767 results about "Cloud processing" patented technology

A system and method for adaptive edge-cloud data processing dynamically distributes computational tasks between edge devices and cloud infrastructure in response to changing conditions. The system continuously monitors resource availability, network parameters, and workload characteristics while predicting future conditions using hierarchical forecasting models. A multi-objective optimization approach determines optimal task distribution, balancing processing latency, energy consumption, bandwidth utilization, and result quality. The system implements a partitionable processing pipeline that enables seamless task migration through state synchronization protocols and checkpoint mechanisms. During migration, the system preserves processing continuity by establishing dependencies, creating execution checkpoints, and verifying successful state transfer. Performance metrics may be continuously collected and analyzed to improve future decision-making. The system maintains operational resilience during connectivity disruptions through local decision-making capabilities and eventual consistency protocols, making it suitable for diverse applications including industrial IoT, connected vehicles, healthcare wearables, and smart city infrastructure.

The invention provides an intelligent traffic data processing method and system based on edge computing, and the method comprises the steps: collecting multi-source traffic data through an edge computing node, carrying out the preprocessing of the data, and obtaining the preprocessing data; and performing space-time alignment and data association processing on the preprocessed data to generate an associated data set, and completing feature extraction and preliminary analysis at an edge computing node to obtain a local analysis result. The local analysis result is transmitted to the cloud server, deep data mining and mode recognition are carried out, and a global analysis result is generated. And based on the global analysis result, generating a local traffic control strategy at the edge computing node, and generating a global traffic optimization scheme at the cloud server. The local traffic control strategy is issued to traffic facilities for execution, and the global optimization scheme is distributed to each edge computing node to realize cooperative control. The advantages of edge computing and cloud processing are combined, and the real-time performance of traffic data processing and the overall optimization capability of an intelligent traffic system are effectively improved.

The invention discloses a computing power resource collaborative scheduling method and device in a multi-cloud scene and a storage medium, and belongs to the field of computing power resource management. Constructing a virtual computing power resource pool based on the multi-cloud cluster; receiving a to-be-processed task, and obtaining a target server identifier and target computing power resource state information; performing task processing capability evaluation on the target cloud processing server, and judging whether the computing power resource requirement of the to-be-processed task is met or not; when the computing power resource requirement of the to-be-processed task is not met, dividing the to-be-processed task into a plurality of sub-tasks; and performing cross-cloud cooperative distribution on the plurality of sub-tasks, so that the loads of the plurality of cloud processing servers tend to be balanced, generating a target scheduling scheme, and executing cross-cloud cooperative processing of the plurality of sub-tasks. The technical problems of low computing power resource scheduling efficiency and unbalanced load in the multi-cloud environment in the prior art are solved, and the technical effects of improving the computing power resource utilization efficiency in the multi-cloud environment, realizing cross-cloud cooperative processing and optimizing load balancing are achieved.

The invention relates to the technical field of data analysis, in particular to a new energy power generation equipment health management platform based on a large model, which comprises a data acquisition and perception layer, an edge computing layer, a cloud processing layer and an application service layer, compared with the prior art that static historical data or single equipment parameters are adopted as a health detection reference, and the influence of environment dynamic change and equipment aging cannot be reflected, the scheme adopts a multi-dimensional simulation modeling technology, equipment parameters, weather parameters and other real-time working condition data are integrated to construct a digital twinborn model, and the digital twinborn model can be used for real-time health detection. Dynamic health reference values including generating capacity, instantaneous current / voltage, equipment temperature and the like are generated by simulating equipment operation states (such as photovoltaic efficiency attenuation at an extreme temperature and aerodynamic load of a fan in a salt mist environment) in different scenes. The method can accurately capture the interaction effect of the environment and the equipment, enables the health detection threshold to be dynamically adjusted along with the working condition, and improves the anomaly recognition accuracy by more than 35% compared with a traditional method.

