1678results about How to "Improve perception" patented technology

The invention provides a machine room energy consumption optimization method and system based on temperature monitoring, and relates to the technical field of energy consumption optimization, and the method comprises the steps: collecting the temperature data of a machine room in real time, continuously monitoring the temperature gradient of each region, carrying out the fusion processing, and constructing a three-dimensional dynamic temperature distribution model; a hot spot area, an airflow dead angle and a cooling capacity waste area are identified through a deep learning algorithm, a flowing track and a mixing process of cold and hot air in a machine room are predicted through a track regression neural network in combination with machine room layout and equipment distribution, and a machine room temperature control strategy set is generated through adaptive neural fuzzy reasoning, correction and updating of a prediction result; and by taking minimum energy consumption and maximum refrigeration efficiency as optimization objectives and taking a safe operation temperature range of equipment in the machine room as a constraint condition, a plurality of parameters are jointly adjusted through a temperature control optimization algorithm to obtain an optimal temperature control curve, and the optimal temperature control curve is converted into a refrigeration system control instruction, so that collaborative optimization of the temperature and the energy consumption of the machine room is realized.

The invention provides an intelligent fault diagnosis method for a numerical control machine tool, which is applied to a control system of the numerical control machine tool. The method comprises the steps of collecting multi-source sensor data in the operation process of the numerical control machine tool, wherein the multi-source sensor data comprises at least two of a vibration signal, a temperature signal, a current signal and a sound signal; performing preprocessing and feature extraction on the multi-source sensor data to obtain preprocessed data; based on the preprocessed data, constructing and training a machine tool dynamic model to obtain a target machine tool dynamic model; when abnormal operation of the numerical control machine tool is detected, fault state estimation is carried out based on the target machine tool dynamic model and a model prediction control method, and a fault estimation result is obtained; and based on the fault estimation result, constructing a fault evolution path by adopting a Monte Carlo tree search algorithm, and outputting a fault diagnosis result. The accuracy and efficiency of fault diagnosis of the numerical control machine tool can be improved.

The invention provides a distributed computing resource smart evolution method and system based on digital twinning, and relates to the technical field of resource evolution.The distributed computing resource smart evolution method comprises the steps that a smart sensing probe is deployed at a computing node to collect operation data, a double-layer attention mechanism is used for extracting features, and a digital twinning model is constructed based on a graph neural network; a resource allocation strategy and a scheduling decision are optimized in combination with a hierarchical reinforcement learning framework, a prediction sequence is generated through an attention-enhanced sequence prediction network, and finally an optimal configuration scheme is solved by adopting a particle swarm algorithm. According to the method, the utilization rate of distributed computing resources can be improved, the energy consumption of the system is reduced, and intelligent scheduling and optimal configuration of the computing resources are realized.

The invention discloses an expressway service area sewage treatment system based on the Internet of Things, and relates to the technical field of sewage treatment.The expressway service area sewage treatment system is provided with a multi-modal sensor and an edge computing unit, real-time monitoring and data processing of sewage flow and water quality parameters are achieved, the water quality change trend and the parameter change rate are rapidly recognized, and the sewage treatment efficiency is improved. The sensing capacity of the system to load changes is remarkably improved, the system conducts real-time adjustment according to the water quality changes, fluctuation of the treatment effect and waste of energy consumption are avoided, an LSTM model is adopted for conducting dynamic prediction on water quality parameters, in combination with real-time monitoring data, the aeration strategy is optimized through intelligent self-adaptive adjustment and closed-loop fine control, and the aeration efficiency is improved. The LSTM model can predict the future sewage load and ammonia nitrogen concentration change, the aeration intensity and time are adjusted in advance, the fluctuation of the ammonia nitrogen concentration is quickly responded through PID control and closed-loop control of a feedback mechanism, and the ammonia nitrogen removal efficiency and the aeration accuracy are improved.

