18928 results about "The Internet" patented technology

The invention discloses a ring main unit monitoring method and system based on the Internet of Things, and the method comprises the steps: building a multi-source data collection network of a ring main unit, extracting the multi-dimensional time-frequency domain features of a signal through an edge computing device, generating a multi-source feature data set, and constructing a dynamic digital twinborn model, real-time mapping of the running state of the ring main unit is achieved through a physical-virtual synchronization algorithm, and twin data volumes are output; according to the node influence coefficient of the twin data volume, analyzing a topological structure of a ring network system by adopting a graph neural network, identifying a key fault propagation path and generating a priority monitoring node list; based on the priority monitoring node list and the twin data volume, dynamically generating an adaptive monitoring strategy through a reinforcement learning algorithm, executing the adaptive monitoring strategy and collecting feedback data in real time, and completing closed-loop iteration of the monitoring strategy through an online learning algorithm; through closed-loop control of multi-source perception-digital twinning-intelligent decision, high efficiency, intelligence and reliability of ring main unit monitoring are realized.

The invention discloses a smart park full life cycle management system and method based on digital twinning and Internet of Things, and relates to the technical field of smart park management, and the system comprises a sensing edge module, a data governance module, an intelligent analysis module, a life cycle module and a twinning modeling module. According to the invention, multi-protocol access and edge computing capability are supported, and the data transmission efficiency and stability are greatly improved; the intelligent analysis module outputs an accurate analysis result by constructing a multi-class feature matrix and deep multi-task joint modeling mechanism, and provides data support and model guidance for dynamic management and intelligent decision making of the park; the life cycle module integrates a Kepler optimization algorithm and a multi-agent reinforcement learning and simulated annealing algorithm, establishes a collaborative optimization mechanism, realizes combination of global search and local fine tuning of resource scheduling, and effectively optimizes energy consumption, response time, space utilization and safety risks; and the twin modeling module constructs a park three-dimensional model, so that the interactivity and operability of the system are improved.

The invention provides an intelligent building energy conservation and emission reduction digital twin management system and method, and relates to the field of building automation and energy conservation control. The intelligent building energy conservation and emission reduction digital twinborn management system comprises a digital twinborn model construction module, an energy consumption prediction and regulation module, a multi-device cooperative control module, a real-time feedback and self-learning module and an edge-cloud cooperative architecture. By integrating a building information model, real-time sensor data, meteorological data and the like, a three-dimensional visual dynamic twinborn model is constructed, historical data playback and thermodynamic diagram analysis are supported, omnibearing virtual mapping of a physical building is realized, a physical system and virtual model data are synchronized in real time through an Internet of Things sensor network, and the real-time real-time dynamic twinborn model is established. The consistency of a virtual model and an actual system is ensured, the precision and reliability of the model are remarkably improved, artificial illumination is dynamically compensated according to natural illumination, lamps are automatically turned off in an unmanned area through infrared induction, and equipment-level energy saving is achieved in combination with air conditioning system optimization.

The invention discloses an Internet of Things multichannel earthquake early warning emergency linkage system, belongs to the technical field of earthquake early warning, and aims to solve the problems of insufficient real-time performance, reliability, scene coverage and safety in the prior art. The sensing layer collects data cooperatively through various sensors, eliminates environmental interference and ensures high-precision seismic wave detection, the edge calculation layer filters noise in real time by adopting an advanced algorithm, and confirms seismic events through multi-dimensional comparison, so that the local processing efficiency is remarkably improved, the cloud load is greatly reduced, a cloud platform fuses multi-source data, and the seismic detection efficiency is improved. A machine learning model is combined to dynamically optimize an early warning threshold value, the accuracy of earthquake type classification and intensity prediction is improved, full-link optimization from data acquisition to intelligent decision making is realized, a multi-channel distribution module is designed, instructions are transmitted according to priority levels, and urban and rural full-scene coverage is ensured through multiple communication technologies.

