2640 results about "Time control" patented technology

The invention discloses a real-time computing system resource coordination and decision engine system based on a large language model. According to the system, a large language model is innovatively used as a central strategic decision engine, and a'decision-coordination-execution 'three-layer architecture is constructed. According to the system, macroscopic strategy generation and microscopic real-time control are decoupled by introducing a hierarchical decision-making mechanism (a strategic layer, a tactical layer and an execution layer), so that the core contradiction between LLM high reasoning delay and the microsecond / millisecond-level real-time requirement of the system is effectively solved, and the method is suitable for local computing equipment and a cloud data center. The system comprises a predictive strategy preloading system, and transient response can be achieved. Meanwhile, the system adopts an asynchronous event-driven decision-making mechanism for continuous intelligent optimization. According to the method, the top-down, semantic understanding-based and global collaborative intelligent management of the computing resources is realized, and the resource utilization efficiency, the system automation degree and the overall energy efficiency in a complex and dynamic computing environment are remarkably improved.

The invention discloses a modular reconfigurable production line control system integration method, which relates to the technical field of industrial automation, and comprises the following steps: establishing virtual mapping based on physical attribute parameters to form a production line digital twin basic model framework; collecting data in real time based on an on-site sensor, and establishing a digital twinborn dynamic mapping mechanism synchronous with a physical production line state; a reconstruction scheme is imported into a virtual environment, and key performance indexes are analyzed through a production line digital twin model rehearsal module combination process. A virtual production line model is constructed through a digital twin technology, a rehearsal and verification reconstruction scheme in a virtual environment is supported, trial and error time and cost required by traditional physical debugging are remarkably reduced, a reconstruction strategy is further optimized through a cloud AI algorithm, closed-loop optimization from virtual verification to physical execution is achieved in combination with edge end real-time control, and the real-time performance of the virtual production line is improved. The production line can quickly complete local or overall reconstruction according to production requirements, and the response speed and flexibility of the production line are greatly improved.

The invention discloses an electric arc dynamic self-adaptive control method and sensing system based on multi-modal sensing, and relates to the technical field of electric arc control. A multi-modal sensor integrated on a welding gun is used for synchronously collecting an electric arc form image, a molten pool area image, a molten pool oscillation sound signal and current and voltage multi-dimensional data; the collected data are preprocessed, and real-time parameters in the welding process are extracted; calculating and outputting an optimal welding control parameter in real time by combining a depth Q model and taking the welding quality as an optimization target; the control parameters are transmitted to a welding actuator, real-time adjustment of the welding process is completed, and real-time control over the welding arc is achieved; the control method can be used for enhancing the real-time self-adaption of a welding control system to complex welding components and the flexible manufacturing capacity of replacing a product line; the technical problems that in an existing automatic electric arc welding control method, electric arc welding information collection is single, control response is delayed, the flexible degree is low, and welding feature extraction errors are large are solved.

The invention relates to the technical field of asphalt pavement paving, and discloses an asphalt pavement 3D intelligent paving cooperative control system and method based on BIM + GIS, and the system comprises a data fusion layer, a real-time control layer, a multi-machine cooperation and task distribution layer, and a quality closed-loop layer. The real-time control layer comprises multi-source data acquisition (positioning / temperature / compaction degree and the like) and three types of core decisions carried out by the cloud center, and the three types of core decisions carried out by the cloud center comprise feeding demand prediction (based on paving speed + thickness) of a dumper, thickness / speed adaptive adjustment of a paver and dynamic planning of a rolling path of a road roller. According to the method, real-time correction of transverse deviation and elevation deviation is achieved through combination of PID control and fuzzy control, the expansion amount of a hydraulic cylinder of the ironing plate is dynamically adjusted by associating the paving speed, the multi-source sensing technology of Beidou, IMU, laser ranging and infrared imaging is adopted, time sequence consistency data synchronization is achieved through 5G communication, and the real-time correction of the transverse deviation and the elevation deviation is achieved. And the precision and the closed-loop optimization effect can be controlled in real time.