The invention relates to the technical field of data processing, and discloses a road condition monitoring recognition method, device and equipment based on machine vision and a medium. The method comprises the following steps: carrying out multi-source data acquisition on a road environment to obtain an original data set; performing dynamic time warping and multi-modal feature fusion processing on the original data set to obtain a spatial-temporal feature tensor; performing multi-scale pyramid decomposition and depth feature extraction on the spatial-temporal feature tensor to obtain a road condition feature vector; semantic analysis is carried out on the road condition feature vectors through feature mapping and knowledge graph enhancement, and a road condition attribute set is obtained; performing multi-level risk quantification and space-time propagation simulation on the road condition attribute set to obtain a graded early warning index and a risk distribution map; and performing multi-target tradeoff and causal chain reasoning on the graded early warning indexes and the risk distribution map to obtain a self-adaptive traffic regulation and control instruction set. According to the invention, the efficiency and accuracy of road condition monitoring and identification based on machine vision are improved.

The invention discloses a medical visual question-answering method and system based on multi-task modeling, and the method comprises the steps: carrying out the feature extraction of a to-be-analyzed medical image through a visual encoder, obtaining an image feature, inputting a preliminary question instruction and historical question-answering data into a dialogue large model to generate a question instruction, and obtaining a text feature through a text encoder; carrying out feature fusion on the obtained text features and image features, and carrying out self-attention image importance weighting processing and image-text alignment to obtain image-text fusion representation; and inputting a multi-target output projection layer, carrying out multi-task prediction, outputting question answers, image categories and important region masks, inputting the question answers and the image categories into a dialogue large model, and finally generating diagnosis opinions with detailed description in combination with dialogue context and multi-round interaction data and in combination with the important region masks. According to the invention, the interaction experience and the performance of the question and answer system are improved, and more accurate medical diagnosis in multi-modal complex information is facilitated.

The invention provides a vehicle collision risk early warning method, system and device and a medium, and belongs to the technical field of vehicle safety control. The method comprises the steps that surrounding environment information of a vehicle is collected in real time through a vehicle built-in sensor and a GPS module; a multi-sensor fusion device technology is used to carry out comprehensive processing on surrounding environment information of a vehicle, and an environment perception model is generated after data integration; based on the environment perception model, target identification is carried out by using a computer vision technology based on a convolutional neural network so as to identify travelers, vehicles, traffic signs and obstacles; performing behavior prediction based on a deep learning model to predict a behavior track of a front vehicle or pedestrian; evaluating a potential collision risk in the current scene in combination with the target recognition result and the behavior prediction result; when a potential collision risk is detected, an early warning signal is sent to a driver, and when the driver does not take corresponding measures, active safety measures are automatically triggered to avoid collision or reduce accident damage.

The invention relates to the field of inductive control of illuminating lamps, in particular to an inductive control system of an inductive LED illuminating lamp. The method comprises the following steps: the lighting scene module is used for identifying all LED lighting lamps in an area; calculating node positions of all the LED lighting lamps in the area one by one, and identifying use scenes in the area to construct a plurality of LED lighting scene sub-areas; the ambient light feature module is used for acquiring ambient light parameters of a scene sub-region; performing light spatial distribution tracking on the ambient light parameters of the scene sub-regions, and performing ambient light state feature mining to generate ambient light comprehensive state features; and the multi-area lighting cooperation module is used for carrying out dynamic light source output parameter adjustment and multi-area lighting cooperation smoothing on the plurality of LED lighting scene sub-areas based on the environment light comprehensive state characteristics so as to generate multi-area lighting cooperation control parameters. According to the invention, rapid and accurate induction control of the LED illuminating lamp is realized.

