482 results about "Visual interaction" patented technology

The invention discloses an electrical equipment operation state monitoring method, system and terminal, full life cycle health management of equipment is realized through multi-dimensional perception and intelligent analysis, a composite sensor network can be constructed from the level of the method, and high-frequency current, ultrahigh frequency, fiber grating temperature vibration, multi-parameter gas and acoustic sensors are integrated. Electromagnetic characteristics, mechanical states, environmental parameters and voiceprint characteristics are covered; the adaptive signal processing technology performs classification and noise reduction on multi-source data, and the three-dimensional digital twin model realizes time-space fusion of a temperature field, a vibration field, an electric field and a sound field; a lightweight space-time convolutional network is deployed to fuse a time domain waveform, a spectrogram and spatial distribution characteristics for diagnosis, and a hidden semi-Markov model and a particle filter algorithm are combined to dynamically predict the residual service life of equipment. The system architecture comprises a self-organizing sensor network with edge computing capability, a time-sensitive industrial communication network and a containerized analysis engine, and supports mixed reality visual interaction.

The invention discloses an intelligent management system for nuclear power plant personnel situation prediction and risk assessment, and relates to the field of intelligent safety management systems, and the system comprises a data collection unit, a multi-dimensional situation awareness unit, a risk prediction and assessment unit, an intelligent decision intervention unit and a visual interaction unit. And multi-source data acquisition, real-time situation construction, dynamic risk prediction and evaluation, intelligent early warning intervention and information visualization are realized. According to the invention, real-time monitoring, dynamic risk prediction and intelligent management of the safety state of the operating personnel can be realized, and the defects of real-time monitoring, dynamic prediction and intelligent management of the operating personnel in a high-risk area in the prior art are overcome, so that the safety management level is improved, the life safety is guaranteed, and the accident occurrence probability is reduced.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses an operation guidance and optimization method, device, equipment and medium, and the method comprises the steps: obtaining multi-source heterogeneous data to construct a dynamic knowledge graph, and generating an initial guidance scheme containing target operation, and in combination with the visual interaction model, presenting a target operation in real time, collecting real-time dynamic data, generating operation correction information based on data differences, updating display contents of the visual interaction model, and dynamically optimizing a guidance scheme according to an effect analysis value. According to the method, the dynamic knowledge graph is constructed by fusing multi-source heterogeneous data, real-time presentation and data acquisition are performed in combination with a visual interaction model, operation correction information is generated based on data differences, and a guidance scheme is dynamically optimized, so that personalized, dynamically updated and real-time feedback guidance scheme generation and optimization in multi-field application are realized; and the operation accuracy and adaptability are improved.

The invention discloses a software test automation report generation optimization method which comprises the following steps: automatically registering and authenticating a multi-source test data access endpoint, uniformly collecting and normalizing test data with a multi-dimensional attribute tag, and forming a high-quality grouped data set through structured modeling and automatic clustering; further extracting event and result feature nodes, and establishing an interpretable tree rule dependency relationship; according to the method, the minimum redundancy and reusable customized rule path is generated by applying a dependency tree traversal and optimization algorithm and automatic reasoning, and the continuous adaptive evolution of the rule base among multiple projects is realized by combining conflict detection and visual interaction, so that the efficiency and expandability of data processing in a complex test scene are improved, and the test efficiency is improved. And the flexibility and automation level of report customization rule configuration are enhanced.

The invention relates to the technical field of neural engineering and brain-computer interfaces, in particular to a closed-loop multi-modal nerve stimulation system and method based on time interference, and the system comprises a multi-modal stimulation module which is used for integrating electrical stimulation, magnetic stimulation and optical genetic stimulation, and generating a time interference field domain; the neural state sensing module is used for collecting real-time electroencephalogram signals, blood oxygen concentration and neural metabolite level data; the neural control center is used for fusing neural state data and stimulation parameters, and dynamically adjusting time interference frequency and stimulation intensity through an adaptive algorithm; the time sequence cooperation engine predicts a neural response time phase based on a deep learning model, and optimizes a stimulation time sequence and a mode switching strategy; and the visual interaction platform is used for rendering the nerve activation thermodynamic diagram and the stimulation parameter adjustment curve in real time. Therefore, the problems of adjustment strategy solidification, low adjustment precision, insufficient energy conversion efficiency and the like in the prior art are solved.

