511 results about "Visual identification" patented technology

The invention discloses a patient rehabilitation training data acquisition method and system based on visual identification, and relates to the technical field of rehabilitation training data acquisition, and the method comprises the steps: carrying out the image segmentation of a patient rehabilitation training video through a deep learning model, and separating a patient motion feature region from the patient rehabilitation training video; marking key skeleton nodes in the patient motion feature region, and establishing a three-dimensional coordinate system of the key skeleton nodes; calculating a motion angle and displacement data between adjacent skeleton nodes according to the three-dimensional coordinates of the key skeleton nodes; and performing matching degree analysis on the motion angle and displacement data and a preset rehabilitation standard to generate a rehabilitation training score. According to the method, the deep learning model is adopted to perform image segmentation on the rehabilitation training video, the motion feature region of the patient is accurately extracted from the image, and key skeleton nodes are marked and analyzed in combination with the three-dimensional coordinate system, so that accurate acquisition of rehabilitation training data of the patient is realized.

The invention relates to the technical field of safety monitoring, in particular to a smart park safety management method and system based on AI visual identification, and the method comprises the following steps: obtaining a video stream captured by a monitoring camera of a smart park, extracting the movement track data of a person through AI, and carrying out the statistics of the stay duration and access times of a target object in each grid; and constructing retention heat distribution of the grid region to obtain a personnel trajectory retention heat value. According to the invention, through the data processing of the monitoring video stream and the combination of the gridding analysis of the personnel motion trail, the accurate capture of the abnormal cruise behavior is enhanced, and through the calculation of the change rate of the access times between the adjacent grids, the statistics of the round-trip condition of the target among the plurality of areas and the combination of the passing authority data, the abnormal screening is carried out. The method improves the recognition capability of abnormal movement modes, achieves the dynamic adjustment of regional safety risk levels, improves the response capability of park security, enables the recognition capability of abnormal behaviors to be enhanced, and reduces the false alarm rate.

The invention relates to the technical field of unmanned aerial vehicle obstacle avoidance, in particular to an AI-assisted unmanned aerial vehicle low-altitude flight obstacle avoidance method and system. The method comprises the following steps: carrying out real-time unmanned aerial vehicle low-altitude flight environment perception based on a multi-modal perception network, and carrying out static obstacle identification and obstacle radiation range analysis to obtain a plurality of static obstacle radiation paths; carrying out dangerous potential energy field modeling based on the plurality of static barrier radiation paths, carrying out dangerous environment distribution fitting, and constructing a three-dimensional dangerous potential energy map; performing multi-path flight rehearsal according to the three-dimensional danger potential energy map, performing optimal flight path evaluation, and extracting an optimal flight path; and performing unmanned aerial vehicle flight processing based on the optimal flight path, performing dynamic obstacle visual identification and operation path prediction, and constructing a plurality of obstacle movement prediction paths. According to the invention, the task execution efficiency and safety of the unmanned aerial vehicle are improved through rapid and accurate obstacle avoidance decision making of the unmanned aerial vehicle.

The invention discloses a robot visual identification, positioning and grabbing method based on RGBD point cloud, and the method comprises the following steps: 1, carrying out the camera point cloud data processing, synthesizing an RGB image and a depth image into a point cloud image, and carrying out the filtering and denoising processing of the point cloud data; step 2, target detection and capture attitude generation: performing target detection and instance segmentation on the RGB image, identifying a target object and generating a corresponding mask frame, and applying a mask to the depth image to extract depth point cloud data corresponding to the target object; obtaining the depth point cloud data, generating a plurality of groups of grabbing postures, and screening out an optimal grabbing posture; and thirdly, the mechanical arm grabbing execution module receives the optimal grabbing posture information output by the target detection and grabbing posture generation module and controls the robot to complete the grabbing action.

