52244 results about "Control cell" patented technology

The invention provides an intelligent power distribution load prediction and adaptive scheduling method, and aims to improve the response capability and operation efficiency of a power distribution system to load fluctuation. According to the method, historical load data, real-time meteorological information and user power consumption behavior data of a target power distribution area are collected, a multi-dimensional input data set is established, a short-term load prediction model is constructed based on the data set, and prediction of future load levels of multiple feeder lines and key nodes is achieved. According to a prediction result, potential voltage deviation, load imbalance and equipment overload risks are identified, and a dynamic scheduling control strategy is generated by adopting a rolling horizon optimization method in combination with a power grid topological structure and operation constraints; the strategy comprises control measures such as transformer tap position adjustment and capacitor bank switching; through issuing a scheduling strategy to an equipment control unit, active adjustment is completed before load change, voltage stabilization, load balancing and energy optimization are realized, and intelligent and adaptive operation guarantee is provided for a power distribution system.

An electric motor based system includes an electric motor; an electric motor power controller for controlling the electric motor; a first control unit, adapted to provide first control signals for the electric motor power controller; an electric power storage device adapted to power the electric motor power controller; a first sensor adapted to determine information related to power exchange between the electric power storage device and the electric motor power controller; and a second control unit, wherein the second control unit is adapted to pass the first control signals, received from the first control unit, to the electric motor power controller in a first mode and adapted to provide second control signals for the electric motor power controller in a second mode; and the second control unit inputs and exploits the information.

The invention provides an unmanned aerial vehicle multi-system navigation decoy system based on intelligent algorithm optimization, and relates to the technical field of unmanned aerial vehicle processing, a sensing module comprises a cognitive radar, a photoelectric / infrared sensor, a magnetic and acoustic sensor and an electromagnetic environment scanning device, and a digital twinning and countermeasure AI module comprises a digital twinning model module and a countermeasure AI engine; the cross-domain multi-source cheating module comprises a multi-system navigation signal synthesis module and a sensor-level cheating module; the reconfigurable intelligent antenna array comprises a metamaterial unit, a real-time beam controller and a biological heuristic intelligent algorithm module, and the system security hiding module comprises an encryption access control unit, a micro-power frequency expansion and hopping unit and a redundant backup and disaster recovery unit. The response time of the system from detection to interference can be remarkably shortened by performing high-fidelity simulation on the flight control logic of the unmanned aerial vehicle and the actual electromagnetic environment in the digital twin environment and combining the hybrid decoy strategy generated by the adversarial AI.

The invention provides an intelligent regulation and control method for a three-stage constructed wetland recirculating aquaculture system, and belongs to the technical field of machine learning. The method comprises the following steps: firstly, continuously collecting water quality data and operation control data of each control unit, and constructing a multi-source heterogeneous data set under a unified time scale; then, constructing a pollution evolution trend prediction model, and capturing a dynamic evolution trend of water quality along with time and control behavior changes; then, under the guidance of a prediction result, analyzing the similarity of historical states and the sensitivity of regulation and control response, automatically identifying key control parameters which influence the water quality change of the system at present, and reasoning the dynamic adjustable boundary of the key control parameters; and finally, constructing a reinforcement learning strategy network fusing state prediction, a parameter boundary and a control target, realizing multi-target tradeoff among pollutant removal efficiency, a water quality standard-reaching rate and operation energy consumption, and outputting an efficient and steady control strategy through continuous interactive training. According to the invention, efficient, accurate and robust operation of the wetland system can be realized.

The invention relates to the technical field of the Internet of Things, and discloses a machine room equipment environment monitoring method and system based on the Internet of Things, and the method comprises the steps: constructing an edge calculation hardware system of the Internet of Things to obtain an integrated terminal which integrates a communication unit, an acquisition unit and a control unit; performing multi-channel parallel acquisition and edge calculation analysis on equipment and environment parameters in the machine room to obtain a standard data tuple and an analysis data packet; executing local hierarchical storage and logic control to obtain a control instruction and storage data; executing three-layer network communication interaction on the storage data to obtain a remotely accessible monitoring view; according to the method, intelligent alarm and system self-maintenance are achieved, graded and classified alarm information and a system state report are obtained, the data transmission amount is greatly reduced, meanwhile, the system response delay is reduced, the control decision accuracy is improved, and the misoperation risk is reduced.

