10749 results about "Smart control" patented technology

The invention provides an unmanned aerial vehicle low-altitude intelligent traffic dynamic airspace management and control method and system, and relates to the technical field of intelligent control, and the method comprises the steps: taking an electronic fence geographic coordinate set as a monitoring reference boundary, fusing ADS-B data, meteorological information and an unmanned aerial vehicle equipment state, generating a real-time risk thermodynamic diagram, and outputting a grading alarm instruction; receiving a real-time risk thermodynamic diagram and a grading alarm instruction, and combining wind speed prediction and dynamic airspace occupation data; when the grading alarm instruction is triggered, executing the following operations: constructing a route feasible solution space by taking a no-fly zone and a high-risk zone in the risk thermodynamic diagram as constraint conditions; and iterating an evolutionary path population through selection, intersection and mutation operations of a genetic algorithm by taking the lowest energy consumption as an optimization target, so as to output a global final obstacle avoidance bypassing path, and issuing a route updating instruction to an unmanned aerial vehicle flight control system. According to the invention, the utilization of airspace resources is maximized on the premise of ensuring safety.

The invention provides a control method based on differential rotation speed torque distribution, which relates to the technical field of intelligent control and comprises the following steps: performing difference calculation on an initial input quantity and a preset rotation speed instruction to generate a rotation speed deviation signal and a deviation change rate, and establishing a dynamic membership function library through a fuzzy controller; mapping the rotating speed deviation and the change rate thereof into a fuzzy language variable, performing reasoning operation according to a preset fuzzy control rule base, and outputting a frequency regulation quantity and a torque compensation coefficient of the frequency converter; and reading the generated torque compensation coefficient and shaft system torque values actually measured by the two torquemeters, calculating a dynamic difference value between the current total load torque and the target torque, establishing a torque distribution weight model according to the transmission characteristics of the differential mechanism, and generating differential torque correction instructions of the two motors in combination with the real-time rotating speed values. The control performance of differential drive is improved.

The invention relates to the technical field of intelligent control, in particular to a control method and system based on intelligent collaborative production. Comprising the following steps: aiming at a task cut-in influence range, traversing related nodes in a production network graph model, and detecting a device resource occupation conflict with an existing task to obtain a resource conflict detection result; recursively tracking the affected downstream process from the time delay predicted value along the dependence edge of the production network graph model to generate an influence propagation path; according to a weight dynamic quantification result, summarizing weight values of all affected nodes, generating a global view evaluation score, and determining an overall influence degree of new task cut-in; if the global view evaluation score exceeds a preset threshold, adjusting the cut-in node and time of the new task, and re-executing resource conflict detection to obtain an optimized task cut-in scheme; and extracting a process execution sequence and a resource allocation plan from the optimized task cut-in scheme, updating the production network diagram model, and generating a final collaborative production scheduling scheme.

The invention provides a power grid multi-agent large model safety evaluation index calculation method, and relates to the technical field of intelligent control. The method comprises the following steps: acquiring a voltage change rate and a frequency change rate, dividing four stages according to disturbance starting and extreme points, pairing curve trends, judging delay and synchronization characteristics, extracting a continuous change structure, and generating a disturbance subsection evolution structure; according to the method, through staged analysis of node voltage and frequency change trends and combination of trend consistency judgment and abnormal segment identification, dynamic structure reconstruction and trend mapping of a disturbance process are realized, and coherence and precision of index response state identification are improved through trend boundary collection and frequency classification; cooperative characteristic identification is enhanced through the coupling direction and delay analysis of inter-node multi-parameter change, the node adjustment priority is defined through regulation and control response sorting, an adjustment path under multi-dimensional linkage is constructed, and the refined management and control capacity of disturbance response and the pertinence of control decision are improved.

The invention relates to the technical field of intelligent control, in particular to a self-adaptive camera pose intelligent adjusting system which comprises a dynamic recognition module, a pose adjusting module, a mechanism control module, an information filtering module and a feedback association module. According to the method, the foreground pixel brightness gradient and the boundary continuity in the image frame are extracted, the target displacement path can be recognized, the tracking information is constructed, the target coherent perception under rapid scene change is guaranteed, the attitude parameters and the target motion direction and speed are combined, the offset correction instruction is generated, the response stability of the dynamic target is improved, and the target tracking accuracy is improved. The execution structure feedback is used for correcting control output, reducing the driving error accumulation risk, eliminating non-target disturbance through image boundary definition and motion consistency, improving data purity and combining a view angle execution state and path information to realize synchronous control of tracking and view angle adjustment; and the response efficiency and the following robustness of the system in a dynamic environment are enhanced.