The invention discloses a network security situation awareness and analysis platform based on AI, and relates to the technical field of network security situation awareness and analysis, and the platform comprises a multi-source data collection module which integrates flow, logs, assets and threat intelligence data, and carries out encryption transmission and standardization; the data preprocessing module purifies and optimizes data, and guarantees data quality and sensitive information security; the AI situation awareness analysis module extracts features through a deep learning model, dynamically evaluates the situation and identifies threats; the threat early warning and decision-making module triggers graded early warning and generates a targeted emergency response scheme; the visual display and interaction module displays information in multiple dimensions and supports query and report generation; and the data storage and tracing module adopts a mixed storage architecture, so that the data security and traceability are ensured. The platform integrates multi-source data and realizes situation accurate perception and intelligent decision by means of an AI technology; the early warning is accurate, the visual interaction is convenient, and the intelligent and efficient level of network security protection is comprehensively improved.

The invention discloses an offshore wind power fault monitoring method based on a knowledge graph and digital twinning, and aims to solve the problems that multi-source data acquisition is dispersed, the transmission delay is high, and real-time convergence and integration are difficult to realize in the operation state monitoring of offshore wind power equipment. According to the invention, the following modules are provided: a multi-source data acquisition layer is used for solving the problems of dispersed data acquisition and transmission lag and ensuring the real-time performance; the knowledge graph construction module is used for solving the problems of extraction, fusion and storage of equipment entity relationships; the digital twin modeling module realizes visual presentation and accurate simulation of the state of physical equipment; the fault monitoring reasoning module solves the problems of anomaly detection and root cause positioning; the intelligent early warning decision module determines an early warning level and efficiently matches a solution; the visual interaction module solves the problems of scattered information display and poor linkage, supports third-party docking, realizes accurate early warning and root cause positioning, improves the decision-making efficiency, adapts to severe environments, supports large-scale wind power plant monitoring, and reduces the operation and maintenance cost.

The invention relates to the technical field of data processing, discloses a digital twin adaptive decision platform for TBM tunneling and a control method, and aims to solve the problems of single sensing dimension, mutual separation of physical and data models, disjunction of early warning and control links and low man-machine interaction efficiency in the prior art. According to the scheme, the method mainly comprises the steps that multi-source real-time data is collected through a hardware sensing layer, after the multi-source real-time data is processed through a data management and fusion layer, a digital twin model layer constructs a physical-data fusion model and completes risk assessment, finally risk visualization is achieved through an augmented reality interaction layer, and an intelligent control decision-making layer generates a control instruction and outputs the control instruction to a TBM main control system. According to the integrated intelligent decision-making platform and the control method, multi-source perception deep fusion, physical and data model online coupling, risk assessment and equipment control second-level closed loop can be realized, and visual interaction can be realized through augmented reality.

The invention discloses a sewage treatment information intelligent management method, system and equipment based on the Internet of Things, and relates to the technical field of sewage treatment information management, and the method comprises the steps: 1, collecting multi-source water quality data, initializing sensor equipment, and carrying out the primary processing and caching of the data; step 2, carrying out edge intelligent prediction on pollutant concentration and pollution load sudden increase; step 3, establishing a prediction and control response model, introducing a control strategy generation mechanism based on a rule engine and priority tradeoff, and calculating a dosing dynamic sensitivity value; 4, adjusting frequency conversion of a fan, a dosing module and a reflux pump, and feeding back, collecting and uploading in real time; and step 5, performing artificial intelligence strategy center verification and a digital twinborn simulation strategy, analyzing multi-plant load scheduling and implementing optimization measures, and performing visual interaction in real time. The problem of energy and medicament waste caused by lack of a feed-forward pre-judgment mechanism is solved.

The invention provides an intelligent task scheduling and optimizing method and system, a medium and equipment, and the method comprises the steps: analyzing task metadata through a database storage process, generating a task dependency graph according to an obtained task dependency matrix, and converting the task dependency graph into a visual interaction interface; identifying a key link with longest time consumption and a bottleneck task on the key link through the created time prediction model, and generating a multi-dimensional optimization suggestion based on an identification result; automatically adjusting a task scheduling script according to the multi-dimensional optimization suggestion and generating a standardized configuration file, dynamically allocating resources according to the standardized configuration file, and monitoring an execution state of a task through a fault-tolerant mechanism; and performing intelligent early warning according to the task log data collected in real time and the system performance index, and generating a multi-dimensional analysis report. The system is an intelligent scheduling system integrating dependency analysis, link optimization and execution monitoring, and automatic analysis and optimization of a complex task network are achieved.

