33 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 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 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 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 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.

This invention discloses a MIMU-based method for calculating drilling attitude. During drilling, real-time data on triaxial acceleration, triaxial angular velocity, triaxial magnetic field strength, and temperature are collected. Temperature drift compensation is applied to the collected data, and the quaternion derivatives of the gyroscopes are calculated. Using accelerometer and magnetometer data, the gradient directions of the accelerometers and magnetometers are calculated using a gradient descent algorithm. A motion confidence factor is introduced using a Logistic function to control the weights of the accelerometers and magnetometers. The corrected gradients are fused and fed back into the quaternion update equation, combined with the quaternion derivatives predicted by the gyroscopes, and finally, the updated quaternions are used to calculate the three-dimensional attitude of the drilling tool—azimuth angle ψ, inclination angle θ, and tool face angle.

The application discloses an image exposure correction method based on deep curve estimation. First, a pair of image exposure correction data sets are constructed, images are shot by using a camera to determine optimal exposure settings, and an image post-processing software is applied to render optimal exposure images in RAW format at different exposures to obtain overexposed and underexposed images. Then, based on the constructed data sets, a deep curve estimation network is trained to determine the parameter settings of the image-specific exposure correction curve. Finally, the parameters estimated by the deep curve estimation network are substituted into a composite exposure correction model composed of a gamma transformation and a logistic function, and the model is used to correct the image exposure. The image exposure correction method proposed in the application realizes accurate general image exposure correction. Compared with other methods on an internationally common data set, the result shows that the method can obtain a more superior exposure correction effect.

This invention proposes a gallium nitride-based semiconductor laser with a gradient peak rate electric field waveguide layer. The fitting curves of the In ion intensity distribution or In atom concentration distribution, the fitting curve of the peak rate electric field distribution, and the fitting curve of the saturated electron drift velocity distribution obtained from SIMS testing of the upper and lower waveguide layers with the gradient peak rate electric field all satisfy any one of the Logistic function, Logistic 5 function, or Nelder function, forming a high electric field barrier. Electrons transporting from the active region to the p-type layer must overcome this barrier height, suppressing electron leakage from the active region to the p-type layer. The low electric field matches the low-velocity transport of holes, avoiding enhanced hole scattering. High injection efficiency allows carriers in the active region to quickly reach the stimulated emission threshold. The saturated electron drift velocity gradually decreases from high to low, and the low saturated electron drift velocity on the active region side avoids punch-through leakage caused by high-speed electrons rushing through the active region.

The invention discloses a virtual synchronous machine grid-connected optimization control method based on three-parameter self-adaption, and the method comprises the steps: designing a three-parameter self-adaption function through a generalized logistic function, and exerting the adjustment capability of parameters in a virtual synchronous machine control strategy; and meanwhile, the change rate of the function is determined by utilizing the improved particle swarm algorithm, so that the instability of the system caused by overlarge change of the function rate is avoided, a complicated setting mode is not needed, a parameter change spike phenomenon can be better inhibited, the oscillation overshoot problem of inhibiting the output power under the condition of coping with power disturbance is enhanced, and the anti-disturbance capability of the system is enhanced.

The application provides a distributed medical auxiliary diagnosis method with privacy protection and smooth correction, comprising the following steps: S1: constructing a connected network composed of multiple hospitals or medical institutions (hereinafter referred to as nodes), the network contains n nodes, only the basic connected condition needs to be met, that is, any node only needs to be connected with part of the nodes, and can communicate with all nodes in the network, and the node does not need to be directly connected with all nodes; S2: collecting m internal characteristic data to form a vector set; S3: establishing a logistic regression problem for the medical diagnosis problem; S4: calling the Exact diffusion distributed optimization algorithm with smooth correction; S5: calculating the value of the logistic function by using the obtained and new patient characteristic data; S6: making a medical diagnosis. Different from other distributed optimization algorithms, the nodes in the Exact diffusion algorithm do not transfer gradient information and have the smooth correction effect with deviation, which brings more strict protection to the privacy data and higher prediction accuracy.

The invention discloses an energy storage configuration method for improving new energy bearing capacity based on collaborative optimization, which relates to the technical field of new energy power systems, and optimizes a load curve and solves initial energy storage configuration based on a Logistic function by constructing an upper-layer constant volume model; constructing a middle-layer simulation operation model, and calculating a bearing capacity upper limit by taking minimum system operation cost and maximum new energy bearing capacity as double targets; constructing a lower-layer optimization model, optimizing the configuration of the network construction type power supply and the phase modifier, and completing security constraint verification; and iteratively solving the three-layer model by adopting a generalized Benders decomposition algorithm, and outputting a global optimal energy storage configuration and new energy bearing capacity improvement result. According to the method, the load side, power side and system side strategies are optimized in a layered and collaborative manner, economy, reliability and safety are considered, and the new energy bearing and absorption capacity of the power grid is effectively improved.