311 results about "Particle filtering algorithm" patented technology

The invention provides a virtual DPU power plant simulation fault restoration method and system based on digital twinning, and relates to the technical field of digital twinning, and the method comprises the steps: carrying out the preprocessing of collected DPU power plant operation data, including noise reduction, time sequence alignment and abnormal point elimination, carrying out the data quality evaluation, training a fault feature mapping model based on the processed data, and carrying out the fault restoration of the DPU power plant. The model is used for recognizing abnormal clusters in real time, a fault evolution path is searched and determined in combination with a conditional random field and a Monte Carlo tree, an optimal path is determined through a particle filtering algorithm, fault root causes are determined in combination with causal analysis, spectral clustering and a Bayesian network, and a fault diagnosis report is generated.

The invention discloses an electrical equipment operation state monitoring method, system and terminal, full life cycle health management of equipment is realized through multi-dimensional perception and intelligent analysis, a composite sensor network can be constructed from the level of the method, and high-frequency current, ultrahigh frequency, fiber grating temperature vibration, multi-parameter gas and acoustic sensors are integrated. Electromagnetic characteristics, mechanical states, environmental parameters and voiceprint characteristics are covered; the adaptive signal processing technology performs classification and noise reduction on multi-source data, and the three-dimensional digital twin model realizes time-space fusion of a temperature field, a vibration field, an electric field and a sound field; a lightweight space-time convolutional network is deployed to fuse a time domain waveform, a spectrogram and spatial distribution characteristics for diagnosis, and a hidden semi-Markov model and a particle filter algorithm are combined to dynamically predict the residual service life of equipment. The system architecture comprises a self-organizing sensor network with edge computing capability, a time-sensitive industrial communication network and a containerized analysis engine, and supports mixed reality visual interaction.

The invention provides a sea surface target tracking method and system based on infrared and visible light image fusion, and relates to the technical field of ocean monitoring, and the method comprises the steps: collecting visible light and infrared image data of a sea surface target; performing feature extraction and fusion through wavelet transform fusion to generate a comprehensive feature map, and performing target recognition on the comprehensive feature map by using a deep learning target detection model; after target recognition, the system calculates the position of a target based on image data and radar data, performs multi-target matching and association through a Hungary algorithm combined with multi-modal features, predicts the position of the target and updates trajectory information in combination with a Kalman filtering or particle filtering algorithm. The infrared camera and the visible light camera carry out dynamic angle adjustment according to the position and the movement track of the target; whether light information correction is carried out or not is judged based on the light correction threshold value, when light information correction is carried out, light information correction features are constructed based on the visible light compensation model and the infrared compensation model through the image data, and information errors caused by the illumination angle are eliminated.

The invention provides a special lithium ion battery system for a UPS (Uninterrupted Power Supply) based on intelligent management, and relates to the technical field of UPSs, the system comprises a battery state monitoring and predicting module, an intelligent charging management module and a fault diagnosis and fault-tolerant control module of a battery system, and the SOH and SOC of a battery are accurately predicted through a support vector machine, a random forest and a particle filter algorithm. A basis is provided for reasonable charging and discharging, the service life of the battery is prolonged, the intelligent charging management module dynamically adjusts charging parameters according to the SOC and the temperature of the battery through a fuzzy logic control algorithm, balance of rapid charging and battery protection is achieved, the intelligent charging management module adapts to different environments, and the fault diagnosis and fault-tolerant control module detects faults in time with the help of a support vector machine algorithm. A model prediction control algorithm optimizes control input when a fault occurs, it is ensured that the UPS system still operates normally under the battery fault, a data acquisition and transmission module and a system monitoring and display module monitor the battery state and provide historical data query analysis, and a user can conveniently master the battery condition and make a decision.

