CN115695564A - Efficient transmission method for data of Internet of things - Google Patents

Efficient transmission method for data of Internet of things Download PDF

Info

Publication number
CN115695564A
CN115695564A CN202211715398.8A CN202211715398A CN115695564A CN 115695564 A CN115695564 A CN 115695564A CN 202211715398 A CN202211715398 A CN 202211715398A CN 115695564 A CN115695564 A CN 115695564A
Authority
CN
China
Prior art keywords
data
sequence
sub
stage
data sequence
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202211715398.8A
Other languages
Chinese (zh)
Other versions
CN115695564B (en
Inventor
赵魏来
王茂林
郑崇智
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shenzhen Runxin Data Technology Co ltd
Original Assignee
Shenzhen Runxin Data Technology Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shenzhen Runxin Data Technology Co ltd filed Critical Shenzhen Runxin Data Technology Co ltd
Priority to CN202211715398.8A priority Critical patent/CN115695564B/en
Publication of CN115695564A publication Critical patent/CN115695564A/en
Application granted granted Critical
Publication of CN115695564B publication Critical patent/CN115695564B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Landscapes

  • Compression, Expansion, Code Conversion, And Decoders (AREA)

Abstract

The invention relates to an efficient transmission method of data of an internet of things, and relates to the technical field of data transmission. The method comprises the following steps: acquiring a data sequence of the Internet of things of industrial equipment in a time period; acquiring the period of data change in the data sequence; acquiring a plurality of third subdata sequences according to the data in the data sequence corresponding to each new first time node; acquiring the bit number of the data in each stage in each third sub data sequence according to the repetition degree of the data in each stage in each third sub data sequence; performing DACs (digital addressable Cs) coding on the data in each third subdata sequence according to the bit number of the data in each stage in each third subdata sequence; the sequential analogy is that data compression coding is carried out on data sequences of the Internet of things of the industrial equipment in a time period, and the data sequences are sent to a receiving end to be stored. The invention reduces the decoding time aiming at key data while ensuring the compression ratio, thereby achieving the purposes of real time and high efficiency.

