6218 results about "Adaptive control" patented technology

The invention discloses an industrial robot real-time adaptive control method and system based on digital twinning, and relates to the technical field of industrial robots. The digital twin engine module runs a high-fidelity dynamics simulation model and an environment interaction model, performs real-time state estimation, abnormal working condition recognition and twin parameter dynamic updating, is seamlessly integrated with the control execution module, and provides decision support with high robustness and high adaptability for an industrial scene; the adaptive control module performs online rolling optimization on a control strategy based on a deep reinforcement learning algorithm, generates joint space trajectory correction, tail end precision compensation and dynamic load adaptability optimal instructions, and realizes parameter adaptive setting through fuzzy logic or a neural network; and the fault diagnosis module performs multi-scale time sequence analysis by using an LSTM and convolutional neural network fusion model, detects position offset, moment sudden change or temperature overrun and other abnormalities, and triggers emergency shutdown, sound-light alarm and an adaptive recovery strategy.

The invention relates to the technical field of production line control, and discloses an automatic control method and system for a plastic processing production line. The method comprises the steps of collecting technological parameters of a plastic processing production line and transmitting the technological parameters to a central control system to generate a database; multi-dimensional parameter correlation analysis is executed, a parameter and quality mapping relation is established through a CNN-LSTM hybrid network, and an optimization model is formed; calculating an optimal control parameter, generating a control strategy and issuing the control strategy to an execution unit; and monitoring a response result, updating the model in real time, and forming closed-loop adaptive control. According to the method, the mapping relation between the process parameters and the product quality is accurately established through the deep learning model, the optimal control parameters are automatically calculated, and closed-loop adaptive control based on production feedback is realized.

A system and method for multi-scale temporal attention processing in multimodal technology deep learning systems. This system processes time-series, textual, sentiment, and structured tabular data across three hierarchically-organized temporal streams—quarterly, weekly, and intraday levels—with bidirectional cross-temporal information flow. Scale-specific attention mechanisms are optimized for respective temporal granularities, while an adaptive controller dynamically weights each temporal level based on real-time market volatility indicators. A multi-scale fusion processor integrates attention-weighted representations to generate temporally unified representations preserving both short-term market dynamics and long-term trends. This approach enables superior forecasting and risk assessment by leveraging temporal correlations across multiple time scales while automatically adapting to changing market conditions. The system facilitates interpretable AI analysis through attention visualization and enables synthetic scenario generation for model testing.

The invention relates to a welding quality detection system which comprises a process monitoring layer configured with a multi-mode sensor array and used for collecting original performance data in the welding process in real time; the intelligent analysis layer is provided with a multi-source data fusion module and a defect prediction module, and the multi-source data fusion module adopts an improved D-S evidence theory algorithm to perform fusion processing on the original performance data collected by the process monitoring layer to obtain multi-dimensional performance data; the defect prediction module analyzes and captures abnormal data according to the multi-dimensional performance data, dynamically predicts a defect development trend and outputs defect prediction information; the decision execution layer is provided with a self-adaptive control module and is used for dynamically adjusting welding process parameters according to the defect prediction information output by the intelligent analysis layer; and the data communication bus is used for realizing real-time data interaction and closed-loop feedback control among the process monitoring layer, the intelligent analysis layer and the decision execution layer. The method has the effect of effectively improving the detection precision and the detection efficiency.

The invention relates to the technical field of tunnel power supply and distribution, in particular to a tunnel modular prefabricated cabin power supply and distribution intelligent substation self-adaptive regulation and control system based on edge calculation. Comprising an edge calculation and AI decision-making unit which is used for realizing rapid acquisition, processing and instant decision-making of tunnel power supply and distribution multi-dimensional data, generating a power supply and distribution adaptive regulation and control strategy by deploying calculation resources and a machine learning algorithm at edge nodes close to a data source, and converting the strategy into an executable regulation and control instruction; a cloud platform collaborative management unit; and an intelligent sensing and internet-of-things unit. According to the invention, hierarchical decision control of the tunnel power supply and distribution system is realized by constructing a hybrid architecture of edge computing and cloud platform collaboration and a priority judgment mechanism; according to the invention, multi-modal data are integrated through the multi-protocol communication link module and the full-scene data fusion analysis module, and data association analysis is realized through Kalman filtering, D-S evidence theory and other algorithms.

