51results about How to "Realize dynamic matching" patented technology

The invention discloses a one-film six-row wide-narrow-row close-planting staggered planting method for cotton, which comprises the following steps: measuring the soil nutrient content of a region to be planted, calculating the comprehensive score of soil fertility, selecting a basic plant spacing according to the tillering coefficient of a cotton variety, fitting an accumulated temperature correction function, calculating a predicted value of the tillering rate, and calculating a dynamic offset in combination with the latitude of a planting field. Determining a narrow row spacing and a sowing depth according to the dynamic offset, extracting a growth picture of the same variety of cotton in the same region after the flowering and boll-setting period, extracting a planting row spacing mean value of the canopy coverage in a preset range as a wide row spacing, planting cotton according to the basic plant spacing, the narrow row spacing, the wide row spacing and the sowing depth, and monitoring the canopy photosynthetically active radiation in real time. And dynamically regulating and controlling a drip irrigation fertilization strategy according to the photosynthetically active radiation of the canopy. By means of the method, the photosynthetic efficiency, the water and fertilizer utilization efficiency and the stress resistance of cotton groups can be improved, a brand new technical path is provided for high-yield and high-quality cultivation of cotton, and the method has important significance in promoting intelligent transformation of the cotton planting industry.

The invention discloses an electroencephalogram head ring interaction control method and device based on concentration level, and the method comprises the steps: collecting multi-band electroencephalogram signals and user interaction parameters, carrying out spectrum decoupling analysis to recognize a concentration feature baseline, exporting an attention stability coefficient, and building a cognitive state mapping relation; tracking a high-response frequency band core to implement frequency band fusion analysis to form a control channel, and collecting concentration fluctuation data to construct an interaction control network; executing multi-task response analysis to identify an intention switching point, extracting cognitive load parameters to divide a low-load area and a high-load area, and extracting a recovery rate to construct an attention toughness index in an identification conversion process; determining an optimal trigger position in a low-load area according to an attention toughness index, constructing a brain wave feature chain, aligning the brain wave feature chain with an attention stability coefficient, determining a low-interference response window, and setting a self-adaptive threshold group; and the coupling identification false triggering area extracts redundant brain wave components to execute false triggering suppression to generate an interaction control instruction, so that accurate matching between teaching intervention and the cognitive state of the student is realized.

This invention discloses an intelligent health education method, device, equipment, and storage medium for gynecological tumor patients. The method acquires the patient's electronic medical record information and patient interaction data. Based on the electronic medical record information, it identifies the gynecological tumor disease type and treatment stage tags. Based on the patient interaction data, it specifically identifies psychological stress signals related to gynecological tumor treatment, generating a profile of the patient's current psychosomatic state. It matches and dynamically generates personalized health education content and psychological counseling plans from a structured knowledge base. Based on the patient's treatment data, it automatically generates reminders for medication and follow-up examinations. It structurally correlates the output records of personalized health education content and the patient's feedback data on the reminders to form a digital pathmap for patient education. This enables dynamic matching and personalized generation of health education content and psychological counseling plans, improving the patient's cognitive adaptability to health information and the effectiveness of psychological support, achieving closed-loop management throughout the entire hospital and inpatient cycle.

The application discloses a kind of electric truck super-charging facilities collaborative planning method considering traffic network-power grid interaction, according to the energy consumption heterogeneity characteristics of electric truck multi-type transport task, constructs charging demand space distribution model;Introduce node marginal electricity price mechanism, establish the charging station selection equilibrium model with the minimum total cost as target;Based on the theory of direct current optimal power flow model, construct the power distribution network operation model with the minimum total generation cost as target;Build electric truck super-charging facilities collaborative planning model considering traffic-power two network interaction, and carry out transformation processing to obtain several differentiable barrier sub-problems, and solve several differentiable barrier sub-problems using interior point method, obtain the super-charging facility optimization setting scheme of traffic network-power grid interaction.The application solves the collaborative optimization problem of charging facility deployment and power grid impact in the field of port collection and distribution during electric transformation, provides efficient and stable collaborative decision support for port electric truck charging network planning, and has significant application value.

