600 results about "Predictability" patented technology

The invention relates to the technical field of electric power government affair management, and provides a cross-department government affair big data business co-processing method based on a computing power platform and data fusion, comprising the following steps: S1, deploying a heterogeneous computing power cluster, and establishing a computing power resource dynamic scheduling data processing platform comprising a CPU, a GPU, an FPGA and an edge computing node; s2, acquiring multi-dimensional data of power grid equipment data, power grid load data, industry power consumption data and government policy data; and S3, designing a cross-department data access system based on the zero-trust architecture, and developing a multi-dimensional model of a power grid risk prediction model, an industry energy consumption analysis model and a carbon emission accounting model in power management. The space-time joint probability prediction and conflict priority ranking algorithm is applied to power-government affair emergency disposal, intellectualization and precision of cross-department business collaboration are achieved, power faults with serious consequences are rapidly handled according to the priority level, and the harmfulness and unpredictability of the power faults can be reduced.

The invention relates to a coal mine safety risk intelligent management and control method, device and equipment and a medium. The method comprises the following steps: constructing a multi-source heterogeneous coal mine safety data system according to received data of a target coal mine park; the multi-source heterogeneous coal mine safety data system comprises static structure data, dynamic environment data, personnel behavior data and management data; constructing a coal mine three-dimensional space model according to the static structure data and the dynamic environment data; risk indexes in the dynamic environment data are extracted based on multi-algorithm fusion for evaluation, and a risk level is obtained; and if the risk level reaches a preset threshold value, triggering a corresponding linkage response mechanism, and forming a visual result in the coal mine three-dimensional space model. By the adoption of the method, closed-loop logic from sensing, evaluation to linkage treatment can be achieved, and the real-time performance, predictability and controllability of coal mine safety management are effectively improved through algorithm support of each stage and fine design of implementation details.

The invention discloses a lower limb motion data real-time analysis method and system applied to rehabilitation guidance, and the method comprises the steps: calling a personalized model according to user information, converting real-time motion data into historical motion feature vectors, inputting the historical motion feature vectors into a real-time prediction module, and generating multiple frames of future motion feature vectors through coding and decoding; calculating a deviation value between each future motion feature vector and a preset threshold range to obtain a motion deviation value, calculating a motion risk proportion of each future motion feature vector, performing weighted fusion to obtain a motion risk probability, and generating an equipment parameter adjustment value and pose adjustment information through reverse mapping; and finally, according to risk probability assessment training stage promotion, synchronously displaying correction information and adjusting equipment operation parameters. According to the method, the dynamic model is constructed through pre-training, real-time prediction of future motion features is realized in combination with the lightweight LSTM, and the predictability and personalized adaptation ability of rehabilitation guidance are improved.

The invention discloses an integrated collaborative management method for construction site risk operation data, and belongs to the technical field of data management. The method comprises the following steps: constructing and calibrating a space-time risk reference database; deducing a potential risk conduction link set, and generating an associated intervention knowledge base; during operation, through multi-sensor cooperative verification, an abnormal signal is confirmed as a risk event; matching the risk event with a conduction link to calculate a risk upgrade level and dynamically adjust an early warning threshold; and when the early warning is triggered, generating and issuing a dynamic collaborative response instruction, and feeding back a processing result to correct the reference database to form a management closed loop. According to the method, the technical means of constructing the space-time risk reference, deducing the conduction link, cooperatively verifying the risk and dynamically regulating and controlling the threshold are adopted, so that the predictability of project risk management and control, the efficiency of resource cooperative scheduling and the scientificity of overall management decision are improved.