The invention relates to the technical field of intelligent detection in the electronic manufacturing industry, in particular to a PCB warping degree intelligent evaluation system integrating visual positioning and multi-modal sensing, which comprises a multi-modal sensing unit, a visual positioning unit, an intelligent evaluation engine and a closed-loop control unit, the multi-modal sensing unit integrates laser displacement, infrared thermal imaging and strain sensors to acquire three-dimensional deformation, temperature and stress data; the visual positioning unit realizes sub-pixel-level positioning by using a high-resolution industrial camera and a feature point matching algorithm, and compensates vibration errors; the intelligent evaluation engine fuses data based on a time-space synchronization protocol, predicts a thermal deformation trend through an improved multi-modal convolutional neural network, and dynamically adjusts a qualified threshold value; and the closed-loop control unit executes sorting and rechecking according to an evaluation result, and optimizes warping and leveling parameters. According to the system, multi-dimensional accurate detection and intelligent control are realized, the PCB warping degree detection accuracy is effectively improved, the process can be dynamically optimized according to the production working condition, and the equipment fault risk is reduced.

The invention discloses an optical storage direct-flexible system power scheduling method based on market model prediction response, which comprises the following steps: determining the composition of an optical storage direct-flexible system, and collecting operation state data and external environment data; constructing a hybrid prediction model, and predicting the market price and the uncertainty interval of the market price in the scheduling period; establishing a power scheduling optimization model, and solving to obtain an optimal power scheduling strategy; according to the optimal power scheduling strategy, a control instruction is issued to each control unit of the optical storage direct flexible system, and cooperative adjustment is carried out through a bidirectional coupling feedback mechanism of voltage and power; the market price and the system operation state are monitored in real time, and when the market price deviation exceeds a price deviation threshold value, scheduling strategy online correction is triggered. According to the robust optimization architecture, traditional power scheduling is converted into a confrontation game structure between the system and the market, and the resistance of the system to extreme price situations is improved while economic benefits are guaranteed.

To provide a vehicle control system that can suppress a vehicle from operating even after the vehicle is brought into an operable state by an activity of an improper user.SOLUTION: Vehicle control systems (1, 1a and 1b) comprise: start-notification transmitting parts (relay servers 71 and 71a, and an operation support ECU 21a) which transmit operation start-notifications to one or a plurality of registered terminals (portable terminals 6, 6a, 6b, and 6c) associated with vehicles (2, 2a, and 2b) in accordance with operation of starting the vehicles; and operation control parts (driving support ECU 21, 21a, and 21b) which when operation control request operation is performed to the registered terminals in response to the operation-start notifications, switch the vehicles between an operation suppression state and an operation possible state.SELECTED DRAWING: Figure 6

The invention relates to the technical field of power distribution network operation management, and discloses a power distribution network capacity-increase-free access system based on dynamic coordination. The system comprises a multi-source data acquisition and fusion unit which acquires multi-source data and fuses the multi-source data to generate dynamic power grid state characteristics; the multi-time-scale collaborative optimization architecture unit is used for constructing a layered architecture to realize optimization of different time scales; the space-time load prediction unit is used for predicting load distribution based on the space-time diagram model; and the multi-target dynamic optimization solving unit is used for constructing a model to solve an optimal scheduling instruction. In addition, a layered communication protocol unit is arranged to guarantee communication, and a voltage stability cooperative control unit and a distributed energy storage cooperative control unit are arranged to improve the stability of the power grid. Through cooperation of multiple technologies, capacity-increase-free access of the power distribution network is realized, the operation efficiency is improved, the loss is reduced, the voltage stability is ensured, and challenges brought by distributed energy and electric vehicle charging are effectively dealt with.

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 relates to the technical field of digital twinning, in particular to a multi-machine running-in test distributed control system and method based on digital twinning. The method comprises the steps that a sensing execution unit collects equipment operation state data based on a multi-source sensor module and carries out preliminary data processing and detection; the digital twinning unit receives multi-dimensional sensing data of the sensing execution unit based on a dynamic twinning modeling module, establishes an equipment state model, and performs simulation verification and parameter optimization on a control strategy through a Q-learning strategy to obtain optimized control parameters; the intelligent control unit constructs an optimal decision-making model of multi-machine cooperative control based on a cooperative decision-making module, adjusts parameters of an execution mechanism in real time through a PID control algorithm and feed-forward compensation, and generates a self-adaptive control instruction; and the fusion analysis unit constructs a multi-dimensional equipment operation knowledge base based on the knowledge graph module, and generates global optimization parameters through a multi-target genetic algorithm.

