847 results about "State vector" patented technology

The invention discloses a cross-border e-commerce integrated management platform, and relates to the technical field of cross-border e-commerce management, and the platform comprises a multi-modal semantic reconstruction module which is used for collecting multi-modal policy data of a trade country related to cross-border e-commerce business, and generating cross-modal policy features through cross-modal alignment and fusion; the compliance knowledge graph construction module is used for constructing a policy knowledge graph based on the cross-modal policy characteristics, calculating compliance risk values for fuzzy terms in the cross-modal policy characteristics, marking high-risk commodities and adding risk attributes to the policy knowledge graph; the compliance decision driving module is used for constructing a multi-dimensional state vector, generating a sub-node action set through random sampling, constraining a strategy update amplitude and selecting an action corresponding to an optimal sub-node; and the strategy execution and feedback module is used for executing the selected action, synchronously adjusting declaration parameters, optimizing transportation distribution of the split parcels, monitoring a customs clearance result in real time and feeding back a punishment value, so that the accuracy of fuzzy policy understanding is realized.

The invention discloses a hydropower station equipment fault analysis method based on state data mining, and relates to the technical field of hydropower station equipment intelligent fault diagnosis, and the method comprises the steps: collecting key operation parameters through deploying multiple types of sensors, and constructing a unified state time series data set; carrying out supervised training by adopting an LSTM network, extracting a dynamic feature vector, and constructing an AI state analysis model; introducing a micro-fluctuation abnormal coefficient WBYX, and evaluating the operation stability of the equipment; a coupling disturbance collaboration coefficient OHRD is calculated, and a fault conduction relation among multiple devices is identified; and calculating a trend evolution coefficient QSYH based on the state vector included angle offset, and analyzing whether the equipment operation trend is abnormal or not. By setting a multi-level threshold value, generation of a hierarchical early warning mechanism and a response strategy is realized, and the operation safety and the fault prediction capability of hydropower station equipment are effectively improved. The method is suitable for hydropower station key equipment state monitoring and intelligent operation and maintenance management in a complex environment.

The invention relates to the technical field of intelligent operation and maintenance monitoring and self-adaptive rule engines, in particular to a monitoring strategy system and method based on a rule base. The method comprises the following steps: constructing a unified state vector model through multi-source heterogeneous data collection, enhancing state representation in combination with context semantics, and verifying the validity of the state representation; a dynamic rule cutting mechanism is adopted, a graph structure is constructed based on semantic redundancy and conflict relations between rules, and an optimal non-redundant rule subset is screened in combination with a greedy cutting algorithm; generating a strategy graph through rule semantic fusion, aggregating semantics by using a graph neural network, constructing a directed acyclic graph to solve action conflicts, and generating a safe and efficient response sequence; a cross-scene migration mechanism is introduced, and source scene strategy semantics are projected to a target scene through a mapping matrix, so that lightweight migration and self-evolution of a knowledge base are realized. According to the method, the dynamic adaptability of the rule base is improved, the redundancy execution risk is reduced, and multi-scene seamless migration is supported.

The invention discloses an intelligent heat supply regulation and control system based on digital twinning and deep reinforcement learning. The system comprises a physical layer, a control layer and a control layer, wherein the physical layer is a physical heat supply system composed of heat source equipment, a transmission and distribution pipe network and a user terminal; according to the digital twinborn layer, a virtual heat supply system mapped with the physical layer in real time is constructed, the virtual heat supply system comprises a multi-physics field coupling model based on the thermodynamics and fluid mechanics principle, operation data of the physical layer are collected through a distributed sensor network, and the state vector of the virtual system is dynamically updated; and the intelligent decision-making layer is integrated with a DRL intelligent agent, the state space of the DRL intelligent agent is defined as a virtual system state vector output by the digital twin layer, the action space of the DRL intelligent agent is a regulation and control instruction combination of heat source power and pump valve opening, and a reward function fuses an energy consumption penalty term, a room temperature comfort reward term and a pipe network stability constraint term. According to the method, global optimization, high-precision continuous regulation and control and collaborative balance are realized through deep collaboration of digital twinning and deep reinforcement learning.

