279 results about "Continuous data" patented technology

The invention discloses a tunnel structure full life cycle damage detection and safety evaluation method, and belongs to the technical field of damage detection and safety evaluation. The method comprises the following steps: completing installation configuration of a mobile detection platform along the axial direction of a tunnel; generating a moving track and a detection point location distribution scheme of the mobile detection platform; controlling the mobile detection platform to sequentially move to each detection section position according to the detection path, and synchronously starting each detection module to perform continuous data acquisition in the moving process; generating a tunnel three-dimensional mathematical model, a lining structure thickness distribution diagram, a heat radiation anomaly distribution diagram and a crack damage distribution diagram; equally dividing the tunnel three-dimensional model into N detection segments according to a preset interval, and counting the lining thickness mean value, the number of heat radiation abnormal points, the crack length density and the damage level of each segment as safety evaluation indexes; statistical analysis and trend judgment are conducted on the safety indexes of all the segments, abnormal segments exceeding a safety threshold value are recognized, and the risk level of the abnormal segments is determined.

The invention relates to the technical field of data acquisition, in particular to a large-scale node-oriented spectrum acquisition compression system, which is characterized in that the system acquires three types of sensor spectrum data characteristics, presets four types of alarm threshold generation configuration tables, and determines sampling periods of different load intervals according to the configuration tables and node real-time loads to generate scheduling schemes. According to the scheme, a sensor is controlled to collect data, compare a threshold, mark an alarm level and generate a data set, data which does not exceed the threshold is extracted, an amplitude difference is calculated, redundancy is judged, and different data are marked according to a redundancy result to generate a standardized compression format table. According to the method, multiple types of alarm thresholds are preset by obtaining spectrum data characteristics of multiple sensors, sampling periods of different load intervals are determined by combining real-time load data of monitoring nodes, data are collected according to a scheduling period, alarm levels are marked, continuous data which do not exceed the thresholds are extracted, amplitude difference values are calculated, redundancy is judged, and the reliability of the system is improved. Corresponding identifiers are added for different data to generate a standardized compression format, invalid sampling is reduced, redundancy is accurately judged, and the data transmission amount is reduced.

The invention discloses a mine pressure monitoring data optimization and feature extraction method, which comprises the following steps of: firstly, establishing a multi-channel and high-frequency original time sequence data set through continuous acquisition of underground multi-type sensors, and providing an input basis for subsequent fluctuation detection; and then, calculating the adjacent ratio of the data sequence point by point, and adaptively determining a threshold value in combination with a sliding window quantile statistical result, thereby realizing dynamic identification of high and low fluctuation points and providing a marking basis for an optimization strategy. And then, automatically matching a corresponding optimization strategy according to the distribution form of the fluctuation points, performing targeted smoothing and recovery on the abnormal data by means of point pair correlation correction, isolated point backtracking correction, continuous section batch correction, bottom backfilling and the like, and outputting an optimization sequence. And finally, taking the corrected data as new input, performing loop iterative calculation, and continuously optimizing parameters and strategies through correlation, smoothness and information entropy evaluation until indexes converge to obtain a final mine pressure monitoring data result with trend fidelity and noise suppression.

The invention relates to the field of data processing, and discloses a real-time data processing system and method, and the method comprises the steps: carrying out the real-time capturing of a continuous data flow from a multi-source collection end, and generating an initial data sequence with an emergency weight; carrying out cross-source interleaving processing on the initial data sequence, and forming a composite feature set with a time sequence hierarchy characteristic by combining an offset track of a historical data period, buffer areas of adjacent data channels and a conflict frequency of multi-source input; on the basis of the composite feature set, utilizing a partition aggregation deduction algorithm to extract a dynamic occupation path of the data event in a processing cycle, and marking potential abnormal candidate segments; performing re-grading labeling on the abnormal candidate segments, and constructing a local association network with a weight gradient; and according to the local association network, detecting and identifying the data processing units with resource mismatch in the target period, and deducing a corrected data processing sequence. The method has the advantage of improving the real-time performance of data processing.

