6690results about How to "Improve continuity" patented technology

The invention provides an HBase client main-standby switching method and system based on fault perception, and relates to the technical field of database management, and the method comprises the steps: collecting system operation data through a distributed fault monitoring module, building an anomaly detection baseline model, carrying out fault diagnosis based on a system health assessment distribution matrix, and generating a fault characteristic spectrum; and switching is triggered according to a risk level evaluation result, and the optimal standby node is selected for sequential switching by using a deep strategy learning algorithm, so that rapid and accurate fault detection, diagnosis and intelligent switching can be realized, and the reliability and performance of the system are improved.

The invention discloses a data exception traceability tracking and positioning method, system and device and a storage medium. Relates to the technical field of power system data management. At present, the problems of insufficient data anomaly detection accuracy, low traceability positioning efficiency, incomplete knowledge association and the like in analysis and application links of power marketing data are solved. The method comprises the following steps: carrying out preprocessing and feature extraction on power marketing data, constructing a data association relationship network through a clustering analysis and statistical method, analyzing the difference of data features, analyzing the propagation path of an abnormal point identified in a data stream by adopting a path reasoning technology, and constructing a data abnormity tracing positioning model. A semi-supervised learning method is adopted to improve the accuracy of the model, result optimization and hyper-parameter adjustment are carried out, and multi-condition factors influencing business data information are introduced into the model as hyper-parameters. According to the technical scheme, the anomaly detection and traceability positioning capability of the power marketing data is greatly improved.

The invention is suitable for the technical field of video acquisition, and particularly relates to a video stream acquisition method based on deep learning, and the method comprises the steps: carrying out the video acquisition through a self-adaptive strategy, and obtaining real-time collected video data; performing parameter adjustment through edge calculation to obtain preprocessed video data; performing key frame selection on the preprocessed video data to obtain key frame data; performing inter-frame difference compression on the key frame data through the optimized variational auto-encoder model, and outputting video frame compression data; and when the original video needs to be recovered, analyzing the video compression data, reconstructing through the pre-trained neural network model to obtain a reconstructed video sequence, and generating a complete video according to the reconstructed video sequence. According to the method, the non-key frame is directly generated in the decoding process by using the deep learning model, so that the compression ratio can be remarkably improved, and the video playing continuity and the image quality, especially the performance in high-dynamic and complex scenes, can be improved.

The invention discloses a coal mining intelligent scheduling method based on an artificial intelligence platform. The method comprises the following steps: S1, constructing a data set; s2, preprocessing the data set; s3, analyzing the preprocessed data set to generate an analysis result; s4, based on an analysis result, constructing and training a prediction model by adopting a variational auto-encoder and a graph neural network; s5, multi-agent deep reinforcement learning is adopted, a hybrid expert model and a distributed depth deterministic strategy gradient are combined, and an optimization model is constructed and trained; s6, predicting the fault of the equipment by using the prediction model, generating a prediction result, performing early warning in advance, and taking measures; s7, dynamically adjusting a production plan by using the optimization model according to the prediction result and the real-time data, performing resource scheduling, and optimizing resource allocation; and S8, establishing a feedback mechanism, collecting feedback information, and optimizing and improving a resource scheduling strategy. According to the invention, efficient intelligent scheduling is realized by using big data analysis, deep learning and a multi-agent system.

The invention relates to the technical field of coordinate conversion, in particular to a CAD and BIM model coordinate automatic conversion method and system based on coordinate mapping, and the method comprises the following steps: extracting CAD building two-dimensional coordinates input by reference to a building drawing, constructing an initial two-dimensional triangular mesh, extracting key geometric feature points of a building, and combining the floor height parameter and corner information of the building to obtain a CAD and BIM model coordinate automatic conversion model; and mapping the two-dimensional grid to a three-dimensional coordinate space to generate initial building three-dimensional point cloud data. According to the method, a high-quality initial triangular mesh is constructed through a Delaunay triangulation algorithm, coordinate information of a sparse position is accurately captured, a Poisson surface reconstruction technology is combined, global curved surface fitting and smoothing processing are performed on point clouds of a sparse region, and density compensation and precision improvement of coordinate point distribution are realized. According to the optimization mode, the problem of model precision loss is effectively relieved in complex scenes such as building corners, special-shaped spaces and floor height changes.