A peritoneal dialysis (“PD”) system having an ultrafilter is disclosed herein. In one example, the PD system includes a housing and a PD fluid pump. The PD system also includes a filter comprising an outer chamber, a central portion, a membrane, an inlet connected to the outer chamber, an outlet, a first venting port connected to the outer chamber, and a second venting port connected to the central portion. The first venting port has a valve preventing air flow into the filter via the first venting port. The PD system further includes a pressure sensor and a control unit configured to control the PD fluid pump. The control unit is further configured to determine a pressure inside the filter based on an output from the pressure sensor and determine an integrity status of the membrane based on the pressure inside the filter.

The invention relates to a follow-up network type control smooth switching method based on a generalized operation short-circuit ratio index. The follow-up network type control smooth switching method comprises the following steps of 1, introducing a generalized operation short-circuit ratio (GOSCR) as a switching index, and proposing a GOSCR-based hysteresis control mode switching judgment index under different power grid strength and power output working conditions; step 2, establishing a unified new energy grid-connected control unit model: constructing a unified grid-connected control model in two modes of network following control and network construction control, systematically analyzing oscillation characteristics of a grid-connected unit in the two modes, and defining boundary conditions of mode switching; 3, proposing an LADRC-based undisturbed switching control strategy: observing and estimating system current disturbance parameters online by utilizing LESO, realizing real-time compensation of disturbance quantity, and reducing dynamic sudden change in the switching process; the method has the advantages that GOSCR switching indexes are introduced to comprehensively evaluate the strength, a unified grid-connected control model is established, and an undisturbed switching control strategy is provided.

The invention provides a self-adaptive error compensation method and system for focused ion beam equipment, and the method comprises the steps: obtaining a real-time error signal set generated by the focused ion beam equipment in a machining process, training a self-adaptive error compensation model according to the real-time error signal set, and generating an error compensation parameter set; inputting the error compensation parameter set into a control unit of the focused ion beam equipment, and generating a real-time compensation control instruction set; adjusting an ion source parameter and a deflection electrode voltage parameter of the focused ion beam equipment according to the real-time compensation control instruction set, and outputting an adjusted ion beam path parameter set; and performing ion beam processing on the target processing area according to the adjusted ion beam path parameter set, and monitoring morphology feature data of the processed area in real time to verify an error compensation effect. According to the invention, harmful effects caused by thermal effects, electric fields or magnetic fields can be eliminated, and the self-adaptability and long-term reliability of a compensation strategy are ensured.

The invention discloses a thermal power generating unit deep peak regulation control system, and relates to the technical field of thermal power generating unit deep peak regulation, the thermal power generating unit deep peak regulation control system comprises a deep peak regulation control center, and the deep peak regulation control center is in communication connection with a data acquisition module, a load prediction module, a working condition monitoring module, a distributed control module and a safety monitoring early warning module. By introducing a distributed control architecture and bat algorithm optimization, each subsystem of the thermal power generating unit is divided into a plurality of local control units, independent and cooperative control is realized, the flexibility and the response speed of the thermal power generating unit in the deep peak regulation process are remarkably improved, and the reliability of the thermal power generating unit is improved. Distributed control ensures that each subsystem can quickly adjust operation parameters according to actual load requirements, and the global optimization capability of the bat algorithm further improves the self-adaptability and decision-making efficiency of the system in a complex working condition, so that the problem of response lag in a traditional centralized control mode is effectively solved.

The invention relates to steel box girder high-strength bolt operation, in particular to a high-strength bolt automatic tightening machine based on machine vision positioning and a tightening method. The automatic tightening machine for the high-strength bolts is used for automatically tightening the bolts of the steel box girder and comprises a multi-shaft moving support, a tightening assembly and a control unit. The multi-axis moving support achieves plane movement of a steel box girder top plate through a servo-driven moving part, a multi-axis support provides three-dimensional space freedom, and the tightening assembly is driven to accurately adjust the posture. The tightening assembly adopts a double-station design, a first tightening head and a second tightening head which are symmetrically arranged on the two sides synchronously move through a door frame type lifting shaft, the image obtaining set is combined to collect position and posture data of a nut and a screw in real time, and a three-dimensional motion path is generated to guide centering. The control unit coordinates multi-axis motion and torque output based on machine vision feedback, full-process automation of the bolt from recognition, positioning to tightening is achieved, the problems that manual operation is low in efficiency and large in error are solved, and screwing precision and structural reliability are ensured.

