91 results about "Gaussian mixture distribution" patented technology

The invention discloses a deep reinforcement learning-based cooperative control method for an expressway confluence area, and the method comprises the steps: building a LiikeSim-Python joint simulation environment, and setting a coil detector in the simulation environment to obtain the traffic flow data of the upstream and downstream of the expressway confluence area; an EM algorithm based on Gaussian mixture distribution is used as a traffic state classifier, traffic flow data of the highway confluence area are used as input, and traffic states of the highway confluence area are divided; designing a state space, an action space and a reward function; taking the state space of the highway confluence area as input, taking the actions of the variable speed-limiting intelligent agent and the ramp metering intelligent agent as output, and constructing a network model of multi-agent sharing experience under time sequence characteristics; an independent experience pool is set for each of the variable speed limit agent and the ramp metering agent, and interaction experience of the agents and the traffic simulation environment is collected with the control period as the frequency; training an agent model by using the sampled samples; and realizing cooperative control of the highway confluence area by using the trained intelligent agent model. According to the invention, the traffic travel delay of the highway confluence area can be reduced.

The invention discloses a dam progress simulation parameter dynamic updating method based on incremental learning. The dam progress simulation parameter dynamic updating method mainly comprises the steps of obtaining a construction sensing data stream; carrying out abnormal value detection on the construction sensing data flow by adopting an isolated forest algorithm; a cubic spline interpolation method is adopted to carry out interpolation filling on vacancy values in the construction perception data flow; performing simulation parameter distribution modeling on the time sequence data column after interpolation filling by using Gaussian mixture distribution; after Gaussian mixture distribution modeling is realized by using an online kernel density estimation method, continuously learning newly input construction sensing data stream data after interpolation filling processing, updating new Gaussian mixture distribution, and outputting dynamically updated simulation parameters; the rock-fill dam construction simulation parameters are input into the rock-fill dam construction simulation model, the rock-fill dam construction simulation model changes along with dynamic changes of the construction simulation parameters, dynamic changes meeting the actual site are output and generated, high-quality intelligent construction of the engineering site is supported, and decision support is provided for dam progress management.

The invention discloses a power distribution network operation risk assessment method and device, electronic equipment and a storage medium, and relates to the technical field of power systems or other related fields, and the method comprises the steps: obtaining the charging power data of N electric vehicles in a target time period, N being a positive integer; node load flow calculation is carried out based on the charging power data, and probabilistic load flow expressions of N electric vehicles are established; based on the load flow calculation result and the probabilistic load flow expression, establishing a Gaussian mixture distribution body to obtain a Gaussian mixture probability model; and acquiring real-time charging power data of the N electric vehicles, and performing operation risk assessment on the target power distribution network based on the real-time charging power data and the Gaussian mixture probability model to obtain an assessment result. According to the invention, the technical problem of low evaluation efficiency and accuracy caused by high calculation complexity of power distribution network operation risk evaluation when the electric vehicle accesses the power grid in a large scale in the prior art is solved.

The invention relates to the technical field of electric energy metering of a low-voltage power distribution system, and provides an EM algorithm-based district metering device misalignment identification method and system. The method comprises the following steps: establishing a theoretical misalignment value model of each outgoing line in different metering misalignment states, and determining a mapping relation between the different misalignment states and actual electric quantity data; the method comprises the following steps: constructing theoretical deviation values of each outgoing line metering device in different misalignment states on the basis of a transformer area incoming and outgoing line electric energy data and energy conservation relationship, estimating an initial attribution probability of a line state in combination with an index normalization mapping method, and obtaining a posterior attribution probability of each outgoing line line through Gaussian mixture distribution modeling under dynamic prior guidance; and based on the posterior affiliation probability of each outgoing line, constructing an objective function of joint observation value fitting quality, time continuity and misalignment amplitude balance, solving the objective function, and quantifying the electric quantity misalignment proportion of each outgoing line at each moment so as to judge classification and identification of misalignment behaviors.

