378results about How to "Improve system stability" patented technology

The invention discloses an unmanned aerial vehicle cluster control and navigation method based on MAPPO. The method comprises the following steps: extracting basic characteristic data of an unmanned aerial vehicle, calculating a comprehensive capability index of the unmanned aerial vehicle through dynamic characteristic analysis and standardization processing, and constructing a cluster isomerism matrix; calculating a communication quality index according to the real-time communication data, predicting link reliability data, constructing a dynamic communication graph, and generating an optimal communication topological structure; based on the cluster isomerism matrix and the optimal communication topological structure, calculating a task allocation fitness matrix, and constructing a hierarchical strategy model and a hierarchical value function; performing multi-dimensional analysis on the communication data, generating an information importance index, calculating transmission priority data, generating a bandwidth allocation matrix, and constructing a self-adaptive routing strategy; and calculating a strategy uncertainty index, constructing a robustness constraint condition, optimizing a strategy model and realizing distributed execution. According to the invention, the cooperative control efficiency and the system reliability of the heterogeneous unmanned aerial vehicle cluster are improved.

The invention discloses a multi-point ditch flow monitoring method, and particularly relates to the field of branch ditch flow monitoring, which is used for solving the problem of complex branch ditch flow monitoring and optimization, and comprises the following steps of: constructing a branch ditch topological connection relation matrix, generating a flow characteristic curve in combination with flow and water level time sequence data, and marking interference points; generating an interference incidence matrix by utilizing two-channel interaction analysis, extracting upstream and downstream flow offset and disturbance propagation characteristics, judging an interference dominant mode according to a difference degree, and respectively calculating a flow offset compensation matrix and a disturbance propagation correction matrix; the recursive optimization model realizes segmented correction and reduces error residual errors, combines spatial-temporal feature clustering to identify abrupt change sections and generate a high-risk area key monitoring list, extracts refined data based on a high-frequency acquisition strategy to analyze abnormal causes, generates a dynamic correction result by using a prediction model, and updates a traffic balance data set. The multi-mode interference is accurately corrected, the flow distribution is optimized, and the monitoring precision and the system stability are improved.

The invention belongs to the technical field of power system monitoring, and particularly relates to a wireless cable temperature measurement system and method, and the system comprises an operation information collection module, a layout planning analysis module, a data standardization module, a change trend analysis module, an early warning threshold adjustment module, and a fault positioning analysis module. According to the invention, cable operation data and environment information are analyzed, the coverage range, data transmission delay and signal attenuation are considered, the layout of temperature sensors is optimized, the response speed and data accuracy of a temperature sensor network are enhanced, temperature data are formatted and processed, time sequence analysis is carried out, and the temperature change trend is evaluated. Abnormal temperature risk recognition is achieved in the early stage, the accuracy of fault prediction is improved, the cable temperature risk monitoring threshold value is adjusted in combination with cable specifications and meteorological information, the timeliness and effectiveness of risk management are improved, the fault response speed and the system stability are improved, the maintenance cost is reduced, and the reliability and safety of a power grid are improved.

The invention relates to the technical field of mechanical arm control, in particular to a mechanical arm and a control method thereof. The method comprises the following steps that real-time load information of all joints of the mechanical arm in the operation process is obtained, the acceleration and rotational inertia of the mechanical arm are monitored in real time, and real-time inertia force data are generated; the torque compensation amount needed by the mechanical arm is calculated according to the real-time inertia force data, and torque compensation control data are generated; performing dynamic analysis on the torque compensation control data, and performing real-time adjustment on the output torque of the servo motor based on the current load and inertia change condition to generate inertia compensation data; and the motion path and the motion speed of the mechanical arm are dynamically optimized according to the inertia compensation data, and path optimization data are obtained. Through real-time monitoring, dynamic adjustment and intelligent optimization, the operation precision, the movement efficiency, the stability and the self-adaptive capacity of the mechanical arm in the complex working environment are remarkably improved.

