1656 results about "Execution control" patented technology

A data-driven dynamic energy management system for the adaptive coordination of renewable and conventional energy sources, consisting of: a processing unit configured to perform real-time calculations to optimize the generation, storage, and distribution of electrical energy by continuously analyzing operational data, forecasting future energy demand, and generating control instructions to match available generation resources with forecasted consumption demand; a storage unit connected to the processing unit, configured to store records of historical energy production and consumption, environmental data, operating thresholds and learned model parameters, and to provide said data as input for the forecasting and optimization routines performed by the processing unit; a multitude of IoT-based monitoring units, each comprising at least one sensor configured to measure instantaneous parameters of generation, storage level, consumption rate and environmental conditions, with each monitoring unit being configured to periodically transmit measurement packets to the processing unit via a secure communication network; a forecasting unit implemented in the processing unit, configured to process historical and real-time data to create forecast curves for demand and generation using statistical and probabilistic forecasting techniques, and to dynamically update the weights of the forecasting model in response to observed deviations between forecasted and actual output; an optimization control unit implemented in the processing unit and configured to evaluate the outputs of the forecasting unit together with current operational data to determine a set of optimized control variables representing the target generation contribution of each energy source, and to pass these targets to a lower-level controller for execution; a controller that is communicatively connected to the processing unit and the multiple energy generation sources and is configured to regulate the operation of each source by adjusting the activation state, output level and operating priority based on the control signals received from the processing unit; an energy storage management unit comprising at least one battery array and a power conditioning circuit, configured to receive control instructions from the processing unit, store excess generated energy, release stored energy when forecasted demand exceeds available generation, and report charging and discharging characteristics in real time to the processing unit for continuous recalibration; an alarm and notification control unit connected to the processing unit, configured to continuously compare storage levels and generation reserves with stored operating thresholds, trigger predefined responses when critical or abnormal conditions are detected, and transmit acoustic, visual, and digital remote alerts to designated operators; a user interface terminal connected to the processing unit, configured to display real-time generation statistics, demand forecasts, energy storage status, and system alerts, and to accept operator-defined parameter inputs that are transmitted to the processing unit for recalibration of forecast or optimization parameters; and a secure server interface configured to synchronize operational logs, learning data, and performance indicators with a remote monitoring or analysis server for centralized monitoring, long-term data analysis, and distributed decision support.

The invention discloses a virtual-real combination-based power generation side industrial control system network security target construction method. The method comprises the following steps of: constructing a virtual-real combination target environment consisting of a physical equipment layer and a virtual model layer; the heterogeneous industrial control protocol between the physical equipment layer and the virtual model layer is analyzed, protocol semantic information is extracted, and a bidirectional dynamic mapping rule of a physical equipment state and a virtual model state is generated based on the protocol semantic information; based on a state change event of the physical equipment layer, according to a bidirectional dynamic mapping rule, synchronizing changed equipment state data to the virtual model layer in real time, and simulating protocol behavior logic corresponding to the equipment state data in the virtual model layer according to a security test requirement; and based on the attack instruction or the abnormal state signal generated by the virtual model layer, according to the protocol specification format of the target physical equipment, converting the instruction or the signal into an executable control command, and driving the physical equipment layer to execute an operation corresponding to the control command.

The invention relates to the technical field of equalization control of a battery management system, and discloses a lithium battery hierarchical equalization control method based on a hierarchical model predictive control algorithm, comprising the following steps: constructing a hierarchical control architecture which comprises a state monitoring layer, an equalization decision layer and an execution control layer, each layer realizes cooperative control through closed-loop data interaction; the state monitoring layer collects multi-dimensional state parameters of the lithium battery system, pre-processes the collected data and then transmits the data to the equalization decision-making layer, the equalization decision-making layer constructs a multi-target optimization model based on a hierarchical MPC algorithm, and the model takes SOC consistency, equalization energy consumption minimization and cycle life maximization of the lithium battery system as optimization targets. The abnormal state of the sensor or the balancing module can be identified in time through a fault diagnosis mechanism, and when a fault occurs, a standby model is automatically switched or a balancing task is shared through an adjacent module, so that the system is ensured not to generate abrupt reduction of balancing performance due to the fault of a single component.

