72 results about "Outlier elimination" patented technology

The invention relates to the technical field of intelligent fault diagnosis, and discloses a fault diagnosis method for new energy power generation equipment, and the method comprises the steps: obtaining a multi-physical-quantity time dynamic data set; outlier elimination is carried out on the data set, and a deviation degree sequence of each physical quantity is extracted; based on the deviation degree sequence, normalization processing is completed, and a correlation matrix among multiple physical quantities is constructed; generating a preliminary network structure through correlation screening and symmetry completion; calculating a path weight based on the initial network structure and performing topology reconstruction to obtain a final network topology structure; performing deviation propagation analysis according to the final network topology structure, and determining potential fault position distribution; based on the fault position distribution, performing fault grade classification by adopting a support vector machine algorithm, and outputting a fault risk grade label; and in combination with the fault level label and the network topology structure, node risk scoring and area identification are executed, and finally fault positioning is completed. According to the method, accurate positioning of the fault position in a complex system can be realized.

The invention discloses a power equipment safety state assessment method and system, equipment and a readable storage medium, and relates to the technical field of power equipment assessment, and the method comprises the steps: collecting a power equipment communication interaction data flow, carrying out the time synchronization and outlier elimination, and constructing a multi-modal data matrix; evaluating the exception probability score and an exception probability threshold to obtain an exception state flag, and generating a security event data packet in combination with the preliminary exception type and the power equipment communication interaction data stream; adopting a LiNGAM causal discovery algorithm to perform non-Gaussian test and time sequence lag analysis on an abnormal event time sequence in the security event data packet, and constructing a directed acyclic causal graph; dynamically adjusting the causal intensity of the directed acyclic causal graph according to the real-time equipment load rate and the communication quality index, and outputting a dynamic causal graph; according to the method, the accuracy and adaptability of abnormal propagation analysis are improved, and finally the real-time performance and credibility of safety assessment of the power equipment are enhanced.

The invention relates to the technical field of furnace tube intelligent diagnosis, in particular to a big data AI-based furnace tube leakage intelligent diagnosis method, which comprises the following steps: collecting boiler operation data in real time through a multi-source sensor, including temperature, pressure, vibration, sound wave and flue gas component data; performing time sequence alignment, abnormal value elimination and standardized preprocessing on the collected original data; inputting the preprocessed data into a pre-trained deep neural network model, wherein the deep neural network model is obtained through comparison training of historical normal data and leakage accident data; generating a real-time diagnosis result and an early warning level based on the leakage probability value and the feature contribution degree analysis output by the model; through multi-source sensing, AI deep analysis, dynamic early warning and predictive maintenance closed loop, early, accurate and automatic diagnosis of boiler tube leakage is realized, and the safety and operation and maintenance efficiency of industrial equipment are remarkably improved.

The invention provides a gas extraction multi-parameter monitoring method and system based on edge calculation, and relates to the technical field of coal mine gas extraction. The method comprises the following steps: arranging multi-parameter sensor nodes in drill holes, pipelines and gas gathering stations, and accessing an edge calculation unit; performing time synchronization, zero drift correction, outlier elimination and normalization on the data of the gas concentration, the negative pressure, the flow, the temperature, the humidity and the hydrogen sulfide concentration to generate feature vectors; calling a lightweight neural network model for reasoning, and outputting an extraction efficiency score and a leakage risk degree; when the risk degree exceeds the limit, an acousto-optic alarm is triggered and a speed reduction instruction is issued; when the efficiency is insufficient, the negative pressure is adjusted; compressing and encrypting the result, and reporting the result to a cloud platform through a wireless network at regular time; and the cloud performs model incremental training based on historical data and feedback, and issues an update model through hot replacement. According to the invention, local intelligent analysis, rapid early warning and dynamic adaptive optimization are realized.