The invention relates to the field of man-machine interaction, and particularly discloses a multi-mode man-machine interaction application system for sky-eye insight based on a pathologist visual angle, which is characterized in that firstly, a candidate focus thermodynamic diagram is generated through rapid scanning of a full-slice image, low-power lens global browsing of a doctor is simulated, and the doctor is guided to lock a key area interactively through professional judgment; therefore, the processing efficiency of the oversized image is greatly improved. And then, the system only performs high-resolution deep analysis on the focus confirmed by the doctor, and performs multi-modal fusion on the extracted microscopic visual features and the patient text information to generate a preliminary report with an interpretable basis, so that the problem that the multi-modal function deviates from a clinical core task is solved. Finally, the doctor can check and finalize the report through visual interaction, and the dominant position and the final decision making right of the doctor in the diagnosis process are ensured, so that the bottlenecks of black box operation and low clinical acceptability of a traditional AI system are overcome.

The invention discloses an enterprise production risk hidden danger identification method and system based on AI, and the method comprises the steps: building a dynamic risk map through a deep space-time attention mechanism according to the production environment multi-source data collected in real time; based on the dynamic risk map, carrying out risk propagation modeling by adopting a dynamic edge weight adjusted graph neural network to obtain a risk node set and a diffusion path thereof; according to the risk node set, constructing a dual-stage reinforcement learning model to optimize the disposal schemes, and outputting an optimized disposal scheme set containing priority ranking; based on production state data fed back in real time, a risk disposal instruction set is generated, the dynamic risk map is synchronously updated, and visual early warning signals and disposal path guidance are output through a multi-modal interaction interface. By utilizing the embodiment of the invention, full-process closed-loop management of multi-source data fusion, dynamic risk propagation modeling, intelligent disposal scheme optimization and visual interaction can be realized, so that the intelligent level of enterprise production safety management is improved.

The invention discloses a new energy equipment health management intelligent question-answering system based on a large model and a knowledge graph. The system integrates eight modules including a data acquisition and preprocessing module, a multi-source heterogeneous data deep fusion module, a knowledge graph construction module and the like. The data acquisition and preprocessing module is used for collecting and processing multi-source data and providing support for subsequent modules; a multi-source heterogeneous data deep fusion module eliminates data differences; the knowledge graph construction module stores domain knowledge; the large model training module endows the system with semantic understanding and reasoning capabilities. Through cooperation of the modules, the system can realize efficient integration and analysis of multi-source heterogeneous data, accurately diagnose and predict equipment faults, and quickly and accurately perform knowledge retrieval and question and answer. Meanwhile, based on fault prediction and health assessment results, visual interaction and scientific decision support are provided, and full-life-cycle management of the equipment is facilitated. The system improves the health management level of new energy equipment, reduces the operation and maintenance cost, and promotes the intelligent development of the new energy industry.

The invention provides a vision-based cross-network interaction method and system, and relates to the technical field of intelligent interaction, and the method comprises the steps: obtaining a visual interaction sequence, constructing multi-modal feature representation, achieving cross-domain semantic alignment, deconstructing visual information into a hierarchical control instruction set, and transmitting the hierarchical control instruction set to a target network environment for execution after security classification. And a bidirectional mapping relation graph is constructed to realize incremental optimization. According to the method, semantic bridging between heterogeneous networks can be established, the cross-domain control precision is improved, and meanwhile safe interaction in a network isolation environment is guaranteed.

The invention discloses an e-commerce virtual scene generation method and system based on digital twinning and visual interaction, and belongs to the technical field of computer interaction. According to the method, the basic data set of the e-commerce scene is acquired, and then scene modeling is performed by using digital twinning, so that the initial virtual scene model containing the three-dimensional space layout of the commodity and the visual features of the environmental elements can be generated; performing visual interaction feature analysis on the initial model, and innovatively mining user interaction attention region features and interaction operation preference features as key bases for optimizing the virtual scene; and on the basis of the characteristics, interaction optimization adjustment is performed on the initial model, and finally the e-commerce virtual scene supporting dynamic visual interaction is generated, so that the user interaction experience of the e-commerce scene is comprehensively improved, the crossing from the traditional static display to the dynamic and intelligent interaction experience is realized, and the immersive interactive e-commerce virtual scene construction is realized.

