209 results about "Perceptual information" patented technology

The invention provides a multi-mode sensor fusion algorithm for multi-signal processing and a system thereof, and relates to the technical field of sensor data processing, and the system sequentially generates standardized features through Sp1, generates a fusion feature set through Sp2, optimizes fusion parameters through Sp3, generates compensation features through Sp4, outputs perception information through Sp5, and outputs abnormal conditions such as sensor failure and overload calculation. Through feedback mechanism processing, a termination condition of perception information output or new task triggering, and breakthrough in feature retention and data consistency, a fusion result can still keep high precision in a high noise or complex scene, through failure detection and feature association degree priority ranking, other sensor data with high association degree are used for reconstruction, and a fusion result is obtained. According to the method, the accuracy of feature reconstruction is greatly improved, the high real-time performance of edge equipment and the computing power of the cloud are fully utilized by an edge-cloud cooperation mechanism, the resource utilization rate is optimized, and the stability and adaptability of the system in practical application are greatly improved.

The invention belongs to the technical field of image processing, and discloses a polyp image segmentation method based on a double-branch feature progressive fusion network, and the method comprises the steps: constructing a double-branch network based on feature and boundary information extraction, and carrying out the progressive fusion training; the method mainly comprises a double-branch backbone network based on point convolution visual transformation and a reverse perception information layer, a dislocation fusion module, a dislocation single-layer fusion module, a perception information fusion module and a multistage residual decoding module, a trained optimal model is used for segmenting a polyp image, and a result is evaluated. According to the method, boundary information and hierarchical features are effectively extracted through a double-branch structure, global semantic information is reserved and refined in a progressive fusion mode, the limitation of a traditional algorithm in the polyp boundary feature utilization process is overcome, the boundary difference is adopted in the training process to combine with the loss to optimize the model training process, and the training efficiency is improved. And high-precision recognition and segmentation of the polyp region are realized.

The invention discloses an AI-driven robot working environment safety sensing method, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting the visual, laser radar, force sense and environment parameter data of a working environment in real time through a multi-mode sensor; performing feature extraction and semantic understanding on the acquired data by using a deep learning algorithm to generate multi-dimensional perception information of the environment; based on a preset security policy model, in combination with the multi-dimensional perception information, predicting and classifying potential risks; according to a risk classification result, a behavior strategy of the robot is dynamically adjusted, and active safety decision is realized; and through the continuous learning module, the deep learning model and the security policy model are optimized online by using newly collected data so as to adapt to environmental changes.

The present invention provides an intelligent construction control method, apparatus, and system for splitting jet grouting mixing piles, relating to geotechnical engineering. The method includes receiving real-time perception information from a construction information self-perception system, which includes drilling depth, drilling rig output power, drill bit torque, drill rod axial force, and pore water pressure of the drill bit. This information is input into a trained machine-learning model for stratum information, establishing a nonlinear implicit correspondence relationship between the perception information and construction stratum conditions, and outputting real-time stratum state information. The real-time stratum state information is then input into a trained self-matching machine-learning model for optimal construction parameters, establishing a nonlinear implicit correspondence relationship between the stratum state information and optimal construction parameters, and outputting current optimal construction parameters. This invention enables real-time intelligent inversion of stratum information and dynamic self-optimization control of construction parameters.

The invention provides an end-to-end automatic driving track generation method and system, computer equipment and a medium, and belongs to the field of path intelligent planning, and the method comprises the steps: obtaining traffic environment three-dimensional perception information and a natural language instruction; converting the traffic environment three-dimensional perception information to obtain a structured semantic mark, and adjusting the weight of the structured semantic mark through a natural language instruction; based on the weight of the structured semantic mark, visual flow features are extracted from the structured semantic mark, and language flow features are extracted from the natural language instruction; based on a cross-modal attention mechanism, the visual flow features and the language flow features are fused, fusion features are obtained, and a simulation track is generated; real-time collision time is calculated, when the real-time collision time is larger than a collision time threshold value, the simulation track is corrected based on physical constraints and a traffic regulation library, and a corrected driving track is obtained. The automatic driving simulation track constructed by the method conforms to the intention of a user, and the system reliability and the man-machine cooperation efficiency are improved.

