CN115695564A - Efficient transmission method for data of Internet of things - Google Patents
Efficient transmission method for data of Internet of things Download PDFInfo
- 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
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
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:
using two of the same sizeThe windows are respectively arranged at two ends of the data sequence to obtain corresponding data in the two windows, and the corresponding data are adjustedIs iterated, wherein,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:
in the formula (I), the compound is shown in the specification,indicating that the start of the data sequence corresponds to a size ofThe window of (2);
andis to calculate the constant of the time at which,,;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:
in the formula (I), the compound is shown in the specification,an allowable error weight value representing an initial stage;
is shown in the historical data sequenceWithin the initial stage of the second sub-data sequenceA data valueThe probability of occurrence;representing the duration of the initial stage in the second sub-data sequence;the number of the second sub data sequence;is a hyperbolic tangent function for limiting the value of the wholeWithin 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:
in the formula (I), the compound is shown in the specification,is shown asA third sub-data sequenceThe repetition degree of data in each stage;is the firstA third sub-data sequenceIn a stage (a)A data value;is shown asA third sub-data sequenceIn a stage (a)A data value;is the firstThe duration of the phases, wherein,indicating an initial phase, corresponding to a durationIs composed of,Indicating intermediate stages, their corresponding durationsIs composed of,Indicating an end phase, its corresponding durationIs composed of;Is shown asPermission of each stageAn error weight value;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:
in the formula (I), the compound is shown in the specification,is shown asThe first sub-data sequenceThe number of bits of data within a phase;is shown asA third sub-data sequenceThe repetition degree of data in each stage;is a hyper-parameter;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 arrangedThe window of (2); using two identical sizesThe windows are respectively arranged at two ends of the data sequence to obtain corresponding data in the two windows, and the corresponding data are adjustedIs iterated, wherein,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 valueIf a certain size in the course of the iterationIs greater than the threshold, then the selected window is selectedThe value of (d) can be taken as the period of the data sequence. Wherein the threshold valueThe empirical reference values are given in this embodiment, depending on the implementation of the implementation,. 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 temperatureThen, two of the first same size are setThe 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 useThen, two of the first same size are setThe 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 useThen, two of the first same size are setThe 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:
in the formula (I), the compound is shown in the specification,indicating that the start of the data sequence corresponds to a size ofThe window of (1);
andis to calculate the constant of the time that,,;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 satisfiedCan not be too small and the similarity satisfies a certain condition, so the window size is adjustedPerforming iteration, in this embodiment, setting the stop condition of the iteration asWhereinIndicating the length of the acquired data sequence, each calculatedData structure similarity corresponding to valuesA value of (d); establishing a coordinate system and obtaining the similarity of data structuresSelecting the curve corresponding to the peak point of the curveThe value is the period of data change, and the subsequent steps divide the whole time sequence into the sizeThe 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, byA sequence of the historical data is represented,represents the current data sequence, wherein,. Alignment cost matrixIs by calculationData point of (1)Of the distance between data points, matrixTo (1)The element isAndis a distance ofWhereinRepresents 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(the result is the best alignment path), and the corresponding minimum alignment cost value;
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 nodeThe calculation expression of (a) is:
in the formula (I), the compound is shown in the specification,a position correction value representing a first time node;
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 implementationWherein, in the step (A),indicating a period of data change in the data sequence;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 directionA new first time node is obtained at each position;
for example: when calculating3, 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 equipmentCounting the data of each period, wherein: duration of data fluctuation in initial stage (i.e. time length of initial stage)(ii) a Duration of data fluctuation of end phase (i.e. time length of end phase). Calculating the average value of the initial stage duration in all periods (i.e. all the second sub-data sequences)And average of end stage durationsAs the duration of the initial and end phases. For the cycle size isWithin the period time period of (a), the range values of the phases are respectively: initial stageIntermediate stage ofEnd stage。