The invention discloses a driving situation prediction and adaptive strategy generation system based on a cloud multi-mode large model. Comprising a multi-modal data acquisition and situation prediction module, a self-adaptive strategy generation and remote optimization module, a collaborative driving and multi-vehicle strategy coordination module, a cloud driving intelligent monitoring and feedback module and a dynamic risk assessment and path optimization module. Various input data from the vehicle and the environment are processed through the cloud, risk factors such as sudden obstacles, weather deterioration and complex traffic conditions in the automatic driving situation are predicted in advance, cloud computing provides powerful data processing capacity and storage resources, a large model deployed at the cloud can train data collected by the vehicle, and the risk factors can be predicted in advance. Therefore, the perception and prediction capabilities of the automatic driving system are improved, various coping strategies aiming at the risks are generated, and the vehicle is helped to make a safer and more intelligent driving decision in a complex scene through dynamic generation and remote optimization of an adaptive strategy.

The invention relates to the technical field of sleep aiding, in particular to a method and a system for extracting feature information of a sleep state monitoring model. The method comprises the following steps: carrying out multi-mode physiological signal acquisition on a detected object to obtain original sleep monitoring data; performing noise removal and signal enhancement processing on the original sleep monitoring data, and performing signal segmentation to obtain multi-channel sleep signal fragment data; performing feature extraction based on electroencephalogram, eye movement diagram, electromyogram, electrocardiogram and accelerometer signals on the multi-channel sleep signal fragment data to obtain multi-dimensional feature data; and performing ultrasonic feature and acoustic feature extraction on the multichannel sleep signal fragment data to generate acoustic feature data. According to the method, dynamic modeling and trend prediction are carried out on the conversion process of the sleep state, potential sleep disorder events can be accurately recognized, influence evaluation is carried out on the potential sleep disorder events through deep learning, and support is provided for personalized sleep diagnosis.

The invention provides a community management method and system based on an unmanned aerial vehicle, electronic equipment and a storage medium, and relates to the field of community management. The method comprises the steps of obtaining an initial inspection task of an unmanned aerial vehicle cluster and planning an inspection route, and controlling the unmanned aerial vehicle cluster to execute inspection operation to obtain multi-source inspection data. And constructing a community portrait based on the inspection data, identifying community management hidden dangers through a classification reasoning model, and generating an early warning event and a processing task. A processing task is decomposed into hierarchical sub-tasks by utilizing an ontology reasoning framework, a task execution main body is selected, and a task set is generated. And a multi-Agent reinforcement learning framework is adopted to carry out space-time collaborative arrangement on the task set to form an overall collaborative scheme, the overall collaborative scheme is converted into an interpretable scheduling decision instruction, and finally the unmanned aerial vehicle is controlled to execute the task. According to the technical scheme, the emergency response speed of security and protection and the capability of coping with complex conditions are improved.

The invention discloses a park sweeper positioning and environment mapping method based on multi-sensor fusion. The method comprises the following steps: S1, preprocessing multi-modal data acquired by a plurality of sensors; s2, extracting and fusing semantic features and geometric features in the multi-modal data preprocessed by the plurality of sensors; s3, performing geometric feature extraction and recognition on the point cloud data by using a VoxelNet point cloud processing algorithm; s4, performing pose estimation and track plotting by fusing multi-sensor data; s5, identifying places where the motor sweeper repeatedly passes through through loopback detection, and correcting accumulated errors; and S6, constructing a three-dimensional environment map by fusing the laser radar, the visual features and the IMU data, performing global optimization by applying a map optimization technology, correcting errors, improving map precision, and updating the map in real time. According to the invention, the autonomous navigation capability of the sweeper is enhanced, the sweeping efficiency and safety are improved, and the sweeper is suitable for various complex and dynamic park environments.

The invention belongs to the technical field of network security, and provides a network security threat tracing method and system based on association analysis, and the technical scheme is as follows: constructing a network security situation map based on multi-source network security data, and performing association analysis on attack elements by adopting a graph mining algorithm based on the network security situation map to obtain an association analysis result, based on the correlation analysis result, the multi-path traceability strategy is used for tracing the attack and determining the attack source and the propagation path, potential threats can be automatically identified, risks can be evaluated, the optimal attack path can be found out, the accuracy and traceability efficiency of threat identification are improved, and a basis is provided for subsequent defense and countermeasures.

