360 results about "Iot gateway" patented technology

The invention relates to the technical field of industrial Internet of Things, in particular to a predictive maintenance method for intelligent factory Internet of Things equipment, which comprises the following steps of: acquiring equipment operation parameters, environment monitoring data and historical maintenance records, constructing a multi-dimensional feature data set, extracting equipment degradation features by adopting a topological graph attention mechanism and a Bayesian network, and establishing a multi-dimensional feature data set; the method realizes equipment health state modeling and fault probability prediction, combines a dynamic adjacency matrix and a multi-objective optimization algorithm, comprehensively optimizes maintenance cost, equipment fault risk and associated equipment influence, dynamically generates an optimal maintenance plan, carries out constraint optimization based on a mixed integer programming method, automatically generates a maintenance instruction sequence, and achieves the optimal maintenance of the equipment. Tasks are issued through the computerized maintenance management system, the PLC control system and the industrial Internet of Things gateway, and the execution state is monitored and maintained in real time. The intelligent level of equipment maintenance is effectively improved, non-planned shutdown is reduced, and the equipment reliability and the production efficiency are improved.

The invention discloses a coal mine heat energy monitoring and cooling regulation and control method based on the Internet of Things, and relates to the technical field of coal mine heat energy monitoring and cooling regulation and control. The collected temperature data are transmitted to a central monitoring system in real time through an Internet of Things gateway; redundant temperature sensors are arranged at key positions, and data of the temperature sensors in the same area are regularly compared and verified. According to the invention, the monitoring robustness is improved through a redundant temperature sensor and a data fusion algorithm, a monitoring blind area caused by a single-point fault is avoided, and the accident risk is reduced; a self-adaptive learning algorithm is adopted to dynamically optimize a detection threshold value and a compensation strategy, so that the anomaly detection accuracy is improved, and false alarms and missing alarms are reduced; the temperature trend is pre-judged through a predictive control algorithm, cooling is conducted in advance, balance between safety and energy efficiency is achieved, overheating accidents are prevented, and energy consumption is reduced.

The invention relates to the technical field of network management, in particular to an Internet of Things gateway control method and system based on AI computing power, and the method comprises the following steps: obtaining channel real-time signal intensity, noise level and occupancy proportion, monitoring protocol type and transmission condition, analyzing the relationship between retransmission times and interference, deducing protocol combination interference and throughput, and obtaining a real-time channel signal intensity, noise level and occupancy proportion. And configuring an optimal scheduling scheme, evaluating resource consumption and load states, packaging and requesting permission entrustment, and judging permission information in combination with an interaction record and an access control list. According to the method, different protocol distribution combinations are simulated and deduced, the data throughput of the whole network is further estimated and optimized, potential protocol conflicts are reduced, the use efficiency and the overall performance of network resources are effectively improved, consumption of a local computing unit and network bandwidth is comprehensively considered, packaging processing of request information is optimized, and the network performance is improved. And the real-time response capability and manageability of the network state are obviously improved.

The invention provides a heterogeneous network resource virtualization modeling and intelligent arrangement method and system based on a knowledge graph, and relates to unified modeling, dynamic retrieval and intelligent resource arrangement of heterogeneous network equipment. The method specifically comprises: 1, a unified modeling method based on a knowledge graph: integrating protocol attributes, dynamic states and topological relationships of heterogeneous devices such as a 5G base station, an SDN switch, a router, an Internet of Things gateway and the like into a structured knowledge graph, breaking the barrier of a manufacturer private data model, and realizing semantic-level collaborative scheduling of cross-domain resources; 2, designing a semantic retrieval engine: querying dynamic conditional reasoning through a natural language, replacing traditional manual rule definition, and improving retrieval response speed and accuracy; and 3, developing a graph-driven intelligent arrangement framework: combining a graph neural network (GNN) and reinforcement learning (RL), automatically generating a resource allocation strategy according to a real-time network state, reducing manual intervention and improving the resource utilization rate.

