7793results about "Programme control in sequence/logic controllers" patented technology

The invention discloses a DDGS three-dimensional storage device based on PLC control and a management method of the DDGS three-dimensional storage device, relates to the technical field of intelligent storage management, and solves the problems that in traditional DDGS feed storage, the storage space utilization rate is low, the storage turning cost is high, risk control is not timely, and a quick response strategy is lacked. According to the method, the dynamic perception metadata warehouse is constructed, and DDGS material characteristic key data is monitored in real time to support anomaly detection; a storage control topology model is generated by using a graph theory modeling and dynamic attribute injection method to map an exception type; periodically collecting equipment data and fusing and updating a storage state; state diagram nodes are enhanced, risk response priorities are calculated, and grading operation is triggered; pLC control logic is dynamically adjusted based on historical data, and real-time monitoring and multi-device linkage control are achieved; the intelligent level of DDGS three-dimensional warehouse management is remarkably improved, the warehouse space utilization rate is increased, and the risk control capability is enhanced.

The invention relates to the technical field of data processing, and discloses a real-time data processing analysis method and system of an industrial PLC. The method comprises the following steps: transmitting temperature, pressure, current, vibration and acoustic parameters acquired by multiple sensors to an industrial PLC (Programmable Logic Controller) in real time to obtain multi-source heterogeneous original data; preprocessing the multi-source heterogeneous original data to obtain standardized fusion data; correlation calculation and anomaly recognition are carried out through the multivariate analysis model, and an abnormal state classification result is obtained; dynamically adjusting data interaction frequency and sampling rate between the edge nodes and the central PLC, and generating a real-time control decision instruction; and matching the real-time control decision instruction with the current motor load fluctuation state, and outputting the optimal frequency conversion control parameter. According to the invention, the response delay of the system is reduced, the control precision and reliability are improved, the dynamic balance between the safety and the energy efficiency is realized, and the system can intelligently adjust the control strategy according to the real-time safety situation.

The invention discloses a green building intelligent lighting and energy collaborative optimization system based on multi-source data fusion. According to the system, firstly, multi-source information such as indoor and outdoor illumination, weather, personnel occupation and electricity price is subjected to weighted fusion through dynamic confidence, and an accurate real-time environment state is constructed; then, model predictive control is combined with a multi-objective optimization strategy, and a sunshade device, intelligent lighting and a heating ventilation air conditioner are cooperatively adjusted, so that the comprehensive energy consumption or operation cost is minimized on the premise that the indoor illumination and temperature comfort requirements are met; besides, the self-adaptive module continuously learns and adjusts the thermal characteristics and the use mode of the building through online model parameter correction and occupancy probability prediction, and it is ensured that the energy-saving effect is stable for a long time; the system drives each execution device through a standard building self-control interface, is easy to integrate in a newly built or reformed project, and can significantly improve the energy utilization efficiency and the indoor environment quality.

A dog training method for intelligently correcting pet behaviors based on multi-modality comprises the steps that multi-modality perception data of a pet is obtained, the multi-modality perception data comprises acoustic signals, motion signals and track data, an acoustic feature tensor, a motion feature tensor and a position feature tensor are constructed, and a unified coupling tensor is formed; inputting the coupling tensor into a pre-trained behavior recognition model, and outputting a behavior tag; the emotion recognition module outputs an emotion label, and updates a character label in a sliding window mode; inputting the behavior label, the emotion label and the character label into an intervention strategy generation model and outputting an adaptive intervention strategy, wherein the intervention strategy comprises a pacifying type strategy and a stimulation type strategy; according to a historical feedback effect record, adjusting a feedback intensity parameter of the intervention strategy, calculating a feedback convergence index and an emotion recovery index, and dynamically correcting a feedback level and a delay strategy of the intervention strategy.

