880 results about "Target type" patented technology

The invention relates to the technical field of tunnel engineering construction, and discloses an intelligent material transportation and safety monitoring system and method for shield construction, and the system comprises a visual perception unit, a sensor network module, an AI analysis center module, a safety decision module and a human-computer interaction interface. According to the invention, data acquisition is carried out through the visual perception unit and the sensor network module, multi-target detection operation is carried out on image frames through the AI analysis center module after target identification and track prediction, target types, space coordinates, contour boundaries and confidence coefficients are identified and extracted, and safety judgment and early warning output are carried out. According to the invention, by integrating the multi-view camera equipment and the UWB, GNSS and other sensors and adopting a deep learning target detection algorithm, high-precision identification and continuous tracking can be carried out on construction site personnel, equipment, segments and other key objects, and accurate input is provided for subsequent risk analysis.

The invention relates to the technical field of airport safety protection, and discloses an airport bird strike prevention method and system based on multi-sensor fusion. The method comprises the steps of collecting airport peripheral airspace radar detection, radio detection, laser monitoring, weather and other multi-mode sensor data, and performing time synchronization and space registration to generate a fusion data stream; features are extracted based on a dynamic weight distribution algorithm, target types are identified by using a multi-modal classification model, and birds and unmanned aerial vehicles are distinguished; constructing a dynamic risk assessment model based on a Markov chain to generate an intrusion risk level; and when the risk level exceeds a preset threshold value, triggering a multi-level alarm strategy and linking the expelling equipment to execute a self-adaptive expelling instruction. According to the method and system, through multi-sensor fusion, the monitoring precision is improved, the target is accurately recognized, the risk is evaluated in real time, efficient alarming and expelling are achieved, the airport bird strike prevention capacity is effectively improved, and aviation safety is guaranteed.

The invention discloses a data collection method and device, computer equipment, a storage medium and a program product. The method comprises the steps that a control handle in a current interface of a target application program is acquired; the control handle is at least used for determining a control type; wherein the control of which the control type belongs to the target type in the current interface is the target control; the target type comprises a list, a button and an input box; the target control in the current interface comprises at least one of a list control, a button control and an input box control; inputting the feature data of the target control into an acquisition strategy generation model to generate a data acquisition strategy corresponding to the control type of the target control; the feature data at least comprises a control type; and monitoring the target control in real time based on the data acquisition strategy so as to capture the interactive behavior data of the target control when monitoring that the state is changed.

The invention provides an interaction method and device based on image recognition, and relates to the technical field of man-machine interaction, and the technical scheme is characterized in that the method comprises the steps: analyzing the moving speed and change frequency of target and non-target elements in a training scene, and calculating an environment dynamic factor; adjusting a high threshold value and a low threshold value of target identification confidence; comparing the target recognition confidence output by multi-modal image recognition with the adjusted high threshold and low threshold, and determining a visual feedback type; setting a visual feedback intensity parameter according to the determined visual feedback type and the target type; and displaying the set visual feedback in the visual field of the technical object in an overlapping manner. The interaction method and device based on image recognition provided by the invention have the advantages that the cognitive load of the technical object is reduced, and the rapid target recognition capability of the technical object in a complex and uncertain battlefield environment is improved.

The invention relates to the field of image processing, and discloses an object monitoring method and device based on multi-source data and a storage medium, and the method comprises the steps: synchronously collecting multi-channel video streams, environment parameters and equipment position information, and carrying out the time alignment and data association processing, and forming associated data; performing feature matching on the multiple paths of video streams, fusing position information and environment parameters, and establishing a mapping relation between video feature points and a unified space coordinate system; splicing the multiple paths of video streams in real time according to the mapping relation to generate a panoramic video stream; performing dynamic scene analysis based on the panoramic video stream and the environmental parameters, and identifying a target type and a state to obtain an analysis result; and generating an equipment regulation and control strategy based on the analysis result, and generating and issuing a regulation and control instruction for adjusting the working parameters of the multi-view image acquisition device based on the equipment regulation and control strategy. According to the invention, the splicing precision, the real-time performance and the target identification accuracy of panoramic monitoring can be improved, and the dynamic adaptive regulation and control of the equipment can be realized.

