106results about How to "Achieve high-precision forecasting" patented technology

The invention provides a power supply energy analysis method and system for a medium-voltage power distribution network. The method comprises the following steps: collecting multi-modal data of the medium-voltage power distribution network; constructing a health state evaluation model, and predicting a health score and a fault risk of the transformer of the medium-voltage power distribution network according to the fusion features after dimension reduction; constructing an optimization target of the power electronic equipment of the medium-voltage distribution network according to the health score and the fault probability of the transformer; constructing a multi-modal load prediction model to predict the load change of the power electronic equipment in the future; constructing a multi-objective optimization function, and generating an optimal scheduling strategy; and executing the optimal scheduling strategy to actually control the medium-voltage power distribution network equipment. According to the method, the defects of a traditional method in the aspects of equipment monitoring, load prediction and scheduling optimization are overcome, and the overall management level and coping capacity of a power grid are improved by introducing a leading-edge artificial intelligence algorithm and a data fusion technology.

The invention discloses a scenic spot visitor flow prediction method and system, an equipment terminal and a storage medium thereof, particularly relates to the technical field of scenic spot management, and is used for solving the problem that a comprehensive analysis and dynamic prediction method for a scenic spot visitor flow rule is lacked in the prior art. Multi-source tourist behavior data are collected and preprocessed, and a scenic spot functional area node relation model based on a group behavior principle and a geographic information system is constructed; generating a dynamic distribution state of the functional area by calculating a tourist crowding degree parameter and a dynamic distribution parameter; based on the historical visitor flow data and the real-time monitoring data, constructing a visitor flow propagation matrix and optimizing a migration weight; in combination with the propagation matrix and the dynamic distribution parameters, predicting scenic spot people flow in different time periods in the future, and generating time-period people flow distribution data; finally, the potential high-risk area is marked by analyzing the tourist density; and the precision and the dynamic adaptive capacity of scenic spot people flow prediction are improved.

The invention discloses an inertial microsystem thermal coupling analysis method based on a physical information neural network, and the method comprises the steps: S1, setting material parameters and boundary conditions of an inertial microsystem, and building a thermal coupling analysis model; s2, performing electrothermal coupling analysis to obtain temperature distribution of the inertial microsystem; s3, thermal stress distribution of the microsystem is obtained through thermal-mechanical coupling simulation analysis; s4, predicting a temperature field of the microsystem through the physical information neural network; s5, taking the temperature field as a boundary condition of mechanical simulation of the microsystem to obtain mechanical properties such as stress and strain of the microsystem; and S6, performing mechanical-electric coupling simulation analysis, and analyzing the influence of structural deformation on each parameter in electrical performance. According to the method, the neural network solving precision is improved through the improved adaptive weight strategy, the complete polynomial basis function is combined with the neural network, the state dimension is reduced by introducing the expansion basis function, the calculation cost and time are reduced, accurate prediction of the micro-system multi-moment temperature field is achieved, and the micro-system performance under electric heating power multi-physics field coupling is calculated.

The invention relates to a rainfall kinetic energy space-time prediction method based on dual-polarization weather radar observation parameters, and the method comprises the steps: firstly processing minute-resolution raindrop spectrometer data, calculating raindrop diameter distribution, and calibrating the data of a rain gauge; secondly, dual-polarization radar parameters (ZH, ZDR and KDP) are calculated based on a physical formula, rainfall kinetic energy KE is calculated in combination with micro-physical characteristics, and rainfall types are marked; and then, after atmospheric field variables are extracted and physical normalization is carried out, a bidirectional time series kinetic energy prediction network (BTKE-Net) is constructed, and rainfall kinetic energy is accurately predicted through BiLSTM and a deep regression network. And finally, after attenuation and offset correction, inputting the data into the trained BTKE-Net model, and carrying out real-time space-time prediction. The innovation of the invention lies in that the application potential of the dual-polarization weather radar in the water environment field is excavated, large-range high-precision rainfall kinetic energy space-time prediction is realized, and more accurate scientific basis and decision support are provided for water and soil conservation and meteorological disaster prevention and control.

