2697 results about "Performance index" patented technology

The invention relates to a task scheduling optimization method and device based on reinforcement learning, equipment and a medium. The method comprises the steps that firstly, system resource state data are collected in real time, dynamic environment characteristics are determined through preprocessing and time sequence analysis, task characteristic data are analyzed at the same time, and a task priority sequence and a resource demand vector are generated through a priority ranking algorithm and a resource evaluation model; and then a state space and an action space are constructed by adopting a reinforcement learning algorithm, an optimal task allocation scheme is generated through strategy iteration and reward function optimization, and if the scheme meets a resource balance threshold, scheduling is executed, and performance indexes are collected. And finally, fusing real-time indexes with historical data, and updating parameters of the reinforcement learning model through experience playback and gradient descent to form a closed-loop optimized improved scheduling strategy. By adopting the method, the accurate mapping of the resource state and the task requirement can be realized, and the problem of insufficient adaptability of the traditional static scheduling to a complex scene is solved.

Disclosed is a motor fuzzy PID parameter tuning method based on an improved whale algorithm. The method comprises: building a brushless direct-current motor speed control system model, and using a fuzzy PID controller to perform motor speed control. A conventional whale algorithm is optimized by using a chaotic convergence factor, a fractional order, and Levy flights, so as to obtain an improved whale algorithm. Secondly, the overshoot of the system is used as a component of an ITAE performance index to obtain an improved ITAE performance index, and the improved ITAE performance index is used as a fitness function for the improved whale algorithm. Finally, the improved whale algorithm is used to optimize input and output membership functions of a fuzzy controller, so as to obtain optimal ΔKp, ΔKi, and ΔKd values, and Kp, Ki, and Kd parameters of a PID controller are tuned to implement motor speed control. The present invention addresses the difficulty of tuning parameters of conventional PID controllers and solves the problem of low precision of motor speed control, has the advantages of high anti-interference capability, little overshoot, and short adjustment time, and improves the dynamic characteristics and robustness of the controllers.

The invention relates to the technical field of power systems, in particular to a multi-dimensional network performance index evaluation system and method based on measured data of a new energy station. Comprising a data acquisition preprocessing unit; a multi-dimensional index construction unit; the weight adjustment unit dynamically adjusts the index weight according to the operation condition based on a fusion algorithm of an improved analytic hierarchy process and an entropy weight method; the performance evaluation model unit adopts a comprehensive evaluation model fused with a random forest algorithm to generate multi-dimensional performance scores and risk early warning; and the visual interaction terminal unit is used for displaying the evaluation result in real time and generating a trend analysis report. According to the invention, the three-dimensional evaluation system covering the static electrical parameters, the dynamic stability indexes and the harmonic pollution indexes is constructed, so that the limitation of single-dimensional evaluation in the prior art is broken through, and the extension of the network-related performance of the new energy station from local detection to full-dimensional coverage is realized; and the multi-scene evaluation requirements of the power grid on the steady-state operation, the transient response and the electric energy quality of the station are accurately matched.

The invention discloses an enterprise operation global analog simulation method based on digital twinning. The method comprises the following steps: S1, collecting and preprocessing multi-source heterogeneous data; s2, constructing an enterprise operation digital twinning model, and defining an optimization objective function and constraint conditions; s3, optimizing the model by adopting a sea elephant optimization algorithm, and dynamically adjusting structural parameters, control variables and strategy configuration; s4, multi-scene simulation drilling is carried out based on the optimization model, and enterprise operation prediction results under different strategies are obtained; s5, evaluating and comparing key performance indexes corresponding to the strategies, and determining an optimal service configuration scheme; and S6, deploying an optimal service configuration scheme, collecting feedback data, and dynamically updating the model. According to the method, the enterprise operation digital twin model which can be optimized, simulated and fed back is constructed, so that operation simulation and optimal business scheme output under multi-strategy configuration are realized, and the enterprise decision-making intelligence level and the operation efficiency are improved.

The invention relates to the technical field of electrical design and intelligent optimization calculation, and discloses a capacitor structure design optimization method and system and a storage medium. The method comprises the following steps: carrying out modeling processing on capacitor structure parameters, and dividing a parameter space to obtain an initial model set; obtaining a multi-physical field simulation model according to the geometric model and the material attributes; obtaining performance indexes such as capacitance value, heat distribution and stress based on the simulation model; setting a target function and constraint conditions, and operating an optimization algorithm to obtain an optimal solution set; performing feedback control processing on an optimization result, and analyzing a convergence path to obtain a structure parameter; according to the invention, the execution efficiency of capacitor structure design optimization is improved, and the consistency and stability of the optimization process and the performance prediction result are improved.