The invention relates to the technical field of power equipment fault diagnosis, and discloses a power plant equipment multistage fault diagnosis method and system based on a dynamic decision tree, and the method comprises the steps: 1, collecting equipment operation data through multiple sensors, and carrying out the preprocessing of multi-source data; comprising noise filtering, outlier elimination and missing value filling; step 2, extracting feature values according to the time domain features and the frequency domain features, constructing a state decision tree model based on a C4.5 algorithm, optimizing attribute split points by an information gain rate, and optimizing generalization ability by an REP post pruning strategy; and step 3, monitoring operation data of the power plant equipment based on the state decision tree model, diagnosing and predicting equipment faults according to the monitoring data, outputting fault levels according to monitoring results, and triggering corresponding grading responses. The fuel power plant equipment fault diagnosis method provided by the invention has the advantages of high precision, low false alarm, capability of effectively distinguishing fault levels and dynamic self-adaptive capability.

The invention relates to the technical field of power system monitoring, in particular to a bus duct temperature and humidity abnormity monitoring method, which comprises the following steps of S1, periodically acquiring temperature and humidity data through a temperature and humidity sensor, and performing data preprocessing to obtain processed data; s2, mutation detection and trend analysis are carried out according to the processed data, and mutation data and trend data are obtained respectively; s3, according to the abrupt change data and the trend data, a weighting strategy is adjusted in a self-adaptive mode; when the method is used, the credibility of data can be improved, the noise and errors of the sensor can be effectively reduced, the adaptability of the system can be improved, the data subjected to denoising and abnormal value elimination can be used as high-quality input of mutation detection, trend analysis and time sequence prediction, the accuracy of the whole temperature and humidity abnormity monitoring is improved, and the reliability of the system is improved. And short-time window detection and long-time window detection are integrated, so that the accuracy of anomaly monitoring is improved, false alarm caused by purely based on mutation detection is avoided, and the response speed of the method is improved.

The invention belongs to the technical field of airport clearance safety, and discloses an airport clearance ultrahigh ground feature extraction method fusing LiDAR point cloud and a panoramic segmentation model, and the method comprises the steps: collecting point cloud data in an airport clearance protection region through employing an unmanned plane airborne LiDAR, and carrying out the noise filtering, outlier elimination and datum point-based elevation standardization; constructing a data set with semantic tags and instance numbers, inputting the data set into a SuperCluster panoramic segmentation model, sequentially executing feature extraction, adjacency relation construction, edge weight distribution, local supervision and super-point aggregation, and outputting a segmentation result with semantics and instances; extracting a highest point in each instance point set, and performing spatial comparison with an inner horizontal plane, an approach plane and a transition plane limiting plane established according to an ICAO standard to obtain an instance category, a highest point coordinate, an intrusion depth and a spatial relationship; and performing three-dimensional visual display on the judgment result through the clearance supervision platform.

The invention relates to the technical field of agricultural irrigation monitoring, and discloses a multi-source sensing-based irrigation water utilization coefficient intelligent monitoring method and system, and the method comprises the steps: collecting multi-source data through unmanned plane spectrum sensing, a ground Internet of Things sensor and a crop growth model, carrying out the time-space alignment, abnormal value elimination and standardization processing of the multi-source data, and carrying out the monitoring of the utilization coefficient of irrigation water. Constructing a data set; constructing a three-dimensional digital twinborn model of the irrigation area, and simulating hydrological processes under different irrigation strategies to generate a virtual irrigation water utilization coefficient reference value; performing multi-source feature fusion on the data in the data set through a CNN-LSTM-RF model to obtain target feature data; inputting the target feature data into a PPO algorithm, dynamically optimizing irrigation parameters, maximizing an actual irrigation water utilization coefficient, and outputting an optimal irrigation strategy; irrigation is conducted based on the optimal irrigation strategy, the irrigation water utilization coefficient is monitored in real time, and when the irrigation water utilization coefficient is lower than a set threshold value, early warning information is automatically sent out; according to the invention, irrigation water utilization coefficient monitoring and management are realized.

