299 results about "Industrial Ethernet" patented technology

The invention discloses a cloud edge collaborative multi-source data fusion safety management and control system for intelligent power distribution equipment, relates to the technical field of intelligent power grids and power distribution automation, and is used for managing and controlling 10-35 kV power distribution equipment. The system comprises an edge data acquisition module, a preprocessing module, a cloud storage management module, a cloud edge collaborative scheduling module, a multi-source data fusion module, an intelligent risk assessment module, a safety control execution module, a safety protection module and a man-machine interaction module. The modules are interacted through a 5G / industrial Ethernet; the acquisition module obtains multiple parameters, the preprocessing module cleans standardized data, the cloud side performs hierarchical storage, the scheduling module allocates tasks, the fusion module integrates data, the evaluation module performs grading risk, the protection module guarantees safety, and the interaction module performs visual alarm. The method improves the power distribution data quality and risk assessment precision, optimizes the cloud edge cooperation efficiency, enhances the safety protection capability, achieves the preventive operation and maintenance of equipment, reduces the fault and power failure time, reduces the operation and maintenance cost, and provides support for the safe and efficient operation of a power distribution network.

The invention discloses a data access method based on an industrial internet and an industrial internet integrated system, and the method comprises the steps: collecting real-time operation parameters, packaging the parameters into an information packet, recognizing nodes by an edge gateway, dynamically discovering a model by an edge node, evaluating the nodes to generate availability scores, verifying the consistency of data, and recombining the data to form a complete unit. And distributing to a corresponding processing queue to analyze and extract features. According to the system, through an edge node dynamic discovery model and a data consistency verification algorithm, in combination with industrial internet parameters, collaborative optimization of node dynamic discovery and data verification is achieved, all units are connected and interacted through an industrial Ethernet bus, data access reliability and real-time performance are improved, and the system is suitable for data processing requirements in industrial internet complex scenes.

The invention discloses an industrial process supervision and management system based on multi-modal data processing, which comprises a data acquisition module (100), a data fusion module (200), an anomaly detection module (300), a decision optimization module (400) and a visual interaction center (500), and is characterized in that the data acquisition module (100) transmits multi-source sensing data to the data fusion module (200) in real time through an industrial Ethernet; the data fusion module (200) forms a multi-modal feature vector and sends the multi-modal feature vector to the anomaly detection module (300), and the anomaly detection module (300) presets a threshold value and sends a joint alarm signal to the decision optimization module (400) and the visual interaction center (500). The method has the advantages that breakthrough is achieved in the aspects of detection speed, positioning precision and decision reliability through multi-modal data fusion and dynamic modeling, and the technical bottlenecks of time sequence misalignment, single-source misjudgment, insufficient causal analysis and the like existing in a traditional industrial monitoring system are effectively solved.

The invention discloses a mold injection molding full-process automation system based on layered cooperative control, which comprises a central control layer, a module scheduling layer and an equipment execution layer, and clock synchronization among the layers is realized through an industrial Ethernet. The central control layer comprises a dynamic scheduling server and a digital twinborn verification module, the module scheduling layer comprises a mold intelligent scheduling module, an injection molding process control module and a product quality closed loop module, and the equipment execution layer comprises a manipulator cooperation unit and a sprue material processing unit. By constructing a three-layer cooperative control architecture of a central control layer, a module scheduling layer and an equipment execution layer and combining a modular hardware carrier and a cross-layer data closed loop, common industrial problems such as cooperative level splitting, mold control marginalization and abnormal response delay are systematically solved; and finally, the comprehensive benefits that the die changing efficiency is remarkably improved, the product reject ratio is greatly reduced, and the energy consumption is obviously reduced are achieved.