The invention relates to the technical field of process optimization, and discloses a hot work die steel heat treatment process optimization method and system.The method comprises the steps that hot work die steel samples are collected under multiple sets of different process conditions, and a performance basic data set is obtained; constructing a multi-target coordination optimization model based on the performance basic data set; performing phase change detection on the hot work die steel sample to obtain phase change monitoring data; performing prediction in combination with the phase change monitoring data to obtain a performance prediction result; and solving an optimal process parameter combination based on the performance prediction result and the multi-target coordinated optimization model, and realizing simultaneous optimization and coordinated balance of a plurality of performance indexes in the heat treatment process of the hot work die steel by making full use of associated information among different performance indexes.

The invention discloses an SSD testing method, system, device and equipment and a storage medium, and relates to the technical field of solid state disk testing, and the method comprises the steps that firstly, a test request is responded, and a standardized test environment is constructed by deploying a test environment mirror image; and then an iterative testing process is executed on the SSD to be tested in the environment. The key of the iterative test process is that the real-time performance of the SSD to be tested in each round of test can be obtained, and the performance is compared with a preset performance standard. When a comparison result meets a specific condition, the system automatically adjusts and optimizes execution parameters used in the next round of test. According to the method and the device, the dynamic association between the performance indexes and the subsequent test parameters is established, so that the test process can be self-adjusted according to the actual performance of the SSD to be tested, and the limitation caused by the fact that the test parameters are fixed in the prior art is overcome.

The invention relates to the technical field of cluster video stream processing, and discloses a video stream real-time coding and decoding transmission method under a cluster. The method comprises the following steps: firstly, acquiring video stream coding parameter text data, link state time sequence data and equipment performance index data of multiple nodes of a target cluster to form a transmission link data set; semantic analysis is carried out on the coding parameter text data to obtain a coding semantic feature vector, dynamic fluctuation features are extracted from the link state time sequence data to obtain a link fluctuation feature vector, and cross-modal fusion is carried out to generate a fusion transmission feature set; generating an abnormal association degree score set by using a pre-trained multi-layer sensing network model, and obtaining an abnormal source node and an equipment defect type by combining root cause tracing according to the abnormal association degree score set; and finally, generating a dynamic optimization strategy and feeding back to the transmission control system to trigger parameter calibration. According to the method, the abnormal root cause can be accurately traced, the transmission parameters are optimized, and the cluster video stream transmission quality is improved.

The invention provides a real-time link anomaly detection and sampling method and device based on edge AI driving, and is applied to the technical field of artificial intelligence. Segmenting and aggregating the original link tracking data in each time window through a dynamic time window division mechanism to generate a statistical feature vector of link topology in the time window, the original link tracking data including a link topology relationship, a service calling level and a performance index; inputting the statistical feature vector into an anomaly detection model obtained by pre-training for analysis to obtain a path anomaly probability score; and dynamically adjusting the sampling strategy of the next time window according to the path anomaly probability score. Through edge AI calculation nodes, dynamic time window division, segmented aggregation and dynamic adjustment of a sampling strategy, the real-time performance of anomaly detection and the reliability and practicability of anomaly detection are improved, adaptive sampling is realized, and the sampling precision and efficiency are ensured.

The invention provides an aeronautical part multi-process parameter cooperative surface treatment standardization optimization method, and relates to the technical field of aeronautical manufacturing surface engineering, and the method comprises the following steps: S1, constructing a process parameter hierarchical coding system and a correlation mapping model; s2, establishing a parameter solving method based on multi-objective collaborative optimization; s3, constructing a modular process chain; s4, establishing real-time monitoring and closed-loop control of multi-source data fusion; s5, establishing a digital simulation body and verifying the digital simulation body; and S6, constructing a whole-process quality tracing database. According to the method, dynamic collaborative optimization of multiple process parameters is realized through parameter hierarchical coding, association mapping and a cross-process-chain intelligent decision model, the parameter conflict rate is reduced, and the process decision efficiency is improved. Meanwhile, a self-adaptive optimization engine based on reinforcement learning can correct process parameters in real time, the batch consistency of key performance indexes is improved, and the process stability and the product quality are remarkably improved.

