41 results about "Logistic function" patented technology

The invention discloses an interactive garden design method and system based on landscape garden simulation, particularly relates to the technical field of garden landscape design and computer graphic simulation, and relates to a plurality of modules of environmental data acquisition, three-dimensional modeling, plant growth simulation, water simulation, interactive design, microclimate prediction and the like. According to the method, terrain, climate and soil data are collected through a laser radar and multiple sensors, and a standardized environmental map is constructed; generating a plant topological structure based on a context sensitive type L system, and simulating a growth process by adopting a Logistic function in combination with an environmental factor; the method comprises the following steps: realizing dynamic simulation of a water body by utilizing an SPH particle method and a Navier-Stokes equation, and establishing a plant-water body feedback model; and real-time operation and design optimization are supported through a three-dimensional interaction interface. The system has the advantages of being high in ecological adaptability, high in visualization degree, perfect in feedback mechanism and the like, the intelligence and scientificity of garden design are remarkably improved, and the system is suitable for various scenes such as urban green lands and ecological restoration.

The invention provides a vegetation index spatio-temporal fusion method considering farmland spatio-temporal heterogeneity, which comprises the following steps: simultaneously acquiring high-resolution (fine-resolution) and low-resolution (coarse-resolution) satellite remote sensing data of a target area, preprocessing the satellite remote sensing data, carrying out field parcel segmentation on the high-resolution remote sensing data, and carrying out field parcel segmentation on the low-resolution remote sensing data; optimizing a selection strategy of similar pixels by taking the field parcel as a unit; vegetation index time series data of the high-quality coarse-resolution image are constructed through data reconstruction; fitting a crop vegetation index growth curve of the fine-resolution image by adopting a Double Logistic function, and generating a fine-resolution pixel initial value at a prediction moment; constructing an optimization model and optimizing a fine pixel vegetation index value in combination with coarse and fine resolution pixel data; weighted distribution is carried out based on the spatial consistency in the field, and a prediction image is optimized in combination with spatial filtering. According to the method, the problems of unreasonable assumption of a traditional space-time fusion model on crop spectrum linear growth in a high-heterogeneity farmland scene and inaccurate similar pixel matching are effectively solved, the fusion precision and robustness are improved, and the method has a wide application prospect.

The invention discloses a multi-sensor cooperative partial discharge waveform identification method and system, and the method comprises the steps: extracting an envelope line of an oscillation waveform through Hilbert transformation for a collected partial discharge signal, carrying out the Logistic function fitting and exponential decay function fitting of each sensor signal, and carrying out the recognition of the partial discharge waveform through the Logistic function fitting and exponential decay function fitting. Taking the characteristic parameters extracted by each sensor as node characteristics in a graph structure, establishing an edge connection relation in a graph according to the maximum value starting time difference and the similarity of signals between the sensors, constructing a graph neural network model, performing training and reasoning by utilizing the graph structure, and finally outputting a recognition result of whether a partial discharge event occurs or not; the system comprises a signal conversion module, a feature extraction module, a data fusion module and a partial discharge diagnosis module. According to the invention, the accuracy and adaptability of partial discharge signal identification are improved, and the on-line monitoring requirement of the transformer in a complex electromagnetic environment is met.

The invention provides a wind turbine generator self-adaptive short-time frequency support method based on a system view angle, which comprises the following steps: considering the frequency modulation power response speed cooperation of a wind turbine generator and a thermal power generating unit, and establishing a power track model in a frequency support stage of the wind turbine generator; establishing a wind power frequency modulation power peak value calculation method and a wind power frequency modulation power rise time establishment method under a composite working condition based on a load disturbance degree, a wind power permeability level and a thermal power generating unit output level under the constraint of a safe and stable operation domain of a wind power generating unit according to design parameters of a wind power track model in a support stage; and designing an output power model of the wind turbine generator in the frequency support quit stage by adopting a Logistic function. According to the method, the lowest point in the dynamic process of system frequency response can be remarkably improved, the risk of triggering low-frequency load shedding is effectively reduced, and the frequency overshoot phenomenon is inhibited. Meanwhile, in the implementation process of the method, the decreasing amplitude of the rotating speed is small, the time needed for supporting is short, and economic benefits of wind power manufacturers are taken into account while the power grid frequency supporting requirement is met.

