191results about How to "Accurate tracking" patented technology

The invention discloses an unmanned aerial vehicle antenna dynamic adjustment method and device under single Beidou support, and relates to the technical field of antenna control, and the method comprises the steps: carrying out the positioning of a target unmanned aerial vehicle, and obtaining a spatial positioning data signal; transmitting and receiving signals to obtain a signal transmitting and receiving record log; analyzing the flight area of the target unmanned aerial vehicle to obtain terrain environment parameters, performing flight analysis, and generating flight attitude change information; performing operation calculation on an antenna module of the target unmanned aerial vehicle, generating an antenna dynamic operation coefficient, and formulating an antenna adjustment strategy; and performing optimization, generating an antenna adjustment optimization strategy, and performing dynamic adjustment. The technical problem of poor communication and positioning accuracy and stability caused by insufficient antenna adjustment precision of the unmanned aerial vehicle in a complex environment in the prior art is solved, and the technical effects of realizing accurate tracking of the antenna of the unmanned aerial vehicle on the target signal source in the complex environment and improving the accuracy and stability of communication and positioning of the unmanned aerial vehicle are achieved.

The invention discloses a high-robustness target loss detection and rapid tracking method, and belongs to the technical field of calculation, reckoning or counting. By combining the YOLO algorithm and the MOSSE algorithm, the method can actively search the target and realize re-detection and rapid recovery tracking of the target after the space target is lost for a long time. And when the target moves out of the visual field, the MOSSE algorithm cannot continue tracking, and at the moment, the YOLO algorithm is automatically started to carry out target detection so as to confirm that the target appears again. Once a target is detected, the features of the target are immediately transmitted to the MOSSE algorithm so as to quickly switch back to the tracking mode, thereby ensuring the continuity and stability of tracking. The key point of the method is that a switching mechanism can quickly respond between target loss and reoccurrence, and time delay in the detection and tracking process is remarkably reduced.

The invention discloses a harmonic traceability method based on a harmonic power direction of an electric power system, which belongs to the technical field of electric power quality of the electric power system and comprises the following steps of: constructing matrixes of fundamental waves and harmonic signals, initializing active and reactive power deviation values of nodes, and adopting a decoupling algorithm combined with a Jacobian matrix iteration method to carry out harmonic traceability. And sequentially calculating the fundamental wave voltage and each harmonic current, and finally obtaining the power flow direction and judging the harmonic source attribute. When harmonic power flows from a load to a power grid, the node is identified as a harmonic source; and when the harmonic power flows to the load from the power grid, identifying the node as a harmonic receiving point. An equivalent circuit model is used for separating harmonic influences of a linear load and a non-linear load, and a real-time monitoring technology is combined to realize dynamic tracking and accurate positioning of a harmonic source. The method is low in calculation complexity, excellent in convergence performance and suitable for a modern power distribution network scene where distributed energy and power electronic equipment are widely connected, the harmonic suppression efficiency is remarkably improved, and the safety and stability of power grid operation are ensured.

The invention discloses a multi-agent cooperative control system based on multi-mode brain-computer interface-visual tracking, and the system comprises the following modules: a signal collection and processing module which employs an 8-lead electroencephalogram cap to collect electroencephalogram original data, selects occipital region parts (PO7, PO8, P3, P4, Pz, O1, O2 and Oz) as an electroencephalogram data collection region, and carries out the EEG data collection; the feature extraction and classification module is used for extracting SSVEP signal features by using a space-time convolutional network, optimizing classification precision in combination with an integrated learning strategy, extracting EOG signals based on a Transform model, integrating an LMS adaptive filter and denoising the EEG signals in combination with wavelet transform, expanding the frequency tracking range of the adaptive filter, and reducing signal loss to the greatest extent; the cooperative control strategy design module is used for mapping the electroencephalogram signals into control instructions of different intelligent agents, and cooperative control over the unmanned aerial vehicle and the trolley is achieved; and the target detection and tracking module is used for optimizing an unmanned aerial vehicle visual tracking system based on a PP-YOLOE model and realizing accurate tracking and automatic adjustment of the trolley.