The invention provides a multi-agent collaborative visual completion method and system based on semantic communication, and the method comprises the steps: a first agent collects an occluded overall original image through a visual sensor, generates an occluded region mask through a lightweight semantic segmentation network, extracts mask semantic information, and transmits the mask semantic information to a second agent; the second intelligent agent matches the global semantic feature of the second intelligent agent with the received mask semantic information, locates a sheltered area, then cuts the sheltered area to obtain a semantic feature vector of the area, and returns the semantic feature vector to the first intelligent agent; and after the first intelligent agent receives the image, performing high-fidelity reconstruction on the occlusion area by using a conditional diffusion model to obtain a complemented image with consistent semantics, and performing weighted fusion and local color matching and splicing on the complemented image and the original image to obtain a global perception result. According to the scheme, perceptual information sharing and occlusion area reconstruction with low bit rate, high robustness and high real-time performance can be realized under limited communication resources.

The invention relates to the field of audio detection, and provides an audio deep counterfeiting detection method and system fusing multi-source features and cross-scale modeling, which effectively improves the recognition capability of a hidden counterfeiting mode in a synthetic sample through a double-branch data enhancement strategy, and improves the recognition capability of the hidden counterfeiting mode while keeping the speech naturalness and auditory acceptability. A representative forgery disturbance space is introduced, so that generalization and robustness under multi-type deep forgery attacks are remarkably improved, multi-level deep audio features and physical acoustic features are obtained respectively and are subjected to feature fusion, the sensitivity to physical layer artifacts is improved, the detection accuracy is further improved, and the detection efficiency is improved. And through multi-scale attention enhancement, weak change characteristics across time and frequency scales in the forged audio are efficiently captured, through structural design of grouped convolution and a multi-branch attention path, local and global perception information is effectively integrated, and the accuracy, robustness and generalization ability of audio deep forging detection are improved.

The invention provides a safe flight agent system for executing complex language instructions in a high dynamic environment. The system comprises a perception module and a security module based on a visual language model, a planning module based on a language large model and an unmanned aerial vehicle control interface. In a system operation stage, an intelligent agent receives a command from a user, a planning module splits a task, calls a corresponding task memory and a code generator to generate a corresponding executable code, calls a perceived interface to obtain perceptual information related to the task, and finally, after the perceptual information is checked by a security check module, the perceptual information is sent to a server. And executing the corresponding code to complete the instruction of the user. The system achieves safe and efficient execution of tasks in a highly dynamic environment by combining a high-performance low-frame-rate large model and a high-frame-rate traditional method in each module and using a security detection module involving privacy, potential risk and collision.

The invention discloses a time delay constrained Internet of Vehicles multi-mode cooperative sensing method and system. The method comprises the following steps: collecting point cloud data and image data, and carrying out feature extraction, fusion and space projection; constructing a two-dimensional convolution neural network, and extracting a position-level importance probability graph and a mask used for screening key information; constructing a benefit estimation neural network and a constraint evaluation neural network, and predicting expected values of system benefits and constraint conditions; meanwhile, an overall strategy selection module is established; the motorcade carries out target identification and calculates rewards based on information interaction so as to obtain a perception experience sample; and updating network parameters based on the perception experience sample to obtain a final overall strategy selection module. According to the method, the transmitting power, the transmission channel and the sensing data area of the vehicle are optimized based on reinforcement learning, a strategy correction mechanism is introduced, a risk strategy of high time delay or high packet loss is dynamically corrected, and target identification deviation and decision errors caused by sensing information delay and wrong transmission are avoided.