The invention provides a real-time data monitoring system in a knitted fabric production process, and relates to the technical field of data monitoring, and the system comprises a real-time monitoring module which is used for directly collecting actual production data from equipment and the environment of a production workshop through various sensors; the data analysis module is used for comparing the initial prediction value of the prediction model with actual production data to output final prediction data and generating optimization parameters according to the final prediction data; the prediction production module is used for establishing a virtual production model according to the actual production data, and the prediction model predicts an initial prediction value of the output variable based on the input data; and the dynamic adjustment module is used for generating a dynamic deviation threshold value according to the deviation trend data and generating a corresponding adjustment strategy according to whether the deviation value falls into the deviation threshold value or not. The precision and stability of the production process are improved, and the influence of human factors is reduced.

The invention discloses an event and RGB image fused SNN target tracking method and system, and the method comprises the steps: converting an event flow from an event camera into an event frame image through a representation method based on time stacking, and enabling the event frame image to be aligned with an RGB frame image from an RGB camera in time and space; performing target detection on the event frame image # imgabs0 # and the RGB frame image # imgabs1 # after alignment by using a spiking neural network SNN, wherein the spiking neural network SNN comprises a feature extraction module, a feature fusion module and a detection head; and learning the similarity of the targets by using the anchor frame extracted by the spiking neural network SNN and the fusion feature map # imgabs2 # to perform multi-target tracking. The invention aims to realize fusion of the fusion event and the RGB image so as to ensure high precision and high efficiency of multi-target tracking, and keep stable detection performance and tracking performance in static and high-motion scenes.

The invention relates to the technical field of knitted fabric defect detection, in particular to a knitted fabric defect detection method and system based on machine vision, and the method comprises the steps: firstly obtaining a first image set through collecting the production process data of a scarf; constructing a scarf multi-scale edge detection model to identify the first image set, dynamically adjusting a threshold according to the actual brightness and gradient distribution of the image, and generating a first edge feature set; gradient information fusion is carried out on a background dynamic image and the first edge feature set, a scarf region segmentation model is constructed, a scarf production real-time image is segmented, and a time sequence segmentation image set and scarf flaw edge data are obtained; performing real-time analysis on the scarf flaw edge data and the time sequence segmentation image set to obtain scarf time sequence edge flaw track data; and constructing a scarf flaw edge complexity evaluation model to analyze the scarf time sequence edge flaw trajectory data, obtaining the fractal dimension of the scarf flaw edge, and generating the complexity of the scarf flaw edge.

The invention discloses an intelligent decision analysis system and method based on multi-source data, and belongs to the technical field of artificial intelligence. According to the invention, multi-source data related to bus operation is acquired, real-time acquisition and uploading of bus operation data are realized, data preprocessing is carried out, and the multi-source data are integrated; feature engineering is carried out, and a passenger flow prediction model is established; based on a passenger flow prediction model, obtaining predicted passenger flows in different time periods of the day, and predicting a driver state; establishing a bus scheduling model by using a deep learning model, and inputting predicted passenger flow, predicted driver state, historical data of bus operation and urban traffic condition data to obtain a bus scheduling scheme on that day; when the dispatched buses in a certain period of time of the bus route are not enough, a route from a bus station closest to a starting station of the bus route to the starting station is obtained, and idle buses are dispatched; and if no idle bus exists in the bus station, the buses of the rescue bus route allowed to be dispatched are dispatched.

