504 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 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 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 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 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 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 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 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 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 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.

The invention relates to the technical field of data processing, and discloses a construction monitoring method and system based on artificial intelligence. The method comprises the following steps: collecting construction operation behaviors through a high-speed camera, and performing frequency division filtering on data according to a vibration spectrum to obtain standardized data; inputting the data into a construction behavior feature genetic algorithm, and optimizing a recognition network through behavior feature coding to obtain a lightweight model; carrying out probability calculation on the abnormal behavior according to the model; the probability value is transmitted to edge equipment, and model parameters are compressed through weight pruning to obtain a reasoning result; and early warning signals are obtained through three-level early warning threshold grading judgment. The technical problems of unstable data quality, lack of construction scene adaptability of an AI algorithm, delay of cloud processing and insufficient early warning decision reliability of an existing construction monitoring technology in a complex environment are solved.

The invention discloses a multi-modal robot interaction system, and relates to the technical field of robots, the system comprises a robot subsystem and a cloud processing subsystem which are in communication connection with each other, the robot subsystem comprises a multi-modal sensing module and an interaction execution module, and the cloud processing subsystem comprises a cloud processing module. The multi-mode sensing module is used for collecting target data of at least one target user and sending the target data to the cloud processing module, and the target data comprises at least one of touch data, distance data, posture data, position data, action data and voice data; the cloud processing module is used for analyzing the target data, generating an interaction instruction and sending the interaction instruction to the interaction execution module; and the interaction execution module is used for receiving and executing the interaction instruction, so that the higher expectation of the user in the aspects of emotion accompanying and personalized service is effectively met.

The invention relates to the technical field of AI intelligent management, and discloses an AI-based body weight intelligent management method and system, and the method comprises the steps: carrying out the bioelectrical impedance signal collection of M measurement nodes of a target user through an Internet of Things front-end device, and obtaining a multi-node impedance signal set; biological impedance space-time atlas analysis is executed through the cloud processing platform, and human body composition feature data are obtained; aI analysis is carried out through a Deepseek multi-expert model of the cloud processing platform to obtain a weight management analysis result of each professional field; weight fusion and causal inference are carried out through the cloud processing platform in combination with weight management analysis results of all professional fields and doctor review results, and a personalized weight reduction management strategy is generated; the personalized weight reduction management strategy is received through the mobile terminal, dynamic adjustment is carried out in combination with daily execution feedback data of the target user, an intelligent weight management guidance scheme is generated, and the method improves the management effect of AI weight intelligent management and user experience.

The invention relates to the technical field of Internet of Things, and discloses a remote diagnosis system and method for a complex system in an Internet of Things environment, and the system comprises a data collection module which is used for collecting the operation state information of the complex system; the data transmission module is used for transmitting the running state information to the cloud processing center; the cloud processing center is used for performing fault analysis according to the received operation state information based on a machine learning algorithm, generating a fault diagnosis result and a corresponding maintenance suggestion and transmitting the fault diagnosis result and the corresponding maintenance suggestion to the user terminal; and the user terminal is used for receiving the fault diagnosis result and the maintenance suggestion, remotely accessing the system state report of the complex system and remotely controlling the complex system. According to the invention, efficient remote monitoring, accurate fault diagnosis and real-time maintenance guidance of the complex system in the Internet of Things environment can be realized, the maintenance cost is reduced while the maintenance efficiency is improved, and thus the maintainability and safety of the complex system are enhanced.

The invention provides an ultrasonic nonlinear static parameter monitoring method for creep damage of a high-temperature alloy component, and particularly relates to the technical field of material monitoring. The method comprises the following steps: S1, arranging an ultrasonic probe; s2, determining the emission frequency of the excitation end; s3, respectively calculating an incident angle and an emergent angle of the signal; s4, exciting fundamental frequency ultrasonic waves; s5, receiving the signal and uploading the collected signal to a cloud processing system; s6, carrying out fast Fourier transform and calculating a nonlinear parameter of a static component; s7, establishing a linear relation with creep time, and constructing a creep damage linear quantitative model; s8, calculating a static component nonlinear parameter of the healthy sample; and S9, completing quantitative analysis of the damage by using nonlinear parameters. The method solves the problems that a traditional nonlinear ultrasonic method depends on second harmonic nonlinear parameters, phase velocity matching conditions in the middle and later periods are seriously damaged, response shows a nonlinear trend, unique creep time cannot be determined according to the parameters, and then the unique damage state cannot be determined.