Description

Efficient transmission method of Internet of things data
Technical Field
The invention relates to the technical field of data transmission, in particular to a high-efficiency transmission method of data of an Internet of things.
Background
With the development of science and technology, long-time sequence data of the internet of things show explosive growth. The data of the internet of things of the industrial equipment mainly meets the requirements of real-time performance and reliability, such as real-time alarm and real-time monitoring of the equipment. The storage and transmission of the long-time sequence data of the internet of things of the industrial equipment become an urgent problem to be solved.
In the process of data transmission, massive data needs to be compressed, and a traditional data compression method generates many compression errors, so that important data are lost. And as the time series data is accumulated, the data growth mode is exponentially increased, so that the traditional data compression method is more unsuitable. In particular, in the process of compressing binary data, common lossless compression methods are classified into fixed-length coding and variable-length coding. For fixed-length encoding, the largest binary encoding bit number in data is used as the uniform encoding length, and the method has low compression rate, but can directly extract the data without decoding from the beginning; for variable length coding, the coding length of each data is different, the compression rate is high, but when the data is extracted, decoding needs to be started from the beginning, and the method is not favorable for quick reading of the data. Therefore, an efficient transmission method of the internet of things for industrial equipment is needed, and long-time sequence data is compressed and decoded in the transmission process.
Disclosure of Invention
The invention provides an efficient transmission method of data of an internet of things. And calculating the bit number of the current stage according to the data repetition degree of the same stage in different periods to perform DACs coding compression, reducing the decoding time aiming at key data while ensuring the compression ratio, and achieving the purposes of real time and high efficiency.
The invention discloses a high-efficiency transmission method of data of an Internet of things, which comprises the following steps of:
acquiring a data sequence of the Internet of things of industrial equipment in a time period; acquiring the time corresponding to each data in the data sequence;
acquiring the period of data change in the data sequence according to the periodicity characteristics of the data in the data sequence and the time corresponding to each data in the data sequence;
sequentially dividing the data sequence into a plurality of first sub-data sequences according to the period of data change, and acquiring first time nodes divided by each first sub-data sequence according to the time corresponding to the tail data in each first sub-data sequence;
acquiring a plurality of second sub data sequences and second time nodes divided by each second sub data sequence according to the historical data sequences acquired under the normal operation condition of the industrial equipment and the period of data change corresponding to the historical data sequences;
acquiring a minimum alignment cost value through a DTW dynamic time warping algorithm according to each first subdata sequence and a first time node divided correspondingly to the first subdata sequence and each second subdata sequence and a second time node divided correspondingly to the second subdata sequence;
acquiring a position correction value of a first time node according to the minimum alignment cost value;
adjusting each first time node according to the position correction value to obtain a plurality of new first time nodes;
acquiring a plurality of third subdata sequences according to the data in the data sequence corresponding to each new first time node;
dividing each third sub data sequence into three stages in sequence according to the time length of data fluctuation in each second sub data sequence in the historical data sequence;
respectively obtaining allowable error weight values of the three stages according to the historical data sequence;
acquiring the repetition degree of data in each stage in each third sub data sequence according to the allowable error weight values of the three stages and the data value in each stage in each third sub data sequence;
acquiring the bit number of the data in each stage in each third sub data sequence according to the repetition degree of the data in each stage in each third sub data sequence;
performing DACs (digital addressable Cs) coding on the data in each third subdata sequence according to the bit number of the data in each stage in each third subdata sequence;
and sequentially analogizing the data sequences of the Internet of things of the industrial equipment in a time period to perform data compression coding, and sending the data sequences to a receiving end for storage.
In one embodiment, the period of data change in the data sequence is obtained according to the following steps:
two same sizes are arranged
Figure 820485DEST_PATH_IMAGE001
The window of (2);
using two of the same size
Figure 684536DEST_PATH_IMAGE002
The windows are respectively arranged at two ends of the data sequence to obtain corresponding data in the two windows, and the corresponding data are adjusted
Figure 233329DEST_PATH_IMAGE003
Is iterated, wherein,
Figure 790213DEST_PATH_IMAGE003
is 2, the step size is set to 1;
the similarity of the data structures in the two windows is obtained by counting the difference between the data in the two windows in each iteration process;
and judging the period of data change in the acquired data sequence according to the similarity of the data in the two windows.
In one embodiment, the similarity calculation formula of the data structure is as follows:
Figure 526087DEST_PATH_IMAGE004
in the formula (I), the compound is shown in the specification,
Figure 459408DEST_PATH_IMAGE005
indicating that the start of the data sequence corresponds to a size of
Figure 862708DEST_PATH_IMAGE006
The window of (2);
Figure 324913DEST_PATH_IMAGE007
indicates that the end of the data sequence corresponds to a size of
Figure 813663DEST_PATH_IMAGE006
The window of (2);
Figure 285096DEST_PATH_IMAGE008
express correspondence
Figure 277323DEST_PATH_IMAGE009
Mean of data within the window;
Figure 441588DEST_PATH_IMAGE010
represent a correspondence
Figure 417634DEST_PATH_IMAGE011
Mean value of data within window;
Figure 161599DEST_PATH_IMAGE012
represent a correspondence
Figure 539491DEST_PATH_IMAGE013
Variance of data within the window;
Figure 343499DEST_PATH_IMAGE014
represent a correspondence
Figure 806841DEST_PATH_IMAGE015
Variance of data within the window;
Figure 620076DEST_PATH_IMAGE016
represent
Figure 586895DEST_PATH_IMAGE017
Window and
Figure 92963DEST_PATH_IMAGE018
covariance of data within the window;
Figure 778022DEST_PATH_IMAGE019
and
Figure 394948DEST_PATH_IMAGE020
is to calculate the constant of the time at which,
Figure 216274DEST_PATH_IMAGE021
，
Figure 913751DEST_PATH_IMAGE022
；
Figure 86106DEST_PATH_IMAGE023
is the maximum value of the data in the data sequence.
In one embodiment, the three phases include an initial phase, an intermediate phase, and an end phase.
In an embodiment, each of the third sub-data sequences is sequentially divided into three stages according to the following steps:
acquiring the division duration of the initial stage of the third sub-data sequence by counting the duration of the data fluctuation of the initial stage in each second sub-data sequence;
acquiring the time length divided by the ending stage of the third sub-data sequence by counting the time length of data fluctuation of the ending stage in each second sub-data sequence;
acquiring the middle stage division duration of the third sub data sequence according to the initial stage division duration and the end stage division duration of the third sub data sequence;
and dividing each third sub data sequence according to the time length of the initial stage division, the time length of the middle stage division and the time length of the end stage division in the third sub data sequences.
In one embodiment, the allowable error weight value of each stage is obtained according to the duration of the corresponding division of each stage and the probability of the corresponding data in each stage appearing in the stage.
In one embodiment, the allowable error weight value at the initial stage is calculated as follows:
Figure 975565DEST_PATH_IMAGE024
in the formula (I), the compound is shown in the specification,
Figure 182555DEST_PATH_IMAGE025
an allowable error weight value representing an initial stage;
Figure 30425DEST_PATH_IMAGE026
is shown in the historical data sequence
Figure 424498DEST_PATH_IMAGE027
Within the initial stage of the second sub-data sequence
Figure 383226DEST_PATH_IMAGE028
A data value
Figure 179144DEST_PATH_IMAGE029
The probability of occurrence;
Figure 932336DEST_PATH_IMAGE030
representing the duration of the initial stage in the second sub-data sequence;
Figure 79284DEST_PATH_IMAGE031