The invention relates to the technical field of industrial control, and discloses an energy-saving air conditioner intelligent control system which comprises an edge computing node, a dynamic load prediction module, a self-adaptive control module, an energy consumption optimization evaluation module and a fault diagnosis module. A data acquisition module, a real-time control module and a communication interface are integrated; based on the historical environment data and the air conditioner operation log, a long-short-term memory network model is adopted to predict the expected load demand in 2-4 hours in the future; dynamically adjusting the operation number of chilled water units, the frequency conversion frequency of water pumps and the opening degree of fresh air valves according to the expected load requirement; the unit cold energy consumption of a single air conditioner is calculated in real time, and a global optimization instruction is generated in combination with a time-of-use electricity price strategy; motor current harmonic characteristics are collected through the edge calculation nodes, the abnormity of the air conditioner is recognized, graded response is conducted, the intelligent control efficiency of the energy-saving air conditioner is improved, and the overall energy consumption cost of the air conditioner is reduced.

The invention relates to the technical field of mechanical arm control, in particular to a self-adaptive fuzzy fixed time control method based on an output-limited mechanical arm, and the method comprises the following steps: S1, constructing a mechanical arm dynamic model; s2, setting constraint conditions of an output state of the mechanical arm dynamic model; s3, defining a tracking error and a virtual control law of the mechanical arm dynamic model; s4, on the basis of output state limitation and fixed time control, an actual control law is designed to serve as input of the mechanical arm dynamic model, and an adaptive law is constructed; and S5, a three-level logarithmic obstacle Lyapunov function is constructed, the three-level logarithmic obstacle Lyapunov function is combined with the virtual control law, the actual control law and the self-adaptive law step by step and substituted into the virtual control law, the actual control law and the self-adaptive law, and then it is guaranteed that the output state of the mechanical arm dynamic model does not exceed the preset safety boundary according to the actual fixed time control lemma. According to the invention, high-precision trajectory tracking can be realized on the premise of ensuring safe operation of the mechanical arm.

The invention relates to a buoyancy tank wharf real-time assembly quality control method, and belongs to the technical field of port engineering automatic construction. According to the method, aiming at the problems of large geometric positioning deviation, uneven contact surface stress and difficulty in real-time control of attitude instability in buoyancy tank assembly, laser range finders are mounted at four corners of a buoyancy tank through a geometric positioning layer to monitor horizontal deviation, and pressure sensors are arranged on a butt joint surface through a mechanical sensing layer to generate a pre-tightening force curve; the attitude monitoring layer adopts a bottom inclination angle sensor to detect inclination; according to the horizontal deviation, the curve similarity deviation and the inclination angle deviation, the real-time management and control platform triggers a hydraulic adjusting mechanism, a torque wrench and a balance weight drive to conduct adjustment; meanwhile, data are stored through a buoyancy tank number, a timestamp and a sensor type three-dimensional index, and a rechecking instruction is generated based on the difference between the current adjusting quantity and the first three mean values. According to the invention, real-time control and dynamic adjustment of the assembly process are realized, and the modular assembly precision and safety of the floating wharf are improved.

The invention relates to the field of waterproof sockets, and discloses a multi-scene adaptive intelligent waterproof socket remote regulation and control system and method, and the method comprises the steps: obtaining multi-source sensing data of a socket deployment environment, and constructing a multi-scene adaptive input data set in combination with a parameter fusion strategy and a signal interference compensation mechanism; environment feature recognition is carried out on the multi-scene adaptive input data set, and typical application modes in different scenes are extracted by using a socket state tag extraction algorithm; based on the state mapping relation model, a remote regulation and control strategy optimization algorithm is adopted to dynamically analyze the operation state of the socket, and the matching degree of the current operation mode and the environment scene is identified; an intelligent judgment mechanism is introduced, pre-execution verification is carried out on the multi-scene regulation and control suggestion set, and potential control conflicts are recognized in combination with historical operation data and real-time control instructions; and performing instruction issuing management on the remote control behavior of the socket based on the optimized execution strategy. The invention has the advantage of improving the universality of the intelligent waterproof socket.