The invention provides an underwater vehicle multi-source information fusion accurate navigation method and system. The method comprises the following steps: firstly, acquiring inertial navigation data, DVL data and ocean current information of an underwater vehicle and observation information of a distance between the underwater vehicle and a beacon, and preprocessing the inertial navigation data, the DVL data and the ocean current information; then, establishing a state model according to the kinematics principle of the underwater vehicle, and establishing an observation model by combining various types of information; carrying out fusion processing on the state model and the observation model by adopting a self-adaptive Kalman filtering algorithm; and finally, estimating the current position of the underwater vehicle according to the fused state vector, comparing the current position with a preset target position to calculate a deviation, and correcting the deviation by adjusting a rudder angle and the rotating speed of a propeller. According to the method, multi-source information is integrated, innovation is carried out on construction of a state model and an observation model and position estimation and deviation correction, adaptive Kalman filtering is adopted, and the method has the advantages that the navigation precision is improved, the ocean current influence is considered, adaptive adjustment is realized, and real-time navigation is realized.

The invention relates to the technical field of environment perception, and discloses an optimization method and device for a multi-climate self-adaptive environment perception cooling fan system, and the method comprises the steps: collecting multi-point temperature, humidity and air pressure data, and carrying out the filtering processing, and obtaining an environment state vector; executing mathematical modeling of the multi-degree-of-freedom cooling system according to the environment state vector to obtain a linearized state space model; performing temperature gradient estimation and humidity compensation based on the linearized state space model to obtain an adaptive control law; inputting the adaptive control law into a model-free adaptive prediction controller to obtain a rolling optimization control sequence; weight self-adaptive adjustment and heat dissipation efficiency hierarchical prediction control are carried out on the rolling optimization control sequence based on climate conditions, fan rotating speed control and wind direction adjustment execution signals are generated, the problem that a traditional heat dissipation system is not sensitive to space temperature distribution and humidity changes is solved, and the heat dissipation control precision under different humidity conditions is improved.

The invention relates to the technical field of multifunctional detection, in particular to a multifunctional metering detection system which comprises a time alignment module, an analog-to-digital conversion module, a data coupling module, a vector construction module and an event packaging module. According to the invention, the time alignment is carried out on the measurement data in multiple channels, the synchronization consistency of heterogeneous data is improved, the dynamic analysis of amplitude fluctuation characteristics is combined, the real-time adjustment of the analog-to-digital conversion resolution is realized, the response sensitivity of the system to unsteady signals is enhanced, and the reliability of the system is improved. A composite parameter sequence is constructed by using a cross-channel amplitude and trend relationship, the coupling expression capability among multiple parameters is optimized, the recognition precision of a multi-node linkage trend is improved through state vector modeling and direction difference judgment, an abnormal event path is reconstructed by adopting a time sequence, and a process record is packaged. And the detection data is converted into structured event chain data from original sampling, so that the organizability and traceability of the anomaly detection result are enhanced.

The invention provides an inertial navigation attitude resolving method and device based on sensor fusion. The method comprises the following steps: carrying out multi-clock domain synchronous calibration processing on a double-antenna GNSS system, an IMU inertial measurement unit and an antenna servo system in a vehicle-mounted communication-in-motion system; according to the time synchronization reference, carrying out quality evaluation and weight distribution processing on the GNSS satellite signal under the satellite communication in motion antenna pointing constraint; and carrying out multi-sensor fusion resolving processing on the inertial attitude of the vehicle body according to the time synchronization reference and the GNSS observation data weight distribution result. According to the method, millisecond-level time synchronization is realized by establishing a robust extended Kalman filtering model of a multi-dimensional extended state vector, GNSS signal quality is optimized by adopting an adaptive weight distribution strategy of antenna pointing constraint, and attitude fusion precision is enhanced by utilizing high-precision angle feedback of an antenna servo system. The problem that a traditional attitude resolving method in a vehicle-mounted communication-in-motion system is not high in precision is solved.