The invention provides a remote sensing building group depth detection and change intelligent evaluation method for urban planning, and the method comprises the steps: carrying out the multi-modal data collection of a target urban region, and carrying out the standardization processing, abnormal value elimination, multi-modal alignment and interference suppression processing, and obtaining a multi-modal data set; constructing a multi-branch hybrid network, and performing triplet feature extraction on the multi-modal data set through the multi-branch hybrid network to obtain a building group fusion feature map; performing change area detection and change type classification on the two building group fusion feature maps with different time phases to obtain building group change information; and constructing a dynamic evaluation index based on the urban planning core demand, and performing building group change evaluation in combination with the building group change information. According to the method, through multi-source and multi-modal data acquisition and processing, multi-branch hybrid network triplet feature extraction and change detection and dynamic evaluation index application, urban planning core requirements are adapted, and building group detection precision and change evaluation effectiveness are improved.

The invention discloses a photovoltaic power prediction method based on improved empirical mode decomposition and an optimized long short-term memory network, and the method comprises the steps: firstly carrying out the preprocessing of abnormal value elimination, missing value filling, normalization and the like of photovoltaic power and related meteorological data, and improving the data quality; then, an improved empirical mode decomposition (EE-ANEMD) algorithm is adopted to decompose the preprocessed power sequence into a multi-scale intrinsic mode function component and a residual term, and high-frequency noise, intermediate-frequency fluctuation and a low-frequency trend are effectively separated; global optimization is carried out on the hidden layer unit number, the initial learning rate and the maximum number of training times of the LSTM network through an improved sparrow search algorithm (ISSA), finally, the optimized LSTM is utilized to carry out training prediction on each component, and results are fused and subjected to reverse normalization to obtain a final value. Experiments show that the test set RMSE of the method is reduced compared with that of a single LSTM, the mid-term prediction precision is remarkably improved, and reliable technical support is provided for power system dispatching, new energy consumption planning and photovoltaic power station operation and maintenance.

The invention relates to an autonomous measurement and control orbit determination method and system for a high-orbit satellite. The method comprises the following steps: acquiring satellite-borne GNSS broadcast ephemeris data and single-frequency pseudo-range observation data of a high-orbit satellite; based on the GNSS broadcast ephemeris data and the single-frequency pseudo-range observation data, coarse orbit data of the high-orbit satellite are generated through a dynamic pseudo-range orbit determination algorithm; performing outlier elimination preprocessing on the coarse orbit data to obtain orbit data after outlier elimination; performing smooth fitting and equal-interval sampling on the orbit data after outlier elimination to obtain smooth orbit data; determining an orbit determination arc section according to the scheduling time and the orbit maneuvering time; and using the motion equation and the state transition matrix of the high-orbit satellite to process least square iteration in batches to obtain optimal orbit data, and executing orbit forecasting. The method provided by the invention does not depend on information transmission of ground measurement and control resources, realizes on-satellite autonomous orbit determination, reduces the complexity of data fusion calculation, and is small in calculation amount.

The invention relates to the technical field of computer vision and automatic control, and discloses an automatic checking method for the number of stacked sheet materials, which comprises the following steps: identifying a standard number table image to obtain a platform standard identifier and a material standard number; performing visual identification processing on the field material image, identifying a platform identification target and all material targets corresponding to the platform identification target, and obtaining position information of the material targets; identifying the platform identification target to obtain a platform analysis identifier; and outlier elimination processing and gap filling processing are carried out on the material target according to the position information. And finally obtaining a platform analysis identifier and a material analysis quantity. And matching the platform analysis identifier with the platform standard identifier, comparing the material analysis quantity corresponding to the matched platform with the material standard quantity, and outputting a verification conclusion according to a comparison result. Through the arrangement, accurate detection of the material quantity is realized, and the accuracy, the efficiency and the automation level of warehouse checking work are improved.

