1601 results about "Industrial Internet" patented technology

The invention relates to an industrial Internet of Things anomaly detection method based on time sequence and text joint modeling, and belongs to the technical field of industrial Internet of Things anomaly detection. The method comprises the following steps: constructing text prompt information based on collected industrial Internet of Things time sequence data, and respectively taking the text prompt information as inputs of a time sequence channel and a text prompt channel; a sensor association graph is constructed by using a multi-hop GCN, and on the basis of the association graph, time feature modeling from local to global is completed by using multi-scale expansion convolution and combining a differential attention mechanism; performing word segmentation processing on the text prompt information through a word segmentation device, and encoding the text prompt information into vector representation; and calculating attention weight between time sequence embedding and text prompt embedding, fusing to obtain joint embedding representation, enhancing the joint embedding representation, inputting the enhanced joint embedding representation into MLP for reconstruction, calculating an abnormal score through a reconstruction error, and carrying out industrial Internet of Things anomaly detection according to the abnormal score. The method is high in anomaly detection accuracy, and can improve the equipment anomaly perception and risk early warning capability.

The invention discloses an electronic material life cycle quality tracing method based on digital twinning, and relates to the technical field of industrial Internet of Things, the digital twinning of an electronic material is constructed, a material constitutive equation, a process parameter threshold library and historical quality data are integrated, and a multi-dimensional virtual model is formed; a production line real-time data stream including an equipment state, environmental parameters and material attributes is collected. According to the method, the virtual model containing the material constitutive equation and the process parameter threshold library is constructed, the real-time data flow dynamic evolution is combined, and the graph calculation and the causal reasoning algorithm are applied, so that the interaction effect of the equipment state, the environmental parameters and the material attributes can be associated, the core influence factor chain of the quality abnormality can be positioned, the single-point alarm limitation is broken through, and the quality abnormality can be accurately detected. The quality problem is deeply analyzed from the angle of multi-factor coupling, a comprehensive and systematic analysis framework is provided for accurate attribution, the source of the quality problem can be quickly and accurately found, and the efficiency and accuracy of quality tracing are improved.

The invention discloses an industrial Internet of Things time sequence self-supervision anomaly detection method and a monitoring and early warning system, and relates to the field of industrial Internet of Things, and the method comprises the steps: S1, constructing an anomaly detection model, and S2, obtaining a training data set; s3, training and optimizing an anomaly detection model; s4, acquiring to-be-detected data in real time; s5, performing anomaly detection analysis on the to-be-detected data, and outputting an anomaly detection result; through a time sequence and relation learning module, a dynamic graph topological structure learning module and an enhancement module, internal characteristics of a time sequence in a time domain and a space domain are deeply mined. The time sequence and relation learning module comprehensively captures a multi-scale time pattern, and the dynamic graph topological structure learning module eliminates dependence on a predefined graph structure; the enhancement module enhances the invariant representation under noise, and improves the recognition capability of the model to a normal mode; through wide experiments, the advancement of the method in detection performance is verified, and reliable support is provided for intelligent manufacturing and infrastructure diagnosis.

The invention discloses an enterprise production management method based on digital twinning and workflow simulation, and particularly relates to the technical field of industrial internet and intelligent manufacturing, and the method comprises the steps: constructing a physical production system digital twinning body and business process workflow model, and carrying out the dynamic association through a model fusion engine to form an integrated digital twinning model; real-time event driving is used for deducing and simulating a future production process, and bottleneck and conflict prediction is output; based on the prediction result, utilizing a multi-objective optimization engine to generate a plurality of alternative scheduling schemes; performing parallel simulation quantitative evaluation on the KPI of each scheme, selecting an optimal scheme, analyzing the optimal scheme into a control instruction, and issuing and executing the control instruction; and model self-correction and closed-loop optimization are realized through real-time monitoring and feedback. According to the method, the problem of service and physical state disjunction caused by digital twinning and workflow independence is solved, the whole-process closed-loop management from prediction to execution is realized, and the production self-adaption and intelligent level is improved.