The invention relates to an autonomous energy-saving soaring flight path planning method for a small-sized low-cost aircraft, belongs to the technical field of aircraft trajectory planning, solves the problem of low-cost wind field energy acquisition of the small-sized low-cost aircraft in the prior art, and comprises the following steps: S1, configuring a sensor for the aircraft, and measuring through the sensor to obtain observation parameters; s2, establishing a state vector of the aircraft; s3, establishing an aerodynamic force model, introducing a dynamic equation and a state transition equation, and performing accurate modeling on aerodynamic force; s4, performing multi-source data fusion by adopting extended Kalman filtering, establishing an extended Kalman filter of a nonlinear system, and executing real-time wind vector high-precision sensing; and S5, performing global wind field modeling, estimating a wind field environment, and performing energy-obtaining flight path planning to obtain an optimal energy-obtaining soaring flight path planning scheme.

The invention relates to the field of industrial automation and intelligent control, and discloses an intelligent temperature automatic control system for a digital glass mold. The method comprises the following steps: acquiring mold surface temperature and heat flow data through a thermocouple array and a thermal infrared imager, and constructing heat flux and a disturbance coefficient; forming state vectors are established in combination with the forming process parameters and the heat flow distribution, and forming stability levels are generated through support vector regression; building a heat balance deviation model based on the thermophysical parameters and the environment variables, and predicting a temperature trend; a temperature control instruction is generated through the fuzzy neural controller, and temperature dynamic closed-loop control is achieved. The system improves the temperature control precision and thermal field balance of the glass mold, and is suitable for intelligent temperature control management in the glass container forming process.

The invention discloses an unmanned aerial vehicle visual positioning flight optimization system based on multi-source data fusion, and belongs to the technical field of unmanned aerial vehicle navigation and control. The system comprises a data acquisition module used for acquiring RGB-D image data and multiple physical data; the environment modeling module is used for constructing a local three-dimensional environment map; the path planning module is used for generating an obstacle avoidance flight path; the instruction generation module is used for converting the obstacle avoidance flight path into an attitude control instruction and an accelerator instruction; the state prediction module is used for obtaining an unmanned aerial vehicle prediction state vector at the next moment; the positioning switching module is used for generating compensated fusion positioning data; and the flight control module is used for executing the attitude control instruction and the accelerator instruction. Through the multi-source data fusion and state prediction compensation mechanism, the problems of positioning interruption and trajectory divergence caused by loss of visual signals and unstable flight caused by sudden change of multi-source coordinate switching in the prior art are solved.

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 provides a breakwater monitoring data preprocessing method and system based on Kalman filtering, and relates to the technical field of breakwater structure safety monitoring. The method comprises the following steps: acquiring original motion data of acceleration, inclination and displacement through a motion attitude sensor to obtain an original data sequence; initializing a state vector and an error covariance matrix; dynamically correcting the state transition matrix and calculating a prediction state vector and a prediction error covariance matrix; a Kalman gain is generated; updating a state vector and an error covariance matrix; and extracting the filtered motion data as a preprocessing result. According to the method, the state transition matrix is dynamically corrected by introducing the wave force feedback, so that the Kalman filtering algorithm can adapt to the wave impact environment, noise interference in monitoring data is effectively inhibited, and the accuracy and reliability of key motion parameter data of the breakwater are remarkably improved.

The invention discloses a sea condition perception and path prediction navigation method and system for a high-speed unmanned ship, and relates to the technical field of multi-source path decision optimization, and the method comprises the steps: collecting storm real-time perception data through a sensing device, carrying out the preprocessing, outputting a standardized sea condition state vector, and providing an experience reference for real-time path planning through ship-borne historical data; constructing a multi-target navigation prediction algorithm model to predict and generate a path candidate scheme set on the basis of the storm real-time sensing data and the ship-borne historical data, and performing evaluation through a conditional random field in combination with path candidate schemes generated by the storm real-time sensing data, the ship-borne historical data and target navigation prediction; and carrying out multi-dimensional safety risk scoring on an evaluation result through a fuzzy analytic hierarchy process by combining the wave height and direction influence, the hull stability and the obstacle avoidance complexity, and determining an optimal sailing path. According to the invention, fusion of multi-source sea condition perception and dynamic path optimization is realized, and the navigation stability, the adaptability and the risk control capability of the high-speed unmanned ship are improved.

The invention relates to the technical field of automatic control, in particular to a multi-parameter cooperative control method and device for a clamping system of a precision boring and milling machine. According to the method, an industrial camera is used for collecting and processing a workpiece image and extracting visual features, and the visual features are fused with process parameters of a workpiece and geometric distribution parameters of a workpiece key area obtained from a process database; generating a comprehensive state vector; based on the vector, outputting a target clamping force adjustment coefficient by a pre-trained MLP decision model, and calculating a final target clamping force in combination with a safe clamping force range; in the main clamping stage, vibration, temperature and pressure signals are synchronously collected, multi-modal disturbance characteristics are extracted, observation vectors are constructed, the observation vectors are input into a preset fuzzy rule base for reasoning, and compensation activation factors are obtained; and finally, a current correction instruction is dynamically generated according to the clamping force deviation and the compensation factor, and an electro-hydraulic proportional valve is driven to achieve accurate compensation. According to the invention, the stability and the machining reliability of the clamping system are obviously improved.

The invention discloses a charging pile load prediction method based on deep learning. The method comprises the following steps: S1, constructing a regional grid and initializing a cellular basic attribute vector; s2, collecting historical load data and external influence factors, and unifying the historical load data and the external influence factors into an input tensor; s3, generating a time continuous evolution state by using an ordinary differential equation modeling module; s4, constructing a state vector at the current moment; s5, the differentiable cellular automaton module executes primary spatial state propagation; s6, performing multiple rounds of spatial propagation iteration, and outputting enhanced state representation; s7, the load prediction module outputs a future multi-moment prediction value; s8, errors are calculated, and end-to-end training optimization model parameters are executed; and S9, outputting a final optimization model as a load prediction method. According to the method, the response capability of the model to sudden disturbance and the stability of multi-region collaborative prediction are remarkably improved, and the method can be widely applied to multiple application scenes such as smart energy management, electric traffic scheduling and urban public infrastructure intelligent optimization.

The invention discloses a charging pile cooling control method and system based on AI prediction reinforcement learning, and relates to the technical field of cooling control. The charging pile cooling control method and system based on AI prediction reinforcement learning comprises the following steps: S1, collecting charging heat dissipation data of a charging pile, and preprocessing the charging heat dissipation data; s2, constructing a time sequence characteristic matrix, inputting the time sequence characteristic matrix into a temperature prediction model, outputting a temperature prediction sequence of a controlled target, evaluating a thermal runaway risk in a prediction stage, and constructing a thermal risk identification sequence; s3, constructing a cooling strategy optimization model, inputting the real-time state vector into the cooling strategy optimization model, outputting an adjustment instruction, and issuing and executing the adjustment instruction; and S4, the execution deviation of the adjustment instruction is evaluated, the cooling strategy is adjusted based on the evaluation result, and a cooling strategy feedback sample is generated. The problems of energy consumption waste and cooling imbalance caused by lack of real-time prediction and self-adaptive regulation and control capabilities in the cooling control process of the existing charging pile are solved.

The invention relates to the technical field of mine filling control, in particular to a multivariable layered adaptive filling regulation and control method based on reinforcement learning, and the method comprises the steps: constructing a state vector; inputting the state vector into a target reinforcement learning strategy network model to obtain a target action parameter and state value estimation; judging whether an expected standard is met or not based on the state value estimation, and if yes, adjusting a filling control parameter of the filling system based on the target action parameter; in response to completion of adjustment of the filling control parameters of the filling system, state characteristic functions corresponding to the filling system in the current control period are constructed, and a multi-dimensional reward function is constructed based on the state characteristic functions; and judging whether to trigger strategy update based on the control cycle number and the current accumulated reward value, and if so, updating the target reinforcement learning strategy network based on the multi-dimensional reward function. According to the invention, adaptive coordination optimization of a multivariable target can be realized, and the precision and stability of filling control are effectively improved.

The invention discloses a wind power generation tower pose monitoring method based on a GNSS / MEMS IMU / magnetometer combination, and the method comprises the steps: carrying out the estimation of a state vector containing the position, speed, attitude error, sensor zero offset and scale factor error through error state Kalman filtering based on the GNSS / MEMS IMU combination, and carrying out the correction and fusion of GNSS and IMU data through a lever arm effect; a complementary filtering algorithm is adopted, an error vector is constructed by combining measured values of an accelerometer and a magnetometer, a gyro angular velocity is corrected through a PI controller, an attitude quaternion is recurred, a course angle is calculated, and the course angle is used as an auxiliary observed quantity to update filtering. According to the invention, by adding course information provided by the MEMS IMU and the magnetometer, the position and the attitude are comprehensively estimated, and high-precision six-degree-of-freedom pose monitoring is realized.

The invention discloses a high-speed train multi-information fusion positioning method based on a Beidou satellite, and relates to the technical field of satellite navigation and positioning. The method comprises the following steps: designing a high-speed train multi-information fusion positioning scheme; based on a train multi-information fusion positioning scheme, establishing a nonlinear train state model based on a train kinematics equation; the position, speed, acceleration, course angle and pitch angle of the train are used as state vectors, and a train measurement model is constructed; based on a tracking target set by a train state model and a train measurement model, the train state is estimated by fusing IMM-UKF / PF models of unscented Kalman filter (UKF) and particle filter (PF) and introducing ACW and a short-term error feedback mechanism in state fusion. According to the method, the positioning precision and robustness can be improved, and the dynamic response capability in a complex scene can be enhanced.

The invention relates to the technical field of temperature control, in particular to a temperature sensor fusion compensation method and system based on Kalman filtering, and the method comprises the steps: collecting an original measurement signal, and carrying out the preprocessing; constructing a feature vector matrix and a diagonal feature value matrix of each sensor according to a preprocessing result; updating the state vector and the covariance matrix through a Kalman filtering algorithm to generate an optimal state estimation value; according to the residual distribution of each sensor and KL divergence evaluation, adjusting the weight of each sensor; and after the sensor weights of the normal sensor and the abnormal sensor are adjusted, weighted summation is carried out on the weight of each sensor and the optimal state estimation value, and a final temperature measurement value is generated. Through the Kalman filtering algorithm, the judgment of people on the influence degree of high-frequency noise and equipment errors on the sensor is improved; and the weight of each sensor is dynamically adjusted through residual calculation and KL divergence evaluation, so that the robustness and compensation precision of the system are remarkably improved.

The invention relates to the technical field of industrial automation and intelligent control, in particular to a general control system strategy optimization method based on reinforcement learning, and the method comprises the steps: firstly collecting the operation data of a system, constructing a state vector of reinforcement learning, and enabling a reinforcement learning agent to fully understand the current operation condition of the system; then, the state vector is input into a reinforcement learning strategy network, an action for adjusting a control strategy is generated by the network, and the action can be used for modifying the proportion, integral or differential coefficient of PID and can also be used for adjusting the prediction step length, weight coefficient or constraint strength of model prediction control, so that the adaptive capacity of a controller to external changes is enhanced; then, a reward signal is constructed according to a response result of the reference controller; the reward function comprehensively considers the error size, the steady-state characteristic, the system energy consumption, the control smoothness and the stability requirement, so that the reinforcement learning not only pays attention to the error minimization when optimizing the strategy, but also considers the low energy consumption, the smooth action and the anti-interference performance at the same time.

