876 results about "Target acquisition" patented technology

The invention provides a multi-target tracking detection method and system based on cascade matching and trajectory confirmation, and relates to the technical field of digital video monitoring and tracking, and the method comprises the steps: obtaining a personnel construction video, and decomposing a video frame into a plurality of images; identifying a target in the current frame of image based on an improved YOLOv5 network, obtaining a target detection frame, and obtaining a prediction trajectory frame of the current frame through a Kalman filtering algorithm based on the trajectory frame of the previous frame; performing multi-target tracking on the target detection frame and the prediction trajectory frame of the current frame by using a DeepSort algorithm, respectively obtaining motion features and appearance features of the target detection frame and the prediction trajectory frame, performing cascade matching on the motion features and the appearance features of the target detection frame and the prediction trajectory frame by using a Hungary algorithm, and performing matching optimization by using an IOU matching algorithm; and carrying out fusion updating on the successfully matched target detection frame and prediction trajectory frame by using a Kalman filtering algorithm, and replacing the original prediction trajectory frame to obtain a tracking result.

The invention discloses a mode switching control method and device for an air-sea amphibious unmanned aerial vehicle, and the method comprises the steps: monitoring the unmanned aerial vehicle through a sensor, and obtaining target collection data; on the basis of an extended Kalman filtering algorithm, state estimation is carried out on the unmanned aerial vehicle through the target acquisition data, and a state estimation result is obtained; monitoring the mode switching state of the unmanned aerial vehicle according to the state estimation result to obtain a state monitoring result; and executing a mode switching strategy according to the state monitoring result to realize mode switching control of the unmanned aerial vehicle. The method can improve the stability and accuracy of mode switching control of the air-sea amphibious unmanned aerial vehicle, and can be widely applied to the technical field of unmanned aerial vehicles.

The invention provides an intelligent fuel gas accessory part safety monitoring method, an Internet of Things system and a medium, and relates to the field of fuel gas pipeline accessory facility monitoring. The method is executed by an intelligent gas accessory safety monitoring Internet of Things system, and comprises the following steps: determining a first abnormal value; determining a target acquisition parameter based on the first abnormal value; determining an acquisition instruction based on the target acquisition parameter; determining a second abnormal value; and determining a maintenance instruction and / or a parameter adjustment instruction based on the second abnormal value. The Internet of Things system comprises a government safety supervision management platform, a government safety supervision sensing network platform, a government safety supervision object platform, a gas company management platform, a gas company sensing network platform, a gas equipment object platform and a gas pipeline maintenance object platform. The method can be operated after a computer instruction stored in a computer readable storage medium is read. According to the method, real-time monitoring and intelligent management of the appurtenances can be realized, so that the detection result is more comprehensive and accurate.

The invention discloses an infrared unmanned aerial vehicle multi-target tracking method, and relates to the field of computer vision. The method comprises the following steps: reading a video of an infrared camera, and detecting a target by using YOLOv8 to obtain information; a camera motion compensation correction position is introduced, and a Kalman filtering algorithm is adopted to predict a track and determine a state; marking a low-matching-degree target through a pre-matching strategy; extracting appearance characteristics of the target by using RepO-LightMBN, calculating a cost matrix by combining an appearance characteristic distance and motion information, and associating a detection frame and a track through cascade matching and a B-IoU + matching algorithm; matching the last observation value and detection value of the track by adopting B-IoU + and OCR matching algorithms according to the mismatched target and track in cascade matching; and finally, updating a matched track state by using Kalman filtering, deleting an unmatched track and outputting a result. The method effectively improves the speed and precision of multi-target tracking of the unmanned aerial vehicle in the infrared mode, is innovative and practical, and can be used in the fields of anti-unmanned aerial vehicle monitoring and the like.

The invention provides a chemical industrial park unmanned aerial vehicle group patrol path optimization method, device and equipment, and relates to the technical field of unmanned aerial vehicle patrol, and the method comprises the steps: obtaining the environment data of a target chemical industrial park, and the attributes and initial patrol paths of all unmanned aerial vehicles; aiming at any unmanned aerial vehicle, acquiring each patrol position on the initial patrol path and a real-time monitoring value of the corresponding patrol position; for any patrol target in charge of the unmanned aerial vehicle, obtaining the weight when the unmanned aerial vehicle arrives at the patrol target; inputting each patrol position and the corresponding real-time monitoring value, the target position and weight of the patrol target, the environment data and the initial patrol path into a pre-constructed multi-target optimization model, and calculating a score of the initial patrol path; according to the weight, the target position, the environment data, the attribute and the initial patrol path and the position of each unmanned aerial vehicle, determining an optimal patrol path of the unmanned aerial vehicle by adopting dynamic neighborhood search; according to the method, the optimal patrol path with high efficiency, safety, high quality and high coverage rate can be generated.

