589 results about "Kernel density estimation" patented technology

The invention discloses a box-type substation state monitoring and early warning method based on artificial intelligence, relates to the technical field of intelligent power grids, and aims to solve the problems of missing report, false report and response lag caused by the fact that an existing static threshold ignores multi-physical coupling and a depth model highly depends on scarce fault samples. According to the scheme, sliding window kernel density estimation is carried out on a multi-channel time sequence signal, a dynamic coupling matrix is constructed through recursion Copula decomposition, a three-level threshold surface is generated through time-varying quantile regression, abnormal samples and graph attention network extraction state representation are generated in combination with a conditional variation auto-encoder, lightweight recursion pruning is carried out, and the dynamic coupling matrix is obtained. An abnormal score is generated through a multilayer Bayesian network and particle filtering, a multi-step risk trend is discriminated through a Gaussian kernel derivative slope, and finally unscented Kalman filtering is used for smoothing and online threshold correction; according to the method, the detection sensitivity and the early warning recall rate of the box-type substation to the transient coupling fault are remarkably improved, the response speed is improved, and the false alarm frequency is effectively reduced.

The invention provides a logistics distribution demand prediction analysis method and system based on big data, and relates to the technical field of logistics distribution, and the method comprises the steps: dividing a distribution region into hexagonal grid units, building an order density evaluation model based on historical order data, extracting order propagation features through combining with a graph attention network, and carrying out the prediction analysis of the logistics distribution demand. And an external factor encoder is introduced to obtain an influence factor, probability correction is carried out by adopting kernel density estimation, and finally order demand prediction distribution with a confidence interval is obtained. The logistics distribution demand can be accurately predicted, the distribution efficiency is improved, the operation cost is reduced, and intelligent scheduling is realized.

The invention discloses a three-dimensional scene reconstruction method and device based on large model geometric prior, and a medium, and aims to solve the problems that a conventional 3DGS is liable to have artifacts and detail loss in geometric discontinuity, data redundancy and illumination variation scenes, and predicts a dense depth map and a normal map from a monocular image by using a pre-trained large model. The position and form of the Gaussian kernel are constrained as additional geometric priori; a primitive adjustment strategy based on kernel density estimation is introduced in the training stage, small Gaussian primitives with similar structures and adjacent spaces are combined into a large Gaussian primitive, the rendering quality is kept, redundancy is reduced, and the volume of the model is reduced; an exposure coefficient is adaptively estimated for each input image, an exposure compensation image loss function is constructed, and floating artifacts caused by illumination differences at shooting moments are eliminated. Experiments show that compared with the prior art, the method improves the three-dimensional reconstruction precision and real-time rendering quality of complex illumination and less-texture areas in a public data set and an unmanned aerial vehicle aerial photography scene.

The invention relates to the technical field of industrial water system safety monitoring, in particular to an intelligent source tracing method for a process medium leaked in circulating water, which comprises the following steps of: acquiring multi-dimensional operating parameters such as conductivity, pH value, turbidity, dissolved oxygen, temperature, pressure and characteristic ion concentration; a standardized water quality parameter matrix is generated after space-time alignment and wavelet noise reduction; the method comprises the following steps: extracting an abnormal fluctuation signal by using a leakage feature recognition model based on transfer learning, simulating a diffusion process through a three-dimensional leakage diffusion model, realizing leakage source positioning by combining reverse particle tracking and kernel density estimation, associating a high-probability leakage region with upstream process equipment, extracting backtracking path features, and matching a process medium feature library, thereby realizing leakage source positioning. The leakage medium type is judged; and finally generating a structured traceability report. According to the method, high-precision identification, positioning and medium analysis of process leakage in a complex circulating water system can be realized, and the method has relatively high practicability and popularization value.