The application discloses a device control method and device, equipment, storage medium and computer program product, and is used to solve the problem that the user plane device running state and service load lack dynamic matching ability under the existing device scheduling mechanism, resulting in device idling and high energy consumption at low load. The method comprises the following steps: acquiring energy efficiency index data and service traffic data corresponding to each user plane device in a device pool; determining predicted traffic in a future preset time window based on a pre-trained prediction model according to the service traffic data; determining a control strategy for at least one user plane device in the device pool according to the predicted traffic and the energy efficiency index data, wherein the control strategy is used to control the user plane device to enter a sleep state; generating a control instruction according to the control strategy and sending the control instruction to the user plane device to control the user plane device to perform a corresponding sleep operation.

The application relates to the technical field of intelligent trolley walking mechanisms, in particular to an intelligent trolley walking mechanism for logistics carrying, which comprises a moving chassis mechanism and a supporting bottom plate installed on the top of the moving chassis mechanism, one side of the top of the supporting bottom plate is fixedly provided with a vertical fixing plate, one side of the vertical fixing plate, which faces the center of the supporting bottom plate, is rotationally connected with a closed-loop chain type conveying belt; equidistant through shaft sleeves are fixedly arranged in the closed-loop chain type conveying belt; and supporting shafts are rotationally connected in the shaft sleeves; the closed-loop chain type conveying belt and the multiple material carrying frames arranged on the vertical fixing plate realize dynamic matching of the material carrying stations and the taking and placing points. All the material carrying frames can be circulated to the butt joints of the same height as required, so that the matching clamping mechanism does not need to perform a complex vertical lifting stroke, the motion freedom degree of the clamping mechanism is greatly simplified, and the taking and placing operation cycle period is greatly shortened compared with a traditional multi-layer rack AGV.

The present application relates to the technical field of material performance testing, and discloses a kind of test methods for evaluating the chemical solvent penetration resistance of reinforced plastic, comprising the following steps: constructing inverse transfer function to compensate platform lag, using historical strain data to predict the starting time of microcrack opening in the next cycle, combining platform inherent delay to calculate instruction sending time, based on stiffness attenuation rate calculated in real time to adaptively adjust the best disturbance frequency, using inverse transfer function to generate modified disturbance instruction and superimposed injection, deducting fluid viscous damping work previously calibrated from single cycle data to obtain effective dissipation density, determining failure end point based on the second derivative change of effective dissipation density sequence.The present application realizes the time synchronization of pressure wave and crack opening through prediction feedforward strategy, maintains deep penetration driving force using adaptive disturbance, and eliminates fluid damping interference through energy decoupling mechanism, improving the authenticity and result accuracy of stress-assisted solvent penetration test.

The present application relates to the technical field of alfalfa planting, and discloses a phosphorus application quota distribution method for improving alfalfa seed yield, and the specific process is as follows: first, preparing the planting plot, cleaning the plot weeds in advance, leveling the land, installing the underground drip irrigation system, then, sowing and applying base fertilizer, applying the base fertilizer when finishing the land, planting the test variety by using the wide-narrow row mulching sowing method, uniformly dripping seedling water the next day to ensure uniform seedling emergence, setting the total irrigation amount and the total phosphorus application amount per hectare, and through the water and phosphorus management mode of implementing irrigation and phosphorus application quota distribution, the water and phosphorus are proportionally controlled to carry out underground drip irrigation in the growth periods of the branching stage, the squaring stage, the flowering stage and the podding stage of alfalfa, the technical scheme provided by the present application realizes the synergistic effect of differentiated irrigation amount and phosphorus application amount quota distribution in different growth periods through setting different irrigation and phosphorus application quota distribution modes, and thus improves the alfalfa seed yield and the water and phosphorus utilization efficiency.