The invention relates to a seat cushion robot self-adaptive tightening system and an operation method. The system provided by the invention is composed of an integrated industrial personal computer, an AGV mobile platform, a collaborative robot, a tightening end effector, an industrial camera and accessories thereof. The collaborative robot is installed on the AGV mobile platform, the tightening end effector and the industrial camera are installed at the tail end of the AGV mobile platform, and the whole system is controlled by the integrated industrial personal computer. The accurate position of the center of the to-be-tightened bolt can be obtained through a visual identification method in combination with a designed multi-layer screening method; meanwhile, different feeding modes are set according to different pre-tightening conditions, automatic cap searching is achieved, and it is guaranteed that tightening is in place; in addition, the counter-acting force arm is added to counteract counter-acting force in the tightening process, and robot damage is avoided. The tightening system and the operation method provided by the invention not only have extremely high flexibility, but also can adapt to a complex tightening environment, so that the accuracy of tightening work is ensured, and the working efficiency is also improved.

The invention relates to an intelligent visual identification technology-based cold solder joint microcrack intelligent discrimination method, which comprises the steps of collecting an initial RGB image of a to-be-detected welding spot, carrying out two-dimensional discrete cosine transform and inverse two-dimensional discrete cosine transform on the initial RGB image to obtain an enhanced image, and fusing the enhanced image with an R channel of the initial RGB image to obtain a fused image; forming a dual-channel feature map; calculating the phase consistency of the dual-channel feature map, and obtaining a suspected candidate region of the pseudo soldering microcrack through an adaptive threshold segmentation method; acquiring an RGB image sequence of a continuous time sequence of the welding spots, and performing anomaly detection to obtain an abnormal region set; and constructing a welding spot thermal diffusion model, and inputting the geometric parameters and the environmental parameters in the abnormal region set into the welding spot thermal diffusion model to obtain a final judgment result of the pseudo soldering microcracks. According to the method, through multi-dimensional feature fusion and continuous time sequence dynamic tracking, the detection precision of the pseudo soldering microcracks is remarkably improved, the false detection rate is reduced, and the final judgment result is more accurate.

The invention discloses a mobile phone silica gel shell appearance detection system based on an optical image sensor, and relates to the technical field of appearance defect detection and intelligent visual identification. An image acquisition module acquires a high-quality multi-channel image through multi-angle synchronous acquisition; the dynamic light field regulation and control module realizes self-adaptive regulation of a regional light source based on reflectivity perception and a gray gradient change rate; the image processing and defect identification module fuses edge features, texture changes and a deep learning model to realize pixel-level defect classification; the material identification and shielding module identifies a non-silica gel area through multi-dimensional spectral features and eliminates interference; a time sequence defect evolution analysis module models a defect evolution track; the trend prediction module is used for predicting the extension trend of potential cracks and fatigue areas; the control feedback module realizes intelligent judgment and response linked with the manufacturing system; the system can realize high-precision and high-robustness automatic defect detection and risk prediction, and is suitable for online quality monitoring of large-batch flexible products.

The invention relates to the technical field of aluminum formwork stacking planning, in particular to a stacking planning control system and method for building aluminum formworks. The method comprises the following steps: acquiring a multi-angle monitoring image of a building aluminum template to be stacked, performing structural feature point visual identification on the multi-angle monitoring image of the building aluminum template, performing three-dimensional form point cloud modeling, and constructing a three-dimensional structure model of each aluminum template; obtaining a building aluminum template production operation log; physical state feature analysis is carried out based on the building aluminum formwork production operation log, stacking planning constraint rule evolution is carried out, and therefore an aluminum formwork overall stacking planning constraint rule is obtained; and intelligent stacking planning is carried out on each aluminum template three-dimensional structure model based on an aluminum template overall stacking planning constraint rule, optimal stacking planning evaluation is carried out, and an optimal aluminum template stacking planning scheme is extracted. According to the invention, stacking planning control with high space utilization rate and high efficiency is realized.

