634 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 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 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 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 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 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 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 discloses a dynamic graph convolution-based target point cloud completion method for a battery replacement robot, and the method comprises the steps: 1, reconstructing a complete target fastener model from a multi-view image through SFM and MVS technologies, and obtaining complete point cloud data; 2, constructing an incomplete-complete point cloud pair as training data by using a geometric constraint-based adaptive cutting strategy; 3, multi-resolution point cloud processing is adopted, and feature extraction and fusion are carried out according to three-level resolution; 4, on the basis of DGCNN dynamic graph convolution, in combination with multi-stage Edge Conv dynamic edge convolution and a Transform coding module, local geometric feature capture and global relation perception are realized; 5, point cloud generation adopts a pyramid step-by-step refining method, geometric details are added to each layer based on a previous layer result, and the number of points is gradually expanded; and 6, the training process is guided through a multi-target joint loss function, and the point cloud quality is improved while the precision is ensured. The geometric structure integrity of the point cloud of the target fastener is improved, and the operation error of follow-up operation of the battery replacement robot is reduced.

The invention discloses an active dangerous situation recognition and intelligent rescue system for an elevator, belongs to the technical field of elevator monitoring, and solves the problems that the data processing amount of a data processing module is increased and the adverse risk judgment accuracy is insufficient due to the fact that an existing system does not carry out pre-recognition processing on multi-source heterogeneous data. The system comprises a data acquisition end, a cloud processing platform, a response feedback module and a visual terminal, and the cloud processing platform identifies and analyzes a monitoring information set based on a dangerous situation identification model and calculates an elevator dangerous situation value based on a dangerous situation risk factor; according to the method, the data acquisition end pre-identifies the elevator monitoring data based on the federated learning algorithm in combination with the relation attention mechanism, so that data distributed pre-identification processing is achieved, the method is suitable for multi-source heterogeneous elevator monitoring scenes possibly involving different main body data, the efficiency and accuracy of data processing of the dangerous situation identification model are improved, and the safety of the dangerous situation identification model is improved. Therefore, the active recognition response speed and the comprehensive evaluation precision of the elevator dangerous situation are ensured.

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 embodiment of the invention provides a cylinder detection method and device, electronic equipment and a storage medium, and relates to the technical field of point cloud processing. The method comprises the following steps of: segmenting a point cloud to be detected to obtain a plurality of segmented parts, selecting a projection surface for each segmented part, projecting the segmented parts to obtain a projection point cloud, simplifying the projection point cloud to obtain a simplified projection point cloud, clustering the simplified projection point cloud to obtain at least one clustering point cloud, and obtaining at least one clustering point cloud. And performing cylinder detection on each clustering point cloud to determine all cylinders in the segmentation part. According to the method, the to-be-detected point cloud is segmented, the segmented part is projected and simplified, clustering is carried out, and cylinder detection is carried out on each cluster, so that all short cylinders in the point cloud can be detected more accurately.

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 discloses a privacy security-oriented end-cloud hybrid retrieval enhancement generation system and method, and relates to the field of computer science and technology, and the system comprises a local processing module and a cloud processing module. And the cloud processing module performs data interaction with the local processing module. The local processing module generates an enhanced cue word based on the user query information, and performs desensitization operation on the enhanced cue word to obtain a desensitized cue word; the cloud processing module rewrites the desensitized cue word to obtain a rewritten cue word, and adopts a large language model to generate encrypted answer information based on the rewritten cue word; the local processing module also generates final answer information based on the encrypted answer information. The data security can be improved while the expansibility and the system performance are improved, and the potential risk of privacy disclosure is reduced.

The invention discloses a photovoltaic power station comprehensive intelligent operation and maintenance method and system based on cloud processing, and relates to the technical field of photovoltaic power station operation and maintenance management, the photovoltaic power station comprehensive intelligent operation and maintenance method based on cloud processing comprises the following steps: obtaining characteristic data of operation state data and parameter data, and storing the characteristic data in a cloud platform; evaluating the power generation performance of the photovoltaic equipment in real time by using a solar energy conversion efficiency model; formulating an optimal cleaning period of the photovoltaic equipment; and inputting a detection result into a photovoltaic equipment operation fault abnormity database for fault judgment. The power generation efficiency is evaluated in real time through the photoelectric conversion model, so that operation and maintenance personnel can know the power generation condition of the photovoltaic power station, frequent cleaning or overlong cleaning interval is avoided, the fault data discovery and recognition capability can be improved, the service life of equipment is prolonged, the operation efficiency of the equipment is improved, and the power generation efficiency is improved. And the operation and maintenance efficiency of the photovoltaic power station is 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.