The invention discloses a cleaning control method and system based on an electronic cleaning agent, and the method comprises the steps: obtaining the distribution characteristics of pollutants and a material sensitivity judgment result through obtaining the micron-sized particle distribution data, chemical residue concentration data and surface roughness parameters of the pollutants on the surface of an electronic workpiece; according to the pollutant distribution characteristics and the material sensitivity judgment result, matching a candidate formula from a preset cleaning agent formula database, calculating pollutant removal rate predicted values of different concentration and time combinations through a linear regression algorithm, and determining optimized cleaning agent formula parameters and cleaning strength control parameters; and in the cleaning process, the residual quantity of pollutants and the surface damage degree are collected in real time, if the residual quantity of the pollutants exceeds a preset threshold value T1 or the surface damage degree exceeds a preset threshold value T2, cleaning agent injection pressure and cleaning time parameters are dynamically adjusted, and updated cleaning execution parameters are generated. Accurate and efficient cleaning of the surface of the electronic workpiece is achieved.

The invention relates to the technical field of artificial intelligence, in particular to a family service method and system based on artificial intelligence, and the method comprises the following steps: obtaining a statement analysis action sequence and a task direction, collecting the feedback of a device to generate a state structure, judging an instruction trend, recombining a statement to generate a behavior chain, and disassembling an action recognition conflict to extract a main control path. Analyzing a behavior time sequence generation prominent trend, and adjusting a display sequence to generate a function scheme. According to the method, through semantic component extraction and equipment state mapping, the accuracy of instruction target recognition is improved, word order rearrangement and logic connection enhance the continuity of a behavior chain, a control path is optimized and sorted according to verb density and object association, the task scheduling precision is improved, and behavior time sequence analysis recognizes a high-frequency operation forward trend; according to the method, priority dynamic adjustment and rearrangement of recommended content in combination with instruction frequency and time sequence characteristics are realized, the response initiative and the service matching degree are enhanced, and collaborative optimization of semantic understanding, behavior prediction and content recommendation is integrally realized.

The invention relates to the technical field of automatic control, in particular to an intelligent aquaculture automatic control system which comprises a feeding feedback control module, a feeding rhythm allocation module, a water body disturbance partition module, a control unit recognition module and a control path construction module. According to the method, the bait residue density is quantified through image recognition, a feeding state dynamic judgment mechanism is constructed, the accuracy and timeliness of feeding response are improved, the feeding speed is calculated through linkage of time difference and density change, a rhythm allocation strategy based on behavior feedback is formulated, the matching degree of the feeding rhythm to the actual feeding ability is enhanced, and the feeding speed is increased. A main flow path continuous block is constructed by combining flow velocity and flow direction, disturbance levels are marked in a grading manner, a key equipment action area is judged according to cross of fish school active density and dissolved oxygen trend, and an equipment linkage path is constructed by connectivity of a physical channel and an inhabitation area. And an intelligent control scheme of cooperative linkage of ingestion behavior driving, disturbance partition recognition and space layout optimization is realized.

The invention belongs to the technical field of cold chain warehouse management, and particularly relates to a cold storage dynamic scheduling cooperative control method and system based on the Internet of Things, and the method comprises the steps: obtaining operation parameters and environment parameters; according to the operation parameters and the environment parameters, an E-LSTM model is used for analyzing the operation state of the refrigeration equipment, an analysis result is generated, and through a TFT neural network model, the environment parameters and the current time and production plan are combined, and the refrigeration demand change trend is predicted; based on the analysis result and the refrigeration demand change trend, a dynamic scheduling strategy of the refrigeration equipment is generated by combining a scheduling rule and an optimization target and utilizing a PPO algorithm; the scheduling strategy is sent to the PLC control end of the refrigeration equipment through the Internet of Things communication network, the PLC control end performs cooperative control on the refrigeration equipment according to the scheduling strategy, and meanwhile, the execution state of the equipment is fed back to the cloud server. Therefore, the problems of rigid scheduling mode, serious energy waste, insufficient emergency capability and the like in the prior art are solved.