Will be provided withRepresenting the time length of the division of the initial stage of the third sub data sequence; will be provided withRepresenting the time length of the division of the end stage of the third sub data sequence; will be provided withThe 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:
in the formula (I), the compound is shown in the specification,an allowable error weight value representing an initial stage;
is shown in the historical data sequenceWithin the initial stage of the second sub-data sequenceA data valueThe probability of occurrence;representing the duration of the initial stage in the second sub-data sequence;the number of the second subdata sequences;is a hyperbolic tangent function for limiting the value of the wholeWithin the range;
calculating the allowable error weight value of the intermediate stage by analogy in turnAnd an allowable error weight value of the end stage, noted。
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:
in the formula (I), the compound is shown in the specification,denotes the firstThe first sub-data sequenceThe repetition degree of data in each stage;is the firstThe third sub-data sequenceIn a first stageA data value;denotes the firstA third sub-data sequenceIn a stage (a)A data value;is the firstThe duration of the phases, wherein,indicating an initial phase, corresponding to a durationIs composed of,Indicating intermediate stages, their corresponding durationsIs composed of,Indicating an end phase, its corresponding durationIs composed of;Is shown asThe allowable error weight value of each stage specifically comprises,And;representing a rounding function; wherein for whenIn time, the data repetition degree cannot be calculated, and therefore, the data repetition degree is set to 0 and taken。
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:
in the formula (I), the compound is shown in the specification,denotes the firstThe first sub-data sequenceThe number of bits of data within a phase;is shown asThe first sub-data sequenceThe repetition degree of data in each stage;is shown asThe 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 data1/4 of the binary number of the mean of the phases;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 firstA third sub-data sequenceIntra-phase data sequenceThe number of bits isThe bit block size of the coding block is. Will be provided withCoded into a plurality of sizes ofEach bit block has an identifier indicating whether the block is the most significant bitThe last block of (a);
2) Each size isBit block ofBy usingAn identifier representing a block of bits, usingRepresenting the remainder of a block of bitsOne bit then has. Starting with the last bit, slicing each timeIf 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 phaseNumber of bitsThe value of (b) is 2. The results of DACs encoding are shown in table 1 below;
As can be seen from table 1, taking data 24 as an example: the binary encoding of the data 24 is 11000,is 2, then each bit block size isEach 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)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 dataFor example, for a data sequenceThe 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;
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 1If 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 numbersThe 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:
using two identical sizesThe windows are respectively arranged at two ends of the data sequence to obtain corresponding data in the two windows, and the corresponding data are adjustedIs iterated, wherein,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:
in the formula (I), the compound is shown in the specification,indicating that the start of the data sequence corresponds to a size ofThe window of (1);
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:
in the formula (I), the compound is shown in the specification,an allowable error weight value representing an initial stage;
is shown in the historical data sequenceWithin the initial stage of the second sub-data sequenceA data valueThe probability of occurrence;is shown asThe duration of the initial stage in the two subdata sequences;the number of the second sub data sequence;is a hyperbolic tangent function for limiting the value of the wholeWithin 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:
in the formula (I), the compound is shown in the specification,is shown asA third sub-data sequenceThe repetition degree of data in each stage;is the firstThe third sub-data sequenceIn a stage (a)A data value;is shown asThe first sub-data sequenceIn a stage (a)A data value;is the firstThe duration of the phases, wherein,indicating an initial phase, corresponding to a durationIs composed of,Indicating intermediate stages, their corresponding durationsIs composed of,Indicating an end stage, its corresponding durationIs composed of;Is shown asAllowable error weight values for individual phases;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:
in the formula (I), the compound is shown in the specification,denotes the firstA third sub data sequenceIn the column the firstThe number of bits of data within a phase;is shown asA third sub-data sequenceThe repetition degree of data in each stage;is a hyper-parameter;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.
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)
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)
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 |
-
2022
- 2022-12-30 CN CN202211715398.8A patent/CN115695564B/en active Active
Patent Citations (7)
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)
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 |