1102 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 provides a vehicle automatic sound production control method based on infrared recognition, which comprises the following steps: acquiring an infrared image in a preset range in front of a vehicle, recognizing a heat source target in the image, and judging whether the heat source target is an animal or a human according to the shape, size and temperature distribution characteristics of the heat source target; the prediction unit is used for predicting the moving track of the animal and the human in a period of time in the future according to typical moving track modes of the animal and the human so as to obtain predicted moving position and speed information, and transmitting the predicted information to the acoustic control unit; the acoustic control unit dynamically adjusts the sound pressure level and the frequency of the loudspeaker according to the received predicted moving information and the target type so as to generate an acoustic warning mode matched with the predicted moving position, the predicted moving speed and the target type; when the acoustic signal is sent out, the actual moving track of an animal or a person is continuously tracked, and the track prediction method is optimized by comparing the deviation between the actual track and the predicted track, so that the optimal avoidance effect is achieved.

The invention belongs to the technical field of communication, and discloses a detection and identification method and system based on a radio frequency signal of an unmanned aerial vehicle, and the method considers how to utilize zero-intermediate-frequency radio frequency signal collection, short-time Fourier transform and an improved deep learning model in a complex urban electromagnetic interference environment, reduces the detection delay as much as possible, and improves the detection accuracy. And high-accuracy identification of the low-slow small unmanned aerial vehicle is maintained, and the method has a rapid expansion capability for a new target type. Through the detection and identification method provided by the invention, high-precision detection and classification of common civil unmanned aerial vehicles and potential novel unmanned aerial vehicles can still be realized under the conditions of urban environment multi-source interference, complex noise and variable flight heights of low-altitude small targets.

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.

A method for data processing, a computer, and a storage medium are provided. A service processing request text sent by a first service object is obtained on an intelligent conversation page, and the service processing request text is parsed to obtain service semantic information and intention requirement information. A service database associated with the first service object is determined. A first target type indicated by the service semantic information is obtained, and a first service data set of the first target type is obtained from classified data corresponding to N service types in the service database respectively. Information retrieval is performed in the first service data set to obtain retrieval content associated with the first target component, a service result for the service processing request text is generated based on the intention requirement information and the retrieval content, and the service result is displayed on the intelligent conversation page.

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 navigation notification message classification method and apparatus, and an electronic device, and relates to the field of artificial intelligence. The method comprises the following steps: extracting original feature data of a target navigation notification message; inputting the original feature data of the target navigation announcement message into a first model, and determining category probability distribution information when the target navigation announcement message belongs to various messages through the first model according to the original feature data; taking the category probability distribution information as target feature data of the target navigation notification message; and determining a target type of the target navigation notification message according to the original feature data and the target feature data. According to the method and the device, the technical problem that the classification efficiency is low when the navigation notification messages are classified based on expert rules in the prior art is solved.

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 discloses a ship target tracking method and system based on visual twin coding and a medium, and the method comprises the steps: obtaining a ship video sequence, carrying out the target detection of the ship video sequence, and obtaining a detection frame corresponding to each frame of image; performing update prediction on the detection frame to obtain a target prediction frame corresponding to each ship target in the current frame image and a target detection frame corresponding to each ship target in the next frame image; constructing a cost matrix according to each target prediction frame and each target detection frame through a visual twin coding model and an intersection-to-parallel ratio calculation formula, and then carrying out target association according to the cost matrix through a Hungary algorithm to obtain a ship target initial tracking result; and classifying the detection frame to obtain a ship target type, performing similarity comparison on an evanescent target and a new target, and performing target re-identification according to a similarity comparison result to obtain a ship target secondary tracking result. The ship target tracking method can realize accurate tracking of the ship target, and can be widely applied to the technical field of target tracking.

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 electronics, and discloses a deep learning operator optimization and fusion operator calculation instruction generation method and device.The deep learning operator optimization method comprises the steps that all nodes in a deep learning calculation graph are traversed to obtain at least one target node set, the target node set comprises multiple target nodes, and the target nodes in the target node set are extracted; the operator corresponding to each target node is a target operator of a target type, and different target operators have an association relationship; and fusing the operators corresponding to different target nodes in the target node set to obtain a fused operator. The target nodes which have the incidence relation in the computational graph and the corresponding operators are the target type operators are used as a set, the target operators corresponding to the target nodes in the same set are fused, the fused operators can be fully optimized in the aspect of specific instruction implementation, and the execution efficiency of the operators is improved.

The invention discloses a task processing method, a model training method and a code generating method. The method comprises the following steps: determining target request information and target prompt information of a target task; the target request information and the target prompt information are input into a first large language model to be processed, reply information corresponding to the target task is obtained, the first large language model is obtained based on extended corpus training, and the extended corpus is corpus obtained through extension of initial corpus associated with the text generation task of the target type. The technical problem that the accuracy of the output result of the text generation task processed through the large language model is low due to the fact that the corpus for training the large language model of the specific industry or field is difficult to obtain is solved.