The invention relates to the technical field of industrial automation and intelligent manufacturing, and discloses an industrial big data simulation verification system based on digital twinning, and the system comprises a physical entity module which is used for deploying an industrial sensor array and collecting the real-time parameter data of production equipment; the digital twin mirror image module is linked with the physical entity module through a data mapping engine, and synchronously updates a virtual mirror image model at a millisecond level according to the parameter change of the physical entity; and the data transmission module is used for transmitting the data acquired by the physical entity module to the digital twin mirror image module and the multi-source data fusion module through a 5G or industrial Ethernet, and is used for cleaning the acquired multi-modal industrial data and eliminating noise data. Through the physical entity-digital twin mirror image real-time linkage design, the limitation that traditional simulation is disjointed with entities is broken through. The industrial sensor array is combined with the edge calculation module, so that rigid linkage of physical production equipment, a real-time database and a digital twin mirror image is realized.

The invention discloses a charging pile liquid cooling module intelligent cooperative control system based on an industrial Ethernet, and relates to the technical field of liquid cooling control. The charging pile liquid cooling module intelligent cooperative control system based on the industrial Ethernet comprises a liquid cooling data acquisition and preprocessing unit used for acquiring and preprocessing liquid cooling cooperative operation data; the thermal load sensing and intelligent liquid cooling regulation and control unit is used for dividing independent cooling branches, evaluating the thermal load degree of each cooling branch, judging the real-time thermal load state and generating and executing a control instruction; the anomaly detection and cooling redundancy control unit is used for evaluating the thermal response deviation condition of a cooling branch and judging whether a double-ring redundancy pipeline is started or not; and the fault root cause judgment and recovery enhancement unit is used for evaluating the component fault risk degree in the abnormal branch and executing fault-tolerant control and branch recovery strategies. The problem that a whole set of liquid cooling system is shut down due to the fact that local fault tolerance cannot be achieved when a local branch breaks down in a traditional pipeline structure is solved.

The invention relates to the technical field of power electronics and power supply and distribution, and discloses a data center AC / DC power distribution system input and output voltage and current monitoring method, which comprises the following steps: deploying an intelligent sensing unit with edge computing capability at each monitoring node, sampling according to a basic frequency, and extracting effective values, harmonic distortion rates, differential change rates and other characteristics of voltage and current in real time; judging that the system is in a steady state, a quasi transient state or a strong transient state according to the multi-stage dynamic threshold value, and respectively executing feature abstract uploading, local high-frequency backtracking or collaborative full-waveform synchronous acquisition; and the central server classifies and stores the data, constructs a state evolution model, and dynamically optimizes threshold parameters. The system comprises an intelligent sensing unit, a central monitoring server and an industrial Ethernet link. According to the method, the steady-state data transmission quantity is remarkably reduced, transient details are completely captured, and adaptive adjustment of the monitoring sensitivity is realized.

The invention provides a coal mine underground MR safety training system, which comprises an intrinsically safe MR head display device, a multi-mode communication center module, a roadway environment sensing unit, a virtual-real fusion training engine and an intelligent evaluation system, and is characterized in that the intrinsically safe MR head display device comprises an annular distributed antenna array and a wide bandgap semiconductor circuit; the antenna is embedded in an anti-collision beam of the mining helmet, and intrinsic safety is achieved through dynamic power control; the multi-mode communication central module forms a dual-channel redundant architecture by an industrial Ethernet interface and a mining wireless unit, is internally provided with a dynamic routing chip, and automatically switches communication modes according to methane concentration and dust detection values to ensure low-delay and high-reliability transmission; the roadway environment sensing unit integrates a geological radar and a micro-seismic monitoring device, constructs a three-dimensional dynamic geological model in real time, and continuously updates roof displacement data.

The invention provides an energy complex flexible dynamic aggregation method and system based on reinforcement learning, and the method comprises the steps: collecting the real-time operation parameters of electric, gas, heat and multi-energy coupling equipment in an energy complex through an industrial Ethernet protocol, and constructing a continuous time dynamic system model through employing a Shenchang differential equation process modeling technology; and the nonlinear dynamic characteristics of various energy resources are accurately described. Modeling an aggregation process into a Markov decision process, and training and optimizing by adopting an improved twin delay depth deterministic strategy gradient algorithm to generate a reinforcement learning aggregation strategy model. The system realizes self-adaptive dynamic optimization through an online learning updating mechanism, and the renewable energy consumption rate and the system operation efficiency are remarkably improved.