The invention discloses an intelligent illumination control system and method based on a multi-modal sensor, and particularly relates to the field of data analysis, and the system comprises a multi-modal data collection module, a feature calculation module, a space-time coupling analysis module, a multi-modal verification module, a biological rhythm compensation module and an intelligent decision control module. According to the invention, through cooperative acquisition of the multi-modal sensor and space-time coupling modeling, accurate environment perception is realized, and the photo-thermal interaction effect is quantified to eliminate control deviation; an innovative biological rhythm compensation mechanism is fused with the ultraviolet dose and the human body activity data, and a dynamic safety protection boundary is established; and an LSTM-Transform hybrid architecture is combined with a rolling time domain optimization strategy, so that 200ms-level quick response and complex scene self-adaptive control are realized, and the dynamic performance and the security of the system are remarkably improved.

The invention relates to the technical field of adaptive control, in particular to an unmanned aerial vehicle adaptive flight control system and method based on multi-modal fusion, and the system comprises an airspace analysis module, a signal screening control module, a state recognition module, a parameter matching module and a feedback adjustment module. According to the method, through a multi-source task data analysis and trend signal screening mechanism, the accuracy and real-time performance of path planning and adjustment are improved, through combination of channel fluctuation trend identification and abnormal state locking, the stability of sensing data input is enhanced, and the identification accuracy of an unbalance state is analyzed and optimized by using attitude change direction consistency. The attitude error trend and control parameter segment similarity comparison mode is adopted, parameter matching has dynamic responsiveness, control weight configuration is adjusted through collaborative analysis of the path direction component and the attitude adjustment trend, rhythm self-adaptive adjustment of aircraft control output is achieved, and the control precision of the aircraft is improved. And the dynamic adjustment capability and the safety redundancy response capability of flight control are enhanced.

The invention discloses a bionic group intelligent low-altitude logistics unmanned aerial vehicle cluster anti-wind interference cooperation method, and the method comprises the steps: collecting the historical flight data and three-dimensional wind field data of an unmanned aerial vehicle cluster, and generating a bionic formation feature set with a wind field label; inputting the bionic formation feature set into a swarm intelligence model fused with fluid mechanics, and generating a dynamic formation topology instruction; according to the dynamic formation topology instruction, adjusting the relative position and attitude angle of each unmanned aerial vehicle through a distributed cooperative control algorithm, and generating an anti-wind disturbance cooperative flight state; and continuously monitoring the deviation between the three-dimensional wind field change and the cooperative flight state, dynamically correcting the weight of the formation density-anti-wind disturbance intensity mapping relation through a reinforcement learning algorithm, updating a dynamic formation topology instruction, and realizing adaptive control of bionic group anti-wind disturbance cooperation. According to the embodiment of the invention, high-disturbance-rejection cooperative flight of the unmanned aerial vehicle cluster in the dynamic wind field can be realized, the formation energy consumption is reduced, and the obstacle avoidance capability under the sudden wind condition is improved.

The invention discloses a hydropower station unit state online monitoring system, relates to the technical field of hydropower station unit monitoring control, and adopts a hybrid digital twin modeling technology combining a physical mechanism main model and a liquid neural network residual compensation model to construct a high-fidelity unit operation state model. The system comprises a data acquisition module, a digital twin modeling module, a fault evolution prediction module, a multi-target optimization module and an adaptive control generation module. Multi-source heterogeneous data fusion is realized through a space-time adaptive weight distribution algorithm, and residual compensation modeling is performed by using dynamic time constant characteristics of a liquid neural network. Virtual fault injection and fault evolution trajectory prediction are realized, and passive fault response is converted into active fault prediction. A two-stage optimization strategy is adopted to realize'safety-efficiency-life 'three-dimensional target collaborative optimization, and a continuous and smooth adaptive control parameter trajectory is generated by controlling a liquid neural network. The modeling precision is improved, and the fault early warning time is advanced.