The invention provides an image processing method and device and electronic equipment, and the method comprises the steps: obtaining a continuous frame image, carrying out the motion detection of the continuous frame image, obtaining each motion region in the continuous frame image and the speed of each motion region, determining the target type of each motion region through semantic recognition, determining a priority corresponding to each motion area according to the speed and the target type of each motion area, and performing perception recognition on the motion areas with different priorities by adopting corresponding perception strategies to obtain a perception result; according to the method, perception identification is focused on a dynamic area and differentiated processing is carried out, a large amount of redundant calculation in a static or non-key area is effectively avoided, system calculation resources are saved, hardware loads are reduced, key targets can be ensured to be preferential, the real-time performance and response speed of the system are improved, and vehicle driving safety is ensured.

The invention provides a lightweight remote sensing target detection method and related equipment, and relates to the technical field of remote sensing image target detection. The method comprises the steps of obtaining a to-be-measured remote sensing image; inputting the to-be-detected remote sensing image into a preset remote sensing target detection model, and outputting a detection result; the detection result comprises a target type and a corresponding confidence coefficient; the remote sensing target detection model comprises a backbone network, a neck network and a detection head; wherein the backbone network is used for extracting multi-level features of a to-be-detected remote sensing image from a spatial domain and a frequency domain; the neck network is used for enhancing the relation among the multi-level features output by the backbone network so as to fuse the semantic information of the high-level features and the detail information of the low-level features and generate multi-scale fusion features; and the detection head is used for performing target detection on each scale fusion feature. According to the method, the light weight of the model is kept, and meanwhile, the detection precision higher than that of other mainstream target detection algorithms is achieved.

The invention discloses an agent and large language model evaluation method and device, a medium and electronic equipment, and relates to the technical field of large models and agents, and the evaluation method comprises the steps: creating an evaluation task which is used for evaluating at least one dialogue of a plurality of to-be-evaluated objects in a dialogue process from at least one evaluation dimension, the plurality of to-be-evaluated objects are used for realizing dialogue tasks in the same field; the evaluation task is executed to obtain an evaluation result, and the evaluation result is used for comparing the dialogue ability among the multiple to-be-evaluated objects; displaying the evaluation result on the first interface, and displaying a second interface; in response to the configuration operation for the evaluator of the target type in the second interface, a configured evaluator is obtained, the configured evaluator is used for evaluating the dialogue from the corresponding evaluation dimension, a user can conveniently analyze the building effect of the agents of different configurations, in addition, user-defined configuration can be carried out based on the configuration operation, and the user experience is improved. And thus, evaluators of different evaluation dimensions are obtained.

The invention provides a structured target detection method, device and equipment based on a multi-modal language model, and relates to the technical field of target detection. The method comprises the following steps: inputting acquired image data and cue words into a multi-modal language model; the multi-modal language model comprises a visual encoder, a cross attention module and a decoder; performing feature extraction on the image data through a visual encoder; performing multi-modal interaction in a cross attention module based on the cue word and the data after feature extraction; wherein an Adapter module is inserted into the cross attention module so as to realize fusion of image and language information; performing low-rank fine tuning updating on the weights of the query vector and the value vector of the cross attention module, and keeping the weights of the other models frozen; and reasoning and outputting a plurality of target token group sequences at least comprising a target type and bounding box coordinates thereof through a decoder. According to the method, an additional target detection module is not needed, and complete structure information of multiple targets can be generated at a time through the improved multi-mode language model.

The invention relates to the technical field of electric digital data processing, and provides a secondary development code cross-product transplantation method and device, computer equipment and a medium. The method comprises the steps of loading a source project, screening a to-be-transplanted source code file, identifying an original API type, determining a mapping relation between the original API type and a target API type, and generating an adaptation layer code framework according to the mapping relation; copying the to-be-transplanted source code file into the target project; on the premise of keeping the project hierarchical structure of the source project, replacing the calling node of the original API type with the calling node of the adapter type, and replacing the namespace reference with the corresponding namespace in the target project to obtain a migrated source code file; and obtaining a secondary development project of the target project based on the second product API according to the adaptation layer code framework and the migrated source code file. According to the method and the device, the transplantation efficiency and accuracy of the transplantation of the cross-product secondary development codes are improved.