The invention discloses a high-resolution large-scale forest biomass remote sensing prediction method and system. The method comprises the following steps: acquiring multi-source data in a to-be-predicted geographic range; performing geographical registration and feature variable extraction on the multi-source data in combination with the geometric features and the regional features; the method comprises the steps that a forest biomass prediction model backbone network is input, an image serialization module, a channel prior fused double-branch feature extraction network and a regression network are included, the image serialization module slices an image and converts the image into an image sequence, and then the image sequence is input into the double-branch feature extraction network; the double-branch feature extraction network firstly extracts a feature map subjected to channel and space weighting through a channel prior module, then performs layer-by-layer down-sampling on the weighted feature map in four stages, and performs double-branch feature extraction through a state space feature extraction module. And finally, performing up-sampling regression through a regression network to output a forest biomass prediction result in a prediction geographical range. According to the method, the forest biomass can be predicted.

The invention relates to the technical field of big data and deep learning, in particular to a big data urban traffic flow prediction method and device based on geographic information and a computer readable storage medium. According to the method, through the technical means of multi-source data fusion, spatial feature extraction, time sequence analysis, external factor dynamic fusion and the like, high-precision prediction of the urban traffic flow is realized. The method has remarkable technical effects in the aspects of data quality, multi-source data fusion, spatial feature extraction, time sequence analysis, real-time performance and the like, provides powerful technical support for urban traffic management, planning and decision making, and has wide application prospects and important social and economic values.

The invention provides an electron microscope automatic focusing method and system based on a deep learning model, and relates to the technical field of electron microscope automatic focusing, and the method comprises the steps: collecting a plurality of images at different focusing distances through an electron microscope, and recording the focusing distance of each image; calculating the characteristic value of each collected image, combining the focusing distance and the characteristic value of the image into a characteristic vector, and generating a characteristic sequence according to the characteristic vectors obtained under different focusing distances; training a pre-constructed neural network model by using the feature sequence to obtain a trained neural network model; and inputting a to-be-tested image into the trained neural network model, calculating a difference value between the predicted optimal focusing distance and the focusing distance of the collected image, and judging whether the difference value is within a set range or not. According to the invention, the microscopic image acquisition efficiency and quality are obviously improved.

The invention discloses a charging station loss prediction method based on an LSTM deep learning method, and relates to the technical field of charging stations, and the method comprises the following steps: 1, collecting data including real-time power load data, a power grid topological structure and time sequence data; 2, feature engineering: carrying out preprocessing and feature extraction on the data, and extracting expert knowledge features; step 3, establishing an LSTM model in combination with a PPO algorithm; 4, performing hyper-parameter tuning on the LSTM model through a random search method; and step 5, the optimized miniaturized model is then deployed to each charging station, and local real-time energy loss prediction is realized. In order to further improve the prediction accuracy, expert knowledge is creatively integrated in a feature engineering part, multi-dimensional loss feature recognition is carried out, and the performance of the model in processing a complex nonlinear relation is improved by combining the input of an electric power system expert, recognizing factors such as equipment aging and the like.

The invention relates to a tunnel ponding early warning method and device, computer equipment and a storage medium. The method comprises the steps that at least two first flow detection units are arranged at an inlet and an outlet of a tunnel, at least one second flow detection unit is arranged at a water outlet of the tunnel, local water accumulation threshold values of a plurality of water accumulation sensitive areas are determined based on historical water accumulation data, and the local water accumulation threshold values are arranged according to the threshold values to obtain a threshold value distribution sequence; dividing the threshold distribution sequence into a first sequence and a second sequence based on a preset proportion, and determining a first global threshold by a local ponding threshold at the boundary of the first sequence and the second sequence; calculating the inlet and outlet flow difference according to the first flow data and the second flow data, and further determining the water accumulation amount; and adjusting a second global threshold value according to the ponding risk index, and starting a corresponding risk early warning strategy when the ponding amount exceeds the first global threshold value or the second global threshold value. Low-cost and high-precision tunnel ponding monitoring is realized based on simplified sensing data, and the safety of tunnel passage is improved.

The invention relates to a ship hybrid formation control method, device and equipment and a medium, and belongs to the technical field of ships, and the ship hybrid formation control method comprises the steps: constructing a hybrid formation of unmanned ships guided by a man-driven ship through a LoRa technology, obtaining the dynamic data of the hybrid formation, fusing the dynamic data, and obtaining the hybrid formation based on the fused dynamic data. An EMD-LSTM network is adopted to predict the navigation trajectory of the mixed formation to obtain a predicted trajectory, a navigation risk early warning system is constructed based on the obtained actual trajectory of the mixed formation and the predicted trajectory, the navigation risk of the mixed formation is evaluated based on the navigation risk early warning system to obtain an evaluation result, and the mixed formation is controlled based on the evaluation result. And the formation decision-making efficiency and accuracy are improved.