The invention provides a data acquisition method and device, electronic equipment and a storage medium, belongs to the field of data acquisition, and is suitable for the field of financial science and technology. The method comprises the following steps: acquiring a data acquisition instruction for a plurality of target data sources and data source information corresponding to each target data source; configuring a corresponding data acquisition parameter for each target data source according to the data source information and a preset parameter configuration template; wherein the data acquisition parameter comprises a task execution level corresponding to the target data source; constructing data acquisition tasks for the corresponding target data sources based on the data acquisition parameters to obtain a plurality of data acquisition tasks; distributing the data acquisition tasks with the same task execution level to the same acquisition task group to obtain a plurality of acquisition task groups; and executing data acquisition operation on each acquisition task group according to the task execution level to obtain target acquisition data. According to the invention, large-scale and multi-interface financial data acquisition efficiency can be improved.

The invention provides a crane hoisting monitoring method and system based on multi-normal-form visual fusion and a medium, and relates to the technical field of computer vision. Acquiring an image of the target acquisition area through an image acquisition device, and obtaining a real-time image acquisition video stream; preprocessing the real-time image acquisition video stream to obtain real-time image frame data; performing real-time image frame data identification based on the visual detection model to generate detection information; inputting the detection information into a visual tracking module, and tracking the identification object to obtain tracking information; acquiring a fixed dangerous area or a dynamic dangerous area based on the detection information and the tracking information; and identifying a dangerous condition in the video stream according to the dynamic dangerous area or the fixed dangerous area, obtaining dangerous condition accumulated data and triggering an alarm. The technical problems that in the prior art, the monitoring means is single, and dynamic dangers are difficult to accurately recognize are solved, and the technical effect of accurately recognizing potential dangers of hoisting operation in real time is achieved.

The invention discloses an unmanned aerial vehicle cooperative navigation method, system and device based on joint perception, and the method comprises the steps: constructing a multi-unmanned aerial vehicle joint state description through combining the original vision of an unmanned aerial vehicle, the semantic information of key elements, the geometric data of the distance between the unmanned aerial vehicles and the execution action at the current moment; an end-to-end multi-unmanned aerial vehicle collaborative decision-making network is constructed, mapping from joint state description to a multi-unmanned aerial vehicle joint action value is formed, and an executable unmanned aerial vehicle motion control instruction is generated by using an action selection strategy and a value function. The functions of exploration, target acquisition, dynamic target allocation, obstacle avoidance and the like of a multi-unmanned aerial vehicle system in an unknown environment are realized, and the task that each unmanned aerial vehicle reaches the respective target without collision is completed; according to the method, cooperative control based on multi-agent deep reinforcement learning is combined with a time sequence prediction method, so that the unmanned aerial vehicle autonomously completes a cooperative navigation task of multiple unmanned aerial vehicles in an unknown environment without manual intervention, and the autonomy and intelligence of the unmanned aerial vehicle are improved.

The invention discloses a target attribute identification method, electronic equipment and a computer readable storage medium, and the method comprises the steps: obtaining a current frame, and determining a scheduling interval state matched with the current frame, and a motion information state and a quality information state matched with a target in the current frame; the scheduling interval state comprises preferentially identified attributes in the current frame, the dependence condition of part of the attributes is a moving target in the video frame, and the dependence condition of part of the attributes is the image quality of the target in the video frame; determining an identification priority corresponding to a target in the current frame based on the scheduling interval state, the motion information state and the quality information state, and selecting at least part of the target in the current frame as a target to be identified according to the identification priority; and obtaining attributes of the to-be-identified target, and obtaining an attribute identification result of at least part of the target in the current frame based on the identified attributes in the current frame and the identified attributes in at least part of historical frames before the current frame. According to the scheme, the precision and comprehensiveness of target attribute recognition can be improved.