The invention relates to the technical field of equipment abnormity control, and discloses an equipment abnormity self-closed-loop control system. An anomaly detection engine of the system collects operation state data of target equipment in real time, and performs preliminary anomaly marking according to a preset anomaly judgment rule; the dynamic analysis unit receives the preliminary anomaly marks, generates anomaly feature vectors in combination with historical operation data, and inputs the anomaly feature vectors into a multi-dimensional evaluation model to divide anomaly levels; a strategy generator is matched with a preset repair strategy library according to the abnormal grade division result, and a corresponding repair instruction sequence is generated; the execution control module analyzes the repair instruction sequence, and dispatches hardware resources to execute repair operation according to a preset execution priority; and the feedback verification unit collects the running state data again after restoration, and compares the running state data with the initial abnormal mark to generate a restoration verification result. The system realizes full-process automatic processing of equipment exceptions, and improves exception processing efficiency and equipment operation stability.

The invention relates to the technical field of micro-grids, and discloses a micro-grid real-time load balancing scheduling method and system based on deep reinforcement learning, and the system comprises a multi-source sensing module, an intelligent decision module, a safety protection module, an execution control module, a digital twin module and an energy efficiency evaluation module. When real-time load balancing scheduling of the micro-grid is carried out, a multi-time-scale scheduling strategy is generated in real time by dynamically coordinating economical efficiency, environmental protection and power supply reliability targets through a deep reinforcement learning agent, so that the problems of high operation cost, standard exceeding of carbon emission and voltage instability caused by a single optimization target in a traditional method are further solved, and the real-time load balancing scheduling of the micro-grid is realized. According to the method, the multi-dimensional collaborative optimization of the micro-grid in a complex operation environment is ensured, and meanwhile, a cloud global optimization module generates a long-cycle strategy, so that the problems of response delay and insufficient expandability of a centralized control architecture are further solved, and the real-time power balance capability in a high-permeability renewable energy source scene is improved.

The invention relates to the technical field of industrial robot control, and particularly discloses an intelligent foreign matter sorting robot control system based on AI recognition, which comprises a dynamic spatial feature extraction module, a manipulator motion state coding module, a collaborative conflict detection module, a dynamic trajectory optimization module and an execution control adjustment module, constructing a three-dimensional dynamic space model through multi-sensor fusion, and extracting spatial topological features by utilizing continuous coherence analysis; manipulator motion parameters are converted into topological space representation, and a track feature coding matrix is established; detecting interaction conflicts among the manipulators in real time by adopting a multi-scale coherence analysis method, and generating graded early warning signals; a collision avoidance track is optimized based on topological constraints and a virtual rejection field technology; precise execution is achieved through inverse kinematics of the Lie group theory and self-adaptive control.

The invention discloses a self-adaptive gait control method, system and device for a quadruped robot in a limited space and a medium, and belongs to the technical field of gait control. Identifying terrain categories through a classifier and acquiring initial gait parameters from a gait library; adjusting the gait parameters according to the terrain gradient and the roughness to obtain optimized gait parameters; calculating stability indexes and fusing scores; when the score is lower than a threshold value, an adjustment strategy is determined according to an instability mode to correct gait parameters; a foot end track is generated and control is executed; and the executed information is fed back to terrain perception to form a closed loop. According to the method, the terrain recognition accuracy is improved through multi-modal information fusion, the adaptive mapping from the terrain to the gait is established to achieve automatic gait switching and parameter optimization, the stability is evaluated through multi-index fusion, a targeted adjustment strategy is selected according to an instability mode, the adaptive gait control problem under the limited space and the complex terrain is solved, and the adaptive gait recognition accuracy is improved. And the movement efficiency and the stability are improved.