The invention provides a production line scheduling method and equipment fusing edge computing and machine learning, and belongs to the technical field of production line scheduling, and the method comprises the steps: deploying edge nodes in an industrial production line, and carrying out the life cycle management and control of state monitoring, task issuing, resource configuration and version management on the edge nodes by a cloud; the edge nodes collect production line data in real time; constructing a scheduling model at the cloud, and issuing the scheduling model to the edge node; outputting execution task priority judgment and path and resource optimization in combination with the scheduling model, generating a scheduling instruction, and issuing the scheduling instruction to a field control system to execute task scheduling; recording all scheduling processes and state changes, and synchronizing to the cloud after the network is recovered; according to the invention, the analysis rule and the model parameters can be adjusted according to the actual working condition and the monitoring scene of the equipment, accurate description and abnormal early warning of the operation state of the equipment are realized, and the efficiency, the accuracy and the adaptability of equipment monitoring in the industrial Internet of Things environment are effectively improved.

The invention relates to a production abnormality automatic identification and recovery process control method, which comprises the following steps of S1, realizing second-level synchronization of multi-source heterogeneous data, constructing a real-time data flow pipeline and generating a total-factor production situation data flow through a distributed message queue by adopting a Modbus / TCP protocol analysis algorithm based on industrial Internet of Things edge calculation; through the cooperative effect of industrial protocol analysis and distributed message queues, second-level synchronization of multi-source heterogeneous data is realized, a real-time data flow pipeline is constructed, data acquisition delay is effectively eliminated, the timeliness of anomaly detection is ensured, a dynamic weight distribution mechanism of a rule engine and a long and short-term memory network prediction model is adopted, and the real-time performance of the system is improved. By combining sliding window threshold detection, the accuracy and coverage of anomaly recognition are improved, false alarm and missing alarm caused by a single detection mechanism are reduced, and multi-dimensional root cause tracing is performed by combining a fault mode knowledge base through combined application of time sequence correlation analysis and a causal diagram inference engine.

The invention discloses a metal formwork production full-process management and control system based on a cloud platform, and relates to the technical field of industrial manufacturing, and the system comprises an industrial cloud platform which is in communication connection with the following modules: a global element perception processing module, an industrial Internet-of-Things terminal used for combined deployment, and a cloud platform module. And multi-source heterogeneous data including equipment state, cutter service life information, material circulation information, personnel operation information and workpiece quality detection data are collected in real time. According to the method, the industrial Internet of Things terminal is deployed, multi-source heterogeneous data such as equipment state, cutter life and material circulation are collected in real time, a virtual production environment synchronized with a physical workshop is constructed in combination with a digital twinning technology, a production scheduling problem is converted into a path planning problem based on an ant colony algorithm, an optimal scheduling scheme is generated in real time, and the scheduling efficiency is improved. The problems that a traditional system is rigid in plan and slow in response are solved, and the flexibility and efficiency of production scheduling are remarkably improved.

The invention provides a factory energy safety dynamic monitoring method based on the Internet of Things and multi-mode perception, and belongs to the technical field of industrial Internet of Things. Comprising the following steps: acquiring electrical parameters, temperature data, combustible gas leakage concentration and vibration signals of equipment in real time; carrying out aggregation and protocol conversion on the electrical parameters, the temperature data, the combustible gas leakage concentration and the vibration signals through a multi-protocol intelligent gateway, and uploading the electrical parameters, the temperature data, the combustible gas leakage concentration and the vibration signals to a locally deployed edge computing node in a preset period; preprocessing the received data at the edge computing node, and generating a quantitative risk index based on a dynamic risk assessment model fusing the real-time state of the equipment, the historical aging trend and the environmental parameters; and carrying out risk grade judgment according to the quantitative risk index, and executing a corresponding grading response. Through fusion of multi-modal sensing data and edge intelligence, comprehensive sensing, real-time quantitative evaluation and hierarchical intelligent response of factory energy safety risks are realized.