The invention discloses an image acquisition method, which comprises the following steps that: a control unit sends an image acquisition control signal to each pixel unit through a first control pin, generates a first analog signal based on the pixel control signal, and sends the first analog signal to a first data output line through a first column line; the even-numbered row pixel units generate a second analog signal based on the pixel control signal and send the second analog signal to a second data output line through a second column line; the reading circuit sends the first analog signal, the second analog signal and the state signal to the image processing unit through the data pin and / or the first control pin; and the image processing unit obtains a target acquisition image according to the first analog signal and the second analog signal. According to the invention, the photosensitive analog signals from the pixel matrix can be transmitted to two different multiplexing pins in parallel through the two data lines of the readout circuit, and the frame rate of signal transmission is improved under the condition that a clock is not changed.

The invention relates to the field of data processing, in particular to a pedestrian retrieval method and device based on video data, a medium and equipment, and the method comprises the steps: obtaining a first initial collection video according to a first prediction track set, clustering first initial pedestrian segmentation images to obtain a first initial pedestrian segmentation image set, obtaining a first association degree between the first initial pedestrian segmentation image and a target pedestrian by combining a first similarity between the first initial pedestrian segmentation image and a preset image and the number of first video acquisition devices corresponding to the first initial pedestrian segmentation image set, and screening out a first target acquisition video, the influence of interference factors on a retrieval result is eliminated through image segmentation, and the relevance of the same pedestrian in different first initial acquisition videos and the difference between different pedestrians are represented through clustering, so that a first relevance degree is obtained by combining a first similarity and the number of first video acquisition devices; and the retrieval accuracy and the tracking accuracy of the target pedestrian are improved.

The invention provides a camera dynamic calibration method, a vehicle, electronic equipment and a program product, and relates to the technical field of artificial intelligence. The method comprises the following steps: when a camera dynamic calibration starting condition is satisfied, acquiring a reference acquisition image of a reference camera of a target vehicle and a target acquisition image of a target camera; determining a lane line point set of each lane line according to the reference acquisition image, and determining an external parameter matrix of the reference camera based on the lane line point sets; the lane line point set comprises lane line pixel position coordinates of corresponding lane lines; performing feature matching based on common-view areas of the reference acquisition image and the target acquisition image acquired at the same moment to obtain a target matching feature point pair set; and determining an external parameter matrix of the target camera according to the target matching feature point pair set and the external parameter matrix of the reference camera. According to the invention, the driving camera calibration of the intelligent driving vehicle can give consideration to both efficiency and precision.

The present invention discloses a target tracking method, device, system and program product based on pan-tilt linkage, and relates to the technical field of target tracking. In order to solve the defects that the existing pan-tilt linkage imaging devices use single coordinate and position information when tracking targets, which may lead to target loss and poor automatic tracking accuracy, the method includes: obtaining the tracking target; obtaining the target movement speed of the tracking target relative to the search device or tracking device that collects the tracking target; obtaining the pan-tilt movement speed of the pan-tilt device connected to the tracking device; and controlling the movement of the tracking device according to the target movement speed and the pan-tilt movement speed. The present invention is mainly used for tracking targets.

The invention relates to the field of data processing, in particular to a data processing method and device for pedestrian retrieval, a medium and equipment, and aims to represent a first clustering reliability degree corresponding to a second initial pedestrian segmentation image set by combining third image similarity between second initial pedestrian segmentation images in the second initial pedestrian segmentation image set. Representing a third correlation degree between the current second initial pedestrian segmentation image and the target pedestrian by combining a first clustering reliability degree corresponding to the second initial pedestrian segmentation image set according to the first clustering reliability degree corresponding to the current second initial pedestrian segmentation image set, the number of track points and the number of devices; therefore, the accuracy of the third association degree is improved, the accuracy of the third target acquisition video is improved, and the retrieval accuracy and the tracking accuracy of the target pedestrian are improved.

The invention provides an image acquisition control method, device and equipment based on machine vision. The method comprises the following steps: selecting a high-frame-rate image sensor acquisition mode according to environment state parameters; adjusting exposure parameters of the high-frame-rate image sensor according to the selected acquisition mode; fusing the optical flow speed and the brightness of the image flow into a weighting factor according to the exposure parameters, and injecting the weighting factor into an image space to generate a scene partition mask; and the scene partition mask is aligned with the exposure images with different exposure parameters, and the exposure images are fused into a target acquisition image. Through the implementation of the scheme of the invention, the environment state parameters are generated in real time, the corresponding acquisition mode is selected, the exposure parameters are optimized, the optical flow and the brightness data are fused to generate the weighting factor, the scene partition mask is created, the multi-exposure image is aligned through the scene partition mask, the optimal exposure part of each area is accurately fused, and the high-quality target image is obtained. Therefore, the image acquisition efficiency is improved.