The invention discloses an intelligent robot with a body and a robot motion control system and method.The intelligent robot with the body is provided with an execution component, a moving module and the robot motion control system, and the robot motion control system comprises a multi-modal input encoder, a multi-modal output encoder and a multi-modal output encoder, the multi-modal feature fusion module is used for collecting and processing multi-modal data to obtain multi-modal features and fusing the multi-modal features to obtain a multi-modal feature token sequence; the action expert network is used for mapping the fused multi-modal feature token sequence into an abstract robot action sequence block; and the execution controller is used for converting the abstract robot action sequence block into a control instruction which can be executed by bottom hardware. According to the method, direct mapping from environment perception and semantic understanding to movement control and execution of specified task actions can be realized, so that the task understanding, response speed and execution completion degree of the robot in a complex environment are improved.

The invention relates to the technical field of industrial mechanical arm control, in particular to a mechanical arm execution control method for industrial production, which comprises the following steps: collecting parameters in real time; generating a geometric risk index; generating a mechanical risk index; extracting image features; abnormal mechanical arm judgment; determining an adjusting mechanical arm; and generating an adjustment instruction. According to the method, the inclination angle, the plane offset distance, the contact image features, the tightening torque and the force value of the tail end of the mechanical arm are monitored in real time in a multi-dimensional mode, a dual-risk judgment model based on geometric offset and mechanical loading is constructed, image texture fluctuation and posture change are further combined, the continuously abnormal mechanical arm is accurately positioned, and the safety of the mechanical arm is improved. And geometric and mechanical risk indexes are evaluated again after adjustment, so that the aluminum shell positioning and bolt assembling precision is guaranteed, and the problems that the production efficiency is reduced and the product defect rate is increased due to assembling quality fluctuation caused by mechanical arm positioning errors and inaccurate force control are effectively solved.

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 autonomous decision-making system of a multi-level intelligent agent in an urban area and an operation method thereof. Each end-side decision-making unit broadcasts an equipment state variable to a side-side decision-making unit; the side decision-making unit determines a global reference state and generates a control instruction of each end decision-making unit; after the equipment executes the control instruction, each end-side decision-making unit updates an equipment state variable according to the real-time operation data of the equipment, and when an abnormal event is judged to occur, a side decision-making unit predicts a decision-making variable of each piece of equipment based on a collaborative optimization model of an equipment target and an urban area target; the cloud regulation and control platform predicts a global consistency variable based on the collaborative optimization model of all the side decision units; predicting a regulation and control instruction of each side decision-making unit according to a global dynamic optimization target under a system operation constraint; and the side decision-making unit decomposes the regulation and control instruction into the control instruction of each end decision-making unit, so that the problems that the global cooperative capability of the urban regional integrated energy system is weak and cooperative scheduling is not timely under extreme events are solved.

The invention discloses a storage robot intelligent obstacle avoidance system under the Internet of Things, and belongs to the technical field of the Internet of Things, and the system comprises a multi-source data collection and fusion module, a path planning module, a multi-robot cooperation module, an execution control module, a task scheduling module, a safety monitoring module and a self-optimization module. According to the method, laser radar and visual data are integrated into a dynamic occupation grid map, a dynamic obstacle trajectory prediction model is introduced, active avoidance of moving obstacles is realized, and path smoothness and energy consumption efficiency can be optimized while a safe distance is ensured by quantifying distance and time dual cost and path planning. Besides, the path is decomposed into a time interval attitude sequence, and a curvature optimization objective function is combined, so that the robot can still keep efficient and stable obstacle avoidance capability in a storage environment with dense people streams or frequent movement, and the task execution success rate is remarkably improved.