The invention discloses an atmosphere laser radar data analysis method based on deep learning, and the method comprises the following steps: S1, collecting and preprocessing atmosphere laser radar echo signal data, and generating a time series data set; s2, constructing a long sequence prediction model based on Transform, and forming a preliminary prediction result sequence; s3, optimizing model structure parameters and training parameters by using a particle filtering algorithm to obtain an optimized model; s4, using the optimized model to predict current and future moment data, and generating a final prediction sequence; s5, performing residual analysis on the final prediction sequence and the real-time observation data, and identifying abnormal points; and S6, sudden disturbance is detected, particle filtering optimization is restarted, and the prediction model is dynamically updated. According to the invention, by fusing the deep learning long sequence prediction model and the particle filter optimization algorithm, high-precision prediction and abnormal dynamic identification of atmospheric laser radar data are realized.

The invention discloses a tornado identification and path prediction method based on X-band dual-polarization radar data, which mainly utilizes six observed quantities of two lowest elevation angles of a radar, combines a deep learning model Unet, introduces an attention mechanism and position coding information, optimizes a data transmission process, and constructs a weak supervision type lightweight identification model TD-Unet. The model carries out feature extraction on multi-elevation-angle radar data, and generates a tornado identification probability and a geographic azimuth map. In the recognition result, a geographic coordinate sequence where the tornado is located is further extracted, a particle filtering algorithm is optimized based on tornado motion features, a path prediction algorithm is constructed, and path prediction is achieved through the extracted real-time tornado coordinate sequence. According to the method, the accuracy and efficiency of tornado identification can be improved, a scientific and feasible method is provided for path prediction, and powerful technical support is provided for meteorological early warning and disaster prevention and control.

A distribution network fault positioning method based on particle filter algorithm multi-information fusion comprises the following steps: extracting transient information, and extracting a transient zero-sequence current high-frequency component by using discrete wavelet transform; calculating a similar entropy and a skewness coefficient of a transient zero-sequence current high-frequency component of each measurement point to obtain a respective corresponding initial fault section position; fusing the two preliminary fault section positions by using a particle filtering algorithm to obtain a fault section position; determining head and tail fault steady-state zero-sequence voltage and power of the fault section; and cutting a fault section, obtaining the zero-sequence voltage and power of a next cutting point by using a particle filter algorithm, carrying out iterative cutting until a threshold condition is met, and outputting a fault position. According to the method, transient zero-sequence current high-frequency components and steady-state zero-sequence voltage power serve as fault characteristic quantities, and the fault positioning function under the conditions of different fault positions, fault closing angles and white noise interference of the active distribution network is achieved.

The invention discloses a lithium battery health assessment method based on an adaptive extended particle filter algorithm. The method comprises the following steps: constructing a second-order RC equivalent circuit model of a lithium battery; determining all parameters in the lithium battery second-order RC equivalent circuit model by using a recursive least square algorithm with a forgetting factor to obtain a determined lithium battery second-order RC equivalent circuit; estimating SOC parameters of the lithium battery at the moment by combining a particle filtering algorithm with an adaptive extended Kalman filtering algorithm; correcting the terminal voltage of the second-order RC equivalent circuit of the lithium battery at the moment to obtain the terminal voltage of the equivalent circuit; and based on the lithium battery second-order RC equivalent circuit and the corrected terminal voltage, estimating the SOE parameter of the lithium battery at the moment k by using a particle filtering algorithm in combination with an extended Kalman filtering algorithm. The SOC and the SOE of the lithium battery are accurately estimated, and the sampling efficiency and the estimation stability of particle filtering are improved. Cooperative joint estimation of SOC and SOE complements each other, and the accuracy of overall state estimation is further improved.

The invention relates to an equipment residual life prediction method based on a physical-data model, and the method comprises the steps: building a degradation model based on a physical degradation mechanism and a standard Wiener process model; obtaining fixed parameters in the degradation model by using a maximum likelihood estimation method in combination with a nonlinear regression method; acquiring an equipment state observation value in real time, and updating random parameters in the degradation model by using a weight optimization particle filter algorithm; and utilizing the degradation model, the fixed parameters in the degradation model and the random parameters in the degradation model to obtain a probability density function of the residual life of the equipment under a random failure threshold value, and performing numerical integration on the probability density function of the residual life of the equipment to obtain an expected value of the residual life of the equipment, and outputting the expected value as the residual life of the equipment. Compared with the prior art, high-precision and high-reliability residual life prediction is realized by fusing a physical mechanism and a data driving method.