The invention discloses a fault simulation method and equipment in a complex flight scene, belongs to the technical field of flight fault simulation, and aims to improve the fault-tolerant capability of a system and realize full-coverage simulation of the complex flight scene through state injection and fault simulation. The problems of insufficient fault scene coverage, real-time control and data reproducibility in the prior art can be solved; moreover, the method can inject the fault states of a plurality of devices in parallel in the same simulation process, and compared with a traditional method which only supports a single-device single-fault simulation mode, the method has the advantages that the capability of restoring a composite fault scene is greatly enhanced, and the method is suitable for limiting condition verification of a high-reliability aircraft system.

The invention relates to the technical field of automatic laying of composite materials, in particular to an automatic compensation method, system and equipment for errors between silk laying belts and a storage medium. The method for compensating the error between the automatic silk laying belts comprises the following steps: establishing a mathematical mapping relation between the error compensation amount between the belts and robot control parameters through a standard sample piece laying test, and forming an error compensation theoretical model; the method comprises the following steps: aiming at a robot type fiber placement machine structure, constructing an inter-band error model based on pose kinematics modeling, robot and additional axis multi-axis interpolation modeling and error vector decomposition; the tail end target position, the attitude angle and the error compensation amount in the laying program are read in real time, and real-time control input is obtained; and on the basis of the error model, the corrected tail end position is inversely solved into a real-time adjustment angle of each joint, a robot inverse solution algorithm is corrected, and a joint control instruction is generated. According to the automatic tow belt laying device, the laying position precision of the automatic tow belt laying can be remarkably improved.

The invention discloses a double-capacity water tank control method based on multi-target reward Q learning, and belongs to the technical field of machine learning and automatic control. The method comprises the following steps: firstly, establishing a discretization mathematical model of a double-capacity water tank liquid level control system, and designing a multi-target reward function; secondly, empirical data are stored only when the liquid level tracking error exceeds the limit or the control quantity suddenly changes, and sampling weights are distributed according to award absolute values; and finally, updating the Q table based on the time difference error, and performing closed-loop real-time control on the double-tank water tank according to the dynamically adjusted PID parameter. According to the method, a five-dimensional reward function is designed, and multi-index dynamic balance is realized through linear weighting; the empirical tuple is stored only when the liquid level tracking error exceeds the limit, and the quick response requirement of the dynamic time-varying scene of the double-capacity water tank is met; dimensionality reduction is performed on a continuous state space by adopting a non-uniform grading strategy, so that the calculation complexity is remarkably reduced; a perception-decision-execution integrated framework is constructed, and the dependence of a traditional control method on a mathematical model of the water tank is abandoned.

The invention discloses an engineering equipment operation intelligent monitoring and automatic adjusting system and method, and the method comprises the steps: collecting the multi-dimensional operation data of equipment in real time, constructing a three-dimensional operation state evaluation model through an intelligent algorithm, accurately recognizing a stable operation region, and generating a dynamic control reference and a feedback adjustment loop; a control channel decoupling and selective reconstruction technology is adopted, a beneficial synergistic effect is reserved while harmful coupling is eliminated, potential risk points such as resonance are identified through spectral analysis, and a safe operation boundary for perfecting a is established; the analysis equipment responds to dead zone characteristics, the dead zone characteristics are converted into an energy-saving working zone through energy analysis, an optimized power distribution strategy is made, and a multi-stage emergency reserve system is established; a fault early warning mechanism based on vibration characteristics is established, and rapid detection and power compensation of an abnormal state are realized; through equipment response matrix construction and time delay compensation analysis, an accurate real-time control instruction is generated, and the operation efficiency, the stability and the energy-saving level of engineering equipment are comprehensively improved.