The invention provides a resource adjustment method and device for edge computing nodes, equipment and a medium, and belongs to the technical field of computers.The method comprises the steps that the use condition and service scene data of target resources associated with all the edge computing nodes are monitored, the target resource comprises at least one of a central processing unit (CPU), a memory and a network bandwidth; according to the monitored data, a target scheduling strategy is selected from a pre-established scheduling strategy library, and the scheduling strategy library comprises resource scheduling strategies for different service scenes and resource use conditions; and according to the target scheduling strategy and the current use condition of the target resource by each edge computing node, adjusting the resource currently allocated to each edge computing node.

The invention discloses a ship classification method based on multi-source information feature level fusion, and the method comprises the steps: firstly obtaining the HRRP and SAR original radar data of a ship target, carrying out the preprocessing of the HRRP original radar data, and generating two formats of HRRP samples; secondly, performing feature extraction on the HRRP sample in the first format to form traditional features; secondly, constructing a sequential network model branch, taking an HRRP sample in a second format as input, embedding a traditional feature, and outputting a mixed feature; a two-dimensional convolution model branch is constructed, SAR original radar data passes through a lightweight CNN network, and ship target structure features are extracted. And finally, designing a feature fusion module of an attention weighting mechanism, carrying out weighted fusion on the mixed features and ship target structure features, and outputting a ship classification result. According to the method, the problem that automatic target radar recognition is limited in recognition precision and performance based on single-modal data is solved, and the classification accuracy is improved.

The invention relates to the technical field of sensor upgrading, in particular to a remote upgrading method and system for a tire pressure sensor. The method comprises the following steps: acquiring real-time state data of a tire pressure sensor; carrying out outlier parameter elimination on the real-time state data of the tire pressure sensor, and carrying out state diagnosis mining, so as to generate a state diagnosis report of the tire pressure sensor; performing upgrade demand prediction based on the tire pressure sensor state diagnosis report, and performing priority upgrade analysis to extract a plurality of upgradable sensors; performing byte-by-byte difference identification on the plurality of upgradable sensors based on preset latest version information, and performing incremental data encapsulation to generate a plurality of incremental upgrading data blocks; acquiring a historical running log of the vehicle; performing dynamic vehicle using behavior analysis and vehicle idle time period prediction on the vehicle historical operation log to generate a plurality of idle prediction time periods; the upgrading efficiency and the safety performance of the tire pressure sensor are improved.

The invention discloses a high-flexibility structure nonlinear aerodynamic damping estimation method based on a Sage-Husa algorithm and square root cubature Kalman filtering. According to the method, aerodynamic damping with nonlinear characteristics can be effectively identified from free vibration data, forced vibration data, steady-state vibration data and random vibration data of high-flexibility structures such as high-rise buildings and fan towers under crosswind excitation, and process noise can be automatically adjusted according to the current state; the interested parameters of the high-dimensional system are accurately predicted, the noise level does not need to be known sufficiently, and a theoretical basis can be provided for evaluating the structural response by calculating the aerodynamic damping of the high-flexibility structure more conveniently and accurately in actual engineering. The invention further discloses a high-flexibility structure nonlinear aerodynamic damping estimation system based on the Sage-Husa algorithm and the square root cubature Kalman filter and a storage medium.

The invention discloses a distributed energy dynamic load adjusting method and system based on a virtual power plant, and relates to the technical field, and the method comprises the following steps: obtaining first data of the virtual power plant, and building a topological structure of the virtual power plant; constructing a distributed energy output prediction model based on an LSTM deep learning algorithm; combining the output of the distributed energy output prediction model and the topological structure to generate an energy scheduling scheme in real time; monitoring real-time data of the distributed energy, comparing the real-time data with the energy scheduling scheme, and performing feedback updating; according to the invention, a virtual power plant topological structure is constructed, a prediction model is constructed based on an LSTM algorithm, an energy scheduling scheme is generated in combination with model output and the topological structure, and a feedback updating technology is carried out. The problems that existing regulation and control based on a static model cannot adapt to dynamic changes of energy production and load requirements in real time, and due to the fact that an effective cooperation mechanism is lacked between distributed energy sources, energy waste is caused, and the regulation efficiency is low are solved.