The invention relates to an industrial internet multilayer causal motif abnormal propagation path identification method and system, and the method comprises the steps: firstly carrying out the construction and extraction of a multilayer high-order motif, extracting a motif unit which expresses the local high-order structure features through the construction of a semantic hierarchical graph structure in combination with a frequent sub-graph mining and cross-layer motif alignment mechanism, and carrying out the recognition of the abnormal propagation path of the multilayer causal motif. Stable and uniform multi-layer motif representation is formed; then, on the basis of the structural equation model, motif variables are regarded as endogenous variables of a causal model, a causal path between motifs is mined by introducing conditional mutual information and a Bayesian structure learning algorithm, an average causal effect is calculated to construct a causal consistency matrix, and causal community division is realized in combination with a weighted modularity optimization method; and finally, quantifying the dynamic change of a community causal structure by constructing a causal deviation graph between an expected causal graph and an observed causal graph, and assisting in identifying a causal-driven abnormal propagation path. According to the method and the system, accurate detection and causal traceability of equipment-level and subsystem-level abnormal modes in an industrial system can be realized.

The invention discloses a commodity receiving method and system based on digital twinning, belongs to the technical field of industrial internet and supply chain management, and aims to solve the technical problems of non-transparent information, tedious process, low efficiency, difficult inventory management and lack of effective monitoring in traditional commodity receiving. According to the technical scheme, data collection and twinborn modeling are carried out, specifically, digital and intelligent management of commodity receiving is achieved by building real-time mapping of a physical warehouse and a virtual twinborn body, multi-source data of material information, inventory data and position information of the physical warehouse are collected through Internet of Things equipment, and after cleaning processing is carried out, the real-time mapping of the physical warehouse and the virtual twinborn body is carried out; a three-dimensional modeling technology is adopted to construct a virtual twinborn body, and the consistency of the virtual twinborn body and a physical warehouse is ensured through a dynamic updating mechanism; real-time data processing and synchronization; intelligent receiving is realized; inventory monitoring and prediction; and performing user management and authority control.

The invention discloses a data privacy protection method for an industrial internet platform, and relates to the technical field of data security and privacy protection. According to the method, the sensitive information is accurately identified and deeply analyzed through the sensitive information feature library and the hierarchical matching algorithm, the problem of insufficient accuracy and flexibility during large-scale data processing is solved, the accuracy and reliability of data desensitization are remarkably improved, personal privacy is effectively protected, data availability is maximized, and the method is suitable for large-scale data processing. A hierarchical processing algorithm and a self-adaptive desensitization rule base are utilized, desensitization rules are dynamically adjusted according to dynamic access requirements and sensitivity levels of data, data security and availability are balanced, accurate protection under different scenes is ensured, system adaptability and flexibility are improved, and the data access process is monitored in real time, anomaly detection and rule verification are performed, so that the data access efficiency is improved. And the desensitization rule is dynamically adjusted, so that the security and reliability of the system are enhanced, the user credibility is improved, and the transparency and credibility of the data processing process are ensured.

Methods and systems for a monitoring system for data collection in an industrial environment including a data collector communicatively coupled to a plurality of input channels connected to data collection points operationally coupled to at least one industrial component in at least one of an industrial boiler system or industrial pipeline system; a data storage structured to store a library of stored noise patterns associated with operation of the at least one industrial component; a data acquisition circuit structured to interpret a plurality of detection values from the collected data; and a data analysis circuit structured to: analyze the collected data, determine a measured noise pattern for the at least one industrial component, and compare the measured noise pattern to the library of stored noise patterns to identify a changed condition of the at least one industrial component.