The invention provides an intelligent temperature field analysis-based heat dissipation optimization method for an IGBT (Insulated Gate Bipolar Translator) power module of an offshore flexible direct current converter valve. According to the method, an IGBT module space temperature data matrix and multi-point temperature sensor data are collected, and filtering processing is carried out through a multi-scale sliding window and a self-adaptive threshold value; establishing a multi-layer thermal network parameter model, and analyzing temperature dynamic change characteristics; layering the feature data according to the thermal response speed, and performing adaptive mapping and weight calculation; establishing a reinforcement learning model based on the heat dissipation efficiency index set to optimize a heat dissipation strategy; and predicting the temperature field distribution by using the graph structure neural network model. Accurate sensing, dynamic characteristic analysis, self-adaptive control and predictive maintenance of the temperature field of the IGBT module are realized, the heat dissipation efficiency and the temperature uniformity are improved, and the service life of equipment is prolonged.

The invention provides an unmanned aerial vehicle autonomous obstacle avoidance and path planning method and system based on deep learning, and relates to the field of unmanned aerial vehicle control, and the method comprises the steps: obtaining position information and environment perception data, constructing a spatial-temporal feature matrix, extracting target motion and background feature vectors, and mapping the target motion and background feature vectors into a target-environment fusion feature field; calculating an accessibility matrix and a cost matrix to construct a track search space, generating a candidate track set and determining an optimal planned track; and performing segmented optimization on the planned track to obtain a continuous attitude sequence, and generating an adaptive control strategy. According to the invention, intelligent obstacle avoidance and efficient path planning of the unmanned aerial vehicle in a complex environment are realized.

The invention discloses a health food production line control method and system based on deep management, and relates to the technical field of food production control. A raw material feature database is established, a first analysis result is output as a raw material deviation evaluation reference, data of a current batch of raw materials are collected, differences are compared, and a raw material deviation result is output; the method comprises the following steps: collecting process data, analyzing whether the process data is abnormal or not by combining with raw material deviation, generating an adjustment suggestion, executing formula and parameter adjustment, triggering a self-repairing mechanism when the process data is abnormal, identifying an intermediate product state, evaluating a quality risk level and triggering early warning. Dynamic optimization of a formula and process parameters is achieved, meanwhile, an image and spectrum fusion early warning technology is introduced, the quality control ability of intermediate products is improved, finally, optimization parameter configuration is generated for the next batch through whole-process data chain construction and causal reasoning analysis, and self-adaptive control and quality steady-state improvement in a production closed loop are achieved.

The invention relates to the technical field of digital twinning, in particular to a multi-machine running-in test distributed control system and method based on digital twinning. The method comprises the steps that a sensing execution unit collects equipment operation state data based on a multi-source sensor module and carries out preliminary data processing and detection; the digital twinning unit receives multi-dimensional sensing data of the sensing execution unit based on a dynamic twinning modeling module, establishes an equipment state model, and performs simulation verification and parameter optimization on a control strategy through a Q-learning strategy to obtain optimized control parameters; the intelligent control unit constructs an optimal decision-making model of multi-machine cooperative control based on a cooperative decision-making module, adjusts parameters of an execution mechanism in real time through a PID control algorithm and feed-forward compensation, and generates a self-adaptive control instruction; and the fusion analysis unit constructs a multi-dimensional equipment operation knowledge base based on the knowledge graph module, and generates global optimization parameters through a multi-target genetic algorithm.