The invention discloses a spatial data management method and system based on artificial intelligence, and relates to the technical field of spatial data management, and the method comprises the steps: defining the number of wavelet decomposition layers, calculating a self-adaptive threshold value, screening edges of an incidence matrix, generating a sparse incidence matrix, constructing a projection matrix, and projecting a feature matrix to a feature vector space through employing a matrix multiplication method. Generating a comprehensive feature matrix; and using a logistic function to define a nonlinear stream function, forming state vectors, calculating a mean value of the state vectors, fusing the mean value with the node feature matrix to form a dynamic feature matrix, and constructing a graph convolutional network model to predict the fault probability of the nodes. According to the method, fine-grained dynamic features of spatial data are accurately mined through combination of multi-scale wavelet decomposition and adaptive threshold screening, feature energy screening and a cosine distance matrix are introduced, the discrimination ability of feature selection and dimension reduction is improved, and the spatial data are extracted by using the graph convolutional network and combining a sparse embedding mechanism. And the generalization ability of the model in node fault prediction is obviously enhanced.

The invention discloses a typhoon disaster risk assessment method for quantifying wind and rain combined loss, and belongs to the field of typhoon disaster risk early warning and loss assessment, and the method comprises the steps: determining the grid range of a typhoon core disaster area based on typhoon center path data and wind circle size data; constructing a typhoon wind and rain comprehensive index by using the wind speed and rainfall rasterized data sequence; the counted county-level typhoon disaster direct economic loss is scientifically distributed to a grid scale; taking the calculated typhoon wind and rain comprehensive index as an independent variable, taking rasterized typhoon disaster direct economic loss statistical data as a dependent variable, adopting a four-parameter Logistic function for fitting, and constructing a typhoon disaster vulnerability curve model. According to the method, the typhoon vulnerability curve model considering the wind and rain disaster-induced capability is constructed, the mathematical relationship between the economic loss and the disaster intensity is established, the limitation of a traditional qualitative evaluation method is overcome, and a scientific and accurate quantitative basis is provided for the economic influence of typhoon disasters.

The invention discloses a wind turbine generator multi-stage self-adaptive droop control method considering rotor kinetic energy constraint and frequency change rate, which comprises the following steps: 1, constructing a rotation speed influence factor based on a Logistic function, mapping a rotor rotation speed into a frequency modulation contribution coefficient, and quantitatively evaluating a rotor kinetic energy releasable range of a fan under different operation conditions; 2, designing a self-adaptive droop control strategy based on a segmented frequency change rate, adjusting a droop coefficient in real time by identifying a frequency change rate interval, forming a hierarchical response mechanism, providing strong active support in a sharp frequency drop stage to quickly suppress frequency drop, and converting into moderate support in a frequency recovery stage to maintain stability and prevent secondary drop; and 3, building a wind-fire combined system simulation model, and verifying the effectiveness of the provided strategy in the aspects of improving the frequency response characteristic of the system and improving the frequency stability through a multi-scene contrast experiment. According to the method, the frequency regulation capability of the power system under the high wind power permeability can be remarkably enhanced.

The present application relates to a soft peak clipping method and device for suppressing peak-to-average ratio (PAR) for OFDM, and relates to the field of signal processing technology. The method comprises: calculating the envelope amplitude value of the in-phase and quadrature signals based on the in-phase and quadrature components of the in-phase and quadrature signals obtained by OFDM modulation; calculating the envelope amplitude value of the in-phase and quadrature signals after peak clipping based on a preset peak clipping function and the envelope amplitude value of the in-phase and quadrature signals; wherein the peak clipping function is determined by a peak clipping threshold and a basic function, the peak clipping threshold is determined by the PAR, and the basic function includes any one of a Rapp model function, a Logistic function, and an inverse tangent function; and reducing the in-phase and quadrature components based on the envelope amplitude values ​​of the in-phase and quadrature signals before and after peak clipping. The present application can ensure that the OFDM signal after peak clipping meets the requirements of the spectrum emission template, while ensuring that the error vector amplitude performance meets the requirements.

