155 results about "Online model" patented technology

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

The invention discloses an inverter intelligent control method based on a self-adaptive algorithm, and relates to the technical field of inverter intelligent control, and the method comprises the steps: multi-source temperature collection and environment recognition, machine learning-based electric-thermal coupling prediction, self-adaptive inverter parameter optimization, and cooperative thermal management and fault diagnosis self-repairing. According to the high-temperature or low-temperature failure risk of the motor and the inverter under the extreme climate, temperature distribution and load information are obtained in real time, the temperature rise trend is predicted in advance, and the current, voltage and modulation strategy of the inverter are actively adjusted, so that safe derating or torque compensation is achieved; multi-sensor cross validation and observer fusion are carried out when sensors drift or devices are aged, stable operation and efficient energy utilization of the system are kept, and the drivability and the whole vehicle reliability in an extreme environment are remarkably improved; in the process, fault diagnosis and self-repairing can be further improved through online model updating, and the durability and economical efficiency of the electric drive system are improved.

The invention discloses a dynamic error real-time compensation method and system for a heavy-load vertical machining center, and belongs to the technical field of high-end numerical control equipment, precision manufacturing and intelligent control. Inputting the state vector into a dynamic error model to solve a three-dimensional space dynamic error vector; processing the error vector to generate a real-time compensation instruction; a compensation instruction is injected into the numerical control system to correct the machining track online; and obtaining a real error to update the dynamic error model on line. According to the method, the technology of combining multi-physics field data fusion and a neural network agent model is adopted, frequency decoupling and dual-channel compensation injection are performed on errors, and an online model self-optimization feedback closed loop is established, so that real-time and high-precision compensation on multi-source coupling dynamic errors such as thermal-induced and force-induced multi-source coupling dynamic errors can be realized; and the limit machining precision and stability under the heavy-load machining condition are remarkably improved.

The invention discloses a target intelligent cooperative identification method based on an unmanned aerial vehicle cluster, and belongs to the field of unmanned aerial vehicle cluster control and computer vision. According to the method, cluster networking and model initialization are realized through a dynamic heterogeneous federated learning architecture; a space-time attention mechanism is adopted to optimize task allocation, and a deformable network is utilized to extract multi-view target features; a cascade characteristic distillation fusion strategy is provided, and modal compression and cross-modal gating fusion are carried out on multi-source data such as multispectral data and laser radar data; an anti-interference elastic communication mechanism based on meta-learning is designed, and the system robustness is enhanced by combining space-time confrontation detection and a dynamic spectrum sensing technology; an unsupervised federal incremental learning system is established, and online evolution of the model is realized through momentum weighted aggregation. According to the method, the target identification accuracy is improved by 35% in a complex environment, the time delay is reduced to 200 ms, and high-precision real-time identification support is provided for military reconnaissance, disaster rescue and other scenes.

An offline pre-training and online fine-tuning apparatus based on reinforcement learning includes a data management unit collecting and processing data for offline reinforcement learning in advance; an offline model training unit training an offline policy network and an offline state-action value function network using a previously collected dataset that includes a state, an action, a next state, a reward, and an accumulated reward collected by the data management unit; and an online model training unit performing fine-tuning to update parameters of the offline policy network using an online dataset that includes action information determined based on state information acquired through interaction with the offline policy network and an environment, state information at a next time point according to the action information, and the reward, and the previously collected dataset.

The invention relates to a weak supervision online video moment positioning method and system based on memory perception, and belongs to the technical field of artificial intelligence, and the method comprises the steps: carrying out the multi-modal feature fusion of a given video and a text query thereof, and obtaining the unified frame level representation at each stage; inputting the fused features into an offline module and an online module in an integral and frame-by-frame manner by using an offline guide online model architecture; in the off-line module, generating a Gaussian mask to reconstruct query of a covered part of words, and obtaining a proposal of an action starting moment; in the on-line module, the long-term historical memory in the window is used for enhancing the score, the attention weight of the score in the window is dynamically generated, and the score of the current frame is calculated in a weighted mode; taking the proposal obtained by the offline module as a pseudo tag, and providing supervision information for the score sequence of the online module; and high-performance weak supervision on-line moment positioning can be completed only by independently deducing the on-line module. The expansion capability and the application value of the model are remarkably improved.