The invention relates to the technical field of transformer monitoring, and discloses an explosion-proof intelligent monitoring and early warning device for a transformer. The device comprises a multi-source sensing module used for collecting multi-dimensional heterogeneous data of transformer operation; the feature extraction module is used for fusing data cross-domain features to generate various feature representations; the anomaly detection module is used for generating an abnormal signal space-time incidence matrix based on a dynamic causal network construction model; the risk early warning module outputs a risk level and an early warning instruction through a multi-task decision-making mechanism; and the self-adaptive regulation and control module is used for optimizing monitoring parameters and hardware resource allocation according to instructions. The device also can carry out critical state identification and emergency intervention, and constructs an insulation degradation prediction model to correct an early warning threshold value. According to the device, omnibearing monitoring, accurate early warning and intelligent regulation and control of the transformer are realized, the operation safety and reliability of the transformer are effectively improved, and the fault risk and loss are reduced.

The invention relates to the technical field of press monitoring, in particular to a digital twinning-based three-dimensional monitoring system for a precision servo press, which comprises a physical layer sensing module for acquiring real-time operating parameters, environment variables and workpiece processing data of the press; the dynamic twin construction module constructs a total-factor digital twin, and simulates a force-heat-deformation coupling effect by using finite element analysis and a multi-body dynamics algorithm based on physical attributes and process parameters; the intelligent analysis center identifies a potential fault mode of the press machine and locates an abnormal source through multi-physics field simulation data in combination with an improved CNN-LSTM model; the three-dimensional visual interaction unit constructs an interactive immersive three-dimensional virtual scene, renders a running state and a processing process in real time, and generates a maintenance strategy; and the self-adaptive regulation and control unit predicts the residual life of the key component and dynamically adjusts parameters according to a maintenance strategy and real-time monitoring data. Therefore, the problems of single monitoring dimension, disjunction of maintenance strategies and the like in the prior art are solved.

The invention discloses a dynamic path planning and self-adaptive control method and system for a coating robot, and relates to the technical field of coating automation. The method comprises the following steps: acquiring point cloud data through three-dimensional scanning equipment, constructing a dynamically updated workpiece curved surface model, and extracting curvature, edge and high-curvature mutation region features; a spraying path is generated based on a reinforcement learning algorithm, and path density, speed and coating supply are dynamically adjusted for a high-curvature area; distance, force feedback and environment parameters are fused, and mechanical arm and spray gun parameters are dynamically adjusted; dividing operation sub-areas and distributing tasks based on robot capability characteristics to realize multi-machine cooperation; and fault redundancy control and multispectral imaging are added to optimize the coating quality. The system comprises a sensing module, a decision-making module, an execution module, a redundancy control module and a communication module. According to the invention, the uniformity of the complex curved surface coating, the multi-machine cooperation efficiency and the system anti-interference capability are improved, and the method is suitable for spraying large workpieces such as aircraft fuselages and high-speed rail vehicle bodies.

The invention discloses a storage battery capacity checking method for a parallel intelligent direct-current power supply system, and relates to the technical field of storage battery capacity checking, which comprises the following steps of: respectively deploying electromagnetic sensing modules with nanosecond-level response capability at a master control end and a slave node end of a communication bus, acquiring voltage disturbance waveform signals of the communication bus in an operation process, and performing feature extraction operation on the transient high-frequency interference pulse detected each time, and constructing a multi-dimensional feature sequence of the high-energy transient electromagnetic interference. By deploying the high-response electromagnetic sensing module and combining an active suppression mechanism of interference feature extraction and risk index driving, time alignment modeling and self-adaptive regulation and control of electromagnetic interference and communication performance are realized, the communication stability, the battery state recognition accuracy and the capacity accounting continuity of the system in a strong interference environment are effectively improved, and the system reliability is improved. And the anti-interference capability and the operation safety of the parallel direct-current power supply system are obviously enhanced.