The application provides a network load balancing algorithm based on a Xinyuan platform and a Xinyuan terminal, and relates to the technical field of Xinyuan, which comprises the following steps: collecting real-time traffic data from multiple ports; performing normalization, outlier elimination and protocol field standardization preprocessing on the collected data; performing multi-dimensional protocol fingerprint feature extraction on the traffic of each port based on a protocol analysis algorithm; modeling the historical and real-time traffic of each port using a time series neural network, and outputting traffic prediction values and abnormal traffic probability evaluation in a future short period; dynamically generating a multi-port distribution weight factor based on the protocol characteristics and traffic change trend of each port; inputting the prediction results and real-time load state into a reinforcement learning scheduling agent; and immediately performing feedforward load distribution adjustment when abnormal traffic trend is detected. The method can improve the data processing efficiency, realize the intelligence of load scheduling, ensure the resource allocation priority of the ports of the Xinyuan terminal, and guarantee service continuity.

The application discloses an AI model training optimization method and system, relates to the technical field of model training optimization, and comprises the following steps: collecting real-time running data in an AI model training process, and generating a multidimensional real-time running data matrix; performing outlier elimination on the real-time running data matrix, and generating a standardized running data stream; extracting characteristic parameters and coding into a training state characteristic vector; performing multidimensional evaluation on the training state characteristic vector, and generating an original optimization score; generating a comprehensive optimization suggestion; generating hierarchical optimization content, and converting into a personalized optimization report; and forming a training process self-adaptive adjustment cycle based on optimization result feedback. The application solves the problems of low optimization efficiency and difficulty in accurate positioning caused by manual tuning depending on artificial experience, improves resource utilization and optimization accuracy of the AI model training, and realizes intelligent dynamic adjustment of the training process through a closed-loop self-adaptive control mechanism.

The invention discloses a smog methane composite detection method and device, relates to the technical field of smog methane detection, and solves the technical problems of weak anti-interference capability in a data processing link and lack of a multi-parameter compensation and calibration system. The effective acquisition range is dynamically calculated in combination with a factory calibration interval, the signal amplification factor is adaptively adjusted by using a digital potentiometer, the electric signal strength is ensured to be stabilized in the optimal interval, the influence of laser loss and environment temperature on basic data is eliminated from the source, and the filtering coefficient is dynamically adjusted in combination with the signal-to-noise ratio by using a layered filtering strategy. Random noise is filtered out, and key features of an absorption peak are reserved; through segmented weighted polynomial fitting, accurate extraction of core parameters of the absorption peak is realized; and a unified multi-round outlier elimination mechanism eliminates the interference of abnormal data on the methane concentration, the absorption peak position and the smoke sensing value, and improves the credibility of the data.

The invention relates to the technical field of energy conservation and environmental protection, in particular to an AI modeling-based energy station efficient operation system and an operation method thereof, and the method comprises the following steps: collecting equipment operation data at an energy station site through a multi-source sensor, and uploading the equipment operation data to a cloud through an edge gateway; performing time synchronization, abnormal value elimination, missing value processing and standardization processing on the acquired operation data; carrying out working condition clustering on the operation state of the energy station based on the historical preprocessing data, and establishing a corresponding energy consumption baseline; and performing time sequence prediction on the energy station load and the equipment state based on the real-time data and the historical data, and identifying energy consumption abnormity. According to the invention, the accuracy of energy consumption assessment and abnormity identification can be effectively improved, the risk of sudden failure is reduced, invalid start and stop of equipment and energy waste are reduced, the energy utilization efficiency and the equipment operation reliability are improved, the linkage of operation management and operation and maintenance disposal is realized, and the intelligent operation level and comprehensive operation benefits of the energy station are integrally improved.

The invention provides a rockburst intensity forecasting method based on a model-independent element learning improved RNN-DNN hybrid neural network, belongs to the technical field of rock-soil rockburst, and aims to solve the problems of low forecasting accuracy and insufficient cross-engineering scene adaptability in a traditional forecasting method. The method comprises the following steps: widely collecting multi-source rockburst influence factor data, determining a rockburst grade division standard, and constructing a rockburst database; carrying out missing value complementation through Bayesian interpolation to obtain first-level data; performing abnormal value elimination by using an ECOD abnormal detection algorithm to obtain secondary data; dividing secondary data into a training set and a verification set; adopting a model independent element learning framework to optimize initial parameters of the RNN-DNN hybrid neural network; and loading the initial parameters to the hybrid neural network, and carrying out model training by combining the training set and the verification set to obtain a rockburst intensity prediction model. According to the method, the accuracy and cross-scene universality of rockburst intensity prediction can be improved, and technical support is provided for disaster prevention and control of deep underground engineering.