The invention relates to the technical field of monitoring, and particularly discloses an anti-esophageal reflux monitoring system for old patients with dysphagia, which is used for solving the problems of oesophageal elastic degeneration, frequent body position change, low swallowing amplitude and the like of the old patients with dysphagia. Therefore, the problem that an existing reflux monitoring system based on single-point sensing, static threshold control and periodic model updating cannot meet the dynamic reflux early warning requirements of individualization, high sensitivity, real-time performance and low power consumption is solved. Comprising a wearable esophagus comprehensive monitoring module, a body position and esophagus deformation recognition module, a personalized pressure threshold self-adaption module, an intelligent prediction and early warning module, an air bag execution module and a cloud model training and updating module. According to the invention, multi-modal sensing and dynamic threshold, self-adaptive sampling and online model fine tuning are fused, low-amplitude swallowing and early-stage reflux signals are effectively captured, the iteration period is shortened by cloud differential updating, the equipment endurance is prolonged, and the esophageal injury risk is reduced.

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 metal rolling automation, and discloses a computer intelligent control system of a rolling mill, which comprises a rolling mill production line, and the rolling mill production line further comprises a tensiometer, a pressure sensor and a displacement sensor; and the computer intelligent control system is electrically connected with an executing mechanism and a sensor of the rolling mill production line and comprises a tension control module, an automatic thickness control module and an online model self-adaptive module. The method comprises the following steps: controlling strip steel tension through a tension control module; the strip steel outlet thickness is adjusted through the thickness automatic control module; and model parameters such as rolling mill rigidity and friction compensation are identified in real time in the rolling process through an online model self-adaption module, and continuous optimization of the control model is achieved. By introducing the online model adaptive module, real-time online correction of the core physical model of the control system is realized, and the problem of model mismatch caused by equipment wear and working condition change is solved.

The invention discloses a water pump frequency optimization control method and system based on flow-energy consumption feedback, and the method comprises the steps: obtaining a target demand flow of a water pump and a current pipe network resistance parameter, inputting the target demand flow and the current pipe network resistance parameter into a preset energy efficiency optimization model, and obtaining an initial optimal frequency set value and a corresponding predicted energy efficiency value; the actual flow and the actual total power consumption of the water pump operating at the value are obtained, and the actual energy efficiency value in the current operating state is calculated; whether the energy efficiency error between the energy efficiency value and the predicted energy efficiency value is smaller than a preset error threshold value or not is judged; if yes, taking the initial optimal frequency set value as a target frequency set value; if not, correcting the initial optimal frequency set value according to the absolute value of the energy efficiency error to obtain a target frequency set value; and controlling the water pump to operate according to the target frequency set value. By combining real-time energy efficiency feedback, iterative closed-loop correction, multi-stage fine optimization, online model calibration and other means, the effects of remarkable energy saving, high self-adaption and quick and accurate optimization are achieved.

The invention provides a civil aircraft flight control system dynamic reconstruction method fusing uncertainty quantification and toughness decision, and belongs to the field of aviation electromechanical product reliability engineering, and the method comprises the steps: S1, multi-source uncertainty modeling and optimization; s2, running state monitoring and risk prediction; s3, evaluating the toughness margin of the flight control system; S4, generating a candidate toughness response strategy; s5, multi-attribute utility evaluation based on online model prediction; s6, selecting an optimal robust toughness decision; s7, executing an optimal robust toughness decision; and S8, empirical learning and model adaptive optimization are carried out. The invention provides a novel operation safety guarantee theory and method which deeply integrates uncertainty quantification, a toughness engineering principle and a multi-criterion robust decision-making aiming at severe uncertainty faced by a dynamic reconfigurable civil aircraft flight control system in a complex operation environment and challenge on flight safety. And the safety decision quality and the overall operation toughness of the system under uncertainty can be obviously improved.