The invention discloses a self-adaptive grouting control plugging method based on mining-induced fracture real-time monitoring, and relates to the technical field of mine safety engineering and hydrogeology. Through cooperative work of the grouting mechanism, the data monitoring system, the data collecting and processing module and the self-adaptive control module, automation and intellectualization of grouting protection can be achieved, and through introduction of the fracture roughness coefficient and the time-dependent viscosity, rough fracture flow resistance and grout rheology and time-varying characteristics are quantified, and the grout diffusion radius calculation error is reduced. The data weight is dynamically adjusted through a micro-seismic travel time residual error and sound wave velocity field joint inversion algorithm in combination with a weighted robust LM algorithm, and accurate fracture positioning is achieved. And a PID gain compensation and saturation function mechanism is adopted, so that self-adaptive safe and efficient grouting is realized. The fracture dynamic state is monitored in real time through the multi-source data fusion technology, grouting parameters are dynamically optimized in combination with an intelligent algorithm, complex geological interference is effectively restrained, slurry waste is reduced, and efficient and accurate plugging of mining-induced fractures is achieved.

The invention relates to the technical field of tunnel engineering intelligent construction, and discloses a hard rock TBM shield tunneling machine auxiliary tunneling parameter dynamic adaptation regulation and control system, which comprises a high-precision multi-source sensing network, a high-precision multi-source sensing network, a high-precision multi-source sensing network, a high-precision multi-source sensing network, a high-precision multi-source sensing network, a high-precision multi-source sensing network and a high-precision multi-source sensing network, the TBM-geological environment digital twin predicts the tunneling short-term trend based on the high-fidelity physical simulation and data assimilation technology; and the multi-modal deep learning collaborative decision-making module deeply fuses real-time and prediction data and generates an optimal parameter solution set through a network trade-off tunneling multi-conflict target based on Pareto optimization. And the system executes a decision and forms closed-loop feedback through a parameter dynamic adaptation and adaptive learning module, and continuously optimizes a self model and a knowledge base. According to the method, passive response of TBM tunneling is converted into active pre-judgment, the decision accuracy, the construction safety and the comprehensive tunneling efficiency under the complex working condition are remarkably improved, and the method has the sustainable evolution capacity.

The invention relates to the technical field of self-adaptive control, and discloses a self-adaptive temperature control method and system for an automobile heater, and the method comprises the steps: calculating the real-time temperature deviation and temperature change rate of an actuator according to multi-dimensional temperature data; thermal inertia dynamic analysis is carried out on the historical heating power and temperature response relation through a preset thermal inertia compensation function, and a thermal inertia index of the actuator is obtained; the real-time temperature deviation, the temperature change rate and the thermal inertia index are jointly input into a preset fuzzy PID controller, and a preliminary power adjustment instruction of an actuator is output; analyzing the temperature change trend of the actuator in the time period, and performing prospective correction on the initial power adjustment instruction based on the temperature change trend; and controlling an actuator to execute heating operation according to the temperature control instruction, collecting real-time feedback temperature after the heating operation is executed, and performing joint online correction on parameters of the thermal inertia compensation function and a prediction coefficient of the time sequence model according to the real-time feedback temperature. According to the invention, the accuracy of self-adaptive temperature control of the heater can be improved.

The invention relates to the technical field of intelligent manufacturing, and discloses an extreme manufacturing process technological parameter optimization method and system fused with machine learning. The method comprises the following steps: acquiring multi-source data from a manufacturing equipment sensor, and fusing to generate a material state vector; inputting a pre-training model to obtain a material coefficient transition trend; judging whether the trend fluctuation amplitude exceeds a preset threshold value or not, and if yes, marking key nodes and extracting feature parameters; for the key nodes, according to the characteristic parameters and the real-time data of the key nodes, a control algorithm is adopted to calculate the parameter adjustment amount; optimizing the control parameters based on the parameter adjustment amount, generating a control instruction sequence and transmitting the control instruction sequence to an actuator; and obtaining adjusted feedback data, comparing the adjusted feedback data with the transition trend, and if the deviation exceeds an allowable range, updating the pre-training model. Through deep fusion of predictive monitoring and intelligent control, accurate optimization and adaptive control of process parameters are realized, the stability of the extreme manufacturing process and the product quality are improved, and the energy consumption and the defect rate are reduced.