The invention discloses an unattended substation autonomous operation and maintenance management platform based on multi-modal data fusion. Multi-source data such as equipment state, intrusion behavior, flood prevention safety and environmental parameters are acquired through a multi-modal data sensing module; the cross-modal space-time fusion module performs timestamp alignment, space coordinate mapping and feature level fusion on the data to generate a unified representation vector; the intelligent analysis module outputs analysis results such as equipment fault probability and intrusion threat level by using a multi-task parallel deep learning model; the autonomous decision-making module dynamically generates an operation and maintenance strategy set based on the rule base and the knowledge graph; the execution driving module converts the strategy into an executable instruction and issues the executable instruction to the edge computing node; the visual interaction module realizes strategy verification through a three-dimensional digital twinborn body; and the edge computing node drives an execution mechanism to complete specific operation and return a result. According to the invention, multi-source data fusion, intelligent analysis and autonomous decision making of the unattended substation are realized, and the operation and maintenance efficiency and reliability are improved.

The invention provides a 3D application generation method and device and electronic equipment, and relates to the technical field of computers.The method comprises the steps that natural language information input by a user is analyzed through a large language model, and structured scene description information is generated; generating interactive three-dimensional scene preview according to the scene description information; receiving an operation instruction sent by a user for the scene description information based on the three-dimensional scene preview, and determining a final scene configuration parameter according to the operation instruction; and according to the final scene configuration parameters, constructing the 3D application in stages, and after at least one stage is completed, providing a runnable 3D application intermediate version for the user. According to the method, the natural language understanding ability of the large language model is deeply fused with a visual interaction guide mechanism, so that low-threshold and high-efficiency 3D application creation is realized, a user with zero programming basis can quickly and accurately convert the creation into a high-quality immersive application, and the flexibility of 3D application creation is improved.

The invention discloses an AI diffusion model pattern generation platform and method, the generation platform comprises a visual interaction module, an image generation module and a pattern mapping module, and the image generation module comprises a training sub-module and a reasoning sub-module; the generation method comprises the following steps: preprocessing a text input by a user and an optional structure image, and outputting standardized data; a semantic embedding vector is generated through text coding; carrying out LoRA fine tuning to obtain a style weight module; a ControlNet structure condition is injected; performing diffusion sampling to generate candidate patterns; performing CLIP scoring to screen an optimal pattern; and outputting the optimal pattern and previewing the carrier map. According to the platform and the method provided by the invention, the LoRA low-rank fine tuning, the ControlNet structure condition control and the CLIP similarity evaluation algorithm are fused, so that the effects of customizing the special style pattern with a small number of samples at low cost and accurately controlling the structure and style of the pattern are realized.

The invention relates to the technical field of industrial carbon emission monitoring, and particularly discloses an industrial carbon emission visual monitoring method and system based on digital twinning, and the method comprises the following steps: S1, carrying out the collaborative collection and preprocessing of carbon emission data; s2, dynamically updating the hierarchical digital twin model; s3, intelligent early warning and accurate tracing are carried out; and S4, virtual-real linkage visual interaction is carried out. According to the invention, by establishing a multi-factor driven hierarchical iteration mechanism, hierarchical updating and closed-loop verification of parameters-structures are realized, by establishing a space-time correlation-process coupling-mode matching three-dimensional fusion early warning rule, the false alarm rate and the missing report rate are reduced, a visual interaction system of virtual-real linkage is built, the decision-making efficiency is improved, and the decision-making efficiency is improved. And an accurate and efficient carbon emission control technical support is provided for multi-process industrial scenes such as metallurgy and chemical engineering.

The invention discloses a visual interaction artificial intelligence algorithm automatic arrangement and deployment method, and belongs to the technical field of artificial intelligence development and deployment. The method comprises the steps that after a training data set is uploaded, a system automatically analyzes a format and generates a visual analysis report; importing an algorithm operation environment and codes, and configuring parameters and a required data set through a visual interface; after a user inputs a training task, a system dynamically schedules an algorithm and a data node automatically arranges a process and generates a directed acyclic graph; the intelligent resource scheduler allocates computing resources and monitors a training state; and after the training is completed, deploying a pipeline publishing model API through servitization. According to the method, the training process of the algorithm can be automatically arranged, the training process of the algorithm is greatly shortened, and the problems that in the prior art, an automatic arrangement function is lacked, automatic parameter mapping is not supported, the resource scheduling strategy is single, and the multi-mechanism joint training requirement cannot be met are solved.