The invention provides an esophagus pressure measuring method, system and device based on big data, and relates to the technical field of medical image processing, and the method comprises the steps: obtaining historical detection data; performing three-dimensional reconstruction processing according to the historical detection data to obtain a three-dimensional model set; performing feature extraction according to the three-dimensional model set, and integrating to obtain a structural feature set; performing simulation processing according to the structural feature set to obtain an esophageal pressure labeling data set; performing model construction processing according to the esophageal pressure labeling data set and the structural feature set, and obtaining an esophageal pressure prediction model by analyzing a mapping relation between esophageal structural features and local pressure and training a neural network model; and performing prediction processing on the real-time endoscope video based on the esophageal pressure prediction model to obtain an esophageal pressure real-time prediction value. Through direct analysis and three-dimensional reconstruction processing of endoscope video data, in combination with deep learning and mechanical simulation methods, non-invasive real-time prediction of esophageal pressure is realized.

The invention discloses a cutter wear state on-line monitoring method based on a physically guided deep learning network, which combines a physical law and a deep learning technology in a cutter wear degradation process, and comprises the following steps: 1, collecting processing experiment data and constructing an actual training data set; 2, the physical model expands training data samples and constructs a simulation training data set; 3, adding a periodic feature extraction unit, a monotone increasing unit and a physical guidance module into the deep learning network, and constructing a physical guidance deep learning tool wear state online monitoring network; 4, constructing a physically guided deep learning loss function; and 5, pre-training the model by using simulation experiment data, and then performing fine tuning optimization on the model by using a small amount of actual data to obtain an optimal tool wear state monitoring network so as to predict the current tool wear. The method is high in adaptability, good in robustness and capable of accurately monitoring the state of the cutter in a complex and changeable machining environment.

The invention provides a method for predicting the sand-bearing state of a ballast track bed based on deep learning. The method comprises the steps that a discrete element simulation model of a sandstorm track bed is established, and rigidity parameters of the sandstorm track bed in different positions and depth areas are obtained through analysis; based on the rigidity parameters, a vehicle-desertification ballast track coupling finite element simulation model is established, and the vibration acceleration of the sleeper in different sand-containing states is obtained through analysis; and inputting the vibration acceleration as training data into the constructed sand-bearing state prediction model to train the sand-bearing state prediction model to obtain a trained sand-bearing state prediction model, and performing sand-bearing state prediction by using the trained sand-bearing state prediction model to obtain a sand-bearing state prediction result. The sand-bearing state prediction model is a deep learning model based on a convolutional neural network and a long short-term memory network. According to the invention, the sand-bearing state of the ballast bed can be predicted with high precision. The method has important reference value for guiding line maintenance work and guaranteeing stable operation of the train.

The invention discloses a multi-mode traffic demand prediction method based on a graph neural network, which is characterized in that technologies such as multi-source fusion, multi-graph processing and meta-information embedding are introduced, and a complex space-time dependency relationship of multi-mode data of an urban traffic system is captured; and carrying out traffic demand prediction on the fused multi-mode data by adopting a space-time diagram convolutional network, a diffusion convolutional recurrent neural network and a Graph-WaveNet network model. Compared with the prior art, the method has the advantages of adjusting distribution of traffic resources, optimizing scheduling, reducing congestion, improving traffic efficiency and the like, modeling is carried out by collecting spatio-temporal data of different traffic modes and combining multiple graph structures, high-precision prediction of multi-mode traffic demands is achieved, and the method is suitable for popularization and application. The method solves the problems of mutual influence between complex traffic modes in urban traffic, spatial heterogeneity and multi-mode data fusion, can be widely applied to the fields of intelligent traffic, urban planning, public traffic scheduling and the like, and has a good application prospect.

The invention relates to the technical field of marine science, in particular to a method for acquiring data of different depths of seawater based on a salinity sensor, and the method comprises the following steps: S1, collecting salinity data of seawater at different depths in real time; s2, preprocessing the collected salinity data; s3, constructing a depth-salinity mathematical model by adopting a Gaussian process regression algorithm; s4, acquiring environmental data in real time, and dynamically correcting parameters of the depth-salinity model according to the environmental data; and S5, inputting the preprocessed salinity data in the S2 into the dynamically corrected depth-salinity mathematical model, and calculating corresponding seawater depth information. According to the method, a nonlinear mathematical model of the depth and the salinity is constructed through a Gaussian process regression algorithm and a dynamic correction technology, and accurate prediction and dynamic adaptation of the relationship between the salinity and the depth in a complex marine environment are realized in combination with data preprocessing and inverse calculation.

The invention discloses a hydraulic pump life prediction method and system, and relates to the technical field of hydraulic pump life prediction, and the method comprises the steps: collecting vibration signals, pressure signals and temperature signals of a hydraulic pump under different working conditions, and forming original monitoring data; performing wavelet decomposition on the original monitoring data, extracting a high-frequency coefficient and a low-frequency coefficient of the original monitoring data, and constructing a feature vector based on the high-frequency coefficient and the low-frequency coefficient; inputting the feature vector into a pre-trained health estimation model to obtain a health index of the hydraulic pump; establishing a Weibull distribution model based on the health index, and calculating a scale parameter and a shape parameter of the Weibull distribution model through a maximum likelihood estimation method; and calculating the remaining service life of the hydraulic pump according to the scale parameters and the shape parameters. According to the method, the health index is combined with the Weibull distribution model, the scale parameter and the shape parameter are accurately calculated by using the maximum likelihood estimation method, and then the remaining service life of the hydraulic pump is predicted.

The invention discloses a visibility prediction method and system, and the method employs a dynamic window division and abnormal value processing technology based on data variability, solves a problem that meteorological data abnormal values are difficult to accurately recognize, achieves the precise recognition and processing of the abnormal values, and improves the data quality. By adopting the missing value interpolation method based on the weighted distance, the problem that the missing value of the meteorological data is difficult to fill is solved, the accurate interpolation of the missing value is realized, and the integrity and the reliability of the data are enhanced. According to the method, a dynamic normalization processing method is adopted, the problems of different scales of meteorological factor data and local characteristic differences are solved, adaptive normalization of the meteorological factor data is achieved, important fluctuation information is reserved, and the sensitivity of a model to data details is improved.

The invention provides a crustal stress prediction method based on multi-modal deep learning, and the method comprises the steps: S11, obtaining seismic data, horizon data and logging data of an oil and gas reservoir region, and carrying out the preprocessing of the obtained seismic data and horizon data; s12, extracting deep features from the seismic data by using a convolutional neural network, performing sequence modeling on the horizon data by using a bidirectional long-short term memory network, and capturing time sequence features of horizon changes; s13, constructing a multi-modal ground stress prediction model; and S14, applying the multi-modal crustal stress prediction model to the new seismic data and horizon data for crustal stress prediction, and generating a report for displaying a prediction result. By fusing the seismic data and the horizon data, constructing the multi-modal deep learning model and comprehensively utilizing the information of the multi-source data, more comprehensive crustal stress distribution information can be provided, comparison verification with the logging data can be carried out, the reliability of the prediction result is further improved, and higher-precision crustal stress prediction is realized.

The invention provides a capacity attenuation trend prediction method of a low-temperature lithium battery, and relates to the technical field of battery health management and prediction. The capacity fading trend prediction method of the low-temperature lithium battery specifically comprises the following steps of S1, initial data acquisition, S2, feature extraction, S3, hybrid prediction model construction, S4, model training and offline verification, S5, online deployment and dynamic correction, and S6, SOH evaluation and early warning output. A hybrid prediction model is constructed by fusing an electrochemical mechanism and deep learning, so that high-precision prediction and full-life-cycle intelligent health management of lithium battery capacity fading in a low-temperature environment are realized, and the problems of accelerated battery aging and prominent safety risk in an alpine region are effectively solved; early warning and intelligent management of the health state of the battery can be realized, and the service life of the battery under low-temperature application can be prolonged.

The invention discloses an air jet loom auxiliary jet air consumption prediction method and device, equipment and a medium, and belongs to the technical field of industrial automation. The method comprises the following steps: obtaining to-be-measured structure parameters of a to-be-measured nozzle in the air jet loom, wherein the to-be-measured structure parameters comprise an inlet diameter, a straight pipe diameter, a nozzle taper, an elliptical outlet long axis and an elliptical outlet short axis; the to-be-measured structure parameters are input into an air flow prediction model, the nozzle air consumption, output by the air flow prediction model, of the to-be-measured nozzle is obtained, and the air flow prediction model is obtained based on sample structure parameters of the sample nozzle and air consumption label training. According to the method, the gas consumption of the nozzle can be accurately predicted in a complex industrial environment.

The invention discloses a human body lower limb posture monitoring system based on a flexible sensor. The human body lower limb posture monitoring system comprises an array type flexible piezoresistive sensor module, a data acquisition and processing module, a feature selection module, a pressure data color mapping module, a wavelet neural network module and a joint bending angle result output module. An array type flexible piezoresistive sensor used for obtaining initial plantar pressure distribution data is designed, a method based on gait intervals is adopted to select data points, key feature points most related to joint angle changes are screened out based on time sequence information of gait events, the dimensionality of input data is reduced, and the accuracy of the input data is improved. And the structure and the training process of the network are optimized, and high-precision prediction of the lower limb joint angle is realized.

The invention relates to a wellbore risk prediction method and system, processing equipment and a storage medium, and the method is characterized in that the method comprises the steps: obtaining wellbore production data of a to-be-detected wellbore, and carrying out the preprocessing; adopting a statistical analysis method to select feature data in the preprocessed wellbore production data to obtain a plurality of first feature data most related to a target in the wellbore production data of the wellbore to be detected; performing standardization processing on the selected feature data to obtain feature data after standardization processing; the feature data obtained after standardization processing is input into a pre-trained multi-scale Transform model, and a wellbore annulus pressure prediction result of the wellbore to be detected is obtained.According to the wellbore annulus pressure prediction method and device, the Transform model and a multi-scale mechanism are combined, high-precision prediction of the wellbore annulus pressure is achieved by accurately analyzing the time sequence of the wellbore production data, and the wellbore annulus pressure prediction accuracy is improved. The method can be widely applied to the field of wellbore risk prediction.

The invention discloses a lithium ion battery life prediction method based on a life prediction combination model, and the method specifically comprises the following steps: S1, carrying out the fitting analysis according to the data of a battery capacity recession experiment, and relates to the technical field of lithium ion battery life detection. According to the lithium ion battery life prediction method based on the life prediction combination model, the prediction precision of the battery life is improved, the life prediction model is established on the basis of a capacity attenuation curve fitting formula and an empirical model, and model parameters are updated on the basis of the improved empirical model; the established prediction model not only can accurately reflect the trend of battery capacity decline, but also can effectively combine the actual use state of the battery, so that the high-precision prediction of the residual service life of the lithium ion battery in different health states can be realized, and the prediction accuracy of the lithium ion battery in different health states is improved. And the development of the battery echelon use industry is facilitated.

A fiber composite material compression strength estimation method based on 3D printing comprises the following steps: collecting sample data, determining process parameters, preparing a sample, and compressing, testing and recording data; preprocessing the original data to form a data set; secondly, constructing a recurrent neural network model, capturing a long-term dependency relationship by utilizing the sequence data processing capability of the recurrent neural network model to estimate the strength, training through a training set, optimizing the performance by using a verification set, and evaluating through a test set to determine a final model; and finally, inputting new sample material characteristics and printing parameters into the model to obtain an intensity estimation value. The method has an effective evaluation verification and updating mechanism, and can adapt to different environments and material characteristic changes.

The present invention provides a method for predicting the thermal error of an electric spindle in advance based on a deep learning algorithm. The method collects the surface temperature and the displacement data of the electric spindle by arranging multiple temperature sensors and displacement sensors on the surface of the electric spindle, and trains a thermal error prediction network composed of a time domain convolution network, a gated loop network, and a fully connected network to achieve the thermal error prediction of the electric spindle in advance. The present invention adopts a thermal error prediction network to predict the advance amount of the thermal error of the electric spindle, and can compensate before the error occurs, thereby improving the machining accuracy and the stability of the electric spindle. At the same time, the thermal error prediction network adopts a multi-way parallel neural network and a fully connected network design, which enhances the adaptability and robustness of the model and provides a flexible solution for electric spindles of different types and specifications.

The invention discloses a distributed photovoltaic load prediction method and device based on ISO and LightGBM, and a medium, and the method comprises the steps: carrying out the load prediction of meteorological load data through a LightGBM model if the data size of the meteorological load data is larger than a preset threshold range, and obtaining a load prediction value; if the data volume is within the threshold range, performing load prediction on the meteorological load data through a physical model to obtain a load prediction value; if the data volume is smaller than the threshold range, load prediction is carried out on the meteorological load data in a power curve fitting mode, and a load prediction value is obtained. According to the distributed photovoltaic load prediction method and device based on ISO and LightGBM and the medium provided by the invention, a machine learning model, a physical model and a statistical method are comprehensively applied, and an appropriate prediction method is selected according to different data conditions, so that the advantages of various methods can be brought into full play, the prediction accuracy and efficiency are improved, and the prediction efficiency is improved. The problem that the accuracy of distributed photovoltaic load prediction is difficult to improve can be solved.

The embodiment of the invention provides a satellite ephemeris updating method and device and spaceborne equipment, and relates to the technical field of aerospace, and the method comprises the steps: obtaining the current position and current space environment parameters of a satellite; searching a grid adjacent to the current position from the plurality of grids, wherein the grid is associated with the position, the space environment parameter interval and the atmospheric density; predicting the atmospheric density of the orbit where the satellite is located by using the position, the space environment parameter interval and the atmospheric density associated with the adjacent grids, the current position and the current space environment parameters; and updating the ephemeris of the satellite by using the predicted atmospheric density. By applying the technical scheme provided by the embodiment of the invention, the prediction precision of the atmospheric density can be improved and the satellite ephemeris prediction precision can be improved under the condition that the computing power and storage of satellite-borne equipment are limited.

The invention provides an electric spindle thermal error advanced prediction method based on a deep learning algorithm, and the method comprises the steps: collecting the surface temperature of an electric spindle and the displacement data of the tail end of the electric spindle through arranging a plurality of temperature sensors and displacement sensors on the surface of the electric spindle; and training a thermal error advanced prediction network composed of the time domain convolutional network, the gating circulation network and the full-connection network to realize thermal error advanced prediction of the electric spindle. The advanced quantity of the thermal error of the motorized spindle is predicted by adopting the thermal error advanced prediction network, compensation can be carried out before the error occurs, the machining precision and the stability of the motorized spindle are improved, meanwhile, the thermal error advanced prediction network adopts the design of a multi-path parallel neural network and a full-connection network, the adaptability and robustness of the model are enhanced, and the accuracy of the model is improved. And a flexible solution is provided for motorized spindles of different types and specifications.

The invention belongs to the field of medical data processing, and particularly relates to an epileptic seizure prediction method based on a lightweight deep statistical model, and the method specifically comprises the following steps: preparing a data set: preparing a CHB-MIT data set, the first 70% of which is used for label generation and network training; the last 30% of the CHB-MIT data set is used for final model testing; and S2, constructing a mixed data enhancement model. According to the method, a mixed data enhancement model including non-stationary data segmentation, low-response data rejection and random channel replacement designed by the invention is established; a depth statistical model comprising a statistical model and a neural network structure is constructed; a simple and efficient neural network is designed, a complete network fully-supervised training framework is closed to enable the network to be successfully converged, and a new loss function and evaluation indexes are designed; the efficiency and accuracy of an epileptic seizure prediction model are improved, and the problems that training data is insufficient and a traditional machine learning epileptic seizure prediction method is unstable are solved.

The invention discloses a matching method and system based on a 5G network, Beidou positioning and non-line-of-sight microwave, and relates to the technical field of target signal positioning, and the matching method comprises the steps: building a static and dynamic combined calibration data set through obtaining high-precision geographic position information and real-time signal intensity data, and carrying out the calibration of the calibration data set; and adaptive direction adjustment and rapid focusing of the antenna are supported, and efficient communication and accurate positioning in a complex scene are realized. The calibration matching model is trained based on the calibration data set, so that the precision and adaptability of the model under multiple geographical environments and non-line-of-sight conditions are improved, an efficient antenna positioning mechanism is provided, and the stability and real-time performance of a communication link are guaranteed; the method is particularly suitable for complex environments such as disaster rescue, accident personnel can be quickly and accurately positioned, and rescue efficiency and action reliability are remarkably improved.