The invention relates to the technical field of data processing, and discloses an operation state monitoring method and system for vibration test equipment. The method comprises the following steps: generating a multi-dimensional physical characteristic data stream through vibration waveform calibration according to operation parameters collected by each sensor of vibration test equipment; performing frequency response analysis on the data stream to obtain physical response characteristics; based on this, a test calibration reference is established; outputting test state parameters according to the reference analysis real-time response characteristics; and performing performance attenuation analysis according to the parameters, and generating an equipment quality evaluation report. According to the comprehensive analysis method based on the multi-dimensional physical characteristic data flow, real-time monitoring, multi-parameter joint evaluation and performance attenuation trend analysis of the dynamic characteristics of the equipment are achieved, so that the accuracy, comprehensiveness and predictability of state monitoring of the vibration test equipment are improved, and the reliability and consistency of test results are ensured.

The invention discloses an AI-NWP three-dimensional closed-loop bidirectional dynamic feedback coupling method for extreme rainfall event area simulation and a program product, and belongs to the technical field of meteorological numerical simulation and artificial intelligence fusion. According to the method, a mid-term forecast field is generated based on an AI global weather prediction model, an analysis field is constructed by assimilating multi-source observation data, and a high-resolution NWP region mode is driven to perform rolling simulation. Furthermore, a space-time residual field is constructed by using the difference between an NWP region simulation result and live data, a residual learning model is trained, an AI model prediction structure is fed back and corrected, and dynamic weight adjustment updating of the AI prediction model is realized. A closed-loop two-way feedback system among AI output, NWP simulation and residual evaluation is integrally formed, the space structure reduction capability and the area positioning precision of a medium-term heavy rainfall event are effectively improved, the continuous predictability and the simulation credibility of an extreme weather process are remarkably enhanced, and the method has good stability, universality and engineering expansion value.

The invention relates to the technical field of fault detection of the Internet of Things, in particular to a valve path detection method and system based on the Internet of Things, and the method comprises the following steps: based on real-time data of a valve path, removing high-frequency noise and high-frequency components in temperature fluctuation through a low-pass filter, carrying out the mean value smoothing operation of pressure data, and carrying out the mean value smoothing operation; and performing data standardization conversion on the flow data to obtain valve operation state data. According to the method, through real-time collection and optimization processing of valve operation data, it is ensured that the data is accurate and stable, weighted calculation is carried out in combination with the data influence degree, the representativeness of state feature extraction and the perception ability of working condition changes are enhanced, and based on state feature trend analysis, the sensitivity of fault detection is improved; and health state evaluation is carried out in combination with the operation duration and the load change, so that health state scoring is realized, the predictability of the long-term operation state is improved, false alarms and missing alarms are reduced, and the accuracy of fault early warning and the equipment health monitoring capability are enhanced.

The invention discloses a boiler state monitoring and predicting system based on artificial intelligence, and relates to the technical field of state monitoring. The system comprises a data acquisition part, a combustion stability analysis part, a hydrodynamic instability risk assessment part, a steam drum water level fluctuation prediction part and a fault probability and service life assessment part. The data acquisition part is used for acquiring boiler operation data and water circulation data; the combustion stability evaluation part is used for calculating a thermodynamic efficiency index; the combustion stability analysis part is used for calculating a combustion stability coefficient; the hydrodynamic instability risk assessment part is used for calculating a hydrodynamic instability risk index; the steam drum water level fluctuation prediction part is used for calculating and predicting steam drum water level fluctuation; and the fault probability and service life evaluation part is used for evaluating the fault probability and the residual service life of the boiler. According to the invention, the comprehensiveness, accuracy and predictability of boiler operation state monitoring are obviously improved.

Systems and methods facilitate saving data storage through data storage management of storage location(s) by training, via an iterative training and testing loop, a predictive model using training data to detect data redundancies from two or more datasets stored to data storage location(s), the training includes testing the predictive model by predicting a target variable and iteratively adjusting weights and calculations during each subsequent iteration to improve predictability of the target variable, where the predictive model is trained to identify data similarities among the two or more datasets. Based on any error in predicting the target variable being less than a predetermined level, the predictive model is deployed and applied to at least two datasets to quantify a percentage of similarity among the at least two datasets. If it is determined the percentage of similarity surpasses a predefined threshold percentage, then an electronic notification is transmitted.

