866 results about "Decision control" patented technology

The invention discloses an agent autonomous decision control method based on multi-modal data fusion. The method comprises the following steps: S1, synchronously collecting multi-source heterogeneous data; s2, dynamic weight adaptive fusion is carried out; s3, generating a task-driven decision; and S4, performing autonomous decision closed-loop optimization. According to the method, through dynamic weight distribution and space-time correlation modeling, the problems of heterogeneity and environment adaptation in multi-modal data fusion are solved; furthermore, a risk-sensitive reinforcement learning framework and a closed-loop feedback mechanism are combined, so that full-link cooperative control from data fusion, strategy generation to optimization execution is realized. In the mechanism level, the method breaks through the limitations of static fusion, single-target optimization and offline training, can adapt to a dynamic environment, ensures that the intelligent agent is in a complex scene such as noise interference, illumination abrupt change and task emergency switching, and meets the requirements of decision-making efficiency, safety and environment robustness at the same time.

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 temperature control, in particular to an intelligent temperature control system for the whole cable production process. Comprising a cable parameter acquisition module, a production process modeling module, a temperature control decision module, an execution control module and a monitoring early warning module. According to the system, cable production parameters are collected, and a graph neural network and a symbolic regression algorithm are used for modeling production temperature distribution and a temperature-quality relation. And based on the model, an optimal temperature control strategy is generated by adopting reinforcement learning, and precise temperature control is realized through adaptive fuzzy control. Meanwhile, the system monitors temperature parameters in real time, early warning signals are sent out when abnormal detection, over-limit detection or equipment failure are detected in combination with sparse coding and variation inference, and the production stability and the product quality are ensured. According to the invention, through intelligent modeling, decision making, control and monitoring means, the temperature control precision and efficiency of the whole cable production process are comprehensively improved.

The invention discloses a multi-objective optimization decision control method for shrimp and silkworm polyculture environment parameters, and relates to the technical field of polyculture control. The method comprises the following steps: deploying a water quality sensor network, and combining prawn and nereis growth monitoring equipment; extracting criticality of environmental parameters and biological growth indexes by using historical breeding data and experimental data; constructing a dynamic coupling model, and establishing a dynamic model of the polyculture system based on a differential equation; developing a hybrid optimization algorithm to balance the competition and symbiotic relationship between the prawns and the nereis; and generating a control instruction set based on an optimization result, and adjusting the control equipment through a fuzzy PID controller. According to the invention, intelligent regulation and control of shrimp and silkworm polyculture environment parameters are realized, and economic benefits of shrimp and silkworm polyculture are improved.

The invention discloses a group aircraft collaborative perception decision control method and system in a complex high-dynamic environment, and relates to the technical field of cluster aircrafts, and the method comprises the steps: obtaining the performance parameters of a target group aircraft, building a task scene model in combination with the geographic information of a target task area, and building a target optimization function. Iteratively optimizing an aircraft and task allocation scheme through a graph neural network algorithm, outputting a task allocation result of each aircraft, and then according to a flight path constraint condition, analyzing a matching degree between a flight cost generated in a flight path and a corresponding task demand; and generating a globally optimal task allocation and flight path cooperation scheme. Task distribution and path planning are dynamically adjusted by sensing task requirements and environment changes in real time and combining performance parameters of all aircrafts, and compared with a traditional static method, the method can quickly respond to dynamic changes of task scenes, and task distribution reasonability and path planning effectiveness are ensured.

The invention relates to the technical field of computers, and discloses a digital twin hydraulic engineering operation and maintenance monitoring system which comprises a data acquisition module, a twin modeling module, a principal stress extraction module, a stress evolution prediction module, a safety domain judgment module, a regulation and control decision module, a control execution module and a state feedback module. The invention also discloses a digital twin hydraulic engineering operation and maintenance monitoring method, which comprises the following steps: data acquisition: real-time sensing data of a hydraulic engineering structure area is acquired through the data acquisition module, and the sensing data comprises strain, water pressure, temperature and displacement information and is used for representing the current state of the structure; and carrying out twin modeling, and receiving the real-time sensing data transmitted by the data acquisition module. According to the method, intelligent sensing and dynamic regulation and control of the structure state are realized by constructing a closed-loop system of principal stress prediction, regulation and control decision, control execution and state feedback.