the number of the second sub data sequence;
Figure 841704DEST_PATH_IMAGE032
is a hyperbolic tangent function for limiting the value of the whole
Figure 757707DEST_PATH_IMAGE033
Within the range;
and calculating the allowable error weight value of the intermediate stage and the end stage by analogy in sequence.
In an embodiment, the repetition degree calculation formula of the data in each stage in each third sub-data sequence is as follows:
Figure 416221DEST_PATH_IMAGE034
in the formula (I), the compound is shown in the specification,
Figure 50465DEST_PATH_IMAGE035
is shown as
Figure 350996DEST_PATH_IMAGE036
A third sub-data sequence
Figure 121506DEST_PATH_IMAGE037
The repetition degree of data in each stage;
Figure 216501DEST_PATH_IMAGE038
is the first
Figure 72462DEST_PATH_IMAGE039
A third sub-data sequence
Figure 176684DEST_PATH_IMAGE040
In a stage (a)
Figure 801701DEST_PATH_IMAGE041
A data value;
Figure 333176DEST_PATH_IMAGE042
is shown as
Figure 410853DEST_PATH_IMAGE043
A third sub-data sequence
Figure 318767DEST_PATH_IMAGE040
In a stage (a)
Figure 63869DEST_PATH_IMAGE041
A data value;
Figure 766245DEST_PATH_IMAGE044
is the first
Figure 328289DEST_PATH_IMAGE040
The duration of the phases, wherein,
Figure 774314DEST_PATH_IMAGE045
indicating an initial phase, corresponding to a duration
Figure 373923DEST_PATH_IMAGE044
Is composed of
Figure 981622DEST_PATH_IMAGE046
，
Figure 299470DEST_PATH_IMAGE047
Indicating intermediate stages, their corresponding durations
Figure 283607DEST_PATH_IMAGE044
Is composed of
Figure 737722DEST_PATH_IMAGE048
，
Figure 781901DEST_PATH_IMAGE049
Indicating an end phase, its corresponding duration
Figure 587046DEST_PATH_IMAGE044
Is composed of
Figure 109295DEST_PATH_IMAGE050
；
Figure 683495DEST_PATH_IMAGE051
Is shown as
Figure 898576DEST_PATH_IMAGE040
Permission of each stageAn error weight value;
Figure 659859DEST_PATH_IMAGE052
representing a rounding function.
In an embodiment, the bit number of the data in each stage in each third sub data sequence is calculated as follows:
Figure 251377DEST_PATH_IMAGE053
in the formula (I), the compound is shown in the specification,
Figure 680084DEST_PATH_IMAGE054
is shown as
Figure 66066DEST_PATH_IMAGE055
The first sub-data sequence
Figure 580224DEST_PATH_IMAGE056
The number of bits of data within a phase;
Figure 709854DEST_PATH_IMAGE057
is shown as
Figure 727489DEST_PATH_IMAGE055
A third sub-data sequence
Figure 549951DEST_PATH_IMAGE040
The repetition degree of data in each stage;
Figure 551405DEST_PATH_IMAGE058
is a hyper-parameter;
Figure 219147DEST_PATH_IMAGE052
representing a rounding function.
In an embodiment, when the data in each third sub-data sequence is DACs-encoded, the data in each third sub-data sequence is converted into binary data and then DACs-encoded.
The invention has the beneficial effects that: according to the efficient transmission method of the data of the Internet of things, the period of the data sequence is obtained in a self-adaptive mode through the acquired data sequence of the Internet of things of industrial equipment in a certain time period, the division of different stages in the same period is obtained through analysis of historical data, and the allowable error weight value is calculated. And calculating the bit number of the current stage according to the data repetition degree of the same stage in different periods to perform DACs coding compression, reducing the decoding time aiming at key data while ensuring the compression ratio, and achieving the purposes of real time and high efficiency.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flowchart of general steps of an embodiment of a method for efficiently transmitting data of the internet of things according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Aiming at industrial equipment, in order to obtain a data sequence of the Internet of things of the industrial equipment within a certain time period, a sensor is required to be installed on the industrial equipment to detect the data sequence in the operation process of the industrial equipment, and the obtained data sequence is analyzed to be used for detecting the operation state of the industrial equipment.
According to the invention, the collected sensor data sequence of the industrial equipment needs to be compressed and transmitted, and in the transmission process, the collected sensor data is compressed through binary coding. Therefore, binary conversion of the acquired data sequence is required, and specific data conversion is performed in the known technology and is not described in detail.
The sensor equipment comprises a data acquisition and transmission system for acquiring the sensors of the industrial equipment and storing and compressing sensor data.
The data acquisition and transmission system comprises a storage device for storing image data, a data acquisition device for analyzing and compressing, an Internet of things device for transmitting data and an antenna.
The invention provides a high-efficiency transmission method of Internet of things data, which comprises the following steps:
firstly, according to the periodic characteristics of a data sequence (for example, equipment is restarted once in a day), a time node is determined in a self-adaptive mode, and the whole sequence is divided into a plurality of time periods;
then, according to the periodic characteristics of the data characteristics, the repetition degree between time periods in a plurality of time periods is calculated, and the weight value is determined; in the analysis process in a single time period, the allowable errors of the initial data, the middle data and the final data in the time are considered;
finally, the weight of the data set is smaller when the repetition degree is larger, the writing rate (compression rate) is ensured, and the bit number is small; the smaller the repetition degree, the greater the weight of the data setting (i.e., the required reading speed), the guaranteed reading rate (the number of layers is small), and the number of bits is large.
The method mainly aims at the problem that a large amount of long-time sequence data of the Internet of things can be generated in the operation monitoring process of industrial equipment, and the data is coded according to the data; the method comprises the steps of obtaining the cycle size of long-time sequence data in a self-adaptive mode through the acquired Internet of things data of the long-time sequence of the industrial equipment, obtaining the division of different stages in the same cycle by combining the analysis of historical data, and calculating an allowable error weight value. And calculating the optimal bit value of the current stage according to the data repetition degree of the same stage in different periods to carry out DACs coding compression.
Referring to fig. 1, the method for efficiently transmitting data of the internet of things provided by the invention includes:
s1, acquiring a data sequence of the Internet of things of industrial equipment in a time period; acquiring the time corresponding to each data in the data sequence;
in this embodiment, in order to obtain a data sequence of the internet of things of the industrial equipment within a time period, a sensor needs to be installed on the industrial equipment to detect data in an operation process of the industrial equipment, and the obtained data sequence is analyzed to detect an operation state of the industrial equipment.
S2, acquiring the period of data change in the data sequence according to the periodic characteristics of the data in the data sequence and the time corresponding to each data in the data sequence;
it should be noted that the most basic attribute of the acquired data sequence is a time attribute, each data has a respective time point, and the data sequence generally has a fixed sampling frequency, for example, a value is acquired every 5 minutes. For the data of the internet of things of the industrial equipment, the data generally has periodic characteristics, and under the condition that the equipment normally operates, the data difference generated at the similar time point is small. Therefore, in order to achieve the optimal bit number in the DACs method, the data sequence is subjected to self-adaptive acquisition of the period of data change through the periodic characteristics, and the Internet of things data of the whole long-time sequence is subjected to self-adaptive segmentation, so that the optimal bit number of each time period is acquired.
In this embodiment, the period of data change in the data sequence is obtained according to the following steps:
two same sizes are arranged
Figure 356867DEST_PATH_IMAGE059
The window of (2); using two identical sizes
Figure 350231DEST_PATH_IMAGE059
The windows are respectively arranged at two ends of the data sequence to obtain corresponding data in the two windows, and the corresponding data are adjusted
Figure 570473DEST_PATH_IMAGE060
Is iterated, wherein,
Figure 41905DEST_PATH_IMAGE060
is 2, the step length is set to 1;
the similarity of the data structures in the two windows is obtained by counting the difference between the data in the two windows in each iteration process;
and judging and acquiring the period of data change in the data sequence according to the similarity of the data in the two windows.
