162 results about "Data decomposition" patented technology

The invention relates to the technical field of data center dynamic modeling, and discloses a twin model dynamic modeling method for a data center DCIM platform, which comprises the following steps: constructing a discrete state space model containing a thermal coupling matrix and a system matrix, collecting real-time power and temperature time sequence data, and calculating a cross-correlation function to lock hot air dynamic transmission lag time; calculating cut-off frequency based on physical attributes of the cabinet and decomposing data into high and low frequency components by using a complementary filter; according to the method, the model parameters are made to return to a physical source through a frequency domain decoupling mechanism, the problem of aliasing of airflow coupling and structural thermal inertia parameters in a traditional single-scale identification method is solved, and the method is suitable for large-scale identification. And the physical authenticity and prediction robustness of the twin model under a complex working condition are improved.

The invention discloses a reservoir flood forecasting method based on physical constraint and space-time double-flow coupling. The reservoir flood forecasting method comprises the following steps: S1, acquiring multi-source hydrometeorological data; s2, decomposing the multi-source hydro meteorological data into a historical state sequence and a future driving sequence; s3, performing feature extraction on the historical state sequence data through the physical enhanced long-short term memory network of the historical inertial feature extraction branch to obtain historical inertial features, and performing feature extraction on a future driving sequence through the time domain convolutional network of the future forced feature extraction branch to obtain future forced features; s4, performing weighted fusion on the historical inertial features and the future forced features to generate fusion features; and S5, inputting the fused features into a decoder to obtain a predicted water level increment, and superposing the predicted water level increment to the current water level to obtain a predicted value. The prediction timeliness is improved, the prediction precision of the water recession stage is improved, and the physical consistency of the prediction result is enhanced.

The invention discloses a landslide early warning method based on multi-source fusion and an intelligent algorithm. The landslide early warning method comprises the following steps: acquiring meteorological and hydrological monitoring data, remote sensing image data and landslide displacement monitoring data of a target landslide area; performing data decomposition on the landslide displacement monitoring data to obtain a trend term and a periodic term; extracting ground monitoring data composed of a plurality of factors and response time of the plurality of factors from the meteorological and hydrological monitoring data; performing fusion processing on the remote sensing image data and the ground monitoring data to obtain key disaster-causing factors; constructing a prediction model used for predicting the landslide displacement rate; triggering an early warning signal based on the predicted value of the landslide displacement rate; according to the method, the landslide multi-scale response rule and the lag effect can be identified more accurately, and the dynamic prediction and early warning capability of the landslide is improved.

The invention relates to the technical field of industrial visual intelligent detection, in particular to a magnetic attraction robot inspection method and system based on weld visual tracking. The specific implementation process comprises the following steps: performing illumination equalization processing on continuous frame original images, decomposing and filtering texture layer data by using total variation regularization, and retaining structural layer data; predicting a region of interest by using Kalman filtering, and inputting the region of interest into a double-flow feature fusion semantic segmentation network; extracting welding seam features by utilizing a spatial detail branch and a context semantic branch, and generating a welding seam mask through an attention gating mechanism; performing skeleton vectorization on the mask image, calculating a curvature change rate and a perspective gradient rate, and constructing a mapping model; and generating a visual tracking control vector in combination with the transverse position deviation and the angle deviation. According to the method, by means of total variation regularization decomposition and perspective gradient analysis, the welding seam features can be extracted under the complex industrial texture background, and robot inspection robustness and tracking precision are improved.

The invention relates to the technical field of pollution discharge intelligent sensing monitoring and early warning systems, in particular to a drainage pipe network pollution monitoring and advanced early warning system based on the Internet of Things. The method comprises the steps of firstly obtaining water quality data of a current monitoring period; further identifying a water quality fluctuation event according to fluctuation characteristics of data in each water quality data dimension, and extracting an event center moment and an event waveform characteristic vector; further constructing a matching deviation matrix; further obtaining a matching deviation parameter based on the optimal matching result, comparing the matching deviation parameter with a preset historical mode statistical baseline, and obtaining a mode deviation degree corresponding to the COD data dimension and each non-COD data dimension; and finally, based on the mode deviation degree, decomposing the COD data and extracting a real pollution load, and generating an early warning report based on the real pollution load, so that the false alarm rate is reduced, and the early warning accuracy is improved.