The invention relates to a railway bridge post-earthquake traffic safety probability evaluation method and device, which are applied to the technical field of traffic safety, and the method comprises the steps: obtaining an earthquake-induced damage value set of each component through a probability distribution function of different material parameters of a railway track-bridge system; the method comprises the following steps: acquiring a mapping relation between the earthquake-induced damage of a key component and track irregularity through a balance differential equation of a bridge and railway track structural mechanical model, and acquiring earthquake-induced track random irregularity samples of different components based on an earthquake-induced damage value set of each component and the mapping relation between the earthquake-induced damage of the key component and track irregularity; constructing a power spectrum of the track irregularity caused by vibration of different components; establishing a rapid prediction model of the driving performance indexes on the axle after the earthquake through the earthquake-induced track irregularity sample and the coupling dynamic response result; through a Monte Carlo method, based on the earthquake-induced track irregularity power spectrum and the rapid prediction model, the overrun probability and the confidence interval of the driving safety on the axle after the earthquake are rapidly and accurately obtained.

The invention relates to the technical field of air-cooled air conditioner control, in particular to an air-cooled air conditioner control method and system based on deep learning, and the method comprises the following steps: obtaining sensor readings of indoor temperature, outdoor temperature and humidity and Wi-Fi channel state information, judging the position number and the activity state of personnel, calculating the position distribution of the personnel in combination with a time window, and calculating the position distribution of the personnel. And integrating to obtain a multi-dimensional environment and load state vector. According to the method, indoor and outdoor temperature, humidity, Wi-Fi channel state information and personnel position distribution and activity states are integrated through a multi-dimensional environment and load state vector, the personnel density change trend is dynamically calculated through a time window, the comprehensiveness and real-time performance of environment perception are enhanced, and high-dimensional data support is provided for control decision making. And a basic comfort degree deviation index is constructed based on the temperature deviation value and the humidity deviation value, and a multi-target performance index set is generated in combination with the wind speed value, the compressor start-stop frequency and the energy consumption power.

The invention discloses an intelligent resource scheduling system and method based on an elastic threshold value and AI prediction, and relates to the technical field of computer resource management. For the limitation of the existing resource scheduling method, the provided scheme comprises an elastic threshold configuration module used for defining performance indexes of computing resources, allocating weights and setting initial upper and lower limits of an elastic threshold; the dynamic threshold value generation module is used for collecting and calculating resource performance index data in real time, calculating a real-time dynamic threshold value after preprocessing, and comparing the real-time dynamic threshold value with an elastic threshold value range; the AI prediction module is used for training and optimizing a prediction model, and the model obtains a future computing resource demand prediction result based on the new data; and the real-time scheduling engine module is used for formulating a resource scheduling strategy, distributing computing resources, executing computing resource increasing and decreasing operation, monitoring a scheduling result and feeding back the scheduling result, so that the modules are adjusted, and a dynamic optimization closed loop of the resource scheduling strategy is formed. The method is used for improving the computing resource utilization rate.

The invention belongs to the technical field of automatic control, and relates to a self-adaptive control method based on multi-physical modeling. According to the method, by collecting multi-source data of a controlled object, a thermal, electric and force coupling relation used for control analysis is established so as to describe dynamic responses under different operation conditions. And calculating stress, motor power, energy consumption and temperature rise change in the operation process based on a coupling relation to obtain system performance data, verifying stability and safety of different control parameter combinations in a simulation environment, and obtaining performance indexes including operation retardation risk, overload safety margin and safety response time limit. And according to a simulation result, under the condition of meeting safety constraints, taking energy consumption and temperature rise as optimization targets, adjusting control parameters, generating optimized control parameter configuration data, and feeding back the optimized control parameter configuration data to a control unit, so that closed-loop adaptive control and performance optimization are realized. According to the invention, through multi-physical coupling modeling and simulation optimization, the adaptability and reliability of the automatic control system are improved.

The invention relates to the technical field of equipment monitoring and management, and particularly discloses an AI oT-based distributed industrial equipment monitoring and management system and method, and the system comprises a distributed equipment monitoring module which collects operation parameters, performance indexes and environment data in real time through a plurality of types of sensors disposed on industrial equipment, a dynamic hierarchical edge architecture is utilized to process data and evaluate a static health state, and task intelligent layering and cloud collaboration are supported. And the multi-modal perception analysis module generates an equipment association graph based on the collaborative task, extracts multi-modal features by using a cross-node collaborative algorithm, and realizes dynamic health assessment by combining a CNN-LSTM fusion model and equipment implicit dependency relationship analysis. And the distributed equipment management module fuses equipment dynamic and static health data, cooperates with a task association graph and a fault tracing model, and realizes efficient resource configuration and fault prevention. The system guarantees intelligent monitoring and management of the whole life cycle of industrial equipment through a cooperation mechanism of edge intelligence and cloud deep analysis.