The invention discloses a modularized extensible Internet of Things multi-scene dynamic regulation and control system and a regulation and control method, and belongs to the field of intelligent regulation and control. The invention discloses a modularized extensible Internet of Things multi-scene dynamic regulation and control system and method. The system comprises a data acquisition unit, a cloud analysis unit and a regulation and control execution unit. According to the invention, the problem of single scene in the prior art is solved, the efficiency and reliability of data transmission are ensured by collecting multi-scene data of the Internet of Things in real time and automatically selecting the optimal communication protocol and link according to the network condition, cloud analysis is combined, and according to the preset scene feature model, the data is subjected to multi-scene data transmission. According to the invention, the scene type can be automatically identified and the corresponding regulation and control rule can be called, the accurate and real-time regulation and control of the Internet of Things equipment can be realized according to the regulation and control rule, the operation efficiency of the equipment and the overall performance of the system can be effectively improved, the regulation and control process can be evaluated and analyzed through feedback information, the regulation and control rule can be automatically adjusted, and a continuously optimized closed-loop control system can be formed.

The invention discloses a packaging workshop production line intelligent management system and method based on big data, and relates to the technical field of packaging workshop production management, and the system comprises an Internet of Things data collection module, a big data analysis module, a dynamic production scheduling module, a real-time control module, and a man-machine interaction module. Multi-dimensional data of equipment states, order demands and material inventory are captured in real time through the Internet of Things data acquisition module, and deep analysis is performed through the big data analysis module, so that the dynamic production scheduling module can respond to changes of production elements in real time, and equipment failure rate prediction and order delivery cycle prediction results are fully fused; a production scheduling scheme considering efficiency, cost and delivery reliability is accurately generated, the matching relation between orders and equipment is dynamically adjusted through real-time data, idle and overload operation of the equipment are avoided, optimal configuration of materials, the equipment and human resources is achieved, and the adaptability of a production system to complex and changeable production environments is greatly improved.

The invention discloses an intelligent bonding copper wire equipment control method and system based on the Internet of Things, and relates to the technical field of semiconductor manufacturing. An intelligent bonding copper wire equipment control system based on the Internet of Things comprises copper wire bonding equipment, an edge computing gateway, a cloud platform and a communication module. According to the invention, a three-level cooperative intelligent control system combining real-time control of an equipment end, intelligent prediction of an edge layer and dynamic adjustment of parameters, and long-term optimization of a cloud end and remote intervention of experts is innovatively constructed, and a hierarchical uploading and distributed cooperative control strategy of real-time data and historical data is combined; data integrity, real-time performance, intelligence and system expandability are effectively considered, and an intelligent copper wire bonding control overall solution for complex scenes is formed.

In an aspect, systems and methods of connected nodes, including electronic control units (ECUs) in a vehicle, integrate remote direct memory access (RDMA) capabilities with time-sensitive networking (TSN) traffic shaper configurations in a manner that guarantees lossless, bounded-latency critical traffic (CT) streams, while also allowing for different classes of traffic to flow through the connected nodes. In another aspect, mechanisms are introduced to enable an automotive software framework (e.g., classic AUTOSAR) to support RDMA communications.

The invention relates to the technical field of computer network data acquisition and processing, in particular to an intelligent news crawling system and method based on SpiderFlow, and the system comprises a dynamic time control module, an intelligent duplicate removal module, a paging processing module, a fault-tolerant data extraction module and a dynamic parameter configuration module. The dynamic time control module generates a time variable through a date calculation engine to realize accurate limitation of a crawling time range; the intelligent deduplication module adopts a three-level deduplication mechanism to ensure data uniqueness; the paging processing module supports static parameter and dynamic AJAX loading dual-mode paging, and realizes full-amount acquisition in combination with a self-increasing page number iterator and an intelligent termination strategy; the fault-tolerant data extraction module is integrated with CSS / XPath double parsers, and data integrity is guaranteed through field missing detection and messy code transcoding. Through multi-module cooperation and an intelligent algorithm, crawling efficiency, data quality and system stability are remarkably improved, and challenges such as webpage structure change and anti-crawling limitation are effectively handled.