The invention provides an intelligent Internet of Things public security management and control system and method based on multi-source data fusion, and belongs to the technical field of Internet of Things security. According to the system, unified collection and standardized processing of multi-source data are achieved by recognizing a system operation scene and loading corresponding model parameters and response strategies, and a unified semantic representation structure is constructed. And the system executes anomaly detection at an edge node, completes risk scoring and grading alarm judgment in combination with a semantic structure and anomaly feature information, and generates a control response instruction based on strategy matching. Meanwhile, dynamic optimization of the scoring model and identity authentication, behavior auditing and data compliance export in the operation process are supported. The system has intelligent identification, adaptive analysis and response closed loop capabilities, and improves the precision and credibility of public security management in the Internet of Things environment.

The invention provides a risk control big data mining method and system based on the Internet of Vehicles, and the method comprises the steps: collecting a real-time operation data flow of a target vehicle in an Internet of Vehicles environment, including vehicle state time sequence data, a driving behavior event set, external environment perception data and a vehicle-mounted communication interaction record; and then respectively extracting a combined operation feature vector, an abnormal behavior mode identifier and a dynamic environment risk factor from different data, converting to generate a structured communication feature matrix, constructing a space-time association risk analysis map based on the structured communication feature matrix, and carrying out driving risk dynamic assessment on a target vehicle through the space-time association risk analysis map. And finally, generating a risk control decision instruction set according to the result, and feeding back the risk control decision instruction set to the vehicle-mounted control terminal and the Internet of Vehicles cloud platform, thereby realizing comprehensive assessment and effective management and control of vehicle risks.

The invention relates to the technical field of data processing, and discloses a bridle machine fault monitoring method and system based on the Internet of Things. The method comprises the following steps: collecting multi-source operation data through a sensor; performing filtering segmentation processing on the data to obtain feature data; analyzing the feature data to obtain an abnormal identifier and a fault type; establishing a component association map to position a fault source and predict a development trend; calculating a maintenance priority to generate a maintenance scheme; and recording the actual maintenance result and the prediction deviation to update the fault library and the knowledge parameters. Through multi-source data fusion, intelligent fault diagnosis, predictive maintenance and knowledge self-adaptive updating mechanisms, early recognition and accurate positioning of faults of the bridle machine are realized, the non-planned downtime is effectively shortened, and the equipment reliability and the production efficiency are improved.

The invention belongs to the technical field of industrial equipment monitoring, and discloses an industrial production equipment monitoring and early warning system based on the Internet of Things and edge intelligence, and the system collects multi-modal data in real time through a distributed sensor network, and carries out the real-time processing and feature extraction through the edge intelligence technology. Data fusion and optimization are carried out through a cloud deep learning model, a self-adaptive early warning and decision module dynamically adjusts a threshold value and optimizes an operation strategy, an environmental adaptability optimization module ensures the stability of equipment under extreme conditions, and an equipment health management and collaborative maintenance module realizes cross-equipment collaborative maintenance. The remote monitoring and interaction module provides man-machine interaction type control, and the cross-device collaborative learning module improves the model performance through federal learning. According to the system, the accuracy of equipment monitoring, the timeliness of early warning and the intelligent level of maintenance are remarkably improved.

The invention relates to the technical field of machine tool intelligent monitoring, and discloses a numerical control machine tool intelligent monitoring method and system based on the Internet of Things and a storage medium. The method comprises the steps of collecting machine tool multi-source data and transmitting the data to an edge node; original data is processed to obtain a working condition feature set, the working condition feature set is input into a recursive convolution hybrid memory network to construct a common dependency relation graph, and state parameter prediction is realized through time-frequency domain joint analysis; converting the two-dimensional feature map into a two-dimensional feature map, and extracting interaction features through SE-ResNet; fusing the two types of features to execute state classification; and predicting the life and generating a diagnosis report based on the health state judgment result in combination with the process parameters and historical data. According to the method, effective fusion analysis of multi-source heterogeneous data of the numerical control machine tool is realized, and a self-adaptive state recognition and fault early warning mechanism is constructed, so that accurate monitoring of the operation state of the machine tool, intelligent diagnosis of early faults and accurate prediction of residual life are realized.