The invention discloses a power grid data acquisition and analysis system based on big data, particularly relates to the technical field of power network monitoring, and is used for solving the problem of key feature loss caused by cross-hierarchy data semantic association missing of an existing hierarchical aggregation mechanism. Performing standardized filling and time label layered alignment processing on the original data through the equipment acquisition module; the feature extraction module screens an abnormal waveform fragment and a steady-state parameter offset based on the equipment type and the real-time fluctuation amplitude; the semantic association module generates a space-time association weight in combination with the regional physical topological relation and the reactive circulation path sensitivity; the feature clustering module reconstructs a region-level feature aggregation packet through kernel density estimation and spatial weighted fusion; the cross-layer analysis module dynamically corrects the aggregation weight based on the current flow direction and the transient energy distribution; and the decision generation module generates an equipment positioning instruction and load scheduling strategy combination through alarm template matching, and finally realizes accurate positioning of fault equipment and generation of a scheduling strategy.

The invention provides an electricity-hydrogen coupling intelligent regulation and control method and system considering a wind-solar prediction error, and relates to the technical field of electric power, and the method comprises the steps: collecting wind-solar historical power generation data, and obtaining prediction error probability distribution based on adaptive combination kernel density estimation; the distribution serves as an uncertainty constraint, a dual neural network is adopted to execute deep reinforcement learning, an action value network dynamically adjusts a decision space, and a target network evaluates risks and restrains decisions based on conditional risks and state values; a distributed collaborative network is constructed, and hydrogen production power and hydrogen desorption power are controlled in real time based on a dynamic consistency theory and a synchronous potential function. The operation stability and economical efficiency of the electricity-hydrogen coupling system are improved, and the uncertainty of wind and light power generation is effectively dealt with.

The invention discloses an image data annotation method and system, and relates to the technical field of computer vision, and the method comprises the steps: collecting image and text data, carrying out the updating through kernel density estimation and a kinetic equation, carrying out the optimization through employing Euler discretization in combination with CLP propagation and EA, carrying out the conversion through amplitude coding, and carrying out the construction through a quantum state circuit. The method comprises the following steps: carrying out calculation by using partial traces, generating topological weighted entanglement entropy through weighted fusion, carrying out updating based on an attention mechanism, generating potential conflict pairs, calculating priorities, generating final conflict pairs through a CNN model, outputting region features through a NeRF model, carrying out extraction by using a CLIP model, and carrying out updating through a loss function. According to the method, VAE is combined with GLP propagation and EA for optimization, the precision and consistency of labeling are improved, and through joint verification of quantum entanglement entropy calculation and the NeBF model, the efficiency and accuracy of labeling are improved.

The invention relates to the technical field of network security management, in particular to a network security management system based on big data, which comprises a data acquisition and integration unit, a dynamic threshold setting unit, a fuzzy comprehensive evaluation unit, a machine learning optimization unit and a control unit. The acquisition process has intelligent characteristics and adopts a block chain to cache data, the dynamic threshold setting unit constructs a model to adjust a threshold by combining a hidden Markov model, kernel density estimation and a reinforcement learning algorithm for different network crime types, and the fuzzy comprehensive evaluation unit determines a membership function and distributes weights by applying a fuzzy mathematical algorithm. And the machine learning optimization unit uses historical data to train and update the model, assists in case-related account determination, effectively solves the problems of missed determination and misjudgment caused by a fixed threshold value in traditional account risk assessment, and improves the accuracy of network criminal account risk assessment.

The invention relates to an offshore wind power operation environment scene construction method considering typhoon forecast errors. The method comprises the steps of selecting similar historical typhoon forecast data according to current typhoon forecast information; based on wind speeds of similar historical typhoon forecast and current typhoon forecast, combining marine environment element information to construct a marine environment scene information space; a kernel density estimation model of historical typhoon wind speed forecast errors is constructed, corresponding kernel density bandwidths are calculated and updated according to different typhoon forecast timeliness, and historical forecast wind speed error distribution is obtained; and inputting the marine environment scene information space and the historical forecast wind speed error distribution into the improved generative adversarial network, and generating a marine environment scene of which the wind speed interval meets the historical typhoon wind speed forecast error distribution through the wind speed interval constraint loss. Compared with the prior art, the marine environment scene is updated according to the wind speed error distribution under different forecast time periods of the typhoon, and the scene generation quality is improved.