The invention relates to the technical field of electronic equipment detection, and discloses an FPC connector quality detection method and system based on multi-modal fusion, and the method comprises the steps: obtaining an original multi-modal data set containing electrical stress, thermal stress and mechanical stress, and carrying out the filtering and time sequence alignment preprocessing, and obtaining a standardized multi-physical field sequence; constructing a three-dimensional space field intensity distribution diagram according to the sequence, and identifying field intensity anomalies by adopting density clustering so as to accurately position potential defects; learning a time sequence evolution mode based on the multi-moment observation value of the defect position to predict the future diffusion trend of the defect; and integrating the diffusion trend, the real-time field intensity and the environment variable to carry out comprehensive modeling and evaluation, and generating a dynamically updated and final quality detection report. According to the method, root cause diagnosis of connector defects, accurate three-dimensional space fault positioning and accurate prediction of future evolution trends can be realized, and the detection precision and predictability are remarkably improved.

The invention discloses a soft start control method for intelligent temperature control of a glue injection mold, and particularly relates to the technical field of glue injection molds. Initial temperatures of a plurality of heating areas of the mold are collected, and a temperature distribution model is constructed; calculating a thermal inertia score value based on the thermal response characteristic and the thermal capacity parameter of each region; predicting temperature rise rates of different areas by using the thermal inertia score value, the thermal coupling degree abnormal value and the historical control deviation frequency, and setting differentiated soft start amplitude limiting parameters; temperature feedback is collected in real time in the heating process, a temperature rise rate error sequence is constructed, and the future deviation risk is predicted through the dynamic Bayesian network; when the risk value exceeds the limit, the PWM duty ratio or the conduction angle of the heating area is dynamically corrected, and power output self-adaptive adjustment is achieved; the method has the advantages of being high in predictability, accurate in response and intelligent in control, and is suitable for high-precision heating control application of the complex glue injection mold.

The invention discloses a multi-agent task arrangement method and system, and relates to the technical field of artificial intelligence. In the method, firstly, a task demand is obtained, key information and intention of the task demand are extracted, and task semantics are obtained; secondly, based on the task semantics and the business standard process template, obtaining a business standard process template matched with the task demand; then, according to the matched business standard process template and task semantics, a task context is constructed, and a domain specific language DSL is generated through reasoning according to the task context; thirdly, the DSL is analyzed, a task dependency graph is constructed according to the DSL analysis result, and the task dependency graph is converted into BPMN process model data; and finally, sub-task scheduling and execution of the sub-agents are carried out according to the BPMN process model data. According to the method provided by the invention, the dependence on professional developers or process engineers can be reduced, the response and planning time of complex tasks can be shortened, the result predictability can be improved, and the reasoning cost can be obviously reduced.

The invention relates to the technical field of computer vision, in particular to a station platform limit intelligent detection method and system based on computer vision, and the method comprises the following steps: obtaining platform region spectral data, extracting spectral features according to the reflectivity value of each pixel point in a spectral range, comparing the spectral features with the features of various known materials, and determining the position of the station platform limit. And identifying material types of the plurality of positions, detecting obstacles, and generating material classification feature values. According to the method, the spectral data is acquired, the reflectivity value is compared to identify the material type, the texture feature and the gradient change rate are utilized to analyze and identify the fuzzy area, misjudgment on the fuzzy identification is reduced, point cloud data and image information are adopted for time-space synchronous registration, and multi-source sensing information is fused to enhance the time-space consistency of three-dimensional reconstruction. The size accuracy of the platform model is improved, the space offset is calculated by comparing engineering design drawing data, a structure deviation quantitative evaluation system is established, a deformation area is positioned, and the foreseeability of safety early warning is improved.