The invention provides an intelligent factory data processing method and system based on the industrial Internet, and the method comprises the steps: firstly obtaining a real-time industrial data set collected by a multi-mode sensor in a target production region, covering various data, such as equipment vibration signals, carrying out the time sequence synchronization processing of the real-time industrial data set, and generating a synchronization data block; the method comprises the following steps of: extracting dynamic state characteristics of a production line from the data, calling a pre-trained anomaly detection model to carry out multi-dimensional anomaly detection, outputting an anomaly detection result, matching a preset expert knowledge base according to the anomaly detection result, and generating a dynamic control instruction set containing equipment adjustment parameters and the like; finally, production parameter configuration is adjusted based on the dynamic control instruction set and fed back to the industrial control terminal in real time, optimization of current production resource configuration is achieved, and the production efficiency and the resource utilization rate of an intelligent factory are effectively improved.

The invention discloses a park energy router multi-agent coordination control method based on a dynamic game model, and relates to the technical field of energy internet optimization control. The method comprises the following steps: according to real-time meteorological data and historical load fluctuation characteristics, identifying multi-energy supply and demand time sequence conflicts, constructing a multi-subject game framework, quantifying dynamic coupling strength of meteorological parameters and load fluctuation, and generating a dynamic constraint boundary; then, combining dynamic constraints and operation states, dynamically adjusting economic, reliability and low-carbon index weights, superposing a risk penalty term of a future supply and demand difference value, and constructing a coordination revenue function; when the photovoltaic output and the thermal load change rate exceed threshold values, an emergency game mode is triggered, Nash equilibrium is solved through a distributed optimization algorithm, and a coordination control instruction is output; and finally, collecting execution data, calculating a supply and demand deviation correction rate and an income balance degree, adjusting a coupling weight and a constraint boundary based on feedback, and realizing multi-subject benefit dynamic balance and stable and efficient operation of the park energy system.

The invention provides an intelligent classroom teaching optimization method and system combining behavior recognition and the Internet of Things, and relates to the technical field of smart classs.The method comprises the steps that student behavior data, audio and video data and environment data are acquired through a distributed Internet of Things data acquisition system deployed at a student terminal; processing the data by adopting a multi-modal feature fusion network to obtain student interaction state features; and constructing a remote teaching interaction evaluation model based on the features, calculating the attention level and the classroom participation degree of the students in real time, and generating a personalized teaching guidance strategy. According to the method, intelligent sensing of student states and dynamic optimization of teaching strategies in the remote teaching process are realized.

The invention relates to the technical field of the Internet of Things, and discloses a machine room equipment environment monitoring method and system based on the Internet of Things, and the method comprises the steps: constructing an edge calculation hardware system of the Internet of Things to obtain an integrated terminal which integrates a communication unit, an acquisition unit and a control unit; performing multi-channel parallel acquisition and edge calculation analysis on equipment and environment parameters in the machine room to obtain a standard data tuple and an analysis data packet; executing local hierarchical storage and logic control to obtain a control instruction and storage data; executing three-layer network communication interaction on the storage data to obtain a remotely accessible monitoring view; according to the method, intelligent alarm and system self-maintenance are achieved, graded and classified alarm information and a system state report are obtained, the data transmission amount is greatly reduced, meanwhile, the system response delay is reduced, the control decision accuracy is improved, and the misoperation risk is reduced.