The invention relates to the technical field of candid shooting detection, and discloses a monitoring screen area candid shooting detection method based on real-time visual identification, which comprises the following steps: determining a screen area, judging whether a suspected candid shooting behavior exists or not according to an overlapping result of a current image and a previous image of acquisition equipment, identifying a gesture action of the screen image, the method comprises the following steps: determining an object contour of a screen image according to an edge detection algorithm, searching an object contour value in a preset object contour table, obtaining a lens brightness value according to effective lens pixel points, outputting a contour weight factor of the object contour value and a brightness weight factor of the lens brightness value by adopting a candid shooting behavior model based on a gesture action, and comparing the candid photographing behavior value with a candid photographing behavior threshold value, and judging whether to send candid photographing early warning information to the signal shielding device or not according to a comparison result. The candid photographing behavior is comprehensively measured through the object contour value, the lens brightness value and the gesture action, and the candid photographing detection reliability is improved.

Precise nerve detection during surgery is crucial for safe operational outcomes, which largely depend on the surgeon's skills. Therefore, automatic real-time identification of anatomical structures is of great importance. However, image processing times must be kept as low as possible to be a viable solution for real-time visual identification. By using a general purpose graphics processing unit (GPGPU) implementation for birefringence mapping, the process can achieve rates of 43 frames per second (FPS); a 118×gain from a CPU implementation. Furthermore, by including a deep learning network, the complete framework can automatically detect nerves close to 12 FPS. By leveraging GPU for the processing task, the framework can run on compact devices with NVIDIA modules, such as Jetson Xavier AGX, while still achieving reasonably fast nerve identification and visualization.

The invention relates to the technical field of solid waste sorting scheduling, in particular to a real-time monitoring optimization method and device for a solid waste collection, transportation and sorting ERP system. The method comprises the following steps: obtaining a real-time solid waste sorting log based on a solid waste collection, transportation and sorting ERP system; solid waste carrying vehicle tracking is carried out on the real-time solid waste sorting log, and a carrying track distribution visual view is constructed; acquiring an omnibearing monitoring image of a real-time sorting site; equipment sorting behavior visual identification is carried out on the real-time sorting site omnibearing monitoring image, dynamic sorting evolution is carried out, and a dynamic sorting twinborn model is constructed; carrying out multi-time-point solid waste sorting simulation on the dynamic sorting twinborn model, carrying out sorting load demand prediction, and generating a sorting load demand prediction value; and carrying out vehicle load calculation on the sorting load demand prediction value one by one, and then carrying out dynamic vehicle scheduling decision making to obtain a dynamic vehicle scheduling strategy. According to the invention, efficient and real-time solid waste sorting scheduling optimization is realized.

The embodiment of the invention provides a warehouse body intelligent unblocking method and system based on visual identification and air cannon linkage, and the method comprises the steps: collecting images in a warehouse in real time through a plurality of high-definition anti-explosion cameras disposed in the warehouse, carrying out the fusion processing of the collected images in the warehouse, and constructing a warehouse three-dimensional dynamic model; based on the warehouse three-dimensional dynamic model, key features are extracted by adopting a neural network; taking the extracted key features as input, analyzing associated parameters through a time sequence of a long-short-term memory network, establishing a congestion cause knowledge base, and labeling congestion types in a classified manner; constructing a congestion risk grading model based on the congestion cause library and a machine learning algorithm; and activating a corresponding air cannon based on the level corresponding to the three-level early warning and a visual positioning matching result, dynamically adjusting injection parameters, and executing unblocking operation according to an optimized time sequence. According to the embodiment of the invention, closed-loop intelligence from visual perception, reason modeling, artificial intelligence decision making to accurate execution is constructed.

The invention discloses a full-automatic intelligent port inspection robot system and method. The system mainly comprises an unmanned vehicle and an unmanned aerial vehicle which are autonomously coordinated. The unmanned vehicle platform comprises a vehicle body, an omni-directional driving wheel set arranged at the bottom of the vehicle body, and a multi-source sensing system, a 5G / Beidou dual-mode communication module, an emergency equipment cabin, an edge computing controller and an intelligent charging system which are arranged on the vehicle body, and real-time data interaction is achieved between the communication module and the unmanned aerial vehicle as well as between the communication module and the port central dispatching system. According to the invention, it is ensured that the robot system can complete core inspection tasks such as container three-dimensional scanning, container body state monitoring and storage yard path planning in an all-weather manner, automatic docking of the unmanned aerial vehicle and the unmanned vehicle with emergency equipment is realized, and a'sensing-decision-execution 'closed-loop system is formed; the functions of high-precision visual identification, multi-mode environment perception, dynamic obstacle avoidance planning, convenient replacement of unmanned aerial vehicle equipment, intelligent charging and the like are integrated, the port inspection efficiency can be remarkably improved, and the new-generation intelligent port equipment of the emergency response process can be optimized.