The invention relates to the field of power grid energy scheduling, in particular to a multi-microgrid intelligent energy scheduling method and system based on deep learning. The method comprises the following steps: acquiring a plurality of micro-grid multi-dimensional state parameters; performing multi-time-window change analysis on the multi-dimensional state parameters of the plurality of microgrids, and performing deep convolution learning to generate state convolution diagrams of the plurality of microgrids; identifying a plurality of microgrid nodes; performing node relative position analysis on the plurality of micro-grid nodes one by one, and performing node topological association mining to generate micro-grid node topological association representation; performing dynamic state attribute mapping based on the micro-grid node topology association representation and the state convolution graphs of the plurality of micro-grids, and constructing a micro-grid topology twinborn model; and performing power grid state trend evolution on the state convolution graphs of the plurality of micro-grids one by one, and performing energy demand prediction to generate a plurality of micro-grid energy demand prediction values. According to the invention, high-efficiency and high-utilization-rate energy scheduling is realized.

The invention relates to a time-frequency system integrity monitoring method, device and equipment and a storage medium. The method comprises the following steps: acquiring a time difference measurement result of each time-frequency signal link and a temperature measurement result of a time-frequency system; and inputting the time difference measurement result and the temperature measurement result into a time difference measurement combination estimation model to generate a temperature change coefficient and a noise normalization coefficient. And according to the temperature change coefficient and the temperature measurement result, calculating a temperature removal time difference measurement result to obtain a first time difference measurement result, and according to the first time difference measurement result and a preset sliding window length, constructing a factor graph model. According to the factor graph model, time difference forecast deviation and time difference frequency deviation of each time frequency signal link are estimated, and according to the time difference forecast deviation, the time difference frequency deviation, the noise normalization coefficient and preset integrity monitoring parameters, an RAIM algorithm is adopted to carry out integrity monitoring on the time frequency system. By adopting the method, the deviation alarm efficiency and accuracy of the time-frequency system can be improved.

The invention discloses an intelligent assistance method and system for software development, particularly relates to the field of software development assistance, is used for solving the problem of code snippet screening and recommendation, and aims to analyze an abstract syntax tree of code snippets through a systematic method, construct a dependency graph and a time sequence dependency graph, and calculate early warning isolation and a functional functional rate. Screening high-value code snippets by combining the indexes; converting the code snippets into high-dimensional vectors by using a deep learning model, capturing grammar and semantic features, understanding the intention of a developer in real time through a situation analysis model, and adjusting a code recommendation strategy; clustering and classifying the code snippets by using an unsupervised learning algorithm to form a code snippet library with similar semantics and related functions; the accuracy of classification, clustering and similarity calculation of the code snippets is improved, code search and recommendation are effectively supported, the development process is optimized, the reuse rate and the system stability are improved, and the maintenance cost is reduced.

The invention discloses a dynamic power distribution method for a direct current charging pile, and relates to the technical field of power distribution of the direct current charging pile, and the method specifically comprises the following steps: obtaining the dynamic power characteristic information of all electric vehicles which are accessed to the direct current charging pile for charging in real time, and carrying out the analysis after obtaining, respectively generating a fluctuation risk index and a dynamic load adaptability coefficient of each electric vehicle; and constructing a fluctuation sensitivity evaluation model for the generated fluctuation risk index and the dynamic load adaptability coefficient of each electric vehicle, generating a fluctuation sensitivity evaluation coefficient of each electric vehicle, analyzing the fluctuation sensitivity evaluation coefficient after generation, and evaluating the sensitivity degree of each electric vehicle to the bus voltage fluctuation. And the electric vehicles are divided into high-sensitivity vehicles, medium-sensitivity vehicles and low-sensitivity vehicles according to evaluation results. The problem that charging of sensitive vehicles is interrupted due to the fact that the direct-current charging pile cannot accurately distribute power is solved, and the stability and resource utilization efficiency of a charging system are improved.