The invention discloses a cross-system fault diagnosis method and system combined with multi-dimensional anomaly detection, and relates to the technical field of fault diagnos.The method comprises the steps that a graph neural network with a liquid time constant network unit as a node is constructed through a dynamic topology dependency relationship and a multi-dimensional key performance index flow; each unit describes state evolution through a coupled ordinary differential equation system, and a liquid state time constant can be adaptively adjusted. A time back propagation algorithm is adopted to train a model to learn a normal behavior track contour reference, and anomaly is detected through a dynamic time warping distance. And determining a fault propagation path and a root cause through anti-fact intervention and forward integral solution. And generating an optimal diagnosis action sequence in a liquid graph neural network simulation environment, and calculating a reward value based on execution efficiency, accuracy and a repair effect to carry out strategy optimization. The abnormal detection accuracy and the root cause positioning precision are improved, the fault repair time is shortened, the operation and maintenance cost is reduced, and an intelligent fault diagnosis solution is provided for a complex information technology system.

The invention relates to a multi-parameter fusion motor performance evaluation method, equipment and a medium. The method comprises the following steps: firstly, acquiring electrical signals, mechanical vibration signals and thermal temperature data in operation of a motor, and performing preprocessing and standardization to form a first data set; secondly, dimension reduction is carried out on multi-dimensional parameters of the first data set, and core parameter combinations are extracted to generate a second feature data set; then, classifying and marking the second feature data set by utilizing a classification algorithm, and determining a key performance index distribution interval to obtain a third classification result set; and finally, constructing a time sequence anomaly detection model based on the third classification result set, analyzing an index change trend, identifying an abnormal point deviating from a preset threshold value, and generating a fourth anomaly detection result. According to the method, the data quality and consistency can be improved, the abnormal state in motor operation can be effectively identified, data support is provided for motor performance evaluation and fault early warning, and the method is suitable for real-time monitoring and operation optimization of a motor system.

The invention provides a server cluster operation and maintenance method based on multi-source heterogeneous data fusion and a dynamic knowledge graph, and the method comprises the following steps: collecting a performance index, a log text and topological structure data of a server cluster, splicing the performance data and the log data based on a unified time window, and generating a multi-modal feature sequence; and analyzing the sequence by using an unsupervised deep learning model, constructing a dynamic health baseline, and generating a health degree portrait through the deviation with real-time data. When an exception is detected, mapping an exception event into a dynamic topological graph constructed based on a topological structure; analyzing a fault propagation probability between nodes by using a graph neural network algorithm, positioning a root cause node, and generating a disposal strategy to execute disposal operation; and collecting the processed recovery data as a feedback signal, and updating the deep learning model by using incremental learning. The method has the beneficial effects that the fault discovery accuracy is improved, the alarm storm is effectively inhibited, the root cause is directly positioned, and the model self-iteration adaptability is higher.

The invention relates to the technical field of ship intelligent navigation and trajectory analysis, and discloses a ship trajectory similarity judgment method and system based on multiple dimensions and dynamic weights, and the method comprises the steps: obtaining load data, real-time navigation parameters and environmental parameters, and generating a load state classification result; calculating according to the load state classification result to obtain an acceleration performance index and a turning radius change trend; establishing a mathematical relationship model according to the load state classification result and the turning radius change trend; generating a dynamic incidence matrix according to the mathematical relationship model and the acceleration performance index; according to the dynamic incidence matrix, dynamically adjusting a weight value of a load state to obtain a weight distribution result; and performing calculation according to the weight distribution result and the real-time navigation parameters to obtain a final similarity judgment result. According to the method, the dynamic association of the ship load state and the trajectory characteristics can be realized, and the weight self-adaptive adjustment is realized in the similarity judgment process.

The invention discloses a process intelligent simulation method and system based on rational number fusion, and belongs to the technical field of high-end equipment manufacturing and artificial intelligence. The method comprises the following steps of: constructing a fusion database used for storing a mapping relationship between process parameters and organization characteristics by taking a mathematical model fusion normal form as a core, taking the mathematical model data as theoretical model data and taking the mathematical model data as industrial field data; constructing a multi-scale AI simulation agent model corrected by the industrial field data, and constructing an intelligent prediction model used for representing an association relationship between organization characteristics and service performance; and finally, receiving a target performance index, carrying out global optimization under double constraints of a physical boundary of a theory and a simulation environment which is corrected by a number by utilizing an AI reverse design engine, and carrying out reverse solution to obtain an optimal process parameter. Through a two-way closed-loop mechanism of a theoretic constraint number and a number correction theoretic, the fundamental problems of distortion of a theoretical model caused by a scale effect and lack of physical constraint with an AI model are solved.