The invention relates to the technical field of data processing, and particularly provides a low-altitude aircraft power supply facility cooperative control method, system and device and a medium, and the method comprises the steps: predicting the renewable energy power generation power of a power supply facility and the charging load power of a low-altitude aircraft based on obtained multi-source information, and generating a prediction scene set representing uncertainty; based on the prediction scene set, through a multi-time scale optimization strategy, solving a scheduling model with optimal operation cost as a target, and successively obtaining a pre-scheduling plan of a day-ahead stage and a real-time control instruction of an intra-day stage; the multi-time scale optimization strategy comprises random optimization based on a scene and feedback optimization based on a rolling time domain; according to the real-time control instruction, power distribution is conducted on the hybrid energy storage system, and a power type energy storage unit preferentially responds to the transient power requirement for charging of the low-altitude aircraft. The comprehensive operation cost is effectively reduced, and particularly, the service life of equipment is prolonged through quantitative energy storage degradation.

The invention discloses a multi-mode communication switching method of a large unmanned aerial vehicle and a related device, and the method comprises the steps: setting an auxiliary communication link to be in a hot standby state after an airborne end of the large unmanned aerial vehicle is in communication connection with a ground end through a main communication link, and carrying out the real-time monitoring of a current communication link, and obtaining a real-time monitoring result; when the real-time monitoring result is that the current communication link is abnormal in communication, the airborne end switches communication connection from a main communication link to an auxiliary communication link in a hot backup state, and controls the main communication link to enter a diagnosis mode; and after the main communication link executes the diagnosis mode and returns to normal, controlling the main communication link to enter a hot standby state, and switching the communication connection from the auxiliary communication link to the main communication link. In the embodiment of the invention, the problems that a signal coverage blind area exists in a single link and the single link is susceptible to interference are solved, so that the real-time control requirement of a large unmanned aerial vehicle is met, and the reliability and the stability in a long-distance communication scene are improved.

The invention relates to the technical field of pipeline anomaly detection, in particular to a petroleum pipeline network anomaly recognition and early warning system which comprises a pressure fluctuation monitoring module, a flow anomaly analysis module, a state association optimization module, an early warning strategy adjustment module and an anomaly early warning generation module. According to the method, deep correlation analysis is carried out on the relation between pressure fluctuation and flow abnormity, the change trend of pipeline pressure and flow can be accurately captured, the abnormal state in pipe network operation can be recognized in time, the abnormity early warning structure is dynamically adjusted, the early warning strategy can be flexibly optimized along with the change of the working condition, and therefore the detection precision and the response speed are improved; the state can be mastered in real time in a complex pipe network environment, missing detection and false alarm phenomena caused by insufficient data interpretation or early warning strategy solidification are effectively reduced, the adaptability of the system to emergency situations is enhanced, and the problems of response lag and insufficient sensitivity caused by single data source under variable working conditions are avoided.

The invention relates to the technical field of power conversion, in particular to a collaborative optimization control method, system and equipment of a multi-mode power conversion device and a storage medium. The method comprises the following steps: constructing a double-layer collaborative architecture by collecting multi-source heterogeneous operation parameters, obtaining a device operation state, power grid dynamic characteristics and environment change information, establishing a cross-level communication protocol to realize data synchronization, and outputting a real-time working condition feature vector; deploying a dynamic mode switching engine based on a real-time working condition feature vector, triggering multi-mode operation strategy reconstruction through a state transition probability model, constructing a self-adaptive switching mechanism comprising a parallel power supply mode, an island operation mode and a hybrid modulation mode, and establishing a composite fault response system to realize pre-diagnosis and grading disposal of an abnormal state; millisecond-level real-time control is implemented in the edge control layer to generate a pulse width modulation driving signal, multi-target global optimization is implemented in the cloud optimization layer to dynamically adjust an operation parameter combination, and closed-loop control is formed through bidirectional data interaction and strategy feedback.