The invention relates to the technical field of computers, and discloses a cross-region advertisement putting analysis and decision method and system based on artificial intelligence, and the method comprises the steps: responding to a cross-region advertisement putting instruction, carrying out the analysis of the cross-region advertisement putting instruction, and determining a target advertisement putting region in the cross-region advertisement putting instruction; obtaining multi-dimensional data of the target advertisement putting area; performing semantic analysis and code conversion on the multi-dimensional data, and constructing a structured regional data matrix; feature extraction is performed on the regional data matrix based on a preset deep learning algorithm, and potential features related to the advertisement putting effect are mined; and inputting the potential features into an advertisement putting decision model to obtain an advertisement putting strategy output by the advertisement putting decision model for the target advertisement putting area. According to the method, the problems that the dynamic change and potential trend of the market cannot be accurately predicted and the advertisement putting strategy is lack of accuracy are solved, and the accuracy and predictability of cross-region advertisement putting analysis decision are improved.

The invention relates to the technical field of data testing, in particular to an intelligent test analysis system and method for an integrated circuit. The method comprises the following steps: acquiring circuit monitoring data; performing data preprocessing on the circuit monitoring data to generate circuit monitoring preprocessing data; performing multi-dimensional feature extraction on the circuit monitoring preprocessing data to generate a circuit monitoring multi-dimensional feature data set; performing feature set fusion on the circuit monitoring multi-dimensional feature data set to generate a circuit monitoring feature fusion data set; therefore, the problems of insufficient precision, response delay and high maintenance cost in traditional circuit monitoring are solved through an accurate data processing and intelligent analysis method, and the real-time performance, the accuracy and the predictability of intelligent testing of the integrated circuit are improved.

The invention relates to a virtual power plant load prediction and dynamic response method and system integrated with vehicle network interaction. The method comprises the following steps: acquiring and processing historical charging data to generate a user data set; extracting and analyzing the user data set to generate a charging behavior prediction result; collecting traffic flow data in real time and matching to generate a traffic influence coefficient; constructing a regional charging demand prediction model, and generating regional charging demand distribution; acquiring external environment event data and processing the data to generate a sudden load correction value; a comprehensive load prediction value is generated by fusing multi-level factors, the power distribution and the charging priority of the charging pile are adjusted, and a scheduling instruction is output to complete closed-loop operation, so that the problem of load prediction deviation caused by user behavior randomness, traffic dynamic coupling and unpredictability of emergencies is solved; accurate modeling and collaborative optimization of multi-level dynamic factors are realized, and the scheduling efficiency of the virtual power plant to the vehicle network interaction resources is improved.

The invention discloses a dynamic water quality monitoring system based on an unmanned ship and a data acquisition and distribution algorithm. Comprising an unmanned ship platform module, an initial navigation path setting module, a water quality data acquisition module, a dynamic adjustment and optimization module, a control interaction module and a dynamic adjustment and optimization module, wherein the dynamic adjustment and optimization module dynamically adjusts the navigation strategy and sampling frequency of the unmanned ship in a target to-be-detected water area through a second branch of a first neural network model; the unmanned ship can sail autonomously, continuous and global water quality monitoring is achieved, through an efficient data processing algorithm, the accuracy and reliability of data are improved, a scientific basis is provided for water quality management, a distributed algorithm model is achieved to conduct predictive analysis on each specific target sampling point, the accurate predictability of unmanned ship monitoring is improved, and the water quality monitoring efficiency is improved. The monitoring strategy is dynamically adjusted according to actual requirements, optimal configuration of monitoring resources is achieved, the monitoring cost is reduced, the water quality change trend is found in advance through prediction analysis of the neural network model, and powerful support is provided for early warning and emergency response.