The invention relates to the technical field of visual identification, and provides an AI visual system for shaft part stain identification and cleaning equipment, an image acquisition module acquires a surface image of a shaft part, generates an image of the shaft part, captures the surface image of the shaft part in real time, and provides a data basis for subsequent stain identification; the stain identification module processes the shaft part image, identifies the local contrast of each shaft sub-block in the shaft part image to obtain a stain candidate area, detects whether a stain edge exists in each shaft sub-block in the stain candidate area, combines the shaft sub-blocks to obtain a stain area, and triggers an equipment signal; the real-time monitoring and identification capability enables the production process to be more efficient, and problems can be found and processed in time. Corresponding cleaning operation can be automatically executed according to equipment signals of the visual system, the shaft parts are cleaned, manual intervention is reduced through the automatic process, and the production efficiency and the product quality are improved.

The utility model discloses the field of agricultural machinery, and particularly relates to a spraying machine tool and an all-terrain spraying vehicle, the spraying machine tool comprises a hollow shell, a visual identification device and a spraying device are arranged on the two sides of the shell in the width direction respectively, the visual identification device comprises a supporting rod, the supporting rod extends in the height direction of the shell, and the spraying device is arranged on the supporting rod. A camera is arranged at one end of the supporting rod, the other opposite end of the supporting rod is connected with the shell, the spraying device comprises a water pipe, the water pipe extends in the height direction of the shell, a signal control switch valve is arranged on the water pipe, a spraying pipe is connected to the top of the water pipe, and a plurality of nozzles are arranged on the spraying pipe at intervals. According to the utility model, the visual identification device and the spraying device are arranged, and the visual identification device is used for judging whether spraying is carried out or not, so that the pertinence is stronger during spraying, the investment of labor cost can be reduced, the physical and psychological health of staff is greatly maintained, and the spraying device is simple in structure and convenient to popularize.

The invention belongs to the technical field of visual identification, and particularly discloses a thick plate weld groove orthogonal section contour three-dimensional point cloud reconstruction method and system. Comprising the following steps: dividing a three-dimensional point cloud of a groove contour into a plurality of parts according to geometric characteristics of a weld groove; receiving designation of a target part, extracting a point cloud subset at the left junction of the target part, and further fitting to obtain an edge line; confirming a point closest to the three-dimensional point cloud of the current frame on the edge line; calculating a tangent vector of the edge line at the nearest point as a normal vector of an orthogonal section; calculating the distance between each point in the three-dimensional point cloud of the current frame and the normal vector of the orthogonal section, and screening out all points which do not exceed a preset threshold value; and calculating a projection point of each screened point on the orthogonal section to jointly form a reconstructed thick plate weld groove orthogonal section contour. According to the method, the coordinate system orthogonally perpendicular to the welding seam in real time is constructed, the three-dimensional point cloud is projected to the orthogonally perpendicular plane, the orthogonal section contour of the groove of the welding seam is obtained, and the measurement error caused by the pose of the sensor is eliminated.

A mapping positioning method based on inertial navigation-laser-loopback-identification positioning tight coupling comprises an IMU odometer module, a laser radar odometer module, a loopback detection module and a visual identification positioning module, wherein the IMU odometer module and the laser radar odometer module are arranged on a mobile robot. According to the method, the basic pose of the mobile robot is estimated, the map is constructed, the global position information is generated according to detection, the corresponding weight is assigned according to the autonomously established evaluation standard, integration is carried out, the mapping function is finally completed, and high-precision positioning is achieved.