The invention discloses a wind power multi-scale decomposition prediction method. At present, single-point prediction is not comprehensive and accurate enough, and cannot adapt to quantitative accurate requirements of a wind power plant and a power grid dispatching mechanism in risk management. The method comprises the following steps of: forming an original wind power sequence from actually acquired wind power data, sequentially performing feature selection and data decomposition processing to form multi-scale modal data, and constructing a depth prediction model according to the multi-scale modal data; a probability prediction interval determination process is completed in the residual error distribution mode depth prediction model through adaptive bandwidth kernel density estimation; after actually obtained wind power data form an original wind power sequence, an initial model is established, feature selection processing is performed on the initial model, that is, weighted marginal contribution is calculated for each feature of the initial model according to all involved feature subsets by using an SHAP algorithm based on a Shapley value in a game theory, and the weighted marginal contribution of each feature of the initial model is calculated; and completing a feature data acquisition process of accurately quantifying interdependence and interaction effect between features.

The invention relates to the technical field of optical elements, and discloses a fog display point diffusion optical system and a preparation method thereof, and the preparation method comprises the steps: obtaining user visual parameters, and building a visual behavior probability density model through kernel density estimation; designing the front surface of the lens based on the model, and generating an asymmetric defocus system coupled with a gazing habit; the rear surface of the lens is designed, and the distribution density of a fog display point diffusion unit is cooperatively modulated by the front surface defocusing amount, the fixation probability and the pupil diameter; and finally integrating front and rear surface design to form an optical lens body. According to the method, the asymmetric defocus field on the front surface and the fog display point diffusion field on the rear surface are subjected to collaborative design, and the defocus intensity and the contrast modulation intensity are accurately applied to the effective retina area of the user through the visual behavior probability density model; the technical problem that a traditional out-of-focus lens is fixed in signal and cannot adapt to individual staring habits and physiological parameter changes is solved.

The invention discloses an industry load prediction method, system and device fusing standard mutual information and improved bidirectional LSTM, and relates to the technical field of power systems. At present, industry monthly load prediction is not accurate. The method comprises the following steps: carrying out clustering processing on information including a load sequence, a consumption level, an air temperature and vacation by adopting an improved fuzzy C-means clustering algorithm considering kernel density estimation; standard mutual information calculation is carried out based on a clustering result, and quantitative analysis is carried out on relevance between information factors and industry monthly loads; the method comprises the following steps of: selecting an industry load, distributing weights, constructing a bidirectional LSTM neural network to capture a time sequence change rule of the industry load, analyzing external feature influence by adopting a multi-head attention mechanism, and outputting an industry monthly load prediction result through convolutional neural network connection. According to the technical scheme, the influence of external influence factors on the industry monthly load can be effectively considered, and the accuracy of an industry monthly load prediction result is improved.

The invention discloses a key sensor short-time abnormal distribution drift detection method in a unit start-stop process, and belongs to the technical field of gas turbine power plant financial supervision and artificial intelligence, and the method comprises the steps: synchronously triggering multi-channel signal collection through a main clock, and achieving noise suppression and data pre-screening through the combination of first-order difference and threshold filtering; constructing a nonlinear weighted feature matrix, and fusing a time attenuation coefficient and a shafting acceleration factor to enhance the transient feature expression capability; generating a sensor association graph based on double-threshold determination of weighted Pearson's correlation coefficients and mutual information, and dividing stable subgroups by using an incremental label propagation algorithm; designing a double-layer Cluster-GCN model, aggregating subgroup internal characteristics in the first layer, introducing a fuel valve position-acceleration comparison gating mechanism in the second layer to correct a global edge weight, and generating a node embedding vector sensitive to working condition change; gaussian kernel density estimation and an instantaneous deviation index of embedding similarity are fused, and a historical sliding mean value and subgroup connectivity analysis are combined, so that sensor faults and working condition abrupt changes are distinguished.