The invention relates to the technical field of data processing, and discloses a salinity drift correction method and system for buoy observation data. The method comprises the steps that historical buoy observation data and current environment parameters are collected and preprocessed, and a multi-source heterogeneous data set is obtained; performing multi-dimensional environmental factor analysis on the multi-source heterogeneous data set to obtain a salinity influence factor weight; drifting detection and quantification are carried out on the buoy salinity data through the salinity influence factor weight, and an accumulated deviation integral value is obtained; performing multi-scale correction calculation on the accumulated deviation integral value to obtain preliminary correction data; performing dynamic space-time weight optimization on the preliminary correction data to obtain optimized correction data; and performing real-time performance evaluation and automatic threshold adjustment on the optimized correction data to obtain target correction data and a parameter updating strategy. According to the invention, the accuracy of salinity drift correction for buoy observation data is improved.

The invention discloses a transmission network fault positioning method, system and device and a storage medium, and relates to the field of communication, and the method comprises the steps: firstly, collecting network data to construct a multi-granularity network topology model, then obtaining original alarm data, analyzing the topology association relationship between alarms, and obtaining a multi-granularity network topology model; and mining horizontal peer association and vertical upstream and downstream association among the alarm objects, and identifying multi-alarm data of each alarm object. Thirdly, constructing an associated alarm directed graph based on the multi-alarm data, and performing associated alarm compression on the associated alarm directed graph to obtain a compressed alarm set; and finally, constructing an alarm propagation graph based on the compressed alarm set and the multi-granularity network topology model, querying the alarm propagation graph through a reverse hierarchical sorting algorithm, and determining a root alarm. According to the method, through innovative methods such as topological modeling, association mining, graph compression and reverse sequencing, alarm data and a topological structure are closely combined, an alarm propagation rule is analyzed, and fault root causes are diagnosed.

The invention discloses an efficient flight control system of a low-altitude manned unmanned aerial vehicle based on tilting composite wings, and relates to the technical field of unmanned aerial vehicle control systems. The low-altitude manned unmanned aerial vehicle efficient flight control system based on the tilting composite wings comprises an initialization and pre-check module, a vertical take-off and landing control module, a transition flight control module, an obstacle avoidance and path planning module and a landing control module. Through integration of vertical take-off and landing, smooth transition to cruise, intelligent obstacle avoidance, path planning and other modules, autonomous and efficient flight in a complex environment is realized, and the application scene of the unmanned aerial vehicle is greatly widened. Meanwhile, the accurate flight control and data post-processing analysis capability not only guarantees the safety and comfort of passengers, but also promotes the continuous optimization of the system performance through the real-time performance evaluation and feedback mechanism. In addition, the aerial refueling / charging technology is supported, the cruising ability is prolonged, and the task execution continuity and flexibility are enhanced.

The invention discloses a data monitoring and processing method based on dam stability, and relates to the technical field of dam monitoring, and the method comprises the following steps: arranging sensors: arranging high-precision sensors, such as a strain gauge, a displacement meter and the like, at key positions according to the structural characteristics of a dam; comprehensively analyzing the structure of the dam; according to the method, the high-precision sensors are arranged at key positions of the dam, real-time acquisition of dam stability parameters including strain, displacement, pressure, temperature and the like is achieved, the real-time data acquisition mechanism remarkably improves the monitoring frequency and continuity, the real-time performance and the accuracy of data are ensured, and compared with traditional regular manual inspection, the real-time monitoring accuracy is improved. According to the invention, more accurate and continuous monitoring data can be provided, so that the stability state of the dam can be evaluated more effectively.