The invention discloses an information interaction method for an intelligent robot with a body, and the method comprises the steps: S1, receiving the information which is transmitted by a user and comprises a task instruction, S2, carrying out the analysis of the information based on a context understanding module, so as to recognize the specific meaning and environment conditions of the task instruction, s3, operation corresponding to the specific meaning is executed, and execution parameters are adjusted in a specific environment to ensure that the task instruction is accurately implemented; according to the information interaction method of the intelligent robot with the body, environment perception and adaptive capacity are enhanced, various perception information such as vision, hearing and touch can be integrated, comprehensive perception of the environment is achieved, instruction understanding and execution accuracy is improved, the intelligent robot with the body can analyze complex user instructions more accurately, and user experience is improved. Through real-time multi-modal data fusion and decision optimization, the robot can realize real-time integration of different data sources in a highly dynamic scene, and the task execution efficiency and the user experience are improved.

The invention relates to the field of robot motion, in particular to a multi-modal information fusion robot motion planning method, which comprises the following steps: information acquisition: acquiring multi-modal data of a robot through various sensors; preprocessing: carrying out normalization, denoising and synchronization processing on the multi-modal data; fusion: coding and fusing the preprocessed data by adopting a feature level fusion method, and generating an environment model based on an information fusion algorithm; motion planning: combining an environment modeling and path planning algorithm, and generating a motion path according to the fused multi-modal information; and feedback adjustment: dynamically adjusting the motion track by adopting a feedback control mechanism based on the environment change sensed in real time. The invention provides a multi-modal information fused robot motion planning method and equipment, which are used for improving the perception capability of a robot to a complex environment, enhancing motion planning and precision and improving task execution efficiency and safety by fusing multi-modal information.

The invention relates to the technical field of lithium battery charging and discharging management, and discloses a lithium battery charging and discharging detection and management method, device and equipment and a storage medium, and the method comprises the steps: carrying out the data collection of the charging and discharging process of a lithium battery pack, applying consistent Gaussian noise, and obtaining an electrode-level expansion data set and a battery-level expansion data set; performing feature mapping and feature mixing by minimizing the maximum average difference loss function to obtain a mapping feature vector; estimating the state of charge and the state of health of the battery through a fusion model of a recurrent neural network and a physical information hybrid neural network to obtain a state of charge estimated value and a state of health estimated value; and constructing a battery pack electric heating model of the lithium battery pack, and generating a corresponding charging and discharging control instruction sequence. Self-adaptive adjustment can be carried out according to the real-time state of the battery, potential safety hazards such as over-charging and over-discharging and out-of-control temperature are effectively avoided, and the use safety of the battery pack is remarkably improved.

The invention discloses a network slice access control method based on a mixed service type, relates to the technical field of network slices, and aims to solve the problem that the network slice access control method does not comprehensively consider slice network quality, and comprises the following steps: calculating a mixed service regulation factor according to a waiting delay mean value of all slices; according to the residual bandwidth of the slice and the signal-to-noise ratio, evaluating the residual capability of the link; calculating any slice service quality parameter according to the reference signal receiving power lower limit value; calculating a service delay parameter and a service throughput parameter of any slice, and determining a qualified set containing the slices; and selecting the slice as a target slice according to the comprehensive evaluation value of the slice in the qualified set to implement access control. According to the control method provided by the invention, the comprehensive evaluation values of all the slices in the qualified slice set can be calculated, so that the new service can be dynamically accessed; the service can be ensured to be always in a good slicing environment, and real-time guarantee is provided for improving customer perception.

The invention provides a natural language control method for a humanoid robot. By constructing the simple skill library and the complex skill library of the robot, the dependence on a large amount of operation data in a specific field is effectively reduced. According to the method, a large language model is used as a base of a task planner, and the capabilities of scene learning, semantic understanding and common sense reasoning of the task planner are brought into full play. By introducing a visual model in the field of computer vision, high-level semantic information and low-level spatial information of the environment can be obtained, and an accurate scene graph is constructed to serve as environment state representation. According to the voice interaction interface constructed by the invention, the naturalness of man-machine interaction is improved, so that a user can more conveniently communicate with the robot and input instructions, the user experience is effectively improved, and popularization of the humanoid robot in an actual application scene is facilitated.