The invention relates to power transmission line unmanned aerial vehicle inspection, and discloses a power transmission line unmanned aerial vehicle inspection path dynamic planning system, which comprises a quantum high-precision positioning unit, an environment perception and electromagnetic field modeling unit, a dynamic path planning unit, an unmanned aerial vehicle flight control unit and a ground station task management unit. The quantum positioning unit fuses quantum inertial navigation, GNSS and quantum magnetometer data to improve the positioning precision under strong interference; the environment sensing unit obtains a three-dimensional environment and electromagnetic field distribution of the power transmission line; the path planning unit dynamically optimizes an inspection path based on positioning and environment information; the flight control unit performs accurate execution; and ground station management tasks and data analysis. The anti-interference positioning precision of the unmanned aerial vehicle is remarkably improved, accurate path planning and electromagnetic field avoidance are realized, the inspection risk is effectively reduced, and the efficiency, the safety and the adaptability are improved.

The invention relates to the technical field of industrial automation control, and discloses a change gear assembly processing technological parameter intelligent optimization system, which comprises a multi-dimensional data synchronous acquisition unit, an angular domain static map construction unit, a phase lead constraint control unit and an angle trigger execution unit, according to the method, a static angle-load topological graph with a main shaft angle as an index is established, a phase lead angle is calculated by combining inherent physical response lag time of a servo system, and a parameter adjustment instruction sequence is directly injected into a servo current loop at a preset angle window before physical load impact occurs; according to the feed-forward control mechanism based on angular domain reconstruction and phase lead constraint, the problem of physical lag of traditional time domain feedback control in response to high-frequency intermittent cutting is avoided, and zero time difference of periodic load impact and even pre-judgment torque compensation are achieved.

The invention relates to the technical field of single-phase asynchronous motors, and discloses an electronic soft start control method for a single-phase asynchronous motor, which accurately controls the start process of the motor by combining an MCU (Microprogrammed Control Unit) with an electricity metering chip and a silicon controlled rectifier technology. After the system is powered on, the MCU control unit firstly carries out initialization and reads initial circuit parameters collected by the electricity metering chip, and whether starting conditions are met or not is judged based on the parameters. Once the starting condition is confirmed to be met, the MCU sequentially triggers the auxiliary silicon controlled rectifiers to switch on the starting winding and then triggers the main silicon controlled rectifiers to switch on the main winding, so that the motor enters a running state. As the motor reaches a stable operation state, the MCU control unit closes the auxiliary silicon controlled rectifier to reduce unnecessary energy consumption, and adaptively adjusts the conduction time of the starting winding according to the actual load condition, thereby ensuring that the whole starting process is stable and efficient.

The invention relates to the technical field of intelligent pianos, in particular to a piano automatic playing driving method and system, and the method comprises the steps: obtaining first external data which is used for describing the position change of keys at first playing time; converting the first external data into discrete data, wherein the discrete data is associated with a time sequence label; the discrete data is sent to a main control module, and the main control module forwards the discrete data to a control unit of the exercise device; monitoring second external data of the key at the first moment, and searching a corresponding discrete position through time sequence label association according to the first moment; and calculating a difference value between the second external data and the discrete position, and outputting an adjusting current value through the difference value by adopting a closed-loop control algorithm. The invention provides a lossless built-in driving technology and a driving method, and based on the driving method, a low-cost and miniaturized exerciser can be adopted, so that automatic playing of the piano keys is realized under the condition that the original framework of the piano is not influenced.