The invention discloses a prediction model for representing a relationship between a magnetic Barkhausen noise characteristic value and a stress, and the prediction model for representing the relationship between the magnetic Barkhausen noise characteristic value and the stress is constructed by using a logistic function based on a generation mechanism of the magnetic Barkhausen noise and an action mechanism between the magnetic Barkhausen noise and the stress. The model contains three undetermined characteristic parameters, and the three parameters communicate with a physical coupling relationship between a detection characteristic value and stress. And the prediction model can be trained through small sample experiment data, so that three characteristic parameters of the model are determined. The prediction model can provide theoretical basis and key technical support for subsequent development of low-cost portable stress monitoring instruments and meters.

The invention discloses a rack testing device of a vehicle intelligent system and an acceleration safety evaluation method thereof, and belongs to the technical field of automobile safety, and the device comprises a vehicle testing cabinet, a wire control movable rack, a wire control steering rack, and a self-driving scene display system. According to the method, a tested system is iteratively fitted through a Gaussian process and a logistic function, and an agent model efficiently approaching the real tested system is generated to predict a security test result of the system; three acquisition functions are introduced to respectively explore the whole multi-dimensional continuous scene parameter space, namely, the safety performance of the system is represented by a small number of test scenes; accurately positioning a system security boundary; unsafe areas can be quickly identified, and dangerous scenes can be quickly found; and finally, giving the unsafe probability of the tested system in the test scene. The device and the method provided by the invention have relatively high test efficiency on the premise of ensuring the reliability and the accuracy of the vehicle intelligent system bench test.

The invention discloses a tea garden ecological condition assessment method based on a laser radar point cloud, and belongs to the technical field of agricultural ecological monitoring, and the method comprises the following steps: S1, collecting tea garden point cloud data through an unmanned aerial vehicle-mounted laser radar system; s2, extracting key ecological parameters including canopy density, leaf area index, three-dimensional green quantity, gradient and spatial heterogeneity; s3, analyzing and determining a key threshold value of each parameter based on an empirical cumulative distribution function; s4, respectively adopting a Logistic function, an inverted S-shaped curve and a piecewise linear scoring method to construct a scoring model of each parameter; s5, the final weight of each scoring model is determined through grey correlation degree analysis in combination with expert scoring; s6, performing weighted calculation to obtain a comprehensive ecological index so as to evaluate the ecological condition of the tea garden; and S7, dynamically updating model parameters. According to the method, accurate and objective evaluation of the ecological condition of the tea garden is realized by means of laser radar point cloud data, the evaluation model can be optimized by a dynamic updating mechanism, and scientific guidance is provided for sustainable development of the tea garden.

The invention provides a wind turbine generator active frequency response method considering wind-fire power coordination, which comprises the following steps: establishing an improved SFR model considering the dynamic state of a speed regulator and high-proportion wind power, and solving the improved SFR model to obtain a time domain analytical solution of system frequency response; constructing a power track of a wind turbine generator frequency support stage by considering wind-fire power cooperation and frequency response peak-valley complementation under wind power frequency modulation power and load power disturbance, and solving parameters by using a time domain analytical solution of system frequency response; and designing a power track of the wind turbine generator frequency support quit stage based on a Logistic function. According to the method, the lowest point of system frequency drop under power disturbance can be improved, low-frequency load shedding can be prevented from being triggered or low-frequency load shedding turns can be reduced, and power supply reliability is guaranteed; according to the method, the initial frequency change rate can be improved, the off-network risk of distributed energy sources can be reduced, more online power generation capacity can be maintained, and cascading failures can be prevented; and popularization and application of the governor dynamic SFR model can be considered.