The invention relates to the technical field of industrial equipment control, and discloses an industrial equipment autonomous decision-making control method based on reinforcement learning, and the method comprises the steps: obtaining the time sequence data of a vibration sensor, the continuous data of a temperature transmitter and the discrete data of a pressure instrument of industrial equipment through a multi-mode sensing module; multi-modal data alignment is achieved through a feature space mapping algorithm, the thermal drift compensation amount is calculated in combination with a thermodynamic state model, self-adaptive threshold segmentation processing is conducted on pressure data, the processed data are input into a reinforcement learning decision model to generate a fusion decision result, exploration-utilization balance parameters are adjusted through a strategy updating mechanism, and the fusion decision result is obtained. And outputting the autonomous decision control scheme. The method solves the problems of poor adaptability, low decision-making efficiency and the like of a traditional control method, can improve the autonomous decision-making capability, the control precision and the operation stability of the equipment, reduces manual intervention, reduces the cost, and is suitable for intelligent control of the industrial equipment.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology, medical health and the like, and discloses an abnormity early warning method, device, equipment and medium based on stage penetration, and the method comprises the steps: collecting interaction whole-process behaviors to form a continuous data chain, and constructing a multi-dimensional dynamic portrait; establishing a stage penetration model, and defining progressive migration of three stages of service interaction, service use and exception occurrence; calculating the overlap ratio of the interactive and used feature identifiers at the first level, and calculating the difference degree of the used and abnormal feature identifiers at the second level; training an early warning model in combination with historical data; inputting the current state of the portrait, and outputting an abnormal level; and triggering an early warning signal according to the grade. According to the method, portraits and historical statistics are converted into calculable coincidence degree and difference degree indexes through staged modeling and hierarchical penetration, and the early warning model is formed by weights and threshold values, so that the abnormal level is judged in advance, the recognition accuracy and the response timeliness are improved, and preferential disposal of resources is facilitated.

The invention discloses a seasonal frozen region salinization roadbed deformation monitoring and early warning method based on a neural network. Comprising the steps of static attribute data acquisition, dynamic monitoring data acquisition, action process data acquisition, data gridding fusion, construction and training of a first neural network model, construction and training of a second neural network model, regular monitoring and intelligent diagnosis and intelligent prediction and beforehand early warning. Data fusion is performed on spatial continuous data, investigation data, traffic load, freeze-thaw cycle and other action process data of a vertical deformation field and a salt concentration field of a roadbed, and then a serial neural network model is utilized to predict the disease type and severity of the roadbed under current and future working conditions. Full-chain intelligent analysis from multi-source data fusion, deformation prediction to disease identification is realized, the problems of monitoring fragmentation and early warning lag of a traditional method are solved, and the perception ability and the beforehand early warning level of the health state of the salinized roadbed in the seasonal frozen region are remarkably improved.

The invention relates to a landslide hidden danger point land settlement prediction method and device based on multi-modal data, and the method considers a plurality of physical factors which greatly affect land settlement. The method comprises the following steps: firstly, dividing modal data of numerous physical factors into static data, time-varying continuous data and future predictable data according to trends of change, time, parameter types and the like, performing feature extraction on various types of data through a neural network, and capturing a complex nonlinear relationship among different modal data; the method comprises the following steps: firstly, performing feature enhancement on future predictable features of future predictable data and time-varying features of time-varying continuous data by using static features of static data, and finally, integrating the time-varying features and the future predictable features by using a multi-head attention mechanism so as to predict the land subsidence state of a rainfall-type landslide hidden danger point according to the integrated features. The accuracy of predicting the land settlement state of the landslide hidden danger point can be improved.

The invention provides a multi-parameter environment sensor chip data fusion method based on artificial intelligence, and belongs to the technical field of agricultural product growth environment monitoring. A continuous data sequence is constructed through a multi-parameter environment sensor chip, and the data quality is evaluated and preprocessed by using an abnormal jump matrix and an interference abnormal matrix; a multi-dimensional fusion algorithm is constructed to calculate a convergence index, a stability index and a disorder index to quantify a data fusion state, and a self-adaptive calibration algorithm is combined with a jump compensation matrix and an abnormal compensation matrix to carry out real-time dynamic compensation on sensor baseline drift and sensitivity attenuation phenomena. The number of attention approximation features is dynamically adjusted through a feature adjustment function, finally classification prediction and quality evaluation of the agricultural product growth environment are achieved, and the technical problem that in the long-term operation process of a multi-parameter environment sensor, baseline drift and sensitivity attenuation cause data fusion precision to be remarkably reduced along with time lapse is solved.