The invention relates to the technical field of rail traffic equipment maintenance and artificial intelligence image recognition, in particular to a rail vehicle cloud edge cooperative detection method and system and inspection equipment. The method comprises the following steps: acquiring image data of an area to be detected, synchronously recording attitude information of shooting equipment, carrying out image quality scoring on the image data, if the image quality score reaches a preset threshold value, calling a local target detection model to carry out defect identification on an image, and outputting an identification result containing a target type, a position and confidence; if the confidence coefficient of the recognition result is lower than a set threshold value, the image data and the posture information are uploaded to a cloud server, a cloud high-precision recognition model is called for secondary recognition, and a cloud recognition result is obtained. The real-time requirement is met through quick response of the local model, the cloud model rechecks a low-confidence result, efficiency and precision are both considered, and the contradiction that in the prior art, manual judgment standards are different, and speed and precision are difficult to consider is solved.

The invention discloses an image annotation method, system and device for spacecraft target type and attribute classification and a medium, and belongs to the technical field of image recognition. The method comprises the following steps: constructing a multi-dimensional attribute model based on key elements of a spacecraft image; based on the multi-dimensional attribute model, constructing a reference prompt template, obtaining a preliminary output result through the reference prompt template, and carrying out optimization processing on the reference prompt template and the preliminary output result by adopting few-sample learning to obtain a first output result; performing output supervision on the thinking process of the first output result in combination with the thinking chain, and obtaining a second output result through an optimization prompt template; and according to the first output result, the second output result and the plurality of output results, generating an output result data set, and obtaining an image annotation result. According to the method, through a semi-supervised labeling method combining multi-attribute modeling and context learning, the accuracy and robustness of fine-grained labeling of the spacecraft image in a few-sample scene are improved by utilizing a generative large model.

The invention discloses a method for realizing holographic traffic at an intersection. The method comprises the following steps: acquiring a real-time video stream at the intersection; motor vehicles, non-motor vehicles and pedestrians in the video are identified based on a deep learning target detection algorithm, a continuous motion track of each target is generated through a multi-target tracking algorithm, identity association is carried out on cross-camera targets in combination with a re-identification algorithm, and structured traffic data including target types, real-time speeds and motion directions are output; obtaining coordinate points of each lane of the intersection based on a geographic information system, and constructing a lane network topological relation; combining with the structured traffic data to generate target trajectory data; and constructing an intersection three-dimensional model based on an unreal engine, generating a virtual target in the three-dimensional model according to the target trajectory data, and driving the virtual target to move in real time. According to the method, a holographic traffic implementation scheme is provided for a business scene which is free of radar and only monitored by a video.

The invention discloses a data processing method and device for a power distribution network and a nonvolatile storage medium. The method comprises the following steps: acquiring environment data of a power distribution network; determining a data change rate of the to-be-collected data based on the environment data; based on the data change rate, determining a target sampling frequency of the sensor, a target type of the target computing device and a target sensing frequency of the sensor in a sampling period when the age of the target information reaches a predetermined target; and based on the target sampling frequency and the target sensing frequency, controlling the sensor to collect target data of the power distribution network, and processing the target data collected by the sensor based on the target computing device of the target type. The technical problem that the data real-time performance is poor due to the fact that a traditional sensor data processing method is difficult to adapt to distributed energy access is solved.

The invention provides a document processing method and system based on a multi-modal large model. According to the method, firstly, a target type document is converted into a target picture based on a multi-modal large model according to a preset document segmentation rule, then structured text information is extracted from the target picture based on a preset cue word and by utilizing an advanced optical character recognition technology PaddleOCR technology, the multi-modal large model is guided to generate a text abstract for each target picture, and the text abstract is extracted from the target picture. And the integrated graphic and text information and the condensed picture content are converted into a Markdown document comprising a picture link of the target picture and text summary information. Through the steps of format recognition, image-text separation, content condensation and the like, a target type document is converted into a format which is easy to manage and retrieve, rapid retrieval of information is achieved by means of RAG retrieval enhancement, and documents and information related to user query can be rapidly found to serve as candidate answers. Compared with a traditional OCR technology, the method has the advantages that the missing rate of key information is remarkably reduced, and the accuracy of document processing is improved.