The invention discloses a virtual character behavior processing method and system based on semantic decoupling and elastic coupling and a computer readable storage medium. According to the method, unstructured action data is decomposed into physical layer intention descriptors, measurable style descriptors and high-dimensional semantic layer features by utilizing a parallel physical calculation engine and an AI semantic analysis engine through a double-track feature decoupling mechanism. Besides, an elastic coupling mechanism between intentions and styles is introduced, style parameters are dynamically clamped based on a physical priority principle, and physical topology collapse caused by style overload is prevented. According to the method, a physical-semantic dual index system is constructed, database autonomous evolution based on manifold density analysis is supported, and the problems that in the prior art, virtual character action generation is poor in physical controllability, semantic understanding is lacked, and cross-scene generalization ability is weak are effectively solved.

The invention provides a KPIs anomaly detection method based on MVMD decomposition. The KPIs anomaly detection method comprises the following steps: S1, obtaining multi-dimensional key performance index KPIs time sequence data in a micro-service system; s2, decomposing the normalized multivariable KPIs time sequence data into K intrinsic mode function (IMF) components; s3, dividing the time sequence of each IMF component into a plurality of fixed-length subsequences through a sliding window; s4, respectively constructing and training a variational auto-encoder VAE model, and calculating a reconstruction error of each IMF component at each time point; s5, obtaining a comprehensive abnormal score of each time point; s6, based on a grid search method, determining an optimal anomaly score threshold value for anomaly judgment; and S7, comparing the comprehensive abnormal score with an optimal abnormal score threshold value, and if the comprehensive abnormal score at a certain time point exceeds the threshold value, judging that the KPIs at the time point is abnormal. According to the invention, the modeling capability of abnormal modes of different frequency components can be enhanced, so that the accuracy of overall anomaly detection is improved.

The invention belongs to the technical field of water quality prediction, and particularly relates to a water quality dynamic prediction method and system in a marine ranch culture environment, and the method comprises the steps: collecting historical water quality time sequence data, and carrying out the standardization processing; decomposing the standardized water quality time sequence data into a trend component and a season / disturbance component, and performing multi-scale feature coding; based on the seasonal multi-scale features, dynamically adjusting the frequency domain resolution through fractional Fourier transform, and obtaining time domain enhancement features after inverse transformation in combination with multi-head self-attention; based on trend multi-scale features, calculating a Pearson correlation matrix to form a mask matrix, and calculating output channel frequency domain enhancement features in combination with a mask attention mechanism; utilizing a global time sequence attention mechanism to obtain a globally enhanced trunk feature; and performing dynamic gating fusion on the three paths of features to obtain a fused spatial-temporal feature, inputting the fused spatial-temporal feature into a prediction head, and outputting a future value of the water quality time sequence data.

The invention discloses an ocean sound velocity field prediction method and system, and relates to the technical field of data processing, and the method comprises the steps: carrying out the quantitative diagnosis of key driving factors affecting the evolution of an ocean sound velocity field of a target sea area from the ocean environment data, and building a causal network diagram based on the information flow of the key driving factors and a structural equation model, obtaining a core feature set; the ocean sound velocity field sequence data in the core feature set are decomposed into data in a space-time coherent mode through dynamic mode decomposition, and then the data are mapped into low-dimensional space-time features; learning the evolution dynamic state of the ocean sound velocity field according to the low-dimensional spatial-temporal characteristics by using a KAN network, and outputting an ocean sound velocity field prediction result at a future moment; future guide learning is used in the training stage of the KAN network, and real observation data in future set time is used as an auxiliary supervision signal; and using a dynamic feedback mechanism to calibrate the ocean sound velocity field prediction result based on the real-time observation data in the online prediction stage. The accuracy of predicting the ocean sound velocity can be improved.

A surface deformation monitoring method and systems based on time-series InSAR (TS-InSAR) are provided. The method includes acquiring Synthetic Aperture Radar (SAR) data; performing differential interferometry; performing a robust two-tier multi-temporal InSAR method for detection of Persistent Scatterer (PS) and Distributed Scatterer (DS) candidates to acquire time series data for surface deformation monitoring; performing a variational mode decomposition (VMD) method to decompose TS-InSAR data into a plurality of components; reconstructing time series data; performing a gated recurrent unit (GRU) method; extracting trend of surface deformation; and performing continuous large-scale deformation monitoring. The InSAR-based deformation monitoring method integrates VMD and GRU, offering significant improvements in robustness and accuracy over the existing methods.