The invention discloses a multi-modal diagnosis and treatment data analysis and auxiliary diagnosis method for internet medical treatment, which comprises the following steps: S1, performing feature extraction and fusion on multi-modal diagnosis and treatment data through a nonlinear mapping algorithm based on quantum state optimization; s2, constructing a self-adaptive dynamic relation network, and capturing a complex interaction relation between cross-domain features in the diagnosis and treatment data; s3, fusing implicit features in the sparse medical data with semantic association; s4, optimizing the diagnosis and treatment decision process by applying a causal inference technology; s5, processing anomalies in the diagnosis and treatment data by using a fusion generative adversarial network and sparse tensor decomposition technology; s6, constructing a decentralized encrypted federated learning network to protect the privacy of the patient; s7, predicting a disease evolution path in real time to form dynamic decision support; and S8, performing accelerated analysis on the high-dimensional feature mode in the diagnosis and treatment data in combination with quantum calculation. The method has the advantages of high data fusion capability, high diagnosis precision and high real-time decision support capability.

The invention relates to the technical field of the Internet of Things, in particular to an intelligent construction site management method and system based on the Internet of Things. The method comprises the following steps: acquiring construction site environment monitoring data in real time based on multiple Internet of Things sensor nodes; performing real-time environment state change mining on the construction site environment monitoring data, performing global environment perception modeling, and constructing a construction site digital environment perception map; obtaining a construction site omnibearing monitoring video and construction site construction equipment state monitoring data; carrying out target dynamic optical flow tracking on the omnibearing monitoring video of the construction site one by one, carrying out dynamic operation state evolution, and constructing a real-time behavior portrait model of each worker; and performing operation state equipment temperature change trend analysis and dynamic load change evolution according to the state monitoring data of the construction site construction equipment, and generating comprehensive state characteristics of each piece of equipment. The construction site management efficiency and the construction site construction safety are improved.

The invention discloses a self-adaptive multi-dimensional adjustment metering box based on the Internet of Things, and relates to the technical field of intelligent monitoring of power equipment. The problem of measurement distortion caused by high fixed threshold false triggering rate, weak anti-aliasing capability and multi-source data asynchronization in a high-dynamic industrial scene in the prior art is solved. Multi-parameter acquisition is realized through the sensing acquisition module and the annular buffer area; a reconfigurable FIR filtering module is adopted to dynamically switch a low-pass / band elimination mode to suppress aliasing interference; noise features are extracted by combining spectral kurtosis analysis and concept drift detection, and a dynamic threshold value is generated by using exponentially weighted moving average and a sliding window standard deviation; a multi-channel time sequence is aligned through an IEEE 1588 protocol, and cloud parameter closed-loop optimization is realized based on extended Kalman filtering and a particle swarm algorithm; according to the method, the threshold fault tolerance, the high-frequency transient signal capturing precision and the multi-source heterogeneous data fusion reliability in a high-noise environment are remarkably improved.

The invention relates to a digital integrated quality management system based on multi-source data fusion, and belongs to the technical field of industrial internet and quality management. A data acquisition layer of the system obtains real-time and static multi-source heterogeneous data through a multi-source adapter; the data processing layer is used for cleaning, converting and standardizing the acquired data; the intelligent analysis layer performs deep analysis and prediction on the data by using an adaptive quality prediction model, an anomaly detection module and a root cause analysis engine; the application service layer displays a quality trend and an anomaly detection result through a visual billboard, and provides credible tracing and collaborative decision-making functions; and the feedback closed layer adjusts system processing logic according to the decision support data to form closed-loop quality control. According to the method, real-time fusion and efficient utilization of multi-source data are realized through a dynamic routing technology, an adaptive quality prediction model and a block chain evidence storage mechanism, and the intelligent level and decision-making efficiency of quality management are remarkably improved.