The invention discloses a photovoltaic power prediction method for Gaussian mixture error probability distribution compensation based on initial value optimization, and the method comprises the steps: carrying out the fitting of a distribution rule of a photovoltaic power preliminary prediction error through employing a Gaussian mixture model (GMM) based on initial value optimization according to a photovoltaic prediction error, and carrying out the calculation of the distribution rule of the photovoltaic power preliminary prediction error; and compensating a predicted value of the error to a point prediction result to realize interval prediction of photovoltaic output. As environmental factors such as illumination intensity, temperature, visibility and the like have great influence on a prediction result of photovoltaic power generation output, historical photovoltaic output data are divided into a plurality of different weather scenes by adopting a K-means clustering algorithm (K-means) based on an irradiance index, an optimal initial value data set selected by inputting a whale optimization algorithm (WOA), and the prediction result of the photovoltaic power generation output is predicted by using a prediction result of the photovoltaic power generation output according to the optimal initial value data set selected by the whale optimization algorithm (WOA). And then a Gaussian mixture distribution model is adopted and an expectation maximization (EM) method is combined to solve a model optimal parameter so as to carry out modeling analysis on probability distribution of photovoltaic prediction errors, and a model fitting result is used to correct a photovoltaic power generation power point prediction value under a specified confidence level interval so as to obtain a final prediction interval. According to the scheme provided by the invention, simulation analysis is carried out by taking photovoltaic power data of a certain photovoltaic power station in Jiangsu province as an example, and a result shows that the method provided by the invention has better fitness and reliability.

A method for multimedia data generation by energy guidance of diffusion model is disclosed. The method comprises obtaining a Gaussian mixture distribution of K components for approximating a diffusion posterior, wherein K is an integer greater than 1, and the diffusion posterior is a distribution of multimedia data to be generated given intermediate noisy multimedia data; estimating an intermediate energy for guiding the multimedia data generation, by a plurality of samples drawn from the obtained Gaussian mixture distribution; and generating the multimedia data with a pre-trained noise prediction network and an intermediate energy guidance computed based on the estimated intermediate energy.

The invention discloses a two-stage optimization scheduling method for a mine energy system. The method comprises the following steps: constructing a mine comprehensive energy system model; establishing a distributed photovoltaic output uncertainty model, quantitatively predicting error space-time correlation through a gamma function and a Gaussian mixture distribution model, and decomposing a regional error by using a node weight coefficient; a distributed ADMM coordination algorithm is designed according to a distributed photovoltaic output uncertainty model, spatial-temporal correlation compensation is realized through a local layer error transfer function and coordination layer global variable updating, and a voltage out-of-limit probability constraint is introduced; and designing a day-ahead-day two-stage optimization scheduling framework, taking the minimum sum of the power generation cost of the gas turbine in the mine integrated energy system model and the photovoltaic light abandoning penalty as a day-ahead stage target, and dynamically adjusting the power deviation through virtual energy storage in the day-ahead stage. The method can effectively solve the problem of mine energy system scheduling under distributed photovoltaic access, and is suitable for a mine comprehensive energy scene with high-proportion new energy access.

The invention discloses an electric vehicle charging demand characteristic probability quantification method based on multivariate Gaussian mixture distribution, and relates to the field of vehicles, and the method comprises the steps: obtaining platform original data; preprocessing the original data of the platform to obtain charging demand characteristics of the electric vehicle; based on the initial model parameters and the charging demand characteristics of the electric vehicle, optimizing the initial model parameters by using an expectation maximization algorithm to obtain optimized model parameters; the initial model parameters are obtained through a random generation method; the model parameters comprise a weight, a mean value, a variance and a skewness coefficient; constructing a Gaussian mixture model based on the optimized model parameters; and quantifying the charging demand characteristics of the electric vehicle through a Gaussian mixture model. According to the invention, the accuracy and effectiveness of electric vehicle charging demand feature quantification are improved.