The invention discloses an electric power resource scheduling method and device suitable for extreme weather, terminal equipment and a storage medium, and belongs to the technical field of electric power scheduling, and the method adopts nonparametric kernel density estimation to construct a wind and light output joint probability distribution function representing output characteristics of wind power output and photovoltaic output in extreme weather. When flexibility demand analysis is carried out, the advantages of nonlinear correlation between variables and tail risks can be analyzed by means of a joint distribution probability function, wind power prediction errors and photovoltaic prediction errors are accurately analyzed, and then adjustment demand risks of net load flexibility demands are quantified in combination with convolution operation. The problems that the joint probability distribution characteristics of wind and light output in extreme weather cannot be accurately described and the regulation demand risk cannot be ignored in the current flexibility demand evaluation method depending on the normal distribution independent assumption are solved. And the power supply reliability and the operation stability of the power system in extreme weather can be ensured by the formulated power dispatching plan.

The invention discloses a high-temperature equipment fault identification method and system based on cooperative imaging, and particularly relates to the technical field of fault identification, and the method comprises the steps: building an offline ROI identification model in combination with historical data, achieving the initial ROI region configuration of high-temperature equipment before the high-temperature equipment is online, and achieving the fault identification of the high-temperature equipment through an online mechanism driven by a multi-modal image and real-time data in the operation process. And continuously and dynamically updating the ROI, performing first-order and second-order differential analysis on temperature gradients of the standard high-temperature equipment and the actual equipment in a monitoring interval based on ROI imaging data of the standard and actual high-temperature equipment, and determining image information of the ROI by using KL divergence calculation based on a kernel density estimation result of a grayscale image, geometric features and spatial positions of initial defects of the high-temperature equipment are extracted, PCA analysis and Euclidean distance calculation are combined, a logistic regression model is constructed, influence information of the ROI is determined, and the fault identification comprehensiveness and accuracy can be improved.

The invention relates to the field of power transmission line channel risk hidden danger monitoring, and provides a data fusion power transmission line channel risk hidden danger monitoring method and system, and the method comprises the steps: collecting the multi-modal sensing data of a power transmission line channel, and generating a multi-modal data flow of a unified time-space coordinate; constructing a three-dimensional space point cloud through a phase unwrapping and stereo matching fusion algorithm, and fusing multi-modal data to generate a space probability tensor; extracting risk semantic latent variables, constructing a Bayesian network and identifying potential risks; performing tensor product on the potential risk and the environmental data to generate a dynamic risk enhancement feature matrix, and constructing a nonlinear dynamic threshold curved surface through quantum annealing and Gaussian process regression; a mechanical equation is constructed, Gaussian kernel density estimation and numerical simulation are combined, the evolution trajectory of the risk in the space-time dimension is predicted, and a risk thermodynamic diagram and early warning information are generated; and generating a structured risk early warning report by adopting a natural language processing method. And the accuracy of power transmission line channel risk hidden danger monitoring is improved.

The invention discloses a sudden death risk real-time assessment system and method based on multi-modal physiological signal fusion, relates to the field of human physiological state monitoring and early warning, and solves the problems of low accuracy and poor real-time performance of sudden death risk assessment by single-modal signals. The system comprises a signal acquisition module, a preprocessing module, a high-dimensional feature extraction module, a multi-modal feature fusion module, a sudden death risk quantification module, a model updating engine module and an early warning feedback module, and each module integrates a self-adaptive filtering unit, a time sequence convolutional network unit, an improved multi-head self-attention mechanism unit and the like. According to the scheme, multi-mode signals such as electrocardio are synchronously collected through multiple channels, a dynamic risk index is calculated through self-adaptive noise reduction, parallel extraction of time-frequency domain nonlinear features and mutual information weighted fusion in combination with kernel density estimation, and online incremental updating and multi-stage early warning of a model are achieved; the sudden death risk can be accurately evaluated in real time, the anomaly detection sensitivity and the early warning timeliness are improved, and the method is suitable for daily health monitoring and high-risk group risk management and control.