The invention relates to the technical field of photovoltaic energy storage methods, and discloses a method for optimizing hybrid photovoltaic energy storage based on a Perturbobserve algorithm, which comprises a load, a photovoltaic power generation subsystem, an energy storage subsystem, a control and management system and a grid-connected system. The specific control method comprises the following steps: S1, obtaining an illumination intensity value within one day through a meteorological station, and obtaining a power grid net load value within one day through a voltage sensor and a current sensor; s2, taking economy optimization as a target, considering photovoltaic energy storage capacity constraints, and establishing an energy storage SOC control strategy target function; and S3, solving is carried out by using a PerturBobserve algorithm, and an optimal control pulse sequence is obtained. By optimizing the charging and discharging time and intensity of the energy storage battery, the consumption rate of photovoltaic power generation is maximized, the light abandoning phenomenon is reduced, and the energy loss in the charging and discharging process is reduced. Meanwhile, the PertuBobserve algorithm can avoid the excessive charging and discharging of the energy storage battery while considering the system load, prolongs the service life of the battery, and solves the problem of insufficient management of the service life of the battery in a traditional method.

The invention belongs to the technical field of wireless communication, and discloses an optimization method based on active reasoning in an MEC-enabled low-altitude element cosmic system, which realizes low energy consumption and low delay of Internet of Things equipment on the premise of ensuring user service quality through joint optimization of a flight path and a resource allocation strategy of an unmanned aerial vehicle, and improves user experience. And meanwhile, an auxiliary means is provided for improving the economic benefits of operators. Aiming at the dynamic nature of the network, the uncertainty of information acquisition and the incompleteness of data use, an optimization problem is converted into a Markov decision process, an improved deep learning algorithm based on active reasoning is adopted, factors except the reward dimension are additionally considered on the guidance of dynamic learning, and the dynamic learning algorithm is optimized. And a personalized strategy is explored according to the preference of the intelligent agent.

The invention relates to the technical field of bioinformatics, in particular to a mining method and system for synthetic biology functional elements and a storage medium. The method comprises the following steps: acquiring functional demand data of a target metabolic pathway; performing evolutionary distance analysis and function prediction on homologous genes of a plurality of species according to the function demand data, and performing protein structural domain conservative classification on preset genome sequence data to obtain candidate element cluster data with function relevance; obtaining expression system data of the host cells; and according to the candidate element cluster data and the expression system data, scoring each functional element based on codon optimization degree, protein folding stability and metabolic load prediction to obtain hierarchical element suitability data. According to the method, factors such as sequence characteristics, expression efficiency and regulation and control network interaction are considered at the same time, multi-level accurate screening is achieved, and therefore the low efficiency and limitation of a traditional screening mode are overcome.

The invention discloses a topological design method and system for a multi-port flexible interconnection system, and relates to the technical field of power systems, and the method comprises the steps: designing a topological structure based on a power distribution network demand objective function and constraint conditions; based on the topological structure, a model is constructed for simulation verification, and a simulation verification result is obtained; and optimizing the topological structure based on a simulation verification result. According to the topology design method of the multi-port flexible interconnection system, the multi-port flexible interconnection system technology is introduced, the particle swarm optimization algorithm and the analytic hierarchy process are combined, and systematic optimization design of equipment layout and a topology connection mode is achieved. Compared with a traditional method, the dynamic adaptive capacity and the system stability of the power distribution network are improved, different load changes and fault conditions can be effectively handled, and the recovery time after system faults is shortened. Meanwhile, the optimized scheme has better performance in the aspects of cost effectiveness, flexibility and the like, and the reliability and effectiveness of the design scheme in actual operation are ensured.