The invention discloses a VR-based unmanned aerial vehicle inspection method and system, a storage medium and a program product, and the method comprises the steps: calculating a low-polygon terrain model, and carrying out the subdivision smoothing processing, and obtaining a three-dimensional terrain image; matching and registering the panoramic image sequence and the three-dimensional topographic image to obtain a point cloud map; generating a virtual mining area environment map; calculating image quality parameters; when the image quality parameter is lower than a preset threshold value, generating a semi-real and semi-virtual fusion display picture; transmitting the semi-real and semi-virtual fusion display picture to a VR helmet for display, and synchronizing the attitude information of the unmanned aerial vehicle to the VR helmet; when it is determined that a fatigue feature appears according to the brain wave data, the unmanned aerial vehicle is controlled to be switched to a preset autonomous flight mode; constructing a virtual anti-collision boundary; and obtaining an optimized flight path according to the virtual anti-collision boundary, and controlling the unmanned aerial vehicle to inspect according to the optimized flight path. The continuity and reliability of the inspection task are improved, and the safety risk of the inspection task is reduced.

The invention discloses a seabed sediment classification method and product based on seabed surface sediment acoustic characteristics, and relates to the field of seabed acoustic detection. The method comprises the following steps: firstly, acquiring actual measurement data of seabed sound reflection of a measurement area in a marine environment, and performing inversion to obtain seabed surface sediment characteristic parameters; by analyzing the uncertainty and correlation of the characteristic parameters of the seabed surface sediments, acoustic characteristic parameters of the seabed surface sediments are screened out; constructing a deep neural network model based on the acoustic characteristic parameters of the seabed surface sediments, wherein the deep neural network model comprises a seabed acoustic-physical information neural network model and a sediment attribute prediction neural network model; expanding the training sample set according to the actual measurement data of seabed sound reflection and the acoustic characteristic parameters of the seabed surface sediments, and training and testing the deep neural network model; the trained seabed sediment classification model is adopted to perform seabed sediment classification of the measurement area, fine classification of the seabed sediment can be realized, and classification continuity and classification precision are improved.

The invention discloses a fault detection method and system for a mine unmanned vehicle, and relates to the field of positioning navigation, and the method comprises the steps: collecting the data of each sensor; according to the laser radar data, a slam point cloud map is constructed through a cartographic algorithm; according to the IMU data and the vehicle speed data, performing fusion through a Kalman filtering algorithm to obtain odometer data; the preprocessed laser radar data, IMU data, vehicle speed data, camera data and odometer data are converted to a slam point cloud map coordinate system through coordinate conversion, fusion is carried out through a Kalman filtering algorithm, and state estimation of the unmanned vehicle is obtained; taking the state estimation of the unmanned vehicle as a predicted value, comparing the predicted value with laser radar data, IMU data, vehicle speed data and camera data, and calculating an observation residual error; and performing unmanned vehicle fault detection according to the observation residual error. Aiming at the low positioning precision of the mine unmanned vehicle in a complex environment in the prior art, the positioning precision is improved through multi-sensor fusion, fault-tolerant switching and the like.

The invention relates to the technical field of financial services, in particular to a testing method and system based on financial environment stability. The method comprises the following steps: carrying out real-time state monitoring on a financial transaction system, and generating system state basic data; constructing a virtual test environment according to the system state basic data, and performing test scene adaptive configuration to generate test environment parameter data; performing multi-dimensional pressure test analysis on the test environment parameter data, calculating a system stability index, and generating stability reference data; performing dynamic load gradient processing according to the stability reference data to obtain system pressure gradient data; and performing risk level mapping on the system pressure gradient data to generate risk assessment mapping data. According to the invention, the virtual test environment is constructed and self-adaptive configuration is carried out, so that a real transaction scene can be simulated, the correlation and effectiveness of the test are improved, and the stable operation of the system under different pressure conditions is ensured.

The invention provides a brain parenchyma MRI image-based infarction change prediction method and system, and relates to the technical field of image recognition, and the method comprises the steps: obtaining a brain parenchyma MRI image, constructing a multi-modal image set, carrying out the screening according to a screening standard, carrying out the artifact recognition, calculating a quality score, carrying out the preprocessing, obtaining standard image data, carrying out the feature extraction, and carrying out the prediction of the infarction change of the brain parenchyma MRI image. Obtaining a brain region segmentation mask; performing feature extraction to obtain multi-modal feature data, constructing a topological relation among different brain regions, extracting high-order semantic features, obtaining brain region feature data, and performing selection to obtain high-quality feature data; constructing a time sequence characteristic matrix, distributing weights, generating residual output, performing prediction through a full-connection neural network, generating an infarction change prediction result, generating a prediction prognosis result, and generating a comprehensive prediction report.