The invention provides an examination room multi-source data fusion abnormal behavior intelligent analysis method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting examination room video and audio data, and carrying out the multi-view skeleton point fusion and sound source positioning to extract features; constructing a hidden Markov model and kernel density estimation to carry out abnormal behavior identification; calculating a seat correlation degree based on a spatial weight coefficient and wavelet decomposition to identify multi-person cooperative cheating; and early warning information is pushed in real time. According to the invention, the cheating behavior identification accuracy is improved, and effective detection of multi-person cooperative cheating behaviors is realized.

The invention discloses an urban rail transit pull-in passenger flow prediction method fusing multi-source spatio-temporal data. The method comprises the following steps: collecting multi-source spatio-temporal data of a target station and a corresponding associated station; kernel density estimation is carried out on the surrounding POI density corresponding to each site, the spatial thermal characteristics of each site are generated, one-hot coding is carried out on the real-time weather index corresponding to each site, the weather influence characteristics of each site are generated, segmented coding is carried out on the date type identifier corresponding to each site, and the periodic effect characteristics of each site are generated. Performing differential stabilization on the historical pull-in passenger flow sequence of each station to generate a time sequence fluctuation characteristic of each station; tensor splicing is carried out on the space thermal characteristics, the weather influence characteristics, the periodic effect characteristics and the time sequence fluctuation characteristics of all the stations, a space-time fusion characteristic tensor is constructed and input into a space-time self-adaptive prediction model, and an inbound passenger flow prediction value of the target station is generated. The accuracy and timeliness of urban rail transit pull-in passenger flow prediction can be improved.

The invention relates to a path planning method and system for navigation guidance of a low-altitude aircraft, and the method comprises the steps: calculating a probability density value of pre-processed prior flight data based on kernel density estimation, mapping the normalized probability density value to a grid map of a rasterized environment model, generating a flight path safety experience distribution map, and carrying out the navigation guidance of the low-altitude aircraft. And expanding the boundary of the obstacle area based on the safety distance constraint matrix, generating a restricted area matrix, generating an initial path according to the bidirectional A *-KDE algorithm cost function, the flight line safety experience distribution map and the restricted area matrix, removing redundant nodes from the initial path, and carrying out smoothing processing to obtain a flight route. The flight route is obtained by using the prior flight data, the deviation between the flight route and the actual navigation path can be reduced, the obstacle area boundary is expanded based on the safety distance constraint matrix, the obstacle safety distance is fully considered, and the risk that the low-altitude aircraft collides with the obstacle can be reduced.

The invention provides a thermal power combustion optimization control method and system based on deep learning, and relates to the technical field of thermal power combustion optimization control, and the method comprises the steps: recognizing an abnormal scene based on a maximum average difference criterion and a multi-kernel density estimation method, and carrying out the sampling of operation data according to the abnormal scene. A mutual information maximization criterion is adopted to carry out dynamic quantization coding on a sample, and a recurrent neural network is utilized to extract a working condition feature vector. And evaluating a system state according to the working condition feature vector, screening control actions meeting constraint conditions through an adversarial learning network, and calculating strategy gradient optimization to obtain an optimal control strategy. And finally, decomposing the optimal strategy into sub-strategies, constructing a distributed consistency coordination model based on a fuzzy decision tree, determining an optimal execution time sequence, and correcting a control signal through a self-adaptive dead-zone compensator to realize optimal control of thermal power combustion. The thermal power generating unit operation stability and economy can be effectively improved, and pollutant emission is reduced.

The invention discloses a transmission system anomaly detection method and system, a medium and equipment, and the method comprises the steps: collecting vibration signals of a transmission system under different operation conditions through a vibration sensor; inputting the vibration signal into an auto-encoder for reconstruction; the working condition information is used as a label, and supervised comparative learning is introduced into an auto-encoder architecture; the distance between the test sample and the training sample center is used as an anomaly detection score to indicate whether the sample is abnormal or not; and adaptively obtaining probability density distribution of an anomaly detection score of a sample through kernel density estimation, and adopting a 5% quantile as a threshold value to realize anomaly detection of the transmission system.