In particular, by setting a threshold value
Figure 299711DEST_PATH_IMAGE061
If a certain size in the course of the iteration
Figure 463976DEST_PATH_IMAGE059
Is greater than the threshold, then the selected window is selected
Figure 908864DEST_PATH_IMAGE060
The value of (d) can be taken as the period of the data sequence. Wherein the threshold value
Figure 183988DEST_PATH_IMAGE062
The empirical reference values are given in this embodiment, depending on the implementation of the implementation,
Figure 296300DEST_PATH_IMAGE063
. It should be noted that, the corresponding similarity is calculated once per iteration, and the specific iterative process includes:
when the temperature is higher than the set temperature
Figure 365887DEST_PATH_IMAGE064
Then, two of the first same size are set
Figure 829230DEST_PATH_IMAGE065
The windows are respectively arranged at two ends of the data sequence to obtain corresponding data in the two windows, the data of the corresponding windows are respectively read, and the similarity is further calculated;
when in use
Figure 376886DEST_PATH_IMAGE066
Then, two of the first same size are set
Figure 609284DEST_PATH_IMAGE067
The windows are respectively arranged at two ends of the data sequence to obtain corresponding data in the two windows, the data of the corresponding windows are respectively read, and the similarity is further calculated;
when in use
Figure 849772DEST_PATH_IMAGE068
Then, two of the first same size are set
Figure 800411DEST_PATH_IMAGE069
The windows are respectively arranged at two ends of the data sequence to obtain corresponding data in the two windows, the data of the corresponding windows are respectively read, and the similarity is further calculated;
calculating the corresponding similarity once per iteration in turn; the similarity calculation formula of the data structure is as follows:
Figure 151758DEST_PATH_IMAGE070
in the formula (I), the compound is shown in the specification,
Figure 238662DEST_PATH_IMAGE005
indicating that the start of the data sequence corresponds to a size of
Figure 915631DEST_PATH_IMAGE006
The window of (1);
Figure 822408DEST_PATH_IMAGE007
the size corresponding to the end of the data sequence is represented as
Figure 977445DEST_PATH_IMAGE006
The window of (1);
Figure 184436DEST_PATH_IMAGE008
express correspondence
Figure 501148DEST_PATH_IMAGE009
Mean of data within the window;
Figure 160799DEST_PATH_IMAGE010
represent a correspondence
Figure 119528DEST_PATH_IMAGE011
Mean of data within the window;
Figure 181025DEST_PATH_IMAGE012
express correspondence
Figure 934217DEST_PATH_IMAGE013
Variance of data within the window;
Figure 78235DEST_PATH_IMAGE014
express correspondence
Figure 309496DEST_PATH_IMAGE015
Variance of data within the window;
Figure 225500DEST_PATH_IMAGE016
represent
Figure 415172DEST_PATH_IMAGE017
Window and
Figure 49416DEST_PATH_IMAGE018
covariance of data within the window;
Figure 84368DEST_PATH_IMAGE019
and
Figure 854878DEST_PATH_IMAGE020
is to calculate the constant of the time that,
Figure 215452DEST_PATH_IMAGE021
，
Figure 71413DEST_PATH_IMAGE022
；
Figure 910056DEST_PATH_IMAGE023
is the maximum value of the data in the data sequence.
In this embodiment, for long-time sequence data of the internet of things of the industrial equipment, the period size needs to be satisfied
Figure 800651DEST_PATH_IMAGE006
Can not be too small and the similarity satisfies a certain condition, so the window size is adjusted
Figure 66548DEST_PATH_IMAGE006
Performing iteration, in this embodiment, setting the stop condition of the iteration as
Figure 409804DEST_PATH_IMAGE071
Wherein
Figure 317718DEST_PATH_IMAGE072
Indicating the length of the acquired data sequence, each calculated
Figure 62820DEST_PATH_IMAGE073
Data structure similarity corresponding to values
Figure 765196DEST_PATH_IMAGE074
A value of (d); establishing a coordinate system and obtaining the similarity of data structures
Figure 330170DEST_PATH_IMAGE074
Selecting the curve corresponding to the peak point of the curve
Figure 510616DEST_PATH_IMAGE073
The value is the period of data change, and the subsequent steps divide the whole time sequence into the size
Figure 375803DEST_PATH_IMAGE073
The time period of (a).
S3, sequentially dividing the data sequence into a plurality of first sub-data sequences according to the period of data change, and acquiring first time nodes divided by each first sub-data sequence according to the time corresponding to the last data in each first sub-data sequence; in fact, the time corresponding to the last data in each first sub-data sequence is a first time node divided corresponding to each first sub-data sequence;
acquiring a plurality of second sub data sequences and second time nodes divided by each second sub data sequence according to the historical data sequences acquired under the normal operation condition of the industrial equipment and the period of data change corresponding to the historical data sequences;
in this embodiment, according to a historical data sequence acquired under a normal operating condition of the industrial equipment, that is, in a known historical data sequence, according to the period of the data change obtained in the above steps, a plurality of second sub-data sequences are obtained according to the historical data sequence and the period of the data change corresponding to the historical data sequence, that is, the historical data sequence is divided into a plurality of second sub-data sequences by using the period of the data change, it should be noted that the second sub-data sequences obtained by dividing the historical data sequence are sub-data sequences of a complete period; meanwhile, a second time node divided by each second sub data sequence is also obtained; it should be noted that the period of data change in the historical data sequence is the same as the period of data change in the current data sequence;
acquiring a minimum alignment cost value through a DTW dynamic time warping algorithm according to each first subdata sequence and a first time node divided correspondingly to the first subdata sequence and each second subdata sequence and a second time node divided correspondingly to the second subdata sequence; acquiring a position correction value of a first time node according to the minimum alignment cost value;
in this embodiment, a DTW dynamic time warping algorithm is used to calculate a data alignment cost matrix to determine a position correction value, and two data points are matched between complete one period data of a history data sequence and one period data obtained in the present application. The specific steps for obtaining the minimum alignment cost value are as follows:
first, by
Figure 983502DEST_PATH_IMAGE075
A sequence of the historical data is represented,
Figure 301351DEST_PATH_IMAGE076
represents the current data sequence, wherein
Figure 285488DEST_PATH_IMAGE077
，
Figure 739603DEST_PATH_IMAGE078
. Alignment cost matrix
Figure 783782DEST_PATH_IMAGE079
Is by calculation
Figure 588927DEST_PATH_IMAGE075
Data point of (1)
Figure 108246DEST_PATH_IMAGE076
Of the distance between data points, matrix
Figure 682446DEST_PATH_IMAGE079
To (1)
Figure 897527DEST_PATH_IMAGE080
The element is
Figure 658810DEST_PATH_IMAGE081
And
Figure 250328DEST_PATH_IMAGE082
is a distance of
Figure 679035DEST_PATH_IMAGE083
Wherein
Figure 65017DEST_PATH_IMAGE084
Represents a 2 norm;
secondly, a path is searched to minimize the accumulated distance between every two data points, and the requirement is as follows: all points must be used, the point pairs cannot be crossed, the matching direction is monotonous, and thus, a result after two data are matched is obtained
Figure 579175DEST_PATH_IMAGE085
(the result is the best alignment path), and the corresponding minimum alignment cost value
Figure 708805DEST_PATH_IMAGE086
；
And finally, obtaining the corresponding optimal alignment path result, namely the position correction value needing to be adjusted. Wherein the position correction value of the first time node
Figure 726440DEST_PATH_IMAGE087
The calculation expression of (a) is:
Figure 548902DEST_PATH_IMAGE088
in the formula (I), the compound is shown in the specification,
Figure 550356DEST_PATH_IMAGE087
a position correction value representing a first time node;
Figure 218098DEST_PATH_IMAGE089
the hyper-parameter is used for adjusting the position correction value, and can be set according to specific implementation conditions, and an empirical reference value is given in the implementation
Figure 355818DEST_PATH_IMAGE090
Wherein, in the step (A),
Figure 349182DEST_PATH_IMAGE091
indicating a period of data change in the data sequence;
Figure 572353DEST_PATH_IMAGE092
is a rounding function.
Adjusting each first time node according to the position correction value to obtain a plurality of new first time nodes; acquiring a plurality of third subdata sequences according to the data in the data sequence corresponding to each new first time node;
adjusting the divided periodic data according to the adjusted position correction value calculated in the step; and carrying out stage division on the adjusted periodic data according to the stage range values of different stages. That is, each first time node is moved in the data sequence to the starting direction