The invention relates to the technical field of tunnel settlement data acquisition and analysis, in particular to a tunnel settlement data acquisition method and system based on laser ranging, and the method comprises the following steps: S1, equipment is a monitoring device comprising a holder and two laser ranging sensors with a fixed included angle, the first sensor horizontally points to the side wall of a tunnel, and the second sensor horizontally points to the side wall of the tunnel; the second sensor points to the tunnel vault at an included angle theta; s2, point distribution and base point selection, wherein monitoring points are distributed in the tunnel according to a preset rule, and a zero reference point is arranged in a stable area of a tunnel portal; in the scheme, through integration of the double laser sensors and automatic holder scanning, high-density and high-precision automatic continuous data acquisition of the tunnel section is realized, and the defects of low efficiency and insufficient coverage of a traditional method are overcome; meanwhile, by means of intelligent coordinate transformation, a deformation calculation model and a multi-stage early warning mechanism, real-time calculation, trend prediction and graded automatic alarm of settlement and convergence parameters are achieved.

The invention relates to the technical field of equipment state monitoring, in particular to an electronic component monitoring system based on big data, which comprises a multi-frequency response analysis module, a frequency domain feature reconstruction module, a stress deviation trend module, a coupling correlation structure module and a grade mapping generation module. According to the invention, through continuous data acquisition and analysis based on multi-frequency response information, electrical parameter changes of components are tracked in real time, change trends under various frequencies are continuously monitored and accurately identified, and through analysis and extraction of frequency domain data features and combination of stress deviation trend analysis, tiny changes caused by external environment changes are identified. According to the electrical parameter change of different time slices, the long-term health state of the component is comprehensively reflected, a dynamic evolution model of the component is constructed, a multi-dimensional data association analysis method is adopted, and the component degradation process is accurately evaluated by judging the relevance among different electrical parameters. And scientific and accurate decision support is provided for component maintenance and replacement.

The invention discloses a multi-sensor fusion road surface real-time detection method, system and device, and the method comprises the steps: carrying out the continuous data collection of a road surface through a laser radar in the advancing process of a construction machine, and obtaining the polar coordinate data; converting the polar coordinate data into rectangular coordinate data, and screening to obtain coordinate points consistent with a pavement coordinate system; correcting the coordinate points according to the height change of the laser radar; according to the angle data obtained through coordinate transformation and the laser radar attitude angle measurement data, cross section gradient data are obtained through calculation; acquiring height data according to the corrected coordinate points to calculate the flatness of the cross section; calculating a moving distance according to the laser radar GPS coordinate, constructing a pavement three-dimensional coordinate in combination with the corrected coordinate point, constructing an interpolation point by using an interpolation method, calculating to obtain an interpolation height, reconstructing the pavement three-dimensional coordinate by using the interpolation height, and calculating to obtain the flatness and gradient of a vertical section. According to the invention, efficient and real-time gradient and flatness detection can be realized, the measurement precision is improved, and the function dimension is expanded.

The invention discloses an electric energy meter data compression method and system, electronic equipment and a storage medium, and the method comprises the steps: carrying out the first-order differential processing of an electric quantity measurement data sequence, obtaining a residual data sequence, greatly reducing the value domain of the residual data sequence, greatly improving the repetition, and remarkably reducing the statistical entropy; low-entropy and high-redundancy ideal input is created for subsequent coding, and the compression ratio is integrally improved. According to the method, the residual data sequence is subjected to coding processing, positive and negative information is reserved, the problem that complement storage equivalent entropy is not reduced is avoided, bit column recombination processing or byte column recombination processing is performed on the residual coding sequence, and a large number of continuous data streams of local repeated fragments are generated, so that in an electric energy meter environment in which the MCU computing power and the RAM are limited subsequently, the residual data sequence is subjected to bit column recombination processing or byte column recombination processing. The lightweight compression algorithm can also quickly discover and reuse redundancy in an extremely short window, and the overall compression efficiency is remarkably improved.