The invention relates to the technical field of agricultural product storage management, in particular to an agricultural product storage intelligent management system based on big data, which comprises a multi-source data acquisition layer, an edge processing layer, a cloud processing layer and an execution control layer, compared with the prior art which only depends on manual recording of the picking date or simple visual sampling inspection for judging the freshness, the method has the advantages that the freshness key indexes such as the skin color degree and the texture structure of the agricultural product are synchronously and quantitatively extracted through the deep learning model in the warehousing link of the agricultural product; performing dual cross validation with the registration time to eliminate abnormal samples; the verified freshness reference data and parameters such as temperature, humidity, gas concentration and the like monitored in real time in a corresponding storage microenvironment are fused and input, and a self-adaptive shelf life prediction model based on a dynamic environmental stress factor is constructed; therefore, continuous and accurate quantification and early warning of decay processes of different types of agricultural products under various storage conditions are realized.

The invention provides an interference self-adjusting carrier communication system based on a complex environment, and relates to the technical field of communication data adjustment, and the system architecture comprises a quantum-assisted interference detection and protection layer, a multi-domain fused dynamic anti-interference core layer, and an edge computing driven real-time anti-interference optimization layer. The quantum-assisted interference detection and protection layer can accurately capture quantum state information of interference signals and quickly determine key parameters of the interference signals through the quantum sensor array and the quantum signal processing unit, and the multi-domain fused dynamic anti-interference core layer integrates space-time-frequency-energy multi-domain resources. An interference rule is predicted in advance in a time domain and communication is reasonably scheduled, an interference source and a terminal position are accurately positioned in a space domain to optimize an antenna beam, a frequency spectrum is sensed in real time in a frequency domain and parameters are optimized, and energy consumption is intelligently regulated and controlled in an energy domain. The real-time anti-interference optimization layer driven by edge computing realizes quick analysis and processing of local interference by means of distributed edge computing nodes, and data transmission delay and cloud processing pressure are effectively reduced.

The invention discloses a vehicle-mounted streaming language dialogue interaction system and method based on a large language model, and the system comprises the large language model disposed at a vehicle-mounted end, and the large language model is used for carrying out the local processing and cloud processing of information inputted by a user, so as to obtain a processing result. Wherein the large language model is connected with the edge calculation and privacy protection module, the edge calculation and privacy protection module is used for carrying out edge calculation on a processing task in local equipment and distributing a data processing reasoning process to local and cloud service for processing, and the proportion of a local processing part is larger than that of the cloud service. The advantages of the LLMs are fully utilized, the special requirements of the vehicle-mounted environment can be met, and the method has important significance for improving the intelligent level of vehicle-mounted man-machine interaction and the user experience.

The invention belongs to the technical field of data quality management, and particularly discloses a power transmission and transformation equipment manufacturing full-process data checking method and system, a terminal and a medium, and the method comprises the steps: collecting original business data of each stage of power transmission and transformation equipment manufacturing, and carrying out the standardization, format normalization and preliminary abnormity elimination; uploading the processed data to a cloud processing module, and executing semantic fusion and field mapping processing; calling a corresponding checking rule set according to the manufacturing state, and executing field-level and record-level quality checking on the structured data; generating an abnormal identifier for the unqualified data, feeding back the abnormal identifier to a client interface, and storing the qualified data to the block chain module to realize credible evidence storage; and finally, generating a data quality checking report and performing structured visual display. According to the method, the problems that existing manufacturing data semantics are not uniform, rules cannot be dynamically adapted, abnormity is difficult to identify with high confidence, and data evidence storage is missing are solved.

The invention provides a high-precision intelligent welding system adapting to complex working conditions, and relates to the technical field of laser welding. The high-precision intelligent welding system adapting to the complex working conditions comprises the steps of S1, based on a high-resolution visual sensor and a laser scanner, S2, based on preprocessed point cloud data, S3, based on a workpiece three-dimensional model, S4, based on an optimized welding path and a material attribute database, S5, based on a dynamic welding parameter set, S6, based on a real-time welding track, and S7, based on multi-sensor monitoring data. S8, based on a defect detection report; and S9, based on a repaired welding result and historical welding data. According to the invention, the geometric data of the workpiece are collected through the high-resolution visual sensor and the laser scanner, the filtering algorithm is applied to remove noise to generate high-quality point cloud data, and a subsequent processing foundation is laid; workpiece features are identified by using a point cloud processing algorithm and a machine learning method based on the point cloud data, and an accurate digital model is generated through a three-dimensional reconstruction algorithm.

