CN117094450A - Block chain-based chemical production line data optimization processing method - Google Patents
Block chain-based chemical production line data optimization processing method Download PDFInfo
- Publication number
- CN117094450A CN117094450A CN202311353792.6A CN202311353792A CN117094450A CN 117094450 A CN117094450 A CN 117094450A CN 202311353792 A CN202311353792 A CN 202311353792A CN 117094450 A CN117094450 A CN 117094450A
- Authority
- CN
- China
- Prior art keywords
- wavelet
- sequence
- temperature history
- history data
- coefficient
- 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
- 238000012824 chemical production Methods 0.000 title claims abstract description 34
- 238000005457 optimization Methods 0.000 title claims abstract description 19
- 238000003672 processing method Methods 0.000 title claims abstract description 19
- 238000004519 manufacturing process Methods 0.000 claims abstract description 27
- 238000000354 decomposition reaction Methods 0.000 claims abstract description 26
- 230000006835 compression Effects 0.000 claims abstract description 24
- 238000007906 compression Methods 0.000 claims abstract description 23
- 239000000126 substance Substances 0.000 claims abstract description 22
- 238000000034 method Methods 0.000 claims abstract description 21
- 238000002360 preparation method Methods 0.000 claims abstract description 17
- 230000009466 transformation Effects 0.000 claims abstract description 15
- 238000009826 distribution Methods 0.000 claims description 26
- 239000013598 vector Substances 0.000 claims description 20
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000003860 storage Methods 0.000 abstract description 8
- 238000013144 data compression Methods 0.000 description 6
- 238000012544 monitoring process Methods 0.000 description 6
- 230000007613 environmental effect Effects 0.000 description 3
- 238000005070 sampling Methods 0.000 description 3
- UFWIBTONFRDIAS-UHFFFAOYSA-N Naphthalene Chemical compound C1=CC=CC2=CC=CC=C21 UFWIBTONFRDIAS-UHFFFAOYSA-N 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 239000012847 fine chemical Substances 0.000 description 1
- 239000000446 fuel Substances 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
- G06F17/148—Wavelet transforms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/04—Manufacturing
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Physics (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Mathematical Analysis (AREA)
- Computational Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Data Mining & Analysis (AREA)
- General Business, Economics & Management (AREA)
- Marketing (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Entrepreneurship & Innovation (AREA)
- Quality & Reliability (AREA)
- Primary Health Care (AREA)
- Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Manufacturing & Machinery (AREA)
- Development Economics (AREA)
- General Health & Medical Sciences (AREA)
- Algebra (AREA)
- Operations Research (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to the technical field of wavelet transformation, in particular to a block chain-based chemical production line data optimization processing method. The method obtains a temperature history data sequence of a production and preparation link under the quality index of the same chemical product; performing wavelet decomposition on the temperature history data sequence to obtain a wavelet coefficient sequence; obtaining the fluctuation degree of the wavelet coefficient according to the fluctuation condition of the wavelet coefficient sequence corresponding to each temperature history data sequence; determining the confidence coefficient of the corresponding wavelet coefficient according to the discrete condition of the fluctuation degree of the wavelet coefficient; based on the confidence coefficient, carrying out wavelet transformation sparsification treatment on wavelet coefficients in the wavelet coefficient sequence to obtain a wavelet reconstruction sequence; and compressing wavelet coefficients in the wavelet reconstruction sequence after differential encoding to obtain compressed data corresponding to the temperature history data sequence. The invention improves the compression efficiency and the optimized compression storage processing efficiency of the temperature data.
Description
Technical Field
The invention relates to the technical field of wavelet transformation, in particular to a block chain-based chemical production line data optimization processing method.
Background
In a chemical production line data storage scene related to fine chemical product naphthalene, the block chain technology can effectively ensure the authenticity, the integrity and the safety of stored chemical production line data, has the important significance and advantages of improving the data transparency and the traceability, protecting privacy, promoting data sharing and cooperation and the like, and is beneficial to improving the efficiency and the quality of the chemical production line. However, the storage characteristic of the blockchain technology limits the storage efficiency of the general data, so that the data compression is required according to the data characteristics of the chemical production line, and the overall storage rate is improved.