The invention discloses a power system random scheduling method based on wind-solar joint probability distribution and double-layer dynamic optimization. The method comprises the following steps: firstly, constructing a wind and light output joint probability distribution model by adopting nonparametric kernel density estimation and a Frank Copula function so as to accurately describe space-time correlation; secondly, generating a wind-solar combined output scene through Monte Carlo simulation, and performing reduction by adopting a k-means + + clustering algorithm to obtain a typical scene set; secondly, establishing a double-layer optimization model, wherein the upper layer takes the minimum net load variance as a target to coordinate wind and light storage output stabilizing fluctuation; and the lower layer optimizes the output of the traditional unit by taking the minimum operation cost of the system as a target. Finally, a dynamic climbing constraint mechanism is introduced, the net load fluctuation standard deviation output by the upper layer is converted into a dynamic constraint threshold value of the climbing rate of the lower layer unit, collaborative optimization of economical efficiency and stability is achieved, and the dispatching robustness of the high-proportion renewable energy power system is remarkably improved.

The invention discloses a dam safety monitoring data effective information extraction method and system based on adaptive time sequence decomposition. The method comprises the following steps: firstly, obtaining a dam single-measuring-point monitoring time sequence; adopting a multi-population Jaya algorithm to adaptively optimize control parameters of variational mode decomposition (VMD); performing non-recursive decomposition on a data sequence by using the optimized VMD, and screening high-frequency and medium-and-low-frequency sub-modals through a correlation coefficient-energy entropy composite index; superposing the high-frequency sub-modes to form a high-frequency sequence, constructing a composite threshold in combination with kernel density estimation and a box graph method, and eliminating local singular values in the high-frequency sequence to generate a residual sequence; and finally, superposing and reconstructing the low and medium frequency sub-modes and the residual high-frequency sequence to realize effective information extraction of the monitoring data. According to the method, the feature extraction precision of dam monitoring data is remarkably improved through parameter adaptive optimization and composite criteria.

The invention discloses a power distribution network photovoltaic openable capacity dynamic evaluation method fusing voltage stability margin and neural network optimization, and aims to solve the problems that a traditional method does not fully consider dynamic stability and is low in calculation efficiency. Firstly, a prediction method combining kernel density estimation and quantile regression is adopted to accurately quantify the uncertainty of distributed photovoltaic output. The core innovation of the method is that on the basis of traditional security constraints, a static voltage stability margin (VDSM) is introduced as a key constraint condition, and a double-layer interval analysis model capable of guaranteeing the dynamic stability of a power grid is constructed. Secondly, in order to efficiently solve, the invention provides a framework of'neural network pre-screening + parallel optimization ': after a model is decomposed into optimistic sub-problems and pessimistic sub-problems through an interval decoupling technology, massive candidate solutions are quickly screened by utilizing a neural network model, so that a feasible solution space is greatly reduced, and the solution efficiency is improved; and carrying out parallel optimization solution on the sub-models in combination with an improved particle swarm optimization algorithm.

The invention relates to the technical field of power system planning, and discloses a new energy grid-connectable capacity margin evaluation method based on spatio-temporal distribution characteristics, which comprises the following steps: constructing a joint probability distribution model based on nonparametric kernel density estimation and a Copula function by using historical and meteorological data of a wind and light station, and generating a random output scene reflecting the spatio-temporal complementarity of wind and light resources; constructing a multi-dimensional stability constraint set containing a generalized short circuit ratio and broadband oscillation as a safety boundary of a random output scene; establishing a grid source interaction control strategy considering energy storage adjustment and direct current power modulation by taking grid-connected capacity maximization as a target, solving an optimal installed capacity scheme, and activating the grid source interaction control strategy to perform dynamic correction when a scene is out of limit; the maximum grid-connected capacity distribution meeting the constraint is counted, and the shadow price of the constraint is calculated to identify the bottleneck. According to the method, the wind and light resource complementary benefit and the system regulation potential can be quantified, and the new energy grid-connected capacity evaluation margin is improved on the premise of guaranteeing the safety.