The invention discloses a flood disaster vulnerability curve model construction method, which comprises the following steps of: selecting an optimal daily scale extreme precipitation threshold and an optimal continuous day number, and carrying out grid-by-grid calculation to obtain annual total extreme precipitation data corresponding to a precipitation parameter combination; carrying out statistics on the land coverage change data and the slope data by adopting a density calculation method to obtain a corresponding weight matrix; in combination with the weight matrix, rasterizing county-level annual total loss data obtained through statistics by county and year; and based on the rasterized county-level annual total loss data and annual total extreme rainfall data, dynamically constructing a flood disaster loss curve by adopting a deep learning multi-objective optimization four-parameter Logistic function. The technical problems that in the existing flood disaster loss curve construction process, the data size is insufficient, precision is low, and the method is limited to flood events or local cities and cannot be popularized to a large scale and a large range can be solved.

The invention discloses an advanced driving assistance system safety assessment method and device, a medium and a product, and relates to the technical field of safety assessment, and the method comprises the steps: obtaining an observation data point of an advanced driving assistance system, building an agent model of the advanced driving assistance system based on the observation data point and combining a logit function and a logistic function based on a Gaussian process, and carrying out the safety assessment of the advanced driving assistance system. According to the method, a proxy model is constructed, a predicted value of the proxy model is obtained, three acquisition functions are adopted, an optimal candidate scene is determined based on a trust region search algorithm, and a safety evaluation result is determined according to all the optimal candidate scenes and corresponding test results. According to the method, the test efficiency of a safety evaluation algorithm can be improved, and exploration of a risk scene set, safe, convenient and accurate identification of a system and estimation of a fault probability can be completed at the same time.

The invention discloses a low-carbon park capacity configuration and optimization method for photovoltaic and electric heavy trucks. A Monte Carlo sampling method is adopted to simulate the daily driving mileage and the charging starting time of the pure electric heavy truck, and a pure electric heavy truck charging quantity model is established; the method comprises the following steps of: carrying out scientific division on peak, flat and valley periods by adopting a self-organizing map (SOM) neural network clustering method, and optimizing time-of-use electricity price and the charging amount of the pure electric heavy truck by taking a fuzzy demand response mechanism based on a Logistic function as a basis and adopting a non-dominated sorting genetic algorithm (non-dominated sorting genetic algorithm-II); an upper layer system capacity configuration planning model with the minimum park equipment investment cost as the target and a lower layer low-carbon optimization scheduling model with the minimum system operation cost as the target are established, and a green power supply mode of photovoltaic power station-charging pile-pure electric heavy truck is established. According to the strategy provided by the invention, carbon emission and economic cost are reduced, and collaborative development of green low-carbon power supply and economic benefits is realized.

This invention discloses a method for simulating soil relative humidity based on meteorological indices, relating to the field of soil moisture simulation application technology. The method includes: acquiring daily meteorological data of the target area and calculating the evapotranspiration index of previous precipitation; constructing a logistic function relationship between the previous precipitation evapotranspiration index and soil relative humidity based on the S-shaped variation characteristics of the soil moisture absorption process; fitting the parameters of the logistic function using soil relative humidity observation samples to obtain a soil relative humidity meteorological simulation model for outputting soil relative humidity simulation results. This invention solves the problems of existing drought indices such as SPEI and SAPEI failing to accurately reflect the daily dynamic changes in farmland soil moisture and the inconsistency between drought levels and actual agricultural soil moisture.

The invention relates to a line spectrum contour inflection point detection method using dynamic planning and Logistic fitting, which uses dynamic planning and Logistic fitting to realize contour inflection point detection of a line spectrum, performs data acquisition through a spectrum confocal displacement sensor, performs data cleaning in a data cleaning module, performs abrupt change point detection in a midpoint detection module, and performs detection of the abrupt change point in the midpoint detection module. The optimal mutation point can be found by using a dynamic planning method; in the data segmentation module, abrupt change data is intercepted, data of a certain window is selected before and after an abrupt change point, a Logistic function is used in the step fitting module for fitting, and an inflection point is selected through a derivative of the Logistic function in the inflection point screening module. The method can effectively solve the problem of data segmentation of a line spectrum or similar line contour sensor in measurement of object height difference and measurement of light and shade alternating objects, so that object areas with different heights can be effectively segmented, and the data analysis efficiency in a scene of the contour sensor is improved.

The invention discloses a near infrared spectrum light distance correction method for a box, and the method comprises the steps: S1, carrying out the fitting of a collected near infrared spectrum signal, introducing a penalty factor to obtain the optimal distortion degree and roughness, and obtaining an optimal fitting signal based on the optimal distortion degree and roughness; s2, performing linear regression through nonlinear transformation of a generalized logistic function to predict different weight values, and distributing weights for the acquired near infrared spectrum data so as to obtain an optimal fitting baseline; s3, performing variable screening on the corrected near infrared spectrum data by using a BOSS algorithm to determine an optimal subset of variables; and S4, utilizing a particle swarm algorithm to optimize penalty parameters and kernel function parameters of support vector regression, and establishing a prediction model.

The application discloses a kind of crop phenophase satellite remote sensing monitoring methods, utilize Sentinel-2 satellite remote sensing data, calculate crop from sowing to harvest period positive NDVI time series data and positive NDVI peak value, reverse NDVI time series data and reverse NDVI peak value;Set positive reverse NDVI difference threshold;In threshold range, take the time point corresponding to the maximum NDVI value as final NDVI peak point;At final NDVI peak point, positive, reverse NDVI time series data are spliced and segmented, and are reconstructed into continuous NDVI time series data;Using S-G filter smoothing, using Logistic function fitting, combine curve curvature method and dynamic threshold method to extract the key phenophase of crop.The application is characterized in that the improved maximum synthesis method is used, and the NDVI data is smoothed by combining S-G filtering, which greatly improves the accuracy of crop phenophase monitoring in the process of remote sensing detection, cloud, atmospheric interference and vegetation coverage change.

The present application belongs to the technical field of equipment maintenance support decision, discloses a kind of equipment maintenance task priority evaluation method and system, the present application obtains maintenance task dataset and attribute evaluation set, based on scene discrimination parameter selection evaluation path;In hesitant fuzzy evaluation path, hesitant fuzzy matrix is constructed, the expected value and variance value are calculated, the weight optimization objective function is constructed based on attribute correlation degree to solve weight vector, normalization and comprehensive evaluation sorting are carried out;In analytic hierarchy process-approximate ideal solution evaluation path, hierarchical structure model and judgment matrix are constructed to calculate weight, standardized using Logistic function, determine positive and negative ideal solution, calculate closeness degree and sort, the present application realizes the unity of wartime rapid response and daily fine evaluation by the scene adaptive double-path evaluation mechanism, effectively solves the problem of insufficient description of decision ambiguity and lack of dynamic adaptation ability.

This invention discloses a multi-sensor collaborative method and system for partial discharge waveform recognition. The method includes extracting the envelope of the oscillating waveform from the acquired partial discharge signal using Hilbert transform; fitting each sensor signal with a Logistic function and an exponential decay function; using the feature parameters extracted from each sensor as node features in a graph structure; establishing edge connections in the graph based on the time difference and similarity between the maximum values ​​of the signals from different sensors; constructing a graph neural network model; using the graph structure for training and inference; and finally outputting a result indicating whether it is a partial discharge event. The system includes a signal conversion module, a feature extraction module, a data fusion module, and a partial discharge diagnosis module. This invention improves the accuracy and adaptability of partial discharge signal recognition, meeting the online monitoring needs of transformers in complex electromagnetic environments.

The invention discloses a Bayesian inference-based high-temperature stress vegetation stomata adjustment modeling system and method, and relates to the technical field of ecological process simulation. A data acquisition module is used for acquiring and preprocessing meteorological driving data; the stomatal conductance calculation module is used for calculating basic stomatal conductance based on a Jarvis stomatal conductance empirical model; the high-temperature stress correction module is used for correcting Rubisco enzyme related parameters based on a temperature power function and constructing a high-temperature stress stomatal conductance correction function through a Logistic function; the correction parameter inversion module is used for inverting optimal parameters in combination with the actually measured carbon water flux data, the Bayesian theorem and the Monte Carlo-Markov chain algorithm; and the carbon-water flux simulation module is used for substituting the optimal parameters into a high-temperature stress stomatal conductance correction function to perform high-temperature stress correction on the basic stomatal conductance, and after the corrected stomatal conductance is obtained, the corrected stomatal conductance is coupled to the ecological process model to simulate the carbon-water flux process. The simulation precision of the model at extreme high temperature can be improved.