Figure 43786DEST_PATH_IMAGE087
A new first time node is obtained at each position;
for example: when calculating
Figure 301592DEST_PATH_IMAGE087
3, a first time node is 10:30' and 30 ", then adjust 3 positions forward in time, get the new first time node as 10:30 '27'; the new first time node is taken as 10: and taking the data in the data sequence corresponding to the 30 '27' as the last data in the corresponding third subdata sequence. It should be noted that, a new first time node before two adjacent new first time nodes is a start position of a third next sub data sequence. And sequentially simulating to obtain a new first time node from each first time node through adjustment, and obtaining a third sub-data sequence corresponding to each new first time node.
S4, sequentially dividing each third sub data sequence into three stages according to the time length of data fluctuation in each second sub data sequence in the historical data sequence; wherein the three phases include an initial phase, an intermediate phase, and an end phase.
It should be noted that, in the process of daily operation of the industrial equipment, the error rate is allowed to be large due to the fact that the equipment is just started to operate in the initial time period; the allowable error rate in the intermediate time period is small; the latter period allows a large error rate due to the fast stopping of the device.
In this embodiment, each third sub-data sequence is sequentially divided into three stages according to the following steps:
acquiring the division duration of the initial stage of the third sub-data sequence by counting the duration of the data fluctuation of the initial stage in each second sub-data sequence;
acquiring the time length divided by the ending stage of the third sub-data sequence by counting the time length of data fluctuation of the ending stage in each second sub-data sequence;
acquiring the middle stage division duration of the third sub data sequence according to the initial stage division duration of the third sub data sequence and the end stage division duration of the third sub data sequence;
and dividing each third sub-data sequence according to the time length of the initial stage division, the time length of the middle stage division and the time length of the end stage division in the third sub-data sequences.
In particular, the data is complete in the history data under the normal operation condition of the industrial equipment
Figure 200278DEST_PATH_IMAGE093
Counting the data of each period, wherein: duration of data fluctuation in initial stage (i.e. time length of initial stage)
Figure 176324DEST_PATH_IMAGE094
(ii) a Duration of data fluctuation of end phase (i.e. time length of end phase)
Figure 185868DEST_PATH_IMAGE095
. Calculating the average value of the initial stage duration in all periods (i.e. all the second sub-data sequences)
Figure 298181DEST_PATH_IMAGE096
And average of end stage durations
Figure 367768DEST_PATH_IMAGE097
As the duration of the initial and end phases. For the cycle size is
Figure 565531DEST_PATH_IMAGE098
Within the period time period of (a), the range values of the phases are respectively: initial stage
Figure 375837DEST_PATH_IMAGE099
Intermediate stage of
Figure 608235DEST_PATH_IMAGE100
End stage
Figure 848723DEST_PATH_IMAGE101
。
Will be provided with
Figure 533783DEST_PATH_IMAGE099
Representing the time length of the division of the initial stage of the third sub data sequence; will be provided with
Figure 150709DEST_PATH_IMAGE101
Representing the time length of the division of the end stage of the third sub data sequence; will be provided with
Figure 237613DEST_PATH_IMAGE102
The time length of the middle stage division of the third sub data sequence; it should be noted that the dividing time lengths of the three stages of each third sub-data sequence are the same; each third sub-data sequence is divided into three stages of data by the duration of each stage.
S5, respectively acquiring allowable error weight values of the three stages according to the historical data sequence;
it should be noted that, in order to calculate the allowable error weight values of different stages, it is necessary to count the fluctuation degrees of different stages in the historical data of normal operation of the industrial equipment, and the allowable error weight values are calculated according to the fluctuation degrees of different stages, and the larger the fluctuation is, the larger the set error is. For example: and calculating the change of the data value of the initial stage for the historical data of normal industrial equipment operation of a complete period, and if the data fluctuation of the stage is large, indicating that the allowable error of the stage is large. Specifically, the allowable error weight value of each stage is obtained according to the duration of the corresponding division of each stage and the probability of the corresponding data in each stage appearing in the stage.
The calculation formula of the allowable error weight value in the initial stage is as follows:
Figure 649003DEST_PATH_IMAGE103
in the formula (I), the compound is shown in the specification,
Figure 555779DEST_PATH_IMAGE104
an allowable error weight value representing an initial stage;
Figure 976396DEST_PATH_IMAGE105
is shown in the historical data sequence
Figure 652228DEST_PATH_IMAGE106
Within the initial stage of the second sub-data sequence
Figure 500099DEST_PATH_IMAGE107
A data value
Figure 159750DEST_PATH_IMAGE108
The probability of occurrence;
Figure 852900DEST_PATH_IMAGE109
representing the duration of the initial stage in the second sub-data sequence;
Figure 914396DEST_PATH_IMAGE110
the number of the second subdata sequences;
Figure 933168DEST_PATH_IMAGE111
is a hyperbolic tangent function for limiting the value of the whole
Figure 548957DEST_PATH_IMAGE112
Within the range;
calculating the allowable error weight value of the intermediate stage by analogy in turn
Figure 311377DEST_PATH_IMAGE113
And an allowable error weight value of the end stage, noted
Figure 227380DEST_PATH_IMAGE114
。
S6, acquiring the repetition degree of data in each stage in each third sub-data sequence according to the allowable error weight values of the three stages and the data value in each stage in each third sub-data sequence;
in this embodiment, the bit number in different stages needs to be calculated according to the data repetition degree in different stages in each third sub data sequence. In order to ensure the compression rate and the reading rate of the data, the larger the repetition degree of the data in the same stage of different third sub-data sequences is, the smaller the importance of the data is, the smaller the set bit number is, and the compression rate of the data is ensured; the smaller the repetition degree of the data in the same phase in different third sub-data sequences is, the greater the importance of the data is, the greater the set bit number is.
The data repetition degrees in the same stage in different third sub-data sequences are an accumulated process, that is, the data repetition degree of a certain stage of the current third sub-data sequence is to be calculated as the repetition value of the stage corresponding to the previous third sub-data sequences, specifically, the data repetition degree calculation formula in each stage in each third sub-data sequence is as follows:
Figure 151474DEST_PATH_IMAGE115
in the formula (I), the compound is shown in the specification,
Figure 785718DEST_PATH_IMAGE035
denotes the first
Figure 86249DEST_PATH_IMAGE036
The first sub-data sequence
Figure 591180DEST_PATH_IMAGE037
The repetition degree of data in each stage;
Figure 951754DEST_PATH_IMAGE038
is the first
Figure 807714DEST_PATH_IMAGE039
The third sub-data sequence
Figure 643428DEST_PATH_IMAGE040
In a first stage
Figure 534023DEST_PATH_IMAGE041
A data value;
Figure 65499DEST_PATH_IMAGE042
denotes the first
Figure 143176DEST_PATH_IMAGE043
A third sub-data sequence
Figure 785510DEST_PATH_IMAGE040
In a stage (a)
Figure 530612DEST_PATH_IMAGE041
A data value;
Figure 232989DEST_PATH_IMAGE044
is the first
Figure 797962DEST_PATH_IMAGE040
The duration of the phases, wherein,
Figure 243987DEST_PATH_IMAGE045
indicating an initial phase, corresponding to a duration
Figure 843596DEST_PATH_IMAGE044
Is composed of
Figure 716874DEST_PATH_IMAGE046
，
Figure 769144DEST_PATH_IMAGE047
Indicating intermediate stages, their corresponding durations
Figure 18859DEST_PATH_IMAGE044
Is composed of
Figure 472974DEST_PATH_IMAGE048
，
Figure 517154DEST_PATH_IMAGE049
Indicating an end phase, its corresponding duration
Figure 56720DEST_PATH_IMAGE044
Is composed of
Figure 578968DEST_PATH_IMAGE050
；
Figure 153169DEST_PATH_IMAGE051
Is shown as
Figure 368249DEST_PATH_IMAGE040
The allowable error weight value of each stage specifically comprises
Figure 395111DEST_PATH_IMAGE116
，
Figure 721050DEST_PATH_IMAGE117
And
Figure 415337DEST_PATH_IMAGE118
；
Figure 535740DEST_PATH_IMAGE052
representing a rounding function; wherein for when
Figure 49897DEST_PATH_IMAGE119
In time, the data repetition degree cannot be calculated, and therefore, the data repetition degree is set to 0 and taken
Figure 176598DEST_PATH_IMAGE120
。