The invention discloses an OpenHarmony multi-queue scheduler intelligent distribution method based on multi-dimensional load feature perception, and relates to an OpenHarmony multi-queue scheduler intelligent distribution method. The problems that the CPU utilization rate is low, the average response delay is high, and resource scheduling self-adaptive adjustment and optimization cannot be achieved are solved. The method comprises the following steps: step 1, deploying a data acquisition module; step 2, feature preprocessing and coding; step 3, task load classification; step 4, executing dynamic optimization of scheduler parameters according to a prediction result; and step 5, performing performance feedback and online model updating. The invention belongs to the technical field of operating system resource management and artificial intelligence.

The invention discloses a flow characteristic adaptive QoS intelligent prediction adjustment method, and relates to the field of network flow management, and the method comprises the steps: 1, collecting flow data in real time, and constructing a multi-dimensional characteristic vector based on protocol types, port numbers and user behavior dynamic classification; 2, high-frequency / low-frequency components are separated, and QoS parameter prediction is output through fusion of an LSTM short-term prediction module and a periodic trend analysis module; 3, solving a resource pre-allocation scheme by adopting reinforcement learning by taking minimization of packet delay as a target; and 4, executing traffic identification, speed limiting and priority queue scheduling by using NPU hardware unloading. According to the method, the precision is improved through a high-frequency / low-frequency combined prediction architecture, decision delay is compressed to a large extent through reinforcement learning and NPU cooperation, and meanwhile online model iteration is achieved through a prediction error triggering mechanism.

The invention relates to the technical field of network fault maintenance, in particular to a network fault self-healing and prediction maintenance method based on artificial intelligence, and the method comprises the steps: S1, constructing a multi-source data real-time collection framework; s2, deploying a lightweight A I model at an edge node; s3, introducing an interpretable AI technology; s4, constructing a causal reasoning module; s5, designing a dynamic self-healing strategy library; s6, establishing an online model learning mechanism; s7, developing a simulation verification environment; and S8, realizing a man-machine cooperative operation and maintenance workflow. According to the scheme, data is subjected to streaming preprocessing and intelligent dimension reduction at the source, the transmission load is greatly reduced, the processing efficiency is improved, real-time, near-real-time and batch processing tasks are further distinguished through the edge side parallel assembly line technology, it is ensured that key indexes are preferentially processed, and compression and acceleration are achieved on the model level through knowledge distillation, quantification and pruning technologies.

The invention discloses a power system scheduling optimization method and system based on deep reinforcement learning, and relates to the technical field of intelligent power grid optimization scheduling. Comprising the following steps: receiving and synchronizing real-time operation data, meteorological data, equipment health indexes and renewable energy output data; constructing a power grid graph and generating a node time sequence matrix, and encoding the node time sequence matrix into multiple space-time embedding vectors; generating a short-term output predicted value and an uncertainty index based on the meteorological and renewable energy output data, and converting the short-term output predicted value and the uncertainty index into compensation factors; calculating a risk score according to the equipment health index and the meteorological data and mapping the risk score into a dynamic weight; inputting the multiple space-time embedding vectors, the compensation factor and the dynamic weight into a deep reinforcement learning model to generate a scheduling strategy, and performing feasibility verification; and if the verification is passed, issuing execution is carried out, and a result is returned for online model updating. According to the invention, by fusing multi-source data and a risk perception mechanism, the security and robustness of power system scheduling are effectively improved.

The invention relates to a precision agriculture monitoring system and method based on multispectral imaging. The system and method are applied to real-time monitoring of crop physiological parameters and variable fertilization decision making. The system comprises an unmanned aerial vehicle imaging module, an edge computing unit and a cloud analysis server. The unmanned aerial vehicle module is provided with a multispectral filter wheel, a three-axis holder and an RTK positioning device and is used for acquiring a high-resolution crop image; the edge calculation unit integrates a radiation correction module, an image splicing module and a canopy segmentation module to realize on-site preprocessing; the cloud server runs a deep learning model and a feature fusion mechanism, outputs estimation of parameters such as nitrogen, chlorophyll and moisture, and generates a high-resolution fertilization prescription map. In the aspect of the method, dynamic monitoring of the nitrogen content of crops is realized through route planning, data synchronization, radiation normalization, multi-source feature fusion and time sequence prediction. The system supports online updating and ground verification of the model, has high precision, low delay and large-area operation capability, and is suitable for intelligent agriculture and precise fertilization scenes.