The invention provides an offshore wind power blade-oriented stain following and unmanned aerial vehicle disturbance detection method, which comprises the following steps of: acquiring a fan blade image by using an unmanned aerial vehicle airborne camera, and preprocessing the fan blade image to obtain a fan blade stain track; performing time sequence processing on the stain track of the fan blade, extracting time and space characteristics of the stain track, decoupling an unmanned aerial vehicle offshore wind power blade cleaning interference source, and obtaining a disturbance detection result; and according to a disturbance detection result, an unmanned aerial vehicle flight control strategy is adjusted, and the stability of the unmanned aerial vehicle during fan blade stain cleaning is kept. According to the method, a plurality of disturbance identification modes are adopted for track time sequence processing from visual identification, identification is carried out without depending on a sensor of the unmanned aerial vehicle, the disturbance type is directly identified through the position change of the stain, the change in a time sequence signal has high identifiability and decoupling performance on the track, disturbance identification is efficiently carried out, and the disturbance identification efficiency is improved. And meanwhile, the unmanned aerial vehicle follows the blade positions.

The invention discloses a robot automatic grabbing path planning method based on visual identification, and relates to the technical field of intelligent grabbing. The method comprises the following steps: acquiring multi-view visual data of a target scene, and generating a scene three-dimensional compact reconstruction model through a multi-modal image fusion algorithm; performing target detection and feature extraction on the model, and screening an optimal capture point in combination with a visual attention mechanism; constructing a dynamic environment obstacle probability map, updating an obstacle state through time sequence visual tracking, and quantifying an interference weight; an initial grabbing path is planned based on an improved fast expansion random tree algorithm, path smoothness constraints and robot joint movement limit parameters are introduced, and path nodes are optimized through a Bezier curve; visual servo feedback and path deviation prediction are fused, path parameters are corrected in real time, and a continuous movement track is generated. The method effectively adapts to the dynamic environment, gives consideration to path safety, smoothness and mechanical arm motion characteristics, and remarkably improves the grabbing success rate and operation reliability.

The utility model relates to the technical field of agricultural picking instruments, in particular to tomato maturity intelligent identification picking equipment which comprises a collecting bin, a camera is installed on the collecting bin, a visual identification system is arranged in the camera, a base is fixedly connected to the middle of the collecting bin, a steering motor is installed on the base, and a control system is installed on the steering motor. The output end of the steering motor is connected with a steering main gear, the steering main gear is in meshed connection with a steering auxiliary gear, the steering auxiliary gear is coaxially and fixedly connected with a steering seat, and the steering seat is rotationally connected with the base. The angle of the scissor mechanical arm is adjusted through the steering motor and the steering base, so that mature tomatoes at different horizontal positions and different heights can be picked, the tomatoes do not need to be picked by adjusting the position of equipment, time and labor are saved, and meanwhile the picking efficiency is improved.

The invention relates to the technical field of robot welding, in particular to a mobile intelligent welding robot for seat production, which comprises an autonomous mobile chassis for autonomous navigation and obstacle avoidance in a seat production workshop; the multi-degree-of-freedom mechanical arm is used for achieving complex space track welding; the welding actuator is used for completing high-precision welding operation; the visual identification system is used for capturing the welding seam position in real time, detecting the welding quality and feeding back to the control system; the central controller is used for coordinating the operation of each module; through the steps, flexible movement of the robot in a production workshop is achieved through the autonomous moving chassis and the SLAM navigation technology, so that the utilization rate of equipment is increased, the occupied area of a production line is reduced, the multi-degree-of-freedom mechanical arm and the high-precision visual recognition system are combined, the complex welding seam track of the seat framework can be recognized in a self-adaptive mode, and the production efficiency is improved. And the pose deviation in the welding process is compensated in real time through force sense feedback, so that the consistency of the welding seam forming quality is ensured.