The invention discloses a digital control integrated system for an auxiliary workshop of a power plant, and relates to the field of digital control, which comprises the steps of configuring sensor layout and measuring points, acquiring operating parameters based on sensors, acquiring processing parameters based on sensors, acquiring noise data based on sensors, and acquiring parameters of a heat supply system based on sensors. A multi-level sensor arrangement and operation parameter acquisition module, an industrial Ethernet control and multi-protocol interconnection communication module, a dynamic threshold comparison and combined cycle working condition analysis module and a cooperative control and multi-system synchronous scheduling module are configured in a power plant auxiliary workshop. The closed-loop monitoring and secondary correction control module and the historical database archiving and optimization learning module realize unified acquisition, centralized control and intelligent optimization of a gas turbine control system, a steam turbine control system, a waste heat boiler control system, a chemical water treatment control module, an environmental protection monitoring system and a heat supply system adjusting device.

The invention discloses an ICS-oriented efficient multi-source data interaction method and system, and relates to the technical field of intelligent power plant ICS data communication. According to the method, interaction variables are limited through a backboard bus agent, and the data interaction security is improved in combination with a dual-access security control mechanism; a multi-link communication architecture fusing optical fiber, 5G and industrial Ethernet is constructed, transmission tasks are dynamically allocated by using an intelligent scheduling algorithm, and a communication procedure and a coding strategy are adaptively adjusted, so that the data transmission efficiency and reliability in different network environments are improved; a distributed cache unit is deployed at each server node, and the data access delay is reduced in combination with a cache consistency regulation and an intelligent scheduling and prefetching mechanism; through an omnibearing fault perception and parallel rapid switching algorithm, rapid takeover under a fault condition is realized, and service continuity is ensured in cooperation with data consistency; the method can be widely applied to industrial control systems with high requirements on data interaction safety, real-time performance and reliability, such as intelligent power plants.

The invention provides an EEPROM (Electrically Erasable Programmable Read-Only Memory) data read-write method and device based on a real-time industrial Ethernet and a medium. The method comprises the following steps: setting an initial offset and an access length of EEPROM access on a target slave station according to EEPROM capacity parameters, and establishing a paging transaction; when the paging transaction is of a reading type, sequentially triggering EEPROM control registers of the target slave station, reading EEPROM data to a buffer area of the master station according to pages from the initial offset, and executing cyclic redundancy check on each page of read data; when the paging transaction is of a write-in type, reading a to-be-written data file into a buffer area of the master station, writing the to-be-written data file into an EEPROM (Electrically Erasable Programmable Read-Only Memory) of a target slave station according to a page sequence from initial offset, and after writing of each page is completed, reading back data of the page and executing cyclic redundancy check; and when all page data of the paging transaction of the reading type or the writing type passes cyclic redundancy check, outputting a transaction completion state to the soft programmable logic controller. According to the method and the device, the technical effects of safely, efficiently and accurately reading and writing the EEPROM on line in batches and ensuring the data consistency can be achieved.

The invention relates to an intelligent security management system for a sewage pumping station, which relates to the technical field of intelligent security management and comprises a front-end sensing layer, a transmission layer, a platform layer and an application layer, the front-end sensing layer is composed of a high-definition network camera deployed on the enclosure wall of the pump station and the top of a process area, a face recognition terminal installed on the side wall of a doorframe of an entrance and exit and a license plate recognition unit, the camera is powered by a POE, and the access controller is connected with a biological recognition card reader through an RS485 bus. Omnibearing monitoring and accurate identity verification of a pump station area are achieved through a high-definition network camera of the front-end sensing layer and the biological recognition technology, illegal invasion is effectively prevented, the transmission layer is combined with the wireless Mesh network through the industrial Ethernet, the stability and the anti-interference capacity of data transmission are ensured, and the service life of the transmission layer is prolonged. And the smoothness of a monitoring picture can be kept in a complex electromagnetic environment, and rapid fusion of multi-source data and efficient processing of service logic are realized.