The invention relates to a tumor image segmentation method. Performing multi-scale feature extraction on the image by using a convolutional neural network (CNN) to capture different-scale information of the tumor; introducing a self-attention mechanism to enhance the attention capability of the model on the tumor area and the accuracy of feature extraction; performing image segmentation through the improved model based on the U-Net architecture; according to the method, the advantages of a CNN and a VisionTransform are combined, local and global features are extracted by using a hybrid architecture, and position codes are added into the Transform so as to enhance the perception capability of spatial position information; adopting a comparative learning strategy to improve the discrimination capability of the model by comparing positive and negative sample pairs, and performing multi-task learning in combination with a tumor classification task and a segmentation task; designing a combination of a weighted cross entropy loss function and a Dice loss function, and improving the sensitivity to a small tumor region; and performing post-processing by using a conditional random field (CRF) to optimize a segmentation boundary, and eliminating small noise points and filling holes of a segmentation region by applying morphological operation to further optimize a segmentation result.

The invention provides a ship remote real-time early warning system based on cloud computing, and relates to the technical field of data processing, and the system comprises a data collection module which is used for collecting ship operation data, environment state data and external interaction data; the data preprocessing module is used for generating standard processing data; the data grouping module is used for dividing a priority data group and a common data group; the data transmission module is used for transmitting the data to the cloud analysis module; the cloud analysis module is used for carrying out multi-source data aggregation processing on the priority data set and the common data set, carrying out real-time evaluation on the ship operation state, identifying abnormal behaviors and predicting emergencies, and generating corresponding final early warning instructions including an avoidance operation instruction and an air route adjustment suggestion; the early warning feedback module is used for sending the early warning instruction to the ship terminal; the local response module is used for executing instant triggering operation on the instruction classification data; according to the invention, autonomy and accuracy of remote real-time early warning of the ship are improved.

The invention discloses an intelligent self-adaptive industrial robot control system and method, and the method comprises the steps: carrying out the automatic recognition and self-inspection of a sensor, an actuator and a controller module through a standardized interface when the system is started; various sensors are used for environment monitoring, acquired data are stored in a cloud end, and a sensor fusion technology is applied for integration; the system state and the environment condition are monitored in real time, machine learning and an artificial intelligence algorithm are used for analyzing the operation state and the task effect, and a corresponding operation strategy is formulated; based on task execution and environment information, an execution path is generated by adopting a path planning algorithm, and an operation strategy and parameters are dynamically adjusted according to real-time feedback; a real-time safety monitoring mechanism is introduced, and a safety threshold value is set; sensor data are analyzed regularly, potential faults are identified, and fault prediction is carried out; according to the intelligent self-adaptive industrial robot control system and method, when the robot is designed, the safety of man-machine cooperation is considered, and safety protection measures are added.

The invention discloses a multi-perception interaction method and system based on virtual reality. The method comprises the following steps: creating a virtual scene and an intelligent agent based on an interaction demand, and constructing a perception model of the virtual scene and the intelligent agent; the intelligent agent perceives visual, auditory and tactile environments, extracts perceived feature time sequence data, divides instructions according to the perceived feature time sequence data, and calculates instruction priorities. And generating a decision network according to a preset knowledge graph, and forming an instruction chain in combination with the instruction priority, so that the intelligent agent obtains a feedback strategy. A dynamic weight value is distributed to a feedback strategy, a virtual scene is updated through fusion of an attention mechanism and a perception model, an interaction mode is obtained, meanwhile, physical and psychological parameters of a user are recorded in real time, abnormal fluctuation segments are recognized, and emotional tendency of the user is learned from corresponding interaction, so that the interaction mode is continuously optimized. According to the method, the interaction experience can be improved, the intelligent agent can better meet the user requirement, the interaction effectiveness and adaptability are enhanced, and meanwhile good interpretability is achieved.