The invention relates to the technical field of genome data analysis, and discloses an intelligent system, method and equipment for assisting multi-step genome data analysis, and the system comprises a dialogue agent which is used for generating a corresponding answer according to a question of a user, or reading an analysis plan file generated by a workflow agent, generating an analysis interpretation text for the analysis plan file; the workflow agent is used for generating a structured task execution plan according to the to-be-executed analysis task and executing the to-be-executed analysis task; and the modeling analysis agent is used for generating a configuration file and a script based on the user request, constructing a model and generating an analysis result corresponding to the user request in combination with the workflow agent. Through multi-agent cooperation, task division and cooperative scheduling are realized, each agent independently completes task planning, execution control, model analysis and other functions, the bottleneck problem of processing of a traditional single model in a complex process is avoided, error accumulation is reduced, and the execution efficiency and stability of the whole process are improved.

The invention relates to the field of heating ventilation air conditioner automatic control, and discloses an efficient central air conditioner cold station optimization control system and method based on physical AI. The system comprises a data acquisition module, an energy efficiency modeling module, a load prediction module, a rolling optimization module, an execution control module and a feedback correction module. The data acquisition module forms a time sequence data set; the energy efficiency modeling module adopts transfer learning to obtain a system energy efficiency model of a target domain cold station; the load prediction module outputs a predicted cold load sequence; the rolling optimization module is used for solving an optimal control sequence under the constraints of cooling capacity balance, an equipment operation boundary and a temperature difference threshold by taking a predicted cooling load sequence, a system energy efficiency model and a current equipment operation state as input under a model prediction control framework; the execution control module issues a first control action to control the water supply temperature, the water pump and fan frequency and unit start and stop; and the feedback correction module calculates a residual error and is used for closed-loop correction of the next period.

The invention discloses a complex terrain-oriented quadruped robot self-adaptive gait generation system and method. The system comprises a sensor system used for collecting terrain data and robot state information; the terrain sensing module identifies terrain types and terrain feature parameters according to sensor data, and quantifies terrain features into parameters; the multi-modal gait library is used for storing a plurality of basic gait modes and corresponding gait parameters; the parameterized gait generation module is used for selecting a basic gait from the gait library and optimizing and generating an optimal gait adaptive to the current environment according to terrain parameters and task parameters; the gait smooth transition algorithm synthesizes all intermediate postures in the time and space interpolation generation transition process to obtain a smooth transition trajectory; the terrain adaptability adjusting module is used for finely adjusting the gait parameters according to real-time feedback; and the execution control module is used for converting the generated gait parameters into joint control instructions for controlling the robot so as to execute corresponding actions. The quadruped robot can adapt to more complex environments.

The invention discloses a machine control system and method based on large language model structured data output, and relates to the technical field of artificial intelligence and robot control. Voice information and image information are acquired through a multi-mode acquisition module, and based on a Gemma 3 large model, a text instruction and a coding image are combined, so that a multi-mode model is obtained; a decision output module generates a structured instruction conforming to predefined JSON Schema, the structured instruction is analyzed by an execution control module to drive a mechanical arm, a memory feedback module is utilized to store interaction data and execute feedback, dynamic switching between a dialogue mode and an action mode is supported, and the mechanical arm is controlled to operate. On the basis of large language model structured output and a multi-module cooperation mechanism, the ambiguity and unreliability of natural language control are effectively solved, and the man-machine interaction precision, the execution reliability and the complex scene adaptation intelligence level are improved.

The invention discloses a mechanical arm control method and system based on fuzzy self-adaptive PID control, relates to the technical field of mechanical arm control, and solves the problem that mechanical arm control is insufficient in flexibility when facing different working conditions. According to the embodiment of the invention, the universe expansion and contraction factor is adjusted through the error, then the close linkage of the quantization factor and universe expansion and contraction is realized through the self-adaptive quantization factor calculation, the fuzzy reasoning technology is combined, the intelligent mapping from the error information to the PID parameter increment is realized, the real-time updating of the PID parameter and the control output calculation are realized, and the real-time updating of the PID parameter is realized. The flexibility and adaptability of the control system are improved; and then output is controlled through incremental PID, so that a complete control link from parameter increment to actuator driving is realized, a complete closed loop is formed by a cascade control architecture, functional modules are tightly coupled, parameters are coordinated, and the whole-process intelligence from error sensing to execution control is realized.