The invention relates to the technical field of multi-sensor fusion anomaly detection, in particular to a multi-sensor data fusion pig farm anomaly detection system which comprises the steps that data are continuously collected through sensors, a unique space-time anchor point is bound to an RFID tag, and a particle filter algorithm is utilized to predict the motion trail of a pig in different sensor view fields; local model training is carried out at each node according to own data, then local model parameters are uploaded to a central node for fusion through a gradient aggregation strategy, and a global anomaly detection model is generated; dividing a dynamic sensing network in the hog house, and evaluating the confusion degree and the abnormal possibility of data by calculating entropy values of grid units; a behavior fingerprint database is established for each pig, pig behavior data are monitored in real time, and matching degree calculation is carried out on the behavior fingerprint matching degree. And various types of sensors are widely deployed, so that multi-dimensional information of pigs can be comprehensively acquired, and the condition of the pig farm can be comprehensively sensed.

The invention provides a micro-milling machining parameter identification method considering the random tool wear influence. The method comprises the steps that a cutting force model under the tool wear influence is established by considering tool bounce and a chip separation mechanism; updating the rotating radius of the tool according to the influence of jumping and abrasion on the edge radius value of the tool, and obtaining a tool nose trajectory equation; tool wear data in the actual machining process are collected, the neural network model is trained, and hyper-parameters in the neural network model are optimized through a bidirectional long-short-term memory network; identifying processing parameter values in the neural network model by adopting a particle filtering algorithm; and simulating and calculating cutting forces and wear values under different working conditions by using a neural network model, comparing experimental results, and evaluating the accuracy of machining parameter identification. According to the method, the randomness of tool wear is fully considered in the modeling process, the prediction precision is remarkably improved, and the method has higher practical application value.

The invention discloses an urban particulate matter migration path identification method based on data fusion of a dynamic diffusion model and multi-source sensing. According to the method, firstly, multi-source pollution related data of a fixed monitoring station, a mobile monitoring device, a meteorological observation node and a traffic sensing system are collected, and standardized input is constructed after time synchronization, coordinate mapping and exception handling. And then establishing a hybrid dynamic diffusion model fusing a two-dimensional Gaussian plume model and a Lagrange particle tracking mechanism, and realizing assimilation of monitoring data and model output in combination with an improved particle filtering algorithm to obtain a correction concentration field. Based on concentration gradient analysis and particle trajectory superposition, a pollution migration path is extracted, then indexes such as a migration intensity index (MII), a path stability factor (PSF) and path average correlation are calculated, and recognition and sorting of a migration direction, a pollution source position and path stability are achieved. Meanwhile, an alpha dynamic weight factor is introduced, adaptive weighting is realized between the Gaussian model and the LPDM model, and the path strength and consistency are considered. Experimental results show that the method is superior to a traditional method in average absolute deviation (MAD) and hot spot overlap ratio (IoU) indexes, diffusion and migration rules of particulate matters under complex urban conditions can be more accurately revealed, and the method has important environmental governance and emergency management application value.

The invention relates to a tunnel construction progress model generation method based on artificial intelligence. The method comprises the following steps: acquiring multi-source heterogeneous data such as tunnel field equipment operation and geological change, and performing multi-modal fusion to form a unified data set; based on this, a particle filter algorithm is used to correct equipment positioning deviation, and a real-time position optimization result is generated. Comparing the time and space with the progress plan, and identifying and quantifying deviation points through a critical path method and a deviation analysis algorithm to form deviation data. And constructing a construction progress model by combining construction basic parameters and adopting a genetic algorithm, generating an adjustment scheme by taking a construction period and resources as targets, and obtaining an optimal scheme through compliance screening and fitness calculation iterative optimization. By the adoption of the method, efficient integration of heterogeneous data can be achieved, the equipment positioning precision is improved to the centimeter level, multi-dimensional quantification of progress deviation is achieved, progress deviation recognition and resource allocation efficiency is improved, and a technical scheme is provided for tunnel intelligent construction.

The invention discloses a ten-thousand-square-meter muddy deep foundation pit supporting structure deformation prediction method which comprises the following steps: establishing a soil-supporting interaction rheological model, simulating stress relaxation and creep effects of muddy soil in combination with a Burgers rheological model, and realizing dynamic evolution prediction of a supporting structure deformation field and an internal force field. According to the method, the particle filter algorithm is adopted to carry out denoising processing on real-time monitoring data, model parameters are updated through the Bayesian method, and the prediction precision is improved. Meanwhile, nonlinear mapping of the deformation rate and the internal force increment is established by using a random forest algorithm, and instability risk probability distribution is generated. According to the method, multi-stage collapse early warning thresholds are set, monitoring data and prediction results are fused by adopting a weighted average method, and abnormal points of a high-risk area are identified. For a high-risk area, a genetic algorithm is applied to optimize the rigidity and prestress distribution of a supporting system, effective control over deformation and internal force of a supporting structure is achieved by dynamically adjusting construction parameters, and construction safety of the deep foundation pit is ensured.

The invention belongs to the technical field of automatic driving, and particularly relates to an automatic driving decision-making method and system with dynamic risk perception and attention focusing and a vehicle, and the method comprises the steps: predicting the track of a surrounding vehicle in real time through a multi-feature Gaussian weighted particle filtering algorithm, and improving the prediction precision through combining a vehicle kinematic model and resampling optimization; constructing a comprehensive evaluation model fusing transverse and longitudinal risks, and dynamically quantifying the collision risk of the vehicle and surrounding vehicles; and inputting the risk value as a key state feature into a double-depth Q network based on attention mechanism enhancement, focusing key information through a feature attention distribution mechanism, and generating an optimal driving decision in combination with a multi-target reward function. Compared with the prior art, the method solves the problems of insufficient quantification of uncertainty factors, incomplete risk assessment and low decision-making efficiency of automatic driving in a complex dynamic environment, and significantly improves the risk perception capability and decision-making safety of the automatic driving vehicle.

The invention relates to the technical field of attitude control, and discloses a method for realizing self-adaptive active leveling compensation, which comprises the following steps: acquiring real-time data of a vehicle, the data being provided by a plurality of sensors; processing the real-time data through a data fusion algorithm; according to the inclination state and the motion parameters of the vehicle, hydraulic supporting leg adjusting parameters are calculated through a multi-objective optimization algorithm; according to the calculated hydraulic supporting leg adjusting parameters, the action of the hydraulic supporting leg is adjusted through a PI D controller for vehicle balance recovery; the stability of the hydraulic supporting leg adjusting system is verified through the Lyapunov stability theory, and the hydraulic supporting leg adjusting system is used for enabling the adjusting process of the hydraulic supporting leg to be completed in all operation states; and the vehicle inclination state is fed back in real time. By fusing multi-sensor data and adopting a Kalman filtering or particle filtering algorithm, high-precision perception of the real-time inclination state and dynamic parameters of the vehicle is realized, and a more accurate and reliable vehicle attitude judgment effect is obtained.

The invention relates to the technical field of equipment operation optimization, in particular to an electric power equipment operation optimization method and system based on digital twinning. The method comprises the following steps: acquiring operation data of physical power equipment through a sensor; building a digital twinborn model of the power equipment by utilizing modeling software Simulink based on the operation data; establishing bidirectional data streams from the physical power equipment to the digital twinborn model and from the digital twinborn model to the physical power equipment, and performing data synchronization; performing state estimation on the synchronized digital twin model by using a particle filtering algorithm, and introducing a physical degradation model of equipment aging in the state estimation process; and based on the estimated state, optimizing the operation condition of the equipment through a particle swarm optimization algorithm. According to the design of the invention, the digital twinborn model synchronized with the physical power equipment is constructed, and the particle filter algorithm is utilized to carry out accurate state estimation, so that the operation state of the power equipment can be monitored in real time.

The invention relates to the technical field of unmanned aerial vehicle control, and particularly discloses a wrist-worn unmanned aerial vehicle countering control method and system, through cooperative work of a radar sensor, an infrared sensor, an optical sensor and a GPS sensor, flight state data of an unmanned aerial vehicle can be obtained, and state prediction and target tracking are performed on the unmanned aerial vehicle by using a particle filtering algorithm; a deviation coefficient between an actual flight trajectory and a preset trajectory is calculated through a dynamic time warping algorithm, and the system can judge whether the unmanned aerial vehicle deviates from the trajectory and adjust an interference strategy in time; the frequency, the intensity and the transmitting direction of the interference signal are dynamically optimized according to the flight state of the unmanned aerial vehicle so as to form an efficient countering strategy; and by comprehensively evaluating the countering response time and the success rate, a countering precision coefficient is calculated, so that data support is provided for optimization of a subsequent countering strategy.

The invention provides an indoor positioning method and system based on dynamic mode switching and data fusion, and relates to the technical field of visible light indoor positioning, and the method comprises the following steps: deploying a VLC transmitting device and an auxiliary positioning point, constructing a VLC fingerprint database, carrying out dynamic signal detection and mode conversion, carrying out VLC fingerprint positioning, carrying out IMU data processing, and carrying out main positioning mode data fusion. VLC signal intensity distance estimation, ultrasonic ranging, IMU relative displacement estimation and standby positioning mode data fusion are carried out. According to the invention, by deploying the visible light communication transmitting device and the auxiliary positioning point, the strength and the stability of the VLC signal are monitored in real time, and dynamic positioning mode switching is realized through fuzzy logic reasoning. In the main positioning mode, VLC fingerprint matching is combined with IMU data, and accurate positioning is carried out through a Kalman filtering algorithm; and when the VLC signal is weak or unstable, a standby positioning mode is switched, and accurate positioning is carried out by fusing data through a particle filter algorithm according to VLC signal intensity distance estimation, ultrasonic ranging and I MU data.

The invention discloses an automatic control gradient LED lamp energy-saving illumination method, which comprises the following steps: S1, asynchronous sampling is carried out through an illumination sensor A and an illumination sensor B, ambient light change data is collected, a sampling timestamp is recorded, a time difference weight is obtained based on the timestamp, data fusion is carried out through the time difference weight, and ambient light intensity data is obtained; s2, performing data calibration on the ambient light intensity data through a state space model, and performing optimization through a weighted moving average method to obtain an ambient light intensity change rate; and S3, acquiring a light change condition based on an environment illumination intensity change rate, adjusting a brightness duty ratio through a probability model based on Bayesian reasoning to output an initial control variable, performing optimization based on the initial control variable in combination with a particle filtering algorithm to obtain a final control scheme, and performing optimization through the probability model and the algorithm to obtain a final control scheme. A relatively accurate brightness adjustment scheme can still be given, and the intelligent level of lamp control is improved.

According to the invention, the internal association relationship between the fault working condition attribute and the fault feature is mined, and the fault information fusion-based power system transient protection method is provided. The fault information fusion method adopts a Kalman filtering algorithm and an improved Kalman filtering algorithm, or adopts a particle filtering algorithm or an improved particle filtering algorithm. And the fault can be reflected more accurately by fusing the standard fault characteristics of the fault working condition attributes. According to the power system transient protection method based on the fault working condition attribute, the adverse effect of the fault initial phase angle and the transition resistance on protection is overcome, and the transient protection reliability is improved.

The invention provides a flow battery energy storage power station full life cycle health degree assessment management method. The method comprises the following steps: constructing an initial health reference library, collecting electrochemical parameters through a reference electrode, collecting electrolyte physical parameters and environmental parameters through a sensor, synchronously recording operation and maintenance data, and carrying out time-space synchronization processing; introducing a hierarchical fusion assessment mechanism, assessing data integrity and constructing an attention mechanism model, calculating a dynamic weight according to a parameter relative change rate and a health degree correlation coefficient, generating a weighted feature vector, and predicting a short-term health degree index by adopting a bidirectional long-short-term memory network Bi-LSTM; a particle filter (PF) algorithm is introduced to be combined with a physical parameter drift distance to correct a long-term health degree index, and then a real-time health degree index is output through fuzzy comprehensive evaluation. The method can achieve the linkage evaluation of the health state in the operation period and the full life cycle trend, corrects the long-term SOH through the PF algorithm in combination with the physical parameter drift distance, and reduces the error of long-term prediction.