The invention discloses a shield muck volume estimation method and system based on image processing, and particularly relates to the technical field of tunnel engineering monitoring, and the method comprises the steps: obtaining muck RGB and depth images through an image collection device, generating high-precision three-dimensional point cloud data through the combination of laser scanning and a multispectral technology, and calculating the muck volume through a slicing method. And fusing the mass flow and water content data of the belt weigher, dynamically generating a calibration factor, and correcting a volume estimation result in real time. The system comprises an image acquisition module, a point cloud processing module, a volume calculation module and a dynamic calibration module, has the characteristics of automation, high precision, real-time feedback and the like, effectively solves the problems of large error and slow response of a traditional manual metering and single weighing mode, and is suitable for continuous and accurate monitoring of the volume of muck in the shield construction process.

The invention provides an electric energy load real-time data acquisition and analysis method based on a low-cost scheme, and the method comprises the following steps: constructing a distributed data acquisition network, employing a combined hardware architecture of a current / voltage sensor and a low-power-consumption MCU, and achieving the data acquisition optimization through a dynamic sampling rate adjustment mechanism, when the load fluctuation exceeds a set threshold value, the sampling rate is automatically increased, when the load is stable, the sampling rate is reduced to a calibration sampling rate, and a Kalman filtering algorithm is adopted to carry out real-time data preprocessing; a three-level data transmission system is established, the three-level data transmission system comprises an edge acquisition node layer, a convergence gateway layer and a cloud processing layer, an optimized AODV routing protocol is adopted among nodes to construct a star-shaped and net-shaped hybrid network topology, and an optimal transmission path is dynamically selected according to the signal strength and the network congestion condition; a differentiated QoS transmission strategy is designed, and data is divided into three priorities of fault alarm data, real-time monitoring data and historical data.

The technology disclosed teaches distributed networks and methods for cloud processing for global navigation satellite system (GNSS) interference data from a plurality of GNSS receivers to alert aircraft personnel to GNSS spoofing of aircraft guidance systems. The technology disclosed includes receiving at a cloud-based server, GNSS interference data from the plurality of GNSS receivers, analyzing the GNSS interference data upon receipt, identifying an interference event from the analyzed GNSS interference data, and in response to an identified interference event, the cloud-based server providing an alert to EFB tablet devices onboard the aircraft thereby causing notification of aircraft personnel of a potential spoofing event.

The invention belongs to the technical field of petrochemical engineering pipeline monitoring, and discloses an intelligent detection and repair system and method for petrochemical engineering pipeline leakage, and the method comprises the following modules: a data collection module which comprehensively monitors the leakage condition and environmental parameters in a pipeline by deploying a high-precision and low-power-consumption multi-parameter sensor network; the data transmission module is responsible for safely and reliably transmitting the collected data to the cloud processing center; the data processing and analyzing module is used for cleaning, processing and analyzing the collected data and extracting feature information; the leakage identification module is used for identifying and judging leakage events based on various algorithms; the leakage positioning module is used for accurately determining a leakage position; and the leakage evaluation module is used for evaluating the leakage degree, type and potential risk.

The invention discloses a wearable immersive multi-modal feedback man-machine interaction system and method, and relates to the technical field of man-machine interaction, the system comprises a sensing module, a physiological monitoring and health management module, a processing module, a feedback module and a user interface module, multiple sensing devices are integrated on a flexible material to form a multi-modal sensor; according to the wearable immersive multi-modal feedback man-machine interaction system and method, by arranging the system and integrating various sensing modules, physiological monitoring and health management, processing and recognition modules and rich feedback mechanisms, highly intelligent and personalized man-machine interaction is achieved through the system; the system can accurately capture the intention and behavior of a user, can monitor the health state in real time and provide timely feedback, and guarantees the instantaneity and high efficiency of data processing in combination with edge calculation and cloud processing. And due to an omnibearing and immersive feedback mechanism and a dynamic user interface, the user experience is greatly improved.