The invention relates to the technical field of wireless, and provides a target sensing method, device and equipment, a storage medium and a program product, the target sensing method is applied to network equipment, and the method comprises the following steps: determining type information of a sensing target in a sensing result based on the sensing result and RID information under the condition that the sensing result and the RID information exist; wherein the sensing result is obtained by performing target sensing by the network equipment; the RID information is sent by the RID receiver. According to the invention, under the condition of determining that the sensing result and the RID information exist, the RID information can be used as the auxiliary information to assist the base station to carry out data processing in the sensing result, and the type information of the sensing target in the sensing result is accurately determined, so that the sensing result and the RID information are fused, and the sensing accuracy is improved. The method can solve the problem that the general sensing integrated system is easy to mistakenly recognize the target type when detecting the target, thereby improving the credibility of the detection result, and guaranteeing the reliability of the general sensing integrated system.

The invention discloses a project quality management method and system based on an AI large model, and relates to the field of project quality management.The project quality management method comprises the steps that panoramic equipment is installed on a tower crane on a project site, and a panoramic image is collected through the panoramic equipment; performing semantic segmentation on the panoramic image by using a pre-constructed risk control model; carrying out hidden danger investigation on each grid region, and collecting a hidden danger target image of a hidden danger target through panoramic equipment; inputting the hidden danger target images into a pre-trained hidden danger classification model, and classifying the hidden danger target images; according to the hidden danger target type label and the position coordinate, collecting a local image of the hidden danger target in a preset range; fusing the hidden danger target image with the corresponding local image; and mapping the fused image into a BIM model through three-dimensional reconstruction to obtain a digital twinborn model of an engineering site, and performing engineering quality management by using the digital twinborn model. Aiming at the problem that hidden danger investigation in the engineering field is not timely in the prior art, the hidden danger investigation efficiency is improved.

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 a target data sample set construction and screening method, device, equipment and medium, and the method comprises the steps: obtaining a target type description, carrying out semantic extension to generate an extension description, generating a reference image sample based on an image generation model, real data units are screened through feature extraction and similarity comparison, and a target data sample set is constructed in combination with knowledge base verification. According to the method, by introducing semantic extension, reference image generation, cross-domain feature comparison and knowledge base consistency verification, real samples highly fitting target type semantics are automatically screened from massive original data, so that the manual annotation dependence is reduced, the illegal sample construction efficiency is improved, and the manual annotation time is shortened. And the training quality and the expansion capability of a subsequent detection model are enhanced.

The invention provides a frequency modulated continuous wave laser radar-based target identification method and a laser radar system, which can be applied to the technical field of laser radars. The method comprises the following steps: acquiring an interference beat frequency signal after polarization orthogonal demodulation; performing signal demodulation on the electric signal of the interference beat frequency signal to obtain a target detection feature; performing feature matching on the target detection features and a typical target polarization feature library to obtain a feature matching result; and under the condition that the feature matching result represents that the target feature similarity meets a first threshold condition, performing identification processing on the target detection features based on an air intrusion target identification model to obtain a target type identification result.

The invention discloses a multi-mode element learning remote sensing reconnaissance target identification method guided by a detection mechanism, and belongs to the field of computer vision and remote sensing image processing. The core of the method is as follows: multi-modal feature extraction and fusion guided by a detection mechanism: aiming at the electromagnetic scattering characteristic of an SAR image, the geometric texture characteristic of a visible light image and the thermal radiation characteristic of an infrared image, respectively designing a special feature extraction network, and introducing a cross-modal fusion module based on an attention mechanism, feature enhancement expression of physical information complementation is realized; according to the meta learning training normal form, an optimization-based meta learning device is constructed, and training is performed on a large number of multi-modal remote sensing task sets, so that the model obtains the capability of quickly learning new target categories from a small number of samples. According to the method, a physical detection mechanism and data-driven meta learning are combined, and the problems that a traditional method is low in recognition precision and poor in generalization ability in a complex reconnaissance scene with scarce samples, variable target types and difficulty in SAR image interpretation are effectively solved.