The invention relates to the technical field of intelligent control, in particular to an unmanned aerial vehicle cluster collaborative operation method and system, and the method comprises the following steps: collecting geometric distribution data and task density information of an unmanned aerial vehicle cluster operation region, carrying out the point set coordinate screening of the geometric distribution data, calculating the distance and spatial relevance between point set distributions, and calculating the task density information of the unmanned aerial vehicle cluster operation region; and obtaining a task density distribution set. According to the method, through comprehensive extraction of task completion time, priorities and position sequences, task windows are divided in combination with time periods and priorities, ordered execution and real-time optimization of tasks are achieved, the flexibility and collaboration of task scheduling are improved, and the task scheduling efficiency is improved based on sequence allocation and time interval optimization of the priorities. Reasonable allocation of time and resources in the task execution process is further ensured, non-critical delay and conflicts are avoided, the operation state and the residual electric quantity of the unmanned aerial vehicle are monitored in real time, and the efficiency, the flexibility and the stability of unmanned aerial vehicle cluster collaborative operation are improved.

The invention provides an adjustable spectrum-based liquid crystal display system and a control method thereof, relates to the technical field of display systems, and aims to overcome the limitations of poor environmental adaptability, easiness in causing visual fatigue, single user experience and the like of an existing display. The system creatively integrates a liquid crystal panel, a high-precision multi-main-color Micro-LED backlight, a pixel inner sensing array, an external multi-mode sensing module, a dynamic addressable micro-optical array, a dynamic polarization modulation layer and an optional transparent energy collection layer. The intelligent controller carries out fusion processing on the real-time biological state, environment information and application context data of the user obtained by the multi-source sensor, and carries out accurate state evaluation and future trend prediction. And determining an optimal space-time-direction-spectrum-polarized light field output characteristic and an intelligent energy management strategy through a multi-objective optimization decision engine, and driving each optical regulation and control component to execute in real time. The visual comfort and health level are remarkably improved, and the information fidelity and task performance in a complex environment are ensured.

The invention discloses an intelligent control method and system for tunnel loudspeakers, and relates to the technical field of tunnel audio control, environmental parameters in a tunnel are collected by adopting a mode of deploying sampling equipment in a distributed manner, and data preprocessing is performed in a targeted manner for different environmental parameters; a sound propagation model is established, and attenuation and delay of sound in different environments are simulated. According to the intelligent control method and system for the tunnel loudspeakers, various temperature and humidity sensors are arranged in the tunnel, and the absolute humidity is calculated in combination with the air pressure data, so that the sound velocity is accurately corrected, and the phase difference of the multiple loudspeakers is reduced; an adaptive Kalman filtering algorithm is adopted to process wind speed data, and reliable input is provided for a sound propagation model; a deep reinforcement learning algorithm is used to carry out collaborative optimization on parameters such as amplitudes and directional angles of multiple loudspeakers, a Bayesian network is used to detect loudspeaker faults, and Delaunay triangulation and a distributed consistency algorithm are combined to realize rapid fault reconstruction.

The invention discloses an intelligent control method and system for AR equipment, and the method specifically comprises the steps: determining a pupil center position coordinate and a cornea reflection point position coordinate of a user according to an eye image of the user, and inputting the pupil center position coordinate and the cornea reflection point position coordinate into a geometric mapping model for matching, obtaining user sight direction information; acquiring image brightness value distribution of an area near a user fixation point, determining a plurality of candidate saliency areas according to the image brightness value distribution, and comparing the candidate saliency areas with a preset saliency area template to determine a user attention focus; and acquiring head posture information of the user in real time by adopting a head posture tracking algorithm, and dynamically controlling the shooting direction of a camera of the AR equipment according to the head posture information, the sight direction information of the user and the attention focus of the user. According to the method and the device, accurate alignment of the AR content and the visual focus of the user is realized, and the immersion and interaction experience of the AR application are greatly improved.