The invention discloses a vehicle-mounted charging and discharging system based on DSP + FPGA control, and relates to the technical field of charging and discharging control, the system is composed of an AC / DC unit, a battery unit, a DC / DC unit, charging and discharging control and an AC / DC charging gun, a DSP + FPGA control architecture is adopted, the performance and reliability of the vehicle-mounted charging and discharging system are greatly optimized, and the charging and discharging efficiency is improved. The powerful computing power of the DSP can optimize the charging and discharging strategy in real time according to the battery state and the requirements of external equipment, safe and efficient charging and discharging of the battery are ensured, the service life of the battery is prolonged, the PWM pulse logic protection realized by the FPGA effectively prevents the power device from being damaged, and the service life of the battery is prolonged. The analog acquisition and multi-path input / output extended function enhances the processing and interaction capability of the system to various signals, and the analog acquisition and multi-path input / output extended function and the multi-path input / output extended function work cooperatively, so that the system can quickly and accurately deal with various working condition changes, the stability and accuracy of the charging and discharging process are improved, and the system fault risk is reduced. And a powerful guarantee is provided for long-term stable operation of the vehicle-mounted charging and discharging system.

The present application relates to the field of wastewater treatment technology, and specifically to a bacterial algae symbiotic treatment agent for aquaculture wastewater, and its preparation method and application. The bacterial algae symbiotic treatment agent of the present application comprises the following components per liter: 100-150g of adsorption carrier, 20-50mg of microalgae, 50-80mL of bacterial seed liquid, and distilled water to 1L; the bacterial seed liquid is taken from aquaculture wastewater, and the concentration of the bacterial species in the bacterial seed liquid is 10 7 ‑10 9 CFU / mL. The bacteria-algae symbiotic treatment agent uses microalgae and bacteria taken from aquaculture wastewater to form a relatively stable bacteria-algae symbiotic system, which can be directly added to aquaculture wastewater for wastewater treatment, and can effectively remove COD, total nitrogen and total phosphorus in aquaculture wastewater, realizing the recycling of resources, and has good convenience of use and water purification effect.

The invention provides a method for synchronizing identity data based on event driving. The method comprises the following steps: S1, event subscription; s2, event monitoring; s3, task scheduling; s4, data processing; s5, sending the data; s6, receiving the data; s7, state feedback; and S8, logging and auditing. According to the event-driven identity data synchronization method provided by the invention, through an intelligent and real-time data processing strategy, a plurality of pain points in a traditional timing synchronization mode are effectively solved, the real-time performance and accuracy of data synchronization and the utilization efficiency of system resources are remarkably improved, and the method has great significance in constructing efficient and reliable digital infrastructures and has great significance in the field of data synchronization. The method has important significance in supporting enterprises or organizations to keep competitive power in a rapidly changing market environment, a traditional data synchronization mode is changed fundamentally, real-time sensing, instant processing and accurate synchronization of identity data changes are achieved, and therefore the consistency and timeliness of data and the use efficiency of system resources are greatly improved.

The invention relates to the field of printing ink, and discloses UV fission printing ink for outer packaging of cigarette cases and a preparation method of the UV fission printing ink. The UV fission ink is prepared from the following raw materials in percentage by weight: 32 to 50 percent of polyurethane acrylate, 30 to 40 percent of functional monomer, 6 to 10 percent of active amine, 4 to 8 percent of photoinitiator, 6 to 8 percent of fission regulator, 2 to 4 percent of polyethylene wax, 0.5 to 1 percent of defoaming agent and 1.5 to 3 percent of flatting agent. The fission regulator is prepared from fumed silica, polydipentaerythritol hexaacrylate, a silane coupling agent and a dispersing agent. The UV fission ink prepared in the invention keeps good fluidity, irregular wrinkle structures or pattern edges formed by printing are not easy to deform, the UV fission ink has good stereoscopic impression and clear lines, and the wrinkle structures or pattern edges are not easy to discolor and fall off.