The invention belongs to the technical field of aircraft control, particularly relates to a range extender control system for an unmanned aerial vehicle and a controller, and aims to solve the problems of limited endurance, poor flight stability and the like caused by discontinuous energy supply and power response lag. The system comprises a range extender power source module, an electric energy conversion and distribution module, a flight state sensing module, a load power prediction module, a multi-target optimization scheduling module and a closed-loop feedback execution module. Through flight state real-time perception and load power prediction based on a recurrent neural network, an optimal power generation instruction is generated in combination with multi-target optimization scheduling, and precise rotating speed tracking is realized through adaptive PID control. A fuel consumption, power supply smooth switching and battery health combined cost function is introduced, dynamic weighting is carried out according to task types, and the cruising ability and the system robustness are improved; the system has the functions of fault emergency response, power battery hot plug and model online updating, and safety and maintainability are enhanced.

The invention relates to the field of remote communication network optimization, and discloses a predictive remote communication method and system. The method comprises the following steps: generating a time sequence sample structure based on historical communication data, and constructing a deployable model through LSTM model training and Bayesian optimization; receiving a communication request in real time, predicting a demand probability, and triggering a dynamic handshake protocol to be connected with the security encryption; generating optimal path configuration by combining network topology analysis and a dynamic programming algorithm; and finally, online updating of the model is realized through performance monitoring and incremental learning. According to the method, time sequence prediction and dynamic path optimization are fused, the problems that model training and network topology are disjointed, dynamic optimization lags and the like in a traditional method are solved, the connection establishment speed and the transmission stability in a high-delay scene are remarkably improved, and meanwhile the self-adaptive capacity of the system is enhanced through embedded packaging and a hot updating mechanism.

An online soft measurement method for dioxin emission concentration in MSWI process includes: performing principal component analysis on process data according to a historical process data set of the MSWI process to obtain a drift index control limit; constructing an offline model based on FTBL, and inputting the process data and historical DXN true value data of the MSWI into an off-line model; performing principal component analysis according to online data, judging whether the online data is drift data according to the drift index control limit; if the typical sample pool is the drift data, constructing an online model based on FTBL, and inputting the process data and the drift data of the typical sample pool and output data of the incremental layer of the offline model into the online model; and determining a DXN emission concentration predicted value according to an offline calculation result and an online calculation result.

The invention relates to the technical field of adaptive control, in particular to a robot dog gait adaptive control method and system facing complex terrains. The specific implementation process comprises the following steps: quantifying external interference generated by unexpected physical interaction through an endogenous interference observer; a configurable gait control loop is used for generating a compensation control signal, and the actual movement gait is adjusted through fusion of a feedforward adjuster and the basic movement gait; continuously collecting and generating a historical interference sequence, and analyzing and extracting a continuous interference factor; and performing online self-adaptive correction on the gait dynamic model by using the continuous interference factor and re-planning the basic motion. According to the method, a dual adaptive control architecture is constructed, and feedforward compensation of instantaneous interference and online model adaptive correction of persistent deviation are combined. The problem that the robot dog is insufficient in robustness when facing complex terrains is effectively solved, and the walking ability of the robot dog in the complex terrains is improved.

The invention provides a production line parameter real-time scheduling method based on reinforcement learning, and belongs to the technical field of production lines, and the method comprises the steps: collecting sensor data to form an original state vector, reducing the dimension of the original state vector into a low-dimensional feature state vector through a state compression encoder, inputting the low-dimensional feature state vector corresponding to a microcosmic scheduling unit into a parameter decision model, and carrying out the real-time scheduling of the microcosmic scheduling unit; the attention weight coefficient of the model is determined by the product of the historical scheduling success rate, the element value of the difference degree matrix and the maximum characteristic value of the inter-stage sensitivity matrix, after a scheduling instruction is output, simulation evaluation is performed in a digital twin platform, and after the scheduling instruction passes, a real production line is issued for execution and multi-time-scale deviation indexes are collected; and calculating a comprehensive reward value according to the indexes, and storing an experience sample to an experience playback buffer area for model online update training, thereby solving the technical problem that production line parameter scheduling is difficult to consider multi-level state feature recognition and dynamic decision weight optimization at the same time.