The invention discloses a nursing data sharing method and system based on the Internet of Things. The method comprises the steps that S1, multi-modal nursing data are collected in real time through a distributed Internet of Things terminal cluster, and a feature signal set is formed; s2, constructing a data coordination engine, executing federal learning preprocessing operation on the feature signal set, and forming a coordination signal flow; s3, dynamically calculating a data contribution degree weight and generating a verification signal chain; s4, monitoring vital sign abnormal indexes in the coordination signal flow in real time, and generating a trigger signal vector; and S5, updating incentive mechanism parameters according to the contribution degree voucher in the verification signal chain, optimizing a service quality strategy based on a network resource allocation scheme in the trigger signal vector, and forming a closed-loop control signal. According to the invention, the problem of data islanding effect in the medical care data sharing process can be solved.

The invention discloses an intelligent supervision management and control method and system, and the method comprises the steps: generating a violation operation recognition result and an equipment abnormality early warning signal according to construction scene multi-mode data collected by an intelligent camera and real-time monitoring information of an Internet of Things sensor; generating a non-tampering block chain evidence storage data chain based on the violation operation identification result and the equipment abnormity early warning signal; according to the engineering change information in the block chain evidence storage data chain, updating a BIM collaboration platform through an incremental three-dimensional model synchronization technology, and generating a multi-party real-time interaction enhanced three-dimensional model; based on an enhanced three-dimensional model and Internet of Things environment monitoring data, a digital twin mirror image space is constructed, a construction process is simulated through a multi-source data fusion deduction engine, and a risk pre-judgment map and a resource configuration optimization strategy are generated. According to the embodiment of the invention, visual supervision, credible tracing and prospective decision making of the construction process can be realized.

The invention belongs to the technical field of intelligent health monitoring, and discloses a retired old people home safety monitoring system based on Internet of Things, which comprises the following steps: deploying multiple types of sensors for the home environment of old people to carry out all-weather data acquisition and analysis to obtain behavior data with activity feature tags; performing correlation analysis to obtain a personalized security risk assessment model; performing real-time monitoring and mode recognition to obtain a behavior track analysis report; performing security level division and intelligent configuration to obtain a virtual security guard area and a self-adaptive reminding rule; performing grade classification processing and early warning signal generation to obtain graded early warning information and a response authority strategy; performing integration analysis to obtain a dynamic intervention strategy, and updating the personalized security risk assessment model according to the dynamic intervention strategy to obtain a target security monitoring model; blind spots and defects of traditional manual monitoring are effectively overcome, and a safe home environment is provided for the old.

The invention relates to the technical field of signal devices, and discloses a traffic signal control method and system based on vehicle-road cloud multi-modal data fusion, and the method comprises the steps: collecting multi-modal traffic data synchronously in real time through a vehicle-end sensor, road-side sensing equipment and a cloud Internet platform; fusing the heterogeneous data by adopting a space-time alignment algorithm, and constructing a standardized space-time feature matrix; traffic flow prediction is carried out based on a multi-layer space-time diagram neural network trained by a federated learning mechanism, and a signal control instruction is generated through reinforcement learning and a multi-objective optimization model; and issuing the green wave parameter, the dynamic timing scheme and the cross-domain coordination strategy to a roadside signal machine through the cloud edge coordination architecture to execute control. The problems that in the prior art, low-delay private network communication cannot be achieved, the data fusion efficiency is low, unmanned driving is not supported, and the deployment cost is high are solved, and the purposes of low-delay communication, high reliability and low risk are achieved.

The invention provides a comprehensive pipe gallery abnormal state early warning method and system based on the Internet of Things, and the method comprises the steps: collecting multi-source monitoring data including environment state parameters, equipment operation parameters and structure deformation parameters in real time through multiple types of sensor nodes disposed in a comprehensive pipe gallery; the multi-source monitoring data is preprocessed to generate a preprocessed monitoring data set, feature fusion processing is carried out on the preprocessed monitoring data set to generate a fusion feature vector set containing time and space related features, and then an abnormal state analysis model is called according to the fusion feature vector set to calculate and obtain real-time state evaluation parameters of the comprehensive pipe gallery. And finally, the real-time state evaluation parameters are compared with a preset early warning threshold interval, an early warning strategy is generated and fed back to a monitoring system to trigger early warning response operation, and timely early warning of the abnormal state of the comprehensive pipe gallery is achieved.