Because the production data acquired on the chemical production line has the characteristic of periodical change due to the standardization and periodicity of the production links, the production data also has the characteristic of periodical change under the condition that the production links and production indexes are not changed, the differential encoding is adopted to compress the data of the chemical production line, but the complex environmental factors can influence the local fluctuation characteristics of the data, so that the compression efficiency of the differential encoding on highly repeated data is influenced.
Disclosure of Invention
In order to solve the technical problem that differential encoding is influenced on the compression efficiency of highly repeated data under complex environmental factors, the invention aims to provide a block chain-based chemical production line data optimization processing method, and the adopted technical scheme is as follows:
acquiring a temperature history data sequence of a production and preparation link under the quality index of the same chemical product;
performing wavelet decomposition on the temperature history data sequence to obtain a wavelet coefficient sequence;
obtaining the fluctuation degree of the wavelet coefficient according to the fluctuation condition of the wavelet coefficient sequence corresponding to each temperature history data sequence;
determining the confidence coefficient of the corresponding wavelet coefficient according to the discrete condition of the fluctuation degree of the wavelet coefficient;
based on the confidence coefficient, carrying out wavelet transformation sparsification treatment on wavelet coefficients in the wavelet coefficient sequence to obtain a wavelet reconstruction sequence; and compressing wavelet coefficients in the wavelet reconstruction sequence after differential encoding to obtain compressed data corresponding to the temperature history data sequence.
Preferably, the calculation formula of the fluctuation degree of the wavelet coefficient is as follows:
wherein,the fluctuation degree of the wavelet coefficient of the jth wavelet coefficient in the ith temperature history data sequence; n is the number of temperature history data sequences under the quality index of the same chemical product;similarity of energy distribution structure vectors for the i-th temperature history data sequence and the i+1th temperature history data sequence;the absolute value of the difference value of the jth wavelet coefficient in the wavelet coefficient sequence corresponding to the ith temperature history data sequence and the jth wavelet coefficient in the wavelet coefficient sequence corresponding to the (i+1) th temperature history data sequence is obtained.
Preferably, the method for obtaining the energy distribution structure vector comprises the following steps:
and taking a vector formed by the modes of wavelet coefficients in the wavelet coefficient sequence corresponding to the temperature history data sequence as an energy distribution structure vector.
Preferably, the similarity of the energy distribution structure vectors of the two temperature history data sequences is: cosine similarity of energy distribution structure vectors of two temperature history data sequences.
Preferably, the calculation formula of the confidence coefficient of the wavelet coefficient is as follows:
wherein,confidence level of wavelet coefficient under current frequency band;a normalized value of a modulus of a wavelet coefficient of the temperature history data sequence under the current frequency band under the overall frequency band;the fluctuation degree of the wavelet coefficient corresponding to the temperature history data sequence under the current frequency band is obtained;the average value of the fluctuation degree of the wavelet coefficient corresponding to all the temperature history data sequences in the current frequency band.
Preferably, the performing wavelet transform sparsification processing on the wavelet coefficients in the wavelet coefficient sequence based on the confidence coefficient to obtain a wavelet reconstruction sequence includes:
and reserving the wavelet coefficients which are larger than or equal to a preset dividing threshold value in the wavelet coefficient sequence, deleting the wavelet coefficients which are smaller than the preset dividing threshold value in the wavelet coefficient sequence, and constructing a wavelet reconstruction sequence by the reserved wavelet coefficients.
Preferably, the compressing the wavelet coefficient in the wavelet reconstruction sequence after differential encoding to obtain the compressed data corresponding to the temperature history data sequence includes:
and carrying out differential encoding on the wavelet coefficients in the wavelet reconstruction sequence, and storing the wavelet coefficients subjected to differential encoding as compressed data corresponding to the temperature history data sequence.
Preferably, the performing wavelet decomposition on the temperature history data sequence to obtain a wavelet coefficient sequence includes:
and carrying out wavelet decomposition on the temperature history data sequence to obtain a wavelet coefficient sequence with low-to-high frequency arrangement.
Preferably, the wavelet coefficient sequence is composed of wavelet coefficients obtained by wavelet decomposition.
Preferably, after the wavelet coefficients in the wavelet reconstruction sequence after the compression differential encoding obtain the compressed data corresponding to the temperature history data sequence, the method further includes:
the compressed data is converted back into a temperature history data sequence by inverse wavelet transform.
The embodiment of the invention has at least the following beneficial effects:
the invention relates to the technical field of wavelet transformation. According to the method, firstly, the temperature history data sequence of the production and preparation links is obtained under the quality index of the same chemical product, and the temperature history data in the production process of the same chemical product is analyzed, so that the abnormal condition of the temperature history data can be more accurately compared. And carrying out wavelet decomposition on the temperature history data sequence to obtain wavelet coefficients of different frequency band components, and then analyzing the wavelet coefficients to obtain the fluctuation degree of the wavelet coefficients. And determining the confidence level of the corresponding wavelet coefficient by combining the discrete condition of the fluctuation degree of the wavelet coefficient. And carrying out wavelet transformation sparsification processing on the wavelet coefficients based on the confidence coefficient to obtain a wavelet reconstruction sequence. And finally, the differential coding is combined to realize the compression of the chemical production line data to different degrees, so that monitoring data within acceptable loss degree of temperature data of temperature history actual data can be reserved as far as possible, the compressed data are stored on a block chain, after the temperature history data are decompressed, the information loss is acceptable, and the production quality of chemical products is not affected according to the temperature data. Compared with the conventional algorithm, the method has the advantages that wavelet decomposition rarefaction and differential coding secondary compression are performed, and the compression efficiency and the optimized compression storage processing of temperature data are higher.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for optimizing data of a blockchain-based chemical production line according to an embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following detailed description is given below of the specific implementation, structure, characteristics and effects of the block chain-based chemical production line data optimization processing method according to the invention by combining the attached drawings and the preferred embodiment. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The embodiment of the invention provides a concrete implementation method of a block chain-based chemical production line data optimization processing method, which is suitable for a chemical production line data compression scene. The method aims to solve the technical problem that the compression efficiency of differential coding on highly repeated data is affected under the complex environment factors. Compared with the conventional algorithm, the method for optimizing compression storage processing of the temperature data has higher compression efficiency and better compression storage processing of the temperature data by wavelet decomposition sparsification and differential coding secondary compression.
The following specifically describes a specific scheme of the blockchain-based chemical production line data optimization processing method provided by the invention with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of steps of a blockchain-based chemical production line data optimization processing method according to an embodiment of the present invention is shown, where the method includes the following steps:
step S100, acquiring a temperature history data sequence of a production and preparation link under the quality index of the same chemical product.
According to the invention, temperature change data sampling and monitoring are carried out in the process of preparing products on a chemical production line through temperature monitoring equipment. The temperature change data of each production and preparation link adopts the same data analysis and compression mode, and the sampling frequency is 1 time/second, so that the sampling temperature data of each production and preparation link is obtained. And then the quality index Q of the chemical product is obtained through the conventional step of product quality detection. The scheme is to analyze the temperature data of the production and preparation links under the quality index Q of the same chemical product.
And acquiring temperature data in the chemical fuel production and preparation process through temperature monitoring equipment. It should be noted that, different quality indexes of chemical products correspond to different types of temperature history data sequences, and under the same quality index of chemical products, there are a plurality of temperature history data sequences of production and preparation links at different moments.
And step S200, carrying out wavelet decomposition on the temperature history data sequence to obtain a wavelet coefficient sequence.
Because of the production and preparation links under the same chemical product quality, the relative fixing of each process parameter setting on the chemical production line can be realized, the temperature history monitoring data of the production and preparation links basically show the same change trend and have high similarity, but the actual production process still has small-range fluctuation, and the compression efficiency of differential coding is affected.
Therefore, the invention realizes the temperature data adjustment within the acceptable error range by analyzing the temperature change characteristics of the temperature history data of the production and preparation links under the same quality of the chemical products. It should be noted that, the acceptable error range refers to that the slight change of the temperature data does not affect the quality of the output of the product at the temperature, and the temperature change characteristic of the product particularly represents the distribution, namely the change characteristic, of the wavelet coefficients of each frequency band of the wavelet decomposition temperature history data, so as to realize the wavelet transformation sparsification processing, and facilitate the subsequent realization of the compression of the chemical production line data in different degrees by combining with differential coding.
Because of the complex environmental influence factors existing in the process of preparing chemical products by the chemical production line, the obtained temperature history data can fluctuate in a small range, and the compression efficiency of data compression can be greatly influenced if the temperature data is not regulated. Therefore, the invention carries out wavelet decomposition on the obtained temperature history data sequence to obtain wavelet coefficients on each frequency band.
And carrying out wavelet decomposition on the temperature history data sequence to obtain a wavelet coefficient sequence. Wherein the wavelet coefficient sequence is composed of wavelet coefficients obtained by wavelet decomposition. Specific: performing wavelet decomposition on a temperature history data sequence obtained in a production and preparation link under the quality of the same chemical product to obtain a wavelet coefficient sequence with low-to-high frequency arrangement; namely, the wavelet coefficients obtained by wavelet decomposition are sequenced according to the sequence from low frequency to high frequency, and a wavelet coefficient sequence is obtained. It should be noted that wavelet decomposition divides a sequence into wavelet coefficients of different scales and frequencies.
Step S300, according to the fluctuation condition of the wavelet coefficient sequence corresponding to each temperature history data sequence, obtaining the fluctuation degree of the wavelet coefficient.
The wavelet coefficients of each frequency band reflect the decomposition characteristics of the change trend on the temperature history data sequence, the wavelet decomposition energy distribution change characteristics of a plurality of temperature history data sequences in the temperature history data are analyzed, the energy distribution change degree of the whole temperature history data sequence is obtained, the total energy change condition of the temperature history data sequence is represented, and the change condition of the wavelet coefficients of each frequency band can reflect the fluctuation degree of the wavelet coefficients.
For the temperature history data sequence, in the production and preparation links under the same chemical product quality, the sequence variation trend tends to be consistent, the fluctuation error caused by the complex environment is mainly small-amplitude fluctuation of a local neighborhood, the wavelet coefficient obtained by wavelet decomposition is reflected to have larger energy value variation amplitude of a high-frequency coefficient, the variation amplitude of each frequency band can be regarded as a possible fluctuation error part, so that the similarity of the overall energy distribution of the temperature history data sequence is a precondition for solving the fluctuation error, and the fluctuation degree of the wavelet coefficient under the similarity of the overall energy distribution is a fluctuation error part required to be solved, thereby obtaining the fluctuation degree of the wavelet coefficient of each frequency band.
In the production and preparation links of the same chemical product under the quality, the obtained temperature history data sequence is subjected to wavelet decomposition to obtain wavelet coefficient sequences with frequencies arranged in sequence from low to highIts first oneThe energy distribution structure of the strip temperature history data sequence can be a vector formed by the modulus of wavelet coefficients thereofRepresentation, the firstThe energy distribution structure of the strip temperature history data sequence can be a vector formed by the modulus of wavelet coefficients thereofThe higher the degree of similarity of the energy distribution structure, the more reliable the degree of fluctuation of the wavelet coefficient of each frequency band is, and the fluctuation of the wavelet coefficient can be represented by the absolute value of the difference of the modes of the wavelet coefficient on each frequency band.
The calculation formula of the fluctuation degree of the wavelet coefficient is as follows:
wherein,the fluctuation degree of the wavelet coefficient of the jth wavelet coefficient in the ith temperature history data sequence; n is the number of temperature history data sequences under the quality index of the same chemical product;similarity of energy distribution structure vectors for the i-th temperature history data sequence and the i+1th temperature history data sequence;the absolute value of the difference value of the jth wavelet coefficient in the wavelet coefficient sequence corresponding to the ith temperature history data sequence and the jth wavelet coefficient in the wavelet coefficient sequence corresponding to the (i+1) th temperature history data sequence is obtained.
Wherein the degree of fluctuation of the wavelet coefficientReflecting the degree of similarity of the energy distribution structure and the wavelet coefficient differences over the frequency band associated with the temperature history data sequence. Degree of fluctuation of obtained wavelet coefficientCorresponding to each frequency band wavelet coefficient of the temperature history data sequence, wherein each frequency band wavelet coefficient of each temperature history data sequence corresponds to one wavelet coefficient fluctuation degreeThe larger the value of (c) is, the larger the fluctuation degree of the obtained wavelet coefficient is based on the wavelet coefficient of the current temperature history data sequence on the current frequency band.
The method for acquiring the energy distribution structure vector comprises the following steps: and taking a vector formed by the modes of wavelet coefficients in the wavelet coefficient sequence corresponding to the temperature history data sequence as an energy distribution structure vector.
The similarity of the energy distribution structure vectors of the two temperature history data sequences is as follows: cosine similarity of energy distribution structure vectors of two temperature history data sequences.
Step S400, according to the discrete condition of the fluctuation degree of the wavelet coefficient, the confidence degree of the corresponding wavelet coefficient is determined.
Since the degree of fluctuation of the wavelet coefficients on each frequency band of the temperature history data sequence characterizes the average degree of fluctuation of the wavelet coefficients corresponding to the frequency bands under the structural similarity of the energy distribution of the whole temperature history data sequence, the average degree of fluctuation of the wavelet coefficients of each frequency band is closely related to the respective frequency bands, but the closer the degree of fluctuation of the wavelet coefficients of each frequency band is to the average degree of fluctuation, the better the degree of fluctuation of the wavelet coefficients of each frequency band is, but the closer the average degree of fluctuation of the wavelet coefficients of each frequency band is to the overall optimal real temperature history data sequence is. And the module size of the wavelet coefficient represents the importance degree of the change trend of the temperature history data sequence corresponding to the wavelet coefficient.
Therefore, according to the obtained fluctuation degree of the wavelet coefficient and the fluctuation condition of the fluctuation degree of the wavelet coefficient, the smaller the fluctuation degree of the wavelet coefficient in the same frequency band is, the closer the wavelet coefficient in the frequency band of the corresponding temperature history data sequence is to the wavelet coefficient in the current frequency band of the overall optimal real temperature history data sequence, the temperature history data sequence is the overall optimal real temperature history data sequence, and the confidence degree of the wavelet coefficient of each frequency band is obtained by combining the frequency band information importance represented by the module size of the wavelet coefficient.
Because the obtained wavelet coefficient fluctuation degree is related to the obtained temperature history data sequence, different temperature history data correspond to different wavelet coefficient fluctuation degrees of each frequency band, and the more important the frequency band information is under the same frequency band, the more concentrated the wavelet coefficient fluctuation change degree is, and the higher the wavelet coefficient fluctuation confidence degree is under the current frequency band of the temperature history data sequence. The concentration degree of the fluctuation degree of the wavelet coefficient can be expressed by the relative discrete degree of the wavelet coefficient fluctuation degree sequence which is arranged from small to large through the fluctuation degree of different wavelet coefficients under the same frequency band. Thus, the wavelet coefficients in the current band fluctuate confidence level.
And determining the confidence coefficient of the corresponding wavelet coefficient according to the discrete condition of the fluctuation degree of the wavelet coefficient. The confidence coefficient of the wavelet coefficient is calculated by the following formula:
wherein,confidence level of wavelet coefficient under current frequency band;a normalized value of a modulus of a wavelet coefficient of the temperature history data sequence under the current frequency band under the overall frequency band;the fluctuation degree of the wavelet coefficient corresponding to the temperature history data sequence under the current frequency band is obtained;the average value of the fluctuation degree of the wavelet coefficient corresponding to all the temperature history data sequences in the current frequency band.
Wherein,reflecting the relative discrete degree of the wavelet coefficient fluctuation degree sequence in the wavelet coefficient fluctuation degree sequence; the confidence level of the wavelet coefficient reflects the confidence level of the fluctuation of the wavelet coefficient; confidence of wavelet coefficients obtainedIs a degree of fluctuation of a wavelet coefficient corresponding to each frequency band wavelet coefficient of each temperature history data sequenceCorrespondingly, the greater the importance of the frequency band information indicating the fluctuation degree of the wavelet coefficient of the current temperature history data sequence on the current frequency band, the more concentrated the fluctuation degree of the wavelet coefficient in sequence, and the greater the value thereof.
Confidence levels of wavelet coefficients of respective frequency bands are obtained by processing according to the obtained degree of variation in wavelet decomposition energy distribution.
Step S500, carrying out wavelet transformation sparsification processing on wavelet coefficients in the wavelet coefficient sequence based on the confidence coefficient to obtain a wavelet reconstruction sequence; and compressing wavelet coefficients in the wavelet reconstruction sequence after differential encoding to obtain compressed data corresponding to the temperature history data sequence.