S7, acquiring the bit number of the data in each stage in each third sub data sequence according to the repetition degree of the data in each stage in each third sub data sequence;
in this embodiment, the number of bits required to be set is calculated according to the data repetition degrees in the same phase in different third sub-data sequences. The larger the repetition degree is, the smaller the set bit number is; the smaller the repetition degree is, the larger the number of bits set. Specifically, the bit number calculation formula of the data in each stage in each third sub-data sequence is as follows:
Figure 459812DEST_PATH_IMAGE121
in the formula (I), the compound is shown in the specification,
Figure 16695DEST_PATH_IMAGE054
denotes the first
Figure 18149DEST_PATH_IMAGE122
The first sub-data sequence
Figure 951470DEST_PATH_IMAGE123
The number of bits of data within a phase;
Figure 89190DEST_PATH_IMAGE124
is shown as
Figure 816975DEST_PATH_IMAGE122
The first sub-data sequence
Figure 305725DEST_PATH_IMAGE123
The repetition degree of data in each stage;
Figure 42737DEST_PATH_IMAGE125
is shown as
Figure 34964DEST_PATH_IMAGE123
The meta-parameter of each stage for adjusting the value of the whole number of bits can be determined according to the specific implementation of the implementer, and the empirical reference value given in this embodiment is calculated from the historical data
Figure 668070DEST_PATH_IMAGE123
1/4 of the binary number of the mean of the phases;
Figure 644116DEST_PATH_IMAGE126
representing a rounding function.
S8, performing DACs (digital audio coding) on data in each third sub-data sequence according to the bit number of the data in each stage in each third sub-data sequence; and when DACs are carried out on the data in each third sub-data sequence, converting the data in each third sub-data sequence into binary data and then carrying out DACs coding.
The sequential analogy is that data compression coding is carried out on data sequences of the Internet of things of the industrial equipment in a time period, and the data sequences are sent to a receiving end to be stored.
The specific encoding process of DACs is as follows:
1) Is provided with the first
Figure 919240DEST_PATH_IMAGE127
A third sub-data sequence
Figure 765973DEST_PATH_IMAGE128
Intra-phase data sequence
Figure 101140DEST_PATH_IMAGE129
The number of bits is
Figure 298903DEST_PATH_IMAGE130
The bit block size of the coding block is
Figure 112138DEST_PATH_IMAGE131
. Will be provided with
Figure 78957DEST_PATH_IMAGE132
Coded into a plurality of sizes of
Figure 585025DEST_PATH_IMAGE133
Each bit block has an identifier indicating whether the block is the most significant bit
Figure 270084DEST_PATH_IMAGE132
The last block of (a);
2) Each size is
Figure 621431DEST_PATH_IMAGE133
Bit block of
Figure 708336DEST_PATH_IMAGE134
By using
Figure 385305DEST_PATH_IMAGE135
An identifier representing a block of bits, using
Figure 292081DEST_PATH_IMAGE136
Representing the remainder of a block of bits
Figure 733206DEST_PATH_IMAGE137
One bit then has
Figure 674617DEST_PATH_IMAGE138
. Starting with the last bit, slicing each time
Figure 256908DEST_PATH_IMAGE139
If the number of bits is not enough, 0 is added. For the identifier of the current bit block, if the current bit block is not the last bit block, the identifier is 1; if the current bit block is the last bit block, the identifier is 0;
3) By way of example: data sequence of a certain phase
Figure 916559DEST_PATH_IMAGE140
Number of bits
Figure 875288DEST_PATH_IMAGE141
The value of (b) is 2. The results of DACs encoding are shown in table 1 below;
table 1 is a data sequence
Figure 671206DEST_PATH_IMAGE140
DACs encoding process
Figure 424398DEST_PATH_IMAGE142
As can be seen from table 1, taking data 24 as an example: the binary encoding of the data 24 is 11000,
Figure 571346DEST_PATH_IMAGE141
is 2, then each bit block size is
Figure 333765DEST_PATH_IMAGE143
Each bit block is composed of an identifier and remaining bits. Therefore, the binary coding 11000 of the data 24 is segmented from back to front, and the segmentation result is as follows: 01 (deficiency)
Figure 984189DEST_PATH_IMAGE141
Bit complement 0) 1000, the corresponding identifier for each bit block is: 0 (last bit block) 1 (not last bit block), the combination is: 001 110 100.
4) And (3) DACs coding recombination results:
by a sequence of data
Figure 173862DEST_PATH_IMAGE144
For example, for a data sequence
Figure 542527DEST_PATH_IMAGE144
The coding structure obtained after DACs coding is shown in the following table 2;
wherein B1 denotes a first bit block, C1 denotes an identifier of the first bit block, and D1 denotes remaining bits of the first bit block; b2 denotes a second bit block, C2 denotes an identifier of the second bit block, and D2 denotes remaining bits of the second bit block; b3 denotes a third bit block, C3 denotes an identifier of the third bit block, and D3 denotes remaining bits of the third bit block;
table 2 is the data sequence
Figure 577479DEST_PATH_IMAGE145
Obtaining an encoded structure after DACs encoding
Figure 613568DEST_PATH_IMAGE146
As shown in tables 1 and 2, the decoding process is (also taking data 24 as an example): the data to be decoded is shown in Table 1
Figure 974142DEST_PATH_IMAGE147
If the 5 th position is found in the B1 layer, the corresponding identifier C1=1, and the corresponding bit D1=00, where the identifier is 1, which indicates that the current bit block is not the last bit block; continuing to search at the B2 layer, since the data is located at the third (i.e. the third 1) in C1=1 of the B1 layer, and thus the 3 rd position is searched in the B2 layer, the corresponding identifier C2=1 and the corresponding bit D2=10, where the identifier is 1, which indicates that the current bit block is not the last bit block; continuing with the search at B3 level, since the data is located first (i.e. first 1) in C2=1 of B2 level, the first location is searched in B3 level, and the corresponding identifier C3=0, and the corresponding bit D3=01, and the identifier is 0, which indicates that the current bit block is the last bit block. Thus, a binary of the 5 th data is obtained: 11000, the corresponding data is 24.
In this embodiment, the bit numbers of different stages in different third sub-data sequences are obtained according to the obtained bit numbers
Figure 830103DEST_PATH_IMAGE148
The values are DACs encoded. Operating an industrial plantIn the method, data compression coding is carried out on the collected data of the Internet of things, the data are transmitted through a data collection and transmission system and sent to a cloud server for storage. When data of a certain time period needs to be checked, the data of the time period can be decoded.
In summary, according to the efficient transmission method for the data of the internet of things, the period of the data sequence is obtained in a self-adaptive manner through the acquired data sequence of the internet of things of the industrial equipment in a certain time period, the division of different stages in the same period is obtained through analysis of historical data, and the allowable error weight value is calculated. And calculating the bit number of the current stage according to the data repetition degree of the same stage in different periods to perform DACs coding compression, reducing the decoding time aiming at key data while ensuring the compression ratio, and achieving the purposes of real time and high efficiency.
The present invention is not limited to the above preferred embodiments, and any modifications, equivalent substitutions, improvements, etc. within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. An efficient transmission method of data of the Internet of things is characterized by comprising the following steps:
acquiring a data sequence of the Internet of things of industrial equipment in a time period; acquiring the time corresponding to each data in the data sequence;
acquiring the period of data change in the data sequence according to the periodicity characteristics of the data in the data sequence and the time corresponding to each data in the data sequence;
sequentially dividing the data sequence into a plurality of first sub-data sequences according to the period of data change, and acquiring first time nodes divided by each first sub-data sequence according to the time corresponding to the tail data in each first sub-data sequence;
acquiring a plurality of second sub data sequences and second time nodes divided by each second sub data sequence according to the historical data sequences acquired under the normal operation condition of the industrial equipment and the period of data change corresponding to the historical data sequences;
acquiring a minimum alignment cost value through a DTW dynamic time warping algorithm according to each first subdata sequence and a first time node correspondingly divided by the first subdata sequence and each second subdata sequence and a second time node correspondingly divided by the second subdata sequence;
acquiring a position correction value of a first time node according to the minimum alignment cost value;
adjusting each first time node according to the position correction value to obtain a plurality of new first time nodes;