The invention belongs to the technical field of intelligent power grids, and particularly relates to a circuit breaker control method, system and device of the intelligent Internet of Things, and the method comprises the steps: obtaining the operation data of a sensor; data extraction is carried out on the operation parameters of the sensor, the operation parameters are transmitted to a cloud server through the wide area Internet of Things for analysis, and whether the circuit breaker has an abnormal condition or not is judged according to a preset rule and a historical data training model; if an abnormal condition exists, based on a risk assessment matrix and real-time data monitoring, generating a safety control strategy and establishing a strategy library, performing different-level response according to an abnormal type, and generating a control instruction; and after an instruction is received, dynamically adjusting parameters according to the state of the circuit breaker, interactively verifying the instruction through the block chain node, operating the circuit breaker according to a safety control strategy after the instruction passes, and meanwhile, feeding back and adjusting the strategy. Therefore, the problems that in the prior art, the data processing dimension is single, strategy response lags behind, and the abnormal type recognition capacity is insufficient are solved.

The invention relates to the technical field of automatic production control and industrial data processing, in particular to a PLC-based gypsum-based flame-retardant plate production line control system and method, and the system comprises a data collection module which collects sensor data in real time; the ideal state construction module is used for constructing a pure ideal state vector; the disturbance simulation module is used for generating a theoretical damaged state vector corresponding to a specific fault mode; the difference calculation module is used for generating real and theoretical deviation vectors; the coupling verification control module is used for calculating a similarity value between the actual deviation vector and the theoretical deviation vector; if the similarity value is larger than a preset judgment threshold value, it is judged that a real physical fault exists at present, and a PLC precise compensation instruction is generated and sent to a production line execution mechanism; otherwise, judging that non-physical noise exists at present, keeping the control parameters of the production line unchanged, and starting a dynamic filtering program; according to the method, the problem that physical faults and sensor clutters are difficult to distinguish in a high-noise environment is effectively solved, and missing report is avoided.

The invention relates to the technical field of concrete vibration, in particular to an intelligent concrete vibration system and method. The intelligent concrete vibrating system comprises a planning module used for selecting a current vibrating area from a to-be-vibrated range according to construction parameters of the to-be-vibrated range; the multi-dimensional sensor is used for collecting multi-dimensional parameters of the vibrating equipment during vibrating in the current vibrating area; the multi-dimensional parameters comprise vibration frequency, vibration pressure and concrete temperature; the decision module is used for determining a current vibration strategy required by the current vibration area according to multi-dimensional parameters during vibration in the current vibration area, and determining a next vibration area corresponding to the current vibration area in each to-be-vibrated area in the to-be-vibrated range; and the control module is used for controlling the vibrating equipment to perform vibrating operation in the current vibrating area according to the current vibrating strategy, and controlling the vibrating equipment to perform vibrating on the next vibrating area after the vibrating operation of the current vibrating area is executed.

The invention relates to the field of energy storage power supply management, in particular to a power supply management method, system and equipment based on a household energy storage power supply system. The method comprises the following steps: collecting a household power utilization monitoring data stream, carrying out equipment use behavior preference mining and multi-scale state transition deep learning, and generating a user equipment behavior prediction model; mining power utilization behaviors of power utilization terminals one by one based on the household power utilization monitoring data flow, performing multi-time-point power utilization demand prediction based on a user equipment behavior prediction model, and constructing a multi-time-point power utilization demand prediction map of the user; and obtaining meteorological environment parameters of a current user family area, performing climate disturbance demand adjustment on the power demand prediction maps at the multiple time points based on the meteorological environment parameters, and constructing a climate disturbance power demand prediction map. According to the invention, through dynamic user demand changes, the energy efficiency management level and the energy allocation precision of long-term operation of the energy storage system are improved.