The invention belongs to the technical field of intelligent early warning, and discloses a water meter well remote ponding early warning method and system based on the Internet of Things. The method comprises the steps that collected water meter well measurement data are integrated through an Internet of Things gateway, comprehensive analysis is conducted on the measurement data, comprehensive characteristics are obtained, the measurement data and the comprehensive characteristics serve as input of an accumulated water analysis model, and an accumulated water characteristic mode is obtained; determining a leakage point coordinate through a fusion positioning algorithm in combination with sound wave positioning derivative features in the comprehensive features; performing dynamic analysis on the measurement data to obtain derivative dynamic characteristics, and taking the measurement data and the derivative dynamic characteristics as input of a dynamic characteristic model to obtain a dynamically adjusted graded ponding early warning threshold value; corresponding graded early warning is carried out according to the relation between the actual water accumulation amount and the graded water accumulation early warning threshold value; according to the invention, through multi-dimensional sensor data fusion and intelligent feature engineering, a closed-loop system integrating real-time monitoring, abnormity diagnosis, accurate positioning and grading early warning is constructed.

The invention relates to the technical field of Internet of Things gateways, in particular to an Internet of Things gateway and an operation method thereof. The method comprises the following steps: acquiring power system data, and constructing a power grid node topological structure diagram, so as to form a power system Internet of Things gateway; based on an Internet of Things gateway, power grid transmission demand prediction is carried out, an abnormal load state is analyzed, abnormal fluctuation of voltage and frequency is detected, and the stability of a power system is ensured; according to the frequency abnormity and voltage fluctuation data, the aging trend of power grid equipment is estimated, power resource loss estimation is carried out, abnormity early warning is carried out on power resource loss and equipment damage prediction data by utilizing an Internet of Things gateway, safe operation of a power grid is ensured, and early warning information is fed back to a terminal in time. According to the method, the intelligent level of the power system is effectively improved, and efficient resource configuration and fault management can be realized. According to the method, the Internet of Things gateway early warning is optimized, so that the Internet of Things gateway prediction is more efficient and accurate.

The invention relates to a zero-trust forest and grass Internet of Things identity authentication method and system based on a block chain. The method comprises the following steps: S1, initializing the block chain and formulating an intelligent contract; s2, registering the forest and grass Internet of Things equipment; s3, the forest and grass Internet of Things gateway accesses the forest and grass cloud platform and initializes the forest and grass cloud platform, the step S4 is executed after identity authentication, and otherwise, the step S5 is executed; s4, the forest and grass Internet of Things terminal accesses the forest and grass Internet of Things gateway and performs identity authentication, the step S6 is executed if yes, and the step S7 is executed if not; s5, forest and grass cloud platform security situation awareness and emergency disposal; s6, the forest and grass Internet of Things gateway transmits data to the forest and grass cloud platform; and S7, ending. The system is used for executing the authentication method. According to the method, a continuous dynamic identity authentication mechanism is constructed among the forest and grass Internet of Things terminal, the forest and grass Internet of Things gateway and the forest and grass cloud platform, and the intelligent contract technology of the block chain is utilized to record the access session into the block chain network, so that the access session and data exchange information are transparent, non-tampering and traceable.

The invention relates to the technical field of data processing, and discloses an Internet of Things gateway data grading processing method and system based on edge computing, and the method comprises the steps: collecting original operation data and a data traffic value received by an Internet of Things gateway; performing feature extraction and clustering grouping according to the original operation data, calculating an average influence coefficient of samples in a group in combination with a preset service weight, and determining an importance level corresponding to the group; important data is screened out for distribution immediate evaluation; performing aging characteristic analysis according to the important data, and predicting a data processing priority; analyzing resource demand allocation according to the priority sequence and the data flow value, and constructing a resource allocation proportion; according to a resource allocation proportion and an allocation proportion lower limit, identifying nodes with insufficient proportions and optimizing the priority sequence to obtain a final scheduling scheme; according to the final scheduling scheme, the processing sequence is applied to the original operation data, and data grading output is obtained. According to the method, efficient and accurate data processing is realized.

An information handling system of an orchestrating Internet of Things (IoT) gateway executing a carbon dioxide (CO2) minimizing IoT edge gateway data processing orchestration system comprises a network interface device receiving processed IoT sensor data generated by alteration of unprocessed IoT sensor data via access point (AP) IoT servers executing a common data processing method, a processor executing code instructions to determine that an initial volume of CO2 emitted per unit of processed sensor data transferred from the AP IoT servers to a cloud platform, via the orchestrating IoT gateway in an enterprise wide area network, is greater than an adjusted volume of estimated CO2 that would be emitted per unit of data if the common data processing method were performed at the orchestrating IoT gateway instead of the AP IoT servers, and transmitting an instruction to cease execution of the common data processing method at the AP IoT servers.

The invention relates to the technical field of Internet of Things gateways, and discloses an Internet of Things gateway device and system based on edge computing, and the device comprises a hardware heterogeneous computing module and a multi-interface protocol adaptation module. The edge calculation local processing module is used for performing preprocessing, feature extraction and local decision on the acquired terminal data; the network redundancy and self-healing module is used for realizing accurate network disconnection detection, seamless switching of a main network and a standby network and equipment fault self-recovery through a multi-dimensional network health degree evaluation algorithm; the local autonomous storage module is used for realizing hierarchical storage of high-frequency data and network disconnection emergency data and seamless synchronization after network recovery through network disconnection data hierarchical caching and an incremental synchronization algorithm; and the cloud collaborative management module is used for realizing remote configuration, OTA upgrading and state monitoring. The method is high in integration, low in delay and fast in response, adapts to real-time scenes, saves bandwidth cost, improves cloud efficiency, is high in reliability, and realizes network disconnection and continuous operation.

The invention relates to the technical field of port operation, big data processing and Internet of Things, and discloses a port operation optimization method based on Internet of Things and big data intelligent analysis, and the method comprises the steps: collecting various types of data based on an Internet of Things gateway, and constructing a standard data set according to the various types of preprocessed data; performing feature mining and analysis on the data in the standard data set, determining a predicted to-be-optimized link and predicted to-be-optimized data, and generating a first prediction optimization strategy; carrying out analogue simulation on the first prediction optimization strategy, generating a simulation optimization coefficient according to an analogue simulation result, judging whether the first prediction optimization strategy is corrected or not, and if yes, obtaining a second prediction optimization strategy; and judging whether a conflict exists in the second prediction optimization strategy or not, if so, calculating a priority coefficient, and generating a port operation optimization strategy according to the priority coefficient and the second prediction optimization strategy, thereby improving the port operation efficiency.

The invention discloses an intelligent medical big data information management system, and the system comprises a data collection layer which serves as a sensing neural network, employs an Internet of Things gateway to be compatible with a medical equipment protocol, and deploys an edge calculation node for physiological signal preprocessing; the intelligent preprocessing layer is used for cleaning and standardizing the original data transmitted by the data acquisition layer; the informatization service layer is used for realizing fine-grained authority control based on an RBAC (Role Based Access Control) model, realizing inspection process automation by integrating an LIS (Line Information System) interface through a Hyperdger Fabric block chain evidence storage key operation hash, and constructing a zero-footprint DICOM (Digital Imaging and Communications in Medicine) viewer; the intelligent analysis layer is used for carrying out data life cycle management by adopting a mixed storage architecture, carrying out feature engineering on data, carrying out label matching and classified storage by using efficient neighbor search and similarity / distance measurement, quantifying contribution of each feature or feature combination to a single prediction result through an SHAP value, generating an interpretation result, and outputting the interpretation result; and presenting an interpretation result to a clinician or related personnel through a query interface.