A thin film type cargo containment system datum line marking method based on an unmanned aerial vehicle rapidly and accurately completes marking construction work of a thin film type containment system, and theoretical coordinate values of 16 vertexes, theoretical side lengths of 8 longitudinal sides, theoretical coordinates of a datum axis loop line and the theoretical length of a liquid dome in the X direction are obtained according to a design drawing; and actual coordinate values of 16 vertexes, actual side lengths of 8 longitudinal sides, actual coordinates of a reference axis loop line and the actual length of the liquid dome in the X direction are obtained through point cloud processing software, and when the difference value between the actual measured value and the theoretical value of the cargo hold does not exceed the inspection length, all optimal loop lines are selected for spraying. The cargo compartment modeling method based on the unmanned aerial vehicle laser radar surveying and mapping technology is more accurate, scribing work of the thin film compartment can be completed without an installation platform or depending on GTT scribing drawings and software of the GTT scribing drawings, manual construction work and types of scribing equipment tools are greatly reduced, the scribing precision is improved, and the scribing construction period is shortened.

The invention relates to a point cloud static denoising method based on geometric constraint and deep learning, belongs to the technical field of point cloud processing, and solves the problem of poor point cloud denoising effect caused by poor geometric fidelity and weak generalization ability for complex noise in the prior art. Comprising the following steps: after preprocessing a to-be-denoised point cloud, dividing the to-be-denoised point cloud into a plurality of point cloud blocks with overlapping regions; for each point cloud block, performing multi-round iterative optimization on the coordinates of each point cloud by using the trained denoising network, and optimizing the predicted coordinates of each point cloud output by the denoising network in each round of iteration by using normal guided filtering and incremental updating to obtain the coordinates of each point cloud for the next round of iteration; the denoising network sequentially comprises a dynamic graph convolutional network, a first multi-layer perceptron and a second multi-layer perceptron; and after the optimization results of the point cloud blocks are fused, carrying out post-processing filtering to obtain a denoised point cloud. The point cloud denoising precision and the detail retention capability are improved.

The invention provides a deep well roadway surrounding rock area stress optimization control method, and belongs to the technical field of surrounding rock area stress optimization, and the method comprises the following steps: obtaining key data of deep well roadway surrounding rock; identifying a potential stress concentration area; performing grading evaluation on the identified potential stress concentration area, and determining the grade of the surrounding rock according to an evaluation result; according to the grade of the surrounding rock, a corresponding supporting strategy is formulated; the advanced presplitting blasting technology is applied to all grades of surrounding rocks; arranging a plurality of sensor nodes in the stress concentration area; the sensor nodes send monitoring data to the cloud processing center in a wireless mode, and the cloud processing center predicts the stability of surrounding rock and sends a prediction result to the expert terminal; and an expert formulates a new support strategy or adjusts an existing support strategy according to the prediction result of the expert terminal. According to the stress optimization control method for the surrounding rock area of the deep well roadway, the stability of the surrounding rock is improved through comprehensive technical means, and safe and efficient mining of a mine is guaranteed.

The invention relates to the technical field of power system operation or management, in particular to a partial discharge monitoring strategy optimization method and system based on dynamic resource allocation, and the method comprises the steps: constructing a three-stage monitoring system comprising a sensor node, a sink node and a cloud processing center, firstly initializing monitoring parameters, and then obtaining system state information periodically or in a triggering manner, calculating the risk level of each monitoring point in combination with a dynamic risk evaluation model; constructing an efficiency-maximized resource allocation optimization model based on risk levels and resource constraints, solving an optimal scheme by adopting an improved multi-target particle swarm algorithm, and issuing the optimal scheme to each node to adjust monitoring behaviors to form closed-loop optimization; and meanwhile, model parameters are dynamically updated through an online learning mechanism. According to the method, dynamic matching of risks and resources is realized, the monitoring accuracy and the resource utilization rate are improved, the adaptability of the system to the equipment state and the environment change is enhanced, and the method is suitable for partial discharge monitoring scenes of various power equipment.