The invention discloses a motion state monitoring and feedback system and method based on electroencephalogram signals, in the system, an electrode array is arranged corresponding to a forehead part and used for collecting the electroencephalogram signals, a motion sensor is used for collecting motion data signals, and a vibration motor is used for sending out vibration feedback; the electroencephalogram collection hair band sends the electroencephalogram signals and the motion data signals to the mobile terminal, and the mobile terminal sends the electroencephalogram signals and the motion data signals to the cloud processing platform; the cloud processing platform is used for predicting input electroencephalogram signals and motion data signals by adopting a physical and mental state prediction model based on a deep neural network to obtain physical and mental state indexes; triggering a self-adaptive feedback intervention strategy according to the physical and mental state indexes, and controlling a vibration motor to send vibration feedback in a preset mode. According to the technical scheme of the invention, the recognition accuracy of the physical and mental state characteristics of the movement of the user is improved, and the wearing comfort and convenience of the signal acquisition device are improved.

The invention relates to a control method, system and device for a security robot and a medium, and the method solves the problem of perception failure in smog and low-light scenes through the adaptive adjustment of the weight of a multi-modal sensor through dynamic weighting space-time alignment processing. Constructing a cross-modal threat field function based on a lightweight Transform, and realizing quantitative modeling of threats in a complex environment; a federal reinforcement learning framework is utilized to fuse local decision and encrypted global strategy update, and cloud processing delay is avoided while data privacy is guaranteed; a mixed potential field path is generated in combination with the threat field gradient and strategy parameters, and the limitation of traditional path planning on dynamic threat avoidance is broken through; and finally, a multi-robot surrounding track is generated through a distributed cooperation protocol, so that the high-risk target surrounding efficiency and the system robustness are remarkably improved. According to the method, perception-decision-execution full-link optimization is realized, and a high-response, strong-privacy and collaborative intelligent solution is provided for a complex security scene.

The invention provides an Internet of Things smart farm monitoring method and system, and relates to the technical field of agricultural Internet of Things, and the method comprises the steps: executing the triggering type sensing operation of crop growth and environment abnormality at a field end side visual node of an Internet of Things smart farm based on a preset triggering condition, and outputting an end side initial sensing data set; performing non-redundant feature screening on an end-side initial sensing data set, extracting a key feature set, executing a staged slow aggregation operation through a low-power-consumption communication network, outputting batch feature data packets according to a link state, and realizing field light-flow transmission; the cloud processing platform receives the batch feature data packets, then calls the agronomy knowledge set for restoration and diagnosis, outputs a farm monitoring diagnosis result, generates a field equipment linkage instruction according to the farm monitoring diagnosis result, sends the field equipment linkage instruction to the execution equipment, and outputs an equipment linkage response result, so that the farm monitoring efficiency and accuracy are improved, and the farm monitoring accuracy is improved. Intelligent management is realized, and the field operation cost is reduced.

The invention discloses an aircraft dragging risk early warning method based on head-mounted display equipment and a laser radar. The method comprises the following steps: establishing an aircraft dragging process risk early warning system; calibrating an RGB camera and a laser radar; training the two-dimensional target detection model to realize two-dimensional detection; determining a target point cloud and an obstacle point cloud; determining a target collision area point cloud and a safety distance; determining the curved surface type of the risk surface; correcting the risk patterns in a partitioned manner according to the curved surface type; generating a grid; and fitting the corrected risk image to the grid, and the like. According to the aircraft dragging risk early warning method based on the head-mounted display equipment and the laser radar, a system which takes the head-mounted display equipment as a carrier, integrates multiple sensors such as the laser radar, an RGB camera and a TOF camera and integrates multiple technologies such as augmented reality, deep learning, point cloud processing and distortion correction is designed; the method has stronger visibility, real-time performance and accuracy, and can be used for monitoring and early warning various risks in a complex environment.