The invention discloses a keratoconus severity automatic grading detection method and a related device, and relates to the technical field of medical data processing, and the method comprises the steps: obtaining cornea image data, extracting cornea biomechanical feature data and cornea morphological feature data, inputting the feature data into a variational auto-encoder, and obtaining a variational auto-encoder; the method comprises the following steps: analyzing cornea feature data distribution by introducing potential variables to obtain a mean value and a variance of the potential variable distribution, finally inputting the mean value and the variance of the potential variable distribution into a Bayesian Gaussian mixture distribution model, carrying out unsupervised clustering on the cornea feature data, and outputting the cornea conus severity level of cornea image data. According to the scheme, the potential variable space of the cornea features is generated through the variational automatic encoder, richer data representation of the keratoconus quantitative features is learned, the potential law or structure of keratoconus severity grading is captured, and the keratoconus severity grade can be accurately detected.

The invention discloses a redundant space robot inverse kinematics solving method based on a graph neural network, and relates to the technical field of robot motion control and artificial intelligence crossing. A distance geometric graph is constructed to represent the configuration of the mechanical arm, an inverse kinematics problem is converted into a completion problem of a partial graph, joint configuration is sampled, and data pairs of a complete graph and the partial graph are stored as a data set; training based on an isotropic graph neural network and a conditional variation auto-encoder, and training a model based on a loss function of an evidence lower bound; and in the reasoning process, constructing a partial graph, inputting the partial graph into the trained prior network, outputting hidden variables meeting Gaussian mixture distribution, extracting sampling points, and generating a reconstructed complete graph to obtain joint angle information. The mechanical arm structure is represented through the distance geometric diagram, the inverse kinematics problem is converted into the complementation problem of partial diagrams, probability distribution of a solution space is learned through a conditional variation auto-encoder frame, multi-solution generation of different mechanical arm configurations is supported, and the solving precision and efficiency are high.

The invention relates to a deep learning image compression method based on an importance scale and a Gaussian mixture model, and belongs to the technical field of image processing. According to the method, an importance scale network is constructed, the potential representation obtained by a main encoder is processed through the importance scale network, bit distribution of adaptive content is realized, effective extraction of key feature information is ensured, and thus the potential representation with enhanced importance is obtained. A Gaussian mixture model is introduced into both a content stream and a model stream, and the Gaussian mixture model comprises a plurality of Gaussian distributions, so that more accurate probability distributions can be obtained for guiding entropy coding and decoding to obtain more accurate reconstruction potential representation or neurogrammar information. According to the method, content self-adaptive optimization is carried out on the potential representation, a more reasonable and flexible probability distribution model is obtained by using Gaussian mixture distribution prediction, redundant information in the potential representation or neurogrammar is reduced, and the rate distortion performance of a deep learning image compression model is improved while a reconstructed image with a better visual effect is obtained.

The invention provides an electric vehicle charging load prediction and evaluation method based on multi-scale probability distribution modeling. The method focuses on the multi-time-domain characteristics of charging behaviors and the influence of the multi-time-domain characteristics on the operation state of a power grid. The method comprises the following steps of: firstly, constructing a flow distribution simulation network, and accurately describing instantaneous load and vehicle waiting characteristics of a charging station by fusing traffic and charging behavior data so as to realize short-term load dynamic analysis; secondly, providing a Bayesian hidden Markov model fusing Gaussian mixture distribution and logarithmic normal distribution, effectively fitting the travel time distribution of the electric vehicle, and improving the evaluation precision of the medium-term charging behavior; and finally, designing a variable self-adaptive regulation and control Transform network, and combining a power grid load with external factors to enhance the modeling capability of a long-term load evolution trend. According to the method, fine prediction and system evaluation of the charging load of the electric vehicle under different time scales are realized, and the operation safety and management decision level of a power grid are remarkably improved.