The invention discloses an AGV intelligent scheduling and management system based on digital twinning, and the system comprises a digital twinning module which carries out the simulation optimization of the scheduling state data after fusion processing through an AGV digital twinning body, and carries out the task simulation in a virtual environment according to the actual layout of a workshop. Counting performance indexes under different scheduling strategies and obtaining an optimal index, and obtaining an initial scheduling strategy; and the advanced scheduling module is used for constructing a high-level strategy enhancement model based on a reinforcement learning algorithm and a strategy network, taking the initial scheduling strategy as model input, and outputting a final optimized AGV scheduling scheme. The problems of path congestion, unclear task priority distinguishing and the like caused by the fact that an existing mode cannot be adjusted according to workshop dynamic changes are effectively solved.

The invention discloses a multi-source data fusion foam concrete construction process monitoring method and system, and relates to the technical field of concrete construction, the method comprises the following steps: obtaining a standard response data set of foam concrete, including raw material ratio parameters and performance index parameters which are stored in an associated manner; constructing and training a hybrid hierarchical performance prediction model, wherein the model comprises a shared prediction layer based on integrated learning and a plurality of independent output layers connected with the shared prediction layer; in combination with a multi-objective optimization algorithm, by taking compressive strength and cost optimization as an objective, constructing an evaluation function for global optimization, and obtaining a target mix proportion scheme; acquiring real-time process data associated with the target mix proportion scheme, and inputting the real-time process data into the mixing layering performance prediction model to obtain a prediction result; and comparing with a preset design target, and generating an adjustment instruction for adjusting the matching parameters of the subsequent stirring batches. The problem that in the prior art, the performance fluctuation of foam concrete of different batches is large due to the fact that the construction condition cannot be dynamically adjusted in real time is solved.

The invention provides a composite material performance prediction and process optimization method based on a neural network, and the method comprises the steps: firstly collecting multi-source data in the preparation and test process of a composite material, carrying out the preprocessing of the data, screening key feature variables as input variables, constructing a feedforward artificial neural network model, and predicting and outputting the performance indexes of the composite material. And training the model, performing iterative optimization on model parameters, and optimizing composite material process parameters by using the optimized model based on a reverse optimization strategy of a genetic algorithm to obtain an optimal process parameter combination. The invention provides a scientific, efficient and reliable tool for design and optimization of composite materials, and particularly has wide application prospects in high-requirement industries such as aerospace and the like.

The invention relates to the technical field of industrial equipment control, and discloses an industrial equipment intelligent control system and method based on deep reinforcement learning. The system comprises an equipment state monitoring module, a reinforcement learning decision-making module, a control parameter adjustment module, an abnormity intervention module and an equipment performance optimization module. The equipment state monitoring module collects real-time operation data of equipment, analyzes state change by means of a deep reinforcement learning algorithm, and generates a state feature data set; the reinforcement learning decision-making module calculates a control strategy by using a deep reinforcement learning model and generates a control strategy parameter set; the control parameter adjusting module optimizes the operation efficiency according to the adjusted parameters and generates an optimized control parameter set; the abnormity intervention module monitors abnormity, recognizes intervention through deep reinforcement learning, and generates an abnormity intervention adjustment data set; and the equipment performance optimization module optimizes the performance indexes according to the parameters and generates a performance optimization parameter table. The system realizes accurate monitoring of equipment, intelligent optimization of strategies, timely intervention of abnormities and performance improvement, and meets intelligent management and control requirements of the equipment.

The invention belongs to the technical field of cloud computing and edge computing collaboration, and particularly discloses an intelligent scheduling and optimizing method for a cloud-edge collaboration network based on reinforcement learning. By constructing the state sensing matrix and generating the action decision vector, the problem that a traditional scheduling method is insufficient in correlation analysis of multi-dimensional operation state data in a complex network environment is solved, and the comprehensive sensing capability of the operation state of the network node is improved; a dynamic mapping mechanism among the running state, the resource limitation and the task allocation strategy is established, the task allocation and resource scheduling strategy is automatically and differentially adjusted according to the real-time state of the node, and the optimal matching between the task demand and the resource supply and the dynamic balance between the performance and the efficiency are realized; through performance index monitoring and closed-loop feedback optimization, the scheduling effect is mastered in real time, continuous iterative optimization is performed on the reinforcement learning model according to objective data, and resource waste and scheduling delay are reduced.

The invention discloses a design optimization method of a radio frequency power amplifier, and relates to the technical field of design optimization. The method comprises the following steps: collecting the working frequency and the circuit topological structure of the radio frequency power amplifier, establishing a digital twin simulation model, and generating an initial impedance value; simulation performance indexes of the amplifier under the input signal are obtained through simulation; matching network parameters are adjusted according to a dynamic reconstruction matching network algorithm, and a dynamic optimization impedance set is generated; controlling the tunable element to reconstruct the actual impedance value of the radio frequency power amplifier, and collecting the actual performance index of the output signal; comparing an error between the simulation performance index and the actual performance index, and if the error exceeds a preset error threshold, iteratively updating a parasitic parameter of the digital twinborn model; and if the error is within the threshold value, locking the actual impedance value as the optimal configuration, and amplifying and outputting the input signal. The working efficiency of the radio frequency power amplifier is improved.

The invention discloses a backup disaster recovery optimization method, and relates to the technical field of IT operation and maintenance and disaster recovery, and the method comprises the steps: analyzing a statistical dependency relationship between basic performance indexes through employing a causal discovery algorithm based on a standardized monitoring data flow, and constructing a directed weighted graph; when a basic performance index in the directed weighted graph deviates from a normal performance baseline, triggering a reverse traceability positioning root cause component, identifying a service influence path through forward path analysis, and generating a backup enhancement instruction; in a digital twin environment, according to historical drill records and a strategy library, multiple potential disaster recovery switching schemes are generated, parallel deduction is carried out, the performance influence of each potential disaster recovery switching scheme on a virtual service link is quantitatively evaluated in the deduction process, and the optimal disaster recovery switching scheme is selected by comparing evaluation results. According to the invention, the prospective optimization of the disaster recovery decision and the minimization of the business influence are realized, and the automation, intelligence and business adaptation capabilities of the backup disaster recovery system are enhanced.

The invention discloses a test evaluation method and device for an industrial park integrated management system. The method comprises the following steps: acquiring a performance index data set of the industrial park integrated management system; the performance index data set comprises a performance index test data subset of each subsystem; the performance index test data subset comprises a test data sequence of each performance index; pre-processing the performance index data set to obtain a to-be-evaluated data set; and performing test evaluation processing on the to-be-evaluated data set to obtain a performance evaluation result value of the industrial park integrated management system.

The invention relates to an intelligent customer service self-learning method and system, and the method comprises the steps: collecting the interaction data of a user and an intelligent customer service, and forming a multi-dimensional data set; based on a PID (Proportion Integration Differentiation) controller, processing the performance indexes in the multi-dimensional data set, calculating a current error signal, and generating a corresponding control instruction according to the current error signal so as to adjust the response behavior of the intelligent customer service system in real time; evaluating the system performance data adjusted by the control instruction to obtain evaluation feedback; and according to the evaluation feedback, dynamically adjusting the parameters of the PID controller through a self-adaptive control strategy, and feeding back the adjusted parameters to the PID controller in the step S2. According to the invention, by introducing a closed-loop feedback mechanism based on the PID controller and a parameter adaptive optimization strategy, real-time regulation and control of the response behavior of the intelligent customer service system are realized, and the stability, the response speed and the user satisfaction of the system are remarkably improved.

The invention provides a method and a system for digital twinborn construction and multi-performance real-time evaluation of equipment, the core of the method is to construct a digital twinborn body of the equipment and develop a multi-performance real-time evaluation agent model, and the method comprises the following steps: constructing a multi-field simulation virtual prototype of the equipment based on a joint simulation technology; various performance indexes of the equipment are comprehensively presented through simulation analysis; the model of the virtual prototype is combined with sensing data, and after visualization processing, a digital twinborn body capable of reflecting direct observation information of equipment is constructed; a robust agent model construction strategy is provided, and batch joint simulation data is adopted to train the model, so that the indirect performance index of the equipment can be accurately evaluated; sensor data for collecting working conditions in real time and an agent model for evaluating performance in real time are integrated in a digital twinborn body, and a model-data dual-drive performance evaluation mechanism is formed.