The application discloses an electric energy meter data compression method and system, an electronic device and a storage medium. The method first performs first-order difference processing on an electric quantity measurement data sequence to obtain a residual data sequence. The value range of the residual data sequence is greatly reduced, the repetition degree is greatly increased, and the statistical entropy is significantly reduced, thus creating an ideal input with low entropy and high redundancy for subsequent coding, and improving the compression ratio as a whole. The residual data sequence is then coded, the positive and negative information is retained, and the problem of equivalent entropy not being reduced due to complement storage is avoided. The residual coded sequence is then subjected to bit column reorganization processing or byte column reorganization processing, and a continuous data stream with a large number of locally repeated segments is generated, so that in the subsequent electric energy meter environment with limited MCU computing power and RAM, a lightweight compression algorithm can also quickly find and reuse redundancy in a very short window, and the overall compression efficiency is significantly improved.

A detection engine for detecting threats to a computing system is disclosed. The detection engine includes an interceptor that is positioned in a data path and configured to intercept IOs. The interceptor transmits a data stream, which may include data and / or metadata or the intercepted IOs, to a detector. The detector perform a detection analysis. When a threat is detected, a response may be initiated. The interceptor is configured to perform the response.

The invention discloses a distributed rock stress real-time inversion method and system, and the method comprises the steps: discretizing a target region rock structure through a discontinuous structure stress field model, collecting parameters, and constructing a geological-physical constraint inversion frame; initial stress tensor distribution is obtained by applying a seismic source mechanism solution and a stress tensor linear inversion algorithm, and optimization is carried out in combination with a constraint framework; distributed rock stress parameters are introduced to establish a mapping relation to invert initial distribution, and real-time updating is achieved through continuous data collection and repeated processing. The system comprises a plurality of association units which respectively execute the functions of discrete acquisition, framework construction, inversion optimization and the like. According to the method and the system, the geological-physical constraint quantification accuracy is improved, the problem that a real-time updating mechanism is incomplete is solved, the accuracy, comprehensiveness and efficiency of inversion are improved, and reliable technical support is provided for geological engineering, disaster early warning and the like.

The invention relates to the technical field of state monitoring, can be applied to scenes of intelligent traffic operation monitoring, operation and maintenance treatment and the like, and discloses a sensing quality guarantee method, device and equipment based on self-diagnosis feedback and a medium. Performing time correction and structured processing on the data; performing multi-dimensional self-diagnosis on the sensing equipment to generate a health index, and performing fusion analysis in combination with historical accuracy and environmental data to form a comprehensive monitoring parameter; and when an exception occurs, generating an operation and maintenance work order and outputting replacement monitoring data, and after maintenance is completed, executing equipment calibration and recovering a normal data processing mode. According to the method, through linkage of multi-source data fusion and equipment self-diagnosis, continuous data output is maintained in the abnormal sensing period, automatic calibration and smooth recovery are achieved after restoration, and therefore the stability, reliability and operation and maintenance response efficiency of sensing data are improved.

The invention relates to the technical field of remote monitoring, in particular to a carbon brush intelligent monitoring system which comprises a linkage sampling module, a differential pressure offset module, a curvature tracking module, a track node module and an anomaly identification module. According to the invention, through continuous data acquisition and change trend identification of the operation state of the carbon brush assembly, dynamic monitoring of the rotation speed change and the current jump trend is realized, a periodic data regression determination trigger moment is combined to construct an abnormal characteristic time sequence, and continuous tracking and directional discrimination of the change trend of the carbon brush voltage drop difference are realized. By comparing the abnormal nodes with the periodic jump positions, the abnormal positions and times of the brush blocks in the space and time intersection area are accurately identified, the dynamic identification of the abnormal wear trend and position in the running state of the carbon brush is realized, the accuracy, the timeliness and the relevance of the abnormal wear identification of the carbon brush are improved, and the service life of the carbon brush is prolonged. And fine monitoring and management of the equipment operation state are promoted.

The application discloses a central air conditioning system anomaly detection method based on graph mining, and the method comprises the following steps: acquiring central air conditioning system operation data and outdoor meteorological data, and constructing a multi-source data set; performing data cleaning on continuous data in the multi-source data set, performing feature screening on the multi-source data set after the data cleaning is completed, and acquiring sensitive features of an anomaly detection model; constructing a data graph network, performing frequent subgraph mining on a historical data graph network, and acquiring frequent operation modes of the central air conditioning system; calculating an anomaly score of an anomaly data graph; determining a threshold according to an inflection point of the anomaly data graph, marking the frequent operation modes with anomaly scores exceeding the threshold as abnormal operation modes; and marking data that does not match the data graph network and the frequent subgraph under the same operation mode. The application solves the problem of high latitude and high coupling of equipment operation data, and improves the efficiency of graph mining.