The invention discloses a display system and a vehicle. The display system comprises a display assembly and a light control assembly. The display assembly is used for displaying multi-view image information of a target object. The light control assembly is used for transmitting light corresponding to the image information of each visual angle of the display assembly to different preset visual areas, and the visual angles are in one-to-one correspondence with the preset visual areas. Therefore, the display system can form images of different visual angles in the air so as to form a suspended three-dimensional image corresponding to the target object in the air. Meanwhile, the visual angle corresponding to the image formed in each preset visual area is the same as the visual angle when the real target object is seen in the preset visual area, so that the image effect seen when the target object image is seen in each visual angle is the same as the image effect seen when the real target object is seen in the visual angle; therefore, it is ensured that the image suspended in the air has real 3D information, the display effect of the target object image is good, and then it is ensured that a user can obtain more real man-machine visual interaction experience.

The invention relates to the technical field of medical data analysis and rehabilitation, in particular to a cerebral apoplexy patient rehabilitation exercise analysis system based on medical big data, which comprises a data acquisition module used for acquiring electromyographic signals, exercise data and behavior data of a patient to form a data set; the data processing and fusion module is used for preprocessing the electromyographic signals in the data set, normalizing motion and behavior data and fusing the motion and behavior data into a multi-modal feature vector; the hierarchical analysis module is used for performing motion intention recognition according to the preprocessed electromyographic signals and outputting an intention classification result, recognizing the motion intention and predicting a rehabilitation effect through a double-flow Transform encoder, and evaluating a behavior risk level in combination with a risk factor formula; the optimization module is used for dynamically adjusting the mirror image treatment intensity according to the risk level and optimizing the prediction model by adopting incremental learning; and the interaction module is used for generating a thermodynamic diagram superposition rehabilitation curve in the X-Y coordinate system, and marking doctor intervention suggestions to complete visual interaction.

The invention relates to the technical field of artificial intelligence. The power equipment fault prediction and diagnosis method based on the artificial intelligence algorithm comprises the steps of performing multi-modal data acquisition and preprocessing on a vibration signal, a temperature signal, an electromagnetic field signal and a partial discharge signal of power equipment, and generating a preprocessed multi-modal feature vector; performing dynamic time reference axis construction processing on the preprocessed multi-modal feature vector to generate multi-modal data after time calibration; semantic enhancement feature fusion processing is carried out according to the multi-modal data, and global semantic consistency features are generated; performing fault prediction and diagnosis model training processing to generate a fault type classification result and a root cause tracing result; and based on a fault type classification result and a root cause tracing result, carrying out visual interaction interface generation processing, and outputting a decision view comprising a fault probability hot area distribution diagram and an economic disposal strategy so as to solve the problems of multi-modal data time sequence dislocation and physical association missing in related technologies.

The invention discloses a mineral resource metallogenic law simulation and target prediction system based on big data, and relates to the technical field of geological exploration. The multi-source data acquisition and standardization module supports acquisition of five types of data, and the data are preprocessed and then transmitted to the distributed database; mining key elements by using an improved Apriori algorithm, and constructing a correlation graph; building a model based on Unity 3D, and integrating three types of dynamic simulation; performing three-level prediction by using a CNN-LSTM model; three-dimensional rendering and multifunctional display are supported; and automatically updating data, models and rules to form an optimized closed loop. According to the method, multi-source data is integrated to improve ore-forming element recognition comprehensiveness, dynamic simulation fits geological reality, AI prediction is accurate, uncertainty analysis is included, visual interaction is practical, iterative optimization continuously improves efficiency, invalid exploration cost is reduced, and mineral exploration is efficiently guided.

The invention relates to an internet-of-things monitoring and early warning system for lake water quality. The internet-of-things monitoring and early warning system comprises a multi-mode intelligent sensor acquisition system, a self-adaptive wireless transmission system, a distributed data storage system, an intelligent data analysis system, a visual interaction system, an intelligent early warning system and a remote intelligent control and repair system. The invention also relates to a monitoring method using the lake water quality Internet of Things monitoring and early warning system. Traditional and innovative sensors are cooperated to comprehensively collect water quality and ecological data, the biosensor accurately detects specific pollutants, the hyperspectral imaging sensor monitors algae distribution and water color change, rich and high-precision data are provided for lake water quality monitoring, the monitoring comprehensiveness and precision are improved, and the water quality monitoring system is suitable for being popularized and applied. The lake ecological protection work is powerfully supported.