The invention discloses a UWB (Ultra Wideband) ranging error suppression method fusing a black-wing plinary algorithm and an information entropy adaptive adjustment mechanism. The method comprises the following steps: firstly, identifying a non-line-of-sight signal by combining a ranging residual error, a first-order difference and a sliding window strategy; abnormal values are removed based on residual errors, missing data are compensated through segmented three-time Hermite interpolation, and data continuity and smoothness are guaranteed; calculating Shannon information entropy by using sliding window kernel density estimation, dynamically quantifying residual distribution uncertainty, adaptively adjusting Kalman gain, and realizing weight real-time adjustment; meanwhile, the initial parameter configuration of the filter is optimized by adopting a black-wing algorithm, and the convergence and the overall stability of adaptive filtering are improved. Aiming at the problems of measurement abnormity and precision reduction caused by the fact that UWB distance measurement is easy to be shielded and interfered in the NLOS environment, the method can effectively inhibit distance measurement errors, overcomes the limitation that a traditional method depends on manual parameter adjustment, and provides powerful technical support for high-precision UWB distance measurement in the NLOS environment.

The invention provides a radar and optical observation segmental arc correlation method and system for space debris. A joint probability density function is obtained through a kernel density estimation algorithm. A radar observation arc section and an optical observation arc section are input. And converting the observation data into a geocentric celestial coordinate system. And acquiring system parameters of the observation equipment. Radar observation data and optical observation data are unified to the same dimension, and a distance measurement value weight and an angle measurement value weight are set. And calculating an initial orbit by the radar observation arc section. And calculating a joint objective function corresponding to the current orbit position speed. And if the convergence condition is met, outputting the minimum value of the joint objective function and the orbit position speed corresponding to the minimum value. Otherwise, updating the track position speed, and repeating until the convergence condition is met. And according to the minimum value of the joint objective function and the joint probability density function, judging whether association is carried out, and outputting confidence during association. The method is easy to implement and can be effectively applied to association of the space debris radar and the optical observation arc section.

The invention discloses a small fault detection method based on a prediction error sum of squares and JS divergence, and relates to the technical field of engineering detection, the algorithm of the application replaces a traditional algorithm for calculating JS divergence based on projection data after PCA dimension reduction, and a probability density function of the prediction error sum of squares of projected data is fitted by using kernel density estimation, so that the detection accuracy is improved. And calculating the JS divergence between different data distributions. The sum of squares of prediction errors reflects parts, which cannot be well reconstructed in the PCA model, of original data points, and the parts may contain abnormal or fault information. According to the method, the fault extraction capability of the prediction error sum of squares in the JS divergence is utilized, so that tiny faults can be better detected. By introducing the time sliding window, the method can more sensitively detect the local abnormal condition causing the increase of the JS divergence value, thereby timely and accurately capturing the process change, providing the early warning about the fault occurrence, and ensuring the safety and reliability of industrial production.

The invention discloses a 3D GS cultural relic digital reconstruction method and system based on a block chain, and the method comprises the steps: collecting the RGB image data and depth perception data of a cultural relic, eliminating the influence of different shooting conditions through an illumination separation processing technology, and building a standardized image data set; recognizing a surface area suitable for reconstruction based on image analysis, determining feature point distribution by using kernel density estimation, and generating initial three-dimensional representation through Gaussian ellipsoid fitting; performing gradient calculation and feature extraction on the depth data, and combining with Gaussian representation to form a geometric constraint mechanism; self-adaptive encryption based on visual importance is executed for a sparse region, and a layered rendering effect is achieved through opacity parameter adjustment; the rendering characteristics and the conversion relation of different view angles are analyzed, key observation points are determined through stability analysis, and a smooth multi-view-angle display sequence is constructed; and integrating multi-view rendering information to generate volumetric representation, and completing right confirmation of the high-quality three-dimensional digital model through digital signature.