The invention relates to the technical field of image recognition, in particular to a method and system for early warning below a portal crane, and the method comprises the steps: obtaining site environment sensing data, constructing a space map, and recognizing the position and motion state of equipment in real time; predicting an equipment motion track and a load swing range, and generating a predicted dangerous area; image acquisition devices are installed at a plurality of fixed positions, field images are acquired, an image recognition model is used for detecting a target, and the spatial position and the posture state of the target are obtained; tracking a target trajectory based on a motion estimation model and predicting a future position of the target trajectory; dynamically switching the image acquisition devices according to the motion state of the equipment; and comparing the future position of the target with the predicted dangerous area in real time, when the future position and the predicted dangerous area coincide, carrying out potential interference prediction, calculating predicted interference time, and carrying out grading processing in combination with the attitude state of the target. According to the invention, equipment dynamic perception and personnel behavior prediction are realized, and the intelligent protection capability of the operation area is improved.

The invention belongs to the technical field of automatic focusing, and particularly relates to a dynamic focusing method based on an industrial measurement system. Comprising the following steps: S1, fixing a target on a measured target, and aligning a lens of an industrial measurement system with the measured target to obtain an image data set of the measured target; s2, sequentially inputting the detected target images into a target acquisition network to obtain a target image data set; s3, correspondingly calculating the working distance of the target relative to the camera based on the prediction frame of each target image; s4, all the target images are subjected to denoising and illumination normalization processing, and a processed target image data set is obtained; s5, predicting the predicted defocus distance of each processed target image by using a deep regression network; s6, performing static adjustment on the lens to obtain a clear first frame of target image; and S7, dynamically adjusting the lens according to the motion state of the target to be measured and the working distance of the target relative to the camera. The invention has the capability of dynamic focusing.

The invention is suitable for the technical field of semiconductor testing, and provides a data acquisition method and device based on a DDIC chip, and testing equipment, and the data acquisition method comprises the steps: receiving a test pattern, controlling the DDIC chip to output a corresponding gray scale voltage signal, and achieving the comprehensive testing of the response capability of the chip in a full gray scale range; secondly, a target acquisition link composed of functional units including impedance conversion, single-ended to differential conversion, programmable gain amplification, filtering, analog-to-digital conversion and the like is utilized, so that the signal driving capability and the anti-interference capability are effectively improved, meanwhile, high-frequency noise and nonlinear distortion are reduced, and the basic quality of sampled data is guaranteed; on the basis, sampling optimization processing is carried out on the sampling data based on the FPGA unit, and the data stability is improved. According to the scheme, through high-precision transmission of the signal link, the data acquisition precision of the DDIC chip is improved, and then the technical requirements of high-precision DDIC chip testing are met.

The invention relates to the technical field of data processing, and discloses a capital project collaborative monitoring method and system based on a typical construction period. The method comprises the following steps: performing index correlation analysis processing on a multi-dimensional collaborative planning system to obtain a collaborative monitoring index system with a hierarchical structure and a data acquisition scheme; based on the data acquisition scheme, performing data acquisition according to the collaborative monitoring index system to obtain target acquisition data, and performing dynamic analysis processing on the target acquisition data to obtain a project monitoring data set; performing multi-level response strategy analysis processing on the project monitoring data set through a decision tree algorithm to obtain a collaborative response mechanism; and performing effect evaluation processing on an execution result of the collaborative response mechanism through an iterative optimization algorithm to obtain a construction period reference parameter set and a monitoring early warning rule. According to the invention, the efficiency and accuracy of capital project collaborative monitoring based on the typical construction period are improved.

The invention provides a motion video key clip extraction method based on behavior analysis. The method comprises the following steps: firstly, performing decoding and frame standardization on a match video stream, detecting and positioning a coach target, and obtaining and expanding a bounding box; extracting a candidate skeleton key point set in the extended region, selecting the skeleton with the most key points as the skeleton of the coach, and performing cross-frame tracking; analyzing the skeleton in real time, triggering hand fine analysis and extracting hand key points when a scoring trend appears, comprehensively judging based on geometry, kinematics and time sequence rules, and generating a trigger signal when a specific scoring gesture is detected; and the background thread extracts fragments before and after the triggering moment from the video stream as key fragments, and stores the key fragments in association with metadata such as event types, coach identities and competition states.

The invention discloses a river water level detection method, and relates to the technical field of computer processing, and the method comprises the following steps: S01, presetting a distributed pressure sensor array in each target collection region to obtain underwater pressure data k and real-time water flow data v of a plurality of target collection regions of a river to be detected; s02, establishing a dynamic pressure water level model based on the water flow direction pressure conduction quantity estimation of the plurality of target acquisition areas in combination with real-time water flow velocity and wave period parameters; s03, performing signal attenuation compensation on the underwater pressure data according to the dynamic pressure water level model to obtain initial water level data; s04, multi-source data fusion calibration is carried out on the preliminary water level data, a river water level detection result is generated, and the result comprises a wave height frequency spectrum and a water level change curve after peak elimination correction; and S05, generating a wave energy spectral density map according to the calibrated water level data, performing real-time comparison according to the wave energy spectral density map and a preset threshold value, and performing result return according to a comparison structure. The small-wave-height short-period wave measurement precision and the water level detection reliability are remarkably improved, the intelligent flood prevention early warning and dynamic adjusting capacity is achieved, and it is guaranteed that data collection is accurate and consistent.

The invention relates to the technical field of path planning, and provides a path planning method, device and equipment for an underwater unmanned vehicle, and a storage medium, and the method comprises the steps: obtaining a node access sequence of the underwater unmanned vehicle; determining the current state of the underwater unmanned vehicle by taking the first acquisition node as a target acquisition node; executing a current action corresponding to the current state to obtain a next state and a node residual data volume corresponding to the next state; calculating a dynamic composite reward value; judging whether the residual data volume of the node is 0; if the residual data volume of the node is not 0 and is within a preset communication range, updating a corresponding expected return value in a preset action-value table by using the dynamic composite reward value to obtain a target action-value table; and obtaining a data acquisition path of the underwater unmanned vehicle based on the target action-value table. Therefore, the throughput and efficiency of data acquisition of the unmanned underwater vehicle can be improved.

The invention relates to a data acquisition system, a data acquisition method and related equipment. The system comprises a cloud server, data acquisition equipment and a plurality of industrial equipment, the cloud server sends a data acquisition task to the target data acquisition device based on the data acquisition instruction for the target industrial device; the target data acquisition device sends an acquisition resource acquisition request to the cloud server based on the data acquisition task; the cloud server determines a target acquisition resource corresponding to the target data acquisition device, and sends the target acquisition resource to the data acquisition device; and the target data acquisition device obtains target acquisition data based on the target data acquisition tool and the data acquisition strategy in the target acquisition resource, and then sends the target acquisition data to the cloud server. The target data acquisition device can acquire the data acquisition tool and the acquisition strategy corresponding to the data acquisition task at the cloud, so that the data acquisition requirements of different industrial devices can be met, and the use cost of the data acquisition device is reduced.

The invention relates to an unmanned aerial vehicle indoor three-dimensional reconstruction autonomous acquisition method and system based on reinforcement learning, and the method comprises the following steps: constructing a virtual indoor simulation space and a virtual unmanned aerial vehicle model, and laying target acquisition points; a reinforcement learning model is constructed and trained, in the training process, in each time step, the unmanned aerial vehicle obtains target collection point information and inputs the target collection point information into the reinforcement learning model, the reinforcement learning model generates an action instruction and transmits the action instruction to the virtual unmanned aerial vehicle model, and the virtual unmanned aerial vehicle executes geometric transformation, updates the position and orientation and reconstructs view information; and deploying the trained reinforcement learning model in an untrained virtual simulation environment, carrying out an unmanned aerial vehicle autonomous flight experiment and data acquisition, and carrying out quantitative evaluation on the acquisition performance through a plurality of performance indexes. Compared with the prior art, efficient and stable data acquisition can be realized in a complex environment.

The invention relates to an intelligent mechanical fault diagnosis method based on a progressive transfer learning network, and belongs to the technical field of mechanical part monitoring and fault diagnosis. The method comprises the following steps: respectively acquiring original signal data in a laboratory environment and a real industrial environment, and preprocessing the data; establishing a fault identification model based on a progressive transfer learning network architecture, and obtaining an optimal network parameter by combining a loss function in a propagation process and by taking a classification error of minimizing a source domain sample and a target domain sample and an inter-domain difference of minimizing sample distribution as targets; and collecting real-time signal data in a real industrial environment, inputting the real-time signal data into the optimized fault identification model based on the progressive transfer learning network architecture, and outputting a fault type by the fault identification model. According to the method, the sample migration success rate and the fault identification accuracy are improved by gradually constraining feature mapping in the sample migration learning process.