The embodiment of the invention provides a method and system for providing ward round service. The method comprises the following steps: acquiring perception information, wherein the perception information is acquired by perception equipment in a ward where a patient is located during the ward round period of a doctor; during the ward round period, based on the perception information, detecting an instruction sent by a doctor for calling patient data; responding to the instruction, and controlling terminal equipment in ward equipment to display patient data by utilizing an augmented reality technology; and after the ward-round period, based on the perception information, generating a ward-round record and displaying the ward-round record to the doctor for confirmation.

The invention relates to the technical field of intelligent sorting control. The multi-modal sensing sorting control method comprises the steps of obtaining multi-modal original sensing information, processing the multi-modal original sensing information, extracting category information and three-dimensional space pose information of a to-be-sorted target, generating a target recognition result, and recognizing the to-be-sorted target on the basis of the target recognition result. The method comprises the following steps: executing a path planning operation to obtain sorting path information containing a grabbing point location and a movement track, obtaining preset target clamping pose information, and generating a clamping control instruction based on the sorting path information and the preset target clamping pose information. The method has the effect of improving the sorting efficiency in a complex operation scene.

The invention relates to the technical field of medical image processing, and discloses an ultrasonic image edge segmentation method and system, a terminal and a medium, and the ultrasonic image edge segmentation method comprises the steps: constructing an initial model, obtaining a training set, inputting the training set into the initial model, and obtaining a prediction probability graph; performing data preprocessing on the training set to obtain boundary supervision information; boundary sensing information is obtained according to the prediction probability graph and the boundary supervision information; obtaining a target model according to the boundary perception information, the training set and the initial model; and acquiring an echocardiogram of a user, and inputting the echocardiogram into the target model to obtain a segmentation probability graph. According to the method, the boundary monitoring information is provided by preprocessing the training set to solve the problem of boundary blur, the perceptual ability of the model to the boundary is enhanced through the boundary monitoring information, and the boundary segmentation precision of the ultrasonic image can be improved.

The invention relates to a multi-vehicle cooperative sensing method based on an attention mechanism, and belongs to the technical field of mobile communication. A multi-vehicle collaborative perception scene lacks selective attention to perception information of different values, so that perception characteristics of each vehicle are difficult to effectively utilize. At the same time, in a high mobility scene, it is difficult to align sensing information time delay and asynchronous features, resulting in low dynamic target sensing precision. In order to solve the problem, the method comprises the following steps: firstly, filtering redundant sensing information of cooperative vehicles, and constructing a screening model based on aerial view feature intersection-to-union ratio so as to select high-quality cooperative vehicles; spatial calibration is completed in combination with sparse consensus foreground features and geometric consistency verification, and feature fusion is realized through an attention mechanism; and finally, finishing time calibration of the dynamic target by adopting a time alignment strategy based on distillation learning. The method is suitable for multi-vehicle cooperative perception in an Internet of Vehicles scene, the focus on high-quality perception information is improved, and meanwhile, the real-time perception precision and robustness are enhanced.

The present disclosure provides a camera calibration method based on vehicle localization, mainly considering that a vehicle is not of an ideal rigid structure, and shaking of a vehicle body during operation, uneven road surfaces, or different loads will cause variations in extrinsic parameters of a camera system, resulting in perceptual information errors. Therefore, through perceptual information from a fused image, an inertial measurement unit, a speedometer, and the like, combined with the road and marking information provided by a high-definition map, the relative motion between the vehicle body and a vehicle chassis is simultaneously described with a mass-spring-damper model, so as to complete the determination of six-degree-of-freedom positions of the vehicle body and the vehicle chassis, and finally the extrinsic parameters of cameras are immediately calibrated with the vehicle chassis as reference coordinates.