The invention relates to the technical field of industrial internet-of-things data security, in particular to an edge calculation differential privacy industrial internet-of-things data desensitization verification system and method.According to the system and the method, edge nodes are divided in a three-dimensional mode according to resource capacity, function positioning and privacy requirements, differential differential privacy parameters are formulated in combination with data attributes and leakage influences, and the safety of the industrial internet-of-things data is improved. And a dynamic adaptive grid is matched to realize hierarchical alignment, scene binding and elastic reconstruction, so that a cross-hierarchical privacy risk is avoided, and verification logic is simplified. Noise intensity is dynamically corrected by integrating multi-dimensional factors based on grids, lightweight, medium and deep hierarchical desensitization is designed for three types of nodes, and privacy protection and data availability are balanced. A privacy security and data availability two-dimension, terminal-gateway-area three-level verification chain is constructed, the desensitization effect is comprehensively evaluated, closed-loop iteration is achieved through hierarchical judgment and accurate adjustment, and industrial scene requirements are efficiently met.

The invention relates to the technical field of industrial internet of things operation and maintenance, and particularly provides a remote maintenance auxiliary method integrating video monitoring and three-dimensional modeling. The method comprises the following steps: acquiring engineering graphic data and point cloud scanning data of maintenance equipment, and collecting video stream data of a maintenance equipment site; the video stream data is used for describing the operation state of maintenance equipment; the video stream data comprises a plurality of video frames; matching the point cloud scanning data with the engineering graphic data, and constructing a watertight three-dimensional grid model according to a matching result; mapping texture features of the maintenance equipment in a target video frame to the surface of the watertight three-dimensional grid model to obtain a target three-dimensional model; and receiving a first maintenance instruction marked in the target three-dimensional model by a remote expert, and sending the first maintenance instruction to a video picture of a client of an on-site maintainer. According to the technical scheme provided by the invention, the time consumption for positioning the overhaul part can be reduced.

The invention discloses a wireless data uploading method and system for an industrial internet of things, and relates to the field of wireless data processing, and the method comprises the steps: firstly, comprehensively considering the inherent service attributes of data to determine the initial service priority of the data; meanwhile, terminal side sensing data and global network state data obtained from the gateway are fused, and a more accurate and comprehensive network quality score is calculated through double-source information. Finally, the initial priority and the network quality are dynamically combined according to the two key dimensions, and a final transmission strategy is generated. According to the method, a data uploading decision is no longer static and one-dimensional, but a two-dimensional dynamic decision which can be adaptively adjusted according to a real-time network condition, so that the sending opportunity or mode of the high-priority data is intelligently adjusted when the network is congested, the blocking of key services due to blind sending is effectively avoided, and the service quality is improved. And the robustness and the high efficiency of data uploading in a complex industrial environment are ensured.

The invention provides an intelligent resource decision-making method based on industrial chain collaborative optimization, and relates to the technical field of industrial internet management, and the method comprises the steps: integrating resource data, business data, external environment and other multi-source heterogeneous data from all participants, combining a causal conduction rule and an operation constraint rule, and carrying out the collaborative optimization of an industrial chain. According to the method, a dynamic digital model capable of simulating a real industrial chain operation mechanism is generated, concurrent extreme conditions of multiple risks in reality are simulated through a composite interference scene for deduction, and a corresponding multi-dimensional toughness index and a visual toughness profile are generated based on a simulation result; precise positioning of key fragile nodes and fragile conduction paths is realized, deduction of different types of composite interference scenes can simulate influences of various complex factors appearing in an actual industrial chain, so that corresponding resource allocation strategies are generated, a coping basis and a precaution basis are provided for appearing of the actual interference scenes, and the resource allocation efficiency is improved. And the coping capability of the industrial chain network to the emergency scene interference is improved.

The invention discloses an assemblage multi-robot collaborative welding system in ship sections, and relates to the technical field of industrial robot collaborative control, the assemblage multi-robot collaborative welding system comprises an industrial internet platform, the industrial internet platform is in communication connection with the following modules: a visual inspection modeling module, which is used for scanning the surface of a workpiece through a 3D camera, constructing a point cloud model, and establishing a point cloud model; matching the point cloud model with a design drawing, and identifying a deviation between an actual workpiece and the drawing; and the path planning and correcting module is used for planning a collision-free welding path in combination with the visual detection data and the motion range of the robot. Through high-precision visual inspection scanning and an SLAM matching algorithm, a workpiece point cloud model is constructed in real time and is accurately aligned with a design drawing, the welding seam position and the workpiece geometric deviation can be accurately recognized, a collision-free welding path is generated in combination with robot kinematics constraint and a safety boundary, it is ensured that the pose of the tail end of a welding gun is attached to the surface of the workpiece, and the welding quality is improved. Welding deviation is reduced, and process stability is ensured.

Provide are a system and a method for data management based on an Industrial IIoT data center. The system includes an IIoT user platform, an IIoT service platform, an IIoT management platform, an IIoT sensor network platform, and an IIoT sensing and control platform. The IIoT management platform is configured to: determine a future acquisition parameter of a sensor network sub-platform based on a remaining storage space, a first retrieval feature, and a future retrieval feature; determine a pre-increment storage capacity based on the future acquisition parameter; determine a group to be deleted based on the pre-increment storage capacity, the remaining storage space, and a group retrieval feature and a group data volume corresponding to each of a plurality of data groups; generate a parameter update instruction based on the future acquisition parameter; and generate a data deletion instruction based on the group to be deleted.

The multi-agent cooperative scheduling method for the incremental mixed flow in the industrial time-sensitive network comprises the following steps: analyzing transmission characteristics of an AVB flow and a TT flow in the industrial time-sensitive network, and carrying out refined modeling on dynamic interference caused by the increment of the mixed flow based on a network calculation theory; determining key transmission management variables of the AVB stream and the TT stream in combination with a dynamic interference factor introduced by the incremental mixed stream; designing a flow scheduling model based on multi-agent cooperation, and generating an end-to-end transmission scheme based on multi-agent cooperation decision when incremental mixed flow is accessed; and packaging the trained agents into a multi-agent cluster and deploying the multi-agent cluster into an industrial time-sensitive network for generating a transmission scheme for the incremental mixed flow on line. According to the method, the transmission requirement of the incremental mixed flow can be met while the existing AVB flow delay constraint is guaranteed, the transmission scheme is optimized to improve the service quality of an industrial time-sensitive network, and stable operation of advanced applications in the industrial Internet of Things is fully supported.

The invention belongs to the technical field of industrial Internet security, and discloses an industrial Internet security situation assessment and prediction method and system based on multi-stage feature enhancement and spatial-temporal feature fusion, and the method comprises the steps: collecting multivariate data in an industrial Internet, carrying out the feature analysis, carrying out the missing value processing, abnormal value elimination, and normalization operation, and carrying out the prediction of the security situation of the industrial Internet. Forming a standardized security situation data sequence; extracting features of the security situation data sequence by using the constructed situation assessment model based on multi-stage enhancement; and on the basis of the extracted features of the security situation data sequence, constructing a situation prediction model based on spatio-temporal feature fusion to realize attack identification and prediction.

The invention discloses a block chain and digital twinborn integrated production whole process credible tracing method, and belongs to the technical field of intelligent manufacturing and digital tracing. The system comprises a digital twin construction module, a block chain evidence storage module, a tracing verification module and a data acquisition module. The method comprises the following steps: creating a digital twinborn body for each product instance, and mapping material, process, equipment and quality data of the whole manufacturing process in real time; recording the twinborn key state hash value to a block chain in real time through an intelligent contract; credible tracing verification is provided based on block chain evidence storage and digital twin data; industrial Internet of Things equipment is used for collecting manufacturing data in real time to drive twin updating. According to the method, millisecond-level slice tracing in the manufacturing process is realized, the deep description capability of digital twinning and credible guarantee of the block chain are fused, the problems of insufficient data depth and low credibility of a traditional tracing system are solved, and judicial-level evidence support is provided for quality disputes.

The invention relates to the field of industrial Internet of Things data processing, in particular to an industrial Internet of Things data acquisition and elastic transmission method. The method comprises the following steps: acquiring a process unit topology and monitoring target configuration, performing process unit code registration, probe layout scheme generation and acquisition session configuration structure registration processing, and generating an acquisition session configuration structure; time anchor point unified processing, change node alignment sequence generation and contact value grading threshold registration processing are executed, and a data importance grading table structure is generated; executing fingerprint detection session list generation, network state fingerprint matrix construction and network state category judgment processing, and generating a network state category structure; and carrying out elastic transmission strategy graph construction and strategy version record registration processing to generate an elastic transmission execution configuration structure. According to the invention, through multi-level data processing and intelligent state perception, accurate alignment of data acquisition and elastic self-adaption of transmission are realized, and the reliability and efficiency of an industrial Internet of Things system are effectively improved.

The invention discloses an intelligent water operation management and control system based on the Internet of Things, and relates to the technical field of industrial Internet of Things crossing, and the system comprises the steps: collecting multi-dimensional operation data in a water supply network, carrying out the preliminary time mark alignment and preprocessing, and generating a multi-source synchronous perception data set; importing the comprehensive risk list into a digital twinborn body, dynamically injecting corresponding fault and abnormal scene parameters in a digital twinborn environment, and performing large-scale parallel deduction through a Monte Carlo simulation algorithm to generate a rehearsal consequence data set; and carrying out multi-objective decision analysis on the rehearsal consequence data set, carrying out prediction comprehensive plan utility scoring based on a preset safety weight and an economic weight, and calculating an optimized management and control plan through iterative optimization. According to the invention, the multi-source heterogeneous data is deeply fused through the graph neural network of the risk identification module, the dynamic impedance characteristics of the pipe network are accurately extracted, the early abnormality is identified, and advanced diagnosis of the multi-modal coupling risk is realized.

The invention relates to the technical field of power plant equipment maintenance, in particular to an industrial internet power plant equipment predictive maintenance system based on digital twinning. The system comprises an equipment data acquisition unit, a digital twinning construction unit, a state prediction unit, a maintenance strategy generation unit and a dynamic adjustment unit. The equipment data acquisition unit acquires multi-equipment real-time operation data, and a multi-dimensional data set is formed through time alignment and space registration; the digital twin construction unit performs sparse reconstruction on the data set to obtain target data and abnormal indication data; the state prediction unit generates a health state prediction result containing the remaining service life and the fault occurrence probability in combination with the target data, the historical maintenance records and the operation parameters; a maintenance strategy generation unit generates a scheme containing a maintenance priority and a time window; the dynamic adjustment unit adjusts the scheme according to the abnormal indication data. The system realizes comprehensive monitoring, accurate prediction and dynamic maintenance of equipment.

The invention relates to the technical field of production state management, in particular to an LCD display screen production state collaborative management method based on the industrial internet, which comprises the following steps of: acquiring execution cycles and response delays of multiple devices, performing aggregation analysis, generating a rhythm synchronization parameter table, performing time sequence alignment to calculate time sequence offset and generating a time sequence calibration instruction; the method comprises the following steps of: analyzing current power and cycle deviation to form an equipment coupling trend, monitoring a scheduling rhythm correction execution sequence, dynamically estimating delay and optimizing a production state management cooperative control result, realizing cooperative measurement and difference identification through multi-equipment rhythm normalization aggregation, and improving state response precision and synchronization consistency through time sequence alignment; load coupling analysis balances energy consumption distribution and rhythm, whole-line scheduling correction realizes production rhythm balance and continuous operation, the resource utilization rate and the production efficiency are improved, process waiting and energy consumption fluctuation are reduced, and the system cooperation and self-adaptive regulation and control capability is enhanced.

The invention discloses a heating furnace group control system and method based on the Internet of Things, and relates to the technical field of industrial Internet of Things heating furnace control. The adaptive edge calculation module fuses algorithm processing data and reduces response delay; the dynamic thermal efficiency optimization module predicts and improves thermal efficiency; the digital twinborn decision support module realizes fault prediction; the collaborative energy scheduling module optimizes multi-furnace operation; the block chain security management module guarantees data security; and the cognitive interaction interface module realizes multi-modal interaction, and the system also comprises extension modules such as a multi-scale combustion optimization module and the like, so that accurate control and intelligent management are realized. According to the invention, accurate acquisition and real-time processing of multi-dimensional data of the heating furnace group are realized, the heat efficiency is improved, and energy consumption and emission are reduced; the system has the capabilities of fault prediction, cooperative scheduling and data security guarantee; and the man-machine interaction experience is improved, the production efficiency is improved, and intelligent upgrading of industrial heating is promoted.

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 discloses an industrial Internet of Things federated learning method based on a federated increment decision tree, and the method comprises the steps: a cloud server deploys and initializes a federated learning global model and the federated increment decision tree, and sets a statistical histogram bucket boundary set of each feature value; in each federated learning iteration, the cloud server broadcasts a federated learning global model parameter, each industrial device adopts a local data set to train and count to obtain a local gradient histogram parameter, the edge server performs local aggregation on the local gradient histogram parameter and the federated learning model parameter, and the edge server performs local aggregation on the local gradient histogram parameter and the federated learning global model parameter; and the cloud server globally aggregates the local aggregation parameters of the gradient histogram and then incrementally trains the federated increment decision tree, simultaneously aggregates the parameters of a federated learning global model, and adaptively calculates an aggregation weight based on a second-order gradient value during local aggregation and global aggregation of the parameters of the federated learning model. The federal learning overhead can be effectively reduced, and the performance of the federal learning model is improved.

The invention relates to the technical field of industrial internet of things, in particular to an automatic equipment information collection management method and system, and the method comprises the steps: 1, transmitting a multi-protocol detection instruction sequence to target equipment, analyzing response data, and dynamically generating a protocol feature code; 2, analyzing a production process logic to generate an equipment action dependency chain, and creating a time sequence trigger condition expression according to a dependency relationship; step 3, equipment fault events are detected in real time, a dynamic acquisition strategy is generated based on the fault conduction relation, and the strategy comprises an acquisition frequency adjustment rule of associated equipment; and 4, executing a dynamic acquisition strategy, starting data acquisition of specified equipment when a time sequence triggering condition is met, and dynamically adjusting the acquisition frequency according to the fault conduction strength. The dynamic acquisition strategy generated based on the fault conduction relation can adjust the acquisition frequency in real time, the flexibility and timeliness of fault response are improved, and efficient and safe operation of the production line is ensured.

The invention relates to the technical field of coke tower foam layer measurement, and provides a high-temperature-resistant ultrasonic sensing array coke tower foam layer measurement system based on the Internet of Things, and the system comprises an ultrasonic sensor module which is installed at the central position of the top of a coke tower and vertically and downwards transmits and receives an ultrasonic signal; the signal processing module is connected with the ultrasonic sensor and is responsible for transmitting, receiving and preprocessing ultrasonic signals; the environment monitoring module is used for monitoring environment parameters in the tower in real time and providing environment compensation data for ultrasonic signal processing; the data transmission module is used for realizing data transmission between the signal processing module and the platform layer; the edge computing node is deployed in the field control cabinet, directly communicates with the signal processing module and is responsible for real-time data processing and local decision making; and the cloud platform is constructed based on an industrial internet platform and realizes data storage, deep data analysis and equipment remote monitoring management. The problems of potential safety hazards, insufficient precision and high cost of a traditional monitoring mode are solved.