The present application discloses an industrial Ethernet communication method and system, a network device, and a computer storage medium. The method comprises: receiving a PDO packet sent by an industrial master station or a previous industrial slave station, and determining whether to forward the PDO packet to a next industrial slave station; and when a frame processing unit receives the PDO packet returned from the next industrial slave station, or determines that the PDO packet is not forwarded to the next industrial slave station, and if it is determined, on the basis of a preset address mapping rule, that a read instruction is present in the PDO packet, responding to the read instruction to write read target data into a target position of the PDO packet, and then forwarding the PDO packet to the industrial master station or the previous industrial slave station.

The invention discloses a piece placing sequence mistake proofing device which comprises a multi-dimensional sensing module, an intelligent control module, a reconfigurable positioning module, an execution intervention module, a block chain tracing module and a man-machine interaction module, and all the modules communicate through an industrial Ethernet. The multi-dimensional sensing module and the intelligent control module adopt a time-sensitive network protocol to guarantee the certainty of data transmission, the multi-dimensional sensing module integrates an AI vision assembly, an ultrahigh frequency radio frequency identification read-write unit and a polarized light detection assembly, and parallel acquisition and cross validation of cutting piece multi-dimensional information are realized through a time sequence synchronous triggering mechanism. According to the method, a full-dimensional recognition system can be constructed through cooperation of multi-modal sensing fusion and an intelligent algorithm to avoid a single detection blind area, multi-variety production requirements are adapted by relying on AI incremental learning and rapid remodeling design, non-tampering tracing of quality data is realized in combination with a block chain technology, and the method is suitable for mass production. And meanwhile, reliable operation of the system is guaranteed through redundancy control and emergency design.

The invention provides an intelligent agent decision-making system based on reinforcement learning, which comprises a three-dimensional visual sensor, a mechanical arm controller, a central processing unit, a conveyor belt simulation platform and an industrial Ethernet switch which are connected through the switch. Pose data of objects on the conveying belt are collected in real time through the three-dimensional vision sensor, and the mechanical arm controller executes track grabbing action and feeds back a result. The central processing unit is provided with a plurality of object pose points and grabbing track points, controls the conveying belt simulation platform to generate the set pose points, coordinates the robot, the sensor and the controller to complete pose evaluation, track decision and grabbing reward calculation under each pose point, and obtains a pose state value, a track action value and a grabbing reward value. The robot is connected with the sensor and the controller through a data interface and installed on the conveyor belt simulation platform, and sorting decision optimization in a dynamic environment is achieved. According to the method, the grabbing precision, the trajectory planning efficiency and the anti-offset capability of the industrial robot can be improved, and the labor cost is reduced.

The invention discloses an industrial intelligent safety risk early warning and management and control platform, and relates to the technical field of industrial safety monitoring and management and control. The industrial intelligent security risk early warning and management and control platform comprises a full-scene perception layer, a data management layer, an intelligent risk identification layer, a hierarchical management and control layer and a global operation and maintenance layer, and each layer and an edge computing node construct a distributed data interaction network through an industrial Ethernet; the scene sensing layer is used for realizing all-region and all-time security data acquisition of industrial production and comprises a multi-mode sensing terminal array, an equipment protocol adaptation module, a personnel state monitoring unit and an acquisition scheduling module; according to the industrial intelligent safety risk early warning and management and control platform, through a multi-mode terminal array and a dynamic acquisition scheduling mechanism of a full-scene sensing layer, full-area and full-time-period automatic monitoring of industrial production is realized, dependence on manual inspection is thoroughly eliminated, the problems of low manual inspection efficiency and missing inspection of hidden risk points are effectively solved, and the industrial production safety risk early warning and management and control platform is suitable for popularization and application. And the monitoring coverage rate is improved to 100%.