acquiring a plurality of third subdata sequences according to the data in the data sequence corresponding to each new first time node;
dividing each third sub data sequence into three stages in sequence according to the time length of data fluctuation in each second sub data sequence in the historical data sequence; respectively acquiring allowable error weight values of the three stages according to the historical data sequence;
acquiring the repetition degree of data in each stage in each third sub-data sequence according to the allowable error weight values of the three stages and the data value in each stage in each third sub-data sequence;
acquiring the bit number of the data in each stage in each third sub-data sequence according to the repetition degree of the data in each stage in each third sub-data sequence;
performing DACs (digital addressable Cs) coding on the data in each third subdata sequence according to the bit number of the data in each stage in each third subdata sequence;
the sequential analogy is that data compression coding is carried out on data sequences of the Internet of things of the industrial equipment in a time period, and the data sequences are sent to a receiving end to be stored.
2. The method for efficiently transmitting data of the internet of things according to claim 1, wherein the period of data change in the data sequence is obtained according to the following steps:
two same sizes are arranged
Figure 240096DEST_PATH_IMAGE001
The window of (1);
using two identical sizes
Figure 924499DEST_PATH_IMAGE002
The windows are respectively arranged at two ends of the data sequence to obtain corresponding data in the two windows, and the corresponding data are adjusted
Figure 987133DEST_PATH_IMAGE003
Is iterated, wherein,
Figure 127128DEST_PATH_IMAGE003
is 2, the step length is set to 1;
the similarity of the data structures in the two windows is obtained by counting the difference between the data in the two windows in each iteration process;
and judging and acquiring the period of data change in the data sequence according to the similarity of the data in the two windows.
3. The method for efficiently transmitting data of the internet of things as claimed in claim 2, wherein the similarity calculation formula of the data structure is as follows:
Figure 300620DEST_PATH_IMAGE004
in the formula (I), the compound is shown in the specification,
Figure 576881DEST_PATH_IMAGE005
indicating that the start of the data sequence corresponds to a size of
Figure 75995DEST_PATH_IMAGE006
The window of (1);
Figure 437706DEST_PATH_IMAGE007
indicates that the end of the data sequence corresponds to a size of
Figure 680469DEST_PATH_IMAGE006
The window of (1);
Figure 811236DEST_PATH_IMAGE008
represent a correspondence
Figure 215672DEST_PATH_IMAGE009
Mean of data within the window;
Figure 330259DEST_PATH_IMAGE010
express correspondence
Figure 111133DEST_PATH_IMAGE011
Mean of data within the window;
Figure 96407DEST_PATH_IMAGE012
represent a correspondence
Figure 937324DEST_PATH_IMAGE013
Variance of data within the window;
Figure 273627DEST_PATH_IMAGE014
express correspondence
Figure 592613DEST_PATH_IMAGE015
Variance of data within the window;
Figure 963551DEST_PATH_IMAGE016
represent
Figure 709791DEST_PATH_IMAGE017
Window and
Figure 533390DEST_PATH_IMAGE018
covariance of data within the window;
Figure 656067DEST_PATH_IMAGE019
and
Figure 615933DEST_PATH_IMAGE020
is to calculate the constant of the time at which,
Figure 798652DEST_PATH_IMAGE021
，
Figure 843969DEST_PATH_IMAGE022
；
Figure 770336DEST_PATH_IMAGE023
is the maximum value of the data in the data sequence.
4. The method for efficiently transmitting data of the internet of things as claimed in claim 1, wherein the three phases comprise an initial phase, an intermediate phase and an end phase.
5. The method for efficient transmission of data of the Internet of things of claim 4,
each third subdata sequence is divided into three stages in sequence according to the following steps:
acquiring the division duration of the initial stage of the third sub-data sequence by counting the duration of the data fluctuation of the initial stage in each second sub-data sequence;
acquiring the time length divided by the ending stage of the third sub-data sequence by counting the time length of data fluctuation of the ending stage in each second sub-data sequence;
acquiring the middle stage division duration of the third sub data sequence according to the initial stage division duration of the third sub data sequence and the end stage division duration of the third sub data sequence;
and dividing each third sub-data sequence according to the time length of the initial stage division, the time length of the middle stage division and the time length of the end stage division in the third sub-data sequences.
6. The method for efficiently transmitting data of the internet of things according to claim 5, wherein the allowable error weight value of each stage is obtained according to the duration of the corresponding division of each stage and the probability of the corresponding data in each stage appearing in the stage.
7. The method for efficiently transmitting data of the internet of things according to claim 6, wherein the allowable error weight value at the initial stage is calculated by the following formula:
Figure 853218DEST_PATH_IMAGE024
in the formula (I), the compound is shown in the specification,
Figure 941259DEST_PATH_IMAGE025
an allowable error weight value representing an initial stage;
Figure 739451DEST_PATH_IMAGE026
is shown in the historical data sequence
Figure 203930DEST_PATH_IMAGE027
Within the initial stage of the second sub-data sequence
Figure 138388DEST_PATH_IMAGE028
A data value
Figure 397331DEST_PATH_IMAGE029
The probability of occurrence;
Figure 682819DEST_PATH_IMAGE030
is shown asThe duration of the initial stage in the two subdata sequences;
Figure 685410DEST_PATH_IMAGE031
the number of the second sub data sequence;
Figure 474375DEST_PATH_IMAGE032
is a hyperbolic tangent function for limiting the value of the whole
Figure 435377DEST_PATH_IMAGE033
Within the range;
and calculating the allowable error weight value of the intermediate stage and the end stage by analogy in sequence.
8. The method for efficiently transmitting data of the internet of things according to claim 7, wherein a formula for calculating the repetition degree of the data in each stage in each third sub-data sequence is as follows:
Figure 942582DEST_PATH_IMAGE034
in the formula (I), the compound is shown in the specification,
Figure 748864DEST_PATH_IMAGE035
is shown as
Figure 657914DEST_PATH_IMAGE036
A third sub-data sequence
Figure 258660DEST_PATH_IMAGE037
The repetition degree of data in each stage;
Figure 253161DEST_PATH_IMAGE038
is the first
Figure 863134DEST_PATH_IMAGE039
The third sub-data sequence
Figure 626690DEST_PATH_IMAGE040
In a stage (a)
Figure 663916DEST_PATH_IMAGE041
A data value;
Figure 145713DEST_PATH_IMAGE042
is shown as
Figure 293798DEST_PATH_IMAGE043
The first sub-data sequence
Figure 911861DEST_PATH_IMAGE040
In a stage (a)
Figure 385568DEST_PATH_IMAGE041
A data value;
Figure 89082DEST_PATH_IMAGE044
is the first
Figure 775278DEST_PATH_IMAGE040
The duration of the phases, wherein,
Figure 513427DEST_PATH_IMAGE045
indicating an initial phase, corresponding to a duration
Figure 178542DEST_PATH_IMAGE044
Is composed of
Figure 369352DEST_PATH_IMAGE046
，
Figure 859240DEST_PATH_IMAGE047
Indicating intermediate stages, their corresponding durations
Figure 451895DEST_PATH_IMAGE044
Is composed of
Figure 1825DEST_PATH_IMAGE048
，
Figure 679931DEST_PATH_IMAGE049
Indicating an end stage, its corresponding duration
Figure 973509DEST_PATH_IMAGE044
Is composed of
Figure 686250DEST_PATH_IMAGE050
；
Figure 141502DEST_PATH_IMAGE051
Is shown as
Figure 306904DEST_PATH_IMAGE040
Allowable error weight values for individual phases;
Figure 404173DEST_PATH_IMAGE052
representing a rounding function.
9. The method for efficiently transmitting data of the internet of things according to claim 8, wherein a calculation formula of the number of bits of the data in each stage in each third sub-data sequence is as follows:
Figure 971421DEST_PATH_IMAGE053
in the formula (I), the compound is shown in the specification,
Figure 597574DEST_PATH_IMAGE054
denotes the first
Figure 250273DEST_PATH_IMAGE055
A third sub data sequenceIn the column the first
Figure 151232DEST_PATH_IMAGE056
The number of bits of data within a phase;
Figure 572987DEST_PATH_IMAGE057
is shown as
Figure 635620DEST_PATH_IMAGE055
A third sub-data sequence
Figure 775615DEST_PATH_IMAGE040
The repetition degree of data in each stage;
Figure 214686DEST_PATH_IMAGE058
is a hyper-parameter;
Figure 490947DEST_PATH_IMAGE052
representing a rounding function.
10. The method for efficiently transmitting data of the internet of things according to claim 1, wherein when the data in each third sub-data sequence is subjected to DACs coding, the data in each third sub-data sequence is converted into binary data, and then the DACs coding is performed.
CN202211715398.8A 2022-12-30 2022-12-30 Efficient transmission method of Internet of things data Active CN115695564B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202211715398.8A CN115695564B (en) 2022-12-30 2022-12-30 Efficient transmission method of Internet of things data