The invention provides an industrial internet of things-oriented general inductance calculation and control integrated method and system. The method comprises the following steps: a controller dispatches sensors in a work area to collect environment data and production state data; receiving and aggregating the data acquired by each sensor to generate a control task; evaluating local and cloud resource states, and respectively calculating the total time delay of the control task processed in the local and the cloud; calculating the control cost based on the optimal control theory; generating an unloading decision of the control task according to the total time delay and the control cost; according to the unloading decision, the control task is distributed to the local or the cloud for processing, and a processing result is generated; and a control instruction is generated according to a processing result, and is issued to corresponding production equipment and sensors for adjustment, sensor parameter optimization and unloading decision making, so that a control closed loop is formed. According to the method provided by the invention, the resource utilization rate is improved, low time delay and high real-time performance are guaranteed, the system stability and reliability are enhanced, and the operation and maintenance complexity and cost are reduced.

The invention relates to the technical field of computer vision, and particularly provides a tunnel video stream three-dimensional modeling system based on binocular stereo matching and SLAM (Simultaneous Localization and Mapping), which comprises a multi-modal image acquisition unit, a binocular infrared camera and an RGB (Red, Green and Blue) camera are configured to synchronously acquire an infrared image pair, an RGB image pair and inertial measurement unit data of a tunnel environment, the multi-source data is uploaded to the cloud processing platform in real time through the wireless transmission module; based on a dynamic switching mechanism of environmental perception, performing adaptive fusion pose estimation of a feature point method and a direct method on input multi-modal image data; the stereo matching and dense reconstruction unit is used for calculating a disparity map by adopting an improved self-adaptive window stereo matching algorithm, generating a three-dimensional point cloud in combination with the pose information output by the pose estimation unit, and constructing a global consistency point cloud model of the tunnel scene through a time sequence point cloud registration and splicing algorithm; and a multi-algorithm target detection and semantic fusion unit. The method can meet the requirements of precision and robustness of tunnel reconstruction.

The invention discloses a curved surface workpiece processing three-dimensional reconstruction method and system based on point cloud data, and relates to the field of data processing, and the system comprises an acquisition module which is used for adaptively adjusting the scanning path, sampling density and scanning distance parameters of an acquisition device, and driving the acquisition device to obtain workpiece surface point cloud data; the preprocessing module is used for hierarchically dividing the acquired point cloud data according to the curvature change of the curved surface of the workpiece, removing abnormal points in each hierarchy and complementing missing point clouds; according to the method, scanning parameters are flexibly adjusted in combination with workpiece machining area parameters, comprehensive and accurate point cloud data collection is ensured, point cloud processing can be optimized according to curvature, features are accurately extracted, a model is constructed in a layered mode, the integrity of the model is guaranteed through splicing constraint, and it is ensured that the model meets machining requirements through precision verification and correction.

The invention provides an intelligent building fire safety early warning system based on intelligent fire protection, and relates to the field of fire safety, and the system comprises a distributed data collection module which is used for achieving data real-time collection and network disconnection local processing through heterogeneous sensor networking and dual-mode communication; the edge processing module is used for carrying out edge processing on the output standardized environment data flow by adopting a fire behavior triple verification model and a dynamic weight algorithm, and carrying out cross-node model sharing in combination with a federated learning framework so as to obtain a fire risk score and a fire source positioning coordinate; the decision execution module is used for dynamically planning an evacuation path; the feedback module is used for performing real-time rendering fluid dynamics fire spreading simulation on the evacuation path and generating multi-terminal early warning; and the dynamic evacuation optimization module simulates and optimizes the exit position and the path planning through fluid dynamics to obtain the optimized path planning. The method and the device are used for overcoming the defects of cloud processing delay and failure during network disconnection in the prior art.