The invention relates to the technical field of electric vehicle control, and provides an intelligent new energy locomotive power distribution and energy recovery control system and method. The system comprises a multi-source information sensing layer, an intelligent decision control layer and an execution feedback layer. The multi-source information sensing layer collects the state of a vehicle and external environment data. The intelligent decision control layer generates a power distribution and energy recovery instruction through data fusion and preprocessing, fuzzy logic assistance, reinforcement learning decision and cooperative work of a multi-objective optimization module; and the execution feedback layer executes the instruction, monitors feedback in real time, and optimizes related actions. According to the method, advanced technologies such as reinforcement learning and fuzzy logic are fused, complex and changeable driving scenes are accurately dealt with, self-adaptive optimization of power distribution and energy recovery strategies is achieved, the energy recovery efficiency is improved, power distribution is accurate and efficient, dynamic adjustment can be achieved according to driving intentions and working conditions, the driving safety and comfort are guaranteed, and meanwhile the driving efficiency is improved. And efficient utilization of energy is realized.

The invention provides an end-to-end automatic driving decision control method and system, and belongs to the technical field of automatic driving. The invention relates to an end-to-end automatic driving decision control method based on multi-modal perception and hierarchical trajectory optimization, and the method comprises the steps: constructing an end-to-end decision closed loop through combining the zero sample migration capability of a vision-language-action (VLA) model with a hierarchical optimization architecture: analyzing multi-modal input (vision, language and point cloud) by using a pre-trained VLA model to generate path points; the vehicle pose is dynamically adjusted through upper-layer optimization to expand a feasible solution space, a smooth track meeting dynamics and collision avoidance constraints is solved in real time through lower-layer optimization, and finally a vehicle control instruction is output. According to the method, a multi-modal sensing and hierarchical trajectory optimization mechanism is fused, and the sensing generalization ability, the path planning feasibility and the control execution robustness of the system in a complex traffic environment are effectively improved.

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 collaborative unmanned aerial vehicle cluster path planning and scheduling system, and particularly relates to the technical field of unmanned aerial vehicle intelligent control, and the system comprises a multi-mode sensing unit which is composed of a heterogeneous sensor array composed of LiDAR, binocular vision and millimeter wave radar, and an output dynamically updated three-dimensional Gaussian mixture map; the decision control unit is used for implementing double-layer optimization of mixed integer programming task allocation and artificial potential field path planning; the dynamic communication network adopts a hybrid networking protocol of TDMA backbone nodes and 802.11 ax terminal nodes; an energy management module; aiming at the insufficient environment perception and dynamic modeling capability in the prior art, the method achieves the effects that the centimeter-level positioning precision and the dynamic obstacle recognition rate are greater than 92%, the environment model is delayed and compressed to be within 200ms, the response speed is increased by 5 times by setting multi-modal sensor fusion, constructing a dynamically updated 3D Gaussian mixture map and combining an LSTM network to predict the obstacle trajectory in real time, and the dynamic obstacle recognition rate is greater than 92%. And the obstacle avoidance reliability in a complex scene is obviously enhanced.

The invention relates to the technical field of wind power operation and maintenance technologies, and discloses a combined heat dissipation system of a fan, the system comprises a multi-source sensing module, a decision control module, a heat dissipation execution module and a safety early warning module, and by setting multi-mode cooperative control, when fan heat dissipation regulation and control are carried out, the heat dissipation of the fan can be controlled in a multi-mode cooperative mode; a dynamic matching mechanism of temperature field characteristics and heat dissipation modes is established, and air cooling, liquid cooling and phase change strengthening modes are automatically switched in real time according to the temperature rise rate and the heat distribution gradient, so that the system can accurately adapt to heat dissipation requirements under different working conditions, timely heat dissipation of a high-temperature core area is guaranteed, the heat dissipation response accuracy is improved, and the service life of the system is prolonged. By arranging the three-dimensional thermal field balancing system, the hot area distribution difference of the bearing, the winding and the power module is recognized in real time, the cooling liquid flow and the airflow guide angle are dynamically adjusted, the three-dimensional thermal unbalance phenomenon in a traditional heat dissipation mode is eliminated, and it is guaranteed that the temperature gradient of all areas in the motor is always kept within a safety threshold value.

The invention provides an intelligent factory decision control method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining equipment operation state data, customer cooperation data, customer order data and material feature data, determining a customer value level according to the customer cooperation data, and carrying out the health assessment based on the equipment operation state data, thereby achieving the intelligent factory decision control. Equipment state evaluation parameters are obtained, an initial scheduling plan is generated according to the customer order data, the customer value level and the equipment state evaluation parameters, a preset production line digital twin model is called in combination with the material feature data to perform scheduling verification optimization, scheduling optimization compensation parameters are obtained, and an optimized scheduling plan is obtained after optimization adjustment; according to the method, multi-dimensional association is constructed through clients, equipment and materials, global resource optimal allocation of scheduling is achieved, delay risks are found in advance through a twin model, adjustment and optimization are conducted through dynamic feedback, active optimization is achieved based on pre-verification and real-time compensation, decision global optimization coverage is reserved, and dynamic adaptability is improved.

The invention provides a dynamic fault-tolerant control method based on an expected function safety mechanism, and the method comprises the steps: 1, selecting the model of a sensor, and enabling the sensor to be distributed at the periphery of a vehicle body; 2, building a decision algorithm model, carrying out environment scene recognition, planning a driving path and speed according to data obtained by a sensing layer, evaluating safety risks of different decisions, and carrying out judgment and decision making on various conditions; 3, control interfaces of a vehicle power system, a steering system and a braking system are established, and the electronic control unit converts a decision control instruction output by the decision algorithm model into actual vehicle operation through the control interfaces; 4, performing an expected function safety mechanism, performing real-time monitoring on state parameters of the vehicle, and analyzing detection data through a fault detection algorithm; 5, implementing a fault tolerance strategy; and 6, continuously monitoring the actual driving state and the system response condition of the vehicle, and adjusting the fault tolerance strategy.

The invention relates to the technical field of elevator control and fault diagnosis, and discloses an elevator inverter fault pre-diagnosis and fault-tolerant control system and method based on digital twinning, and the system comprises a high-fidelity digital twinning body building module which is used for building a digital twinning body of an elevator inverter, the digital twin integrates an electric-thermal-stress coupling model, a device-level aging model, a parasitic parameter influence model and a driving circuit and protection logic model; wherein the electric-thermal-stress coupling model is used for simulating loss, junction temperature fluctuation and thermal stress circulation of a power device, and the device-level aging model is used for representing a time-varying degradation rule of device parameters. Through deep fusion of a digital twin technology, fault diagnosis and fault-tolerant control, a closed-loop system of'perception-pre-diagnosis-decision-control 'is constructed, the safety, reliability and economical efficiency of the elevator inverter are comprehensively improved, and the method has important significance in promoting transformation of the elevator industry to intelligent and unmanned maintenance.

The invention discloses a complex terrain self-adaptive motion control method and system for a double-wheel-foot robot, and relates to the technical field of robot motion control. The method comprises the following steps: constructing a complex terrain model comprising a robot model, obstacles and environmental constraints; a motion decision controller is constructed, a reinforcement learning model is utilized to perform multi-target collaborative optimization training on the motion decision controller by adopting an asymmetric training strategy for different complex terrain models, and risk constraints are introduced during updating of the asymmetric training strategy to constrain behaviors of the strategy; strategy gradient back propagation is carried out according to a training result, and an asymmetric training strategy is optimized by using a self-adaptive learning rate adjustment method based on performance feedback. According to the method, an asymmetric training strategy and a segmented training mechanism are designed in the reinforcement learning process, and the efficient, robust and self-adaptive motion control problem of the double-wheel-foot robot in various complex terrain environments is solved.

The invention relates to the technical field of power distribution network analysis, in particular to a distributed energy storage cooperative control system and device for an intelligent power distribution network, in the distributed energy storage cooperative control system and device for the intelligent power distribution network, inverter phase angles and bus voltage fluctuation characteristics are collected in real time through a dynamic virtual impedance parameter library, and a virtual impedance parameter library reflecting a disturbance propagation path is constructed; a lower-layer region dynamic division unit of the double-layer decision control module dynamically divides cooperative control sub-regions based on a virtual impedance deviation value and a preset clustering interval, and improves local disturbance suppression precision in combination with power grid topology constraints, and an upper-layer weight distribution unit generates a dynamic power distribution weight coefficient according to an inverse proportion relation of an equivalent impedance ratio; the overload risk caused by traditional fixed weight distribution and impedance characteristic mismatch is solved; when the energy storage unit is off-network, the power disturbance buffer factor calculation module adjusts the upper limit of output of the remaining units according to the original weight proportion and the equivalent impedance ratio in an equal-ratio scaling mode, and output adjustment and the impedance attenuation direction are maintained to be synchronous.