The invention discloses an energy storage equipment management method and system based on the Internet of Things, and relates to the technical field of energy storage management, and the method comprises the steps: constructing an initial topological graph mathematical model processed by concurrent block energy storage equipment based on the Internet of Things and a block chain network; according to the method, energy distribution is efficiently optimized by means of a directed acyclic graph, and under the condition that the management decision effect is not reduced, the operation steps are greatly reduced, and meanwhile, the decision and the state of the energy storage equipment are deeply optimized and managed; meanwhile, powerful support is provided for scenes such as equipment state monitoring, scheduling decision making and energy transaction, the complexity of concurrent block processing in the block chain system is effectively reduced through the topological graph, redundant reference relations are reduced, so that sorting logic is simplified, the method is suitable for shared energy storage power station scenes needing to rapidly process high-frequency energy storage services, and the efficiency of the shared energy storage power station scenes is improved. Multi-dimensional state vectors are utilized to effectively represent interaction information of energy storage equipment and a power grid, and accurate control is performed in combination with discrete and continuous action instructions.

The invention relates to a multi-driving-style high-risk automatic driving cut-in scene test method and system, and the method comprises the steps: collecting original driving track data, carrying out the clustering of driving styles, and generating a risk cut-in scene track cluster with consistent driving styles through employing a Cutin-TimeGAN network model in combination with physical feasibility constraints; constructing a cost function, calculating a cost function value for each risk cut-in scene trajectory cluster, and selecting a target cut-in reference trajectory as a target reference state vector; constructing a game framework, and combining a target reference state vector to construct a utility function according to the state vectors of the VUT and the test confrontation vehicle in the framework; constructing a risk confrontation utility function based on the predicted collision time; a game optimization problem is constructed and solved, an optimal interaction track of the test confrontation vehicle is obtained, and a closed-loop high-risk automatic driving scene switching test is carried out based on the optimal interaction track; the system is used for implementing the method. Compared with the prior art, the method has the advantages that testing of authenticity and high-risk confrontation of multiple driving styles is considered.

The invention belongs to the technical field of expressway operation and maintenance, and relates to an intelligent operation and maintenance method and device for an expressway strong current system, and the method comprises the following steps: collecting and preprocessing multi-source data; constructing a state space model and defining a system state vector; obtaining an estimated state value by adopting a state estimation algorithm; performing fault anomaly detection based on the estimated state value and the multi-source data; extracting a device feature vector and calculating a health index; predicting the remaining service life of the equipment and judging the potential fault risk, and generating a maintenance work order and determining the priority when the threshold value is exceeded. According to the technical scheme of the invention, work orders are generated through multi-source data acquisition, state modeling and estimation, anomaly detection, health assessment and life prediction, so that the whole-process intelligent operation and maintenance of the highway strong current system is realized, and the real-time sensing and diagnosis capability of the operation state can be improved. And the requirements of operation and maintenance real-time performance improvement, data unified management, intelligent diagnosis enhancement and emergency disposal high efficiency are met.

The invention provides a sequential invariant extended Kalman filtering method for a navigation positioning task, and belongs to the field of automobile navigation. The problems that efficient fusion of multi-coordinate system observation is difficult to achieve under an IEKF framework, and geometric consistency of covariance transfer cannot be guaranteed are solved. The method comprises the following steps: dividing observed quantities of a sensor according to different coordinate systems of the navigation sensor; constructing a state vector in a Lie group form; modeling the output of the inertial measurement unit; performing state propagation according to a Lie group state equation; calculating propagation of a covariance matrix; establishing a left invariant observation equation and a right invariant observation equation according to the two types of divided matrixes; and synchronously updating state estimation through the left invariant extended Kalman filter and the right invariant extended Kalman filter, and mapping the left invariant covariance matrix and the right invariant covariance matrix by using the common state covariance matrix to complete sequential fusion. The method is mainly used in the automatic driving field.