The invention relates to an NUP-based intelligent control system for a thermal power plant, which combines a DCS with an NPU to construct a novel thermal power control system comprising a control layer, a calculation layer, a communication layer and a management layer. In a calculation layer, traditional control logic is executed in a DCS, and advanced algorithms such as artificial intelligence and the like are executed by an NPU acceleration module; a general compiler is adopted to support a mainstream programming language to construct an algorithm library, and a deep learning framework is combined to improve a function; the data management uses a lightweight database; and the communication layer adopts a high-speed industrial Ethernet and is compatible with a mainstream protocol to realize data interaction. According to the scheme, the defects of insufficient response speed, algorithm processing capability and parallel operation capability when a traditional DCS deals with the problems of new energy fluctuation and the like are solved, and the effects of improving the flexibility and stability of a unit, improving the calculation speed of an AI algorithm, reducing the data query and extraction speed, realizing self-adaptive adjustment of control parameters of the unit and iterative optimization of performance are achieved. And the system has good compatibility and expansibility, and can be seamlessly connected with the existing DCS system.

The invention discloses an industrial park energy carbon data visualization terminal and a working method thereof. A data acquisition module of the terminal acquires power grid electric energy quality data in real time; the carbon emission calculation module maps the electric energy data into a carbon emission reduction index based on a power grid emission factor, and dynamically updates a historical trend chart; the display module dynamically displays the electric energy quality data, the carbon emission statistical result and the historical trend chart; the communication module supports an industrial Ethernet, an RS485 interface and an IRIG-B code time synchronization function, and realizes data interaction with an external management system; the storage module manages the electric energy parameters and the carbon emission data by adopting a circulation strategy; the control module integrates an ARM processor and a DSP data acquisition board, and operates a Linux system to coordinate data acquisition, processing and communication tasks. According to the invention, end-to-end communication of energy-carbon data is realized through a modular architecture, real-time monitoring, historical tracing and abnormal alarm are supported, visual data support is provided for energy efficiency optimization and carbon emission reduction decision making of an industrial park, and low-carbon operation is assisted.

The invention discloses an information security risk early warning and cooperative defense system oriented to industrial control environment resource optimal allocation, and relates to the field of information security. The system comprises a central control scheduling module, a risk early warning module, a resource optimization distribution module and a cooperative defense module. The central control scheduling module is in bidirectional communication connection with the risk early warning module, the resource optimization distribution module and the cooperative defense module through an industrial Ethernet. A central control scheduling module serves as a core hub, receives risk data every 100 ms, receives resource data every 200 ms, receives defense state data every 300 ms, dynamically generates a control instruction based on the data, issues and iteratively optimizes according to the sequence of risk early warning, resource optimization and cooperative defense, retries twice if receipt is not received, and re-schedules if an execution result is not consistent. And triple requirements of key infrastructures such as electric power, chemical engineering, intelligent manufacturing and the like on safety, continuity and resource efficiency are met.

The invention discloses a biogas energy comprehensive utilization efficiency optimization system and method based on the Internet of Things, and belongs to the technical field of industrial automation control and energy management, the system realizes intelligent management and optimal scheduling of the whole life cycle of biogas energy through a distributed multi-level calculation control architecture, and the core is a multi-agent cooperative control platform. Comprising a fermentation agent, a purification agent, a storage agent and a utilization agent, all the agents can independently execute local control tasks and can cooperatively work to achieve global optimization, an Internet of Things sensor is deployed in the system to collect operation state data, real-time data transmission is achieved through the industrial Ethernet, and the intelligent control system is established. The marginal intelligent control node carries out local preprocessing and real-time decision making, a self-adaptive control algorithm library is innovatively integrated, the optimal control algorithm is dynamically selected according to different process links, the biogas yield and quality are remarkably improved, and practical application shows that the biogas yield is improved by 15%-25%, and energy consumption is reduced by 10%-18%.

The invention relates to the technical field of coal mine dynamic mine pressure monitoring and intelligent early warning and prediction, and particularly discloses a coal mine dynamic mine pressure monitoring and intelligent early warning and prediction system, which comprises a monitoring terminal layer, which comprises a plurality of mine pressure monitoring sensors, and the mine pressure monitoring sensors are respectively arranged in different monitoring areas of an underground coal mine. The mine pressure parameter acquisition module is used for acquiring mine pressure parameters of a corresponding area in real time, and the mine pressure parameters comprise roof subsidence, support working resistance, coal seam stress and surrounding rock deformation; and the data transmission layer is connected with the monitoring terminal layer and comprises an underground ring network switch and a ground industrial Ethernet. According to the dynamic mine pressure monitoring and intelligent early warning prediction system for the coal mine, the data reliability is ensured in combination with sensor state monitoring, the mine pressure trend in the next 24 hours is accurately predicted by adopting an improved LSTM neural network and an AWA-GC optimization algorithm, and the system adapts to nonlinear changes under complex geological conditions.