The invention provides a deep and large foundation pit monitoring method and system, a program product and a storage medium, and relates to the technical field of foundation pit detection. The GPS positioning device collects foundation pit slope crest displacement data, the monitoring sensor network collects deep horizontal displacement data, the two data types complement each other, comprehensive information of surface and internal deformation of the foundation pit is provided, and the generated deep horizontal displacement curve chart visually displays the displacement conditions of the supporting structure at all depths. A potential weak area can be identified, a foundation pit excavation response data acquisition device is arranged at the maximum stress part, response data of a key position is obtained, and the quantitative index of overall deformation is provided by measuring and monitoring the position change of a central point of a sensor network and calculating the deformation amount of the foundation pit. According to the multi-dimensional and multi-type data acquisition and integration method, the description comprehensiveness of the foundation pit environment is improved, potential risks can be judged more accurately, and limitation caused by a single data type is overcome.

The invention discloses a robot indoor and outdoor navigation switching method and system based on a large language model, and the method comprises the steps: obtaining multi-dimensional data of an environment where a robot is located through employing a multi-mode sensor, and the multi-dimensional data comprise a visual image, a laser radar point cloud, a GPS signal, and IMU data; the preprocessed image is input into a visual encoder, and key visual features are extracted. And the big language model performs reasoning by using the characteristics, and outputs a scene judgment result in combination with an internal scene knowledge base. In order to enhance the accuracy of scene judgment, the change trend of multi-frame image features is analyzed based on a time sliding window. An extended Kalman filter is adopted in an indoor and outdoor transition area to carry out weighted fusion on multi-mode sensor data, a dual-mode parallel operation mechanism is adopted during navigation mode switching, and SLAM and GPS navigation modes are activated at the same time in the indoor and outdoor transition area. And by dynamically adjusting the weights of the SLAM and the GPS, gradually transitioning to a target navigation mode at a data fusion level, so that smooth switching is realized.

The embodiment of the invention discloses a bridge crack width monitoring method. The method comprises the following steps: acquiring a continuous video image of a bridge surface; generating a pixel-level segmentation mask image of the crack by using the target detection model; when the target detection model is pre-trained, optical flow features between adjacent frames in the first data set are extracted through an optical flow calculation algorithm, and deep fusion is performed on the optical flow features and the image features; contour pixel points of the crack are extracted based on the crack segmentation mask pattern, and the width change information and the width change rate of the crack are obtained; according to the width change information and the width change rate, performing trend prediction on the crack width change by using a time sequence prediction model to obtain a prediction result of the crack width change; in the training process of the time sequence prediction model, a basic model and a complexity capture module are designed, and the weight of a main model is optimized by using a weighted fusion strategy. According to the invention, efficient and accurate crack width monitoring is realized through crack identification and prediction.

The invention discloses a dynamic and static target continuous positioning and tracking method and system based on millimeter waves. The method comprises the steps that dynamic point cloud data and static point cloud data reflected by a target are acquired through a millimeter wave radar sensor, the dynamic point cloud data reflect target motion state information, and the static point cloud data reflect vital sign information of the target in a static state; fusing the dynamic point cloud data and the static point cloud data to obtain fused point cloud data, and clustering the fused point cloud data by using a density-based clustering algorithm by taking a reflection point for distinguishing a target from an environment as a target to obtain a clustering result; and on the basis of the clustering result, executing continuous positioning and tracking on the plurality of targets to obtain a tracking result. The method can effectively extract the dynamic and static information of the target, achieves the continuous positioning and precise tracking of multiple targets, and is higher in practicality and application value.