The invention discloses a millimeter-wave radar fine-grained human body pose sensing method based on multi-dimensional feature extraction, and belongs to the field of computer vision and radar sensing, and the method comprises the steps: managing a millimeter-wave radar sensor through an equipment access module to generate a 4D point cloud; the target tracking module performs multi-target tracking on the 4D point cloud, predicts a target position by using a Kalman filter, divides point cloud affiliation through a gate function, creates a new target by using DBSCAN clustering, eliminates continuous targets without point cloud affiliation, and obtains a target center position; the human body pose detection module converts point clouds to a local coordinate system for normalization based on a target center position and 4D point clouds, fuses continuous multi-frame point clouds, and inputs a deep learning network of PointNet and U-Net improved based on an attention mechanism to output human body pose joint point coordinates. According to the invention, the human body pose detection precision in a limited data mode is improved, and a human body health monitoring function available to a home environment is realized.

The invention discloses a distributed energy storage device data acquisition method, system, device and medium, and relates to the field of energy storage data acquisition, the method comprises the following steps: a cloud platform sends an acquisition instruction to an acquisition terminal in response to received registration information of a target energy storage device; the acquisition terminal responds to a received acquisition instruction, sends a detection frame through a physical port according to a target communication protocol, establishes communication with target energy storage equipment, acquires identification information of the target energy storage equipment, determines a target point location table from a local point location library according to the identification information of the target energy storage equipment, and acquires the target energy storage equipment according to a target acquisition strategy; and obtaining original operation data of the target energy storage equipment, and obtaining target structured data according to the target point location table and the original operation data. The problems of poor compatibility and low expansibility in the prior art can be solved, and the collection efficiency of the energy storage data is improved.

The invention provides a map construction and automatic driving method and system and a storage medium, and the method comprises the steps: obtaining vehicle environment data, carrying out the real-time mapping according to the vehicle environment data, and obtaining and storing a navigation map in a preset range in real time; detecting a building target in the vehicle environment data, and marking the building target in the navigation map; acquiring a feature map according to the navigation map and the building target; and controlling the vehicle to automatically drive to a specified building target according to the feature map. According to the method provided by the invention, the user can efficiently know the current environment information through real-time mapping, the system memory occupation is reduced by storing the navigation map in the preset range in real time, the resource utilization is reduced, and the user experience is improved by automatically driving to the specified building target.

The invention discloses a self-adaptive feature point compensation method for visual SLAM in a dynamic scene, and the method comprises the steps: firstly obtaining a dynamic environment image, recognizing a dynamic target in the image through a YOLO11s target detection model, and obtaining the type of a target detection frame and the coordinate information of the target detection frame; then, a dynamic confidence scoring criterion is constructed based on target prior motion characteristics, high-dynamic object detection frame categories are distinguished, and interference feature points in the high-dynamic object detection frames are accurately eliminated; secondly, designing an adaptive threshold by combining the space coverage rate of a dynamic detection frame and the time distribution characteristic of the number of historical feature points, and dynamically compensating the number of the feature points; and finally, when the number of the feature points is lower than an adaptive threshold, delimiting a compensation area by taking the edge of the high-dynamic detection frame as a reference, compensating the ORB feature points, and ensuring that the number of the feature points is stable. By adopting the technical scheme of the invention, the robustness of the number of feature points and the positioning precision in a dynamic scene are remarkably improved while the real-time performance is ensured.

The invention relates to the technical field of pipeline positioning, in particular to a large-burial-depth pipeline positioning system based on an unmanned aerial vehicle integrated pipeline detector, and the system comprises a task generation module which is used for determining a plurality of target collection points and generating a target flight task; the magnetic field control module is used for determining a magnetic field application point of the target area based on each target acquisition point and controlling magnetic field parameters of an electromagnetic field applied to the target area; the data acquisition module comprises an unmanned aerial vehicle assembly, a pipeline detection assembly and an environment monitoring assembly; the data analysis module is used for constructing a magnetic field distribution model of the target area, determining an initial pipeline parameter of the large-burial-depth pipeline based on the magnetic field distribution model and a magnetic field parameter of an electromagnetic field applied to the target area, and determining a key influence coefficient based on the environment data and an initial flight parameter; and the parameter adjusting module is used for adjusting the pipeline parameter based on the key influence coefficient to obtain a target pipeline parameter. The detection positioning efficiency can be improved.