The invention provides a self-moving device and a recharging method thereof, the self-moving device comprises a visual module, and the method comprises the following steps: obtaining a to-be-processed image containing a charging station through the visual module; performing image processing on the to-be-processed image to obtain a target image so as to obtain a contour line of the charging station; controlling the self-moving equipment to be aligned with the charging station by using the contour line; and after the self-moving device is aligned with the charging station, the self-moving device is controlled to move towards the charging station, so that the self-moving device is in butt joint with a charging head of the charging station, and a recharging function is realized. According to the method and the device, the self-moving equipment can realize automatic recharging without a boundary guide line or a visual identifier, so that the reliability and the success rate of the recharging process are greatly improved, in addition, the special requirements on an external environment and the dependence on additional facilities are reduced, and the maintenance cost and the complexity of the system are reduced.

The invention provides a driving anti-slip optimization control method based on vehicle-road cooperation road fusion estimation, and the method comprises the following steps: S1, fusing vehicle-mounted and roadside visual recognition information, and obtaining a vehicle-road cooperation road state recognition result through employing a weighted voting method in combination with road invariant hypothesis and historical information assistance judgment; s2, adaptively adjusting parameters of a dynamic estimator by using a pavement state recognition result, establishing a sample set based on a pavement fusion estimation result, introducing a fuzzy clustering algorithm of particle swarm optimization to realize self-evolution optimization of the structure and the parameters of the estimator, and outputting a fused attachment coefficient estimation result; and S3, designing a slip rate and wheel acceleration combined control strategy to deal with potential slip in advance, and outputting a control torque result of a slip rate controller. Compared with the prior art, the method integrates visual perception and dynamic information to realize accurate estimation of the pavement state, supports advanced intervention of the slip rate controller before slip, and improves the driving stability of the whole vehicle.

The invention belongs to the technical field of information logistics, and particularly relates to an intelligent logistics control system and method based on machine vision and a storage medium, and the intelligent logistics control method based on machine vision comprises the steps: obtaining discharging information; controlling a visual identification module to run to a photographing position according to the discharging information so as to identify material grabbing position information and identification code information of an identification code; matching a discharging drawer of the storage cabinet module according to the identification code information, and generating matching information; and a robot carrying module is controlled to be matched with a transfer module according to the matching information and the material grabbing position information, so that the robot carrying module places the grabbed packaging parts on the transfer module, and the packaging parts are stored in a storage cabinet module through the transfer module. According to the invention, the classification, storage and taking efficiency of the packaging parts is improved, and the complexity and errors of manual operation are reduced.

The invention discloses a full-automatic deviation rectification control method for strip steel in the finish rolling process, and belongs to the field of hot rolling and finish rolling processes. According to the method, images in the strip steel rolling process are collected through a camera and sent to a server to recognize and calculate the deviation amount of the strip steel, then the deviation amount is sent to an automatic control system, the automatic control system predicts the deviation trend of the strip steel through a deviation amount fitting curve, a rack is leveled most weekly according to the deviation trend of the strip steel and closed-loop control over the deviation amount, and the strip steel is rolled. Therefore, strip steel deviation rectification is realized. Visual identification, artificial intelligence and automatic control can be effectively combined, full-automatic deviation correction of the strip steel in the finish rolling process is achieved, the purpose that a camera and an automatic control system are used for replacing an operator to conduct leveling intervention on deviation of the strip steel in the finish rolling process is achieved, rolling breakage or scrap steel caused by deviation of the finish rolling strip steel can be effectively reduced, and the production efficiency is improved. The loss of the working roller and the risk of manual misoperation are reduced, and the precision rolling stability, the rolling line automation rate, the labor efficiency and the yield are improved.

The invention provides a peritoneal dialysis solution detection data intelligent analysis method based on visual identification, and the method comprises the steps: obtaining a to-be-identified test paper image, carrying out the test paper region segmentation and color correction of the test paper image, and carrying out the brightness equalization processing of a test paper detection region, and obtaining a processed test paper detection region; obtaining a standardized color feature vector of a test paper color development area from the test paper detection area; carrying out mapping matching on the standardized color feature vector and a standard colorimetric database to obtain a colorimetric matching value, carrying out color abnormality identification according to the colorimetric matching value, comparing the color abnormality with a preset abnormal value to obtain a test paper colorimetric analysis result, and carrying out self-adaptive correction on the test paper colorimetric analysis result with abnormality; and constructing a personalized colorimetric model according to the colorimetric matching value in combination with a historical colorimetric matching value, and performing health trend analysis and early warning.

The invention discloses a cabinet U-bit detection method based on dual-stage visual identification and perspective correction, and belongs to the technical field of computer vision and artificial intelligence. The method comprises the following steps: acquiring cabinet image data through a camera deployed in a machine room; performing irregular cabinet detection on the image by adopting a first-stage visual identification model, and outputting coordinates of four vertexes of the cabinet; performing image perspective correction according to the vertex coordinates to obtain a standard rectangular cabinet front image; inputting the corrected image into a second-stage visual identification model, identifying a U-bit occupation state and constructing a U-bit state matrix; calculating a cabinet vacancy rate based on the state matrix and displaying a detection result in a thermodynamic diagram form; and submitting a detection result of which the confidence coefficient is lower than a preset threshold value to manual auditing, and performing incremental training and optimization on the model. The method has the advantages of high precision, low cost, strong generalization, strong real-time performance, sustainable optimization and the like, and can be widely applied to data center cabinet U bit intelligent monitoring in a complex scene.

The invention discloses a visual process management method and device and a storage medium, and the method comprises the steps: obtaining the input data of task state change, and carrying out the model initialization processing, and obtaining a task network model; according to the task network model, performing path search by adopting a graph traversal algorithm to obtain an influence path set; according to the influence path set, carrying out influence matrix construction operation to obtain an influence degree matrix; according to the influence degree matrix and the task network model, performing dynamic influence path mapping to obtain a dynamic propagation path diagram; key nodes are extracted according to the influence degree matrix and the dynamic propagation path diagram, and a decision point set is obtained; performing priority calculation and visual identification processing according to the decision point set to obtain a deviation node identification result; and performing model optimization processing according to the deviation node identification result to obtain an optimization task network model. According to the method, real-time dynamic layout adjustment of process management can be realized.

The invention relates to the technical field of unmanned aerial vehicles, and discloses an unmanned aerial vehicle positioning method, device and equipment based on scene recognition matching, and the method comprises the steps: carrying out the marking of a collected view field image of an unmanned aerial vehicle flight region through a target detection model, obtaining an unmanned aerial vehicle view angle scene data set, taking the data set as training data, and training a scene perception model; marking the satellite image, and constructing a visual knowledge base; training the scene recognition algorithm model by using a visual knowledge base and taking FaceNet as a base model, and carrying out specific scene classification; and carrying out similarity calculation on a scene perception result identified by using the trained scene perception model and an instance under a corresponding scene category in the visual knowledge base to obtain the position information of the unmanned aerial vehicle. According to the invention, a visual identification matching technology is adopted to get rid of the dependence of an unmanned aerial vehicle navigation technology on terminals such as satellites and the like; the image matching execution efficiency is improved, unmanned aerial vehicle positioning is realized, and the unmanned aerial vehicle navigation efficiency is improved.

The invention discloses a highway hazardous chemical substance liquid leakage accident monitoring method and a highway hazardous chemical substance liquid leakage accident monitoring system based on computer vision. The method comprises the following steps: arranging and installing an identifier configured for hazardous chemical substance vehicle identification on a vehicle for loading and transporting hazardous chemical substances; the identifier comprises an optical identifier configured to be used for improving the visual identification degree of the dangerous goods vehicle and a digital identifier configured to realize encrypted storage and quick reading of vehicle information; on the basis of an existing road monitoring system, a lightweight identification model configured for detecting the states of an optical identifier and a digital identifier in real time is deployed and loaded in the existing road monitoring system; performing visual positioning on the road surface projection pattern through a camera, and calculating the real-time position of the vehicle by combining map coordinates so as to supplement the positioning blind area of the GPS in the tunnel mountain area; accident probability calculation is carried out based on the identification state to determine whether the vehicle is suspected to have an accident; and when the accident is judged to be suspected, triggering an accident response process. And the accident response efficiency is obviously improved.