4612 results about "Target control" patented technology

The invention relates to a V2G-based alternating current charging power adjusting method and device. The method comprises the following steps: carrying out multi-scale data acquisition and preprocessing on a V2G alternating current charging system to obtain a standardized system state data set; constructing a system dynamic model comprising a power grid voltage characteristic and a charging power characteristic; carrying out decomposition to obtain a power output mapping relation calculation, and obtaining a power modulation control parameter; establishing a fast and slow time scale coordination control framework, and designing a power regulation agent and a voltage regulation agent to obtain a coordination control strategy; checking a charging power limit value constraint, a voltage limit value constraint and a power factor constraint to obtain a constraint violation judgment result, and executing constraint projection calculation to obtain a target control instruction; and controlling a power conversion circuit of the charging pile to execute power regulation operation. According to the invention, a hierarchical control structure is adopted, the control complexity of the system is reduced, and the robustness and the adaptive ability of the control strategy are improved.

The invention relates to the technical field of processing temperature control, and discloses a temperature control method and system for a hot processing process and a storage medium. The method comprises the following steps: collecting hot working whole process temperature data through multiple sensors, calculating a temperature change rate, an extreme point and a uniformity index, and obtaining key characteristic parameters; establishing a quantitative relation matrix of the process parameters and the temperature field by using an orthogonal test; calculating an optimal process parameter combination based on the matrix by using a rolling time domain optimization algorithm to generate a control instruction; and performing execution opportunity and duration prediction and correction on the instruction to form a precise target control instruction. The temperature control precision and stability in the hot working process of the large alloy steel component are improved, so that the product quality is improved, the energy consumption is reduced, and the production period is shortened.

The invention belongs to the technical field of stirring control, and particularly relates to an intelligent speed regulation stirring control method and system and computer equipment, and the method comprises the steps: determining a target control value of stirring control according to the requirements of a stirring technology; various sensors are installed on the stirring equipment, it is ensured that key parameters in the stirring process are accurately collected in real time, the data collection frequency is set, and it is ensured that dynamic changes in the stirring process are captured; filtering processing is carried out on the collected sensor data, noise interference is removed, the sensor data is corrected, and the accuracy of the data is ensured; key features in the stirring process of the combustion furnace are extracted through a data analysis method, and a mathematical model of the stirring process is established for prediction and control; a closed-loop control system is established, the stirring speed of the combustion furnace is adjusted according to data monitored in real time, the stability and accuracy of the stirring process are ensured through an intelligent algorithm and real-time parameter monitoring, insufficient stirring or excessive stirring is avoided, and therefore the product quality and the production efficiency are improved.

The invention discloses an automatic control code generation and verification method and device, equipment and a storage medium, and relates to the technical field of automatic control, the method is applied to a large language model, and a natural language command is received; performing matching retrieval on the vector database according to the natural language command to obtain an example code snippet; obtaining API structured information corresponding to the example code snippets from a knowledge graph database; generating an initial control code based on the example code snippet and the API structured information; and performing multi-stage virtual operation verification on the initial control code in the software motion control system, and generating a target control code according to a multi-stage verification result confirmed by a user for multiple times and the initial control code. According to the method, the initial control code is automatically generated through double-database retrieval based on the large language model, then multi-stage code verification is performed through the software motion control system, the target control code is generated in combination with verification results confirmed by a user for multiple times, and the code generation speed and reliability are improved.

The embodiment of the invention provides an optimization method and system for a manipulator control system, and the method comprises the steps: firstly obtaining an operation data set containing a control instruction sequence and corresponding motion state data in a historical operation period of a manipulator, carrying out the feature extraction of the operation data set, and obtaining an associated motion feature set; calling a pre-trained control strategy optimization model to analyze and generate strategy adjustment parameters, adaptively updating the control instruction sequence of the current operation task according to the strategy adjustment parameters to obtain a target control instruction sequence, and finally inputting the target control instruction sequence into a control system to execute operation. And real-time motion state data is collected for subsequent iterative optimization, so that the performance of the manipulator control system can be continuously improved, and the operation precision and efficiency are improved.