The invention discloses a multi-modal large model training method based on a position reflection thinking chain, and the method comprises the following steps: S1, constructing position reflection type thinking chain data, and associating the position reflection type thinking chain data with the coordinates of a chart visual region through distinguishing the data extraction and logical reasoning steps in the thinking chain; automatically generating position annotation data through drawing code editing, re-rendering verification and image analysis technologies; and S2, training a structured reasoning model, constructing a multi-type instruction data set containing visual positioning and logical reasoning, jointly optimizing generation of answer prediction, position positioning and reasoning steps by adopting a multi-task loss function, and enhancing the perception ability of the model to chart elements through a bounding box reflection mechanism. According to the method, the problems of numerical illusion and lack of visual interaction of the thinking chain due to the fact that an existing model depends on OCR are effectively solved, the chart understanding accuracy and the thinking chain interpretation are improved, and the performance is remarkably superior to that of an existing method on the mainstream basis.

The invention discloses an agent configuration method and device based on front-end interaction and a medium, and relates to the technical field of computers.The method comprises the steps that a user interaction behavior is received through a front-end interaction interface, the user interaction behavior is analyzed, and a corresponding response instruction is generated; executing the response instruction, creating a plurality of agent nodes, and configuring corresponding role information; determining a task dependency relationship between the agent nodes, and constructing an agent node relationship graph through a preset task execution rule; receiving and analyzing a to-be-executed task, extracting required agent role information, determining to-be-executed agent nodes, and determining a to-be-executed sequence corresponding to the to-be-executed agent nodes according to the agent node relation graph; and calling the agent models corresponding to the to-be-executed agent nodes in sequence, executing the to-be-executed task, and updating the state parameters. Through deep fusion of front-end visual interaction and automatic process arrangement, the intuition and efficiency of the configuration process are improved.

The invention provides a building outer wall facing layer hollowing defect monitoring device and a monitoring method thereof, and belongs to the technical field of building outer wall facade facing layer defect monitoring. The monitoring method comprises a temperature data acquisition process, a heat flow intelligent analysis process, a hollowing defect adaptive identification process, a temperature field dynamic optimization process, an environmental interference self-learning correction process, a multi-source data fusion diagnosis process, a defect trend prediction process, a visual interaction process and a resource intelligent distribution process. All the processes of the monitoring method are executed through a temperature collecting and processing end, and aiming at the problems of insufficient precision, large environmental interference, low intelligence, prediction deficiency and low resource efficiency in building outer wall facing layer hollowing defect detection, through a temperature sensor matrix, an intelligent data processing process and a multi-source data fusion design, the building outer wall facing layer hollowing defect monitoring method is provided. And the detection precision, the automation degree and the maintenance efficiency are obviously improved.

The invention discloses a multi-modal data collaborative analysis risk quantitative evaluation system, and the system comprises a multi-source data collection unit which is used for collecting different modal data; the data integration unit adopts a data cleaning technology and is used for integrating data of different formats and sources into a unified database; the fusion module is used for applying a deep neural network model based on an attention mechanism to automatically learn importance degrees of different modal data during risk assessment and dynamically distribute weights; the visual interaction unit is used for generating a visual interface according to the multi-modal data; the dynamic evaluation unit is used for constructing a personalized initial evaluation model by utilizing a machine learning algorithm; the method has the beneficial effects that the importance of different modal data in risk assessment is automatically identified by building an omnibearing acquisition system, collecting physiological, behavior, language and text multi-modal information and mining associated features through cross-modal contrast learning, and potential risk signals of a target are more accurately captured.

The invention relates to the technical field of medical image processing, and particularly discloses an intelligent segmentation system for a kidney tumor in a CT image, and the system comprises an image preprocessing module, an ROI automatic detection module, a kidney tumor segmentation module, a segmentation result post-processing module, a visual interaction module, and a model updating module. The system improves image quality through normalization and filtering, uses a target detection network to position a kidney area, realizes precise tumor segmentation based on an integrated channel attention mechanism and a U-Net network guided by structure priori, optimizes a boundary in combination with a conditional random field, adopts a multi-plane fusion strategy to improve three-dimensional consistency, and calculates tumor volume. The system supports federal learning update, improves the cross-mechanism generalization ability, has the advantages of high segmentation precision, strong boundary reducibility, good structural rationality, strong clinical deployment and the like, and is suitable for kidney tumor auxiliary diagnosis and quantitative analysis.