The present disclosure provides a data migration method, device and equipment and storage medium, and relates to the technical field of data processing. The method comprises the following steps: receiving a first migration request sent by a request node, wherein the first migration request comprises position information of the request node and the size of data to be migrated, and the request node is an edge node; determining a target node from a plurality of edge nodes according to the position information of the request node and the size of data to be migrated in combination with storage resource information of the plurality of edge nodes; sending the position information of the target node to the request node, so that the request node sends a second migration request to the target node based on the position information of the target node; and sending a permission proof to the target node, so that the target node sends a confirmation message of accepting migration to the request node in the case that information carried by the second migration request meets the permission proof.

The invention provides a freight heavy truck driving parameter design method, system and device and a medium, and belongs to the technical field of intelligent driving, and the method comprises the steps: S1, classifying the operation scenes of a freight heavy truck, and collecting the driving parameter data of a driver in each operation scene; s2, preprocessing the collected driving parameter data to obtain a driving parameter sample set; and S3, analyzing the driving parameter sample set by adopting a clustering algorithm, and extracting typical driving parameters in each operation scene. Accurate matching of the driving parameters and the freight scene is achieved, and adaptability, safety and efficiency of the intelligent driving system in a complex freight environment are improved.

The invention relates to the technical field of data processing, in particular to a BIM model-based multi-party cooperative construction management and control method and system, and the method comprises the steps: obtaining the point cloud data of a real-time construction project, and determining the optimal thick side length of a voxel block according to the down-sampling rate of the point cloud data and the unit side length of the spatial distribution of the point cloud data; voxelization is carried out on the point cloud data based on the optimal coarse side length to obtain a plurality of voxel blocks, and the average density of point cloud distribution of the point cloud data in the voxel blocks is determined; determining the optimal side length of the voxel block according to the average density, the down-sampling rate, the optimal coarse side length and the unit side length, and performing down-sampling on the point cloud data based on the optimal side length to obtain a down-sampling project point cloud set of the real-time construction project; and constructing a BIM model of the real-time construction project by using the down-sampling project point cloud set, and managing and controlling the real-time construction project by using the BIM model. Therefore, the construction accuracy of the BIM three-dimensional model is improved.

The invention discloses a mobile self-adaptive charging control and thermal control method and system, and belongs to the technical field of thermoelectric control systems.The method comprises the steps that a charging state sensing matrix is constructed by collecting multi-dimensional data such as electric quantity, battery health, environment temperature and load in real time; a neural network model fusing a space-time attention mechanism and a bidirectional LSTM is adopted to accurately identify a charging demand, and a dynamic strategy library is matched to generate an initial charging parameter; in the charging process, the state of a loop is monitored through double-end synchronous sampling larger than 1 kHz, the current and voltage are dynamically optimized in combination with a battery equivalent circuit model of the self-adaptive order, and efficient power matching and ripple suppression are achieved. And when it is detected that the temperature is abnormal, a multi-mode thermal management mechanism is triggered, and a charging-calculation-heat dissipation cooperative control closed loop is formed. The system also supports security OTA strategy updating based on federated learning and differential privacy; according to the invention, the charging efficiency, the safety and the service life of the battery are remarkably improved, and the environment adaptability in a complex mobile scene is enhanced.

The application discloses a big data-based intelligent education guidance evaluation system and method, belongs to the technical field of intelligent education and big data analysis, and comprises a multi-source heterogeneous education data acquisition module, a data preprocessing and feature fusion module, a multi-dimensional capacity evaluation module, a learning behavior sequence analysis module, an individualized guidance recommendation module and a multi-role visual display module. The method integrates multi-source data through a time sequence self-adaptive alignment algorithm, realizes five-dimensional comprehensive capacity evaluation by adopting a subjective and objective fusion weighting algorithm and a double-flow attention knowledge tracking algorithm, performs learning behavior time sequence modeling by using a key behavior node identification algorithm, and generates an individualized learning path by a cognitive load perception path optimization algorithm. The application solves the problems of single evaluation dimension, fragmented data acquisition and lack of data support for individualized guidance in the existing education evaluation system.

The application relates to a multi-source data fusion battery pack thermal runaway fire control method and system, and relates to the technical field of battery packs, which comprises the following steps: acquiring a battery number and a battery temperature corresponding to the battery number; when the battery temperature is greater than a preset thermal runaway temperature, defining the battery number corresponding to the battery temperature as a thermal runaway number; acquiring a real-time temperature corresponding to the thermal runaway number; when the real-time temperature is greater than a preset ignition temperature, accumulating an ignition time; and when the ignition time is greater than a preset time threshold, controlling an atomizing nozzle to spray the battery corresponding to the thermal runaway number according to a preset spraying mode. The application has the effect of improving the fire control efficiency of the battery pack in a thermal runaway scene.

The present application provides a kind of mine roadway support intelligent design and feedback optimization system and method, it is related to mine support technical field, including rock mass quality classification, support parameter automatic design, knowledge graph case reasoning and AI optimization, agent model quick verification, three-dimensional visual support design and digital twin modeling, BIM / GIS integrated interface, multi-source monitoring and early warning, dynamic feedback optimization and surrounding rock parameter inversion, design report generation and multi-roadway progress board nine big subsystems.The intelligent generation and optimization of support scheme are realized based on machine learning algorithm through multi-source data fusion analysis, the three-dimensional visual model of roadway is established using digital twin technology, the dynamic adjustment of support scheme is driven by real-time monitoring data, the scientificity and economy of support design are effectively improved, the complex geological instability risk is effectively resisted, the intelligent management of support full life cycle is realized, and the safe and efficient production of mine is ensured.

The invention provides a control method of cleaning equipment and the cleaning equipment. The control method of the cleaning equipment comprises the steps that a force signal of operation force acting on the cleaning equipment is obtained; according to the signal change rate of the force signal, whether the operating force is instantaneous disturbance force is determined, and according to the duration and the fluctuation amplitude of the force signal, whether the operating force reaches a target stable state is determined; under the condition that the operating force is not the instantaneous disturbance force and reaches the target stable state, the magnitude of the operating force is determined according to the force signal; determining a target rotating speed of the motor according to the magnitude of the operating force based on a nonlinear mapping relation; the actual rotating speed of the motor is adjusted to the target rotating speed, and rotating speed control over the rolling brush is achieved.

The application relates to the technical field of plastic processing, and discloses a plastic melting uniformity analysis method and system. The method comprises the following steps: collecting fluctuation of a supply amount and material distribution data, obtaining a temperature deviation fluctuation interval and a material abnormal distribution graph; combining the temperature deviation and a dynamic disturbance frequency to judge a fluctuation type and generate temperature distribution abnormality prediction data; extracting key features from the temperature distribution abnormality prediction data to determine a plastic flow characteristic change trend; integrating dynamic characteristic data to evaluate the influence degree of a melting process on uniformity; quantifying a shear rate mutation response based on the influence degree to adjust a melting time fluctuation interval parameter; generating a uniformity distribution graph through multi-scenario simulation to determine a product quality quantization index; generating an adjustment strategy according to the index and iteratively optimizing the adjustment strategy to finally determine melting process control parameters. The application solves the problems of poor melting uniformity and control lag caused by disturbance coupling, and improves the stability of a plastic melting process and the consistency of product quality.

The invention relates to the technical field of image processing and vehicle safety protection, and discloses a complex industrial scene-oriented vehicle intelligent auxiliary safety protection system and method. The method comprises the steps of collecting video stream data, carrying out obstacle identification and generating space distribution information, dynamically constructing a three-dimensional warning space, analyzing an intrusive obstacle, tracking a motion track, generating a graded brake control signal and sending the graded brake control signal to a brake execution mechanism. The system comprises a video acquisition unit, an obstacle identification unit, a space mapping unit, a warning space construction unit, a space analysis unit, a target tracking unit, a decision unit and an instruction output unit. According to the method, obstacles are accurately recognized through image recognition and space mapping, a self-adaptive three-dimensional warning space is dynamically constructed according to vehicle driving parameters, graded braking signals are generated in combination with obstacle categories and movement tracks, and the accuracy and reliability of vehicle intelligent auxiliary safety protection in a complex industrial scene are achieved.

The present application belongs to copper pyrometallurgy technical field, specifically discloses a kind of dynamic optimization method of double-side blowing copper smelting slag type based on molten pool working condition real-time monitoring and model driving.The method includes: real-time acquisition molten pool temperature, flue gas oxygen content, slag Fe3O4 Content and viscosity and other multiple key working condition parameters;Parameter is input into the preset control model, the expert rule based on metallurgical thermodynamics and production data is embedded in the model, and the dynamic adjustment amount of SiO2, CaO, MgO three kinds of slag forming agent is calculated accordingly;According to the adjustment amount, the proportion of slag forming agent is adjusted in real time;Periodic acquisition of slag sample analysis, compare measured value with model expected value, and feedback correction model parameters, realize closed-loop optimization.The present application can dynamically adjust slag type according to molten pool real-time working condition, reduce the fluctuation of slag fluidity by more than 40%, stabilize the copper content of slag below 1.5%, and improve the direct recovery rate of copper to more than 97.5%, significantly improve the process stability and metal recovery rate.

The invention provides a heterogeneous model task scheduling method and system based on parallel strategy metadata. The method comprises the steps that a central scheduler receives a calculation task request containing a model identifier and a task parameter set; querying model metadata according to the model identifier, and obtaining a task strategy predefined corresponding to the model version; scheduling decision making is carried out based on a task strategy, and tasks are distributed to computing nodes; the computing node queries a model-environment mapping table according to the model identifier to obtain an environment identifier and loads an isolation runtime environment; acquiring data and executing a task through the data service layer in the isolated runtime environment; the computing nodes report task states and log information, and the central scheduler provides a monitoring interface queried according to model identifiers after aggregation. According to the method, the task strategy is predefined for the model version, so that the scheduling system has the service semantic perception capability, and the parallel processing efficiency is improved while the data consistency is guaranteed.

The application discloses a kind of based on the high-efficiency operation method and system of sewage plant water inlet characteristics, solve the problem that sewage plant operation uses static single processing scheme, cannot adapt to different water inlet characteristics, including steps: setting water inlet classification standard, establish the parameter control database of sewage plant operation;Collect sewage plant water inlet data, according to water inlet data carries out water inlet characteristic analysis, and according to water inlet characteristic carries out classification;According to the type of water inlet characteristic at current time, from parameter control database corresponding sewage plant operation strategy is retrieved, water inlet characteristic is matched with sewage plant operation process parameter, then according to effluent target, the process parameter is fine-tuned;The effluent quality of sewage plant is predicted, according to the feedback regulation of predicted result to sewage plant operation process parameter.It realizes that sewage plant high-efficiency operation is strong in adaptability, energy-efficient, improves the operation efficiency of sewage plant and the treatment effect to sewage, reduces energy consumption and operating cost.

The invention discloses an equipment vacuum degree detection method and device based on current signal analysis, and relates to the technical field of intelligent sensors, and the method comprises the steps: obtaining the feature information of a vacuum generation device, obtaining the predicted vacuum degree based on the feature information, and obtaining the current collection frequency based on the predicted vacuum degree; acquiring an original current signal by adopting the current acquisition frequency, and preprocessing to acquire a current signal; performing feature extraction on the current signal to obtain a current feature parameter; and performing vacuum degree analysis based on the current characteristic parameters to obtain a vacuum degree classification result. According to the invention, the technical problem of poor non-intrusive vacuum degree detection effect in the prior art is solved.