The invention discloses an unmanned aerial vehicle multi-source fusion navigation positioning method independent of satellite signals, and belongs to the technical field of unmanned aerial vehicle docking navigation, and the method comprises the following steps: 1, building a fusion navigation positioning system; 2, performing time registration and outlier elimination; 3, solving an information distribution coefficient; 4, time-variable measurement noise estimation is carried out; 5, updating time and measurement; step 6, fusion and differential feedback of the main filter; according to the scheme, the problems of insufficient sensor redundancy, rigid multi-source fusion navigation algorithm information distribution and fault isolation mechanism and the like of the existing docking navigation positioning system are solved, so that the navigation positioning precision and the anti-interference capability of the unmanned aerial vehicle are improved.

The application belongs to the technical field of three-dimensional reconstruction and intelligent agriculture, and particularly relates to a three-dimensional Gaussian multi-machine collaborative mapping and optimization method and system for complex agricultural and forestry scenes. In view of the problem of low reconstruction accuracy caused by inconsistent exposure, dynamic disturbance of vegetation and sparse multi-machine view angles in complex agricultural and forestry scenes, the application sequentially carries out multi-machine data preprocessing, spatiotemporal outlier elimination, intermediate frame generation based on FILM and DepthAnything, and 3DGS joint optimization. Dynamic masks are generated through spatiotemporal residual double modal judgment to block dynamic interference at the level of loss function; sparse view angles are completed by using virtual intermediate frames to expand the observation data set. The application can effectively eliminate dynamic artifacts and cavities, realize high-precision three-dimensional reconstruction of complex agricultural and forestry scenes, and be directly applied to digital operation scenes such as vegetation monitoring and automatic path planning of forest land and orchards.

The present application belongs to the technical field of data processing, and particularly relates to a reinforced concrete support construction quality detection method based on data processing. The method comprises the following steps: acquiring raw material proportioning, mixing, pouring and vibrating, and curing environment multi-source real-time data of the whole construction process; performing improved DBSCAN clustering preprocessing (missing value filling, outlier elimination, and standardization); constructing a dynamic correlation model by using a neural network, and optimizing hyperparameters by using an improved starling optimization algorithm; calculating influence coefficients and quality correlation degrees of each link; determining the quality by comparing the quality correlation degrees with a threshold value, and iteratively updating the model until the construction is completed. The present application realizes real-time detection throughout the whole cycle, reduces manual deviation, avoids quality defect solidification, reduces repair cost, and improves the reliability of support structure construction.

The application discloses a UAV multi-modal image registration method and device, medium and product, relates to the technical field of remote sensing image processing and computer vision, and comprises the following steps: calculating the normalized mutual information value of each wave band of the image to be registered and a reference image, selecting a wave band with the maximum value as a reference wave band, solving a global affine transformation matrix by using an enhanced correlation coefficient algorithm, performing global geometric coarse correction, and obtaining the image to be registered after coarse registration; calculating the local principal direction of the image to be registered after coarse registration and the reference image, constructing a Gaussian derivative filter, extracting structural features based on the turning theorem, generating respective structural feature maps and dividing the structural feature maps into regular grids, performing grid-by-grid template matching to obtain a sparse displacement field, performing outlier elimination and interpolation smoothing to generate a dense linear deformation field, performing nonlinear geometric correction and applying the dense linear deformation field to all wave bands of the image to be registered after coarse registration, and obtaining a final registration result. The application realizes high-precision registration of multi-modal images.