Disclosed are methods, systems, and one or more computer-readable mediums for receiving, at an edge device from an external server, a rules database comprising a set of rules; receiving, by the edge device, first data from at least one device; processing, by the edge device, the received first data by comparing the received data to each rule among the set of rules; identifying a first triggering event in response to detecting a match between the received data and a rule of the rules database; and outputting an alert corresponding to the first triggering event.

The invention discloses a dual-channel adaptive network stability testing method and system, and belongs to the technical field of computer network testing. The system adopts a dual-channel asynchronous communication architecture in which a control channel and a data channel are physically separated, instructions and state information are transmitted through an independent TCP control channel, and high-speed test data streams are transmitted through a UDP or TCP data channel. The method comprises the following steps: dynamically and adaptively adjusting a timeout parameter based on real-time network delay detection; nanosecond constant interval packet sending control is realized by using a high-precision monotonic clock source and busy waiting compensation mechanism; a packet loss detection, positioning and ACK confirmation mechanism based on the serial number is realized in an application layer; and real-time statistical display and idle detection automatic termination are realized through multi-thread parallel processing. According to the invention, the precision, the stability and the adaptive capability of network testing are remarkably improved, and the method is suitable for long-time stability testing in various scenes such as a high-speed Ethernet, a satellite link, an industrial Internet of Things gateway and the like.

The invention relates to the technical field of intelligent monitoring, and discloses a cloud internet-of-things security monitoring method and system with a fault self-diagnosis function. The method comprises the following steps: an Internet of Things gateway pre-processes environmental data and then generates first processing data, an edge computing device performs real-time computing processing on the first processing data, generates second processing data, performs fusion analysis on all the second processing data, and outputs a first analysis result; the Internet of Things gateway transmits the first processing data, the second processing data and the first analysis result to a cloud server; the cloud server stores the received data, the abnormal state sensing model outputs a second analysis result, the second analysis result is displayed in a map level, and a real-time safety report is generated; the operation state of the monitoring system is monitored in real time, and a real-time fault report is output; and summarizing the real-time safety report and the real-time fault report and then transmitting the summarized report to a terminal. According to the invention, the efficiency and accuracy of cloud Internet of Things security monitoring are improved.

The invention relates to the technical field of Internet of Things data processing, in particular to a multi-modal data real-time processing method for an Internet of Things gateway, and the method comprises the steps: obtaining multi-source operation data, and packaging the multi-source operation data into a standard operation data set; obtaining high-bandwidth modal data in the standard operation data set; when the maximum link delay time is greater than a preset delay abnormal threshold value, obtaining a version effective fingerprint of the target model and a local model fingerprint; when a model version abnormity diagnosis result is obtained, calculating information redundancy; obtaining correction delay time, and comparing the correction delay time with a preset normal reference delay interval; and when the correction delay time is greater than or equal to the upper limit value of the normal reference delay interval, adjusting the current decision period. By monitoring delay in real time, diagnosing consistency of model versions, eliminating redundant information and executing link adjustment, accurate processing of high-bandwidth modal data is realized, and real-time performance and reliability of Internet of Things gateway data processing are improved.

The invention provides an intelligent production line management and control method and system for an automobile brushless motor, and relates to the technical field of motor vehicles, and the method comprises the steps: carrying out the comprehensive defect detection, and obtaining motor defect distribution; process failure mode backtracking is carried out, and P production line devices of P production process nodes are positioned; cross-process parameter coupling analysis is carried out, and M groups of parallel monitoring devices are recombined; m edge detection nodes are configured, the M edge detection nodes are in communication connection with a production line management and control middle table through an Internet of Things gateway protocol, and M multi-modal monitoring tasks are set; after the calibration state is calibrated, the M edge detection nodes dispatch M multi-modal monitoring tasks through dynamic computing power distribution, and dynamic calibration triggering driven by deviation diagnosis is executed. The technical problem that in the prior art, production of motor vehicles such as electric vehicles generally depends on manual inspection and experience judgment to conduct equipment calibration, an automatic real-time monitoring and calibration mechanism is lacked, and therefore the product quality is affected is solved.