The invention discloses a robot grabbing pose generation method and device, and the method comprises the steps: obtaining a natural language grabbing task instruction, and carrying out the analysis based on a large language model, and generating a structured task parameter containing target object identification information and grabbing constraint information; performing open vocabulary target detection on the image based on the identification information, and obtaining a target area mask in combination with pixel-level segmentation; generating a target point cloud based on the mask, the depth map and the camera internal reference, carrying out depth completion or denoising, outlier elimination and threshold confirmation filtering, inputting the processed target point cloud into a grabbing pose prediction network, outputting a plurality of candidate six-degree-of-freedom grabbing poses, carrying out constraint screening according to grabbing constraints, and carrying out collision detection; and selecting the candidate with the optimal confidence coefficient from the collision-free candidates as the optimal grabbing pose to be output. Task constraints penetrate through a target extraction process, a point cloud construction process and a pose screening process, and point cloud processing and collision verification are combined, so that an executable six-degree-of-freedom grabbing pose which meets the constraints is output.

The invention relates to the technical field of new energy automobile batteries, in particular to a new energy automobile battery state prediction method, optimization control method and system, and the method comprises the steps: collecting the real-time operation data of a new energy automobile battery; uploading the real-time operation data to a cloud processing platform in which historical operation data of the battery is stored, and dynamically predicting the state of the battery; fusing the charge state data driven by the real-time model and the charge state data driven by the historical data to carry out charge state estimation prediction, and outputting charge state estimation parameters; a circulation attenuation model is combined with reinforcement learning correction, historical circulation data is used for fitting a capacity attenuation curve, and a dynamic battery health state estimation value is output; a fusion thermal model and an internal resistance model are adopted, a safety voltage / temperature boundary is predicted through digital twin simulation, and a dynamic battery power state estimation value is output; and generating a battery control strategy based on the battery state parameters and issuing the battery control strategy to an onboard BMS module for execution.

Provided in the embodiments of the present disclosure are methods and apparatus for integrating an integrated system into a cloud computing environment, a computing device, a computer-readable storage medium, and a computer program product. A method for integrating an integrated system into a cloud computing environment is applied to a cloud processor in a cloud side device, and comprises: according to a received creation request, determining an integrated system to be called and resource configuration information corresponding to the integrated system to be called; loading a resource back-end driver corresponding to a resource front-end driver of the integrated system to be called, and creating a resource port; and sending, between the cloud processor and the integrated system to be called and via the resource port, the resource configuration information to the integrated system to be called, the resource configuration information being used for generating a target integrated system from the integrated system to be called. Thus, internal intercommunication among the target integrated system and the cloud processor in the cloud side device and a cloud service deployed in the cloud side device is realized, thereby improving the communication efficiency and the overall performance and achieving high stability.

The invention discloses an overhead transmission line point cloud classification method and system based on rule improved RandLA-Net, and a storage medium, and belongs to the field of three-dimensional laser point cloud processing and power system automation. The classification method comprises the following steps: collecting original point cloud data of the overhead transmission line, and performing preprocessing after data conversion and data classification to obtain a standardized point cloud data set; constructing and training an improved RandLA-Net point cloud classification model, performing feature enhancement through a channel attention mechanism, and outputting a preliminary classification result; performing optimization processing on the preliminary classification result based on a prior rule to obtain a final classification result; and outputting the final classification result and displaying the final classification result through a visual tool for supporting power transmission line inspection. Through improvement of a channel attention mechanism, data preprocessing optimization, combination of four types of prior rules and the like, high-precision classification of power transmission equipment is realized, and reliable data support is provided for power inspection.

The invention discloses a concrete member reinforcing steel bar identification and dislocation connection collaborative design method and system. According to the method, point cloud data of an exposed reinforcement area are collected through high-precision three-dimensional laser scanning, and precise three-dimensional geometric parameters of existing reinforcements are obtained through registration, denoising and geometric reconstruction; on the basis, a multi-target optimization model which takes bearing capacity maximization as a core target and takes distribution uniformity and symmetry into consideration is constructed, a two-stage optimization strategy is adopted to automatically solve an optimal arrangement scheme of newly-added reinforcing steel bars, and a digital result capable of being directly constructed is output after performance checking. The system comprises a data acquisition module, a point cloud processing and reconstruction module, a collaborative optimization design module and a scheme output and check module. According to the method, full-process automation from reinforcing steel bar recognition to optimization design is achieved, and the design quality and efficiency of reinforcement engineering are remarkably improved.