The invention discloses a multi-agent cooperative sensing method and system based on double attention, a terminal and a storage medium, and aims to solve the problem that a traditional multi-agent sensing system generally depends on a predefined role allocation and information fusion mechanism, but the methods are insufficient in adaptability in a complex and highly-uncertain environment. The method comprises the steps of 1, extracting point cloud features by using a convolutional neural network; 2, obtaining a sparse graph from the point cloud features through a sparse graph generation module, and transmitting the sparse graph to other agents in a broadcast form; step 3, the adjacent agents determine whether to transmit the features to the agents themselves through the feature selection module; step 4, the own agent receives the features transmitted by the adjacent agent and performs feature fusion through a weighted dual-channel attention module; and step 5, obtaining perception information by the fusion features through a detection module, and obtaining a reliable detection result. The invention relates to the field of multi-agent collaborative awareness.

The invention relates to the technical field of vehicles, in particular to a multi-mode out-of-vehicle interaction method and related equipment based on environment self-adaptation, and the method comprises the steps: obtaining perception information in real time through an environment perception module, the perception information comprising environment conditions, interaction object information and scene semantic understanding information; based on the perception information, utilizing an adaptive decision engine to dynamically analyze an optimized interaction mode and parameters, and generating interaction content; outputting interaction information according to the interaction content generated by the self-adaptive decision engine through a multi-modal output module; the interaction strategy is dynamically adjusted by sensing the environment state in real time, and the safety and the interaction efficiency can be improved.

The invention discloses an equipment operation monitoring and fault diagnosis system based on multi-perception information fusion, and belongs to the technical field of equipment monitoring, and the system comprises the steps: collecting monitoring data in an equipment operation process, and carrying out the preprocessing of the monitoring data, so as to obtain monitoring processing data; obtaining an equipment fault label and corresponding monitoring processing data according to the historical equipment fault record; performing feature extraction on the monitoring processing data, and extracting feature parameters for reflecting the operation state of the equipment; calculating a mutual information value of each feature parameter and the fault tag by using a mutual information method, obtaining key features of which the mutual information values are greater than or equal to a key threshold value, and performing fusion through weighted summation according to a preset weight to form a multi-perception fusion feature; based on the multi-perception fusion features, fault diagnosis is carried out on the equipment by adopting a support vector machine classification algorithm, a diagnosis result is output, and finally the diagnosis result is displayed through a display and early warning module and an early warning signal is sent out.

The invention discloses a space robot compliance control method and device based on multi-dimensional visual constraints, and belongs to the technical field of space robots and autonomous control. The method comprises the following steps: collecting and fusing data through a heterogeneous visual sensor group, and carrying out uncertainty quantification to generate a perception confidence index; constructing and dynamically adjusting a visual constraint function set based on the index, solving a motion feasible region according to the visual constraint function set, and generating an expected motion track; inputting the track into an impedance controller, and adjusting the rigidity and damping parameters in real time according to the sensing confidence so as to output an instruction; the instruction is verified through digital twin simulation, and if the instruction does not pass the verification, a fault-tolerant mechanism is triggered for optimization; and finally, updating the system parameters on line according to the deviation between the actual execution data and the simulation data. According to the method, the technical problems of poor operation safety, low control precision and insufficient autonomy of the space robot in a complex environment caused by single sensing information dimension, lack of uncertainty quantization and fixed control strategy are solved.

According to the driving control method, the cooperative driving method and the related devices, in the method, communication between a vehicle and a roadside sensing device is achieved through a cloud device, so that under the condition that the vehicle cannot sense a first object, sensing information of the first object from the roadside sensing device can be obtained. And fusing the obtained sensing information with the sensing information obtained by the sensor of the vehicle so as to enlarge the sensing range, thereby realizing the driving control of the vehicle based on the fused sensing information. The scheme can effectively reduce potential safety hazards of vehicle driving, and is low in implementation cost and easy to popularize and use.