The invention relates to the technical field of oil filter control, in particular to an oil filter digital intelligent control system based on sensors, which comprises a fusion monitoring unit, an oil temperature control unit and a feedback regulation unit. The oil temperature control unit calculates the optimal heating rate and the target temperature range through a self-adaptive algorithm according to the environment and the initial temperature of oil, compares the optimal heating rate and the target temperature range with the actual oil temperature in the heating process, adjusts the heating power through pulse heating according to the result, judges the working load according to the specific heat capacity of oil and adjusts the frequency of a vacuum pump. And the feedback regulation unit evaluates the system stability based on the target operation frequency and the heating power of the vacuum pump, generates an optimization strategy and feeds back the optimization strategy to the oil temperature control unit, so that accurate oil temperature control is realized, the equipment stability and the energy utilization efficiency are improved, and efficient operation of the oil filter is guaranteed.

The invention provides a switching station control method and system of a subway station. The system comprises a subsystem control unit, a main control unit, a communication module and a data caching module, the subsystem control unit transmits an equipment on-off state, a fault signal and an environment parameter to the main control unit in a hybrid networking mode, and a first cooperative control instruction set is generated in combination with a linkage rule base; a target priority is distributed for the operation instruction to obtain a second cooperative control instruction set, and the communication module adopts a multi-channel parallel distribution mechanism for pushing; in the process of executing the switching station operation, the data caching module stores running state data, identifies a time sequence mode by using a machine learning model, pre-judges a switching station linkage scene and loads a control strategy; and the main control unit detects the response delay of the subsystem control unit and generates a third cooperative control instruction set according to the control strategy so as to optimize the switching station control time sequence. According to the invention, intelligent cooperative control of multiple electromechanical devices can be realized.

The invention discloses an intelligent monitoring system for sewage treatment in the coal chemical industry, and relates to the technical field of sewage treatment.The intelligent monitoring system is characterized in that a water quality monitoring unit is used for collecting parameters such as COD, turbidity, pH and heavy metal ion concentration in real time, the change trend of water quality in a short time is predicted through a long-short term memory (LSTM) network, and then adding parameters of a flocculating agent are dynamically calculated; the defects of a fixed adding curve or artificial experience adjustment are overcome, so that the adding amount, the type and the speed of the flocculating agent can be adaptively adjusted to be matched with the change of sewage components; the addition optimization unit is combined with a CNN-LSTM model to analyze the rheological characteristics of the sludge, so that the addition of the flocculating agent adapts to the rheological change of the sludge, the flocculation effect is optimized, and the sludge settling performance is improved; meanwhile, the stirring control unit adjusts the stirring strength and time in real time based on ultrasonic online monitoring data, so that the flocculating agent is fully dispersed, the particle distribution state is optimized, the flocculation reaction is more uniform, and the subsequent precipitation separation efficiency is improved.

The invention discloses a control method for a cooling circulation system of a multi-core processor, and relates to the technical field of computer hardware heat dissipation, and the control method comprises the following steps: the multi-core processor is responsible for executing a calculation task; the sensor module monitors the temperature of each core; the cooling system control module is used for receiving data from the temperature sensor and the load monitoring module, calculating the cooling requirement of each core according to the data and commanding a cooling unit to adjust; the cooling unit is used for adjusting cooling equipment to reduce the temperature of the processor according to an instruction of the control unit; and the debugging interface is used for monitoring the state of the system and displaying the temperature, the load and the cooling resource distribution condition of each core. The method has the beneficial effects that the rotating speed of the fan, the liquid cooling flow and the working state of the heat pipe system can be accurately controlled by combining the accurate analysis of the load change and the temperature data, the heat dissipation efficiency of the processor is improved, and the energy waste is reduced.

The invention discloses a vacuum preloading soil settlement prediction and control method based on deep learning, and aims to realize precise prediction and dynamic closed-loop control of soil settlement. According to the method, an encoder-decoder architecture is adopted to construct a model framework, and off-line generation of a proprietary model is completed through multi-measurement-point space-time joint training and a combinatorial optimization strategy; multi-source data of a target monitoring point is collected and processed to generate a sequence sample of an adaptive model; in order to adapt to the time-varying characteristics of the geological attributes, deploying an incremental online learning mechanism, and solidifying and finely tuning model parameters through an elastic weight; and based on a prediction result of the updated model, a PID control unit calculates a vacuum degree adjusting amount, a vacuum pump control system is driven to dynamically adjust the operation vacuum degree, and it is ensured that soil settlement is within a reasonable range. Finally, a cooperation mechanism of high-precision settlement prediction and dynamic vacuum degree regulation and control is constructed, and the settlement control precision, the response speed and the construction safety are remarkably improved.