The invention relates to the technical field of state monitoring, in particular to a state monitoring method and system based on a power grid intelligent Internet of Things gateway device, and the method comprises the following steps: based on the Internet of Things gateway device, collecting and analyzing multiple types of sensor signals, carrying out the unified arrangement to form a multi-parameter cooperative monitoring sequence, and comparing the changes of current and voltage signals; and extracting a synchronous response ratio feature group, calculating a thermoelectric trend consistency index in combination with the temperature change trend and the current amplitude, screening an interference level section, and judging abnormal linkage. According to the method, cross-parameter trend extraction, collaborative response, difference comparison and section identification are realized, correlation characteristics of multi-source signals in a continuous time period can be converted into identification basis, an abnormal forming process, a response link and state evolution are discriminated by combined characteristics, and abnormal section, sequence attributes and synchronization factors are jointly applied, so that the accuracy of the multi-source signal identification is improved, and the reliability of the multi-source signal identification is improved. And the identifiability and the classification capability of abnormal events under complex working conditions are improved.

The invention belongs to the technical field of data management, and discloses an inspection sample data tracking identification method and system based on the Internet of Things. The method comprises the following steps: a cloud data center constructs a sample tracking identification model and a sample data analysis model, and deploys a block chain network; the Internet of Things gateway is used for collecting real-time handover records and sending the real-time handover records to the cloud data center; the cloud data center performs tampering verification and generates real-time test sample data; the cloud data center is used for performing sample tracking and identification by using the sample tracking and identification model; the cloud data center is used for performing sample data analysis by using a sample data analysis model; and the cloud data center is used for storing the real-time test sample data, the real-time sample tracking identification result and the real-time sample data analysis result of the detection sample by using a block chain network. According to the invention, the problems of easy data tampering, difficult tracking, inaccurate analysis, low management efficiency and low storage reliability in the prior art are solved.

The invention discloses a communication method, system and device of Internet of Things equipment and a medium, and the method is applied to an Internet of Things gateway, carries out the communication strategy generation of configuration parameters and historical transmission data records according to an Internet of Things communication mode set, obtains a parallel communication strategy, and carries out the communication strategy generation of the configuration parameters and the historical transmission data records. The parallel communication strategy comprises a plurality of target communication modes; secret key generation is carried out on the parallel communication strategy to obtain a communication secret key group, the communication secret key group comprises a plurality of mode secret keys, and each mode secret key corresponds to one target communication mode; and transmitting the to-be-transmitted data of the Internet of Things equipment to the Internet of Things server side in parallel according to the communication key group and the parallel communication strategy. According to the method, the communication stability and reliability of the Internet of Things equipment can be effectively improved. The invention relates to the technical field of Internet of Things.

The invention provides a method for tracing the quality of a high polymer material in down garments based on a block chain. The method belongs to the technical field of clothing quality tracing and information management. The method comprises the following steps: performing Internet of Things tagging on all high polymer raw materials, creating digital assets for each batch of raw materials by utilizing a block chain technology, and uploading key data to a block chain to form a raw material file; related data of the production line are monitored in real time, and the related data are uploaded to the cloud platform in real time through the Internet of Things gateway; a unique logistics label is distributed for finished down garments through RFID and GPS, logistics information is recorded, and the logistics information is linked with raw material archives and production records on a block chain to form a complete supply chain view. Through the block chain technology, the whole process of all high polymer raw materials from purchasing to finished down garments is recorded on the block chain, and a data chain which cannot be tampered is formed.