The invention discloses a traffic subject collaborative cognition method based on semantic representation in the technical field of traffic transportation. The method comprises the following steps: S1, collecting original data including state information of a traffic scene and a traffic subject, integrating the original data into perception information through a data fusion technology, and converting the perception information into structured semantic representation information; s2, establishing a multi-mode collaborative interaction architecture for different traffic scenes, dynamically selecting the collaborative interaction architecture according to a real-time traffic state, and performing ordered information interaction according to tasks; and S3, the edge traffic subjects autonomously generate decisions, and the central traffic subject performs overall optimization to realize multi-traffic subject collaborative cognition and behavior consistency. According to the method, semantic mutual understanding among different traffic subjects is realized, and an information barrier is broken; the method realizes the balance between local decision and global optimization, improves the decision efficiency, and has good expandability and adaptability.

A control method of an auxiliary walking system based on a visual large model (VLM) is applied to an exoskeleton system suitable for being worn by an operator and comprises the following steps of data collection and fusion, perception and analysis, dynamic path planning and strategy generation, humanized interactive design and learning and adaptive optimization. Data feedback in a VLM processing walking process is utilized, model parameters are automatically adjusted through an online learning mechanism in a deep neural network, and the understanding and adaptive capacity to various environments is optimized; meanwhile, according to user feedback and use habits, weights of all dimensions are adjusted in a personalized mode so as to achieve long-term performance optimization. By fusing multi-element perception information and intelligent analysis, the environmental adaptability and user interaction quality of the exoskeleton are remarkably improved.

The invention provides a data alignment method and system and a storage medium. The method comprises the steps that world time information is acquired, and a constraint mode of a trigger signal is selected; outputting a trigger signal to a corresponding sensing device according to the world time information and the constraint mode, so that each sensing device obtains sensing data based on the trigger signal; and based on the constraint mode, selecting a corresponding alignment mode to perform alignment processing on the sensing data. According to the method provided by the invention, the problem that a back-end perception fusion application cannot obtain accurate perception information due to the fact that the perception contents of the perception devices are not matched is solved, and the perception data collected by the multiple perception devices are effectively aligned.

The invention relates to a perceptual information opening method and system, communication equipment and a storage medium. The method comprises the following steps: receiving a perception information acquisition request sent by an application function AF network element, and determining perception equipment according to the perception information acquisition request; sending a sensing measurement request to a sensing device, so that the sensing device collects sensing data according to the sensing measurement request; receiving sensing data sent by the sensing device, and determining a sensing result according to the sensing data; and sending the sensing result to a network open function NEF network element, so that the network open function NEF network element sends the sensing result to an application function AF network element. By adopting the method, the sensing service can be realized by using the basic communication facility.

The invention discloses a target detection system and method based on a multi-modal large language model, and the method employs a DETR to generate a proposal box as an object candidate, and achieves the precise detection of complex targets, such as long-tail classes, combined objects, and the like, through the powerful semantic reasoning capability of an MLLM. The method comprises the following steps: constructing a dual-encoder architecture in which a main visual encoder and an auxiliary visual encoder cooperate, enabling the main visual encoder to continue MLLM global semantic comprehension ability, enabling the auxiliary visual encoder to specially extract local features of an object, and achieving deep complementation of semantic and perceptual information on a dimension level through multi-layer and multi-scale feature splicing; and an object index and coordinate prediction dual-output mode is innovatively designed to form a retrieval-generation dual-guarantee system, so that the target detection precision and robustness are effectively improved.

The invention provides a communication and sensing method and device, a storage medium and a program product, relates to the technical field of communication, and is used for reducing measurement energy consumption and switching delay of a terminal. The method comprises the following steps: a first sensing node obtains sensing information obtained by sensing a first terminal; and sending a switching request message according to the sensing information, the switching request message being used for requesting to switch the sensing service node of the first terminal.

The invention relates to the technical field of image processing, and particularly provides a full-reference image quality evaluation method based on wavelet transform and Hausdorff distance, which comprises the following steps of: realizing efficient evaluation on image quality by combining a frequency domain change technology and deep learning; perceptual relevant features are extracted through wavelet transform, multi-scale perceptual information is captured, and how a human visual system analyzes visual information of various scales and directions in space and frequency domains is simulated. Secondly, the distribution similarity measurement module based on the Hausdorff distance can better evaluate the difference between the feature distribution of the reference image and the feature distribution of the distorted image. The method provided by the invention can accurately capture the perception quality difference without training data or subjective quality scores, and a large number of experiments show that the method is excellent in performance and highlights the ability of the method closely related to the perception of a human visual system.