The invention discloses an intelligent navigation system of a sweeping robot, which relates to the technical field of household intelligent equipment and comprises a multi-source sensor array, a dynamic environment modeling module, a path planning engine, a multi-mode positioning module, an autonomous decision controller, a cloud collaborative optimization unit and a fault degradation processing module. Omnibearing environment perception is provided through a multi-source sensor array, data are fused in real time through a dynamic environment modeling module, a grid map is updated in an incremental mode, obstacle semantic information is marked, an improved A * algorithm and a dynamic window method are fused through a path planning engine, and an optimized path giving consideration to global efficiency and local dynamic obstacle avoidance is generated. The multi-modal positioning module fuses SLAM resolving and IMU data through tight coupling to achieve centimeter-level high-precision and high-robustness positioning, and the autonomous decision controller intelligently and dynamically adjusts the cleaning priority, the advancing speed and the exception handling strategy according to the path, environment state and pose information, so that the cleaning behavior better meets the requirement.

The invention discloses a robot visual identification decision control method based on deep learning, and relates to the technical field of control systems. Comprising the steps of constructing a thinking decision tree according to an input instruction and a real-time visual image, and obtaining a linear relationship between a middle-layer leaf node as a main body and a top-layer leaf node for realizing association between a current visual feature and a task intention. According to the method, deep fusion and unified representation of multi-source heterogeneous data are realized by constructing a multi-layer thinking decision tree structure and combining multi-modal feature fusion and a deep learning engine, and text instructions, visual images and other different modal information are effectively integrated by constructing a fusion search engine and a semantic alignment mechanism, so that the multi-source heterogeneous data fusion and unified representation are realized. Cooperative processing of accurate analysis of task intentions and environment perception is realized, and real-time response and decision accuracy of the service robot to complex tasks are improved.

The invention discloses an intelligent drill bit wear prediction system and method based on multi-source perception, and relates to the technical field of crossing of artificial intelligence and intelligent manufacturing, and the method comprises the steps: synchronously collecting vision, vibration, temperature, pressure and engineering parameter data through a multi-source sensor, and carrying out the time-space alignment and preprocessing; extracting each modal feature by a multi-channel network, generating high-dimensional joint representation through cross-modal attention fusion, inputting a time sequence model formed by a gating circulation unit and an adaptive residual block, and outputting a wear loss prediction sequence and a wear type probability; the system comprises a data acquisition module, a preprocessing module, a feature extraction module, a fusion module, a prediction module and a risk decision module, parameter instructions are optimized, and early warning is realized when a threshold value is triggered. Through multi-source cooperative sensing, dynamic feature fusion and closed-loop decision control, the prediction precision, robustness and real-time performance are remarkably improved, the drilling safety is guaranteed, and the operation and maintenance cost is reduced.

The invention discloses a project decision optimization control method, device and equipment based on a knowledge graph, and relates to the technical field of project decision optimization control, and the method comprises the following steps: constructing a project knowledge graph, and carrying out the consistency verification of the project knowledge graph; performing semantic analysis on the verified knowledge graph based on a multi-target efficiency function to generate a project control strategy initial solution set; performing compression screening on the control strategy initial solution set based on a graph neural network method to obtain a compression control strategy set; converting the compression control strategy set into an executable control instruction, and inputting the executable control instruction into a project implementation system; according to the method, the project knowledge graph based on multi-dimensional semantic association is constructed, deep reasoning and screening of the multi-target efficiency function and the graph neural network are combined, the complex decision problem that optimal configuration and dynamic adjustment are difficult to achieve in project management is effectively solved, and optimal decision control over the whole process of the project is achieved.

The invention discloses a satellite link transmission system and a satellite link transmission method, and relates to the technical field of wireless communication, and the system comprises the steps that a data collection terminal collects and transmits service data and service types, a switching fusion device constructs a plurality of satellite transmission links after receiving the service data and the service types, and determining transmission modes corresponding to different satellite numbers, orbit heights and retransmission mechanisms according to the satellite link state information, the service types and the service data. The service data is transmitted to the data analysis center in a transmission mode for decision analysis, and a decision control instruction is fed back to the data acquisition terminal for real-time field control. Different transmission modes are determined by switching the fusion device, and processing between the terminal and the data analysis center is carried out. Compared with an existing single high-orbit satellite transmission mode, important information can be transmitted at a low speed, the reliability of information transmission is guaranteed, conventional information can be transmitted at a high speed, the timeliness and transmission efficiency of signal transmission are guaranteed, and the utilization rate of satellite resources is increased.