The invention discloses a photovoltaic power station operation state monitoring management system and method, and relates to the technical field of power systems and automatic control, and the method comprises the steps that a dual-mode temperature monitoring module is combined with a ceramic packaging contact sensor and a multispectral filtering non-contact sensor, and a correction temperature value is output through adaptive weight fusion; the environment adaptive calibration unit dynamically corrects the drifting of the sensor based on a CNN-BiLSTM model; the edge computing node detects early symptoms of the hot spots in real time and uploads abnormal data preferentially; the multi-protocol gateway can automatically switch communication channels, and is matched with LZW compression and CRC32 verification to guarantee data transmission; the above modules establish cooperative interaction through an industrial Ethernet, an RS485 bus and the like, and are in bidirectional communication with a cloud management platform. The temperature monitoring precision in a severe environment is improved, the hot spot response time is shortened, the communication bottleneck in a remote area is solved, and stable operation of a power station is guaranteed.

The invention discloses an uncertainty reasoning state scoring and early warning system for gear machining equipment, and relates to the technical field of gear machining monitoring. Data are collected in real time through an industrial Ethernet, time synchronization is realized based on an IEEE1588PTP protocol, and a data cleaning sub-module is designed to repair an abnormal value and keep a record; the method comprises the following steps of: extracting 128-dimensional potential features of a vibration signal by adopting a variational auto-encoder, fusing a temperature drift coefficient and a current harmonic component, quantifying feature uncertainty through Monte Carlo dropout, and marking low-reliability features; and constructing a Bayesian network containing 5 state nodes and 12 observation nodes, calculating a posterior probability through a Markov chain Monte Carlo algorithm, dynamically adjusting a conditional probability table, and fusing multi-feature scores. According to the method, the state evaluation accuracy and the early warning timeliness are improved, the resource consumption is optimized, full-link monitoring is realized, the fault handling efficiency is enhanced, and reliable support is provided for precision manufacturing.

The invention relates to the technical field of ultra-deep geological exploration engineering, and discloses an ultra-deep horizontal hole full-hole coring and trajectory cooperative control system, which integrates a high-precision sensor and professional monitoring equipment through a geological data acquisition module to construct a multi-dimensional data monitoring system; the geological data transmission module adopts a dual backup scheme of drill rod built-in optical fiber and mud pulse transmission, is connected with the geological data acquisition module and the geological data analysis module through an industrial Ethernet, is carried with a 5G industrial module, and supports remote data access and instruction interaction of each functional module; the geological data analysis module comprises a processing unit and an analysis unit, the processing unit preprocesses the acquired data, and the analysis unit extracts the preprocessed data to calculate a trajectory deviation value and a rock core recovery rate; and the decision feedback module sends out feedback processing according to the calculation results of the trajectory deviation value and the core recovery rate. The system solves the problem of insufficient accuracy of traditional track evaluation.

The invention relates to the technical field of material performance testing, and discloses a multi-parameter simulation salt spray environment test system and method, and the system comprises a multi-parameter cooperative control module, a bionic fog drop generation and salt spray adjustment module, an energy recovery and resource circulation module, a salt spray environment adaptive compensation module, and a test data intelligent analysis module. The modules are in closed-loop communication through an industrial Ethernet; the method comprises the steps of test preparation, model initialization, dynamic simulation and compensation, energy resource circulation and data processing ending, specifically, a salt mist concentration-temperature-humidity-wind speed coupling model is constructed, uniform salt mist is generated through bionic atomization and electrostatic adsorption, self-adaptive compensation is achieved in combination with partition sensing and corrosion current detection, and the salt mist concentration-temperature-humidity-wind speed coupling model is obtained. And heat is recovered, and condensate water and non-atomized saline water are recycled. The problems of multi-parameter coupling interference, resource waste and low data value in the prior art are solved, the simulation precision and the resource utilization rate are improved, and the method is suitable for multi-field material corrosion resistance testing.