The invention relates to the technical field of data processing, in particular to a cleaning fusion system for urban traffic multi-source heterogeneous data, which comprises a space-time correlation graph construction module used for creating a unique node identifier for each GPS data record, roadside camera data record and coil data record according to multi-source heterogeneous traffic data, and establishing a spatial adjacent relation edge by judging whether road segment identifiers to which multiple nodes belong are the same or not. According to the method, context awareness evaluation of data source reliability is achieved, meanwhile, when traffic time series data are processed, the data are decomposed into a low-frequency approximation coefficient representing a long-term trend and a high-frequency detail coefficient capturing a sudden change event, and the approximation coefficients of the multi-source data are fused through the obtained context awareness credibility score; meanwhile, a maximum value selection strategy is adopted for detail coefficients, key signal features caused by real traffic events can be reserved, and the fidelity of fused traffic data is improved.

The invention provides a rainwater collection pipe network blockage risk early warning method, which comprises the steps of collecting multi-point flow and environmental parameter data, fusing water level and rainfall intensity, and realizing unified processing of multi-source data through timestamp alignment and mean filtering; using an empirical mode decomposition algorithm to decompose the flow time sequence data into trend, period and high-frequency fluctuation components, and identifying abnormal segments through sliding window energy calculation and dynamic threshold gating; according to the method, the change point detection algorithm and the Gaussian mixture model are combined, energy accumulation sudden change and trend attenuation are verified in a layered mode, and then structured early-stage blockage risk early warning is generated, the accuracy and timeliness of anomaly detection are effectively improved, the pipe network blockage supervision and maintenance cost can be reduced, and the operation reliability of an urban drainage system is enhanced.

A human-factor intelligent driving behavior prediction method, a terminal device and a storage medium are provided. The method includes: obtaining a physiological signal; performing fast Fourier transform on the physiological signal to generate an amplitude-frequency characteristic and obtaining a sampling frequency in the amplitude-frequency characteristic; performing, based on a period of the sampling frequency, multi-period decomposition on the physiological signal to generate a data decomposition result sample; performing two-dimensional spatial expansion on the data decomposition result sample to generate two-dimensional spatial data; performing prediction on target consecutive frames to generate an iteratively predicted future frame; merging the two-dimensional spatial data, the target consecutive frames, and the iteratively predicted future frame to generate a multi-scale three-dimensional feature; analyzing the multi-scale three-dimensional feature to generate a target output feature; and analyzing the target output feature to generate driving behavior description information and driving behavior inference information.

The application discloses a power battery fault prediction method based on a time-frequency hierarchical attention model. The method first collects power battery operation data and pre-processes the data, and decomposes the pre-processed data into trend components and seasonal components by using a time series decomposition method. Secondly, a time-frequency hierarchical attention model TFHformer is constructed, and based on the trend components and the seasonal components, the predicted values of the battery performance parameters are obtained. Finally, according to the error between the predicted values and the actual values, it is judged whether the power battery has a fault risk. The application effectively captures short-term dynamic changes and long-term periodic dependencies, significantly reduces the computing overhead, and helps to timely and accurately predict the fault risk existing in the power battery system, thereby providing protection for the safe use of the power battery at the vehicle end and in the energy storage power station.

The invention discloses a load control simulation method, system and device for simulating an extreme weather power load scene and a medium, and the method comprises the following steps: obtaining historical power load data and historical weather data of a target area, and carrying out the preprocessing of the historical power load data; decomposing the preprocessed historical power load data into non-extreme weather power load data and extreme weather power load data according to a preset extreme weather judgment condition; predicting a basic power load value at a future moment by using the power load time sequence prediction model; and comparing the comprehensive predicted power load value with the schedulable power consumption, judging whether a power gap is generated or not, and issuing an adjustment instruction according to a judgment result. According to the method, the time sequence prediction model and the extreme weather power load feature library are constructed, the comprehensive prediction power load value is calculated in a weighted stacking mode, and accurate reproduction of the power load fluctuation features in the extreme weather is achieved.