The invention relates to the technical field of intelligent control, in particular to a monopotassium phosphate intelligent control method and system.The method comprises the following steps that rhizosphere phosphorus and potassium concentration changes are obtained, whether the rhizosphere phosphorus and potassium concentration changes are in a critical rate interval or not is judged, an effective absorption time period identifier is generated, and an organ amplification ratio is analyzed to judge a cooperation period; and extracting a stalk concentration ratio, comparing the stalk concentration ratio with a reference interval to generate an application inhibition identifier, fusing information to judge a fertilization window, adjusting the concentration according to the root hair amplification and the water consumption rate, and generating a control instruction set. According to the method, the effective time period of phosphorus and potassium nutrient absorption of the crops is determined through crop rhizosphere phosphorus and potassium ion concentration difference calculation and average treatment, the objective interval of crop nutrient absorption rate indexes is defined, blind fertilization is avoided, the pertinence and accuracy of nutrient absorption and utilization of the crops are enhanced, the excessive application state of nutrient elements is dynamically identified, and the crop quality is improved. The risk of excessive nutrients caused by nutrient enrichment of crops is prevented, and resource waste and environmental burden are effectively reduced.

The invention discloses a servo control method and system based on a PLC. The method comprises the steps of track data acquisition, interpolation instruction generation, control instruction output, state information acquisition, synchronous error evaluation, error compensation, track continuity control and the like. Wherein the PLC generates a multi-axis interpolation control instruction according to trajectory data issued by the upper computer, periodically issues the multi-axis interpolation control instruction to each servo driver through a high-speed timed task, collects feedback information in real time, and evaluates multi-axis synchronous deviation. On the basis, the PLC constructs an error vector and dynamically corrects interpolation parameters of the next period to realize synchronous control. Meanwhile, a buffer transition area is arranged at the track section switching position, acceleration and speed changes are adjusted, and it is guaranteed that the motion track is continuous and smooth. The system comprises a PLC, a trajectory data input module, an interpolation generation module, an error evaluation and compensation module and the like, has high real-time performance and high synchronization precision, and can improve motion coordination and system stability in complex trajectory execution.

The invention relates to the technical field of filling automation control, in particular to a full-automatic essential balm filling control system based on PLC online monitoring, which comprises a filling parameter real-time acquisition module, a dynamic coupling analysis module, a multi-axis cooperative execution module, a foreign matter real-time detection module, an interlocking control module and a filling quality feedback module. Wherein the filling parameter real-time acquisition module is used for acquiring temperature data of a filling head, a liquid viscosity value and the flow velocity of a filling pipeline in real time; and the dynamic coupling analysis module is used for establishing a correlation model of the temperature gradient and the viscosity change rate and generating a dynamic compensation parameter set. According to the method, the nonlinear correlation model of the temperature and the viscosity change rate is constructed, and the dynamic compensation parameter set is generated, so that self-adaptive precise control in the filling process and automatic response linkage of abnormal working conditions are realized, and the stability and the intelligent level of a filling system are remarkably improved.

The invention belongs to the technical field of agricultural Internet of Things, and discloses an Internet of Things-based intelligent agricultural cloud platform data acquisition and management system, which comprises the following steps of: acquiring plant rhizosphere microenvironment information, generating rhizosphere original data and performing correlation analysis to obtain rhizosphere electrochemical interaction data; collecting canopy microclimate information, and performing temperature and humidity gradient quantification processing and pore dynamic response characteristic extraction to obtain pore response characteristic data; collecting field illumination information, and carrying out multispectral distribution analysis and photoperiod sensitivity differentiation analysis to obtain a crop photoperiod sensitivity spectrum; performing multi-dimensional association fusion to generate crop physiological status comprehensive evaluation data; agricultural measure analysis and resource allocation optimization are carried out, and finally a resource allocation instruction is generated and agricultural operation scheduling is carried out; according to the invention, intelligent acquisition, analysis and management of data in each link of agricultural production are realized, and the utilization efficiency of agricultural resources and the production accuracy are improved.

The invention discloses a data processing method and system for an industrial-grade data acquisition instrument, and relates to the technical field of electric digital data processing. The system comprises an acquisition module, an analysis module and a management module, the acquisition module acquires operation parameters and production environment data of industrial equipment and transmits the operation parameters and the production environment data to the analysis module; performing single-domain analysis and association processing on the operation parameters and the production environment data to generate a cross-domain association analysis result, generating a threshold range through dynamic threshold adjustment, re-evaluating the operation parameters and the production environment data, generating an analysis result after threshold optimization, transmitting the analysis result to a management module, and managing the operation parameters and the production environment data on the basis of the analysis result after threshold optimization. And generating an optimal scheme and automatically synchronizing the optimal scheme to a workshop system, presetting edge side primary early warning and edge side multi-level early warning mechanisms, mining parameter causality through cross-domain association analysis, dynamically adjusting a threshold value in combination with working conditions and environmental factors, generating an optimal scheme through edge cloud cooperation and multiple algorithms, and enabling industrial intelligent fine management.