The invention provides a face forgery detection method and system based on reconstruction learning and a hybrid expert mode. The method comprises the following steps: extracting a face frame image from a face video; performing data processing on the face frame image to obtain a two-dimensional face image; swin Transform is used as an encoder, a hybrid expert model is introduced at the same time, and spatial feature and long-distance dependence feature extraction is carried out on the two-dimensional face image; enhancing different types of features in the two-dimensional face image by using a hybrid expert model to obtain a high-level semantic feature map; generating a face reconstruction image by using an attention-guided decoding module; and aggregating the face reconstruction image and the corresponding two-dimensional face image by using a feature aggregation module based on reconstruction guidance, and exploring a possible counterfeit region to obtain a final feature map for face counterfeit detection. According to the method, the detection capability of unknown counterfeit types is better, and the accuracy, robustness and generalization of face counterfeit detection can be improved.

The invention provides a path planning method and system for an automatic driving trolley, and relates to the technical field of path planning, and the method comprises the steps: obtaining the data of an acoustic sensor of an underwater environment, and obtaining the contour of the underwater terrain and the position information of an obstacle; carrying out dynamic environment modeling processing; the Euclidean distance is used as a heuristic function to calculate the dynamic model of the underwater environment, and a second optimized path conforming to kinematics and dynamics constraints of the automatic driving trolley is obtained; carrying out smoothing processing on the second optimization path through a dynamic motion primitive technology; constructing a three-dimensional map for path planning and obstacle avoidance; predicting an action track of the robot in a period of time in the future; and through real-time adjustment processing of the self-adaptive control system, path planning of the automatic driving trolley is completed. According to the method, the dynamic characteristics of the underwater environment and the motion constraint of the automatic driving trolley can be comprehensively considered, the three-dimensional map containing the obstacle and topographic information can be constructed in real time, and the future motion trail of the obstacle can be predicted.

The invention relates to the technical field of force feedback handles, in particular to a remote surgical robot control system based on a force feedback handle and a control method thereof. The method comprises the following steps: carrying out multi-dimensional force data acquisition on an end effector of the surgical robot, and carrying out contact force size, direction and time sequence feature extraction to obtain multi-dimensional force feature data; performing surgical operation safety baseline analysis according to the multi-dimensional force feature data to generate surgical operation safety baseline data; performing dynamic force feedback threshold calculation according to the surgical safety operation baseline data to obtain force feedback threshold range data; and performing adaptive compression processing on different force feedback signals according to the force feedback threshold range data to obtain dynamic force feedback control parameters. Through real-time control instruction optimization and a closed-loop feedback strategy, the system can quickly respond to an operation intention, early warning is carried out on potential risks, and it is ensured that the operation process is safer and more reliable.

The invention relates to a collaborative autonomous perception and navigation method in a denial environment, which comprises the following steps: constructing a self-organizing robust communication system to realize dynamic networking and data sharing among multiple unmanned aerial vehicles in the denial environment, and adopting a self-adaptive network protocol to realize automatic configuration of a communication network under network topology change or communication interference; based on a multi-sensor fusion sensing system, environment information and motion information of the unmanned aerial vehicle are processed through a data fusion algorithm; a single unmanned aerial vehicle constructs a reward function based on unmanned aerial vehicle energy consumption, an exploration area, a path overlapping degree and a path danger degree, executes autonomous perception and exploration in an unknown environment and completes mapping; the distributed multiple unmanned aerial vehicles carry out individual communication and game playing in the unmanned aerial vehicle cluster according to map information, respective positions and working states, task distribution is achieved, and path planning and action decision making are completed. According to the invention, autonomy and collaboration of the unmanned aerial vehicle in a denial environment are improved, and an effective solution is provided for navigation in a complex environment.