The invention discloses an intelligent Internet of Things gateway management method. The method comprises the following steps: a gateway receives original data, constructs a data analysis rule, and sets each data analysis node which needs to be used in a data analysis process, wherein each data analysis node is used for one step in the data analysis process; according to the sequence of each step in the data analysis process, determining the sequence of each data analysis node and the corresponding relationship between two adjacent data analysis nodes, and constructing a data analysis rule; for the original data received by the gateway, the edge calculation operates the data analysis nodes one by one according to the sequence of the data analysis nodes determined by the data analysis rule to process the received data so as to complete formatting processing of the original data received by the gateway to obtain data meeting the data format requirement of a target platform; and pushing the data: sending the data obtained by analyzing the original data received by the gateway to the server of the target platform.

The invention provides an access method and device of Internet of Things equipment and an Internet of Things system, and relates to the technical field of Internet of Things. The method is applied to an Internet of Things gateway, and comprises the following steps: obtaining configuration data of an Internet of Things device to be accessed according to unique identification information of the Internet of Things device to be accessed; assembling function points of the object model according to the configuration data in combination with an equipment function model library so as to obtain a target object model of the to-be-accessed Internet of Things equipment; and sending the target object model to the terminal, so that the terminal controls the to-be-accessed Internet of Things device based on the target object model. According to the method and the device, the equipment function model library for unifying the abstract equipment capability is constructed, and the function point assembly of the unique identification information object model of the to-be-accessed Internet of Things equipment is combined, so that dynamic configuration is realized. The problems of low reusability, poor compatibility and high maintenance difficulty in a traditional equipment access mode can be effectively solved, the access period is greatly shortened, and the equipment access development workload and the maintenance cost are reduced.

The invention provides an oil depot state low-cost monitoring method and system based on the Internet of Things, and relates to the technical field of oil depot detection, and the method comprises the steps: deploying a sensor group at the position of an oil depot storage tank, and collecting original monitoring data; preprocessing the collected original monitoring data, and then uploading the data to a cloud server through an Internet of Things gateway; based on the received data, the cloud server performs inversion calculation on the real-time liquid level height of the storage tank through an intelligent algorithm, and performs calibration calculation on the volume of the special-shaped storage tank; state fusion analysis and abnormity diagnosis are carried out based on the real-time liquid level height, the volume data and the multi-source environment data; and triggering graded early warning according to a preset rule based on results of analysis and abnormality diagnosis. According to the method provided by the invention, hardware deployment cost is revolutionarily reduced, and all-around efficiency leap from manual regular inspection to unmanned real-time monitoring, from single parameter measurement to multi-source fusion diagnosis, and from passive reading to active early warning is realized.

The invention relates to an equipment fault prediction method and system based on the Internet of Things, equipment and a readable medium. The method comprises the following steps: acquiring real-time data of equipment; aggregating the real-time data by using an Internet of Things gateway, and carrying out preliminary cleaning on the real-time data; preprocessing the preliminarily cleaned real-time data to obtain real-time feature data; comparing the real-time feature data with historical data to obtain the similarity between the real-time feature data and the historical data; if the similarity is smaller than a similarity threshold value, constructing a fault prediction algorithm model; inputting the real-time feature data into a fault prediction algorithm model for prediction so as to obtain a health state score of the equipment; obtaining a fault risk score based on the health state score; and if the fault risk score is higher than a score threshold, the fault prediction algorithm model outputs a fault type. Potential faults of the equipment can be timely and accurately found and early warned, the maintenance cost is reduced, and the production efficiency is improved.

The invention discloses a geotechnical engineering exploration intelligent management system and method based on the Internet of Things. Data are transmitted to a cloud platform for storage and analysis through an Internet of Things gateway. The cloud platform combines big data analysis and a city information model, carries out descriptive statistics, trend analysis and spatial analysis on the data, identifies potential geological risks in an exploration process, predicts the risks by using an ARMA model, identifies and evaluates the potential risks in advance, provides intelligent decision support, and then carries out decision making on the potential geological risks; on-site data and equipment states are remotely monitored through an informatization management system. When a risk is found, the safety management system automatically executes a corresponding safety operation instruction, generates a safety report and gives an alarm. According to the invention, the whole process of geotechnical engineering exploration can be monitored in real time, risk identification, emergency response capability and operation safety are significantly improved, and efficient and safe exploration operation is ensured.