The embodiment of the invention provides a substation meter identification method, device and equipment. The method comprises the following steps: acquiring a to-be-recognized image; inputting the to-be-recognized image into a pre-acquired target detection model to obtain a target type of a target meter and position information of the target meter in the to-be-recognized image; wherein the target detection model is a model which is obtained by training a lightweight YOLOv11 model based on an image in which the type and the position of the meter are calibrated in advance and is used for classifying the meter in the image and determining the position of the meter; wherein the lightweight YOLOv11 model is obtained by setting a backbone network of the YOLOv11 to be a StarNet lightweight network and setting a detection head to be BNLSCD; cutting out an area where the target meter is located from the to-be-identified image according to the position information to obtain a dial plate image; and reading the meter based on the target type and the dial plate image to obtain the reading of the target meter. The method is used for achieving the effect of reducing computing resources consumed for identifying the meter reading.

The invention relates to the technical field of unmanned aerial vehicle autonomous reconnaissance, in particular to an unmanned aerial vehicle reconnaissance decision-making method, device and equipment based on edge calculation and a medium, and the method comprises the steps: obtaining the multi-dimensional data of an environment and a target through an optical camera, an infrared thermal imager and a radar carried by an unmanned aerial vehicle; target identification and strategy reasoning are carried out on the multi-dimensional data, a target type and attributes are identified from the multi-dimensional data through a lightweight convolutional neural network, and a battlefield situation is obtained in advance to generate a strike strategy; obtaining a threat level and a task priority, and dynamically adjusting the flight path of the unmanned aerial vehicle and the strike strategy according to the threat level and the task priority; and obtaining a strike result corresponding to the strike strategy, analyzing the strike result in real time, judging a target destroy state through an airborne sensor, and updating a subsequent task strategy. According to the invention, the autonomy of the unmanned aerial vehicle system is improved.

The invention discloses a low-altitude unmanned aerial vehicle dynamic scene autonomous navigation adaptation method and system, and relates to the field of intelligent navigation, and the method comprises the following steps: 1, continuously obtaining real-time image data in a flight process through an image collection part carried on an unmanned aerial vehicle, analyzing the real-time image data based on predetermined route information of the unmanned aerial vehicle and a preset route attention area, and judging and identifying whether one or more dynamic targets exist in the route attention area; 2, if the existence of the dynamic target is identified, calling a motion risk prediction model which is constructed and pre-trained based on a deep learning algorithm; the motion risk prediction model integrating the relative speed, the motion direction, the target type and the trajectory prediction uncertainty is introduced, and the potential collision risk is quantitatively evaluated, so that the unmanned aerial vehicle can recognize the danger caused by the sudden dynamic target in time, and the safety guarantee in the densely populated area and airspace mixed environment is enhanced.

The invention discloses a multi-mode nonlinear model predictive control method and system for a visual servo-driven arm-carrying unmanned aerial vehicle, and belongs to the technical field of intelligent control of arm-carrying unmanned aerial vehicles. The objective of the invention is to solve the problems of unstable control mode switching and difficulty in unified control modeling of different target structures in existing unmanned aerial vehicle grabbing control. According to the technical scheme, a task-driven modal judgment mechanism is introduced, the current task modal can be judged in real time based on the target type, the image features and the flight state information, control strategy self-adaptive switching between a rod-shaped target and a plane target is achieved, and continuity and stability of control logic are guaranteed. For a rod-shaped structure target, a remote approaching, servo adjustment and compliant contact three-stage control strategy is designed, and a dynamic weight adjustment mechanism is introduced into NMPC control solution, so that smooth transition of control performance from quick response to fine compliant is realized, and the control precision and compliance of the arm-carrying unmanned aerial vehicle in different operation stages are remarkably improved. For a plane structure target, the invention provides an adsorption control strategy based on impact time prediction and angle feedforward compensation, the contact time can be accurately estimated, the attitude of the aircraft can be adjusted in advance, the adsorption stability is effectively improved, and the contact failure probability caused by inclination of a target surface is reduced.