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202211715398.8A CN115695564B (en) 2022-12-30 2022-12-30 Efficient transmission method of Internet of things data

Publications (2)

Publication Number Publication Date
CN115695564A true CN115695564A (en) 2023-02-03
CN115695564B CN115695564B (en) 2023-03-10

Family

ID=85056981

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202211715398.8A Active CN115695564B (en) 2022-12-30 2022-12-30 Efficient transmission method of Internet of things data

Country Status (1)

Country Link
CN (1) CN115695564B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116155298A (en) * 2023-04-20 2023-05-23 山东水运发展集团有限公司济宁分公司 Ship lock remote management system based on internet data
CN117714380A (en) * 2024-01-24 2024-03-15 陕西小保当矿业有限公司 Internet of things data access method and system

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1404033A (en) * 2001-08-23 2003-03-19 无敌科技股份有限公司 Voice compressing method for quasi-periodical waveform
CN106341842A (en) * 2016-08-23 2017-01-18 北京邮电大学 Method and device for transmitting data of wireless sensor network
US20180089369A1 (en) * 2016-05-19 2018-03-29 Seven Bridges Genomics Inc. Systems and methods for sequence encoding, storage, and compression
CN114372093A (en) * 2021-12-15 2022-04-19 南昌大学 Processing method of DGA (differential global alignment) online monitoring data of transformer
CN114826277A (en) * 2022-03-31 2022-07-29 天津大学 PMU data compression and reconstruction method for power distribution network edge computing device
CN114844507A (en) * 2022-03-23 2022-08-02 福州大学 Analog information converter for realizing quantitative prediction based on observation matrix
CN115184016A (en) * 2022-09-06 2022-10-14 江苏东控自动化科技有限公司 Elevator bearing fault detection method

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1404033A (en) * 2001-08-23 2003-03-19 无敌科技股份有限公司 Voice compressing method for quasi-periodical waveform
US20180089369A1 (en) * 2016-05-19 2018-03-29 Seven Bridges Genomics Inc. Systems and methods for sequence encoding, storage, and compression
CN106341842A (en) * 2016-08-23 2017-01-18 北京邮电大学 Method and device for transmitting data of wireless sensor network
CN114372093A (en) * 2021-12-15 2022-04-19 南昌大学 Processing method of DGA (differential global alignment) online monitoring data of transformer
CN114844507A (en) * 2022-03-23 2022-08-02 福州大学 Analog information converter for realizing quantitative prediction based on observation matrix
CN114826277A (en) * 2022-03-31 2022-07-29 天津大学 PMU data compression and reconstruction method for power distribution network edge computing device
CN115184016A (en) * 2022-09-06 2022-10-14 江苏东控自动化科技有限公司 Elevator bearing fault detection method

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116155298A (en) * 2023-04-20 2023-05-23 山东水运发展集团有限公司济宁分公司 Ship lock remote management system based on internet data
CN116155298B (en) * 2023-04-20 2023-07-04 山东水运发展集团有限公司济宁分公司 Ship lock remote management system based on internet data
CN117714380A (en) * 2024-01-24 2024-03-15 陕西小保当矿业有限公司 Internet of things data access method and system

Also Published As

Publication number Publication date
CN115695564B (en) 2023-03-10

Similar Documents

Publication Publication Date Title
CN115695564B (en) Efficient transmission method of Internet of things data
CN116192971B (en) Intelligent cloud energy operation and maintenance service platform data management method
US11551785B2 (en) Gene sequencing data compression preprocessing, compression and decompression method, system, and computer-readable medium
CN115204754B (en) Heating power supply and demand information management platform based on big data
CN116016606B (en) Sewage treatment operation and maintenance data efficient management system based on intelligent cloud
US20200294629A1 (en) Gene sequencing data compression method and decompression method, system and computer-readable medium
CN115269526B (en) Method and system for processing semiconductor production data
CN113328755A (en) Compressed data transmission method facing edge calculation
CN116170027B (en) Data management system and processing method for poison detection equipment
CN116361256B (en) Data synchronization method and system based on log analysis
CN117692011B (en) Monitoring data early warning method for firefighting rescue environment monitoring system
CN115883670A (en) Medical data analysis and acquisition method and device
CN1426629A (en) Method and apparatus for optimized lossless compression using plurality of coders
CN113676187B (en) Huffman correction coding method, system and related components
CN117294313B (en) Educational robot data intelligent acquisition and storage method and system
CN117097441B (en) Carrier communication system transmission efficiency optimization method based on data analysis
CN117216022B (en) Digital engineering consultation data management system
CN110704408A (en) Clustering-based time sequence data compression method and system
JP2015534795A (en) Secure and lossless data compression
CN115964347B (en) Intelligent storage method for data of market supervision and monitoring center
CN112712855A (en) Joint training-based clustering method for gene microarray containing deletion value
CN115801021A (en) ANS (answer System) grouping coding and decoding method, computer equipment and storage medium
CN114665885A (en) Self-adaptive data compression method for time sequence database
US11818191B1 (en) Stateless lossless compression
CN117896442B (en) Comprehensive energy data management system based on Internet of things

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant