528 results about "Response delay" patented technology

The invention relates to the technical field of intelligent control, provides a flow regulating valve servo force control method and system based on a force sensor, and aims to solve the technical problems of response delay, weak overshoot suppression capability and poor long-term operation stability. The method comprises the following steps: acquiring a servo driving current data set and a valve displacement track data set of a target regulating valve; performing pressure feature mapping processing on the servo driving current data set to generate a pressure fluctuation feature set corresponding to the driving current waveform data; the pressure fluctuation characteristic set and the valve displacement track data set are input into a preset force control decision model for dynamic matching processing, and a servo control instruction set is generated; executing multi-stage dynamic adjustment operation on a servo driving unit of the target adjusting valve according to the servo control instruction set, and generating real-time pressure balance state data; and iteratively updating the dynamic matching processing parameters of the force control decision model based on the deviation value of the real-time pressure balance state data and the preset pressure reference value.

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 computing power scheduling method and system based on dynamic load prediction and resource priority ranking. The computing power scheduling method comprises the following steps: collecting historical load data, task submission data and resource state data of each node in a computing power cluster; on the basis of the preprocessed multi-dimensional load feature data set, constructing an improved hybrid prediction model, optimizing model parameters through training, and predicting the load change trend of each computing power node in a future preset time period by using the trained model to obtain a node load prediction result; extracting a service level protocol parameter, a resource demand type and historical execution efficiency data of a to-be-scheduled task, and establishing a multi-dimensional resource priority evaluation index system; according to the computing power scheduling method, the problems of low resource utilization rate and high task response delay caused by low load prediction precision and mismatching of resource allocation and task priority in a traditional computing power scheduling method are solved, and the overall operation efficiency and service quality of a computing power cluster are improved.

The invention discloses an integrated cold station intelligent control system based on multi-modal data fusion, and relates to the technical field of data fusion, an intelligent decision and optimization module adopts reinforcement learning and model prediction control based on fused data to realize dynamic optimization of start and stop of a water chilling unit, chilled water temperature setting and water pump frequency adjustment; the fault detection and adaptive optimization module evaluates the data fusion accuracy by calculating an environment entropy value and cold station dynamic response delay time, and dynamically adjusts a control strategy based on a prediction deviation rate and the data fusion accuracy; the closed-loop feedback module analyzes and adjusts control parameters through operation data, intelligent control models between different cold stations are optimized through transfer learning, intelligent deployment of a new cold station is accelerated, and the generalization ability of the models is improved. More accurate, more energy-saving and more stable intelligent control of the cold station is realized, and the operation efficiency and reliability of the cold station system are improved.

The invention relates to the technical field of industrial control systems, in particular to a PET extruder multi-zone intelligent temperature regulation and control system, which comprises a temperature field parameter acquisition module for acquiring the input power, the real-time temperature value, the temperature change rate value and the temperature difference value between adjacent temperature zones of an extruder to obtain a temperature zone associated state set; and summarizing and mapping the temperature zone association state set, and establishing temperature field response characteristics. According to the method, through correlation analysis of the temperature states of the multiple areas, independent adjustment of a single area is upgraded to dynamic optimization based on the thermodynamic coupling relation, and the coordination of temperature adjustment of the multiple areas is improved. Dynamic parameters required by temperature regulation and control can be calculated in real time in combination with the heat transfer rate, the temperature response delay time and the temperature response gain coefficient of the temperature interval instead of depending on a fixed set value, different operation working conditions are adapted, and the regulation and control flexibility is improved.

The invention provides a crane remote instruction response delay detection and in-advance compensation method and system, and relates to the technical field of cranes, and the crane remote instruction response delay detection and in-advance compensation method comprises the following steps: adopting an adaptive space-time alignment algorithm to map real-time operation data to a dynamic knowledge graph, and generating a feature vector; inputting the feature vector into a depth map neural network integrated with a causal reasoning mechanism to generate an incidence matrix; a multi-head attention network with a residual structure is adopted to extract time sequence features; constructing a hybrid decision system based on the delay prediction tensor, and outputting an optimal compensation strategy; and establishing a double-closed-loop evolution system with an online learning capability, and dynamically optimizing a prediction and compensation strategy according to a compensation effect. Through the dynamic knowledge graph, causal reasoning, the multi-head attention network and the double-closed-loop evolution system, the remote instruction response delay can be accurately predicted, effective compensation is carried out, and the real-time performance and safety of remote control of the crane are improved.

The invention discloses a measurement and control system adaptive adjustment method and system based on deep learning, and relates to the technical field of intelligent regulation and control, and the method comprises the steps: collecting the real-time operation data of a measurement and control system, constructing the real-time operation data into a system state vector, carrying out the feature extraction of a task in the measurement and control system based on the system state vector, and carrying out the adaptive adjustment of the measurement and control system. The method comprises the steps of classifying tasks by adopting a support vector machine, predicting resource demand quantity for different types of tasks, dynamically adjusting task priorities based on the resource demand quantity, generating task priority information, and performing global resource allocation optimization by adopting a genetic algorithm through a macroscopic scheduling layer; real-time scheduling decision making is carried out through the microcosmic scheduling layer by adopting a reinforcement learning model, and a specific resource allocation instruction is generated based on a resource allocation scheme output after optimization of the macroscopic scheduling layer. The utilization rate of system resources is improved, and task response delay is reduced.

The invention discloses a data processing method and system based on a bidding and tendering management platform. The method comprises the following steps: obtaining a bidding and tendering data set and constructing an initial supplier set; dynamically screening the initial supplier set based on a preset qualification verification rule to generate a compliance supplier set; performing multi-dimensional semantic analysis on bidding document contents in the compliance supplier set to generate a risk assessment report; and generating a recommended supplier sequence based on the risk assessment report and the bid invitation party demand information. The method has the following advantages and effects: semantic mapping and dynamic weight distribution among data are realized; based on dynamic rules and real-time data updating, response delay caused by manual intervention is reduced; through confidence scoring, a quotation rationality model and performance matching degree calculation, a quantifiable risk assessment report is generated, and subjective deviation is reduced; and the recommendation logic is dynamically adjusted in combination with the customized demand of the bid invitation party, and the intelligent level and the result credibility of the bid invitation and tendering process are improved.

The invention discloses an equipment monitoring prediction method and system based on industrial middle platform and edge computing, and the system employs a monitoring collection unit to collect the vibration, temperature and current data of equipment in real time, and carries out the analysis through an innovative three-layer prediction architecture: deploying a lightweight threshold rule model at an edge layer to carry out the real-time anomaly detection, an XGBoost model is deployed in the middle layer to execute trend analysis, a time convolution network model is deployed in the cloud layer to realize deep time sequence analysis, and a corresponding grading early warning mechanism is established. The technical problems that an early warning mechanism is not intelligent enough, a prediction model is single, data preprocessing is not perfect enough and the like in traditional equipment monitoring are solved. Through cooperation of three layers of prediction architectures, omnibearing monitoring is realized, system response delay is reduced, and early warning accuracy is improved. Through the combination of a grading early warning mechanism and parameter correlation analysis, the system reliability is enhanced, and the equipment operation and maintenance efficiency is improved; and an edge calculation mode is adopted, so that the network bandwidth requirement is effectively reduced.

The invention belongs to the technical field of computers, and particularly relates to an Internet of Things security data real-time monitoring and protection method and system, and the method comprises the steps: collecting equipment communication and state data through an edge agent, and analyzing and extracting standardized metadata; constructing an equipment behavior contour vector based on a sliding window, and dynamically maintaining a global equipment topological graph; triggering a primary alarm in combination with behavior deviation detection and topology abnormity; outputting a threat score and an attack intention through rule matching and Bayesian network double-engine collaborative reasoning; and executing automatic response according to grading, and feeding back and correcting a behavior baseline to realize closed-loop optimization. The system comprises a data acquisition module, a protocol analysis module, a behavior modeling module, a topology maintenance module, an anomaly detection module, a collaborative reasoning module, an automatic response module and a baseline correction module. Through full-link real-time modeling and cross-device collaborative analysis, the attack detection rate is significantly increased to 98% or above, the false alarm rate is lower than 2%, the response delay is controlled within 800 milliseconds, and the security and adaptive ability of the Internet of Things system are enhanced.

The invention relates to the technical field of database operation and maintenance, in particular to a database operation and maintenance decision-making method and device based on a large language model.The method comprises the steps that real-time operation indexes and historical log data of a database are collected, and multi-dimensional time sequence features are extracted; and analyzing the characteristics by using a large language model, judging whether a performance bottleneck or an abnormal risk exists or not, and determining the relevance between the performance bottleneck or the abnormal risk and a database performance problem. If the risk exists, acquiring a system load sudden change trend and a resource request rate, quantifying a system pressure degree, and inputting a performance optimization model to generate an adaptive tuning strategy; calculating statement complexity through an SQL audit log, and selecting an index adjustment strategy in combination with an index optimization model; evaluating the influence of the tuning strategy and the index strategy on the throughput, the response delay and the resource utilization rate, and determining a target high-performance strategy; according to the scheme, accurate optimization and efficient operation and maintenance of database performance are realized through intelligent analysis and dynamic adjustment and optimization.

The invention discloses a modeling method based on shield tunneling data feature analysis and parameter relevance, and relates to the field of tunnel engineering data processing. The method comprises the steps that shield tunneling time sequence parameters are obtained, and a non-uniform time sequence is resampled into a space-aligned standardized footage domain sequence through state cleaning and coordinate domain transformation; by means of mixed variable rejection and lagging correlation analysis, environment common cause interference is stripped, physical response delay among parameters is recognized, and a time-delay directed correlation graph model is constructed; and inputting the footage domain sequence and the graph model into a graph neural network, performing feature learning by using a time delay compensation aggregation mechanism, and outputting a key parameter influence degree set with symbols based on a prediction gradient. According to the method, the problem of data space-time dislocation caused by propelling speed fluctuation and the problem of parameter relevance misjudgment caused by physical response lag are solved, and accurate identification and explanation of shield tunneling key parameters are achieved.

The invention relates to the technical field of equipment maintenance scheduling, in particular to an equipment fault repair management system, which comprises a dependency identification and modeling module, a fault state analysis module, a task priority evaluation module, a work order scheduling generation module and a resource scheduling execution module. According to the method, a multi-level dependency topology between devices is constructed through a directed graph traversal algorithm, a trigger relation between physical connection and an operation process is quantitatively analyzed, a classification marking model is established in combination with dynamic parameters such as a performance degradation rate, a fusion influence range and a dependency factor are calculated through scalar superposition, and a priority scoring matrix is dynamically generated. A task queue structure is optimized through a sorting algorithm, a data-driven maintenance decision mechanism is formed, the fault positioning precision is improved, response delay caused by manual intervention is reduced, key node equipment maintenance lag is avoided, the resource configuration efficiency is optimized, the collaboration of fault processing and a production system is strengthened, and formulation of a preventive maintenance strategy is supported.

The invention relates to the technical field of collaborative optimization, in particular to a virtual power plant power generation-utilization-price collaborative optimization system based on an AI large model, and the system comprises a load confidence matching module, a resource stability mapping module, a source-load capacity coupling module, an electricity price interval adjustment module and a comprehensive regulation and control linkage module. According to the method, the confidence interval prediction of the load demand is realized based on the hybrid neural network modeling of the load behavior data and the equipment temperature control characteristic sequence, and the scheduling matching confidence is measured according to the boundary overlapping condition of the prediction interval and the power generation response characteristic; a stability screening mechanism for adjusting resources is constructed in combination with the output fluctuation ratio and the equipment inertia characteristic, the controllability of load adjustment and the real-time performance of source side response are improved, the price adjustment rhythm is corrected through an electricity price response delay factor, dynamic closed-loop linkage between load adjustment and price guidance is achieved, and the load adjustment efficiency is improved. The execution priority is dynamically updated under the condition that multiple response conditions are matched, and the certainty of resource scheduling and the sensitivity of response are improved.

The invention discloses a mechanical arm natural language instruction control system and method based on a large language model, and belongs to the field of intelligent manufacturing. Aiming at the limitation that traditional mechanical arm control depends on pre-programming and a static rule library, a dynamic mapping mode from a natural language instruction to an atomic action sequence is designed, an atomic skill library including detection, grabbing, moving, placement and other operations is constructed, and semantic analysis and a multi-mode cooperation technology are combined, so that the atomic action sequence is obtained. And support is provided for man-machine cooperation of a flexible assembly task. The method specifically comprises the steps that a DeepSeek-Distil-Llam-8B large model and a LoRA fine tuning technology are adopted, and a natural language instruction is converted into an executable atomic action sequence; based on a transfer learning optimized YOLOv8 target detection technology and a binocular vision positioning technology, a sensing module adaptive to an assembly scene is constructed and is fused with a mechanical arm motion planning module, and positioning grabbing of parts and tools is achieved. And an interactive interface is built by combining a voice-to-text large model and a Gradio front-end framework, so that the convenience of man-machine interaction is improved. By optimizing large model reasoning and motion planning cooperation efficiency, response delay from instructions to execution is reduced, and an efficient and extensible solution is provided for man-machine cooperation in intelligent manufacturing.

The invention relates to a dynamic load distribution method, device and equipment based on a vehicle body controller and a medium, and the method comprises the steps: collecting the mechanical load, electrical load and thermal load data of a vehicle body in real time through a distributed sensor network, and generating multi-source heterogeneous initial load data; performing standardization processing on the initial load data, and constructing a three-dimensional data cube type standard load data set; comprehensive characterization features are extracted through feature space mapping, and a fusion load feature set containing a threshold range is dynamically generated in combination with historical data; constructing a load correlation model based on the fusion features, and mapping to generate a steering and braking control parameter combination; and according to the real-time load threshold cross-border state, dynamically adjusting the task priority, allocating real-time calculation core resources to the chassis system, and generating an optimal allocation scheme. According to the method, the problems of multi-source load data fusion failure, resource allocation rigidity and safety task response delay in the prior art are solved, and dynamic load optimization of the vehicle body control system under complex working conditions is realized.

The invention provides a switching station control method and system of a subway station. The system comprises a subsystem control unit, a main control unit, a communication module and a data caching module, the subsystem control unit transmits an equipment on-off state, a fault signal and an environment parameter to the main control unit in a hybrid networking mode, and a first cooperative control instruction set is generated in combination with a linkage rule base; a target priority is distributed for the operation instruction to obtain a second cooperative control instruction set, and the communication module adopts a multi-channel parallel distribution mechanism for pushing; in the process of executing the switching station operation, the data caching module stores running state data, identifies a time sequence mode by using a machine learning model, pre-judges a switching station linkage scene and loads a control strategy; and the main control unit detects the response delay of the subsystem control unit and generates a third cooperative control instruction set according to the control strategy so as to optimize the switching station control time sequence. According to the invention, intelligent cooperative control of multiple electromechanical devices can be realized.

The invention relates to the technical field of safety monitoring, in particular to a safety monitoring management method and system based on the Internet of Things, and the method comprises edge intelligent perception, multi-mode cognitive fusion, danger reasoning and root cause positioning, digital twinborn decision deduction and alarm intelligent merging and response. Compared with the technical defects that in the prior art, response delay is high and key alarms are prone to being missed due to the fact that cloud centralized processing is relied on, a special AI reasoning chip is deployed on the edge side to execute lightweight model real-time preliminary screening, and high-value feature data are uploaded only after abnormity is confirmed; meanwhile, constructing a space-time sensing network deep fusion multi-modal evidence chain at the cloud; according to the architecture, a decision chain of industrial dangerous events from perception to cognition is shortened to be within a second level, collaborative optimization of millisecond-level local blocking of major risks and cloud deep analysis is realized, and the security defense timeliness and reliability of high-risk scenes are greatly improved.

The invention provides a bridge swivel attitude intelligent adjustment method and system based on multi-source data fusion, and the method comprises the steps: obtaining a multi-source monitoring data set generated in a bridge swivel process, carrying out the time-space alignment and fusion processing of the multi-source monitoring data set, generating fusion monitoring data, and carrying out the adjustment of the swivel attitude of a bridge. And based on the fused monitoring data and a preset target attitude parameter set, identifying attitude deviation characteristics between a bridge swivel attitude and a target attitude, generating an attitude adjustment strategy for a bridge swivel execution mechanism according to the attitude deviation characteristics, and executing the attitude adjustment strategy to adjust the attitude of the bridge swivel execution mechanism. And carrying out real-time posture adjustment operation on the bridge turning process. According to the method, the system reliability can be enhanced while the adjustment efficiency is improved, and the problems of insufficient control precision and response delay caused by data isolation and strategy staticization in a traditional method are solved.

The invention is suitable for the technical field of power grid control, and provides a virtual power plant cooperative control scheduling method and system, and the method comprises the following steps: constructing a multi-dimensional resource library, each resource node in the multi-dimensional resource library corresponding to real-time adjustable power, power adjustment cost, response delay time, a carbon emission coefficient and a resource availability coefficient; establishing a global optimization model at the cloud, wherein the global optimization model fuses economy, environmental benefits and system stability; solving the global optimization model by adopting an improved non-dominated sorting genetic algorithm to obtain an optimal scheduling strategy, and issuing the optimal scheduling strategy to a control center and an edge terminal; and determining a real-time state of the resource node based on the edge terminal, and adjusting the optimal scheduling strategy according to the real-time state. According to the method, the response precision and the system stability of the virtual power plant are remarkably improved by constructing the global optimization model and dynamic cooperative control and adopting the improved non-dominated sorting genetic algorithm for solving.

The invention discloses a dynamic task distribution system based on multi-source data fusion, belongs to the technical field of intelligent task scheduling, and aims to solve the problems of response delay and execution failure caused by undifferentiated rule verification, fuzzy resource matching logic and inaccurate real-time state evaluation in a traditional task distribution system. According to the system, a task request is obtained through a task receiving module, multiple indexes are converted through a data standardization processing module, a rule matching engine carries out multi-rule verification and outputs a matching degree score, a real-time capability evaluation module calculates a current capability value of an executor, and a dynamic decision module dynamically adjusts double weights according to a task emergency degree and generates a comprehensive score. And time, resource and skill conflicts are eliminated through the conflict detection unit, and finally the optimal executor is selected by the task allocation unit to issue the task. The system is suitable for various fields requiring efficient and accurate task allocation, such as grid command center order distribution, emergency response, logistics distribution, maintenance service and the like.

The invention discloses a server resource dynamic scheduling method for dealing with video stream high concurrent access, and particularly relates to the technical field of computer network and intelligent scheduling. A user access behavior data set is constructed; a deep learning model is adopted to train an access hot spot prediction model based on the time sequence features to predict a future access hot spot area and a peak trend; acquiring response delay, CPU / GPU occupancy rate and bandwidth load information of the heterogeneous server group, and generating a resource state multi-dimensional parameter set; carrying out joint modeling on the model and the parameter set, constructing a resource scheduling priority model by adopting a graph neural network, and generating an optimal scheduling path graph based on an A * improved algorithm; task transfer, instance elastic expansion, cache preheating and other scheduling operations are executed according to the model; according to the method, the resource utilization efficiency and the service quality of the video system in a high-concurrency scene can be improved, and the method has the advantages of being high in real-time performance, intelligent in scheduling and high in self-learning capability.

The invention discloses an asynchronous motor energy-saving monitoring and dynamic early warning method based on situation awareness, and relates to the technical field of asynchronous motor energy-saving monitoring. The method comprises the steps that S1, multi-source heterogeneous data are synchronously collected, power supply parameters and operation parameters are obtained in real time, and a frequency conversion working condition self-adaptive deviation value and a current stability coefficient are obtained; s2, generating four-dimensional situation factors including an overtemperature danger value, a current stability coefficient, a voltage stability coefficient and a frequency deviation value; s3, constructing a fourth-order transmission network, and generating a primary early warning signal based on the path mark combination; s4, calculating the motor efficiency in real time, triggering an efficiency abnormity mark, fusing the efficiency abnormity mark with the primary early warning signal, and reconstructing a final early warning instruction; and S5, executing hierarchical control according to the final early warning instruction. Through multi-parameter coupling and dynamic early warning transmission, the early-stage capture capability of the composite fault is improved, the early warning response delay is shortened, energy-saving optimization and fault protection of the motor are realized, and the method has a remarkable practical value.

The invention relates to the field of automatic driving vehicle control, in particular to a sightseeing vehicle anti-collision control method and system based on a multi-mode radar. Comprising the following steps: acquiring a multi-modal radar original signal, and performing timestamp alignment and noise reduction processing to generate standardized fusion data; performing multi-target classification based on a convolutional neural network, and outputting a target recognition result in combination with Doppler frequency shift compensation and distance calculation; generating a hierarchical response strategy through dual-threshold comparison and environment interference dynamic weight adjustment; the strategy is converted into a PWM control signal, and a control instruction is generated through relay driving adaptation and hardware compatibility detection; a relay is driven in real time, motor response is collected, and execution effect evaluation and time sequence marking are completed; and brake release and system parameter synchronization are realized through PID closed-loop control. The problems of insufficient multi-target identification precision, poor environmental interference adaptability and response delay are solved, and the active safety performance of the sightseeing vehicle is remarkably improved.

The invention relates to the technical field of virtual power plants, and discloses a virtual power plant optimal scheduling system and method, and the system comprises a data obtaining module, an edge calculation module, a prediction module, a scheduling controller, a topology reconstruction module, and an intelligent terminal device cluster. According to the invention, the edge computing module carries out localization processing and prediction on the sensing data, so that rapid generation and issuing of a scheduling scheme are realized, and the problem of response delay caused by network transmission and centralized computing of a traditional centralized architecture is avoided, thereby supporting millisecond scheduling feedback and improving the scheduling efficiency. The real-time response capability under the sudden load fluctuation or fault condition is remarkably improved, a multi-dimensional perception and prediction mechanism is constructed based on an LSTM neural network prediction model, the recognition and trend prediction capability of the system on meteorological disturbance, equipment aging and operation abnormity is enhanced, the intelligent level of the virtual power plant system is improved, and the real-time performance of the virtual power plant system is improved. The system can dynamically generate an optimal scheduling strategy to ensure stable operation of the virtual power plant under various working conditions.

The invention relates to an intelligent sleep adjusting method and system based on closed-loop acousto-optic brain wave entrainment, electroencephalogram (EEG) signals are collected and preprocessed in real time through a wireless dry electrode, and high-precision sleep staging is achieved through a deep learning model. The system adopts a fuzzy PID (Proportion Integration Differentiation) controller to dynamically adjust acousto-optic synergistic stimulation: an acoustic module generates binaural beat signals, an optical module outputs blue light (470nm) and amber light (590nm) pulses, and the brain wave entrainment efficiency is enhanced through phase synchronization (the phase difference is less than or equal to 10 degrees) and golden section frequency coupling (fL is equal to 1.618 fA). Parameters are updated every 30 seconds through closed-loop feedback, the sound pressure level (30-50 dB) and the light intensity (10-100 lux) are adjusted according to the Weber-Fechner law, and the response delay is lt; the time is 200 ms. A three-level safety mechanism monitors gamma wave abnormity, epilepsy sample discharge and impedance overrun in real time, and triggers graded protection (alarming, cutting off light stimulation and shutdown). Clinical verifications show that the entrainment success rates of the delta wave and the theta wave respectively reach 71% and 68%, the sleep improvement effect is good, and a safe and efficient intervention scheme is provided for sleep disorders.

The invention relates to the technical field of intelligent equipment interaction, in particular to an intelligent equipment intention rapid insight and intelligent response method, which comprises the following steps: acquiring multi-modal interaction data input into intelligent equipment by a user, and generating a standardized intention input vector; performing hierarchical feature extraction on the standardized intention input vector, and outputting a fusion intention feature matrix; inputting the fused intention feature matrix into an intention classifier to obtain initial intention probability distribution data of the user; dynamically correcting the initial intention probability distribution data based on the historical intention record to generate a final intention judgment label; the optimal response action set is matched from the intention response strategy library on the basis of the final intention judgment label, the characteristic representation capacity is improved through multi-modal fusion, intention continuity understanding is enhanced through dynamic context perception, response self-adaptive optimization is achieved through reinforcement learning, and therefore the intention recognition accuracy can be improved in the noise environment, and the intention recognition accuracy is improved. And response delay is reduced.

The invention relates to the technical field of state monitoring, in particular to an electric drive system overheating protection monitoring method and system based on big data, and the method comprises the following steps: obtaining a power rising section and extracting a temperature curve, judging whether the temperature rise lags behind or not, marking response delay, recognizing thermal fluctuation lag through an expansion window, and extracting peak valley to judge trend deviation. And the positioning node tracks the heat path, identifies the interrupt structure and generates an early warning identifier. In the electric drive system operation process, the coupling relation between the power change trend and the temperature rise time sequence is combined, accurate recognition of temperature rise response delay is achieved, the position distribution of a heat path interruption structure is defined through continuous tracing of the heat node temperature rise sequence relation, and linkage performance between heat interruption structures is combined, so that the reliability of the electric drive system is improved, and the reliability of the electric drive system is improved. Concurrent characteristics of overheating abnormity are identified, the response capability and early warning accuracy of an electric drive system in the early stage of overheating risks are improved, and rapid positioning of potential thermal fault risks and intelligent division of risk levels are achieved.

The invention discloses a lightweight AI prediction control method for embedded equipment, and particularly relates to the technical field of embedded control, comprising the following steps: acquiring and preprocessing real-time multi-dimensional data to form a unified time sequence structure; identifying whether to enter a high heat accumulation or control response delay risk area based on the static characteristic threshold value and the dynamic trend function, if so, calculating a lag impact index and a heat load accumulation index, and substituting the indexes into a pre-trained artificial intelligence prediction model to output a scheduling risk coefficient, executing a control path dynamic switching operation according to the scheduling risk coefficient and the combined trend of the two indexes; according to the invention, through unifying a multi-dimensional data time sequence structure, preposed identification of high heat accumulation and control response delay risk is realized; a double quantization mechanism of a hysteresis impact index and a thermal load accumulation index is introduced, so that the operation state expressive power is enhanced; and in combination with artificial intelligence prediction output and an index trend, a control path is dynamically switched, and self-adaptive adjustment and structure optimization of a control strategy are realized.

The invention relates to the technical field of man-machine cooperation, and aims to solve the problems that the traditional man-machine cooperation process mainly depends on a preset flow and timing rhythm control, and is difficult to dynamically capture personnel misoperation, machine response delay or cooperation rhythm imbalance, so that response strategy lagging and low regulation and control efficiency are caused in the face of complex and variable man-machine interaction situations. According to the enterprise intelligent man-machine cooperation method and system, through comprehensive analysis with behavior response offset, cooperation rhythm consistency and a personnel fatigue state as core indexes, a set of intelligent man-machine cooperation process which is distinct in hierarchy, logic closed-loop and sustainable iterative optimization is constructed; the real-time response capability of man-machine cooperation and the production takt time stability are improved, the cooperation efficiency fluctuation can be dynamically monitored, the precise regulation and control strategy is output in time, quantitative evaluation and actual improvement of man-machine cooperation efficiency are achieved, and efficient production of a production line and the quality level of products are guaranteed.