The invention relates to the technical field of vehicle-mounted temperature control, and discloses a method and system for intelligently controlling the temperature and humidity of a vehicle-mounted cold chain and a storage medium. The method comprises the following steps: acquiring and preprocessing temperature and humidity data through a sensor; performing state evaluation based on a three-way decision model; constructing a temperature and humidity trend analysis model to predict a change trend; constructing a control decision system by adopting multi-agent reinforcement learning; executing a multi-mode control strategy; and adaptively updating the control system according to the execution effect. According to the application, the future temperature and humidity change trend is predicted according to the historical data and the real-time state, collaborative optimization control of temperature and humidity is realized, self-learning and continuous optimization capabilities are provided, and the accuracy, stability and energy utilization efficiency of cold chain temperature and humidity control are effectively improved.

The invention discloses a treatment system and a treatment method for chemical production waste gas, and relates to the technical field of industrial waste gas treatment. The system comprises a waste gas collection unit, a pretreatment unit, a multi-stage purification unit and an intelligent control unit. The intelligent control unit monitors data through a sensor network, the data processing and analysis module evaluates the processing effect and predicts the trend, the intelligent decision and adjustment module optimizes the parameters of each unit in sequence, and the remote monitoring and alarm module monitors and alarms in real time. The system can efficiently treat waste gas, realizes automatic intelligent control, ensures the stable treatment effect, reduces the risk of environmental pollution, and is widely applied to the field of chemical production waste gas treatment.

The invention relates to the technical field of battery protection control, and discloses a battery charging protection method and system based on intelligent control and a storage medium. The method comprises the following steps: acquiring and calculating battery parameters to obtain a state value; constructing a neural network model based on the state value to output a charging parameter; generating a charging strategy according to the charging parameters; the charging strategy is input into the control circuit for execution; executing multi-stage safety protection to obtain a result; and updating neural network model optimization control parameters according to a result. According to the invention, the charging control model based on the back-propagation neural network is constructed, and the real-time battery state monitoring, the self-adaptive charging strategy generation, the multi-stage safety protection mechanism and the self-learning optimization function are combined, so that the precise control and the comprehensive protection of the battery charging process are realized, the service life of the battery is effectively prolonged, and the charging safety is improved.

The invention provides a management method and system for coal preparation quality intelligent control, and the method comprises the steps: firstly obtaining raw material quality, equipment operation and environment state index sets of a target production line, then carrying out the dynamic association processing of the index sets, and generating a technological process feature set; then calling a preset optimization decision model to carry out parameter prediction on the technological process feature set to obtain a process regulation parameter set, generating a quality optimization instruction set based on the process regulation parameter set, transmitting the quality optimization instruction set to a production control unit to execute dynamic regulation operation, and finally, according to a quality feedback index set after regulation execution, carrying out dynamic regulation operation. And the parameter weight distribution of the optimization decision model is updated, intelligent control and continuous optimization of the coal dressing quality are realized, and the stability of coal dressing production and the product quality are improved.

The invention discloses a high-precision drilling control method and system for a multi-layer PCB, and relates to the technical field of intelligent control. The method comprises the following steps: scanning and analyzing to obtain layered material characteristics and structural size information of a multi-layer PCB, and carrying out clamping fixation and positioning hole processing on the multi-layer PCB to obtain a PCB positioning hole; a drilling machining three-dimensional coordinate axis is constructed; a PCB drilling strategy space is constructed, the PCB drilling strategy space is adopted to conduct drilling analysis on the layered material characteristics and the structure size information in sequence according to the PCB drilling machining requirement, and layered drilling strategy parameters are obtained; and the layered drilling strategy parameters are mapped to a drilling processing three-dimensional coordinate axis for space route fusion, layered drilling processing parameters are determined, and high-precision drilling and quality detection closed-loop control are executed on the multi-layer PCB. The technical problem that in the prior art, the positioning precision is low in the drilling process of the multi-layer PCB is solved, and the technical effect of improving the drilling machining precision of the multi-layer PCB is achieved.

The invention relates to the technical field of intelligent control, in particular to a numerical control lathe cutting precision optimization method and system for metal part machining, and the method comprises the steps: constructing a multi-mode key feature set with aligned timestamps; the initial compensation amount in the cutting feeding direction is obtained from the tool compensation lookup table; searching a corresponding historical cutting state from the processing state matrix, and inputting the key temperature distribution gradient of the cutter side, the key vibration absolute peak value of the lathe spindle and the corresponding historical cutting state into a weight coefficient model to obtain a temperature compensation weight and a vibration compensation weight of the current cutting state; calculating a dynamic correction factor, and correcting the initial compensation amount in the cutting feeding direction through the dynamic correction factor to obtain a cutting feeding direction compensation amount with a timestamp; the compensation rate of the cutting feeding direction compensation amount and the target feeding displacement under the same timestamp is calculated; and the cutting precision is optimized according to the comparison result of the compensation rate and the cutting key feeding relative deviation rate. And the requirements of high precision, low cost and fast response are met.