The invention relates to the technical field of building energy saving and energy management, in particular to a building park energy-saving potential assessment method and system based on machine learning. During use, a multi-source energy data acquisition module is subdivided into building, floor and equipment levels to acquire energy consumption data, key factors such as meteorology, people flow and equipment working conditions are fused, and the energy-saving potential of a building park is evaluated. A three-principle-based abnormal value elimination scheme and an adaptive data distribution standardization algorithm effectively solve the problems of single data dimension and insufficient continuity, provide high-quality data support for evaluation, extract cross features of temperature and air-conditioning load, people flow and equipment power utilization and the like through feature engineering, and improve the evaluation accuracy. By combining the first sub-model to capture a hybrid model of long and short term dependence and the second sub-model to correct deviation, the problem that a traditional model cannot process multi-factor interaction influence is solved, the prediction precision is improved, and reliable future energy consumption data is provided for energy-saving potential evaluation.

The invention provides a blast furnace molten iron silicon content prediction method based on an improved grey goose algorithm, and relates to the technical field of intelligent prediction, and the method comprises the following steps: collecting multi-dimensional process parameters in a blast furnace operation process, and synchronously recording corresponding silicon content data; performing abnormal value elimination and normalization processing on original parameter data, dividing a training set and a test set, constructing an initial prediction model of the BP neural network, optimizing training parameters of the BP neural network by using an improved grey goose algorithm, and dynamically adjusting step parameters in the algorithm along with the distance between an individual and a global optimal solution. Fusing a velocity field constructed based on a local curvature factor and a local density factor, updating an individual position in a global search stage, reconstructing a final prediction model according to an optimization result, and performing convergence training by using a training set; and finally inputting the test set into the prediction model, and outputting a blast furnace molten iron silicon content prediction result. The method is suitable for blast furnace molten iron component control and has the advantages of being stable in optimization, small in error and high in real-time performance.

The application discloses a Laplace extremely short arc orbit determination algorithm based on a particle swarm algorithm, and relates to the field of short arc orbit determination calculation of a spacecraft. The calculation method comprises the following steps: acquiring a coordinate position vector of an observed satellite at each moment and a direction vector obtained by observation; calculating a Kepler parameter initial value corresponding to the direction vector at each moment and a target position vector corresponding to the direction vector in an inertial system by using a particle swarm algorithm; performing orbit determination, correction and outlier elimination on the target position vector at each moment by using a Laplace method and a least square algorithm; and calculating orbit parameters corresponding to a short arc segment and a true anomaly corresponding to each moment, so that orbit determination calculation from the direction vector to the final orbit parameter of the target is realized, deviation caused by outliers during observation is offset, and extremely short arc orbit determination can be rapidly performed.

The invention provides a frame synchronization and light intensity sampling abnormal value elimination method based on a DLP projection system, and the method comprises the steps: obtaining a to-be-projected original image, extracting a structural feature vector, and calculating the structural prediction light intensity of the original image according to the structural feature vector; collecting at least one light intensity sampling value in a fixed time window after the original image is projected to form a light intensity sequence; calculating the average light intensity of the light intensity sequence, combining the structure prediction light intensity and the structure feature vector, introducing a structure response correction term and a structure adaptability tolerance term, and judging whether the sampling of the current frame is successfully synchronized with the pattern projection or not; if the sampling synchronization of the current frame is judged to be successful, constructing an abnormal value elimination threshold value, and performing abnormal value identification and elimination on the light intensity sequence; calculating the variance based on the residual sampling sequence after the abnormal values are removed; and carrying out weighted fusion on the arithmetic mean value and the median of the residual sequence, and finally generating a light intensity output value and submitting the light intensity output value to an image reconstruction module.