The invention discloses a crop growth real-time monitoring system and method, and relates to the technical field of crop monitoring. According to the crop growth real-time monitoring method, crop image data time sequence and soil state time sequence data are continuously acquired in real time, data analysis is performed respectively to obtain an initial agricultural healthy flow index and a soil conditioning index of each monitoring time period, comprehensive analysis is performed to obtain a comprehensive agricultural healthy flow index of each monitoring time period, and prediction analysis is performed to obtain a real-time crop growth monitoring result. According to the method, the comprehensive agricultural healthy flow index of the next monitoring time period is compared and analyzed with the preset comprehensive agricultural healthy flow index interval, and corresponding early warning measures are taken based on the comparison and analysis result, so that the growth state of crops and the change of the soil environment are tracked in real time; therefore, the health state of the crops and the influence of the soil on the growth of the crops are obtained, and the monitoring accuracy and predictability are improved.

The invention discloses an intelligent mold part machining cutting tool control method and system. The method comprises the following steps that S1, a multi-source sensing information collection layer is established; s2, constructing a cutter health knowledge graph; s3, generating a dynamic decision control instruction; s4, executing a bimodal control response; and S5, realizing closed-loop control optimization. Through quadruple mechanism coupling of global coverage of a multi-mode sensing layer, dynamic deduction of a knowledge graph decision-making layer, risk isolation of a double-track execution layer and intelligent evolution of a closed-loop optimization layer, the method is realized in an industrial control domain for the first time: raising a sensing dimension, and converting a physical world fragmentation signal into a cutter full life cycle digital twinborn body; reconstructing decision logic, replacing traditional threshold judgment with topological correlation, and foreseeably inhibiting ill-conditioned failure; the system is ecological and self-consistent, and the control strategy continuously evolves in operation to form the anti-interference capability; and finally, normal form transition of tool wear control from passive remediation to active immunity is achieved.

The invention discloses a task scheduling method based on predictable resource state atlas modeling, a platform is oriented to a heterogeneous computing environment, a unified resource state atlas is constructed by collecting multi-dimensional resource state parameters of computing nodes, and performance characteristics and communication topological relations among the nodes are comprehensively described. On the basis, a bidirectional time sequence model and an attention mechanism are fused, and the load trend of each node in a future short time is predicted. The platform constructs a multi-factor scheduling scoring function based on a task feature vector and resource state prediction map, integrates parameters such as resource matching degree, prediction load, communication delay and energy consumption cost, dynamically evaluates the adaptability of tasks and resources, and realizes adaptive scheduling and optimal resource allocation of the tasks. Compared with the prior art, the method has the advantages of being high in resource state predictability, high in task allocation intelligence degree, outstanding in platform evolution capability and the like, and is suitable for intelligent task scheduling application in a large-scale heterogeneous resource environment.

The invention discloses a security operation and maintenance management method and system based on a network security event, and relates to the technical field of network security, and the method comprises the steps: collecting and preprocessing multi-dimensional data, and calculating an attack state based on the preprocessed multi-dimensional data; the attack influence degree is calculated by using a weighted propagation model based on the attack state, and a fitness function is constructed and optimized based on the attack influence degree to obtain an optimal protection strategy set; and converting the optimal protection strategy set into a control instruction, executing the control instruction, and storing, collecting and analyzing generated multi-dimensional data. A non-linear ordinary differential equation is constructed through a non-linear reaction diffusion model, the predictability and the coping capacity of network security management and operation and maintenance are improved, the attack influence degree is calculated through a weighted propagation model, accurate estimation of the wide propagation effect of network security threats is achieved, and through construction and optimization of a fitness function, the network security threats can be accurately estimated. The flexibility and adaptability of the protection system are improved, and the requirement for manual intervention is greatly reduced.