The invention discloses an industrial control system and method based on the industrial internet, and relates to the technical field of industrial control, the industrial control system comprises a sensing layer, a network layer, a platform layer and an application layer, all the layers cooperate to achieve intelligent control over the automobile welding process, aiming at strong electromagnetic interference of a welding scene, optical fiber transmission is adopted to replace a traditional cable, and the intelligent control over the automobile welding process is achieved. The anti-interference capability of data transmission is improved; the sampling frequency of the sensor dynamically adapts to the welding process, and precision and energy consumption are balanced; in the reverse optimization process of the spattering amount, the specific operation parameters of the welding robot are automatically adapted to achieve the purpose of reducing the spattering amount, and therefore the spattering risk in the welding process is reduced; the network layer distributes the highest priority time slot for the motion instruction of the welding robot through the backbone network by adopting the TSN and the industrial Ethernet, so that the short-time deterministic transmission is ensured, the collision risk during the cooperation of the mobile equipment and the mechanical arm is avoided, and the protocol conversion delay is shortened.

The invention relates to the technical field of steel rolling production quality detection, in particular to a steel rail bending and torsion online monitoring control method based on a thermal-state 3D full-section contour scanner, which is applied to a finish rolling mill and comprises the following steps: acquiring data of a steel rail, including a bending angle, a torsion angle and temperature data; formulating a corresponding control strategy according to the data of the steel rail; dynamically optimizing a control strategy through a PID (Proportion Integration Differentiation) control algorithm in combination with the monitoring data and the state of the finish rolling mill; and a mapping relation between steel rail deformation and parameters is established, an anti-overregulation mechanism is set, and online monitoring and control of steel rail bending torsion are achieved. Three-dimensional point cloud and temperature data of a plurality of sheet layers of the steel rail are obtained in real time, bending and torsion parameters are calculated through an intelligent algorithm and then transmitted to a finish rolling mill control system in real time through the industrial Ethernet, parameters such as roller pressure and roller gaps are automatically adjusted based on a preset control model, a real-time closed loop of detection-calculation-adjustment is formed, and the real-time control of the steel rail is achieved. And ensuring that the steel rail geometric shape meets the standard in the tapping stage.

The invention provides a micro-grid optical storage capacity optimal configuration method and system, and relates to the technical field of micro-grid optimization, and the method comprises the steps: setting a sectional capacity configuration parameter according to a load characteristic parameter and an energy access condition of a micro-grid; setting a first compensation correction value and a second compensation correction value based on impedance matching characteristics of the micro-grid and charge and discharge efficiency parameters of the energy storage unit; reversely analyzing the capacity optimization configuration parameter combination obtained through optimization into the sectional capacity configuration parameters; and collecting real-time monitoring data in real time through an industrial Ethernet, and performing dynamic refreshing and rolling optimization on a capacity optimization configuration parameter combination according to a preset power balance threshold value and an actual operation deviation of an optical storage cooperative configuration execution scheme. According to the invention, the technical problem of capacity redundancy or insufficiency caused by static and fixed configuration of the optical storage capacity of the micro-grid in the prior art is solved, and the configuration precision of the optical storage capacity of the micro-grid is improved.

The invention relates to the field of deep hole high-speed laser cladding processing, in particular to an ultra-long deep hole high-speed laser cladding method and system based on dynamic light beam regulation and control. The cooperative regulation and control module is used for synchronously controlling the laser power, the feeder and the motion platform through an industrial Ethernet; compared with the prior art, the system has the advantages that data information of in-hole cladding machining is collected at any time by additionally arranging and integrating the thermal infrared imager and the high-speed camera, and the operation states of components such as laser power and a powder feeder are synchronously adjusted through the control system containing the industrial Ethernet; the thickness uniformity of a cladding layer is promoted to be improved to 98%, the bonding strength is larger than or equal to 400 MPa, and when the system and the method are adopted for operation, the machining efficiency can be improved by 40% compared with a traditional method, and energy consumption is reduced by 25%.