The invention relates to the technical field of data storage, and discloses a lossless compression algorithm-based data storage method, which comprises the following steps of: receiving to-be-stored data through a data interface, automatically identifying a data format by using a data analysis tool, and performing feature analysis to obtain the size, the type and the complexity of the data; through deep analysis of data features, the method can flexibly select a proper Huffman coding processing mode, and optimizes a compression process according to different data types and complexity. In addition, a context modeling optimization mechanism is introduced, the coding mode is dynamically adjusted, and the compression ratio and the storage efficiency are further improved; efficient storage and reliability verification of data are ensured through a block storage strategy in combination with metadata management; the problem that an existing compression algorithm is low in efficiency is solved, the integrity and reliability of data storage are improved, and the method is suitable for storage requirements of large-scale data and diversified data.

The invention discloses a Pick ball motion trail tracking and tactical decision-making auxiliary system which adopts a high-frame-rate camera, a micro sensor installed on a ball and an athlete motion sensor worn on an athlete to respectively collect video image data, ball motion data and athlete motion data in the Pick ball motion process. Precise positioning and trajectory tracking are carried out on a ball through a target detection and tracking algorithm based on deep learning, a tactical knowledge model is constructed by combining site information and athlete action data and applying a decision tree and a neural network algorithm, real-time tactical decision suggestions are provided for athletes and coaches, and the training efficiency is improved. And the competitive level and the training effect of the Pick ball sports can be improved.

The invention discloses a phased array radar for unmanned aerial vehicle detection based on multi-target tracking, which comprises a radar antenna array, a signal processing unit, a control unit and a display unit, and is characterized in that the control unit comprises a motion state judgment module; a filtering switching module; a Kalman filtering module; a particle filtering module; and a data association module. The method can achieve the high-precision detection and positioning of an unmanned plane target, has a multi-target tracking capability, and improves the detection efficiency and accuracy of the unmanned plane.

The invention discloses a quality inspection system for a production process of an amomum tsao-ko mixture for guiding qi downward and dredging collaterals, and aims to realize comprehensive monitoring and control of the production process through multiple modules and improve the product quality and the production efficiency. The image data acquisition module is used for acquiring raw material and production process images and spectral analysis images. The timing module records and judges the production process time. The multi-source data acquisition module collects production environment data and raw material weight. The raw material proportion control module monitors a raw material proportion. And the data processing module is used for analyzing and processing images and environment data in the production process. And the user interaction module provides an interface for real-time quality detection results and user modification commands. Through comprehensive data acquisition and processing, intelligent proportion control, accurate time management and efficient environmental condition evaluation, the production quality and safety of the amomum tsao-ko mixture for guiding qi downward and dredging collaterals are remarkably improved, human errors are reduced, and the production efficiency is improved.

The invention relates to a deep learning-based workpiece processing path feature identification method for a numerical control system. According to the method, a G code generated by a numerical control machine tool is analyzed and processed to obtain features of a tool path when a workpiece is machined. Particularly, the method not only performs basic data preprocessing and cleaning, but also performs deeper analysis by establishing cutter path characteristic indexes such as path length, angle, curvature and the like. The data classified through the feature indexes are used as input of a deep learning algorithm, and efficient recognition of the tool path features is further achieved. According to the numerical control machining tool path feature recognition method provided by the invention, the efficiency and the accuracy of feature recognition are improved, the perception capability of a complex machining path is further enhanced through application of a deep learning algorithm, and support is provided for intelligent recognition of the machining path of a numerical control system.

The invention relates to the technical field of the Internet of Things, and discloses an intelligent medicine storage cabinet monitoring system based on the Internet of Things technology, which comprises a medicine storage cabinet, the medicine storage cabinet is in signal connection with an Internet of Things equipment layer, the Internet of Things equipment layer is in network connection with a network communication layer, and the network communication layer is in data connection with a data processing layer. The data processing layer is in data connection with a user identity verification module, the user identity verification module is in signal connection with an access recording unit, and the access recording unit is in data connection with a data encryption module. Meanwhile, all access activities are recorded, subsequent safety auditing and monitoring are facilitated, the central processing system is connected with the real-time monitoring system, it is ensured that environment parameters such as the temperature and humidity of the medicine storage cabinet can be monitored in real time, and abnormal conditions are found and processed in time.

The invention provides a photovoltaic power generation hydrogen production control method and system, a program product and a storage medium, and relates to the technical field of photovoltaic power generation. The system determines whether adjustment is needed or not by judging the relation between the current illumination parameter and the preset threshold value. In the adjusting state, the system calculates the total energy receiving power, and dynamic adjustment of the target power point is achieved. When the target power point is larger than the total energy receiving power, the system analyzes according to the temperature of the electrolytic cell and calculates needed energy and excess energy, so that whether the temperature of the electrolytic cell is increased or the working voltage point is adjusted is determined. According to the dynamic adjustment mechanism based on multiple parameters, the system can flexibly distribute energy according to real-time working conditions, the situation that electric energy is forced to be connected to a grid is avoided, meanwhile, the performance of the system is improved through temperature adjustment when the hydrogen production power is not high, and finally the energy utilization efficiency is improved.

The invention discloses a target detection method and device based on multi-modal fusion, electronic equipment and a medium, and the method comprises the steps: obtaining a first image set and a second image set, the first image set comprises a plurality of visible light images, and the second image set comprises a plurality of infrared images; performing data alignment operation on the first image set and the second image set to obtain a first feature image set corresponding to the first image set and a second feature image set corresponding to the second image set; performing feature fusion on the first feature image set and the second feature image set through a preset dual-scale image fusion algorithm to obtain a fused image set; and performing target detection on the fused image in the fused image set through a preset deep learning model to obtain the object category of the to-be-detected object and mark the bounding box of the to-be-detected object. In the embodiment of the invention, the accuracy of target detection can be improved, and the detection precision is further improved.

The invention provides an identification and early warning method and system for dangerous behaviors of inland ships. The method comprises the following steps: acquiring infrared monitoring video image data of an inland river water area to obtain a standard data set; then, performing ship main shaft identification according to the standard data set, performing bow and stern identification according to a ship gray value, and performing ship motion direction judgment; then, according to a method based on sample generation, a large number of ship target tracking virtual samples are generated; then, inputting the generated ship target tracking virtual sample into a DVIS model tracking training framework to realize tracking training, and obtaining a tracking model; and finally, according to the obtained tracking model and input real sample data, analyzing motion track data and a motion mode of the ship, and carrying out identification and early warning on dangerous behaviors of the ship. According to the method, tracking of ship behaviors under the complex ship shore environment condition is achieved, early warning is conducted on the ship dangerous behaviors, and the recognition rate of the ship navigation dangerous behaviors is further increased.

The invention provides a target tracking method and system based on space channel summation attention, and the method employs a dual-branch feature extraction network based on a space channel summation attention module to carry out image feature extraction, and carries out the processing of a space attention mechanism and a channel attention mechanism in the extraction process. Information interaction between a space domain and a channel domain is achieved, a large amount of structured space information and channel information are obtained, final template features and intermediate search features are obtained, the final template features and the intermediate search features are fused and sent to a head prediction network, and the maximum response position of a tracking target in a search area can be obtained. And target tracking is carried out. According to the method, the advantages of space channel summation attention are fully utilized, so that the tracker can well deal with the difficulties of target shielding, rapid movement, complex background and the like in the tracking process, and more accurate tracking can be realized.

The invention provides an active power integrated closed-loop control method for a photovoltaic power station. The active power integrated closed-loop control method comprises the following steps: obtaining a first instruction value according to a scheduling instruction; calculating first power according to inverter information; obtaining a calculation deviation according to the first instruction value and the first power; comparing the absolute value of the calculated deviation with a deviation dead zone limit value; when the absolute value of the calculation deviation is greater than the deviation dead zone limit value, obtaining the current deviation according to the calculation deviation; comparing the absolute value of the current deviation with the switching boundary value; if the absolute value of the current deviation is larger than the switching boundary value, a second instruction value is calculated through pure proportional control; if the second comparison result is that the absolute value of the current deviation is not greater than the switching boundary value, calculating a second instruction value by adopting proportional-integral control; and allocating a scheduling instruction according to the second instruction value and the inverter information. According to the invention, a control mode of switching between pure proportional control and proportional-integral control is adopted, so that an instruction can be accurately tracked, and the adjusting process is stable.

The invention discloses an automatic leveling platform for a long-focal-length monitoring camera and a control method, and relates to the field of monitoring equipment. According to the invention, the problems of position deviation and even incapability of normally capturing the target when the monitoring camera locates and tracks the target due to inclination of the tower body are solved. The automatic leveling platform comprises a monitoring tower, a base, a leveling lifting support arm group, a rotating platform, a turntable attitude sensor and a controller, the base is fixed on a monitoring tower, the leveling lifting support arm group is arranged on the base, a rotating platform is arranged on the leveling lifting support arm group, and the turntable attitude sensor is mounted on the rotating platform; the controller is connected with the leveling lifting supporting arm set and used for controlling stretching and retracting of the high-precision leveling lifting supporting arm set according to data of the rotary table posture sensor. And a long-focal-length monitoring camera is arranged on the rotating platform. The method is applied to the security field.

The invention discloses a mechanical arm remote cooperation system and method suitable for an inspection robot, and belongs to the technical field of inspection robots, the system comprises a six-axis synchronous mechanical arm, a mechanical arm remote operation device and a communication link, the six-axis synchronous mechanical arm is applied to the inspection robot, and the mechanical arm remote operation device is applied to the inspection robot. The mechanical arm teleoperation device is used for obtaining hand motion data of an operator and generating a corresponding control instruction, the control instruction is transmitted to the six-axis synchronous mechanical arm through the communication link, the six-axis synchronous mechanical arm executes corresponding actions according to the received control instruction, and real-time state information is fed back to the mechanical arm teleoperation device through the communication link. The mechanical arm teleoperation device dynamically adjusts the control instruction according to the fed-back real-time state information, and bidirectional feedback control is achieved. According to the mechanical arm remote cooperation system and method, the operation flexibility, accuracy and efficiency of the inspection robot can be remarkably improved, meanwhile, the labor intensity of an operator is reduced, and it is ensured that an inspection task is safely and efficiently completed.

The invention relates to the technical field of image communication, in particular to an intelligent camera with active defense and a defense method thereof, and the intelligent camera comprises a data acquisition module, an extraction module, a judgment module, a determination module, a screening module and a defense module. According to the invention, through fusion of a multi-parameter analysis technology and combination of the target position, the attitude angle and the height of a real-time image and dynamic changes of sound intensity, accurate identification of an intrusion target is realized, and the judgment module adopts a multi-level logic of combination of boundary judgment and attitude angle change, so that the accuracy of abnormal target judgment is effectively improved; the screening module reduces erroneous judgment through comprehensive screening of sound intensity and position; the defense module calculates a launching track based on the real-time position and height of the target, fixed-point active defense is achieved through a water bomb, passivity of a traditional voice alarm mode is avoided, and the problem that the response speed is not timely due to single fingerprint recognition and vibration monitoring data is effectively solved.

The invention provides a self-protection UPS power supply, and relates to the technical field of UPS power supplies, the self-protection UPS power supply comprises a main control module, a battery module, an inverter module, a power supply input interface and an output interface, the main control module is electrically connected with the battery module, the inverter module, the power supply input interface and the output interface, through the built-in artificial intelligence driving electric energy management system, the UPS power supply can learn the power utilization habit of a user and predict the future load demand, the discharging strategy and the charging scheme are dynamically adjusted, and the battery utilization rate and the charging efficiency are maximized. Besides, when an integrated MPPT (maximum power point tracking) controller is connected to a solar panel, the energy conversion efficiency of solar energy can be accurately tracked and optimized, renewable energy sources are fully utilized, the energy self-sufficient capacity and the endurance time are remarkably improved, and the system is particularly suitable for outdoor environments or environments with unstable electric power conditions.

The invention discloses a method for researching a Tarim red deer population spatial distribution pattern in the field of animal ecology. The method comprises the following steps: step 1, analyzing Tarim red deer wild population distribution characteristics; 2, analyzing forest environment characteristics of the Tarim red deer wild population habitat; 3, analyzing the spatial correlation between the Tarim red deer wild population distribution and the biological environment factors; step 4, tracking and analyzing the motion trail of the Tarim red deer; 5, analyzing the influence of habitat space distribution and abiotic factors on Tarim red deer wild population distribution; and step 6, evaluating habitat suitability of the Tarim red deer wild population. The method is reliable and accurate, and provides data support for construction of a Tarim red deer wild population protected area in the future by analyzing a Tarim red deer wild population space distribution pattern and habitat forest environment characteristics, using the unmanned aerial vehicle to track a motion trail of a red deer group, evaluating habitat suitability of a habitat and evaluating a potential suitable area of the habitat.

The invention discloses an object tracking method, a mobile intelligent device, a medium and a product, and relates to the mobile intelligent device. The method comprises the following steps: when a user object walks in a target area, receiving an area sound signal collected by the acoustic sensor; extracting a target foot sound signal of the user object from the area sound signal; determining relative position information and walking speed of the user object according to the target footstep sound signal; and controlling the moving direction and the moving speed of the mobile device to be matched with the user object according to the relative position information and the walking speed so as to track the display device. The footstep sound signals of the user are accurately captured and tracked through the acoustic sensor, real-time monitoring of user actions and automatic following of the mobile device are achieved, and interactivity and user experience of the device anytime and anywhere are enhanced.

The invention relates to the technical field of image analysis, in particular to an SMT surface defect visual detection device based on AI image recognition, which comprises a template image storage module, an offset frame and mask image generation module, an image acquisition module, a bare board defect detection module and a finished product defect detection module. The method comprises the following steps: firstly, determining a mounting position and an offset error allowable range of each workpiece according to a mounting process file, and generating a plurality of offset frames to obtain a global mask image; an image extraction method based on a global mask image is provided, and an element coverage area image and an element non-coverage area image are extracted through the global mask image, so that partition defect detection is realized; in addition, a PCBA comprehensive defect detection model based on multi-task learning is provided, so that defect detection of an element coverage area is realized. Through respectively carrying out defect analysis on the element coverage area and the element non-coverage area, a PCBA finished product defect detection result is finally generated, and efficient and accurate SMT surface defect detection can be realized.

The invention discloses a face recognition tracking method and equipment based on a holder, and relates to the technical field of face recognition. Receiving a figure locking instruction, determining a target figure, obtaining a first video stream, and carrying out figure feature detection on the first video stream to obtain facial features and motion features; if the facial features of the first video stream cannot be recognized, determining a central area according to the motion features and zooming to obtain a second video stream; and performing path prediction on the target person according to the second video stream to obtain a predicted path, and performing pre-regulation and control on the holder according to the predicted path to perform real-time tracking on the target person. Acquiring a video stream, and detecting face and motion features of a person; and if the facial features cannot be recognized, a new video stream is obtained through motion feature zooming, the path of the target person is predicted, and the holder is pre-regulated and controlled, so that real-time accurate tracking is realized, and the tracking efficiency and the system adaptability are improved.

The invention relates to a sea area management and control early warning evidence obtaining method based on video intelligent analysis, and the method comprises the steps: collecting equipment parameters: collecting internal and external parameters of equipment and environment parameters; equipment screening control is carried out, controllable and controllable equipment with a good detection effect is selected through equipment state analysis and target detection effectiveness analysis, and follow-up monitoring of a target area is realized through pan-tilt control; performing video intelligent analysis, determining a follow-up concerned target through mapping matching of a video intelligent analysis model and different coordinate system positioning information, and performing accurate linkage and video snapshot by fine tuning equipment; according to early warning and evidence obtaining association, structured attribute analysis is performed on a target and an area detected in a video capturing process, and an evidence obtaining result is added to early warning information with an association relationship, so that repeated evidence obtaining is avoided. According to the invention, the accuracy and efficiency of early warning evidence obtaining can be effectively improved, the utilization efficiency of data and resources is improved, and powerful technical support is provided for sea area management and control.

The invention discloses a tower climbing obstacle identification and positioning method based on machine vision. The method mainly comprises the following steps: S1, acquiring comprehensive environment and obstacle information and monitoring illumination conditions in real time; s2, developing an adaptive illumination compensation algorithm, and dynamically adjusting image processing parameters according to real-time illumination conditions; s3, performing semantic segmentation on the image, identifying different types of obstacles, and taking different countermeasures according to the characteristics of the obstacles; s4, constructing a high-precision real-time positioning system, accurately positioning the identified obstacle, and generating three-dimensional coordinate information of the obstacle; s5, tracking the position change of the obstacle in real time in the tower climbing process, and continuously updating the position information of the obstacle; and S6, designing an intelligent path optimization algorithm, planning a safe tower climbing path, and dynamically adjusting the tower climbing path in the tower climbing process. According to the method, misjudgment caused by insufficient illumination or overexposure is greatly reduced, and the safety of tower climbing operation is improved.

The invention belongs to the field of carbon dioxide geological sequestration and oil-gas field development, and relates to a shale carbon dioxide adsorption expansion damage experiment method considering a hydration state, which comprises the following steps: processing a shale sample according to requirements; the shale is subjected to constant-humidity regulation and control pretreatment, and in the process, different relative humidity environments can be used for pretreatment, so that the influence of different clay mineral hydration interlayer spacing states on adsorption expansion is obtained; soaking the pretreated shale in supercritical carbon dioxide, adsorbing the sample when the temperature and pressure conditions reach a set value, soaking until the rock sample reaches adsorption saturation, and treating by using carbon dioxide with different pressures and temperature gradients; according to collection of sample microstructure and mechanical data, damage can be treated through crack initiation position comparison, porosity and gray scale quantitative analysis and macro-mechanical parameter evolution. The method provides an experimental method for carbon dioxide geological sequestration safety assessment and shale gas fracturing parameter optimization.

The invention relates to the technical field of robot autonomous following, in particular to a robot autonomous following method based on a single-line laser radar. Comprising the steps of collecting laser radar data, performing intelligent processing on the laser radar data to obtain accurate laser radar data, identifying contour features of a following target by using a simultaneous positioning and mapping technology, forming a reference point of the following target, and constructing a map; the constructed map is updated and optimized, and an optimized map is obtained; based on the optimized map and the accurate laser radar data, performing dynamic target tracking optimization and target motion prediction by using a dynamic adaptive path planning and target tracking algorithm to obtain a preliminary dynamic path plan; and based on the initial dynamic path planning, introducing a priority-based task scheduling mechanism to obtain the optimal path planning. The technical problems of low environmental adaptability and low tracing accuracy in the autonomous tracing implementation process of the robot in a closed environment are solved.

The invention discloses an air conditioner air outlet partition flow guide control system in the field of smart home, which comprises an air conditioner indoor unit, the bottom of the air outlet end of the air conditioner indoor unit separately communicates with a plurality of independent air outlets and corresponding temperature adjusting cavities, and the inner side wall of each air outlet is rotatably connected with a plurality of up-down swing plates and left-right swing plates. An image sensor, a pickup microphone and a control unit are integrated in the air conditioner indoor unit, the motor, the temperature sensor and the air speed sensor are all connected with the control unit, and the control unit is used for automatically adjusting the air supply temperature and the air speed of an air outlet close to a user according to identity information, position information, environment preference and the like of the user. The swinging angles of the up-down swinging plate and the left-right swinging plate are controlled. The intelligent air conditioner is scientific and reasonable, the multiple air outlets, the temperature adjusting cavity, the image sensor, the pickup microphone and the control unit are integrated, identification and tracking of identity information, user positions and environment preferences are achieved, and accurate regulation and control of air supply parameters are conducted.

The invention discloses a cyclic utilization management method, system and device for digital logistics packaging materials, and relates to the technical field of logistics packaging recovery, and the method comprises the steps: distributing a unique digital identifier for each logistics packaging material, building a cyclic utilization database of the packaging materials, and introducing a block chain technology to carry out the storage and updating of data. The central management system carries out deep analysis on collected and recorded correct data, a machine learning algorithm and a big data analysis technology are applied, a personalized cyclic utilization scheme is formulated according to different types of packaging materials and specific conditions of the packaging materials, and a user scans a digital identifier by logging in an account to obtain detailed information of the logistics packaging materials. According to the method and the system, the unique digital identifier is distributed for each logistics packaging material, so that accurate tracking and efficient management of the packaging materials can be realized, and the authenticity and non-tampering property of the data are ensured through the unique digital identifiers and the block chain technology.

The application discloses a network-constructed photovoltaic station precise active reserve control method and system, relates to the photovoltaic power generation and power system control technical field, and the method comprises the following steps: in the MPPT mode, a global iterative search is carried out on the power-voltage curve of the photovoltaic array by using an intelligent optimization algorithm, and the maximum power point of the photovoltaic array is located; whether the intelligent optimization algorithm converges to the maximum power point is judged by using a multi-index convergence monitoring condition, and the target reserve power is calculated in the case that the intelligent optimization algorithm has converged; the network-constructed photovoltaic station enters a weighted soft switching transition period, and the duty cycle is adjusted based on a dynamic weight; after the weighted soft switching transition period ends, the network-constructed photovoltaic station is switched to an active reserve mode, and in the case that the environment mutates, the network-constructed photovoltaic station is switched back to the MPPT mode. The application realizes precise reservation, stable tracking and fast frequency modulation response of the active reserve of the network-constructed photovoltaic unit.