The invention belongs to the technical field of data processing, and particularly relates to a lightweight index method, system and equipment for high-dimensional data of edge equipment and a medium. The method comprises the following steps: performing feature variance analysis on high-dimensional vector data, decomposing the high-dimensional vector data into high-order features and low-order features according to a variance contribution degree, and performing primary compression processing on the decomposed high-dimensional vector data; an edge adaptation hierarchy coefficient is obtained according to a set memory threshold value, the maximum hierarchy number of HNSW is obtained, clustering calculation is carried out on first-level compressed high-dimensional vector data to obtain data local density, and data hierarchy distribution is carried out according to a clustering result; the maximum connection number of each layer is set according to the number of levels, a distance pruning threshold value is obtained according to the maximum connection number, pruning processing is carried out on corresponding level data according to the distance pruning threshold value, secondary compression processing is carried out on the processed data by using difference value coding, and secondary compression high-dimensional vector data is compressed. And independently storing the compressed data.

This invention relates to a multi-level parallel processing system for massive amounts of data from medium- and high-orbit SAR, comprising: a data storage device for storing multiple full-scene observation data returned by medium- and high-orbit synthetic aperture radar within a preset period; a distributed computing device including a data exchange node and multiple data processing nodes connected to the data exchange node; the data exchange node, connected to the data storage device, for acquiring multiple full-scene observation data, decomposing each full-scene observation data into multiple task blocks, and distributing the multiple task blocks to the multiple data processing nodes; and the multiple data processing nodes for performing parallel processing on the received task blocks using a multi-threaded model and feeding back the corresponding processing results to the data exchange node. This device, through a multi-level joint parallel approach of data-task-algorithm, can effectively improve the throughput of processing massive amounts of data, meeting real-time or near-real-time imaging requirements.

The invention provides a stock K line and buying and selling signal visualization method and system based on multi-index fusion, and relates to the technical field of multi-index fusion, and the method comprises the steps: obtaining network public opinions, market information, ambient light, and buying and selling signal data; performing semantic analysis on the public opinion text to generate an emotion time sequence, and aligning the emotion time sequence with the market data to form multi-dimensional fusion data; decomposing the data into different frequency components by using a time sequence fusion algorithm, and performing weighted fusion to generate a fusion index; k-line geometric coordinates are generated, and contrast and brightness parameters required by display are determined according to the ambient light data; performing color rendering on the coordinates by using the parameters to generate a vector K-line graph, and superposing the vector K-line graph with a buying and selling signal mark to form a fusion visual graph; and finally, the graph is displayed on the mobile equipment, and smooth zooming and translation can be carried out in response to the touch operation. According to the invention, the definition and interaction fluency of stock information visualization in a high-interference environment of the mobile terminal are improved.

The application discloses a photovoltaic ultra-short-term probability prediction method based on data decomposition and reconstruction, solves the problems of low prediction accuracy, weak generalization ability, poor prediction interval calibration, insufficient stability across data sets and low calculation efficiency of existing methods, and cannot meet the 5-minute ultra-short-term photovoltaic probability prediction engineering requirements. The method first collects and pre-processes photovoltaic power and meteorological data, uses the ACEEMDAN algorithm to decompose the photovoltaic power into fixed high-frequency and low-frequency dual components through four stages and regularizes; then the corresponding features of the three types of data are extracted through CNN, iTransformer and BiLSTM respectively, and the unified feature representation is obtained after the multi-head attention mechanism fusion, and then the EQN network is input; finally, the improved EQN loss function containing the width penalty term is used to train the model to convergence, and the probability prediction result is output. The method performs excellently in the deterministic and probability evaluation indexes, has strong stability across data sets, and can be directly applied to power grid dispatching and photovoltaic power generation system operation management.

This invention relates to the field of information platform construction technology, and more particularly to a method and system for constructing a sports information intelligent service platform. The method includes the following steps: collecting user motion image data and user basic health data; extracting key body node position information during the user's movement; generating a user movement trajectory curve based on the key node position information, and calculating the deviation angle and distance from a standard trajectory template to obtain movement correctness data; constructing a user fitness risk assessment model based on the movement correctness data and user basic health data, and analyzing the difference from a preset safety threshold to generate tiered health guidance data; decomposing the tiered health guidance data into quantifiable progress units to generate progress tracking data. This invention, by combining user motion image data and basic health data, constructs an assessment model capable of identifying individualized risk factors, achieving more accurate safety guidance.