The invention belongs to the technical field of power generation equipment, discloses a power generation equipment vibration and temperature combined monitoring method and a related system, and overcomes the defect that a traditional system only depends on a single vibration parameter by continuously acquiring real-time state information of the power generation equipment and comprehensively monitoring various parameters such as vibration and temperature. And more comprehensive equipment state evaluation is provided. According to the method, the defects of the power generation equipment can be quickly identified and fed back based on the grouping preprocessing of the preset period, so that the fault discovery and response time is shortened, and the maintenance efficiency is improved. According to the method, anomaly detection is performed on the periodic sequence information by using the pre-constructed signal mutation model, so that potential problems can be identified more accurately, the risks of missing detection and false detection are reduced, and higher detection sensitivity is ensured. According to the method, deep learning and particle filtering algorithms are combined, and the constructed conjoint analysis model can perform deep analysis on abnormal points, so that an operation and maintenance team is helped to make a more scientific decision.

The invention discloses a pedestrian navigation method and system based on intelligent fusion of optical signals and inertia. The method comprises the following steps: calculating the distance between a receiving end and a transmitting end according to the positions of a visible light transmitting end and a receiving end and visible light original data collected by the receiving end; calculating the collected inertial data by adopting a pedestrian dead reckoning method to obtain position information of a receiving end; and carrying out tight combination fusion on the distance between the receiving end and the transmitting end and the position information of the receiving end based on a particle filtering algorithm to obtain a predicted pedestrian position. According to the method, the pedestrian dead reckoning positioning result and the visible light ranging information are fused, the defects of a single positioning technology are overcome, the method has good performance on a fusion positioning system of visible light and other signals susceptible to environmental interference and inertial and other high-noise signals, the adverse effect of environmental noise on the fusion system is weakened, and the positioning accuracy is improved. And the migration capability and the adaptive capability of the fusion system are effectively improved.

The invention discloses an unmanned ship cooperative target surrounding method, and belongs to the technical field of unmanned ship cooperative control. According to the method, a YOLOv8 target detection method is adopted to train a data set of a collaborative surrounding target, and a real-time target detection model is obtained; a particle filter is utilized to realize collaborative target path determination and conversion, target state distribution is approximated through weighted particles, particle weights are updated in real time according to recognized target states, weighted averaging is carried out, a target trajectory is finally formed in combination with time recursion, and continuity and robustness of the trajectory are ensured. According to the method, the target needing to be surrounded can be identified, the trajectory of the target is obtained through a particle filter algorithm, and the trajectory is converted into a determined path.

The invention relates to a radar point cloud real-time modeling water conservancy earthwork measurement method and device, equipment and a medium. According to the method, a vegetation root system interference area is scanned and recognized through a dual-frequency polarization radar, a coordinate set of the vegetation root system interference area is obtained, an elevation compensation model is constructed based on dielectric constant difference and fractal dimension to generate a compensation parameter set, and original point cloud is processed by combining a bare soil reference curved surface and adopting a dynamic constraint particle filtering algorithm to obtain real ground point cloud. Then, a corrected terrain curved surface is generated through parasitic energy field constraint optimization, and finally, compensation parameters are fused to realize high-precision earth volume calculation in a root system interference environment, so that point cloud distortion and a terrain curved surface systematic lifting effect caused by a vegetation root system are greatly reduced, and an earth measurement result of a complex vegetation coverage area meets an engineering acceptance standard.

The invention relates to the technical field of natural gas pipelines, and provides a pipeline natural gas leakage source parameter inversion method and system. The method comprises the following steps: firstly, acquiring natural gas concentration data at different monitoring sites at a moment t when the pipeline natural gas leaks, and determining a natural gas predicted concentration data set C at a moment t + delta t when the pipeline natural gas leaks based on a leakage diffusion agent model of the pipeline natural gas constructed by intrinsic orthogonal decomposition and an artificial neural network; and then, performing parameter estimation on a leakage source of the pipeline natural gas based on a leakage source inversion model of the pipeline natural gas constructed by a Bayesian network and an improved particle filtering algorithm. Therefore, specific complex concentration distribution characteristics of a natural gas leakage scene are extracted based on the leakage diffusion model, then leakage source parameter information is calculated through continuous iteration of the leakage source inversion model, the problems of pipeline natural gas leakage source positioning, parameters and the like are effectively solved, and technical support is provided for guaranteeing pipeline safety.

The invention relates to the field of substation operation and maintenance, and provides an urban rail transit substation operation and maintenance method and system based on an inspection robot, and the method comprises the steps: obtaining the motion posture data of the inspection robot, and collecting environment point cloud data, visual image data and substation equipment state data in a substation in real time through the inspection robot; by fusing the environment point cloud data, the visual image data and the moving posture data, constructing a substation internal map by adopting a particle filtering algorithm; based on a Dijkstra algorithm and the internal map of the substation, planning an inspection path of the inspection robot; enabling the inspection robot to advance along the inspection path, obtaining the coordinates of the obstacle in real time through the environment point cloud data, and carrying out dynamic obstacle avoidance based on an artificial potential field method; constructing an equipment fault diagnosis model, and identifying the substation equipment state data to obtain a substation equipment anomaly category; and pushing the maintenance notification and the maintenance suggestion to a manager. According to the invention, the operation and maintenance safety and working efficiency of the substation equipment are improved.

The invention discloses a spacecraft sensor abnormal data recovery method based on spatio-temporal information fusion, and relates to a spacecraft sensor abnormal data recovery method. The method aims at solving the problems that in an existing spacecraft sensor abnormal data recovery technology, the signal-to-noise ratio is deteriorated due to environment interference, and multi-dimensional feature modeling has limitation and is insufficient in self-adaptive capacity. The method comprises the following steps: step 1, preprocessing original sensor data through a sliding mean filter; 2, constructing a time-space dual-scale feature extraction network based on Transform, mining a sensor time sequence dependency relationship from a time dimension, and analyzing multi-sensor association features from a space dimension; 3, introducing a particle filter algorithm to carry out optimal fusion on space-time dual-channel output, and quantifying the uncertainty of a data recovery result; and step 4, by comparing the actually measured data with the uncertainty confidence interval, abnormal detection and data recovery of sensor anomalies are realized. The invention belongs to the technical field of satellite sensor abnormal data recovery.

The invention discloses an unmanned aerial vehicle monitoring method and system in a complex electromagnetic environment, and the method comprises the steps: extracting a signal feature from a spectrum signal collected in real time, and obtaining a first feature vector; adopting a pre-constructed random forest model to classify the first feature vectors and match the first feature vectors with known unmanned aerial vehicle information to obtain a first model and a first serial number of the unmanned aerial vehicle; according to the coordinates of the at least three measurement stations, calculating the three-dimensional coordinates of the unmanned aerial vehicle by adopting a time difference of arrival positioning technology and an angle of arrival measurement technology; calculating the radial speed of the unmanned aerial vehicle relative to each measurement station by adopting a Doppler effect, and calculating a three-dimensional speed vector of the unmanned aerial vehicle according to the radial speed; and according to the three-dimensional coordinates and the three-dimensional speed vector of the unmanned aerial vehicle, predicting the change trend of the three-dimensional coordinates and the three-dimensional speed vector of the unmanned aerial vehicle in a preset time window by adopting a particle filtering algorithm. By adopting the embodiment of the invention, the identification accuracy, the positioning precision and the anti-interference capability of unmanned aerial vehicle monitoring in a complex electromagnetic environment can be improved.