The invention discloses a rod pumped well time-sharing optimization design method and system based on edge calculation, and relates to the field of rod pumped well time-sharing optimization, and the method comprises the following steps: S1, constructing an edge sensing layer, and collecting data in real time; s2, edge layer data extraction and working condition diagnosis; s3, time-of-use electricity price response and working condition prediction; s4, generating an edge end real-time control strategy; s5, edge-cloud collaborative reinforcement learning optimization is carried out; and S6, performing dynamic evaluation and strategy correction. According to the time-sharing optimization design method and system for the rod-pumped well based on edge calculation, self-adaptive control over high-stroke-frequency yield increase in the valley period and low-stroke-frequency peak avoidance in the peak period can be achieved, and the phenomenon of empty pumping in the high electricity price period can be avoided; when the liquid supply is sufficient in the valley period, the stroke frequency can be increased, the liquid production capacity in the low-electricity-price period can be maximized, and the problem that timing control is disjointed with the actual working condition is fundamentally solved.

The invention provides a load distribution method, device and system for numerical reservoir simulation. The method comprises the steps that a grid file is obtained, the grid file is a file representing the geological space structure of a target area, the target area is one or more of a coal mine, an oil field, a geothermal field and a gas field, and the grid file comprises a plurality of grids; loads are distributed according to the types of grids in the grid file, the types of the grids comprise structured grids or non-structured grids, the loads are equipment used for reservoir numerical simulation, and the distribution mode comprises average distribution according to the number of the grids and / or average distribution according to the calculation time of the grids, the calculation time is time for load calculation of oil-water distribution of the target area during numerical reservoir simulation; and controlling all loads, and carrying out reservoir numerical simulation based on the distributed grid file. According to the method, the problem that in the prior art, load balancing cannot be achieved during load distribution in numerical reservoir simulation grid calculation is solved.

The invention discloses a multi-motor parallel control system and method based on an FPGA, and belongs to the technical field of multi-motor parallel control. The FPGA module is in communication connection with the control processing unit, and the FPGA module is internally provided with a global synchronous triggering unit used for generating a periodic synchronous triggering signal; the number of the position acquisition sub-modules corresponds to that of the motors, and each position acquisition sub-module comprises a position counter and a hardware latch register, wherein the position counter is used for carrying out counting and direction decoding on encoder pulses in real time; wherein the global synchronous triggering unit is connected with all the position acquisition sub-modules. According to the multi-motor synchronous control system, the position and other state signals of the multiple motors are collected at the same time through the parallel processing capacity of FPGA hardware, real-time control is achieved in combination with the control processing unit, and therefore the multi-motor synchronous control precision is improved, and the response speed is increased.

The invention discloses a multi-mode underwater acoustic communication system and communication method based on a heterogeneous dual-core architecture, and belongs to the technical field of underwater acoustic communication. The multi-mode underwater acoustic communication system comprises a main control module, an acquisition module and a transmitting module; the main control module adopts a dual-core heterogeneous processor OMAPL138 formed by an ARM core processor and a DSP core processor; inter-core communication is carried out between the ARM core processor and the DSP core processor through a shared memory and interruption; the ARM core processor is responsible for protocol analysis, task scheduling and man-machine interaction, and the DSP core processor focuses on signal processing and real-time control and integrates four communication systems, a channel evaluation module and the like. The problems that application of a single communication mode is limited and underwater acoustic communication links in different complex sea areas are unstable are effectively solved, and the deep sea area underwater vehicle information collaboration and return and deep sea area observation and monitoring network system capacity are effectively improved and guaranteed.

The invention discloses an intelligent backboard interaction method supporting terminal interconnection, a medium and an intelligent backboard. The intelligent backboard interaction method comprises the following steps: collecting user space motion data to generate a space-time trajectory sequence and binding a user identity label, segmenting a multi-user shooting action data stream in real time, extracting dynamic association features and combining sphere trajectory data to construct a multi-dimensional feature matrix; inputting the matrix into a distributed intention understanding engine, and fusing voice, a touch event and historical data to generate an interactive decision instruction; a dynamic content generation module is driven according to the instruction, a training difficulty coefficient and a tactical matching degree are calculated in real time, and a multi-terminal cooperative control signal is output; and aggregating multi-user and sphere state feedback data, and continuously adjusting a collaborative strategy of a shooting action evaluation standard, a multi-user confrontation rule and terminal display content. According to the method, accurate distinguishing and collaborative intention understanding of multi-user actions are realized, and the training individuation level and the self-adaptive real-time control capability of multi-user interaction are effectively improved.