The embodiment of the invention provides a data communication method and device based on an edge gateway, a storage medium and a program product. In the scheme, a lightweight target block model customized by a terminal is deployed at an edge gateway of a target terminal, a perception barrier between a center and the terminal is broken, and a multi-dimensional transmission state sequence is utilized to realize microscopic network state prediction; through a two-stage decoupling strategy of'a central macroscopic first block and an edge dynamic second block ', the size of each data packet is matched with the instantaneous receiving capability of the terminal in real time, packet loss and retransmission are remarkably reduced, and the transmission reliability and efficiency are improved. Besides, the target block model forms a'prediction-decision-execution 'closed loop, time sequence dependence is mined, the optimal block is quickly selected according to the time sequence dependence, and decision delay is greatly reduced. Therefore, normal form transformation from a center static state to an edge dynamic state is realized, and an end-edge cooperative adaptive transmission system is constructed, so that high-reliability and high-continuity data transmission experience is continuously provided in a complex network environment.

This invention discloses a method for storing and providing early warning of vibration acceleration data of motor bearings. The system collects vibration acceleration signals from motor bearings using a vibration acceleration sensor, and uses this data to determine the cause of bearing failures and provide early warnings. Simultaneously, a data feedback terminal provides feedback on the actual cause of the failure to supplement and correct the vibration fault database, improving the accuracy of vibration fault diagnosis and early warning. The system is characterized by including a vibration acceleration sensor, an edge cloud, a data storage backend, and a data feedback terminal. The vibration acceleration sensor establishes signal interaction with the data storage backend, the data storage backend establishes signal interaction with the edge cloud, and the data feedback terminal establishes signal interaction with the edge cloud. The vibration acceleration sensor is positioned near the bearing on the motor for continuous data collection of vibration acceleration signals. The data storage backend includes a signal conversion module and a data processing module for data conversion and comparative analysis of the vibration acceleration signals.

The application discloses a continuous acquisition and data model construction system and method of an intelligent sensor. In order to overcome the problems that the training data acquisition of the chemical sensor in the prior art is inconsistent with the actual application, the data acquisition efficiency is low, and effective data is insufficient, in the training process, the sensor unit is placed in the test unit, and the concentration control unit is used to adjust the concentration of different samples, so that the data is continuously acquired. In the prediction process, the sensor unit is placed in the sample input unit, is input into the concentration prediction model after correction by the state calibration unit, and the sample concentration output is obtained. The continuous data sampling method is used, the recovery process is not performed, and the balance state is reached in a relatively short time, so that the training time is greatly reduced; the response and recovery stage data are continuously acquired, so that the training data is consistent with the signal and concentration change process in the actual application; in the prediction process, the state of the sensor is dynamically detected and different models are called, so that the model accuracy is improved.

The embodiment of the application provides a data interaction verification method and system and a WiFi system on chip, the verification method comprises the following steps: converting a received data packet into a data stream, and dividing the data stream into at least two continuous data segments according to the content attribute of the data stream, the content attribute comprising field information and length information corresponding to the field information; when transmitting the at least two continuous data segments, configuring a type identifier for each field information in each data segment, so that the field information and the type identifier in each data segment are sequentially stored according to a preset format, and the type identifiers of different data segments are different; sequentially reading the stored field information according to the order in which each field information is stored, and performing verification according to the preset length information, the actual length information and the type identifier of the read field information. By using the above technical scheme, the data can be verified in real time, and when an error occurs, the interaction process can be terminated in time.

A data quantization method and device, a computer readable storage medium and a chip, the data quantization method comprising: obtaining a data block to be quantized, dividing the data block to be quantized into one or more data groups to be quantized according to a preset grouping method; performing quantization calculation on each data group to be quantized according to a target quantization format, to obtain quantization data corresponding to data in each data group to be quantized and quantization metadata corresponding to each data group to be quantized, wherein the quantization metadata is used for dequantization of the quantization data; and storing the quantization metadata corresponding to the data block to be quantized and the quantization data as a continuous data structure.