The invention discloses a path planning method based on a TEB algorithm, and aims to improve the quality and efficiency of path planning of a mobile robot by improving the algorithm and ensure that the mobile robot can quickly and safely complete tasks in a complex environment. The method comprises the following three main steps: constructing a map by adopting a cost map modeling strategy, rasterizing the map, establishing a world coordinate system and a plurality of robot self-related coordinate systems, and maintaining a main cost layer by a static layer, an obstacle layer and an expansion layer; constructing a mobile robot model by adopting a two-degree-of-freedom Ackerman motion model; and an improved TEB local path planning algorithm is adopted for planning. According to the improved TEB algorithm, a linear velocity factor and an angular velocity factor are introduced into a target function, the projection value of a displacement vector on a direction vector is considered in the linear velocity factor and the angular velocity factor, the linear velocity factor is mapped to a range of [0, 1] by being combined with a Logistic function, and the angular velocity factor is mapped to a range of [-1, 1] by being combined with a Softsign function, so that different driving scenes are adapted, velocity oscillation is reduced, and the driving efficiency is improved. The robot is prevented from retreating on the curve, and the track is smoother.

The present invention discloses a method for constructing a flood vulnerability curve model, comprising: selecting an optimal daily extreme precipitation threshold and an optimal number of consecutive days, and calculating the annual total extreme precipitation data corresponding to this precipitation parameter combination on a grid-by-grid basis; statistically analyzing land cover change data and slope data using a density calculation method to obtain a corresponding weight matrix; combining the weight matrix, rasterizing the statistically obtained county-level annual total loss data by county and year; and dynamically constructing a flood loss curve using a deep learning multi-objective optimization four-parameter logistic function based on the rasterized county-level annual total loss data and annual total extreme precipitation data. This method can address the technical issues of insufficient data, low accuracy, and limitations on flood events or local cities in constructing flood loss curves, preventing their application to large-scale and large-scale data.

The invention discloses an electrical cabinet early warning protection method and system, and relates to the technical field of electrical equipment fault diagnosis and protection, and the method comprises the steps: inputting a dielectric constant deviation parameter, a frequency spectrum feature vector and electrical environment monitoring data into a four-dimensional coupling model, training an LSTM network, and outputting a fault probability distribution result; based on a fault probability distribution result, utilizing Monte Carlo simulation to predict a fault time window, generating a risk level and a protection strategy code, utilizing a dynamic threshold weight mapping formula to dynamically adjust an alarm threshold, and outputting a specific instruction and three-dimensional positioning data; and optimizing the Isin model by using a quantum annealing algorithm, calculating the probability amplitude of a fault time window through a logistic function, integrating the probability amplitude, outputting a final fault prediction result, sending an early warning through an industrial communication protocol, and carrying out power-off isolation. A fault time window is predicted based on fault probability distribution through Monte Carlo simulation, multiple uncertain factors are considered, and prediction accuracy and reliability are improved.

The application discloses a power distribution network vulnerability assessment method and terminal based on typhoon gale and heavy rainfall, and realizes fine identification of composite disasters by constructing a typhoon gale and heavy rainfall composite event identification matrix at an hourly level, and overcomes the limitations of traditional daily scale and single disaster factor methods. According to the composite event identification matrix, the effective influence frequency of the typhoon gale and heavy rainfall composite event is calculated, in order to quantify the composite disaster intensity, the maximum wind speed, the rain intensity, the cumulative rainfall and the effective influence frequency are fused, the composite disaster index is calculated, and the regional adaptation degree is improved. In the aspect of establishing the vulnerability index, the Logistic function is introduced to establish the nonlinear mapping between the composite disaster index and the feeder failure rate, and the vulnerability index is formed. Furthermore, in order to solve the problem of insufficient disaster loss samples in some areas, the similarity interpolation is constructed based on the Mahalanobis distance, the vulnerability parameters of the missing areas are inferred, and the global quantitative evaluation is realized.