The invention relates to the field of intelligent agriculture, and discloses an intelligent drip irrigation control method, system and device and a storage medium, and the method comprises the following steps: data collection: obtaining soil humidity data, environmental meteorological data and crop growth state data of a target area through a plurality of sensors, and transmitting the data to a data processing module; the system comprises a data acquisition module used for acquiring soil humidity data, environmental meteorological data and crop growth state data and transmitting the acquired data to a data processing and storage module; the device comprises a machine case shell, and an intelligent control chip, a wireless communication module, an electromagnetic valve and a water flow meter are installed in the machine case shell. By integrating a sensor network, intelligent analysis and a real-time feedback mechanism, the water demand of crops is accurately calculated, irrigation is automatically adjusted, and water resource utilization is optimized; a personalized irrigation plan is made by combining a deep neural network and time sequence analysis, and the water requirements of crops in different growth stages are ensured.

The invention relates to the technical field of six-degree-of-freedom mechanical arm control, in particular to a six-degree-of-freedom mechanical arm trajectory tracking control method and system. The method comprises the following steps that an operation planning track sequence of the six-degree-of-freedom mechanical arm is obtained; executing real-time trajectory point tracking according to the operation planning trajectory sequence, and performing tail end attitude resolver analysis to generate tail end attitude resolver coupling data; when an end effector of the six-degree-of-freedom mechanical arm executes high-speed rotation load operation, end gyroscopic effect data are calculated; analyzing and estimating an uncontrolled offset vector according to the tail end attitude resolver coupling data and the tail end gyroscopic effect data; and dynamic offset vector correction processing is conducted on the estimated uncontrolled offset vector, so that intelligent control over operation of the six-degree-of-freedom mechanical arm is executed. According to the method, accurate tracking control over the tail end pose of the mechanical arm under the high-speed rotating load working condition is achieved through aerodynamic disturbance torque processing and rigidity weakest shaft recognition, and track deviation caused by composite disturbance is effectively compensated.

The invention provides an intelligent scheduling control method, system and device of an optical storage hybrid inverter, and a medium, and belongs to the technical field of new energy power systems.The method comprises the steps that a scheduling controller collects the output power of a photovoltaic battery, the electric quantity state of an energy storage battery, the power grid electricity price and the load power; generating a photovoltaic power prediction value and a load prediction value by using the prediction model; solving an optimal power distribution scheme by taking economy optimization as a target to obtain photovoltaic side target power, energy storage side target power and power grid interaction target power; the duty ratio of the photovoltaic side bidirectional DC / DC converter is adjusted according to the photovoltaic side target power to achieve maximum power point tracking of the photovoltaic cell, the duty ratio of the energy storage side bidirectional DC / DC converter is adjusted according to the energy storage side target power to achieve charging and discharging power control of the energy storage cell, and the output current of the grid-connected inverter is adjusted according to the power grid interaction target power. According to the invention, efficient and intelligent control of the optical storage hybrid inverter can be realized.

The invention discloses a livestock and poultry manure fermentation monitoring method based on digital twinning, and relates to the technical field of livestock and poultry manure treatment. By constructing a high-precision digital twinborn model, fermentation field sensing data are synchronized in real time, the fermentation state is simulated, and the development trend is predicted; setting an intelligent control strategy fusing data relevance and control priority, and dynamically identifying and avoiding rule conflicts; on this basis, control actions such as pile turning, spraying and ventilation are automatically executed, feedback data are recorded, and twin model parameters are continuously optimized based on feedback results; according to the method, intelligent regulation and control and model self-adaptive updating of the fermentation process are realized, and the stability, the energy utilization efficiency and the prediction accuracy of the system are remarkably improved.

The invention provides an intelligent drilling guide method and system for a petroleum drilling tool face, and relates to the technical field of intelligent control, and the method comprises the steps: determining a first target point, a second target point and a third target point in the process of tracking a target azimuth angle according to a dynamic adjustment instruction; constructing a dynamic triangle through the first target point, the second target point and the third target point, and taking the barycentric coordinate of the dynamic triangle as an azimuth angle correction value; correcting forward and reverse rotation angles and periodic parameters of the drill string according to the azimuth angle correction value so as to obtain a corrected dynamic adjustment instruction; and controlling a drill string to act based on the corrected dynamic adjustment instruction, so that the underground tool surface continuously approaches the target azimuth angle through closed-loop iteration adjustment. The control efficiency and stability of the tool face under the complex working condition can be improved.