The invention discloses a charging pile dynamic power scheduling method, and relates to the technical field of charging pile power scheduling, and the method specifically comprises the following steps: obtaining node state information of each remaining normal node in real time, and generating a remaining bearing capability index and a load balancing index of each normal node; and constructing a power distribution evaluation model for the generated residual bearing capacity index and the load balancing index of each normal node, generating a power distribution evaluation coefficient of each normal node, analyzing the power distribution evaluation coefficient after generation, and evaluating the power bearing capacity of each normal node. Dividing each normal node into a high-bearing node, a middle-bearing node and a low-bearing node according to an evaluation result; and respectively adopting a corresponding power scheduling strategy according to a division result of each normal node. According to the invention, the problem that the power resource cannot be quickly and efficiently allocated after the fault node exits is solved, and efficient and stable dynamic balanced power scheduling and system operation are realized.

The invention discloses a remote sensing target detection method based on a convolutional neural network, and the method comprises the steps: 1, constructing a full-scale enhanced alignment detection network model OSEADet comprising Backbone, Neck and Head, the Neck is an enhanced feature pyramid network EFPN, adding a P2 detection layer on the PAFPN network, enabling a feature map P2 to pass through SPDConv, outputting a feature map rich in small target information, fusing the feature map with a feature map P3, and obtaining a feature map P3; carrying out feature integration by using a CSP thought and a CSPF module; the Head comprises three dynamic task alignment sharing detection heads DSTA, the DSTA comprises a bounding box regression branch and a classification branch which are respectively used for predicting a target position and a target category, the tasks of the three DSTA are combined through group convolution, task interaction features are learned from a plurality of convolution layers, joint features are obtained, interaction among the tasks is increased, and the tasks are dynamically adjusted and aligned through a TaskDecomposition layer; 2, carrying out training and verification on the OSEADet; and 3, the trained OSEADet is used for predicting the position and category of the remote sensing target, so that the detection precision is high, and the remote sensing target can be detected in real time.

The invention provides a deep counterfeit detection method based on self-mixing, and compared with the prior art, the method generates a mixed image containing counterfeit artifacts by using a self-mixing technology, and carries out the precise detection and recognition of an artifact region of an input image through combining a backbone network OfficientNet-b3 and a counterfeit artifact detection module. The method comprises the following steps of: firstly, performing framing processing on a real image, generating a source image and a target image by utilizing a self-mixing technology, and generating a mixed image with counterfeit artifacts by combining a mask generator with an alpha blowing model; and then, the backbone network extracts global features from the mixed image, and the fake artifact detection module reinforces local feature expression of a mixed region through an artifact space cooperation attention module, so that an artifact region is accurately positioned. And finally, fusing global and local features by using residual connection, and transmitting the fused features to a full connection layer to complete classification. According to the method, various forged image types can be effectively detected, and excellent generalization ability is shown in cross-dataset and unknown scenes.

The invention provides an auxiliary dental implant generation method based on a diffusion model, and relates to the technical field of stomatology, and the method comprises the steps: obtaining historical patient oral cavity CBCT image data, carrying out the preprocessing of the historical patient oral cavity CBCT image data, obtaining a CBCT image data set, training a multi-task segmentation network through the CBCT image data set, and obtaining a CBCT image data set; obtaining an intra-oral tissue segmentation result in the segmentation network; respectively training a detection network from three dimensions of a cross section, a coronal plane and a sagittal plane by using an intraoral tissue segmentation result; obtaining detection results in three directions of a cross section, a coronal plane and a sagittal plane by using a detection network; fusing the detection results in the three directions of the cross section, the coronal plane and the sagittal plane, constructing a three-dimensional bounding box by using a majority voting algorithm, and obtaining an edentulous region; the intraoral segmentation result and the edentulous area serve as prompt information, the network is guided through the iteration process to generate the post-implantation effect, the implantation effect obtained through the method is high in accuracy, and a more accurate auxiliary tool can be provided for stomatology.