The invention relates to the technical field of robot joint control, and discloses a robot joint control method and system based on multi-sensor fusion and a medium. The method comprises the following steps: preprocessing angle, current, acceleration and force / torque data; joint kinetic parameters are calculated through sine trajectory testing; performing state estimation according to the kinetic parameters; evaluating the reliability of the sensor and calculating the weight; inputting the fusion data with the weight into a cascade controller to generate a current instruction; performance index calculation is executed, and control parameters are optimized. According to the method, high-precision self-adaptive control of the robot joint can be realized under the condition that the sensor has uncertainty and faults, so that the system can keep stable performance and continuously perform self-optimization under various working conditions.

The invention relates to the technical field of motor control, and discloses a brushless motor adaptive control method and system based on artificial intelligence, and the method comprises the steps: collecting the multi-point temperature data of a motor, building a thermal dynamic model, and reconstructing the complete temperature field distribution; identifying a relation function between motor parameters and temperature, and establishing a temperature sensitive parameter model; in combination with temperature field distribution and a temperature sensitive parameter model, learning long-term influence of control actions on temperature distribution, and predicting temperature evolution trends under different control strategies; constructing a multi-objective optimization control strategy, and generating a motor control instruction and a heat dissipation control instruction; dynamically selecting an optimal heat dissipation strategy combination according to the generated control instruction, the current working condition and the predicted heat influence; according to the invention, high-precision control and thermal management optimization of the brushless motor in a temperature change environment are realized.

The invention relates to the technical field of motor control, in particular to a variable frequency motor dynamic adaptive control method and system based on multi-parameter analysis. The method comprises the following steps: collecting a multi-dimensional electrical operation monitoring parameter flow of a variable frequency motor, carrying out multi-parameter nonlinear correlation analysis and parameter topological correlation reconstruction, and constructing a multi-dimensional feature topological correlation network; a variable frequency motor multi-working-condition travel history database is obtained, multi-working-condition disturbance transient response analysis is carried out, multi-modal behavior prediction evolution is carried out based on the multi-dimensional feature topological association network, and a dynamic multi-modal behavior prediction engine is constructed; and acquiring multi-part electromagnetic sensing parameters of the motor, and performing abnormal electromagnetic intensity fluctuation detection and abnormal part power loss calculation to obtain abnormal part power loss characteristics. The self-adaptive real-time parameter motor control is realized, the energy efficiency of the motor is improved, the maintenance frequency and cost are reduced, and the full-life-cycle optimal control of the motor is realized.

The invention discloses an unmanned aerial vehicle flight path optimization system and method based on artificial intelligence and Internet of Things, and relates to the technical field of unmanned aerial vehicle control, and the method comprises the steps: collecting communication delay, environment data and an unmanned aerial vehicle state in real time; analyzing historical communication delay data by using a Markov chain, and predicting a basic delay value in a short time in the future; correcting the basic delay value through a machine learning model; outputting a final delay prediction result; calculating a delay risk index, and judging a delay risk level; based on the delay risk level, when the delay risk level is low risk, sending a control instruction in advance according to the final delay prediction result; and when the delay risk level is medium or high risk, calling a pre-trained reinforcement learning strategy library, and generating a self-adaptive control instruction. According to the method, the multi-dimensional risk index is constructed, multiple strategies are provided to adjust the flight path according to the real-time data and the risk index, and the flight stability of the unmanned aerial vehicle is improved.

The invention relates to a self-adaptive control rewinding machine tension and coiled material deviation collaborative optimization method in the field of intelligent manufacturing, and the method comprises the steps: deploying a distributed tension sensor network at a key position of a rewinding machine coiled material path, collecting the tension value of each measurement point in real time, and generating a multi-point tension distribution data matrix arranged according to a time sequence; processing the multi-point tension distribution data matrix by adopting a sliding window time sequence analysis algorithm, detecting tension fluctuation abnormity, and if a tension value exceeds a preset threshold range, recording a tension abrupt change timestamp and a change amplitude, and generating tension abrupt change data; based on the working condition state description, the rolling diameter real-time change data sequence and the tension sudden change data, a prediction model reflecting rolling diameter change and tension fluctuation is constructed in real time, and a predicted tension trend is obtained; and comparing the predicted tension trend with a preset ideal tension range through a model prediction control algorithm, and generating a multi-target optimization instruction which comprises a dynamic torque regulation and control quantity and a floating roller position set value.