The invention discloses a dynamic intelligent control method and system for improving the efficiency of a rectifying tower, particularly relates to the technical field of chemical process control, and is used for solving the technical defect that an operation variable is mismatched with a physical state due to the fact that an existing distributed control system cannot recognize a hidden degradation state of equipment. The method comprises the following steps: acquiring temperature gradient distribution of each tower plate of a rectifying tower, thermal load of a reboiler and tower top condensation load parameters in real time, calculating a mass transfer efficiency offset by combining the temperature gradient distribution to generate a scaling trend index, and synchronously calculating a local entropy yield based on the thermal load and the condensation load to generate a thermodynamic irreversibility index. When the double indexes exceed a preset threshold value, it is judged that a recessive degradation state is entered, then a backflow ratio correction coefficient is reversely derived through a dynamic correlation model, and the correction coefficient is input into a distributed control system to dynamically adjust backflow ratio operation parameters; the product purity is maintained, the tower plate scaling process is obviously delayed, and long-term efficient operation of the rectifying tower is guaranteed.

The invention relates to the field of electronic photo frames, and discloses an electronic photo frame intelligent interaction control method and system based on eye movement tracking, and the method comprises the steps: carrying out the real-time tracking of an eyeball position based on the eye image data of a user, and obtaining an eye movement track sequence according to the pupil center coordinate change and the eyelid opening and closing state change; calculating a fixation point coordinate, a fixation time length and an eye hopping frequency based on the eye movement track sequence; the current eye movement behavior type of the user is obtained through a personalized recognition model; matching with a preset eye movement pattern library based on the current eye movement behavior type of the user to obtain a candidate instruction set; and executing photo frame function operation according to the control instruction. The method has the following effect that the convenience and accuracy of man-machine interaction are improved based on non-contact intelligent control of eye movement.

The invention discloses an automatic control method and device for a thermal management system of an electric vehicle, relates to the field of intelligent control, and realizes multi-step prediction of battery and motor heat production and thermal load change in the future by fusing external information such as a navigation path and weather forecast, plans a thermal management strategy in advance, and improves the response perspectiveness of the system. A multi-source disturbance dynamic structured correlation equalization technology is adopted, the coupling relation between heat sources is modeled, cross disturbance is compensated through a joint state function, and multi-heat-loop cooperative control is achieved. On the basis of a model predictive control (MPC) framework, temperature tracking errors and energy consumption penalty are optimized, the energy consumption of actuators such as a compressor and a water pump is reduced while a safe temperature interval is met, and the endurance is prolonged. And in combination with a state estimator fusion model and observation data, the control precision and robustness under a complex working condition are enhanced, and finally global optimal thermal management considering the service life of parts, the comfort of passengers and the energy efficiency is realized.

The invention relates to the field of industrial automation and intelligent control, and discloses an intelligent temperature automatic control system for a digital glass mold. The method comprises the following steps: acquiring mold surface temperature and heat flow data through a thermocouple array and a thermal infrared imager, and constructing heat flux and a disturbance coefficient; forming state vectors are established in combination with the forming process parameters and the heat flow distribution, and forming stability levels are generated through support vector regression; building a heat balance deviation model based on the thermophysical parameters and the environment variables, and predicting a temperature trend; a temperature control instruction is generated through the fuzzy neural controller, and temperature dynamic closed-loop control is achieved. The system improves the temperature control precision and thermal field balance of the glass mold, and is suitable for intelligent temperature control management in the glass container forming process.

The invention relates to the technical field of new energy power systems, and discloses an energy storage coordination system and control method based on meteorological prediction and multi-energy complementation, and the system comprises a power generation end module which carries out the dynamic adjustment according to a power adjustment instruction of a power generation end; the meteorological prediction and data acquisition module is used for acquiring meteorological data in a future time period in real time and sending the meteorological data to the intelligent control and scheduling platform; the energy storage end module sends data including lithium battery SOC and hydrogen storage tank pressure to the intelligent control and scheduling platform and performs charging and discharging according to an energy storage end charging and discharging priority strategy; and the intelligent control and scheduling platform receives the electricity price data of the weather prediction and data acquisition module, the energy storage end module and the power grid in real time, generates a power generation end power regulation instruction and an energy storage end charging and discharging priority strategy, and meanwhile, improves the income and maximizes the hydrogen energy use proportion through electricity price peak-valley arbitrage and green electricity transaction premium. The power grid stability and the energy utilization rate are greatly improved, the energy storage life is prolonged, and the investment payback period is shortened.