The invention discloses an industrial equipment communication method and system based on the industrial internet, and the method comprises the following steps: S1, collecting the multi-modal data of industrial equipment, and generating a preliminary multi-modal fusion feature; s2, performing event-driven analysis on the preliminary multi-modal fusion features to generate event sensing data; s3, constructing a semantic-driven communication protocol, and generating a semantic data queue with a priority identifier; s4, compressing the semantic data queue and carrying out time consistency calibration; s5, optimizing a communication frequency and bandwidth resource allocation strategy in combination with a mixed firefly optimization algorithm and a dynamic differential evolution algorithm; s6, constructing an industrial equipment communication topology model, and predicting path delay and bandwidth occupation indexes of the communication network in combination with the space-time multi-task network; and S7, constructing a zero-sum game model to analyze the potential confrontation relationship in the communication process, and outputting a secure communication scheme. According to the invention, semantic-driven communication, a hybrid optimization algorithm and the like are adopted, and intelligent communication of industrial equipment is realized.

The invention discloses a power system Internet of Things intelligent detection method, and relates to the technical field of power detection, and the system comprises the following components: S1, data collection and preprocessing, S2, big data analysis and feature extraction, S3, equipment life prediction model construction and training, S4, fault prediction and evaluation, S5, predictive maintenance strategy making, and S6, adaptive adjustment. According to the method, the equipment life prediction model of deep neural network self-learning modeling is adopted, the quantum optimization algorithm is used for optimization, accurate prediction of the residual life and the fault occurrence probability of the power equipment is achieved, and meanwhile, the power equipment state comprehensive evaluation model based on the combination of fuzzy logic and the evidence theory is combined, so that the power equipment state comprehensive evaluation efficiency is improved. The equipment health state grade can be comprehensively evaluated, a scientific basis is provided for formulating a predictive maintenance strategy, a maintenance plan can be reasonably arranged in advance, the maintenance cost is reduced, the service life of equipment is prolonged, and economic benefits and social benefits of a power system are improved.

The invention relates to the technical field of Internet of Things, and discloses an OpenHarmony-based Internet of Things control method and platform, and the method comprises the steps: carrying out the distributed discovery and lightweight authentication of Internet of Things equipment, and constructing a dynamic equipment topological graph; generating a device capability description file according to the authentication device set, and creating a virtualization layer; acquiring multi-source sensor data from the virtualization layer and performing fusion processing to obtain a scene control strategy; acquiring network data packets at key nodes of the dynamic equipment topological graph and extracting features to obtain a security situation assessment result; performing deep packet detection based on a security situation assessment result and a scene control strategy, identifying an abnormal behavior, and generating a network optimization strategy; the cluster head nodes are selected according to the network optimization strategy to perform load-balanced data transmission, and the node working mode and the network topology structure are dynamically adjusted to realize global energy efficiency optimization, so that the global energy efficiency optimization is realized, and the overall operation time of the Internet of Things system is prolonged.

The invention discloses a chemical industry park emergency processing method based on digital twinning. High-precision digital modeling is carried out on the physical environment of the chemical industry park, and dynamic changes in the park are sensed and reflected in real time; the Internet of Things equipment model is fused with the twin scene, the Internet of Things equipment model is matched with equipment information of an actual park enterprise, and collaborative linkage of the model and monitoring equipment is achieved; emergency scene simulation of various chemical industrial parks is provided, and different scenes are selected for drilling according to actual requirements; a chemical industrial park accident emergency resource scheduling model is constructed, a digital twinborn simulation technology and a hybrid intelligent optimization algorithm are fused, and resource allocation in a dynamic and complex environment is realized; and a collision-free safe path from the starting point to the ending point is found by comprehensively considering the necessary points and the bypassing areas. According to the invention, through integration of a high-precision digital twinning technology, a comprehensive emergency simulation drilling system and method are provided for the chemical industry park, so that the emergency response capability and the safety management level of the park are comprehensively optimized and improved.