The invention relates to the technical field of wind energy resource assessment, and discloses a wind resource assessment method based on a millimeter wave wind finding radar. The method comprises the steps of collecting wind field data of a target area through a millimeter wave wind measurement radar and performing preprocessing to generate a standardized data set; inputting the data into a prediction model to obtain a wind energy distribution characteristic graph; generating a parameter set by adopting threshold segmentation based on the feature map, and dynamically constructing an evaluation function for simulation evaluation; synchronously collecting actual wind energy data, and calculating a deviation matrix between the actual wind energy data and a predicted value to quantify an evaluation error; performing parameter correction on the prediction model by using the error to obtain an optimized model; and finally, regenerating a feature map by adopting the optimization model and completing resource evaluation. According to the method, through online model correction and dynamic evaluation function construction, closed-loop optimization and data driving in the evaluation process are realized, and the accuracy of an evaluation result and the adaptability to a specific site are improved.

The invention relates to the technical field of intelligent clamping control, in particular to an online prediction method and system for the clamping stability of a flexible manipulator, and the method specifically comprises the following steps: collecting a clamping operation signal in a simulation experiment platform through a sensor network, and constructing a data set marking the clamping instability probability; performing alignment processing on unequal-length signals by adopting dynamic time warping integrated with physical constraints; then constructing an online model including multi-modal feature adaptive extraction fusion, time sequence feature enhancement and key frame dynamic detection and stability probability prediction, and completing model training optimization by using mean square error loss and small-batch gradient descent; and finally, deploying the model to a manipulator system to realize real-time data processing and clamping instability probability online output. The method can effectively adapt to a biochemical vessel clamping scene, the time sequence signal alignment precision and the risk prediction reliability are improved, and a real-time guarantee is provided for the operation stability of the flexible manipulator.

The invention provides an edge real-time video analysis-oriented resource efficient continuous learning method and system. The method comprises the steps of real-time video analysis and model retraining; an online model precision reduction predictor oriented to the RoI granularity is designed; continuously collecting the precision reduction degree of the lightweight model compared with the high-precision model; then designing a double-layer mixed sample pool, and based on the designed RoI granularity-oriented online model precision reduction predictor; randomly introducing a certain proportion of RoI historical samples to relieve the problem of disastrous forgetting; and finally, developing a deep reinforcement learning DRL-based retraining progress controller. Based on the evaluation result of each round, dynamically estimating a model convergence state to determine a retraining termination opportunity; by introducing a behavioral clone BC loss function, the controller converges the optimal strategy more quickly in an offline training stage. The technical scheme of the invention has an important positive significance for improving the SP income in the MEC environment.

The invention discloses a coal mill core component fault prediction method based on vibration analysis, and belongs to the technical field of rotating machinery state monitoring and predictive maintenance, and the method comprises the steps: collecting multi-point vibration signals, and generating an optimization data set; variational mode decomposition and deep embedding are carried out to extract an advanced feature group; establishing a transfer learning model to generate preliminary fault prediction distribution; performing model self-optimization through reinforcement learning feedback circulation to obtain a refining prediction result; and multi-dimensional risk quantification and fault evolution path simulation are carried out to output a decision report. According to the method, a multi-level self-evolution closed-loop intelligent prediction architecture is adopted, online model optimization can be carried out through a reinforcement learning agent based on the uncertainty of prediction distribution, rapid self-adaption to a variable working condition environment is achieved, prospective maintenance decision support is provided in combination with evolution path simulation, and the method has the advantages of being high in adaptability and high in reliability. And the capability of capturing early weak faults of the coal mill, the accuracy of cross-working-condition prediction and the scientificity of final maintenance decision are remarkably improved.