The invention provides a model training method, device and equipment, a medium and a computer program product, and relates to the technical field of artificial intelligence. The method comprises the steps that a pulse model trained according to a dynamic visual data set collected by a dynamic visual sensor and a predefined truncation function are acquired, and an initial quantization pulse model is acquired; according to the first moment of the input variable of the initial quantization pulse model, the credibility estimation value of the initial quantization pulse model is obtained, the initial quantization pulse model is updated, and a quantization pulse model for converting continuous data collected by the dynamic vision sensor into pulse sequence data is obtained. A truncation function is adopted to truncate the pulse model to obtain an initial quantization pulse model, and the prediction accuracy is improved while model memory compression is preliminarily achieved; the credibility estimation value is obtained through the first moment of the input variable, the nonlinear estimation has a closed-form solution, accurate estimation can be realized, and the time sequence data prediction consistency is further improved.

The invention discloses a machine vision-based intelligent attendance system for student guarding posts, which relates to the field of image analysis, and comprises an integrated control module which is used as a central management and control terminal and is used for editing and operating operation indexes of each functional module and receiving and displaying feedback data of each module; the illumination auxiliary module is used as an adjustable light source to cooperatively work with the light sensor, monitors the environment illumination intensity of the preset school gate area in real time, and automatically adjusts the illumination intensity according to the environment illumination intensity; the image acquisition module is used for capturing a real-time video stream of a school gate area in real time through an industrial camera; identity verification, on-duty state judgment and equipment compliance inspection are deeply integrated, and non-inductive and non-contact synchronous identification and verification are performed on personnel such as security personnel, traffic police and parents and volunteers on duty during rush hours when students go to and go to school, so that the manpower management cost is greatly saved, mistakes, omissions and disputes caused by human factors are avoided, and the safety of students is improved. And the traceability of the whole process is ensured by continuous data flow.

This invention provides a pulse oximeter that offers high accuracy in measuring oxygen saturation and provides continuous data suitable for measuring the oxygen saturation decline index. [Solution] The pulse oximeter includes: a saturation measurement unit 21 that measures the blood oxygen saturation of a subject 9 from detection signals of a red light sensor 12 and an infrared light sensor 13 and converts them into a measurement data group 211; an abnormal value detection unit 22 that detects abnormal values ​​221 that appear in the measurement data group 211; a first data processing unit 23 that generates a first data group 233 by removing abnormal value intervals 232 that include the abnormal values ​​221 and adjacent time ranges 231 of a predetermined length; a second data processing unit 24 that generates a second data group 243 by removing low reliability intervals 242 in which the reliability index 241 calculated on the first data group 233 is lower than a predetermined threshold L; and a third data processing unit 25 that performs data interpolation processing on the second data group 243 to generate a third data group 252 of a predetermined time length.

The present invention relates to a method and electronic device for identifying abnormal operation and regulation of a refrigeration station in a central air-conditioning system. The method comprises obtaining historical environmental data, historical operating data, and historical energy efficiency data of the refrigeration station in the central air-conditioning system; preprocessing the historical environmental data, historical operating data, and historical energy efficiency data to obtain a preprocessed data sample set; discretizing the continuous data in the data sample set to obtain all external working conditions and operating strategies for the entire year; clustering the external working conditions using a Kmeans algorithm and determining the optimal clustering result based on a target value; analyzing each clustering result to determine the operating parameters corresponding to a low COP condition and obtain an operating regulation strategy library; and identifying the abnormal cause of an abnormal operating regulation strategy based on the operating regulation strategy library in combination with the operating characteristics and date characteristics of the equipment. The present invention realizes abnormal operation and regulation strategy identification, improves identification accuracy, is low-cost, and has strong scalability, making it applicable to most refrigeration stations.

The present application provides a kind of data processing method and data processing device, electronic equipment and non-transient computer readable storage medium, the method includes in response to the decompression instruction of data, the check of received compressed data is executed;In the case where check passes, according to the preset continuous data length, the compressed data is divided into multiple data segments, wherein the compressed data is compressed according to the preset continuous data length;The decompression operation of the multiple data segments in the preset address space is respectively executed, and the decompressed data is obtained;Whether the decompressed data is consistent with the data amount of the original data corresponding to the compressed data is compared;In the case where data amount is consistent, the decompression task of the compressed data is ended.According to embodiment, by dividing compressed data into multiple data segments and performing double check on decompressed data, not only the accuracy of data decoding is improved, but also the speed and efficiency of decompression are improved.