The invention relates to the technical field of automatic driving and artificial intelligence, in particular to a driving track planning method based on a visual language model and an intelligent driving system. The trajectory planning method comprises the steps of multi-modal data acquisition and preprocessing, visual language model reasoning, execution control and the like. The intelligent driving system comprises a multi-modal data acquisition and preprocessing module, a visual language model reasoning module and an execution control module. The visual language model reasoning module is integrated with a Patch selection module, a token compression module and a speculation decoding module; according to the invention, an end-to-end driving planning reasoning framework is constructed based on the visual language model, the vehicle trajectory planning is directly generated from the original multi-view perception data and the high-level driving intention by a single model, the architecture of an automatic driving planning system is simplified, and the response speed and robustness are improved. The method can effectively meet the requirement of the automatic driving vehicle for generating the safe path in real time in a vehicle-mounted embedded environment, and has important practical value.

The invention relates to the technical field of big data analysis, and discloses a new energy automobile intelligent fast charging scheduling method based on multi-source data analysis, which comprises the following steps: S1, collecting multi-source charging scheduling data: constructing a charging scheduling multi-source data set; s2, multi-source data preprocessing and fusion: obtaining a charging scheduling fusion decision data set; s3, generating a charging scheduling decision: making the charging scheduling decision; s4, instruction issuing and execution control; s5, real-time monitoring and dynamic adjustment: dynamically adjusting a charging strategy; s6, feedback collection and optimization iteration are carried out; multi-source charging scheduling data are collected in real time through a vehicle-mounted terminal, a charging pile terminal, a power grid monitoring terminal and a traffic environment data terminal, a charging scheduling strategy is formulated through a cloud server, opinions are continuously fed back to a mobile phone user through a man-machine interaction terminal, charging scheduling feedback evaluation is carried out, model parameters are optimized, full-dimensional data driving is achieved, and the charging scheduling efficiency is improved. The charging efficiency is effectively improved, and the user experience is comprehensively optimized.

The invention discloses an integrated intelligent management and control method and system for underground water pumping and injection based on the Internet of Things, and the method comprises the steps: carrying out the geological investigation of a target region, constructing a three-dimensional geological model, and planning the layout of a pumping and injection well group and a sensor network according to the three-dimensional geological model; deploying a monitoring network and collecting data based on the layout scheme, and fusing a three-dimensional geologic model to construct a polluted site digital twinborn body; groundwater situation data are extracted through the digital twinborn body, a pollutant migration path and a water level dynamic state are predicted by using an LSTM model, and pollution scene simulation information corrected by geologic features is output; by taking the simulation information as input and coupling geological parameters as constraints, generating a pumping and injection well group cooperative control strategy set by adopting a BLMFO double-layer multi-objective optimization algorithm; and finally, a control instruction is executed, the real-time situation is monitored in the repairing process, strategy change suggestions including the flow adjustment amplitude, the agent optimization scheme and equipment operation parameter correction are judged and pushed, the repairing efficiency is improved, and the method adapts to the complex geological environment.

The invention relates to the technical field of industrial control systems, in particular to an ink path control system of a full-color printer, which comprises the following specific implementation steps of: measuring a plurality of physical quantities in a controlled physical process in real time, and outputting the physical quantities as state variables and external disturbance signals; receiving a state variable and an external disturbance signal, performing future state prediction through a nonlinear dynamic process model, and generating a prediction result; constructing a process performance index function, optimizing the performance index function through a sparrow search optimization algorithm based on a prediction result, and generating an optimal target set value; through a model based on Bayesian reasoning, parameters of a PID control law are updated in a self-adaptive mode according to external disturbance signals; the controller is used for receiving the optimal target set value and the adaptive PID control law parameters, executing the PID control law in combination with a preset undisturbed switching module, and generating a final control instruction; and receiving a final control instruction, and converting the final control instruction into driving signals acting on a plurality of physical actuators so as to realize closed-loop feedback control of the controlled physical process.