The invention provides a real-time multi-scene automatic driving system based on AI multi-sensor fusion. The system can realize multi-scene seamless high-precision real-time automatic driving of the automatic driving vehicle. Adopted key technologies comprise a multi-source heterogeneous fusion automatic driving technology based on AI assistance and an AI enabling enhanced decision execution mechanism. Aiming at the defects of the current automatic driving technology in the aspect of vehicle full situation awareness, the technology improves the multi-source fusion efficiency and accuracy through deep learning, directly maps from multi-sensor original data to a driving decision through an end-to-end learning model to improve the reaction speed, and deeply fuses multiple sensors from an information layer. Meanwhile, aiming at the contradiction between big data processing and real-time computing requirements under the current automatic driving vehicle-machine man-cloud interconnection, the edge computing technology, cloud processing and local computing are used for assistance, so that the power consumption is reduced and the real-time requirements are met. Furthermore, through access integration of an intelligent traffic system, deep combination of traffic big data and AI automatic driving is realized, and relative positioning between vehicles is realized through traffic big data interconnection characteristics. The purposes of responding to complex road conditions in real time and reducing the power consumption of a processing algorithm are achieved through information characteristics such as the vehicle machine, the man-machine and the accurate position between the vehicle machine and the specific marker. In a real-time multi-scene automatic driving system based on AI multi-sensor fusion, the generalization ability of the system is continuously enhanced, and the applicability and transportability of the system are concrete so as to adapt to wider driving scenes.

The invention relates to the technical field of point cloud processing, and discloses a point cloud data processing method, system and device for industrial three-dimensional model construction. The method comprises the following steps: carrying out denoising and down-sampling processing on original point cloud data of an industrial part to obtain preprocessed point cloud data, calculating a local geometric descriptor, converting the local geometric descriptor into an industrial feature recurrent plot through a recurrent plot algorithm, inputting the recurrent plot into a convolutional neural network to extract a high-dimensional geometric feature vector, and mapping the feature vectors into B-spline curved surface control parameters through a deep belief network, and performing point cloud reconstruction quality evaluation according to the control parameters to obtain geometric accuracy error data and curved surface reconstruction integrity data. According to the method, the problems that in existing industrial three-dimensional model construction, a point cloud data processing method lacks intelligent feature recognition, the reconstruction precision is insufficient, and manufacturing constraints are not fully considered are solved, and the recognition accuracy of geometric features of complex industrial parts and the precision of three-dimensional model reconstruction are improved.

The invention provides a pig physical appearance scoring method and device, and the method comprises the steps: carrying out the filtering, registration and segmentation of the point cloud data of a to-be-detected pig, and obtaining the boundary segmentation data and body size data of a first part of the to-be-detected pig; processing the image data based on a target detection model to obtain an identification result of the second part of the to-be-detected pig; scoring the first part according to the boundary segmentation data, and scoring the body length and the body height of the to-be-measured pig according to the body size data to obtain a first scoring result; scoring the second part according to the identification result to obtain a second scoring result; and obtaining a target scoring result according to the first scoring result and the second scoring result. According to the method, the cloud processing technology is combined, multi-dimensional physical features with higher characterization capability are extracted based on a multi-head self-attention and deformable network target detection model, and the accuracy of pig physical feature evaluation is improved.

The invention discloses a real-time translation recognition system under a cloud service framework, belongs to the technical field of real-time translation, and solves the problems that an existing translation system is insufficient in real-time performance, poor in scene adaptability, weak in privacy protection, slow in model evolution and the like. The dynamic model management engine obtains adaptive slices from a model slice factory according to scenes, equipment states and network quality and distributes the adaptive slices to edges, and the adaptive slices are distributed to a cloud-side collaborative reasoning system; the cloud-side collaborative reasoning system comprises a cloud-side collaborative reasoning system, a cloud-side collaborative reasoning system, a cloud-side collaborative reasoning system and a cloud-side collaborative reasoning system; the multi-modal perception engine fuses audio, images and dialogue history to generate a structured context vector and improve translation context fitting degree, the cloud edge cooperation engine takes an edge model as a core, processes different complexity tasks in combination with a cloud end, and constructs a data closed loop by incremental learning and a federation engine to realize model optimization and privacy protection; according to the system, the real-time performance, accuracy and safety are improved through cloud edge collaboration, dynamic adaptation and continuous learning, and the system is suitable for multi-scene real-time translation.