This invention relates to the field of water resources data technology, specifically disclosing an automatic multi-source sensing construction system for integrated water resources databases. This system includes a multi-source sensing feature calculation module, a data link encryption and encapsulation module, a self-evolving heterogeneous topology reconstruction center module, a spatiotemporal semantic holographic fusion engine module, and a blockchain trust anchoring mechanism module. The invention calculates environmental fluctuation entropy and dynamically solves for the optimal sampling frequency using a logistic function. Based on the principle of electrochemical corrosion, it uses an exponential decay model to adjust the sensor calibration cycle according to real-time soil salinity, achieving adaptive adjustment of the sampling frequency to accurately capture transient features. It also provides automatic compensation for sensor drift in highly corrosive environments, significantly improving the spatiotemporal accuracy and reliability of long-term monitoring data. This overcomes the shortcomings of existing technologies that lack dynamic calibration compensation mechanisms in highly corrosive environments, leading to severe sensor zero-point drift, distortion of long-term monitoring data, and thus misleading decision-making.

The application discloses a high-temperature stress vegetation stomatal regulation modeling system and method based on Bayesian inference, and relates to the technical field of ecological process simulation, wherein: a data acquisition module is used for collecting and preprocessing meteorological driving data; a stomatal conductance calculation module is used for calculating basic stomatal conductance based on a Jarvis stomatal conductance empirical model; a high-temperature stress correction module is used for correcting Rubisco enzyme related parameters based on a temperature power function, and constructing a high-temperature stress stomatal conductance correction function through a Logistic function; a correction parameter inversion module is used for inversing optimal parameters in combination with measured carbon flux data, Bayesian theorem and a Monte Carlo-Markov chain algorithm; and a carbon flux simulation module is used for substituting the optimal parameters into the high-temperature stress stomatal conductance correction function to correct the basic stomatal conductance under high-temperature stress, obtaining corrected stomatal conductance, and then coupling to an ecological process model to simulate a carbon flux process. The application can improve the simulation accuracy of the model under extreme high temperature.

The application discloses a salt marsh vegetation classification method, system, device, medium and product, relates to the field of salt marsh vegetation classification, and comprises the following steps: generating a vegetation index time sequence according to satellite remote sensing data; dividing a coastal zone latitude into several classification latitude zones as classification intervals, calculating the mean value and variance index of each vegetation index in each interval per month, and the mean value index and variance index of the current year; based on the normalized difference vegetation index time sequence and the enhanced vegetation index time sequence, calculating the growth period and decay period indexes of the vegetation by using a double logistic function; calculating the J-M distance of each candidate classification index on different vegetation classification samples; based on the J-M distance of the indexes on different vegetation classification samples and the preliminary classification contribution evaluation, screening out the most separable classification index, and constructing a final random forest classification model to classify the salt marsh vegetation in the coastal salt marsh wetland. The application can efficiently complete the coastal plant classification task at low cost.

The invention discloses a DQN-based electric vehicle behavior clustering and peak clipping scheduling method, and belongs to the technical field of electric vehicle and power grid interactive scheduling. The core of the method is that firstly, EV charging and discharging data and power grid operation data are collected, and a Mean Shift and K-means + + joint clustering algorithm is utilized to perform accurate grouping on EV users so as to reduce the decision dimension; then, constructing a dynamic excitation electricity price and a user response uncertainty model based on a Logistic function; then, establishing a layered optimization model; and finally, constructing a Markov decision process based on the model, and performing strategy optimization training by adopting DQN to generate a specific scheduling scheme. According to the method, the problem of curse of dimensionality caused by large-scale EV access is effectively solved, the dual goals of remarkably reducing the peak load of the power grid and optimizing and reducing the charging cost of the user are achieved, and the operation stability and the dispatching economy of the power grid are improved.