The invention relates to a method for optimizing the dosage and the application time sequence of an antibacterial dressing, which is based on the antibacterial dressing with a segmented enabled structure, the dressing comprises at least two antibacterial action sections, each section is preset with an antibacterial dosage and is isolated by a corresponding isolation part, and a load state indication area and a threshold identifier are arranged; the method comprises the following steps: S1, only starting a first section during laying, and keeping the rest sections isolated; s2, acquiring an indication state of the indication area in use and comparing the indication state with a threshold value; s3, when the threshold value is reached, removing the isolation of the next non-enabled section to enable the non-enabled section to be enabled, and updating the effective antibacterial dosage to the sum of the doses of the enabled sections; s4, repeating the steps S2-S3 until all the action sections are started; and S5, generating a replacement signal when indicating that the threshold value is reached again after all starting. Antibacterial dressings capable of being used in a segmented mode are adopted, load indication and threshold value judgment are combined, the antibacterial dosage is released step by step according to needs, the utilization rate is increased, and the use duration is prolonged; through double-region verification, hysteresis control and in-segment hierarchical regulation and control, false triggering is avoided, and time-space collaborative precise drug release is realized.

The invention relates to the technical field of control, and discloses a ground source heat pump system multi-energy flow matching and scheduling system coupled with clean energy, and the system comprises a data collection module which is used for obtaining data such as a soil temperature field, pipe network fluid and clean energy power generation power in real time; the thermal unbalance calculation module is used for calculating enthalpy deviation according to the soil data and outputting a thermal unbalance potential energy index; the fluctuation analysis module is used for calculating real-time surplus power and a fluctuation rate index; the matching scheduling module is used for calculating target hydraulic lagging time according to the volatility, deducing ideal flow and generating a control instruction; and the execution control module converts the instruction into a bottom layer signal and performs safety correction according to the water supply and return temperature. According to the method, dynamic matching of the ground source heat pump and the clean energy is achieved by quantifying the soil enthalpy deviation and stabilizing fluctuation through thermal inertia, the problems of underground thermal unbalance and frequent starting and stopping of equipment are solved, and the multi-energy-flow scheduling stability is improved.

The application discloses a kind of connecting node design method and system of fabricated modular building, including identifying fabricated module contact area and dividing connecting node group, determining the quantity and position of connecting member of each connecting node, determining the first target stiffness of each connecting node group, the second target stiffness is obtained by using point group stiffness optimization objective function optimization, construct connecting node state prediction model, obtain initial stiffness target value by reducing second target stiffness, adjusting connecting node controllable parameter according to initial stiffness target value and connecting node state prediction model, build fabricated modular building, calculate cumulative error and cumulative adaptive tolerance limit, compare data Real-time fine-tuning connecting node part controllable parameter.The method not only can improve the efficiency and accuracy of the connecting node design of fabricated modular building, but also has good explainability, which can be directly applied to the connecting node design system of fabricated modular building.

The application discloses a kind of industrial computer signal transmission multi-channel distribution method, it is related to industrial computer communication technical field, including, to channel monitoring data set executes normalization and fluctuation characteristic calculation, constructs channel stability index set and forms channel stability grade mapping table;Based on standard signal flow set, channel monitoring data set and channel stability grade mapping table, signal distribution and synchronization processing are executed, and synchronization signal distribution result table is generated;Through synchronization signal distribution result table and channel monitoring data set, data check and error correction processing are executed to signal transmission process, and signal transmission check result table is generated;According to signal transmission check result table and channel monitoring data set, signal output and channel dynamic monitoring processing are executed, and signal output record table is generated and signal redistribution is realized.The application is by executing signal distribution and synchronization processing, realizes the dynamic matching of signal priority level identification and channel stability grade.