The invention relates to the technical field of internet of things valve wireless monitoring, and particularly discloses an internet of things-based valve wireless monitoring and early warning system, which comprises a signal acquisition module for acquiring a valve position simulation position signal of a valve in real time through a position sensor; the wireless communication module is used for performing analog-to-digital conversion on the valve position analog position signal and transmitting a valve position digital signal generated after analog-to-digital conversion through a first wireless communication network and a second wireless communication network; the monitoring module is used for sequentially selecting each monitoring point position in the signal acquisition module and sending a monitoring command data packet to the wireless control terminal corresponding to each monitoring point position through the Internet of Things gateway; the control module is used for receiving a valve position control instruction fed back by the monitoring command data packet of each monitoring point position and generating early warning information; and the execution module is used for driving a valve to execute valve action operation according to the valve position control instruction.

The invention belongs to the technical field of edge computing, and discloses an Internet of Things gateway collaborative management system based on edge computing, which comprises a resource virtualization unit used for deploying a protocol independence abstract engine in a gateway cluster, analyzing and adapting various Internet of Things protocols, constructing a proprietary protocol conversion matrix, obtaining standardized equipment attribute data, and storing the standardized equipment attribute data in a server; establishing an equipment shadow model based on the standardized equipment attribute data; the edge collaborative scheduling unit is used for outputting panoramic state data through the equipment shadow model, abstracting the gateway cluster into a tough topology directed graph based on the panoramic state data by adopting an edge computing task scheduling algorithm, and coordinating the gateway cluster; the intelligent load balancing unit is used for monitoring the gateway cluster calculation load and the network transmission load at the same time, and predicting the load imbalance rate according to the gateway cluster calculation load and the network transmission load; the efficient, flexible and intelligent cooperative management of the Internet of Things gateway cluster is realized.

The invention relates to the technical field of forging and pressing monitoring, and discloses a forging and pressing monitoring method and system. According to the method, an Internet of Things sensor is arranged on forging and pressing equipment, various data in the forging and pressing process are collected in real time, and the data are transmitted to a data center through an Internet of Things gateway. In the data center, after data is preprocessed, a deep learning model based on a CTA-GNN algorithm is used for analysis, and whether equipment faults or process risks exist or not is judged. And when the risk probability exceeds a preset threshold value, the system automatically generates early warning information, sends the early warning information to related personnel through a short message, a mail or a system notification and the like, and records a sending state and a receiving confirmation condition at the same time. In addition, the invention further provides a corresponding intelligent monitoring system which comprises a data acquisition module, a data transmission module, a data processing module, an intelligent analysis module and an early warning management module. The system can accurately judge faults, improve monitoring efficiency and accuracy, timely discover and early warn potential risks, and effectively guarantee safety and stability of the forging process.

Disclosed is a system for providing dynamic spectrum access, the system comprising: one or more sensor nodes configured to collect environmental data; a plurality of Internet of things (IoT) gateways arranged in data or signal communication with the one or more sensor nodes via a network protocol, each IoT gateway comprising at least one spectrum channel; a distributed ledger configured for secure data processing between each of the plurality of IoT gateways and the one or more sensor nodes; and a dynamic spectrum access module arranged in data or signal communication with the plurality of IoT gateways, the dynamic spectrum access module configured to implement a deep learning algorithm, the deep learning algorithm configured to obtain the environmental data as input, parse the environmental data to obtain spectrum environment parameters, predict one or more use states of the at least one spectrum channel of each of the plurality of IoT gateways; and provide a spectrum access action based on the predicted one or more use states.