The invention relates to the technical field of construction control, in particular to an underground super-long thick and large concrete structure construction control method and system.The method comprises the following steps that through vertical displacement and strain data, trend extraction nodes are analyzed, difference sections are divided, load recognition influence areas are positioned, and structural sections are sequenced according to fluctuation increments; and adjusting cooling, supporting and loading parameters to form an intervention arrangement, and performing year-on-year adjustment based on construction data to generate feedback to complete regulation and control. According to the method, load-deformation dynamic mapping is established through time sequence association of displacement and strain data, a difference section identification mechanism is constructed based on time difference comparison, natural deformation and load surge response are distinguished, fluctuation characteristics of a load surge section are extracted to form response sensitivity ranking, and cooling parameters and a loading sequence are dynamically adjusted in combination with increment comparison. Graded regulation and control are realized, space-time dimensions of construction records and monitoring data are coupled, the crack positioning precision is improved, the predictability of deformation control is enhanced, and the regulation and control redundancy cost is reduced.

The invention relates to the technical field of data security, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a data processing method, device and equipment based on dynamic association analysis and a medium, and the method comprises the steps: obtaining original data, and converting the original data into initial state information; performing dynamic evolution based on the initial state information to generate a continuous state matrix; calculating correlation information among internal elements according to the continuous state matrix; determining a target hash mapping function based on the correlation information; processing the continuous state matrix by using a target Hash mapping function to generate an intermediate encryption representation; constructing an orthogonally distributed Hash center set; and comparing the intermediate encrypted representation with the Hash center set, and discretizing a comparison result to obtain a final output code stream. According to the method, the Hash mapping function which can be adaptively adjusted along with state evolution is constructed, so that the predictability is reduced, and meanwhile, the resistance to disturbance and statistical attack is enhanced.

The invention relates to the technical field of numerical control machining, in particular to an automatic input system and method for geometric parameters of a numerical control tool. The method comprises the following steps of obtaining point cloud data and material characteristics of a tool, removing abnormal points to clean the data, extracting the edge contour of the tool, performing geometric parameter mapping through the edge contour, determining geometric characteristics of the tool, reconstructing a tool framework, obtaining a numerical control machining task, and performing cutting load simulation on the reconstructed tool. Analyzing the initial wear data, performing heat effect prediction based on cutting load, calculating the thermal deformation degree of the cutter, performing deformation compensation on the initial wear data, generating geometric deformation data, drawing a deformation-time curve, performing time sequence tracking parameter compensation according to the change process of the cutter, and realizing real-time recording and updating. According to the method, the management efficiency and the machining precision of the numerical control tool are integrally improved, and the stability and predictability of the production process are enhanced.

The invention discloses a continuous charging production line intelligent management and control strategy and framework, and the framework further comprises a multi-physics field coupling modeling module which carries out the physical mechanism modeling and state prediction of a production process through a heat-fluid-solid three-field coupling model on the basis of a conventional data collection and control module; the intelligent twin module adopts a mode of dynamically fusing a mechanism model and a data-driven residual model to improve the precision and robustness of system state estimation; the scheduling optimization module is used for realizing dynamic adaptive scheduling of AGV paths and production tasks based on a multi-target deep reinforcement learning strategy; and the multi-layer safety causal reasoning module realizes advanced prediction and active prevention and control of safety risks through a causal map, fuzzy logic and a linkage response mechanism. According to the method, the problem that safety, quality and efficiency are difficult to collaboratively optimize in the prior art is solved, and the predictability, safety, scheduling efficiency and adaptive capacity of the production line are remarkably improved.

The invention relates to the technical field of data processing and analysis, in particular to an intelligent large health system and a data processing method thereof, and the method comprises the steps: collecting the data of a multi-source heterogeneous data source in real time, and comprehensively capturing the multi-dimensional health data of a user; a multi-modal health knowledge graph is dynamically constructed and optimized through an efficient data preprocessing and feature alignment technology, and the integration value and the utilization efficiency of data are improved; a graph neural network and a time sequence analysis model are utilized to realize accurate evaluation of the health state of the user and timely prediction of future risks, and predictability and pertinence of health management are effectively improved; based on a reinforcement learning algorithm, a highly personalized intervention sequence can be generated according to the individual condition of a user, the accuracy of health intervention is enhanced, and the positive change of the health behavior of the user is promoted; an intervention instruction is executed through intelligent equipment, user feedback is monitored in real time, an intervention strategy is dynamically adjusted, and the flexibility and adaptability of intervention measures are ensured.

The invention discloses a digital station building equipment state intelligent monitoring system and method. The system comprises a data acquisition module, a multi-dimensional data processing module, an LSTM neural network analysis module and a state evaluation module. The data acquisition module acquires multi-modal time series data through a multi-type sensor group; the multi-dimensional data processing module performs space-time alignment and feature fusion on the data to construct a composite feature vector; the LSTM neural network analysis module adopts an attention gating mechanism to fuse dual-scale features, and outputs equipment state probability distribution and a residual life prediction value; and the state evaluation module updates the posterior probability of the health state of the equipment based on the dynamic Bayesian network so as to realize graded early warning and maintenance decision. According to the invention, the accuracy and predictability of equipment state monitoring are improved, the requirements of the digital station building for accurate monitoring and predictive maintenance of the equipment state are met, and the intelligent level of operation and maintenance management is improved.

The invention relates to the technical field of mineral processing visual detection, and discloses a visual analysis system for detecting the grade of a phosphorite flotation froth layer. The system comprises an image acquisition and decomposition module, a parallel feature extraction module, a dynamic feature fusion module, a foam evolution analysis module and a grade decision output module. The system performs multi-scale decomposition on a foam image, extracts physical and semantic features in parallel, constructs a dynamic fusion network based on bidirectional mapping to perform iterative interaction, and generates a multi-modal feature descriptor. Therefore, self-organizing growth of a foam evolution graph is driven, an evolution track of a key foam primitive is positioned and tracked, a foam grade state vector is formed, and finally, a regulation and control decision is output in combination with external control parameters. According to the system, deep fusion of physical and semantic features and deep analysis of the foam dynamic evolution process are achieved, the accuracy and predictability of foam grade state sensing are improved, and an effective means is provided for accurate control over the flotation process.

The invention belongs to the technical field of livestock and poultry breeding environment intelligent control, and particularly relates to a meat duck breeding environment temperature regulation and control method and system integrating quantile regression prediction and reinforcement learning. The method comprises the following steps: collecting multi-dimensional data of a breeding environment and operation state information of environment regulation and control equipment, and performing feature construction to obtain a feature vector; using the multi-source data set to predict in-house temperature based on quantile regression to obtain predicted temperatures under different quantiles; modeling a breeding environment temperature regulation problem into a Markov decision process, and dynamically adjusting fan power and a wet curtain equipment state; and setting an online updating model step threshold, and performing actual deployment and application on the reinforcement learning decision model obtained by training and fusing quantile regression information. According to the method, the problems that the meat duck breeding environment temperature regulation and control technology still faces insufficient predictability, regulation and control lag, control strategy static performance, multi-target optimization deficiency and the like are solved.

The invention relates to the technical field of digital twinning, in particular to a digital twinning monitoring system for a precision servo press production line, which comprises a physical production line module, an edge calculation module, a cloud digital twinning module and a visual monitoring module, wherein the physical production line module is used for monitoring and collecting production line data in real time; the edge calculation module preprocesses and analyzes the collected original data, judges product defects, equipment faults and employee abnormal states in real time to trigger edge early warning, and outputs a multi-dimensional data set; the cloud digital twinborn module is used for constructing a digital twinborn body, establishing a real-time coupling simulation model fusing electromagnetism, heat, force and sound to simulate the internal physical process of equipment in real time, predicting the performance change and potential fault of the equipment, positioning the fault root cause and predicting the residual life; and the visual monitoring module is used for carrying out multi-dimensional visual presentation and providing decision support. Therefore, the problems of insufficient production line staff monitoring, single physical field monitoring, lack of fault predictability and the like are solved.