The invention relates to the technical field of data processing, and further relates to a soil environment damage and risk assessment big data analysis system. The system comprises a data acquisition module, a data processing and fusion module, a spatial correlation analysis module and a risk assessment module. The data acquisition module is used for setting a plurality of sampling points in a target soil area and sending the acquired data to the cloud big database; the data processing and fusion module is used for extracting sparse features of the preprocessed data; the spatial correlation analysis module is used for obtaining spatial correlation indexes; the risk assessment module is used for constructing a risk prediction model; and obtaining a current soil environment risk value corresponding to each sampling point. According to the method, the comprehensiveness, the real-time performance and the precision of evaluation are remarkably improved, and meanwhile, the method has the functions of dynamic response, multi-dimensional data integration and automatic early warning.

The invention relates to the technical field of building materials, and particularly discloses high-strength lightweight concrete containing hollow glass beads and a preparation method of the high-strength lightweight concrete. The high-strength lightweight concrete containing the hollow glass beads is prepared from cement, fly ash, carbon fiber grafted hollow glass beads, lightweight sand, deionized water, polypropylene fiber grafted rubber particles and a water reducing agent according to the mass ratio of (58-75): (25-38): (45-65): (20-40): (42-55): (35-55): (5-12) through mixing, mold filling, vibration molding, covering and demolding. The addition amount of the hollow glass beads is strictly controlled, the influence on the overall strength of the concrete is avoided, the density and weight of the concrete are reduced due to the addition of the light sand and the hollow glass beads, the compactness and integrity of the concrete are improved due to the synergistic effect of the two added fibers, and the raw materials have excellent cohesiveness; the concrete can effectively absorb and disperse stress, and has the advantages of excellent strength, light weight, good durability and excellent comprehensive performance.

The invention discloses a heat exchanger descaling and anti-corrosion system and device based on intelligent control, and relates to the technical field of system regulation, the heat exchanger descaling and anti-corrosion system based on intelligent control monitors key parameters of a heat exchanger through a real-time monitoring module so as to effectively prevent faults and performance reduction; the anomaly identification module detects anomaly based on key parameters, constructs a model through historical anomaly image data, judges real-time anomaly types and scaling thickness, and realizes rapid fault identification; the regulation and control module dynamically regulates the pulse width and the period of the power shock wave according to the fluid viscosity data so as to carry out automatic descaling and corrosion prevention on the heat exchanger; the corrosion prediction module predicts the corrosion rate and time based on the data of the corrosion prevention module, provides reference for maintenance, optimizes equipment management, reduces the maintenance cost, and guarantees the continuity and safety of the system. The system integrates real-time monitoring, intelligent identification, automatic corrosion prevention and descaling and prediction regulation and control, and the operation efficiency and reliability of the device are improved.

The invention relates to a low-loss corner-lifting and movement tearing cooperative type film tearing process of a screen display glass protective film. The process comprises the following steps: S1, tearing a film on an A surface; s2, transferring the screen display glass; and S3, tearing the film on the surface B. On one hand, in single-face film tearing, based on cooperation of the film tearing clamping jaw, the rolling wheel and the air nozzle, angle lifting is completed while the film tearing angle adhesive force is kept larger than the protective film tearing force, the damage and fracture rate of an adhesive tape and a corner is reduced, the high angle lifting success rate is achieved, meanwhile, through movement cooperation of the screen display glass and the film tearing clamping jaw, the film tearing difficulty is low, control is easy, and the film tearing efficiency is improved. The integrity and continuity of film tearing are high; on the other hand, overturning of the screen display glass is not needed in double-sided film tearing, transferring of the screen display glass is achieved in the protective film tearing process, the accidental friction rate in the glass overturning process is reduced, the structure is optimized, the layout space is greatly reduced, the equipment is miniaturized, and the double-sided film tearing efficiency of the equipment for treating the screen display glass is also improved.