The invention discloses a DeepSort-based multi-target tracking method, device, equipment and medium, and belongs to the technical field of target tracking, and the method comprises the steps: carrying out the multi-target detection of each frame of image in a collected video stream through an improved YOLOv8 model, and outputting a plurality of candidate bounding boxes for each target; filtering the candidate bounding box to obtain an effective target box; using a DeepSort algorithm to extract appearance feature vectors, predicting state vectors of all current tracking targets in the current frame, and converting the state vectors into a prediction frame; and the DeepSort algorithm performs multi-level matching on all the effective target frames and the prediction frames, if matching succeeds, trajectory information of the corresponding target and a state vector and appearance characteristics of the DeepSort algorithm are updated, and if matching fails, the unmatched effective target frame is initialized as a new tracking target or the unmatched prediction frame is marked as a lost target. The method can achieve the precise recognition and real-time tracking of a small target, and improves the detection precision.

The invention discloses a steering engine accurate control method and system based on an optimization control algorithm, and the system comprises a data collection module which collects key working parameters of a steering engine in real time through a plurality of sensors, and carries out the data processing, thereby forming a state vector; the self-adaptive filtering module is used for carrying out self-adaptive filtering fusion processing on the data acquired by the sensor and dynamically estimating the attitude angle and the corresponding angular velocity of the aircraft; the sliding mode control module constructs a sliding mode surface and a control rate of a steering engine system according to the dynamic error of the system, calculates control input of each axis of the aircraft according to the attitude angle real-time state error, and generates a sliding mode control signal; the driving execution module is used for converting the control signal into a voltage or PWM signal and inputting the voltage or PWM signal into a steering engine, so as to change thrust and realize adjustment and control of a posture or a position; and the real-time feedback module monitors the current state of the steering engine in real time to realize dynamic correction and real-time optimization. The adaptive filtering and sliding mode control method provided by the invention is higher in control precision, quicker in system response and higher in adaptability to complex environments, and can effectively improve the application performance and stability of a steering engine execution system in the fields of industrial automation, aerospace, intelligent manufacturing and the like.

The invention discloses a multivariable predictive control energy consumption adjustment method and system, and belongs to the technical field of automatic control, and the method comprises the steps: collecting a parameter adjustment log in real time through an edge node, carrying out the intervention behavior recognition, and verifying the validity, so as to detect a manual parameter adjustment event; once an artificial parameter adjustment event is detected, multivariable data before and after intervention are extracted, and parameters of the local prediction model are dynamically corrected; performing rehearsal intervention based on the manual intervention parameters, generating space-time coupling constraints, and constructing a hybrid neural network to generate an energy consumption prediction trajectory; dividing types according to operator behavior modes, fusing prediction data to generate a comprehensive state vector, adjusting a reward function, focusing a sensitive variable, and generating a control instruction; and acquiring an actual energy consumption value in real time, comparing the actual energy consumption value with an energy consumption prediction track, calculating an energy consumption deviation, performing secondary optimization, positioning an error root cause through multi-scale decomposition in combination with a semantic tag and a knowledge graph, and performing layered compensation.

A method for predicting gas explosion. The method includes: obtaining a geometric model with spatial obstacles; determining a simulation dataset of the gas explosion based on the geometric model, where the simulation dataset is a dataset including process data of multiple gas explosion scenes in the geometric model, and a state vector of a spatial grid node in the simulation dataset includes a first moment, coordinate information and a target pressure value; optimizing, using the simulation dataset, a physics-informed GNN to obtain an optimized physics-informed GNN; and predicting the gas explosion in an obstructed gas explosion scenario using the optimized physics-informed GNN. This method is applied to use optimized physics-informed GNN to realize spatiotemporal second-level prediction of overpressure distribution and explosion wave propagations occurring in the obstructed gas explosion scenario, enhance the accuracy, the reliability and the efficiency of gas explosion prediction in the obstructed gas explosion scenario.