Curvelet-based low level fusion of camera and RADAR sensor information is disclosed and includes processing RADAR data corresponding to a scene to generate a RADAR point cloud and processing camera image data corresponding to the scene using a curvelet transform to identify a target of interest in the scene and generate for the target of interest a target type, (x,y) coordinate values, and a curvelet magnitude per decomposition level. If discrepancies exist between (x,y) coordinate values of the RADAR point cloud and the target type, (x,y) coordinate values, and curvelet magnitude per composition level of the target of interest, a portion of the RADAR data processing is repeated to regenerate the RADAR point cloud; otherwise, the RADAR point cloud to a perception stack of a vehicle.

The invention relates to the technical field of target recognition, and particularly provides a multi-modal data-based target recognition method, system and device and a storage medium, and the method comprises the steps: collecting radar echo data and an infrared image through a radar and an infrared sensor at the same position, and carrying out the correlation of the radar echo data and the infrared image of a same target, obtaining target radar echo data and a target infrared image; converting the target radar echo data into a distance-Doppler map; extracting radar features from the distance-Doppler image by using a convolutional neural network, and extracting infrared features from the target infrared image; obtaining an environmental parameter and a quality evaluation coefficient of a target infrared image, and generating a feature confidence coefficient according to the environmental parameter and the quality evaluation coefficient; and the radar features and the infrared features are fused according to the feature confidence, and the target type is determined according to the fused features. According to the method, the limitation that single radar identification is interfered by a complex environment is effectively overcome, and the accuracy and reliability of target identification are greatly improved.

The invention discloses a sensing equipment selection method and device and related equipment, and belongs to the technical field of communication, and the method comprises the steps that first equipment obtains at least one measurement value of a first target index, the at least one measurement value comprises a measurement value obtained by performing first measurement by at least one first node based on a first signal sent by at least one second device; the first device determines a second device of a target type for performing a perceptual operation based on at least one measurement of the first target indicator.

The invention discloses a multispectral target identification and laser orientation soft and hard damage anti-unmanned aerial vehicle system. Comprising a multispectral signal acquisition module, a multi-modal data preprocessing module, an unmanned aerial vehicle target feature extraction module, an intelligent target identification and classification module, a laser parameter dynamic configuration module, a directional tracking and aiming module, a soft and hard damage execution module and a damage effect evaluation and feedback module. According to the invention, through multi-spectral fusion acquisition and multi-dimensional feature extraction, haze, camouflage and other environmental interferences are effectively resisted, and the distinguishing precision of the unmanned aerial vehicle and the interference target is improved; laser parameter dynamic configuration and soft and hard damage mode switching can be carried out according to target types and scene requirements, and energy waste or excessive treatment is avoided; and a damage effect evaluation and feedback mechanism ensures thorough target disposal, reduces repeated operation, and improves the system combat efficiency.

The invention discloses a digital circuit design method and device, equipment and a medium, and relates to the technical field of digital circuits, and the method comprises the steps: determining a target type of a to-be-designed digital circuit, and collecting a first training set corresponding to the target type; each training sample in the first training set comprises an input feature item used for designing a target type of historical digital circuit and the actual result quality of the historical digital circuit, and the input feature item comprises a high-level language source code and an HLS constraint parameter; constructing an initial prediction model based on the multi-layer perceptron mixed structure, training the initial prediction model by using the first training set, and searching and optimizing the model architecture based on the network structure to obtain a target prediction model; and predicting the actual result quality corresponding to each parameter combination in a target design space of a to-be-designed digital circuit by using the model, and designing the digital circuit by using the parameter combination of which the quality meets the Pareto frontier. And predicting an accurate actual quality result by utilizing a prediction model without intermediate representation so as to carry out digital circuit design.

The embodiment of the invention provides a method, device and equipment for generating annotation reference information. According to the scheme, the method comprises the steps that vectorization processing is conducted on obtained to-be-labeled data of a target type labeling task, and a to-be-matched vector is obtained; querying a target vector from a vector database corresponding to the target type labeling task, wherein the similarity between the target vector and the vector to be matched meets a preset similarity condition; any group of reference data stored in the vector database comprises a vector obtained by vectorizing labeled data and labeling information for the labeled data; obtaining target labeling information corresponding to the target vector from the vector database; generating annotation reference information based on the target annotation information; the annotation reference information is used as a reference when a data annotation user carries out annotation on the to-be-annotated data.