The invention relates to the technical field of trapping equipment control, and discloses a trapping equipment cooperative control system based on multi-sensor fusion, which comprises a multi-source data acquisition module, a target identification module, a risk assessment module, a cooperative relationship modeling module, a path optimization module, a decision fusion module and an execution control module. The multi-source data acquisition module generates an environment perception map containing temperature, humidity and illumination; the target recognition module analyzes the signals and generates data containing the target size and the motion category in a classified mode; the risk assessment module divides risk areas and inverts threats to generate a risk distribution map; the collaborative relation modeling module extracts collaborative parameters such as communication delay; the path optimization module dynamically plans a path; the decision fusion module performs weighted fusion on the data generation indexes; and the execution control module matches actions and adjusts parameters. The system realizes multi-dimensional perception, accurate identification and cooperative control, improves the trapping efficiency, and is suitable for pest control and other scenes.

The invention relates to the technical field of intelligent electric control systems, and discloses an intelligent electric control system and a control circuit board with the intelligent electric control system. The system comprises a signal acquisition module used for acquiring an input signal and judging a signal partition; the mode mapping module is used for constructing a mapping relation between the signal partition and the control partition; the parameter combination module is used for extracting key partitions and judging a parameter combination matching degree to obtain a bias combination; the state updating module is used for updating the mapping relation based on the deviation combination; and the control management module generates a control management library. The control circuit board integrates each module hardware circuit unit to realize signal transmission and data interaction. Accurate processing, dynamic mapping, optimal parameter combination, state real-time updating and scientific control management of the input signals are achieved, the intelligence, adaptability and reliability of the electronic control system are improved, and the system is suitable for electronic control scenes of various intelligent devices.

The invention discloses a mixing and feeding electric control system with a liquid level sensing function, and relates to the field of control systems. An adaptive parameter configuration module; a multi-stage gradient stirring module; a real-time data twinning processing module; a remote cooperative monitoring module; a fault linkage processing module; and an energy-saving closed-loop control module. According to the system, the material characteristic pre-detection module is combined with the water content-viscosity-medicament dosage three-dimensional mapping table and the viscosity-rotating speed mapping table, so that real-time scanning and parameter quantitative matching of the granularity, the water content and the viscosity of the industrial waste are realized; according to the scheme, the stirring sufficiency of the high-viscosity industrial sludge is improved, the energy consumption of the low-viscosity material is reduced, the dosage of the medicament is automatically matched according to the three-dimensional mapping table, the utilization rate of the curing agent is improved, and the core problems of stirring waste and insufficient stirring are fundamentally solved.

The invention discloses an electromechanical equipment distributed control system based on the Internet of Things, which belongs to the technical field of distributed control, and specifically comprises the following steps: splitting a control task into task units, registration dependencies, data entries and task levels at an edge gateway, and generating a candidate scheduling range according to registration items; a calibration heartbeat with a reference segment is issued, and a driving node executes and returns a measurement record in an idle window for calibrating hardware fingerprints and link overhead and dynamically updating matching parameters; when computing power saturation is triggered, splitting a low-level task unit into suspensible fragments, setting a de-duplication execution identifier, opening a state interface, and completing migration check; selecting a target control node and a transmission path according to a calibration result, issuing fragment and state interface information, driving a target node to continue fragments according to an execution template, and guiding a source node to release occupied resources in sequence; and checking and comparing the execution result, and when the rule is triggered, recovering to the previous stable state according to the mapping rollback table.

