural network architectures, including at least one recurrent neural network (RNN) and at least one attention-based temporal model, the prediction control unit configured to predict short-term and medium-term output trends by sequentially processing the feature vectors; a budget optimization computation unit configured to solve multi-constraint budget allocation problems using a hybrid approach comprising a primary linear programming solver and an additional heuristic optimization technique, wherein the budget optimization computation unit is further configured to dynamically adjust budget allocations based on updated forecasts and user-defined constraints; a security subsystem configured for encryption at rest and in transit, as well as secure key storage in a hardware-based Trusted Platform Module (TPM); and a user interaction interface configured to display budget recommendations and forecasted spending trends through at least one web application, mobile application, or hardware device interface.

The invention discloses a man-machine cooperation disassembly task dynamic planning method under uncertain operation time. The method comprises the following steps: 1) constructing a man-machine cooperation disassembly information model under the uncertain operation time; 1.1) constructing an uncertain operation time description model of the product parts; 1.2) constructing a disassembling tool, direction and station position switching time model by considering the operation characteristics of the robot and the disassembling personnel; 1.3) constructing a product part disassembly constraint relation model; 2) taking the man-machine cooperation disassembly information model under the uncertain operation time constructed in the step 1) as an environment of a reinforcement learning algorithm, and establishing a man-machine cooperation disassembly task dynamic planning model; and 3) using the trained man-machine cooperation disassembly task dynamic planning model to generate a man-machine cooperation disassembly task planning scheme under the uncertain operation time. The uncertainty of the structural adhesive softening time is quantified by constructing the uncertain operation time man-machine cooperation disassembly information model, and the characterization problem of the disassembly operation time is solved.

The invention relates to a two-dimensional multi-view brain tumor medical image segmentation method and a two-dimensional multi-view brain tumor medical image segmentation system based on a Vision Mama time sequence model, which utilize a novel visual representation model to complete focus segmentation of a brain tumor two-dimensional medical image. The method is used for solving the problems that when an existing two-dimensional segmentation method is used for processing two-dimensional brain medical images, space depth information cannot be fully utilized, and three-dimensional structure features are difficult to accurately capture. The method comprises the following steps: 1, acquiring and preprocessing data; 2, constructing a multi-view brain tumor segmentation network based on edge feature fusion and a spatial state model; 3, constructing a combined loss function of weighted cross entropy and weighted Dess loss, and meanwhile, storing an optimal model weight in training for prediction; and 4, predicting a brain tumor medical image by using the trained optimal model, calculating evaluation indexes and performing result comparison. Through the combination of the methods, the boundary feature extraction quality of the fuzzy edge of the complex focus and the two-dimensional segmentation precision of the model on the brain tumor are effectively improved.