The invention provides a method and a device for evaluating the forming quality of a cold-formed steel roller, relates to the technical field of data processing, and aims at analyzing typical frequency band characteristics of monitoring data in the forming process of the roller on multiple scales and determining corresponding joint characteristics so as to carry out quality evaluation. The transient impact characteristic of the roller vibration signal can be accurately captured in the time domain, the main frequency component and the modulation sideband thereof are clearly separated in the frequency domain, and the method adapts to the complex working condition of the forming process. And on the premise that the nonlinear elastic characteristic of the roller is physically constrained, the unsteady-state characteristic corresponding to the roller quality degradation process is determined on the basis of Gaussian mixture distribution of combined characteristics, the internal mechanism of a complex system can be analyzed and explained in combination with the physical deformation of the roller, and the quality of the roller is improved. Different distributions and corresponding non-linear relationships of multi-sensor time sequence data are captured, and timely and accurate quality evaluation can be provided even if the data change slightly or the roller has potential faults.

The present invention provides an unsupervised fault diagnosis method for mechanical equipment based on an adversarial flow model, main steps including: data preprocessing, converting a mechanical vibration signal into a frequency domain signal, and normalizing the amplitude value of the signal into a range of [0, 1]; prior distribution designing: designing a mixture of Gaussian distribution with K subdistributions, wherein K is determined by the number of mechanical equipment status; model construction: constructing an unsupervised fault diagnosis model by combining an autoencoder, a flow model, and a classifier; model training: training the unsupervised fault diagnosis model by using various classes of status data, along with the designed prior distribution, preset training steps, loss functions, and an optimization algorithm; and fault diagnosis: inputting status data of mechanical equipment into the trained unsupervised fault diagnosis model to obtain a data clustering result and a fault diagnosis result.

The invention belongs to the field of artificial intelligence, discloses a meta-heuristic mechanical arm path quick search obstacle avoidance method, and aims to solve the problems of slow convergence, long planning time and the like caused by a random sampling strategy of an existing mechanical arm obstacle avoidance algorithm. The hybrid sampling strategy is implemented by fusing the improved Gaussian hybrid distribution sampling strategy and the map complexity constraint strategy. According to the method, sampling random points in a barrier-free area are reduced, sampling random points close to a barrier area are increased, so that the generation speed and sampling efficiency of a planned path are improved, the whole-course controllability of a joint space is needed in the barrier-avoiding searching process, the physical limits of joint control position quantity and speed quantity are not broken through, and the operation safety of the mechanical arm is guaranteed.

The invention relates to the technical field of artificial intelligence, and discloses a target detection method and device based on a Gaussian mixture model, a vehicle and a medium, and the method comprises the steps: carrying out the image feature extraction of a driving scene image, obtaining a feature sequence, carrying out the extraction through a linear projection layer, and obtaining a query, a key and a value; each key is modeled as a preset number of Gaussian mixture models of Gaussian distribution, the probabilities of the queries in the Gaussian mixture models are calculated, and the attention scores of the current key and all the queries are calculated based on the Gaussian mixture models corresponding to the keys and the probabilities of the queries in the Gaussian mixture models; then attention scores of all keys and all queries are obtained, dot product multiplication is carried out on the attention scores and a value sequence to obtain a target feature sequence, and finally a detection result of a target is determined. Through a self-attention mechanism based on Gaussian mixture distribution, the calculation cost of a self-attention model is reduced, the explanatability of model output is improved, and the detection accuracy of the target is improved. And a better target detection result is realized.

This invention discloses a collaborative control method for highway merging zones based on deep reinforcement learning. A LiikeSim-Python co-simulation environment is established, and loop detectors are set up in the simulation environment to acquire traffic flow data upstream and downstream of the highway merging zone. An EM algorithm based on Gaussian mixture distribution is used as a traffic state classifier, taking the traffic flow data of the highway merging zone as input to classify the traffic state of the merging zone. A state space, action space, and reward function are designed. Using the state space of the highway merging zone as input and the actions of the variable speed limit agent and the ramp metering agent as output, a multi-agent shared experience network model under time-series characteristics is constructed. An independent experience pool is set up for the variable speed limit agent and the ramp metering agent, and the interaction experience between the agent and the traffic simulation environment is collected at the control cycle frequency. The agent model is trained using sampled data. The trained agent model is used to realize collaborative control of the highway merging zone. This invention can reduce travel delays in highway merging zones.