The invention discloses an intelligent gas valve remote control and early warning system based on the Internet of Things, and relates to the technical field of valve monitoring. The valve processing module is used for preparing an intelligent valve through 316N stainless steel subjected to grain boundary processing, and a sealing ring, a fiber grating array and a QCM sensor are arranged on the intelligent valve; and the intelligent control module is used for acquiring the strain size of the fiber bragg grating and vulcanized film data through the fiber bragg grating array and the QCM sensor, and determining the lubricant release amount and the current regulation density. Strain of the valve rod is monitored in real time through the fiber bragg grating array, segmented friction force is obtained, and a nano lubricant is automatically released according to the friction force, so that the friction coefficient can be reduced, flexible opening and closing of the valve are ensured, and meanwhile, the surface density change of a vulcanized film is detected through the QCM sensor; and the valve rod is automatically triggered to rotate for self-cleaning or electrolytic polishing treatment, so that corrosion development can be effectively inhibited.

The invention relates to the technical field of cloud control, in particular to a cloud-based steam cabinet control system, which comprises a humidity sampling processing module, a steam state judgment module, a task stage identification module, a control instruction reconstruction module and a scheduling execution response module. According to the method, the accuracy of steam management is improved through staged recognition of the steam release state, a matching mechanism is formed in combination with the task timestamp and the staged state, the real-time adaptation capacity in the task execution process is enhanced, a dynamic comparison and queue rearrangement mode is adopted in the task instruction updating link, and the task execution efficiency is improved. Synchronous cooperation of a control instruction and a current task state is achieved, targeted optimization of the task control instruction and dynamic adjustment of a control process are achieved through a multi-dimensional state analysis and time sequence mapping means, the feedback response efficiency, task scheduling flexibility and steam release accuracy in the equipment operation process are effectively improved, and the system performance is improved. Therefore, the adaptive capacity and the control precision of the steam cabinet control in various operation states are enhanced.

The invention relates to the technical field of equipment management and control, in particular to an intelligent management and control method and system for a smart park, and proposes the following scheme: dividing a region into regular grids based on spatial data and equipment data, and generating an inspection path in combination with dynamic weights. And the inspection equipment collects equipment operation data and environmental parameters according to the path, and transmits the inspection data to the processing equipment in real time. And the processing equipment accurately identifies equipment operation abnormity, environment parameter abnormity or security risks through time sequence analysis and an abnormity detection model, and links the management and control equipment to process an abnormal area. The method improves the park inspection efficiency and the exception handling capability, and is suitable for industrial park scenes.

The invention relates to the technical field of program control, in particular to a precise powder supply control system, which is characterized in that multi-cycle differential processing of a rotating speed fluctuation ratio of a spiral feeder is performed on multiple key parameters such as a powder batch particle size value, a batch density value, an accumulated discharging amount and a feeding cycle; abnormal fluctuation numbers are extracted in different periods and combined according to the abnormal fluctuation numbers to generate a feeding feature set, a fuzzy neural network is utilized to improve the fitting precision of a discharging rate change trend under a nonlinear condition, the screening capability of extreme value interference is improved, a fluctuation difference value sequence is associated with symbol consistency and a density mean value, and the accuracy of the fluctuation difference value sequence is improved. Analyzing the variation trend of the discharge deviation in the symbol direction and the numerical slope, matching the numbers to establish a feed deviation grade section sequence, adopting a generative adversarial network to construct a target and actual discharge quantity difference value sequence, dividing symbol consistent sections, extracting the coupling trend of the fluctuation slope and the average density value, and obtaining a target discharge quantity difference value sequence; and misjudgment caused by deviation mode covering is avoided.

The invention discloses a control method of an electric actuating mechanism. The electric actuating mechanism is used for driving a valve to be opened and closed. The control method comprises the steps that real-time operation state parameters of the electric actuating mechanism are collected; determining a parameter control target value of the electric actuating mechanism according to a preset control mode; extracting historical operation data of the electric actuating mechanism, and outputting a control signal on the basis of a set target value according to a preset adaptive algorithm in combination with the historical operation data and the current operation state parameter; and driving the electric actuating mechanism based on the control signal. According to the technical scheme, the control precision of the electric actuating mechanism on the valve can be effectively improved.