The invention provides an intelligent tumor nursing monitoring system. The intelligent tumor nursing monitoring system comprises a data acquisition module, a data processing module, a dynamic evaluation module, a multi-stage early warning module and a self-adaptive control module. The data acquisition module acquires physiological sign data, tumor local image data and body fluid biochemical data of a patient in real time through a multi-source biosensor. And the data processing module performs space-time alignment, noise suppression and cross-modal feature association on the multi-modal data to generate a comprehensive feature map. The dynamic evaluation module calculates the tumor progress rate and the complication risk coefficient based on the map, and generates a personalized risk prediction curve. And the multi-stage early warning module triggers an early warning signal according to the curve and generates a risk traceability report. And the adaptive control module generates a personalized scheme based on the early warning signal and the traceability report and controls the medical equipment to execute closed-loop feedback adjustment. The nursing quality and the treatment effect of tumor patients can be improved, and the medical risk is reduced.

The invention relates to the field of artificial intelligence, particularly discloses an intelligent optimal management system for urban traffic congestion based on artificial intelligence, and aims to solve the problems of congestion and low efficiency caused by response delay, local optimization and low data utilization rate of an existing traffic management system. The system comprises a data acquisition and fusion module, a traffic state perception and prediction module, a decision optimization module, an instruction issuing and execution module and a man-machine interaction and visualization module. Through multi-source data fusion, graph neural network prediction and multi-agent reinforcement learning, traffic flow real-time perception, accurate prediction and adaptive control are realized, congestion is effectively relieved, and the overall operation efficiency and toughness of a road network are improved.

The invention provides a seat comfort optimization calculation method based on a sensor and AI modeling, and the method comprises the steps: collecting the pressure distribution data of the sitting posture of a passenger and a spine contact area in real time through an attitude sensor and a pressure sensor which are disposed on a seat; a seat supporting area is divided into a cervical vertebra area, a thoracic vertebra area, a lumbar vertebra area and a sacrococcygeal area, and a pressure distribution prediction model of each area is established; constructing an input vector based on the average pressure, the maximum pressure, the pressure gravity center, the body type parameter and the pathological weight factor, and outputting a target support pressure value, a pressure avoiding curve and a neighbor cell migration proportion of each partition by using an artificial intelligence model; dynamic adjustment is achieved through the partitioned air bag and the driving unit, and secondary fine adjustment and protection are achieved in combination with feedback monitoring. The system comprises a posture sensing module, a pressure sensing module, a data processing module, a model building module, a regulation and control execution module and a feedback monitoring module, and self-adaptive regulation and control of different areas of the spine of a passenger can be achieved.

The invention discloses a multi-mode perception and reinforcement learning engineering machinery intelligent regulation and control method and system, and belongs to the technical field of intelligent control and industrial automation. According to the system, stratum data within the range of 20-50 meters in front of a shield tunneling machine are collected in real time through multi-mode sensing equipment such as a distributed optical fiber sensor, a cutterhead vibration sensor and an electromagnetic wave radar, and original data are processed by adopting a wavelet-Fourier combined noise reduction algorithm; and inputting the processed multi-modal data into a reinforcement learning intelligent decision-making model based on a CNN-LSTM hybrid network architecture. The model is trained through a dynamic reward function, and weight coefficient combinations can be automatically switched according to different construction scenes. According to the system, parameters such as the rotating speed, the thrust and the grouting amount of a shield cutter head are regulated and controlled in real time through the self-adaptive control module, and the slurry utilization rate is increased through the gradient pulse grouting technology. The precision, efficiency and safety of shield construction are remarkably improved, and meanwhile energy consumption and construction cost are reduced.