The invention relates to the field of mine ventilation, in particular to an intelligent adjusting method and system for mine ventilation. The method comprises the following steps: constructing a dynamic topology model taking a roadway as an edge and taking a fan and an air door as nodes based on a mine CAD drawing; acquiring the actual wind pressure and the actual wind volume of each roadway branch in real time, performing inversion to obtain the current wind resistance, and updating the wind resistance attribute of the edge in the dynamic topology model by using the current wind resistance; calculating the optimal air volume of the roadway branch by using the updated dynamic topology model; based on the optimal air volume, target regulation and control parameters of the draught fan and the air door are obtained through solving by means of a particle swarm algorithm; and according to the target regulation and control parameters, the fan and the air door are adjusted through a fuzzy PID algorithm. The problem that in the prior art, mine ventilation adjustment is not intelligent and safe enough is solved, the theoretical optimal air volume is obtained through deep analysis of the mine, then ventilation adjustment is conducted step by step, and the scientificity and efficiency of ventilation adjustment are improved.

The invention relates to an injection mold temperature control method and device and computer equipment. The method comprises the following steps: acquiring temperature data of a plurality of temperature measuring points of the injection mold to obtain a temperature data matrix; establishing a heating and cooling input matrix and a temperature response matrix, and solving to obtain a heat transfer path coefficient matrix; taking the temperature data matrix as state input, constructing a depth deterministic strategy gradient control network comprising a gating circulation unit and a full connection layer, and performing linear mapping to obtain a hybrid action space; inputting the heat transfer path coefficient matrix and the mixed action space into a training environment, performing iterative training on a depth deterministic strategy gradient control network based on an experience playback mechanism, and updating network parameters by calculating a reward function to obtain a temperature control model; and deploying the temperature control model to a target control system, and calculating a real-time control parameter set. According to the invention, coordinated control of the heating stage and the stabilizing stage is realized, the injection molding period is shortened, and the energy consumption is reduced.

The invention relates to the technical field of grease production control, in particular to a grease product production control method and system based on intelligent digitization. The method comprises the following steps: monitoring temperature, viscosity and turbidity data in a grease crystallization process in real time, and generating grease crystallization state snapshot data; performing crystallization state prediction according to the grease crystallization state snapshot data, and performing reaction kettle cooling rate adjustment to obtain a target control cooling rate; performing real-time stirring rotating speed adjustment fuzzy reasoning on the grease crystallization state snapshot data to generate a corrected grease stirring speed; and performing control instruction conversion on the target control cooling rate and the corrected grease stirring speed, executing a production control instruction, and generating a grease crystallization process control report. By accurately controlling the cooling rate and the stirring speed, intelligent control over the grease crystallization process is achieved, and the quality and stability of grease products are remarkably improved.

The invention discloses a task configuration method of a humanoid robot and a related device, which are applied to humanoid robot control system terminal equipment, and the method comprises the following steps: responding to an input operation based on a robot function configuration interface, and obtaining task configuration information; outputting first prompt information, and prompting a user to wear VR wearable equipment to perform task operation demonstration; first information is sent to the VR wearable device, and the VR wearable device is indicated to display a VR environment corresponding to the task type; in response to a selection operation for a target control of the robot function configuration interface, sending second information to the VR wearable device; receiving first posture data and a first video stream of the VR wearable device; and third information carrying the task type, the first video stream and the first attitude data is sent to the server, and the server is indicated to send an association relationship between the task type and the subtask sequence and at least one atomic skill model parameter to the robot. The task execution diversity and flexibility of the humanoid robot can be improved.

The invention belongs to the technical field of monitoring and fault early warning of an intelligent packaging production line, discloses an intelligent packaging production line control system and method, and aims to realize comprehensive monitoring and fault early warning of the intelligent packaging production line. Comprising the steps of collecting electric power parameter data of an electrical component; the power parameter data comprises current, voltage, energy utilization ratio data and a vibration signal of the electrical component; preprocessing the power parameter data to form an electrical characteristic data set; training according to the electrical characteristic data set to obtain a fault state prediction model, and predicting based on the fault state prediction model to obtain a fault probability value of the electrical component; judging whether the intelligent packaging production line has a fault according to the fault probability value of the electrical component; when it is detected that the intelligent packaging production line has a fault, the electrical component having the fault is analyzed, and targeted regulation and control measures are taken, so that efficient fault identification and regulation and control in various special environments are ensured.

The invention discloses a slurry pump control method and related equipment, and relates to the technical field of seawater desalination, and the method comprises the following steps: obtaining sensor data of a slurry pump; data analysis is conducted on the sensor data, and the working condition abnormal index of the slurry pump is determined; determining a target control instruction corresponding to the working condition abnormal index based on the working condition abnormal index and a preset threshold interval; and based on the target control instruction, the slurry pump is controlled to execute corresponding target regulation and control operation. Real-time monitoring and intelligent judgment of the operation state of the slurry pump are achieved, early warning can be triggered at the initial stage of equipment abnormity, the operation parameters can be adjusted in a self-adaptive mode, the response delay problem of a traditional single control mode is effectively avoided, and the operation reliability and safety of the equipment are improved.

The invention relates to the technical field of data sharing, and discloses a multi-department data sharing method and device, equipment and a storage medium. The method comprises the following steps: carrying out acquisition and standardized preprocessing on multi-department data required by enterprise project application to generate an intermediate data packet; establishing an event trigger distributed data control mechanism based on the intermediate data packet, calculating a dynamic trigger threshold, monitoring data change, and generating a target control data stream; performing template-free adaptive learning to generate an optimal data processing flow definition file; executing data classification, self-adaptive compression and increment processing at the edge computing node to generate an optimized data packet and metadata thereof; and inputting the optimized data packet and the metadata thereof into a SaaS cloud server central data warehouse to generate a project declaration data packet, thereby realizing consistency coordination and conflict automatic resolution of data of multiple departments, and realizing unified access and standardized processing of heterogeneous data sources.

The invention provides a method, a device and a system for switching master and standby control centers of a smart home. In the scheme, real-time operation state data of each control center in the smart home and the number of times of downtime in a first preset time period are acquired, and based on the real-time operation state data and the number of times of downtime, a long-term short-term memory network model is used to score each control center to obtain a final score of each control center; sorting all the final scores from large to small, and determining the control centers corresponding to the final scores ranked in the front preset number as candidate control centers; an improved PBFT protocol is adopted to carry out consensus election on the candidate control hub, a target control hub is elected, the main control hub is switched to the target control hub for operation, and the target control hub meets at least one of the following preset conditions: the highest network communication efficiency, the shortest network delay and the lowest power consumption. The problem that the switching efficiency of the main and standby control centers in the smart home system is low is solved.

The invention relates to an audio data processing method and device, electronic equipment and a vehicle, and is applied to the technical field of computers. The audio data processing method comprises the following steps: acquiring target voice data of a user; obtaining at least one grammar element corresponding to the target voice data, and determining a grammar structure integrity score corresponding to the target voice data based on the at least one grammar element; determining an environment interference score corresponding to the target voice data; determining a confidence score corresponding to the target voice data based on the grammatical structure integrity score and the environmental interference score; and under the condition that the confidence score is greater than or equal to the target confidence threshold, determining to execute a target control instruction corresponding to the target voice data, thereby determining whether the target voice data is an instruction for vehicle control by combining the grammatical structure integrity of the voice data and the environmental interference degree. The accuracy of vehicle control and the interaction experience of the user and the vehicle are improved.

The invention relates to the technical field of industrial production intelligent process control, in particular to an AI-based electrolytic manganese process production quality intelligent prediction method and system, and the system comprises a drift calibration module, a structure matching module, a process parameter inversion module, a path correction module and a deviation verification module. According to the method, dynamic compensation of deviation between input parameter drift and output parameters is achieved through the response relation between input and output of all links, the actual influence degree of fluctuation of all the parameters on the target control parameters and product quality is effectively recognized according to correlation matching of the periodic change of the production process and the fluctuation frequency of the target control parameters, and the accuracy of the product quality is improved. Potential interference of key process parameters on stability of each process is locked in time, diffusion starting time and space of impurities are clearly traced through path judgment of residual liquid concentration trend and raw material adding position, and migration path parameters are further adjusted based on ion retention behaviors of boundary detection points.

The invention discloses a semantic parsing and mapping method for cross-platform touch instructions of a same-screen device, and the method comprises the steps: S1, obtaining a touch instruction of a source device through the same-screen device, the touch instruction comprising a touch position, a touch gesture type and touch time sequence information; s2, based on an operating system type and an application scene of a target device, semantic analysis is conducted on the touch instruction, an intermediate semantic layer instruction is generated, and the intermediate semantic layer instruction comprises operation logic, an interaction intention and a target control identifier; s3, dynamically mapping the intermediate semantic layer instruction into a native instruction of the target device according to a platform protocol and an interaction rule of the target device, the native instruction being adapted to a touch event processing mechanism of the target device; and S4, sending the native instruction to the target equipment for execution, and synchronously updating the display interface of the same-screen device and the display content of the target equipment.

The invention discloses a data collection method and device, computer equipment, a storage medium and a program product. The method comprises the steps that a control handle in a current interface of a target application program is acquired; the control handle is at least used for determining a control type; wherein the control of which the control type belongs to the target type in the current interface is the target control; the target type comprises a list, a button and an input box; the target control in the current interface comprises at least one of a list control, a button control and an input box control; inputting the feature data of the target control into an acquisition strategy generation model to generate a data acquisition strategy corresponding to the control type of the target control; the feature data at least comprises a control type; and monitoring the target control in real time based on the data acquisition strategy so as to capture the interactive behavior data of the target control when monitoring that the state is changed.

The invention discloses a robot gait self-adaptive adjustment method and system combined with topographic feature recognition, and relates to the technical field of robot gaits, and the method comprises the steps: collecting a topographic image in front of a robot advancing path, carrying out the deblurring processing, and extracting ground feature data through a lightweight neural network; matching the ground characteristic data with a preset gait control parameter database to obtain a gait control parameter set, and generating a control signal through a proportional-differential control method according to the gait control parameter set; correcting the control signal based on a reinforcement learning algorithm, and generating a target control instruction for driving a joint actuator; in the process of executing the target control instruction, collecting state feedback data in real time, and comparing and optimizing the feedback data with the currently executed target control instruction to realize feedback adjustment; the problems of insufficient terrain recognition precision caused by image blurring, response lag caused by sensing and control decoupling and lack of adaptive ability are effectively solved.

The invention discloses a multi-unmanned aerial vehicle and unmanned ship formation control method and device based on a graph convolutional network and deep reinforcement learning. The method comprises the following steps: constructing a first simulation environment of multi-agent formation; collecting motion control data of the unmanned ship according to the first simulation environment, and constructing a depth control model based on the motion control data and a sequence generation model; constructing a graph convolutional network model, and obtaining compression features according to the graph convolutional network model and the multiple agents; constructing an executor network and an evaluator network based on an independent near-end strategy optimization algorithm; obtaining an initial control model according to the graph convolutional network model, the compression features, the executor network and the evaluator network; and training and testing the initial control model in a first simulation environment according to the depth control model to obtain a target control model. The method can improve the sensing data processing efficiency and the real-time decision response performance, and can be widely applied to the technical field of multi-agent control.

The invention discloses an own task configuration method based on user demonstration data and a related device. The method comprises the following steps: sending a busy / idle query request to a server in response to a first operation of a user; a busy / idle query reply message from the server is received, a task configuration interface is displayed, and the task configuration interface comprises a plurality of task selection controls and a plurality of human-shaped robot selection controls; sending a task configuration message to the server in response to a second operation of the user, wherein the task configuration message carries the target humanoid robot and the target task; a training result message from the server is received, a training result interface is displayed, the training result interface is used for displaying the training score of the target humanoid robot executing the target control scheme, the target control scheme is the content generated by the server according to the user demonstration data, and the user demonstration data is used for representing the action executed by the user for the target task. According to the invention, remote training of the robot can be realized, and the diversity and flexibility of task execution of the humanoid robot are improved.