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 radar identification method for an unknown space cone target. The radar identification method for the unknown space cone target comprises the following steps: acquiring a to-be-detected HRRP sequence; extracting weighted physical features and deep features of the to-be-detected HRRP sequence; fusing the weighted physical features and the deep features to obtain fused features; inputting the fusion features into a pre-trained prototype network to obtain a plurality of to-be-tested embedded vectors based on the prototype network; unknown space cone target radar identification is carried out based on the prototype vector of each target type and the embedded vector to be measured; the target types comprise a space cone target, a light accompanying flying false target and a heavy accompanying flying false target. According to the unknown space cone target radar identification method provided by the invention, the universal problems of weak generalization ability of the space cone target and difficulty in zero sample identification in the existing identification method are solved.

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 discloses a database linkage operation method, a medium and equipment, and relates to the technical field of data storage, and the method comprises the steps: obtaining a target type definition file and a target BIM model corresponding to a target project, constructing a first target database based on the target type definition file and target object data, and constructing a second target database based on the target type definition file and target feature data, according to the BIM data management method and system, the BIM data are subjected to two-way linkage operation, the second target database is constructed, two-way linkage operation of the first target database and the second target database is achieved through the target mixed statements, and it can be known that refined management of the BIM data is achieved, so that the storage mode expansibility is good, different databases are selected for data storage based on different features of the BIM data, and the BIM data management efficiency is improved. Different parts of the same BIM data are stored in a proper database, and the performance of a graph database and the performance of a relational database are brought into full play.

A monitoring method includes: not expecting to monitor a physical downlink control channel (PDCCH) in a target type common search space (CSS) set or not expecting to monitor a PDCCH in a Type1-PDCCH CSS set, if a quasi-co-location type D (QCL-TypeD) property of a first demodulation reference signal (DM-RS) for monitoring the PDCCH in the Type1-PDCCH CSS set is different from a QCL-TypeD property of a second DM-RS for monitoring the PDCCH in the target type CSS set, and if the PDCCH in the Type1-PDCCH CSS set or a physical downlink shared channel (PDSCH) associated with the PDCCH in the Type1-PDCCH CSS set overlaps in at least one symbol with the PDCCH in the target type CSS set or with a PDSCH associated with the PDCCH in the target type CSS set, where the target type is Type0B-PDCCH, and / or Type2A-PDCCH, and / or Type1A-PDCCH.

Embodiments of the invention provide a retrieval result determination method and apparatus, an electronic device and a storage medium. The method comprises the steps of obtaining an input image; determining an open set target of the input image, and extracting a category label of the open set target; generating a content description text for the category label, and obtaining text features of the content description text; acquiring position information of the open set target; generating an unstructured visual coding feature and an attribute feature based on the category label and the position information; an open set target index is constructed through the text features, the unstructured visual coding features and the attribute features; when the retrieval request is obtained, the retrieval result corresponding to the retrieval request is determined based on the open set target index, and therefore the situation that when a new target type appears every time, a model needs to be retrained is avoided, and retrieval for the new target type can be achieved.

The invention discloses a data processing method, computing equipment and electronic equipment, and relates to the field of large model technology and data processing. The method comprises the steps that initial multi-modal data are obtained, and the initial multi-modal data comprise first initial modal data and second initial modal data which are used for describing an object; screening the first initial modal data by using a first network model to obtain first target modal data and an object recognition result, the object recognition result being used for representing an object of a target type included in the first initial modal data; inputting the object recognition result and the second initial modal data into a second network model, and enhancing the second initial modal data by using the second network model to obtain second target modal data; and obtaining target multi-modal data based on the first target modal data and the second target modal data. The technical problem that the quality of data used for model training in the related technology is poor is solved.

The invention relates to a data cleaning method and device and a storage medium. The data cleaning method comprises the steps of obtaining a to-be-cleaned data set; a preset generative adversarial network model is called to process the to-be-cleaned data set to obtain cleaned data, a generator in the generative adversarial network model is used for generating the cleaned data, and a discriminator in the generative adversarial network model is used for determining the probability that the cleaned data generated by the generator is target type data; the target type data is dirty data or clean data; in a generative adversarial network model training stage, the generator is used for generating second type data based on the first type data; in the training stage of the generative adversarial network model, the discriminator is used for distinguishing the second type of data and the first type of data so as to determine the second type of data as non-target type of data. According to the data cleaning method and device, the to-be-cleaned data is generated into the cleaned data through the generative adversarial network model.