The invention discloses a power distribution equipment state monitoring system and monitoring method based on the Internet of Things technology, and relates to the technical field of power distribution equipment monitoring, the system comprises the following components: a sound acquisition module, a data transmission module, a data processing center and an early warning display module; by combining the Internet of Things technology, the expert system and the feature engineering technology, high-precision acquisition and analysis of operation sound signals of the power distribution equipment are realized, and by constructing the fault voiceprint feature library, the system can quickly recognize and match equipment fault features, so that early warning is given out at the initial stage of the fault, and the fault early warning efficiency is improved. In addition, due to the application of the cross-equipment associated fault prediction model and the fault propagation prediction model, the system can predict the propagation conditions of cascading faults and system-level faults, more comprehensive fault information is provided for operation and maintenance personnel, prevention measures can be taken in advance, and the fault monitoring accuracy and timeliness are improved. The influence of faults on a power distribution system is reduced.

The invention relates to a 5G network slice dynamic scheduling method and system based on multi-modal space-time perception and an event knowledge graph, and belongs to the technical field of mobile communication network resource management. According to the method, the change of a physical scene is sensed in real time by constructing a dynamically evolved event knowledge graph and designing a double-flow space-time cross network in combination with visual semantic analysis; dynamically adjusting the resource prediction model by adopting an event-scene dual-drive mechanism, dynamically adjusting parameters of the gated recurrent neural network through an elastic adjustment factor, and optimizing a multi-target resource allocation strategy based on a reinforcement learning algorithm; a two-stage resource scheduling mode is adopted, non-preemptive resource allocation of priority guarantee is implemented in an event triggering stage, and an optimization strategy of continuous adjustment is deployed in a steady-state stage. According to the method, the resource utilization efficiency and the service quality in a high-concurrency scene are remarkably improved, the method is compatible with an O-RAN standard interface, and the method is suitable for high-reliability and low-delay communication scenes such as smart cities and industrial internet.

The invention discloses a power equipment intelligent gateway fault detection system based on the Internet of Things, relates to the technical field of power equipment intelligent gateways, and is used for solving the problem that an existing power equipment monitoring system is inaccurate in fault detection. Comprising a power equipment access module, a data preprocessing and edge calculation module, a data storage and analysis module and a fault diagnosis and alarm module which are in signal connection. The power equipment access module is used for collecting operation state data of power equipment; the data preprocessing and edge calculation module performs cleaning, format conversion and preliminary calculation on the collected data, so that data redundancy is reduced, and the processing efficiency is improved; the data storage and analysis module stores the processed data and extracts feature information by using a big data analysis method; and the fault diagnosis and alarm module carries out fault identification based on an analysis result and triggers an alarm signal so as to realize accurate detection and efficient maintenance of the intelligent gateway of the power equipment.

The invention discloses a mobile storage and charging robot remote scheduling and path planning system based on the Internet of Things, and relates to the technical field of robot control. Comprising a path dependency modeling module, a path conflict analysis module, a resource dependency graph construction module, a decoupling rearrangement scheduling module, a time sequence offset evaluation module and a path weight regulation and control module, and obtaining a path node sequence and an access time period of a current to-be-executed task of each mobile storage and charging robot, and generating a task path pre-occupation graph. By constructing the path dependence model, the conflict prediction mechanism and the task decoupling rearrangement strategy, accurate identification and effective intervention of path conflicts and resource deadlocks in the multi-robot scheduling process are realized, the stability of the scheduling system and the task execution continuity are improved, efficient completion of energy supply is ensured, and the scheduling efficiency is improved. And the operation efficiency and safety of the system are obviously optimized.