The invention relates to the field of environment monitoring and ecological restoration, in particular to a farmland and river ecological collaborative monitoring and regulation system and method. The system comprises a first-stage barrage, a second-stage barrage, a gabion revetment, front-end acquisition equipment, execution equipment and a server, wherein the first-stage barrage and the second-stage barrage are arranged at intervals; the execution equipment comprises irrigation equipment, a first gate, a second gate and an ecological flocculant putting device; the server comprises a data acquisition module, a data storage module, an analysis processing module and a control execution module, inputs control parameters including a first gate opening degree, a second gate opening degree, an irrigation equipment electric valve opening degree and an ecological flocculant putting amount through an LSTM model, and predicts state variables including an upstream water level, a downstream flow, an irrigation water supply amount, a total phosphorus concentration and a total nitrogen concentration; and a multi-target optimization model including agricultural water demand, ecological flow, flood control safety and water quality purification targets is solved in combination with an optimization NSGA-II algorithm, target control parameters are acquired, and a control instruction is generated to drive execution equipment.

The invention discloses an electromechanical equipment distributed control system based on the Internet of Things, which belongs to the technical field of distributed control, and specifically comprises the following steps: splitting a control task into task units, registration dependencies, data entries and task levels at an edge gateway, and generating a candidate scheduling range according to registration items; a calibration heartbeat with a reference segment is issued, and a driving node executes and returns a measurement record in an idle window for calibrating hardware fingerprints and link overhead and dynamically updating matching parameters; when computing power saturation is triggered, splitting a low-level task unit into suspensible fragments, setting a de-duplication execution identifier, opening a state interface, and completing migration check; selecting a target control node and a transmission path according to a calibration result, issuing fragment and state interface information, driving a target node to continue fragments according to an execution template, and guiding a source node to release occupied resources in sequence; and checking and comparing the execution result, and when the rule is triggered, recovering to the previous stable state according to the mapping rollback table.

The invention relates to a vehicle control method and device, computer equipment and a storage medium. The method comprises the steps of obtaining target crosswind information of crosswind acting on a vehicle according to driving information of the vehicle, weather information of a scene where the vehicle is located and scene image information; determining a target yaw velocity of the vehicle according to the vehicle state information of the vehicle; determining a first angular velocity difference value between the target yaw velocity and an actual yaw velocity obtained through a sensor of the vehicle, and determining driving condition information of the vehicle according to the first angular velocity difference value; and target control information of the vehicle is obtained by using the first angular velocity difference value and / or the target crosswind information according to the driving working condition information of the vehicle. By adopting the method, the driving stability of the vehicle in the crosswind environment can be improved.

The invention discloses a self-adaptive algorithm optimization control system and method for intelligent energy management, and relates to the technical field of energy management, and the method comprises the steps: determining the load change sensitivity according to a load change prediction rate; grouping the devices according to the load change sensitivity and the current load state to obtain a plurality of device load groups; mapping the load change prediction rate and the multi-dimensional dynamic threshold matrix to obtain a target control strategy; determining an initial control parameter according to the target control strategy; performing difference analysis on the operation parameter and the initial control parameter to obtain a control parameter difference value; adjusting the target control strategy according to the control parameter difference value so as to update the initial control parameter; the updated initial control parameters are input into an execution control unit; the adjusted load state of the building equipment is fed back in real time; according to the invention, accurate real-time prediction and control of building equipment load change are realized, and the stability and response efficiency of intelligent building energy management are effectively improved.

The invention provides a robot control method and system based on a visual image, and belongs to the technical field of visual images, and the method comprises the steps: determining a guide wire moving path based on a first contrast image; and determining control data of the interventional robot based on the guide wire moving path and the robot interventional control relation model. Guide wire information is determined based on the second contrast image, and a first resistance is determined based on the guide wire information. A target resistance is determined based on the first resistance and the second resistance. The second resistance is data acquired by a force sensor arranged at the joint of the guide wire and the interventional robot. And comparing the target resistance with a resistance threshold to obtain a first comparison result, and updating control data of the interventional robot based on the first comparison result and the guide wire information to obtain target control data. And controlling the interventional robot based on the target control data. The control accuracy of the interventional robot can be improved.