To provide a sample analyzer capable of achieving shortening of a measurement time and improvement of efficiency, in an inspection using a reaction to a plurality of kinds of reaction substances whose measurement ranges are largely different.SOLUTION: A sample analyzer 1 includes: a measurement unit 23 as an imaging device for capturing an image including light reception information based on each reaction between a first reaction substance group comprising a plurality of kinds of reaction substances reacting to a component included in a sample and a second reaction substance which reacts to the component included in the sample and is a different type from a reaction substance included in the first reaction substance group; and a control unit 50 as a control device for analyzing, in the light reception information in the image, the light reception information on the basis of a first determination value corresponding to a first substance group and the light reception information on the basis of a second determination value corresponding to a second substance.SELECTED DRAWING: Figure 10

To provide an information processing system, a control method for the information processing system, and a program that allow a user to distinguish a sound from a specific sound source while leaving the sounds from each sound source as they are, even when a plurality of sound sources are present in a direction of interest that is of interest to the user.SOLUTION: An information processing system 1000 comprises: display means (a display unit 107) capable of displaying an image of a mixed reality space; playback means (an audio output unit 105) capable of playing back sounds from a plurality of sound sources to a user; vibration applying means (a vibration unit 117) for applying vibration to the user; control means (a control unit 111) for controlling displaying a virtual object that can be linked to each sound source on the display means; and selection means for selecting the virtual object (an object selection instruction unit 116). The vibration applying means applies vibration to the user in accordance with the intensity of the sound from the sound source associated with the virtual object if the virtual object is selected.SELECTED DRAWING: Figure 2

The invention discloses a structured text code generation and review method, which comprises the following steps of: receiving a control task demand input by a user, supporting natural language, structured form or graphical process input, performing deep semantic analysis by utilizing a demand analysis module, identifying control conditions, control actions and constraint factors, and generating and reviewing a structured text code. The task is converted into a plurality of control units through the SCoT chain; the SCoT chain reader generates a semantic vector according to a task demand, the AST reader generates AST representation through a grammar rule structure and a hierarchical relationship, and the decoder predicts a next grammar rule and generates an ST code in combination with the SCoT chain and AST features; and verifying grammar and logic of the ST code through an interactive automatic review and repair mechanism, if compiling errors occur, analyzing errors by the teacher model and providing repair suggestions, and performing code repair by the student model according to guidance until the code conforms to an expected function and passes compiling.

The invention relates to the technical field of automatic control, in particular to an intelligent aquaculture automatic control system which comprises a feeding feedback control module, a feeding rhythm allocation module, a water body disturbance partition module, a control unit recognition module and a control path construction module. According to the method, the bait residue density is quantified through image recognition, a feeding state dynamic judgment mechanism is constructed, the accuracy and timeliness of feeding response are improved, the feeding speed is calculated through linkage of time difference and density change, a rhythm allocation strategy based on behavior feedback is formulated, the matching degree of the feeding rhythm to the actual feeding ability is enhanced, and the feeding speed is increased. A main flow path continuous block is constructed by combining flow velocity and flow direction, disturbance levels are marked in a grading manner, a key equipment action area is judged according to cross of fish school active density and dissolved oxygen trend, and an equipment linkage path is constructed by connectivity of a physical channel and an inhabitation area. And an intelligent control scheme of cooperative linkage of ingestion behavior driving, disturbance partition recognition and space layout optimization is realized.

The invention provides a vehicle automatic sound production control method based on infrared recognition, which comprises the following steps: acquiring an infrared image in a preset range in front of a vehicle, recognizing a heat source target in the image, and judging whether the heat source target is an animal or a human according to the shape, size and temperature distribution characteristics of the heat source target; the prediction unit is used for predicting the moving track of the animal and the human in a period of time in the future according to typical moving track modes of the animal and the human so as to obtain predicted moving position and speed information, and transmitting the predicted information to the acoustic control unit; the acoustic control unit dynamically adjusts the sound pressure level and the frequency of the loudspeaker according to the received predicted moving information and the target type so as to generate an acoustic warning mode matched with the predicted moving position, the predicted moving speed and the target type; when the acoustic signal is sent out, the actual moving track of an animal or a person is continuously tracked, and the track prediction method is optimized by comparing the deviation between the actual track and the predicted track, so that the optimal avoidance effect is achieved.