The invention relates to the field of industrial intelligent monitoring and fault detection, and discloses a coal conveying system intelligent monitoring and online anomaly detection system which comprises a data processing module, a feature extraction module, a dynamic tensor fusion module, an anomaly scoring module, a depth detection module and a decision control module. Carrying out pretreatment; the feature extraction module extracts features and converts the features into multi-dimensional feature vectors; the dynamic tensor fusion module fuses the multi-modal data into a unified tensor; the abnormal scoring module constructs a Markov decision process model and optimizes a scoring strategy; the depth detection module is used for extracting correlation characteristics among sensor modes and analyzing a time sequence dependency relationship of data; and the decision control module executes an intelligent control strategy according to the abnormal score and the depth detection result. According to the invention, the whole-process intelligent monitoring of the coal conveying system can be realized, the accuracy and real-time performance of anomaly detection are improved, the manual inspection cost is reduced, and the operation safety and stability of the system are enhanced.

The invention discloses an unmanned aerial vehicle obstacle avoidance method based on sensing fusion and reinforcement learning, and belongs to the technical field of aviation flight control, and the method comprises the following steps: S1, sensor fusion time-space synchronization; s2, sensing fusion obstacle recognition and positioning; s3, obstacle avoidance route planning based on reinforcement learning; the low-resolution global map and a local occupation grid map with the unmanned aerial vehicle as the center are superposed to serve as multi-channel input, and the current state and the historical action sequence of the unmanned aerial vehicle are combined to be input into an intelligent agent model fused by a trained convolutional neural network and a long-short-term memory network; and an obstacle avoidance decision is output through a reward function of the agent model, and the airborne computer executes the obstacle avoidance decision to control the unmanned aerial vehicle to complete an obstacle avoidance action. Through sensor fusion, the obstacle sensing capability under different weather conditions and different flight environments is enhanced, the sensing distance is also increased, and the flight safety under extreme conditions can be ensured.

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 autonomous navigation and obstacle avoidance method for an intelligent aircraft. The autonomous navigation and obstacle avoidance method comprises the following steps: S1, multi-modal data acquisition; s2, generating a motion path; s3, predicting an obstacle path; s4, flight obstacle avoidance; the invention discloses an autonomous navigation and obstacle avoidance system for an intelligent aircraft. The system comprises a sensor group, an edge calculation unit, an execution mechanism, an environment sensing and positioning module, a decision control module and a communication module, the autonomous navigation and obstacle avoidance capabilities of the aircraft are remarkably improved through multi-modal data fusion, three-dimensional environment map generation, dynamic obstacle prediction, path planning and attitude control; semantic tags are introduced to identify obstacle types, geometric consistency scores ensure authenticity and reliability of obstacles, and radar reflection intensity and point cloud density provide spatial distribution and material information; and the dynamic obstacle prediction model dynamically adjusts an obstacle avoidance strategy in combination with semantic tags, geometric consistency scores, radar reflection intensity and point cloud density.

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 provides an anthropomorphic automatic driving simulation test scene construction method. The method comprises the steps that all vehicles in a traffic scene are divided into two types of intelligent agents including a test vehicle and an environment vehicle, the traffic scene is modeled into a Markov decision process, the test vehicle is a vehicle controlled by an automatic driving algorithm, and the environment vehicle is a vehicle controlled by a GAIL-GRU driving strategy model; a Markov decision process is utilized to extract a driving track from the human driving data set, and an expert track data set is generated; and training a GAIL-GRU driving strategy model by using the expert track data set, realizing interaction between an environment vehicle and a test vehicle by using the trained GAIL-GRU driving strategy model, and constructing a simulation test scene. The automatic driving simulation test scene constructed by the invention can effectively expose decision defects of an automatic driving algorithm in a complex interaction situation, has good anthropomorphism and relatively high risk scene coverage, and provides support for a decision control simulation test of a high-level automatic driving vehicle.

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.