The application discloses a kind of power equipment safety state evaluation method and system, equipment, readable storage medium, it is related to power equipment evaluation technical field, including, acquisition power equipment communication interaction data stream, carry out time synchronization and outlier elimination, construct multimodal data matrix;With abnormal probability score and abnormal probability threshold value are evaluated, obtain abnormal state flag, and generate security event data package in combination with preliminary abnormal type and power equipment communication interaction data stream;Using LiNGAM causal discovery algorithm, abnormal event time series in security event data package is non-gaussianity test and time series lag analysis, and directed acyclic causal graph is constructed;According to real-time equipment load rate and communication quality index, the causal strength of directed acyclic causal graph is dynamically adjusted, and dynamic causal graph is output;The application improves the accuracy and adaptability of abnormal propagation analysis, finally enhances the real-time performance and reliability of power equipment safety evaluation.

The present application relates to the technical field of building energy saving and energy management, in particular to a building park energy saving potential evaluation method and system based on machine learning, in use, the multi-source energy data acquisition module is subdivided to building, floor, equipment level to collect energy consumption data, and key factors such as meteorology, passenger flow and equipment working condition are fused, based on the 3-principle outlier elimination scheme and the standardized algorithm adapting to data distribution, the problem of single data dimension and insufficient continuity is effectively solved, high-quality data support is provided for evaluation, at the same time, cross features such as temperature and air conditioning load, passenger flow and equipment power consumption are extracted through feature engineering, combined with the hybrid model of the first sub-model capturing long and short-term dependence and the second sub-model correcting deviation, the problem that the traditional model cannot handle multi-factor interaction is solved, the prediction accuracy is improved, and reliable future energy consumption data is provided for energy saving potential evaluation.

The invention relates to a Bluetooth AOA signal outlier elimination method and system based on deep learning, and the method comprises the steps: initializing system configuration, and cooperatively collecting Bluetooth signals through employing a plurality of Bluetooth base stations; acquiring IQ data in a plurality of Bluetooth base stations, and performing data preprocessing; according to the IQ data, performing calculation to obtain a preliminary positioning result of the Bluetooth terminal; constructing an input data set of the deep learning model and designing a network structure of the deep learning model; training a deep learning model according to the input data set; the training precision of the deep learning model is judged, if the training precision meets the requirement, IQ data collected in real time is input into the trained deep learning model, and a real-time positioning result of the Bluetooth terminal is obtained; otherwise, returning to adjust the network structure; and optimizing the real-time positioning result by using a Kalman filtering algorithm to obtain a final positioning result. Compared with the prior art, the method has the advantages of high positioning accuracy and strong robustness.

The application relates to a fuel cell data outlier elimination method, equipment and storage medium, the method comprises the following steps: acquiring fuel cell data comprising a plurality of samples; mapping each sample as a data point, calculating the average distance and variance between the current data point and K adjacent data points for each data point to obtain the corresponding z-score, eliminating the sample corresponding to the data point with the z-score greater than the threshold value sigma from the fuel cell data; for each sample in the eliminated fuel cell data, fitting the variable value based on the variable associated with the fuel cell voltage, calculating the fitness function value of the sample by using the ant colony algorithm, and calculating the fitting degree index value of the polarization curve corresponding to the sample with the highest fitness function value; obtaining the values of the hyperparameters K and sigma corresponding to the maximum fitting degree index value through grid search to obtain the fuel cell data after eliminating the samples under the corresponding values. The application can effectively eliminate the outliers of the fuel cell data.

The application provides a gear dynamic stress measurement data outlier elimination method under a small sample, comprising the following steps: step 1, determining a gear dynamic stress measurement position and a number requirement; step 2, determining a vibration excitation order of the gear dynamic stress measurement data; step 3, classifying and arranging the gear dynamic stress measurement data; step 4, preliminarily screening abnormal data based on a quartile range algorithm; step 5, determining a statistical characteristic value of the dynamic stress test data; and step 6, finally determining the abnormal data based on a Grubbs criterion method. The method can effectively reduce the influence of gross errors on the mean value and standard deviation of small sample data, improve the identification reliability of abnormal data, and improve the accuracy of gear fatigue risk assessment; the method is fast and convenient, easy to be programmed, improves work efficiency, and is helpful to reduce the cost of manpower and financial resources.