The invention relates to the technical field of laboratory environment control, and discloses an environment parameter control method and system for a complex laboratory, and the method comprises the steps: obtaining environment index data, and carrying out the time sequence processing, and obtaining a real-time change trend; according to the real-time change trend, predicting a future offset by using a prediction model, and performing data fusion when the offset exceeds a threshold value to obtain an accurate environment state estimated value; according to the accurate environmental state estimation value, quantifying the mutual influence among the environmental indexes to obtain the mutual influence relationship of the environmental indexes; determining an environment index priority sequence according to the mutual influence relationship; generating and executing a control instruction according to the priority sequence; and when continuous deviation exists, the prediction model and the mutual influence relation are updated through feedback circulation. According to the method, the dynamic mutual influence among the environmental indexes can be quantified, and accurate and stable cooperative control of multiple parameters in a complex dynamic environment is realized.

The invention relates to the technical field of tunnel and underground engineering construction, and discloses an intelligent closed-loop control system and method for a tunnel construction section contour, and the method comprises the steps: collecting geological-geometric data through the fusion of a three-dimensional laser scanner and a geological radar before blasting; dynamically generating a theoretical blasting contour line and adaptive blasting parameters through a convolutional neural network; iMU positioning and a PID controller are combined to realize millimeter-level accurate control of the drill boom; generating a three-dimensional deviation field by adopting an iterative point cloud registration algorithm after blasting; when the deviation exceeds a threshold value, triggering incremental learning to optimize model parameters; and finally, closed-loop iteration is formed. Through multi-source data fusion, contour and parameter intelligent generation, precise execution control, three-dimensional deviation field feedback and an incremental learning mechanism, a complete intelligent closed-loop control system is constructed, the limitation of traditional blasting design depending on experience is broken through, and the precision, efficiency and economic benefits of tunnel construction are remarkably improved.

The invention discloses a charging pile liquid cooling module intelligent cooperative control system based on an industrial Ethernet, and relates to the technical field of liquid cooling control. The charging pile liquid cooling module intelligent cooperative control system based on the industrial Ethernet comprises a liquid cooling data acquisition and preprocessing unit used for acquiring and preprocessing liquid cooling cooperative operation data; the thermal load sensing and intelligent liquid cooling regulation and control unit is used for dividing independent cooling branches, evaluating the thermal load degree of each cooling branch, judging the real-time thermal load state and generating and executing a control instruction; the anomaly detection and cooling redundancy control unit is used for evaluating the thermal response deviation condition of a cooling branch and judging whether a double-ring redundancy pipeline is started or not; and the fault root cause judgment and recovery enhancement unit is used for evaluating the component fault risk degree in the abnormal branch and executing fault-tolerant control and branch recovery strategies. The problem that a whole set of liquid cooling system is shut down due to the fact that local fault tolerance cannot be achieved when a local branch breaks down in a traditional pipeline structure is solved.

The invention relates to a scraper chain tension dynamic optimization method based on real-time load feedback, and belongs to the technical field of scraper chain control. The method comprises the following steps: acquiring load data of a scraper chain in various operation states; carrying out standardization processing on the load data of various operation states and then extracting features to obtain load time sequence features; carrying out time attention mechanism processing on the load time sequence characteristics and the load data of the multiple operation states to obtain importance weights; a tension value is calculated according to the load data of each operation state and the importance weight of the load data; calculating a real-time working condition coefficient of the current time node, and dynamically adjusting a preset safety tension range to obtain a real-time safety tension range of the current time node; comparing the tension value with a real-time safe tension range, and generating a control instruction according to a comparison result; and executing the control instruction. The tension control method of the scraper chain is optimized, and the control precision of the tension of the scraper chain is improved.