The invention relates to a linear actuator with power-off self-locking and constant supporting force. The linear actuator comprises a driving assembly, a gas spring unit, a braking assembly and a planetary roller lead screw pair. The driving assembly converts electric energy into rotating kinetic energy to drive the actuator to operate. The air spring unit provides constant supporting force through a built-in high-pressure air cavity, and the pressure is adjusted through an inflation valve so as to dynamically adjust the supporting force. The braking assembly realizes power-off self-locking through a movable permanent magnet and a liquid sealing structure; the planetary roller lead screw pair converts rotation kinetic energy of the driving assembly into linear motion. The technical problems that an existing linear actuator is high in energy consumption, unstable in supporting force and lack of a self-locking function are solved.

The invention discloses a dynamic adaptation and multi-level feedback control solid heat storage system and method.The system comprises a heat storage unit, a feedback control system, a self-adaptation control module and a dynamic flow adjusting device, the heat storage unit is connected with the feedback control system and the self-adaptation control module, the self-adaptation control module is connected with the feedback control system, and the dynamic flow adjusting device is connected with the feedback control system. The dynamic flow adjusting device is arranged on the heat storage unit and connected with the feedback control system and the self-adaptive control module. The heat storage unit comprises a plurality of subareas, and a plurality of heat storage modules which are connected in series and in parallel are arranged in each subarea; the feedback control system comprises a multi-point sensor and is used for monitoring temperature and flow data of each subarea; the self-adaptive control module is used for dynamically adjusting the flow and the temperature of each partition in the heat storage unit by utilizing a multi-level feedback algorithm based on real-time data of the feedback control system; the dynamic flow adjusting device is used for adjusting flow distribution of all the subareas in the heat storage unit according to the load requirement.

The invention discloses an intelligent expansion machine residual pressure power generation system and a control method thereof, relates to the technical field of residual pressure power generation, and can monitor load information of an expansion machine in real time so as to obtain a valve regulation demand value and regulate and control the valve regulation demand value. Meanwhile, the system monitors start-stop parameters in the expansion machine, analyzes a pneumatic performance evaluation value, and adjusts the rotating speed of an impeller according to an evaluation result. Besides, gas leakage data of all key positions of the expansion machine are monitored so as to assess the leakage risk and position a leakage source, finally power generation data and operation performance are comprehensively assessed, and an intelligent control decision is formed. And the operation efficiency of the expansion machine is optimized, the energy loss is reduced, the safety and reliability of the system are improved, and the overall efficiency of residual pressure power generation is effectively improved.

The invention relates to the technical field of information communication, and discloses a 5G network slice resource scheduling method for a power distribution network, and the method comprises the steps: firstly collecting the geographic coordinates and priority labels of a service request, carrying out the regional division of a service based on the coordinates, and determining the cluster center of each region; then combining the priorities and the areas to which the requests belong, carrying out hierarchical sorting on all the requests to generate a global scheduling sequence; for each request in the sequence, calculating a path distance to each network resource node according to the coordinate of the request and the cluster center, and selecting a shortest path node as a target resource node; and finally, allocating resource quotas from the resource pool of the target node in sequence. According to the 5G network slice resource scheduling method provided by the invention, the resource matching accuracy and locality are effectively improved, the service transmission delay is reduced, the network flow space distribution is optimized, and the more efficient and reliable bearing of the power distribution network service is realized.

The present invention discloses a redundant control system for charging guns of a charging pile, which belongs to the technical field of charging piles. The technical problem to be solved is that the control board fails in the case of a single gun and a single control, resulting in failure to charge. It includes a dual-channel control board, which is a state acquisition board and a charging control board; the state acquisition board is configured with a gun line state acquisition interface, a CAN interface and a processor MCU, wherein the processor MCU serves as a state acquisition MCU; the charging control board is configured with a processor MCU, a BMS communication interface, an inter-board communication interface, a charging module communication interface and a charging module control line interface; the charging control MCU is used to control the charging process based on the gun line state and the vehicle BMS state stored in its onboard FLASH.

The invention relates to the technical field of fault diagnosis, and provides a nuclear power station equipment fault diagnosis method and system, which can reflect the state of each piece of equipment and the health condition of the whole system in real time by collecting equipment operation data in real time and dynamically constructing and updating an equipment relation graph by using physical connection and function dependency relationship among the equipment. Besides, probability calculation of equipment fault influence is introduced in the method, and the possible influence of the fault equipment on other parts of the system can be accurately analyzed based on the total score of the equipment and the dependency relationship between the equipment, so that quicker and more effective response is made; the problem that an existing method is difficult to accurately analyze the complex dependency relationship and the interaction influence between devices is solved.

The invention discloses an automatic water-cooled power supply host system with independent partitions, and belongs to the technical field of power electronics, and the system is characterized in that the system comprises a host body, the host body is internally provided with a partition shelf, and the partition shelf is internally provided with a cavity. Each cavity is provided with an independent cooling coil, when a heating element corresponding to a certain cavity works to generate heat, a liquid pump sucks cooling liquid from an inlet pipe, the cooling liquid is accurately distributed through a flow dividing pipe and a flow control structure and then enters the cooling coil of the cavity, and by means of the independent cooling mode, when one cavity breaks down, the cooling liquid can be cooled by the liquid pump. The cooling liquid circulation is only influenced in the loop of the cavity, other cavities can still work normally, the reliability of the system is greatly improved, the overall downtime caused by local faults is shortened, and meanwhile, due to the design of the independent cavities, workers can quickly position fault points conveniently, and only the fault cavity is maintained and repaired.

The invention discloses a remote disease and pest monitoring system, and the system comprises a data obtaining module which is used for obtaining environment data and disease and pest image information of a target region; the analysis module is used for analyzing the image information based on a pre-trained deep learning model so as to improve the pest and disease identification precision; the communication early warning module is used for transmitting the recognition result to the remote terminal, displaying the recognition result on the remote terminal and carrying out early warning, the remote disease and pest monitoring system can remarkably improve the recognition precision and the system stability, the image definition and the detail capturing capability under different illumination conditions are enhanced, and the remote disease and pest monitoring system is high in practicability. The method can more effectively cope with the complex ecological environment of the target area, reduces the interference of weeds and non-target crops, improves the recognition accuracy, improves the precision of pest and disease damage monitoring, protects the environment, provides data support for pest and disease damage prevention and control, and reduces the damage of pest and disease damage to garden crops.

The invention relates to the technical field of photovoltaic power generation, in particular to a photovoltaic power generation system performance enhancing system and method based on a self-adaptive MPPT algorithm, through mutual cooperation of a photovoltaic panel battery, an MPPT controller, an inverter, a DC-DC converter, a logic control circuit and a storage battery, the working point of the photovoltaic panel battery is adjusted in real time by using the MPPT controller, and the performance of the photovoltaic power generation system is enhanced. The method comprises the following steps of: controlling a control algorithm module to ensure that the maximum power output is obtained under the current environmental condition, quickly responding to the change of illumination intensity and temperature through the control algorithm module, adjusting a working point to match the optimal power output, and automatically adjusting according to reduced illumination in cloudy or rainy weather, so as to continue to operate at the most efficient power point. Therefore, a high energy utilization rate can be kept under different weather conditions, real-time adjustment under different illumination and temperature adjustment is realized, and the overall power generation efficiency and the system stability are remarkably improved.