The invention discloses a distributed unmanned aerial vehicle detection system, and belongs to the technical field of unmanned aerial vehicle detection planning, and the system specifically comprises the steps: obtaining all to-be-allocated tasks and the number of unmanned aerial vehicles of an unmanned aerial vehicle base station; obtaining importance grade scores of the to-be-allocated tasks, sorting the to-be-allocated tasks according to the importance grade scores to obtain a first sequence, and formulating planning routes of all unmanned aerial vehicles based on the first sequence; any unmanned aerial vehicle is selected and flies according to the corresponding planned route, the consumed electric quantity and the remaining electric quantity of the unmanned aerial vehicle are obtained in real time, whether the electric quantity of the unmanned aerial vehicle meets the task allocation requirement or not is judged, and if yes, the unmanned aerial vehicle is controlled to continue to fly along the current planned route; if not, a target unmanned aerial vehicle is selected from all the remaining unmanned aerial vehicles, the planned route of the target unmanned aerial vehicle is corrected, and a final planned route is obtained, and efficient unmanned aerial vehicle management and route planning are achieved.

The invention relates to a protective film uncovering process based on corner lifting and rolling cooperation type screen display glass. The protective film uncovering process comprises the following steps: S1, corner lifting; s2, tearing off; and S3, collecting the film. On one hand, through cooperation of the film tearing clamping jaw and the rolling wheel, step-by-step peeling with the rolling position and the peeling position aligned is kept, meanwhile, the angle between the connecting section and the screen display glass is gradually increased on the basis that the connecting section is turned inwards under the condition that the adhesion section is not stressed, the success rate of forming an inwards-turned peeling angle is greatly increased, and the production efficiency is improved. The probability that the film-tearing adhesive tape is separated from the protective film or the corner of the protective film is torn is reduced; on the other hand, the complete tearing rate of the protective film is increased based on the relative movement of the glass and the film tearing clamping jaw, the tearing risk of the protective film is reduced, more labor-saving and faster diagonal tearing is achieved, meanwhile, based on pulling formed by the film collecting clamping jaw and the film tearing clamping jaw and airflow blowing cooperation formed by the film collecting clamping jaw and the film tearing clamping jaw, the completeness of film collecting is improved, and the production efficiency is improved. And the problem that the film tearing adhesive tape is adhered to the film tearing clamping jaw is solved, so that the continuity of peeling off the protective film is improved.

The invention discloses a task processing method and system applying robot process automation, and relates to the technical field of robot process automation, and the method comprises the steps: S1, carrying out the priority evaluation of tasks in a task queue based on a preset rule, S2, sorting the evaluation results to generate a priority task queue, s3, allocating execution resources to the tasks according to the priority task queues; S4, monitoring task execution states and dynamically adjusting the priorities of the task queues; according to the task processing method and system applying robot process automation, task scheduling requirements in complex and changeable business scenes are met, efficient operation of the system in a dynamic environment is ensured, key tasks are ensured to be completed on time, continuity and stability of a business process are improved, business risks are reduced, and the task processing efficiency is improved. The problem that how to regulate and control the priority of the task queue to solve the service influence caused by the fact that key tasks are preempted by low-priority tasks is solved.

The embodiment of the invention provides a GIS (Geographic Information System)-based metropolitan area safety video monitoring method and system. The method is applied to the technical field of image processing and comprises the following steps: acquiring a video picture of a coverage area in real time through a camera; performing real-time analysis on the video picture by using a target detection algorithm, and identifying a target in the video picture; converting the position of the target in the video picture into an actual geographic coordinate based on coordinate system conversion; continuously tracking the target in different camera coverage areas through the geographic coordinates; based on perspective transformation, converting the video image into an aerial view angle, and splicing aerial views to form a panoramic view; and visualizing the geographic coordinates of the target in the aerial view. In this way, the efficiency and accuracy of the city monitoring system can be improved, and therefore the requirement for city safety monitoring can be better met.