The embodiment of the invention provides a method and system for providing medical treatment service and a storage medium. The method is executed by at least one processor and comprises the following steps of: performing registration inquiry on a patient through a patient terminal of the patient so as to determine a doctor registered by the patient; based on the department of the doctor, performing pre-inquiry on the patient through the patient terminal to generate a pre-inquiry record of the patient; displaying interface elements related to the pre-inquiry record through a consulting room terminal in a consulting room in response to detecting that the patient starts inquiry, so as to display the pre-inquiry record to the doctor and the patient; the method comprises the following steps: generating an initial diagnosis record based on perception information collected by perception equipment in a consulting room in a treatment process of a patient, and carrying out registration inquiry on the patient through a patient terminal of the patient: displaying a first virtual character through the patient terminal to carry out registration inquiry, the step of pre-inquiry on the patient through the patient terminal comprises the step of displaying the second virtual character through the patient terminal to carry out pre-inquiry.

The invention discloses a wearable tactile perception reconstruction system and method, and the system comprises a myoelectric prosthetic hand, a flexible arm band, a vibration feedback array, and a feedback controller, and the flexible arm band is used for binding the system to the big arm of a subject; the myoelectric prosthetic hand is located at the tail end of the limb of the subject, is connected with the feedback controller and transmits data of the pressure sensor, the distance sensor and the motor encoder to the feedback controller; the vibration feedback array is located on the inner side of the flexible arm band and makes direct contact with the skin of a subject. The feedback controller is located on the outer side of the flexible arm band and at least comprises a control box and a circuit board, and the circuit board is fixed in the control box; in the system, the myoelectric prosthetic hand obtains body sense, force touch sense and proximity sense perception information data, the feedback controller receives the perception information data of the myoelectric prosthetic hand, processes the perception information data and converts the perception information data into stimulation coding instructions, and the vibration feedback array receives the stimulation coding instructions and conducts vibration touch sense feedback to achieve touch sense perception reconstruction.

The invention discloses a multi-agent cooperative sensing system based on an evidence neural network, and relates to a multi-agent cooperative sensing implementation method and implementation system. The invention aims to solve the problems that an intelligent agent cannot deal with noise, delay and sensor errors when processing a dynamic environment, and the sensing precision and the real-time performance cannot be ensured. The method comprises the following steps: step 1, extracting point cloud features by using a convolutional neural network; 2, obtaining a sparse graph from the point cloud features through a sparse graph generation module, and transmitting the sparse graph to other agents in a broadcast form; step 3, the adjacent intelligent agent determines whether to transmit the features to the own intelligent agent or not through a feature selection module; step 4, the self intelligent agent receives the characteristics transmitted by the adjacent intelligent agent and fuses the characteristics; and step 5, the fusion features pass through a detection module and an evidence regression module to obtain perception information and uncertainty estimation, and the perception information is optimized according to the uncertainty estimation module to obtain a reliable detection result. The invention relates to the field of multi-agent collaborative awareness.

The invention provides a perception fusion method, a communication device and a storage medium, relates to the technical field of communication, and is used for improving the fusion accuracy of perception information by a perception node, the method is applied to a first perception node, and comprises the following steps: obtaining first perception information and second perception information, the first sensing information is sensing information obtained by sensing a sensing target by a first sensing node, and the second sensing information is sensing information obtained by sensing the sensing target by a second sensing node; fusing the first sensing information and the second sensing information to obtain fused sensing information of the sensing target; and sending the fused sensing information and global identity information of the sensing target to the second sensing node, wherein the global identity information is used for identifying the sensing target in the sensing network.