The invention discloses a method for optimizing the AGC load regulation rate of a power station, and the method comprises the steps: building a digital twinborn body of a unit, building a boiler thermal positive and negative balance model, a fuel heat value prediction model and a self-adaptive control parameter library based on the historical operation data of a target unit, and receiving and processing the operation data of the unit in real time; dynamically predicting a boiler theoretical calorific value demand corresponding to the AGC instruction by using a long-short-term memory recurrent neural network, and calculating a deviation between an actual calorific value and a theoretical calorific value; when the deviation exceeds a preset threshold value, multi-mode cooperative compensation control of feed-forward compensation, parameter self-tuning and energy closed-loop calibration is implemented in parallel; the AGC instruction response time is controlled within 50-55 seconds, and model self-updating is achieved through online tracking. According to the method, the defects that traditional thermal power station fuel calorific value calibration is slow in response, a single control model is difficult to deal with complex problems, AGC response time is long and the like are overcome, the power station AGC load regulation rate and thermal power generating unit frequency modulation performance are remarkably improved, and power grid frequency stability is guaranteed.

The invention discloses a power distribution network cluster local control method and device, equipment and a medium. The method adopts a cloud edge end three-level collaborative architecture, and comprises the following steps: acquiring electrical parameters through a terminal equipment layer; constructing a graph model based on an electrical distance at the edge calculation layer; performing initial cluster division and issuing on the cloud computing layer; dynamically optimizing cluster division in an edge calculation layer through a lightweight deep reinforcement learning model; local control strategies such as voltage reactive power cooperative control, frequency active power balance control and fault rapid isolation and self-healing are executed based on the final division. The system comprises a terminal device layer, an edge computing layer and a cloud computing layer. According to the method, decoupling of global optimization and local real-time control is realized through cloud-side cooperation, the problems of high data processing pressure, high communication delay, expanded local fault influence range and the like of centralized control are effectively solved, and the real-time performance, reliability and disaster resistance of the power distribution network are remarkably improved.

A power converter having an on-time compensation mechanism is provided. The power converter includes a first switch, a second switch, a third switch, a fourth switch and a control circuit. A first terminal of the first switch is coupled with an input voltage. A first terminal of the second switch is connected to a second terminal of the first switch and a first terminal of an inductor. A first terminal of the fourth switch is connected to a second terminal of the third switch and a second terminal of the inductor. The control circuit, according to an on-time of the fourth switch operating in a boost mode, compensates on-times of the first and the second switches operating in a buck mode. In a buck-boost mode, the control circuit controls the on-times of the first and the second switches to be equal to the compensated on-times, respectively.

The invention provides a system for controlling a manipulator in real time through hand electromyographic signals, which comprises a data acquisition unit, a terminal equipment unit and a tail end execution unit which are in communication connection through serial ports. The data acquisition unit comprises a data acquisition device and a signal acquisition module, and is configured to acquire a multi-channel electromyographic signal of a hand of a user in real time and perform signal conditioning and digitization so as to obtain a digital signal sequence representing a muscle activity intention of the user; the terminal equipment unit comprises a data preprocessing module, a model training module, a gesture recognition module and an instruction generation module; the tail end execution unit comprises a manipulator control unit which is in communication connection with the control output module and is configured to be used for receiving the manipulator control instruction and driving a manipulator to execute corresponding actions; in combination with a control method, the problems of insufficient real-time performance, poor hand action recognition precision and robustness, unstable control instruction, poor user adaptability and the like in the prior art are solved, and the man-machine interaction performance is improved.