The invention discloses a multi-device cooperative online control system and method, belongs to the technical field of multi-device cooperative control, and aims to solve the problems of low synchronization precision and response lag during multi-device cooperative action. Triggering and responding execution equipment is accessed and identified, time reference deviation is calibrated, triggering state and auxiliary state data are received to construct a multi-dimensional state data set, triggering conditions are analyzed and judged, and a click-through triggering initial signal is generated and then compared with a sequence tail end signal timestamp so as to update a sequence and transmit the sequence to a central control platform in an incremental mode; measuring communication delay through a test signal, generating and iterating an online control instruction in real time to drive equipment to cooperatively act, identifying an optimization instruction according to the type, and dynamically updating through an updating interface; and constructing a correction data set based on the returned execution state feedback, and calculating an execution deviation to correct the online control instruction. Finally, high-precision synchronization and efficient response of cooperative actions of multiple devices are realized, and the stability and adaptability of an online control system are improved.

The invention discloses a PLC scheduling algorithm based on a multi-core soft real-time operating system, and relates to the technical field of voltage control, and the algorithm comprises the following four steps: 1, starting a target industrial control system, completing hardware self-inspection, memory allocation and communication module starting, and sending an initialization ready signal after confirming that an assembly is normal; 2, according to PLC task functions and real-time performance, four types of standardized tasks are divided, priority levels are defined, sub-priorities are configured, a dependency relationship is identified, and a task chain is established; 3, counting the number of tasks and task chains, distributing resources by using multiple binding cores, and constructing a global ready queue to adapt to dynamic scheduling; and 4, monitoring an interrupt event through an interrupt manager, realizing high-priority task preemption by semaphores, and allocating tasks to corresponding CPU cores, so that the problems of soft real-time multi-core task preemption and data conflict are solved, the scheduling real-time performance and the data security are improved, the system failure rate is reduced, and the industrial control efficiency and stability are improved.

The invention relates to the technical field of power supply equipment power control. The load power track simulation control method comprises the steps of obtaining voltage information and current information of target power supply equipment, determining power deviation information and a dynamic target voltage range generated on the basis of a preset load behavior prediction model, constructing a combined regulation and control weight vector on the basis of the power deviation information, and obtaining a load power track simulation control result. According to the combined regulation and control weight vector and the dynamic target voltage range, constructing a target power evolution trajectory curve by using a physical response inversion mechanism, executing time discretization processing on the target regulation and control trajectory curve, extracting voltage change values and current change values corresponding to each time step, and calculating the target power evolution trajectory curve; and combining the voltage change value and the current change value according to a time sequence to form a control parameter sequence, and generating an analog control instruction matched with the target power evolution trajectory curve according to the control parameter sequence. The method has the effect of improving the real-time response capability of the power supply equipment under complex working conditions.

The invention belongs to the technical field of intelligent engineering management, and discloses an automatic monitoring and control method of an engineering management system based on the Internet of Things. According to the method, a four-layer Internet of Things architecture comprising a sensing layer, a network layer, a platform layer and an application layer is constructed, and the four-layer Internet of Things architecture is uploaded to a cloud platform through a 5G / narrowband Internet of Things dual-mode transmission channel. A digital twinborn technology is innovatively adopted to construct a three-dimensional visual engineering model, intelligent identification and prediction of abnormal working conditions are realized through a machine learning algorithm, and a multi-stage linkage control mechanism is established. And when construction deviation or equipment failure is detected, the system automatically generates an optimization control strategy and issues the optimization control strategy to the execution terminal, so that accurate regulation and control of the construction machinery and intelligent pushing of early warning information are realized. According to the method, a manual inspection mode of traditional engineering management is broken through, full-process automatic supervision is realized through data fusion analysis and closed-loop control, the construction quality supervision precision is effectively improved by more than 30%, the safety accident rate is reduced by 50%, and the engineering management efficiency is remarkably improved.