The invention discloses a coding method for reducing power consumption of a feed-forward equalizer, and relates to the technical field of equalizer coding, and the coding method is characterized in that each bit is only a value {-1, 0, + 1}; adjacent bits cannot be non-zero at the same time; a first non-zero bit symbol from a high bit to a low bit must be consistent with the overall symbol; the code decomposes the input data into a few symbolized weight bits, and multiplication of the input data and the weight can be converted into shift and addition or subtraction operation. Therefore, for a single tap, corresponding multiplication operation can be realized through shifting and addition or subtraction, and final output can be obtained through summation finally. According to the invention, equivalent multiplication can be completed through a limited number of shift and addition; during implementation, a multiplier is not needed, only symbol control addition is needed, and a very efficient digital system is achieved through the encoding mode and is used for achieving multiplication, especially on occasions needing high throughput rate and low delay.

The invention relates to the technical field of dissolved oxygen prediction, and provides a dissolved oxygen prediction method based on data decomposition and related equipment, and the method comprises the steps: obtaining a plurality of water quality indexes of a target water body, and determining a plurality of target water quality indexes from all the water quality indexes; according to all the target water quality indexes, generating a dissolved oxygen data sequence of the target water body, and decomposing the dissolved oxygen data sequence to obtain a trend component and a seasonal component; predicting the trend component by using a trend prediction model to obtain a first dissolved oxygen predicted value, and predicting the seasonal component by using a seasonal prediction model to obtain a second dissolved oxygen predicted value; and fusing the first dissolved oxygen predicted value and the second dissolved oxygen predicted value to obtain a final dissolved oxygen predicted value of the target water body. According to the method, the dissolved oxygen prediction accuracy can be improved.

The invention discloses a non-centralized multi-agent collaborative scheduling method and system for an everything intelligent connection scene, a terminal and a storage medium, and the method comprises the steps: receiving original task data from everything intelligent connection equipment, and decomposing the original task data into a plurality of atomic subtasks based on a dynamic task decomposition algorithm; a resource scheduling decision agent generates a resource scheduling decision for each atomic subtask according to the real-time state of the system; distributing each atomic subtask carrying the scheduling decision to a corresponding execution agent and a computing node through a non-centralized P2P message bus; and cooperatively executing each atomic subtask by the execution agent and the computing node based on the resource scheduling decision, and synthesizing and outputting an execution result. The performance bottleneck of a centralized scheduling architecture is broken through, and extremely-low-delay collaborative scheduling of massive concurrent tasks, seamless collaboration of cross-protocol equipment and real-time guarantee of high-priority tasks are realized.

This invention discloses a method and system for anomaly detection in satellite on-orbit telemetry data. The method involves acquiring the on-orbit telemetry time-series data of the satellite to be tested; decomposing the data into multiple approximate and detail sequences using the time-frequency characteristics of multi-scale wavelet transform; assigning weights to each approximate and detail sequence, calculating attention weights for each sequence by calculating similarity scores; weighted summing of the hidden states of neurons in the coding layer and their corresponding attention weights within each approximate and detail sequence to calculate a content vector, which is then combined with the current hidden state of neurons in the decoding layer, and so on, to obtain a reconstructed sequence of the on-orbit telemetry time-series data; and outputting anomaly data by comparing the difference between the reconstructed sequence and the on-orbit telemetry time-series data of the satellite to be tested. This method effectively processes the spatiotemporal information of satellite telemetry data, improving the accuracy and robustness of anomaly detection.

This invention proposes a three-dimensional glioma image segmentation method, electronic device, and storage medium. The method includes: acquiring three-dimensional volume data from multimodal magnetic resonance imaging; fusing the three-dimensional volume data with prior information on orientation boundaries to obtain orientation-boundary-enhanced three-dimensional volume data; decomposing the orientation-boundary-enhanced three-dimensional volume data into a two-dimensional slice sequence and performing inter-slice context modeling to aggregate information from adjacent slices to generate a target feature volume; dynamically inferring discrete token combinations based on the target feature volume using a discrete token vocabulary and an attribute predictor to generate high-level semantic cue features; inputting the target feature volume into an orientation-aware dual-domain enhancement branch to obtain enhanced features; and inputting the enhanced features and high-level semantic cue features together into a sparse hybrid expert decoder to output segmentation probability maps of three nested tumor regions. This invention significantly improves the boundary accuracy and small target recognition capability of glioma subregion segmentation, thereby enhancing the accuracy of image segmentation.

