569 results about "Decision control" patented technology

The invention discloses an end-to-end automatic driving method based on dynamic multi-modal fusion in a complex scene, and belongs to the technical field of automatic driving. In order to solve the problems of sensor perception deficiency, cross-modal feature mismatching, unstable trajectory planning and the like easily occurring in night, low-illumination and complex dynamic environments in the existing end-to-end automatic driving method, texture details of a camera mode and geometric structure features of a laser radar mode are respectively enhanced through a double-flow feature refining mechanism; the characteristic difference between different modes is relieved; an information-driven dynamic fusion strategy is designed, the fusion weight is adaptively adjusted according to scene factors such as environment illumination and obstacle density, and the scene sensitivity and discrimination ability of the model are improved; asymmetric convolution and a low-rank-sparse decoupling technology are introduced, multi-order reconstruction of key channels is carried out on the multi-modal features, and the path change modeling capability is enhanced; and in combination with time sequence dependence of waypoints, outputting a future trajectory through an autoregression decoder to realize high-precision trajectory prediction and stable decision control.

The invention discloses a non-point source pollution emergency bypass decision control method, particularly relates to the technical field of automation and process control, and aims to solve the problems that existing scheduling cannot align multi-source measurement and evaluate credibility in minutes under a unified time base, and is lack of risk measurement integrated with a tide level phase and a moisture regain path and same-layer gating. The bypass rhythm is easy to misjudge, and the oscillation is difficult to audit. A minute-level risk density is constructed through multi-source alignment and credibility grading under a unified time reference, injected tide level phases and moisture regaining paths, bypass rhythms are constrained by same-layer gating and tide windows, and beam search, boundary arbitration and fixed window closed-loop correction are matched, so that the risk density is improved. Therefore, under the superposition of rainstorm and tide, decision making and execution are stably limited in a non-backflow red line, bearing and equipment limit, misjudgment and oscillation are remarkably reduced, the problems of unavailability and difficult auditing caused by multi-source asynchronization and moisture regain coupling are solved, intelligent decision support is provided for non-point source pollution treatment, and it is ensured that the treatment technology is accurate and efficient.

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

The invention relates to a robot decision control method based on gradient rarefaction and a robot. The method comprises the following steps: acquiring multi-modal sensor data of a robot; based on a preset sparsification strategy, generating a dynamic mask corresponding to the gradient matrix of the multi-modal large model; based on the generated dynamic mask, screening an effective gradient in a back propagation process of the dynamic mask; updating parameters corresponding to the effective gradient in real time, and obtaining the output of the multi-modal large model based on the updated parameters; according to the obtained multi-modal sensor data and the output of the multi-modal large model based on the updated parameters, feature fusion is carried out, and a combined state code including an environment state, a robot body state and historical decision information is generated; and according to the determined joint state code and based on a time sequence model, generating an action sequence, a force control parameter and a path planning dynamic decision instruction of the robot, so that the robot can act based on the generated dynamic decision instruction, thereby realizing decision control of the robot.

The invention relates to the technical field of machining automation control, and discloses an intelligent tool changing decision-making method based on tool wear perception, which comprises the following steps: acquiring real-time state data of a tool through vibration, acoustic emission, force and temperature sensors, and fusing features of a multi-modal graph neural network to obtain tool wear feature data. And inputting the parameters into a Bayesian decision network, optimizing a tool changing strategy by using a dynamic probabilistic reasoning structure, and generating decision optimization parameters. And a multi-target tool changing optimization model with the highest machining efficiency and the longest service life of the tool as targets is constructed, and an optimal tool changing strategy is determined by adopting an improved particle swarm algorithm. Based on this, a hierarchical decision control model is established and comprises a global evaluation layer, a dynamic adjustment layer and an execution control layer, and intelligent control of tool changing action is realized. In addition, a self-healing control module is embedded in the system to deal with abnormal wear of the cutter. The machining efficiency is improved, the service life of the cutter is prolonged, the machining quality is guaranteed, and intelligent development of machining is promoted.

The invention discloses an intelligent intervention system for children with autism spectrum disorder based on multi-modal neural feedback, which comprises a multi-modal physiological signal acquisition module for acquiring electroencephalogram signals of a target child in real time and generating an original physiological signal set; the multi-modal fusion analysis module receives the original physiological signal set and generates a comprehensive evaluation signal representing the current neurocognitive state and the emotion awakening level of the child; the self-adaptive decision control module receives the comprehensive evaluation signal and generates a self-adaptive control signal containing a neural feedback parameter adjustment instruction and a game interaction strategy instruction; the neural feedback intervention module receives the neural feedback parameter adjustment instruction and generates an adaptive neural feedback stimulation signal to act on the child; and the intelligent interactive game module receives a game interactive strategy instruction and dynamically adjusts game scene contents. The intelligent intervention system for children with autism spectrum disorder based on multi-modal neural feedback can solve the problems of poor individual adaptation of autism intervention, multi-modal data splitting and lack of dynamic adjustment.

The invention belongs to the technical field of crossing of intelligent manufacturing and thermal control systems, particularly relates to an intelligent cooling control system for a bionic micro-channel mold, and aims to solve the problems of low mold cooling efficiency, non-uniform temperature distribution, high energy consumption and the like. The system comprises a bionic microchannel mold body, a distributed temperature sensing network, a multi-area independent flow regulation and control module, a cooling medium dynamic supply unit and a central intelligent decision controller. Through cooperation of a bionic fractal micro-channel structure, partition flow regulation and control and an intelligent algorithm, fine regulation and control of a temperature field are achieved, the cooling efficiency is improved, energy consumption is reduced, and the self-adaptive optimization and fault redundancy capacity is achieved.

The invention provides a CAV lane change decision optimization method based on deep reinforcement learning. The method comprises the following steps: collecting a vehicle microscopic motion state, lane-level macroscopic traffic parameters and global traffic event information; carrying out coding and dynamic weighted fusion on the vehicle microscopic motion state and the lane-level macroscopic traffic parameters by adopting a space-time attention mechanism to obtain a fused environment state representation; processing the fused environmental state representation by adopting a gradient algorithm based on a depth deterministic strategy to generate a preliminary decision instruction; constructing a driving style perception mechanism, dynamically adjusting the weight of a reward function based on the mechanism, and carrying out collaborative optimization on the preliminary decision instruction through the optimized reward function to obtain an optimized decision instruction adapted to the personalized driving style; converting the optimized decision instruction into transverse and longitudinal control quantities by adopting a hybrid fault-tolerant strategy; the vehicle is controlled to change lanes based on the transverse and longitudinal control quantities, and closed-loop decision control under vehicle-road-cloud cooperation is completed; according to the invention, the accuracy of lane changing decision and environmental adaptability can be effectively improved, and the multi-vehicle cooperation efficiency is enhanced.

The invention discloses a heat supply pipe network operation fault diagnosis method based on digital twinning, and the method comprises the following steps: S1, constructing a digital twinning architecture of a heat supply system, and constructing a complete digital twinning system based on a physical entity, a virtual model, a data synchronization layer and a decision control layer; s2, multi-source data fusion and state mapping; s3, a fault prediction and diagnosis mechanism is adopted, an SVM-LSTM joint model is adopted to carry out time sequence anomaly detection and fault prediction, an XGBoost classifier is optimized in combination with an improved sparrow optimization algorithm, and a classification result is output; and S4, performing self-healing control and cooperative regulation and control. According to the invention, a complete digital twin architecture is constructed, through a five-layer system of a physical entity, a sensing and execution network, a data synchronization layer, a virtual model layer and a decision control layer, a full-link closed loop from data acquisition, model driving to intelligent decision is realized, and systematic support is provided for fault diagnosis and self-healing control.

The invention relates to the technical field of intelligent scheduling, and particularly discloses an intelligent scheduling decision control method and system for a distributed car washing robot, and the method comprises the steps: constructing a weighted graph model which comprises a vehicle position, obstacle distribution and path reachability, and dynamically adjusting the weight of an edge according to the path length, energy consumption and time; optimizing the graph structure by adopting an improved minimum spanning tree algorithm, and calculating a path efficiency index in combination with fuzzy logic and a neural network technology; on the basis of the optimized subgraph, applying a shortest path algorithm to obtain a local optimal path, and introducing a dynamic adaptation coefficient to evaluate the response capability of the path to environment change; fusing the path efficiency index and the dynamic adaptation coefficient into a comprehensive scheduling feature vector, inputting the comprehensive scheduling feature vector into a trained deep learning scheduling model, and predicting an optimal scheduling strategy in combination with a task priority and a robot resource state; and dynamically adjusting the moving path and the operation density of the robot according to a scheduling result, and realizing self-adaptive evolution and cooperative control of the system.

The invention relates to the technical field of unmanned aerial vehicle vision, and discloses a vision system of an unmanned aerial vehicle and the unmanned aerial vehicle. The system comprises a visual data acquisition module, a dynamic feature extraction module, an environment modeling module and a decision control module. The visual data acquisition module captures a synchronous frame sequence containing infrared wave bands, visible light wave bands and depth information in a target area through a multispectral sensor array; the dynamic feature extraction module performs cross-modal fusion processing on the original visual data stream to generate a space-time correlation feature tensor containing a target contour geometric invariance descriptor and a motion trail differential topological structure; the environment modeling module constructs a three-dimensional semantic grid map according to the feature tensor, wherein each voxel unit codes the material reflectivity, the dynamic obstacle occurrence frequency and the illumination attenuation coefficient; the decision control module generates a flight path control instruction containing a pitch angle adjustment amount, a yaw angle compensation value and a speed change gradient based on the map, and assists the unmanned aerial vehicle to better cope with a complex environment.

The invention relates to an intelligent driving control method of a vehicle and the vehicle, and belongs to the technical field of intelligent driving, and the method comprises the following steps: obtaining an original data stream of a vehicle sensor network, and carrying out feature extraction on a dynamic feature vector; state vectors of the traffic participants are extracted according to the dynamic feature vectors, edges between the two traffic participants are constructed, a traffic participant interaction graph is constructed, and an asymmetric factor matrix is calculated; acquiring a historical scene data set of the vehicle, calculating correction similarity, and calculating a weight coefficient; performing weighted fusion on the weight coefficient and the historical scene data set to obtain an enhanced data set; and performing updating training on the pre-trained intelligent driving decision model according to the enhanced data set to obtain an updated intelligent driving decision model, generating an intelligent driving decision, and controlling vehicle operation, thereby realizing more accurate characterization of a dynamic game relationship in a complex traffic scene, and improving the accuracy of the dynamic game relationship. And the intelligent driving decision model can continuously adapt to an asymmetric interaction mode effect in a real scene.

The invention relates to the technical field of agricultural machinery automation, in particular to a weeding machine automatic row control translation system and method based on machine intelligence, and the method comprises the steps that a multi-mode sensing module obtains environment data and crop information data; the environment map construction module outputs row reference parameters through a crop row feature extraction algorithm, integrates the environment data and the row reference parameters, solves the three-dimensional pose of the weeding machine through a beam adjustment algorithm, and constructs a high-precision map of the working environment; the decision control module obtains a target row translation track based on a near-end strategy optimization algorithm, and outputs a control instruction; the self-adaptive execution module is used for driving the weeding execution mechanism to automatically perform row-to-row translation weeding along crop rows; and the self-learning module is used for establishing a row translation performance evaluation index according to the full-link operation data, and dynamically updating a target row translation track through a continuous learning algorithm. Therefore, the problems of high seedling injury risk, poor terrain adaptability, low operation efficiency and the like in the prior art are solved.

The invention relates to the technical field of electric vehicle thermal management, discloses an electric vehicle motor and driver integrated intelligent cooling system and a control method, and aims at solving the problems that an existing motor driver thermal management system is low in cooling efficiency, high in energy consumption, complex and insufficient in space utilization rate. The method comprises the following steps: acquiring operation, thermal state and environment data; constructing a collaborative thermal model and predicting a thermal load; generating an optimal cooling strategy based on the predicted value and the target; the cooling liquid pump, the three-way valve and the cooling fan are adjusted, and cooling resources are dynamically distributed to achieve collaborative cooling. The system comprises a data acquisition module, a collaborative thermal model construction prediction module, an intelligent decision control module and an integrated cooling circulation execution module. By the adoption of the technical scheme, the cooling performance can be effectively improved, system energy consumption and complexity are reduced, space layout is optimized, the service life of parts is prolonged, and the reliability, the power performance and the endurance of the whole vehicle are improved.

The invention relates to the technical field of ship navigation risk assessment, in particular to a ship navigation risk assessment system based on multi-source heterogeneous data fusion, which comprises a multi-source data integration module, a spatial-temporal feature mapping module, a dynamic risk detection module, a linkage decision control module and a feedback optimization module. According to the method, standardized operation data is generated through multi-source data cleaning and fusion, a spatial-temporal feature distribution map is generated by using a multi-dimensional dynamic clustering algorithm, a risk index set is extracted in combination with adaptive boundary adjustment and a nonlinear optimization algorithm, and accurate path planning and real-time regulation are realized. In addition, a global sensitivity analysis framework and an early warning module are introduced into the system, and the ship navigation safety and reliability are improved. According to the method, the risk prediction accuracy can be remarkably improved, the navigation accident probability is reduced, the navigation efficiency is optimized, and safe operation of the ship is guaranteed.

The invention discloses an underwater robot with a splicing function and a method thereof, and belongs to the field of underwater robots, the underwater robot comprises a waterproof shell, a sensing module, a main control module, a power supply module and a rotary splicing module; the sensing module is used for environment sensing, the main control module is used for decision control, the power supply module provides energy support, the rotary splicing module is a core module of the robot, a rotary telescopic mechanism comprising a rotary shaft telescopic arm and a limiting rotor is creatively provided, and the rotary telescopic mechanism can be matched with limiting grooves in waterproof shells of other single robots. And splicing of the underwater robot is realized. According to the scheme of combining the underwater robot through the rotary splicing mechanism, independent operation or group operation can be selected according to the use scene, the combination shape can be switched during group operation to meet the task requirement, and the group task which cannot be completed by a traditional individual robot is achieved; therefore, the adaptability and execution capability of the underwater robot to complex tasks and environments are improved.

The invention discloses a multifunctional hydraulic system experiment table, which relates to the technical field of hydraulic system experiment tables and comprises a time base line synchronous control module, a phase lock correction stability control module, a false peak identification and correction module, a multi-dimensional fault-tolerant judgment control module and a reverse energy slow-release anti-reverse module. And the time base line synchronous control module is used for establishing a cross-sensor unified time base line control structure, generating a synchronous reference surface by using a high-stability crystal oscillator signal, and executing nanosecond phase calibration on sampling signals of the pressure sensor and the flow sensor by taking the synchronous reference surface as a unified time reference. According to the invention, high-precision synchronous acquisition of pressure and flow signals is realized through unified time base line and phase locking correction, and high-pressure transient test data is ensured to be real and stable; and meanwhile, control logic is reconstructed, and an energy slow release strategy is introduced, so that the pressure relief process is stable and controllable, pump reverse rotation and structural impact are avoided, and the safety, reliability and self-healing stability of the experiment table are remarkably improved.

The invention discloses an intelligent tea withering control system based on multi-modal feature fusion and time sequence prediction. The system is composed of a multi-modal feature extraction module, a time sequence modeling module, a transfer learning module and an intelligent decision control module, a Transform attention mechanism is adopted to construct a cross-modal fusion framework, RGB images, hyperspectrum and time sequence information are fused, and accurate recognition and trend prediction of the withering state are achieved. According to the system, an adaptive attention fusion network is designed, optimal fusion of images, spectrums and grade information is realized through dynamic distribution of modal weights, and the recognition accuracy and stability are remarkably improved. The classification accuracy of 180 verification samples reaches 93.33%, and is improved by 15.93%-34.83% compared with that of a single-mode method. And cross-environment self-adaption is realized through fusion transfer learning, and the performance is improved by 6.7%-14.3%. The intelligent decision engine optimizes temperature and humidity parameters based on a multivariable coupling control theory, the control precision reaches + / -0.5 DEG C and + / -2.0%, and the response time is 2.5 seconds.

The invention provides a surrounding vehicle track prediction method and system based on an attention mechanism in a network connection environment, and the method comprises the steps: constructing a vehicle network which comprises a road side unit and a vehicle; cooperative driving data of the target vehicle and surrounding vehicles are obtained through a road side unit and a sensor on the vehicle; the driving data is preprocessed; inputting the preprocessed driving data into the trained Transform trajectory prediction model based on the attention mechanism to obtain a trajectory prediction result of the surrounding vehicles; calculating a root-mean-square error between the trajectory prediction result and the actual running trajectory of the vehicle; constructing a vehicle collaborative decision according to the trajectory prediction result and the root-mean-square error, and controlling the operation of the vehicle based on the vehicle collaborative decision; according to the invention, an attention mechanism is adopted to effectively capture a complex dynamic interaction relationship between vehicles, and the precision of trajectory prediction is remarkably improved.

The invention relates to the technical field of intelligent manufacturing and reinforcement learning, and discloses a causal-driven intelligent manufacturing strategy optimization method and system, and a medium. Comprising the following steps: collecting state information of an intelligent agent and a production environment, and constructing an environment data set and a zero action data set; constructing a causal decoupling dynamic model comprising a causal intervention module and an environmental evolution module; mixing the environment data set and the zero action data set, and training a causal decoupling dynamic model by using a first mixed data set; performing intervention simulation on the environment data set by using the trained causal decoupling dynamic model to obtain a model data set; mixing the environment data set and the model data set, and training an unbiased strategy optimizer by using a second mixed data set; and deploying the trained unbiased strategy optimizer into a production system, and driving an intelligent agent to execute production operation according to the state information. Through causal-driven modeling and unbiased strategy optimization, high-precision and high-reliability decision control of the intelligent manufacturing system in a complex dynamic environment is realized.

The embodiment of the invention discloses a dynamic regulation and control system for lead-free brazing filler metal welding process parameters based on reinforcement learning. The dynamic regulation and control system comprises a data acquisition module, a data processing module and a control module. The data acquisition module is used for acquiring original welding data in real time; the data preprocessing module is used for performing space-time alignment on the original welding data to obtain a space-time consistency data set and performing feature compression on the space-time consistency data set to obtain a welding feature vector; the decision generation module is used for fusing the welding feature vectors into enhanced state vectors, and generating a parameter adjustment strategy for the enhanced state vectors through an Actor-Critic network architecture on the basis of a reinforcement learning algorithm; the decision control module is used for converting the parameter adjustment strategy into a welding equipment control instruction, implementing safety constraint on the welding equipment control instruction and outputting a safety verification control instruction; and the decision execution and optimization module is used for executing the security verification control instruction, optimizing network architecture parameters and generating an update adjustment strategy. The welding quality and the welding efficiency can be obviously improved.

The invention relates to the technical field of industrial equipment control, and discloses an industrial equipment autonomous decision-making control method based on reinforcement learning, and the method comprises the steps: obtaining the time sequence data of a vibration sensor, the continuous data of a temperature transmitter and the discrete data of a pressure instrument of industrial equipment through a multi-mode sensing module; multi-modal data alignment is achieved through a feature space mapping algorithm, the thermal drift compensation amount is calculated in combination with a thermodynamic state model, self-adaptive threshold segmentation processing is conducted on pressure data, the processed data are input into a reinforcement learning decision model to generate a fusion decision result, exploration-utilization balance parameters are adjusted through a strategy updating mechanism, and the fusion decision result is obtained. And outputting the autonomous decision control scheme. The method solves the problems of poor adaptability, low decision-making efficiency and the like of a traditional control method, can improve the autonomous decision-making capability, the control precision and the operation stability of the equipment, reduces manual intervention, reduces the cost, and is suitable for intelligent control of the industrial equipment.

The invention relates to the technical field of voice interaction, in particular to a dialogue type AI low-delay response control method and system based on semantic analysis. The method comprises the following steps: firstly, generating a driving intensity correlation factor of a current time window according to the change of CAN bus data; further monitoring the output of the NLU, analyzing the acoustic intonation raising characteristics, and obtaining an interaction suppression coefficient in combination with the driving intensity correlation factor; further acquiring a time pressure coefficient based on the mute duration after the user stops sounding; further comparing the interaction suppression coefficient and the time pressure coefficient of the current time window to obtain a response judgment value; further analyzing the change trend of the interaction inhibition coefficient, and updating an environment improvement flag bit; and finally, according to the driving intensity correlation factor, the environment improvement flag bit and the response decision value of the current time window, carrying out multi-level response decision control, and carrying out selective reset of an interaction state, thereby solving the problems of wrong truncation and response delay of voice interaction under dynamic driving.

The invention discloses a stepless magnetic control system for an intelligent crown block, which belongs to the technical field of electromagnetic control, and comprises a vibration signal acquisition module for acquiring an iron core micro-vibration signal of an electromagnet iron core; the dynamic flux linkage compensation module generates a magnetic flux compensation signal; the inertia trend prediction module generates load inertia change trend information; the multi-dimensional attitude sensing module obtains inclination angle data and tension data; the space-time collaborative decision control module performs space-time weighted fusion on the magnetic flux compensation signal, the load inertia change trend information, the inclination angle data and the tension data to generate collaborative control parameters; the instruction generation module analyzes the cooperative control parameters to generate a magnetic field intensity adjustment instruction; and the cooperative execution module executes the magnetic field intensity adjustment instruction to cooperatively adjust the electromagnetic force. A control strategy of multi-dimensional sensing data fusion and space-time collaborative decision is adopted, magnetic field fluctuation can be effectively compensated, and load swing is actively inhibited.

The invention relates to a different intelligence traffic subject interaction information model construction method based on a three-dimensional digital model. The method comprises the following steps: S1, constructing a multi-level scene based on spatial levels of a road traffic system and designing functions of the multi-level scene; s2, defining autonomous levels of the traffic system, and describing the perception capability, decision logic and execution precision of each terminal device of the vehicle road cloud of each level; s3, according to different attributes of traffic entities, designing a perception-transmission-decision-control four-stage traffic information circulation process and realizing unambiguous collaboration among different intelligence subjects; and S4, converting the standardized and defined information model into a plurality of instantiation units, and deploying the instantiation units into computing entities of various end-edge-cloud traffic subjects to support actual operation of a traffic service scene. According to the method, the problems of difficult cross-domain collaboration, non-uniform information models and insufficient global optimization caused by non-uniform intelligent level of traffic subjects in the prior art are effectively solved, and methodological support is provided for design, verification and implementation of a vehicle-road cloud integrated system.