This invention discloses a fault diagnosis method for aero-engines based on an adaptive spatiotemporal decoupled graph convolutional network, comprising: acquiring and preprocessing multi-channel sensor signals to construct time-series samples; adaptively constructing an adjacency matrix to form a fault signal graph structure representing the spatial correlation of multi-source signals; inputting the fault signal graph structure into a graph convolutional network to extract spatial features; inputting the extracted spatial feature sequence into a bidirectional gated recurrent unit network to obtain temporal features; introducing a global attention mechanism to the temporal features to assign adaptive weights to features at different time steps; decoupling the spatiotemporal features using a variational autoencoder decoupling layer based on a Gaussian mixture distribution; and outputting the fault category of the aero-engine to complete the fault diagnosis. This invention significantly improves the accuracy, robustness, and engineering applicability of aero-engine fault diagnosis under complex operating conditions by jointly modeling the spatiotemporal characteristics of fault signals and effectively decoupling and fusing features.

The invention discloses a micro-earthquake dry and wet event separation method and device based on a Gaussian mixture model, and the method comprises the steps: S1, building a corresponding Gaussian mixture model based on a micro-earthquake detection result, carrying out the random initialization of parameters of the Gaussian mixture model, and setting the number of mixed components; s2, calculating the posterior probability of each microseismic event point relative to each Gaussian component; s3, updating Gaussian mixture distribution parameters based on the posterior probability of each microseismic event point relative to each Gaussian component; s4, based on the updated Gaussian mixture distribution parameters, calculating a maximum likelihood function and evaluating whether the maximum likelihood function accords with a threshold value, if not, returning to S2, and if yes, executing S5; and S5, outputting the Gaussian mixture model after parameter updating and a dry and wet event classification result. According to the method, effective transformation events corresponding to fracturing construction and fracture activity events caused by ground stress changes can be rapidly distinguished.

The embodiment of the application discloses a kind of reactive voltage control methods and devices of regional distribution network, the method comprises: obtaining each regional node sample corresponding to regional distribution network, each regional node sample includes the voltage and power factor corresponding to each regional node;The voltage and power factor corresponding to each regional node sample are input into Gaussian mixture distribution function, determine the target distribution probability corresponding to regional node sample;Based on target distribution probability and preset interval threshold, each regional node is clustered, to determine the target regional category cluster corresponding to each regional node;According to the electrical parameter characteristics corresponding to each target regional category cluster, determine the target control strategy of regional distribution network.Gaussian mixture distribution function is used for the clustering of regional distribution network VQC control, and the characteristics of each electrical parameter characteristic can be obtained according to the category cluster in the regional VQC control, and a targeted target control strategy can be used.

The invention belongs to the technical field of image classification, and particularly relates to a sample weighted domain adaptive image classification method based on source domain variance and Gaussian mixture distribution modeling. In order to improve the accuracy of image classification, the method comprises the following steps: firstly, carrying out probability modeling on feature spaces of a source domain and a target domain by utilizing a Gaussian mixture model so as to accurately characterize data distribution features; secondly, dynamically evaluating the importance of a source domain sample by introducing the variance of source domain classification loss, and adjusting the weight of the sample according to the importance of the source domain sample so as to realize modeling and control on the variability of the sample in the source domain; and finally, the classification model is effectively optimized through a weighted training strategy, and the feature distribution difference between the source domain and the target domain is reduced, so that the recognition accuracy and the model stability in a cross-domain image classification task are improved.

This invention relates to the field of wind power equipment fault diagnosis technology, and provides a method for extracting fault features from wind turbine gearboxes based on Gaussian mixture modeling. The method includes: modeling a mathematical model of the wind turbine gearbox observation signal by superimposing impact fault feature vectors and multi-source noise vectors, wherein the fault feature vector is the product of a redundant dictionary D and a sparse coefficient vector; modeling the multi-source noise vector as a Gaussian mixture distribution; constructing an objective function within a Bayesian framework to solve for the sparse coefficient vector in the mathematical model using maximum a posteriori probability estimation; simplifying the objective function; and using the EM algorithm and ADMM algorithm in a joint alternating iterative solution to obtain the optimized sparse coefficient vector; and reconstructing the impact fault feature vector in the wind turbine gearbox observation signal. This method improves the accuracy and robustness of extracting fault features from the observation signal of offshore wind turbine gearboxes.

The present invention discloses a method for converting the time series loads of the mast of a rotary drilling rig of different models, and belongs to the technical field of load spectrum collection and compilation of rotary drilling rigs of engineering machinery. The method comprises the following steps: (1) extracting the time series loads of the original model under rock entry working conditions, and performing rain flow counting on the time series loads, and statistically calculating the amplitude and mean of the load cycle; (2) fitting the load amplitude with a Gaussian mixture distribution; (3) calculating the limit error value of the probability density value of the load amplitude; (4) randomly reproducing the amplitude load within the limit error; (5) reproducing the mean load according to the parameters of the converted model; (6) correcting the amplitude load according to the upper limit of the maximum value of the converted model; (7) correcting the mean load according to the lower limit of the minimum value of the converted model; (8) performing rain flow reconstruction on the amplitude and mean of the corrected load cycle; and (9) qualitatively comparing the time series load data characteristics of the original model and the converted model. The method solves the problem of difficulty in repeatedly collecting the time series loads of the mast of a rotary drilling rig of different models.

The present application provides a kind of enterprise labor efficiency evaluation model construction method, medium and system, belong to artificial intelligence model technical field, the present application is by gathering staff behavior data to construct high-dimensional sparse feature matrix, after coding by hash embedding layer, gaussian mixture distribution modeling and double network cross screening noise correction are carried out to labeled label, employee collaboration relationship graph is constructed based on training sample set, after hierarchical random neighbor sampling and graph clustering presegmentation, employee performance evaluation vector is obtained by inputting causal inference enhancement double-flow comparative evaluation model, then the efficiency distribution is time series evolution by optimal transport gradient flow continuous flow algorithm, finally, according to the deviation of prediction result and equilibrium threshold, external incentive term is adjusted and intervention strategy is output, the technical problem that employee performance evaluation model cannot accurately estimate causal effect in organization network and realize group efficiency time series evolution prediction is solved.

The invention relates to a nano photonics structure design method and system based on a mixed probability sampling network, and belongs to the technical field of nano photonics structure design, and the method comprises the steps: constructing a data set for training; the data set comprises structure parameters of various nanometer optical structures and corresponding optical property parameters; training the forward pre-training network; predicting one-to-one corresponding optical property parameters by using the structure parameters in the data set for training; training a reverse network; inputting optical property parameters in the data set for training, and outputting a plurality of Gaussian mixture distributions; sampling the Gaussian mixture distribution by using a sampling network to obtain a plurality of candidate structure parameters; and for the candidate structure parameters, utilizing a forward pre-training network to predict corresponding optical property parameters, and selecting the structure parameter with the minimum error with the target optical property parameter as a final nanometer photonics structure. According to the method, accurate and efficient structure reverse design in a complex solution space is realized.

The application discloses an industrial process fault detection method and system based on double subspace division, which firstly performs normality evaluation on process variables, divides a data space into Gaussian subspace and non-Gaussian subspace; a fault detection model based on principal component analysis is established in the Gaussian subspace, and a fault detection model based on support vector data description is established in the non-Gaussian subspace; current test data variables are divided into the Gaussian subspace and the non-Gaussian subspace, and are respectively input into the trained models, so that fault detection statistics corresponding to each subspace are calculated; each subspace statistic is converted into a fault probability by using Bayesian inference and is weightedly fused to construct a comprehensive monitoring statistic, and fault detection is realized. The application effectively solves the problem that a traditional method has poor adaptability to Gaussian and non-Gaussian mixed distribution data, improves the fault detection rate while significantly reducing the false alarm rate, and is suitable for real-time monitoring requirements of complex industrial processes.

The application provides a target detection method, device and equipment based on a Gaussian mixture model, which can be applied to the field of computer vision and the field of digital image processing. The target detection method based on the Gaussian mixture model comprises: processing a target image comprising a target to be detected to obtain a first density map related to the target to be detected, wherein the first density map conforms to a Gaussian mixture distribution, and the first density map serves as a Gaussian mixture model; performing integral processing on the first density map to obtain a first quantity value of the target to be detected; performing local peak value extraction on the first density map to obtain a second quantity value of the target to be detected; obtaining a target mean value and a target variance of the target to be detected based on the first quantity value, the second quantity value and the first density map; and determining position information of the target to be detected according to the target mean value and the target variance.

The application discloses a soft measurement modeling method based on mode perception dynamic variational autoencoding regression, and comprises the following steps: (1) acquiring three-phase flow process data with multiple mode dynamic characteristics; (2) data division and pretreatment operation; (3) establishing a mode perception dynamic variational autoencoding regression model, and realizing the prediction of an online quality variable; and (4) predicting a three-phase flow process pressure variable and performing model performance evaluation. The encoder-decoder network with a convolution-deconvolution structure learns the hidden layer feature representation of dynamic data, the multiple mode characteristics of the data are mined in the hidden space by using a Gaussian mixture distribution, the mapping relationship between the latent variable and the key quality variable is effectively learned, in addition, the reconstruction of dynamic multiple mode data is constrained by using a Wasserstein distance, and finally the purpose of improving the accuracy of key quality inference is achieved.

The present application belongs to the technical field of power system operation, and relates to a double-sided random power grid scheduling method. By analyzing historical data of wind power output, Gaussian mixture distribution fitting is carried out by using software. For determined power system parameters, a double-sided chance constraint random scheduling model is established. Then, the hyperbolic tangent function is used to analytically approximate the cumulative distribution function of random variables in the reserve demand constraint and the line flow constraint, so as to convert the double-sided chance constraint into a deterministic constraint, and convert the original problem into a convex optimization problem which is easy to solve. Finally, the scheduling model is solved to obtain the scheduling strategy. The present application has the advantage that the double-sided chance constraint containing the risk level and the random variable is converted into a solvable deterministic convex constraint by using the hyperbolic tangent function, which effectively improves the solving efficiency of the model and provides a more reasonable scheduling basis for decision makers. The present application can be applied to active power scheduling of a power system containing large-scale renewable energy grid-connected.

The invention relates to a patient trajectory tracking system based on hospital Wi-Fi signal positioning, and the system comprises the following steps: building RSSI fluctuation probability models of each AP in different time periods, and introducing Gaussian mixture distribution adaptive learning; the existence of an electromagnetic interference source is identified through continuous RSSI abnormal jump, and abnormal points are marked and isolated; continuously collecting real-time data in the moving process of the patient, and performing incremental fingerprint correction; the fingerprint database is weighted gradually along with time, and historical stability and real-time accuracy are both considered; converting the positioning point sequence into a space-time velocity vector to form a continuous path vector field; defining an intention potential energy value in the path, and performing comprehensive calculation according to the target point attraction, the path deviation rate and the regional characteristics; if high potential energy transition or potential energy dissipation abnormity repeatedly occurs in a non-diagnosis area, wandering can be judged, and a risk behavior of trying to leave can be attempted; and a track heating power and vector flow view is constructed to assist medical staff in identifying abnormal movement lines.