This specification discloses a method for air-to-ground transmission of flight record data with high temporal integrity, comprising: encapsulating target data to obtain a data assembly containing a header information portion, a data content portion, and a tail information portion; enabling the ground subject to perform data verification operations based on the header information portion and / or the tail information portion after receiving the data assembly; comprising: decomposing the target data into data packets; calculating the feature value of any data packet and determining the flag bit of the data packet for each data packet; setting the header information portion of the first data packet to preset information for the first data packet; using the feature value and flag bit of the previous data packet as the header information portion for any non-first data packet; generating a check code corresponding to the combination of the header information portion and the data content portion as the tail information portion; and forming a data assembly containing the header information portion, the data content portion, and the tail information portion.

The application provides a kind of aero turbofan engine remaining useful life prediction method and system, method includes: the original sensor data is preprocessed, obtains the training set and test set after pre-processing;The pre-processed data is decomposed into several intrinsic mode functions using CSI-EMD method, and the non-stationary sensor data is effectively decomposed into different IMF components;The IMF component obtained by decomposing the training set data is input into the double-channel multi-layer feature fusion network for training, and the result obtained by the IMF component through the network is added as the final output, to obtain the trained network;The IMF component obtained by decomposing the test set data is input into the trained network to obtain the remaining useful life of the test set data, and the superiority of the evaluation index evaluation method is evaluated.The application solves the technical problem of the influence of traditional EMD method on end effect, poor feature learning effect, which leads to the accuracy of turbofan engine life prediction.

This application discloses a method for processing and recognizing image data from a scope, relating to the field of image data processing technology, including the following steps: S1, preprocessing the raw image data acquired by the scope, extracting the RGB values, grayscale values, position information, and gradient features of pixels to obtain preprocessed image data; S2, based on the preprocessed image data, establishing a dynamic background environment model by fusing pixel gradient features through multi-Gaussian distribution, comparing pixel features of newly acquired image data, and decomposing the image data into a background environment part and a suspected target part. This application, through dynamic background modeling, grayscale and contour multi-feature fusion, dual-criteria similarity matching, and trajectory verification mechanisms, can accurately distinguish targets from environmental interference under harsh conditions such as low light, dust, and complex mountain forest backgrounds, effectively filtering out isolated noise and background fluctuations, and significantly reducing the probability of misidentification and missed identification.

The application discloses a street lamp line ground fault intelligent diagnosis system and method, belongs to the technical field of power equipment operation monitoring, and comprises a data acquisition and preprocessing unit, a feature extraction unit and a fault diagnosis unit. The data acquisition and preprocessing unit collects original residual current data of a street lamp line through a residual current transformer and an oscilloscope. The application adopts a parrot optimization algorithm to adaptively determine optimal decomposition parameters of VMD, overcomes the limitations of artificial parameter setting, ensures the quality of data decomposition, lays a foundation for extracting high-quality fault features, solves the problems of lack of reliability of existing fault protection, great influence of human factors, and low accuracy of existing street lamp ground fault diagnosis systems and methods, calculates sample entropy values, constructs a multi-dimensional feature vector set representing line states, inputs the multi-dimensional feature vector set into a fault diagnosis unit, and the fault diagnosis unit trains a kernel extreme learning machine classifier by using the feature vector set.

The application belongs to the field of information security, and specifically discloses a high cost performance storage expansion method and system based on a field programmable gate array, wherein a memory controller writes data transmitted by the field programmable gate array into a pseudo-static random access memory in a burst data stream case, reads one packet of data in the pseudo-static random access memory after processing the last packet of data; meanwhile, in a high concurrency multi-user case, stores security parameters into the pseudo-static random access memory, reads the security parameters in the pseudo-static random access memory when processing user data, updates the security parameters after processing and writes the updated security parameters back into the pseudo-static random access memory; meanwhile, in a bit width decomposition case, decomposes N-bit data into M data segments, and writes the M data segments into the pseudo-static random access memory at the same time, reads out the data in the M pieces of pseudo-static random access memory at the same time when processing data, and combines the data into N-bit width data. The application has the characteristics of low cost and high cost performance.