This invention relates to the field of computing power scheduling technology, specifically to an AI computing power scheduling method and system for private large-scale models. The invention performs attention encoding on multi-source heterogeneous data to obtain format recognition results and acquisition priority labels; generates a memory pressure pre-transmission signal based on the format recognition results; extracts structural features from high-quality data streams and generates computing power affinity encoding vectors through graph neural networks; constructs a state space combined with a secure computing power pre-occupancy mechanism, outputting a resource allocation action sequence; further performs memory oversubscription, core binding, NUMA node crossing, and network bandwidth conflict detection, and resolves conflicts and performs affinity rearrangement based on task priorities to generate a conflict-free instruction sequence. This invention can improve the utilization efficiency of heterogeneous computing power resources, reduce the probability of resource conflicts, and improve the scheduling stability and execution efficiency during the training and inference process of private large-scale models.

The invention relates to the technical field of material performance testing, and discloses a testing method for evaluating the chemical solvent permeation resistance of reinforced plastics, which comprises the following steps: constructing an inverse transfer function to compensate platform lag, and predicting the starting time of microcrack opening in the next period by using historical strain data, the method comprises the following steps: calculating an instruction sending moment by combining inherent delay of a platform, adaptively adjusting an optimal disturbance frequency based on a stiffness attenuation rate calculated in real time, generating a correction disturbance instruction by using an inverse transfer function, performing superposition injection, and deducting pre-calibrated fluid viscous damping work from single-cycle data to obtain effective dissipation density. And determining a failure end point based on the second derivative change of the effective dissipation density sequence. According to the method, the time synchronization of pressure waves and crack opening is realized through a prediction feedforward strategy, the deep penetration driving force is maintained by utilizing self-adaptive disturbance, the fluid damping interference is eliminated through an energy decoupling mechanism, and the authenticity and the result accuracy of the stress-assisted solvent penetration test are improved.

This invention relates to the field of radio monitoring technology, specifically to a multi-target radio monitoring method and system. The method includes: constructing a unified time-frequency energy expression through multi-node time-consistent fusion; reconstructing the target trajectory using dynamic segmentation and bicontinuous constraints driven by the energy change rate; constructing behavioral primitive vectors; jointly modeling the behavior of multiple targets, extracting stable cooperative relationships and forming a weighted cooperative structure graph; using structural entropy to characterize the overall electromagnetic environment organization state; driving adaptive scheduling of monitoring resources with the stability of the cooperative structure as the core, dynamically generating sampling frequency, monitoring frequency band, and time resolution and issuing them for execution; using the historical stable cooperative structure as a baseline, calculating standardized deviations at the edge level, node level, and global level, identifying abnormal targets and types, and selectively triggering dynamic corrections of the cooperative model and monitoring strategy. This invention forms a closed loop of perception, analysis, scheduling, and optimization, achieving long-term refined monitoring and efficient resource utilization.

This invention discloses a big data-driven dynamic compensation control method and system for denitrification systems in thermal power units. The method includes: real-time acquisition of operating data from the thermal power unit to form a multi-source raw dataset; hierarchical preprocessing of the raw dataset, analysis, and extraction of fused feature vectors; inputting the fused feature vectors into a pre-defined full-condition adaptive dynamic prediction model to output the outlet concentration and optimal ammonia injection rate at future times; the prediction model includes a transfer learning module for different load conditions and a catalyst activity decay compensation module; based on the prediction results, a multi-timescale dynamic compensation strategy is adopted to generate and execute ammonia injection control commands, and the control parameters are iteratively optimized based on the feedback outlet concentration and ammonia slip rate. This invention achieves precise cleaning and feature extraction of massive operating data, establishes a dynamic compensation model capable of real-time tracking of operating condition changes and catalyst decay status, and realizes precise dynamic adjustment of ammonia injection rate.