The invention discloses a board card testing method, electronic equipment and a storage medium, and relates to the technical field of servers, and the method comprises the steps: obtaining parameter information of a to-be-tested board card, and calculating a matching degree value of an interface of the to-be-tested board card and a board card interface in a first preset rule base; the characteristics of the to-be-tested board card are accurately identified, the target control script of the board card test fixture suitable for the to-be-tested board card is determined, it is ensured that the matched script is used when the board card test fixture is connected with the to-be-tested board card, and the damage risk caused by version mismatching is avoided to a certain extent; meanwhile, the board card testing jig determines the target testing task according to the parameter information of the board card to be tested and executes testing, and automation and precision of the testing process are achieved. Therefore, the problems that the test accuracy is reduced and the security risk is increased due to the fact that the version change information is not transmitted in time or inaccurately are effectively solved, the dependence on manual checking of engineers is reduced, the working efficiency is improved, and the test accuracy and security are remarkably improved.

The invention relates to the technical field of software testing, and discloses an automatic test script generation method and system, a storage medium and electronic device.The method comprises the steps that test case information and tested environment information are received, and structured case representation is generated; enhancing the structured use case representation based on a preset knowledge base; generating a plurality of positioning candidates for the target control in the testing step, and establishing a priority relationship among the positioning candidates; generating an executable automatic test script; running the script, and when execution failure is detected, automatically adjusting a positioning strategy, a waiting strategy or a business process based on a failure type, and executing again; and feeding back the successfully executed script information to the knowledge base, and updating the content and priority of the knowledge base. Through organic combination of knowledge base enhancement, multi-candidate positioning and a self-healing mechanism, the technical problems that an existing automatic test script is high in maintenance cost, poor in stability and lack of domain knowledge are solved.

The invention discloses a prototype verification method of a DDR4 controller in the technical field of chip verification, the method is applied to a prototype verification platform, a DDR controller and a PHY module in a chip to be verified are matched on the prototype verification platform, and the PHY module integrates a DFI to phy only interface module and a standalone PHY only block module through a python script; the prototype verification method comprises the steps that in response to a verification instruction received by a DDR controller, the DDR controller generates a control instruction according to the verification instruction and sends the control instruction to a DFI to phy only interface module; in response to the control instruction received by the DFI to phy only interface module, the DFI to phy only interface module converts the control instruction from a DFI protocol to a target control instruction of a PHY protocol, and sends the target control instruction to the standalone PHY only block module; in response to the control instruction received by the standalone PHY only block module, the standalone PHY only block module sends a target control instruction to a DDR particle externally connected with the prototype verification platform, so that read-write and verification with the DDR particle are realized; according to the method, the DDR memory interface IP prototype verification code coverage rate can be improved.

The embodiment of the invention provides a virtual power plant intelligent scheduling method and system based on digital twinning, a storage medium, electronic equipment and a computer program product, and belongs to the technical field of digital power grids. The method comprises the following steps: establishing a corresponding action response training data set based on a digital twin proxy matrix constructed by power grid nodes corresponding to distributed power equipment participating in scheduling; generating constraint feedback information reflecting the feasibility of the current target control action based on a power grid operation constraint response model; taking the constraint feedback information as coupling information of a pre-trained scheduling strategy model, and executing scheduling strategy training based on the current target control action to obtain a scheduling strategy model with constraint perception capability; and generating a corresponding scheduling control action based on the scheduling strategy model. According to the scheme, the perception capability and the response capability of the scheduling strategy to the power grid operation constraint are improved, so that the distributed resource scheduling efficiency and the decision intelligent level are dynamically optimized on the premise of meeting the power grid stability.

The invention relates to the technical field of bidirectional power flow control, and discloses an AC / DC charging pile bidirectional power flow control method, device and equipment, and the method comprises the steps: carrying out the detection and parameter collection of an AC / DC charging pile, a power converter and a grid connection point, and obtaining a charging pile system operation parameter set; trapezoidal voltage edge calculation is carried out on the switching process of the power converter, and the dead time compensation amount is superposed on a PWM control signal of the power converter to obtain a target PWM control signal; calculating to obtain an optimal PWM control sequence, and taking the first control quantity of the optimal PWM control sequence as the current control output; when it is detected that the direction of the power flow needs to be changed, the direct-current bus voltage of the power converter is controlled to be stabilized at a set value and then switched to the target working mode, and a new power output control quantity is obtained, the robustness of the alternating-current and direct-current charging pile is enhanced, the alternating-current and direct-current charging pile can adapt to the operation requirements under different working conditions, and the reliability of the charging pile is improved. And the fault rate of the AC / DC charging pile is reduced.