The invention discloses an AI-driven unattended water environment quality monitoring system, and relates to the technical field of water environment monitoring, and the system comprises a data collection module, a cloud processing module, an edge calculation module, an intelligent analysis and early warning module, and a system management module. Through comprehensive and real-time intelligent monitoring of a water body, monitoring of water quality physical indexes is concerned, aquatic organism behavior monitoring is introduced, so that the health condition of the water body can be reflected more comprehensively, and meanwhile, through the data preprocessing and local model fine tuning functions of the edge calculation module, the water quality monitoring accuracy is improved. The intelligent analysis and early warning module can perform correlation analysis, set an early warning threshold value and generate early warning information based on the AI model after cloud side end collaborative optimization, and provides powerful decision support for water quality management.

The invention discloses a high-precision spectral remote sensing atmospheric correction and cloud removal processing method fusing multiple time phases, atmospheric parameters and ground reflectivity, and relates to the technical field of computer vision, deep learning image processing and remote sensing. Comprising the following steps: acquiring an atmosphere top reflectance and surface reflectance model of a Sentinel-2 satellite remote sensing image in a Guilin region, and constructing a data set by atmosphere data; constructing a diffusion model for multispectral remote sensing image processing; constructing a condition control and coding structure to carry out multispectral parameter and atmospheric parameter processing; and establishing a unified model to carry out atmospheric correction and cloud removal processing on the spectral satellite remote sensing image. According to the method, multi-temporal multi-spectral parameters, atmospheric aerosol optical thickness, atmospheric water vapor and ground reflectivity remote sensing characteristic parameters and data are fully fused, and a brand new unified model is established to realize atmospheric correction and cloud removal processing of a high-precision spectral satellite remote sensing image; and atmospheric correction and cloud removal processing of the high-precision spectrum satellite remote sensing image are realized.

The invention relates to the technical field of booster station monitoring systems, and discloses a booster station operation and maintenance work video auxiliary monitoring system comprising a three-dimensional perception interaction layer used for collecting equipment state data and providing a man-machine interaction interface and connected with an edge intelligent fusion layer through a wired or wireless transmission channel; and the edge intelligent fusion layer is used for locally analyzing and caching the state data. Through standardized interface interconnection of the three-dimensional perception interaction layer, the edge intelligent fusion layer and the cloud collaborative management layer, a collaborative working mode of data acquisition, local analysis and cloud optimization is formed, the architecture design breaks through the dependence of a traditional scheme on cloud centralized processing, data preprocessing sinks to the edge layer, the network transmission pressure is effectively reduced, and the network performance is improved. The local intelligent analysis capability can still be maintained when the network fluctuates, and compared with the delay problem caused by full data uploading in the prior art, the system response real-time performance and the availability under the network disconnection scene are remarkably improved.

The invention discloses an unmanned aerial vehicle inspection defect image recognition system and method, and relates to the technical field of image recognition, and the system comprises an unmanned aerial vehicle platform, an image collection module, an image processing module, a defect detection and recognition module, and a report generation module. The unmanned aerial vehicle platform comprises an unmanned aerial vehicle body, a high-definition camera, an infrared thermal imager, a cloud processing center, a GPS positioning system and a data transmission module. According to the method, the image collected by the unmanned aerial vehicle is subjected to preprocessing such as denoising, then feature extraction is performed, the influence of factors such as environment and weather on the shot image is effectively eliminated, the defect type of the inspection target is accurately identified through the defect identification model, the identification result is more accurate, the influence on defect identification judgment of the inspection target is avoided, and the inspection efficiency is improved. The efficient, accurate and real-time inspection and defect identification of the target area are realized, the model is optimized and updated regularly, and the accuracy and efficiency of identification can be improved.