The invention relates to the technical field of take-up tension control, and discloses an intelligent take-up tension control system. A multi-source sensing acquisition unit of the system acquires a wire distribution topological graph through a tension sensing platform, acquires a reference tension parameter of a take-up reference point, and acquires strain characteristics and movement speed characteristics of each wire section; the dynamic feature modeling unit performs multi-modal fusion on the reference tension parameters and the features to generate a multi-modal tension feature matrix; the static path analysis unit constructs a static tension conduction path based on the tension characteristic value in the matrix and the corresponding wire section; the take-up execution control unit controls a take-up track of the take-up mechanism on the adjustable path in combination with the static path conduction node value and the corresponding wire section; the dynamic compensation unit collects the dynamic tension parameter of the current wire section in the take-up process, the tension applying direction of the take-up mechanism is compensated and corrected according to the dynamic tension parameter and the static path conduction node value, and the take-up tension control stability and accuracy are improved.

The invention provides a multi-modal semantic-action alignment method and equipment for end-to-end automatic driving, and belongs to the technical field of automatic driving, and the method comprises the steps: carrying out semantic reasoning through a large language model based on various sensor data and language instruction information of a vehicle, and generating semantic information containing a driving intention; inputting the semantic information into a semantic-action alignment module, and converting the semantic information into corresponding driving action representation through a learned consistency mapping relation from a semantic space to an action space; and generating an executable control track of the vehicle according to the driving action representation. According to the method, the problem of insufficient semantic and action space alignment is solved, information distortion and precision limitation caused by post-processing depending on rules are avoided, the high-level driving intention can be generated based on complex multi-mode information (such as navigation instructions and traffic environments), it is ensured that the final execution action is highly consistent with the intention, and the driving intention is more accurate. And the decision-making rationality of the system in a complex scene is enhanced.

The invention relates to the technical field of financial management methods, and discloses a financial budget management method based on an ERP financial system, and the method comprises the steps: integrating financial, business and external market data through an ERP interface to construct a dynamic model containing a basic budget value and a floating interval, collecting the data in real time, and synchronizing the data to a budget module through cleaning and standardization; budget and actual execution data are compared in real time by means of a preset index system, graded early warning is triggered according to a deviation threshold value and directionally pushed to a responsible department, and dynamic supervision of the whole budget execution process is achieved; after early warning, the system automatically and deeply digs a deviation root, generates a plurality of schemes containing adjustment amplitude and risk coefficients in combination with the model, matches an approval process according to the influence range of the schemes, automatically updates budget data and synchronizes the budget data to a service module after passing, and periodically restores data to optimize model parameters, so that budget execution control is strengthened, and the budget execution efficiency is improved. And the dynamic adjustment and risk response capability is remarkably improved, and the budget management requirements of enterprises are efficiently met.

The invention discloses an intelligent agent planning method, system and device based on context awareness and a medium, and belongs to the technical field of artificial intelligence. The method comprises the steps that firstly, user input is analyzed, structured task requirements are generated, and meanwhile, external tool function attributes and historical interaction data are collected; according to the method, an ordered execution plan is generated in combination with task requirements and tool attributes, subsequent plans are optimized by utilizing historical interaction contexts, in an execution stage, parameters required by a target function can be accurately extracted from the task requirements and context information, corresponding tools are called to complete a task, and finally, an execution result is fed back for display, so that the execution efficiency is improved. And performing execution control based on the function calling state and the result. According to the method, more intelligent task planning and execution are realized through context sensing, and the method is particularly suitable for complex task scenes needing multi-step and multi-tool cooperation.

The present application relates to the technical field of intelligent logistics, and in particular to an automatic warehouse-in and warehouse-out method for intelligent shared pallets, which comprises collecting pallet operation state data through a multi-source sensing module and preprocessing, generating task decomposition parameters based on a task decomposition model, constructing a multi-objective planning model for global scheduling, and outputting execution instructions through a hierarchical execution control model. The present application can realize efficient and accurate warehouse-in and warehouse-out management of shared pallets, optimize the flow efficiency and resource conflicts, and improve the intelligent level and operation efficiency of the logistics system.