The invention belongs to the field of real-time monitoring, particularly relates to a chemical process real-time monitoring method and system based on digital twinning, and aims to solve the technical problem that an existing method lacks an online model correction and updating mechanism. The monitoring method comprises the following steps: S1, constructing a state snapshot data set, and synchronously collecting real-time measurement data; s2, identifying a process fluctuation interval, and selecting to-be-selected state snapshots to form an optimal snapshot matrix; s3, calculating a reduced-order truncation error energy ratio, and constructing a reduced-order model based on a reduced-order basis function; s4, in the real-time monitoring stage, if the norm of the prediction residual error is smaller than a monitoring threshold value, a Kalman filtering algorithm is adopted to correct the state coefficient of the reduced-order model; otherwise, calculating the projection error of the prediction residual on each primary function in the standby primary function library; and outputting a full-order state vector reconstructed based on the corrected or updated reduced-order model. The monitoring method provided by the invention ensures the continuous effectiveness and accuracy of monitoring when the chemical process changes.

The invention relates to the technical field of motor testing, and discloses a motor dynamic characteristic compensation test method and platform based on multi-modal data, the method collects motor data through a multi-modal synchronous test system, and a processing module executes the following steps: constructing a state observer model to estimate winding temperature and loss torque; in the online test, the accuracy of the model is monitored by calculating the residual error between the model prediction and the measured value; and when the residual dynamic characteristic exceeds a threshold value, generating and applying an adaptive excitation sequence according to the residual characteristic, collecting high-resolution data to update model parameters on line, forming a self-optimization closed loop, after the test is finished, adopting a fixed interval smoothing algorithm to be combined with a final model to obtain a global optimal state track, and calculating a dynamic characteristic compensation result according to the global optimal state track. According to the invention, through adaptive excitation of residual error driving and online model updating, accurate identification and compensation of dynamic characteristics of the motor are realized, and the test automation level and accuracy are improved.

The invention relates to the technical field of traffic signal control, and discloses a signal timing optimization method and system based on vehicle-road cooperation and traffic flow prediction, and the method comprises the steps: obtaining the track and road side detection data of a network-connected vehicle, dynamically estimating the permeability through Kalman filtering, and reconstructing the full traffic state of an intersection; predicting future traffic flow parameters by using a long-short-term memory network based on the total state historical sequence; establishing an optimization model aiming at minimizing delay and queuing, and solving by adopting a deep reinforcement learning algorithm to obtain a signal timing scheme; and issuing and executing the scheme, broadcasting a green light speed guide message, and feeding back online update model parameters based on an execution result. According to the method, the problem of state perception in a low permeability environment is solved, active prediction control of traffic signals and vehicle-road collaborative closed-loop optimization are realized, and the traffic efficiency of the intersection is effectively improved.

The invention provides a gait evaluation method and system based on a human body nonlinear system analysis technology, and the method comprises the steps: collecting a gait cycle six-channel high-precision time sequence signal outputted by a wearable inertial measurement unit, eliminating noise and gait difference through wavelet threshold denoising and Z-score standardization, extracting a chaotic feature vector composed of a Lyapunov index spectrum and a Kolmogorov entropy value, and carrying out the recognition of a gait signal, a wavelet neural network is adopted to realize nonlinear mapping of chaotic features and phase-space reconstruction parameters, and a self-adaptive feedback mechanism is introduced to dynamically optimize modeling parameters, so that the accuracy and personalized matching capability of gait pattern recognition and stability evaluation are effectively improved; quantitative characterization of the gait chaos level and real-time online model optimization can be achieved, and high-robustness support is provided for rehabilitation training and exercise aided decision making.

The application relates to the technical field of system-in-package, and discloses a functional chip SIP (System in Package) system-in-package method and system. The method constructs a reduced-order discrete thermal state predictor through multi-order exponential attenuation fitting, generates a DVFS gear-noise hazard degree spectrum mapping table based on a synchronous switching noise spectrum and an ADC noise sensitivity curve, generates a multi-DVFS working condition robust grounding network topology by optimizing a narrow bridge connection structure parameter by using a sequential quadratic programming, quantizes equivalent noise interference amounts of each DVFS gear on an ADC chip, and jointly optimizes DVFS gear selection and power consumption upper limit distribution under a rolling time domain mixed integer quadratic programming framework, simultaneously performs online model correction through exponential weighted moving average and Kalman filtering, and realizes cooperative satisfaction of thermal constraints and noise constraints.