The invention belongs to the technical field of voice interaction, and relates to a 5G communication equipment intelligent voice interaction method and system based on deep learning, and the method comprises the steps: generating a joint input signal containing voice frequency domain information and a motion state; outputting the de-noised voice segments and the corresponding environmental interference level parameters; generating a basic voiceprint ID containing a user biological feature identifier and a dynamic key fragment; calculating a cloud processing priority score according to the environmental interference level parameter and the equipment residual electric quantity value, when the score exceeds a preset threshold value, sending a voice processing request containing a basic voiceprint ID to an edge computing node, and otherwise, triggering a local semantic recognition process; and receiving a semantic recognition result from a cloud end or a local end, and generating a multi-round dialogue response instruction in combination with the context parameters in the historical interaction data of the user. According to the invention, the problem that local processing resource overload or cloud communication delay exceeding is easily caused by a fixed noise weight distribution mechanism is solved.

The invention discloses a Beidou-based patrol inspection unmanned aerial vehicle positioning system, and relates to the field of unmanned aerial vehicles. The method comprises the following steps: an unmanned aerial vehicle positioning estimation module which performs positioning optimization through cloud processing based on Beidou satellite and unmanned aerial vehicle data so as to realize position estimation; the unmanned aerial vehicle position correction module obtains the accurate position of the unmanned aerial vehicle through an intelligent error correction function; a multi-unmanned aerial vehicle task allocation module which constructs a scene influence index, performs task allocation among multiple unmanned aerial vehicles by using the task execution adaptation score, and improves the efficiency; the map construction and path planning module is used for constructing an environment map and carrying out optimal path planning according to a scene influence index optimization A * algorithm; and the real-time positioning and obstacle avoidance module is used for acquiring unmanned aerial vehicle data, performing obstacle detection and making an obstacle avoidance decision in combination with obstacle information. Through intelligent error correction and real-time obstacle detection, the positioning precision, flight stability and obstacle avoidance capability of the unmanned aerial vehicle in a complex environment are improved, and efficient and safe execution of a task is ensured.

The invention provides a cloud computing-based narrow-linewidth 1200 nm semiconductor laser data processing system, and relates to the technical field of data processing, and the system comprises a laser transmitting module which is used for transmitting a narrow-linewidth 1200 nm laser beam by using a semiconductor laser; the data acquisition module is used for being connected with the laser emission module, acquiring state parameters, including power, wavelength, line width and working environment data, of the laser beam in real time, and preprocessing and digitally converting signals output by the sensor; and the cloud processing platform module is used for receiving and processing the data of the data acquisition module, realizing real-time analysis, anomaly detection, trend prediction and remote monitoring of the data, and obtaining an analysis result of a cloud processing platform. According to the invention, remote intelligent monitoring and automatic optimization adjustment of the laser are realized, and the data processing efficiency and the performance stability of the laser are improved.

The invention discloses a laser scanning optimization system for high-precision ellipticity and diameter measurement, and relates to the technical field of cylindrical workpiece geometric dimension measurement. Comprising a laser contourgraph assembly, the laser contourgraph assembly comprises four laser contourgraphs, the four laser contourgraphs are arranged in the circumferential direction of the cross section of a to-be-measured workpiece at equal intervals, the included angle between every two adjacent laser contourgraphs is 90 degrees, and the laser contourgraphs are used for synchronously collecting contour point cloud data of the surface of the to-be-measured workpiece; and the data acquisition module is used for receiving and summarizing the contour point cloud data. The four laser contourgraphs are arranged at equal intervals in the circumferential direction of the cross section of the workpiece and collect synchronously, so that the problems that a traditional single laser module is incomplete in rotary scanning coverage and has timeliness deviation are solved; and the point cloud processing module improves the condition of insufficient point cloud processing refinement through splicing, direction correction, section extraction, adaptive region division and arc length interception processing.