The invention discloses a self-adaptive heat dissipation method and system for a modular battery module, and relates to the technical field of intelligent control, and the method comprises the steps: collecting the working environment monitoring information of a target battery module of deep sea detection equipment in real time, and carrying out the matching to obtain an optimal dynamic heat dissipation mode; a target battery module is divided into a plurality of independent heat dissipation modules, a modular battery heat dissipation framework is built, working state data of the target battery module is collected in real time to perform hot spot distribution analysis, a module temperature gradient is generated, and stepped layered heat dissipation control is performed in combination with the modular battery heat dissipation framework and an optimal dynamic heat dissipation mode. According to the invention, the technical problems that the heat dissipation efficiency is low and the battery performance is reduced due to the fact that an existing battery module cannot effectively cope with high-pressure, low-temperature and corrosive conditions in a deep sea environment are solved, and the technical effects that the heat dissipation efficiency is low and the battery performance is reduced are achieved through a modular battery heat dissipation framework and a stepped layered dynamic heat dissipation strategy; and the heat dissipation efficiency and the performance of the battery module in the deep sea environment are improved.

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 relates to the technical field of intelligent control and unmanned aerial vehicle application, in particular to a bird repelling and unmanned aerial vehicle dispatching integrated platform based on big data analysis. Comprising a multi-source data acquisition module; a dynamic scheduling decision module; and a federal learning optimization module. According to the invention, the millimeter-wave radar, the infrared camera, the voiceprint recognition unit and the meteorological data interface are integrated through the multi-source data acquisition module, so that the defects of one-sided data and extensive evaluation of a traditional scheme are overcome; a bird threat level, unmanned aerial vehicle endurance time, an electric quantity attenuation model and an airspace control rule are fused through a dynamic scheduling decision module, dynamic balance of priority response of a high-risk area and unmanned aerial vehicle endurance and energy consumption is realized, and the problems of response lag and insufficient endurance caused by unreasonable task allocation in a traditional scheme are avoided; the federal learning optimization module adapts to a specific geographical environment through transfer learning, and the problem of strategy stiffness of a traditional scheme in a complex environment is solved.

The invention discloses a forestry intelligent spraying system and spraying method based on multi-source sensor space perception, and relates to the technical field of intelligent control. A target forest region is scanned through a laser radar and a visual sensor carried by an unmanned aerial vehicle, a three-dimensional forest region map is constructed, and a single tree is identified and positioned by using a tree body identification model; evaluating the leaf density of the canopy; identifying diseases and insect pests by utilizing multispectral imaging, and making a pesticide proportioning decision according to the disease and insect pest types and severity; planning and generating an optimal flight path for spraying of the unmanned aerial vehicle; adjusting nozzle parameters according to the leaf density of the canopy and the severity of diseases and pests, performing variable spraying, and storing the spraying operation parameters of the unmanned aerial vehicle. Through multi-source sensor fusion, an intelligent decision algorithm and precise spraying control, autonomous obstacle avoidance, high-precision map construction, tree body recognition and canopy analysis, pest and disease damage detection and dynamic pesticide dispensing and spraying in a forestry scene are realized, the forestry spraying efficiency is remarkably improved, and pesticide waste is reduced.

The invention relates to the technical field of charging stations, and discloses an intelligent charging station monitoring method based on digital twinning, which comprises the steps of 1, data acquisition and preprocessing, 2, digital twinning model construction and calibration, 3, real-time monitoring and intelligent decision making, and 4, full life cycle management. The invention also provides an intelligent charging station monitoring system based on digital twinning, which comprises a physical module, a virtual module, a data module, an application module, a connection module and a visualization module. The system has the beneficial effects that multi-source data are collected in real time through the Internet of Things, a digital twin model is constructed, a perfect 3D visual interface is provided, and information such as the equipment running state, the charging process and the energy flow of the charging station can be accurately displayed in real time. And meanwhile, remote monitoring and intelligent control of charging station equipment, such as functions of remotely adjusting the power of the charging pile, switching equipment and the like, are realized, and convenient and efficient operation experience is provided for a user.