According to the confidence coefficient of the wavelet coefficient of each frequency band, the obtained confidence coefficient indicates that the change trend of the temperature history data sequence corresponding to the wavelet coefficient of the frequency band belongs to the credibility of the fluctuation error part, and the confidence coefficient is the basis of whether adjustment is needed in the wavelet reconstruction process, so that the wavelet reconstruction cutoff parameters are constructed, the wavelet reconstruction cutoff parameters correspond to each wavelet coefficient of the wavelet coefficient sequence corresponding to each temperature history data sequence, and the wavelet coefficient of each frequency band is independently adjusted. Therefore, the thinning processing of wavelet transformation is realized, so that fluctuation errors are removed, the processed temperature detection data has higher compression efficiency in the subsequent differential encoding process, and the aim of optimizing and compressing the blockchain data is fulfilled.
And presetting a division threshold value through specific production implementation environments according to the confidence coefficient of the wavelet coefficient under each frequency band. In the embodiment of the present invention, the preset dividing threshold value is 0.35, and in other embodiments, the practitioner adjusts the value according to the actual situation. And (3) carrying out confidence degree of the wavelet coefficient of each frequency band, and screening the wavelet coefficient, namely carrying out wavelet transformation sparsification processing on the wavelet coefficient in the wavelet coefficient sequence based on the confidence degree, so as to obtain a wavelet reconstruction sequence. Specific: and reserving the wavelet coefficients which are larger than or equal to a preset dividing threshold value in the wavelet coefficient sequence, deleting the wavelet coefficients which are smaller than the preset dividing threshold value in the wavelet coefficient sequence, and constructing a wavelet reconstruction sequence by the reserved wavelet coefficients.
By deleting part of wavelet coefficients in the wavelet coefficient sequence, the wavelet transformation sparsification processing is realized, so that the reserved wavelet coefficients form a new wavelet reconstruction sequence. The purpose of the wavelet transformation sparsification processing is to remove the fluctuation error of the temperature history data sequence, so that the temperature history data sequence is more similar to the overall optimal temperature history data sequence with high wavelet coefficient fluctuation confidence level, the initial lossy compression is carried out, and the compression efficiency of the subsequent differential coding is improved.
Compressing wavelet coefficients in a wavelet reconstruction sequence after differential encoding to obtain compressed data corresponding to a temperature history data sequence, wherein the method comprises the following steps: and carrying out differential encoding on the wavelet coefficients in the wavelet reconstruction sequence, and storing the wavelet coefficients subjected to differential encoding as compressed data corresponding to the temperature history data sequence.
After obtaining the compressed data, the compressed data may also be decompressed, specifically: the compressed data is converted back into a temperature history data sequence by inverse wavelet transform.
Wavelet differential coding compression is commonly used for compression of image, audio and time series data, which can effectively reduce the storage requirement of data and reduce the bandwidth required for data transmission while retaining key information. This method has wide application in the fields of data compression and signal processing.
In summary, according to the temperature history data and related data on the chemical production line obtained by monitoring, according to the temperature history data in the production process of the product in the production and preparation link under the quality index of the same chemical product, wavelet decomposition is utilized to obtain the fluctuation degree of wavelet coefficients of different frequency band components, so as to determine the acceptable degree of temperature data loss of wavelet transformation sparsification processing, and the wavelet transformation sparsification processing is performed on the wavelet coefficients based on the confidence coefficient of the wavelet coefficients, so as to obtain a wavelet reconstruction sequence. And finally, realizing the data compression processing of the chemical production line by combining differential coding, and storing the compressed data on a block chain.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
Claims (10)
1. A block chain-based chemical production line data optimization processing method is characterized by comprising the following steps:
acquiring a temperature history data sequence of a production and preparation link under the quality index of the same chemical product;
performing wavelet decomposition on the temperature history data sequence to obtain a wavelet coefficient sequence;
obtaining the fluctuation degree of the wavelet coefficient according to the fluctuation condition of the wavelet coefficient sequence corresponding to each temperature history data sequence;
determining the confidence coefficient of the corresponding wavelet coefficient according to the discrete condition of the fluctuation degree of the wavelet coefficient;
based on the confidence coefficient, carrying out wavelet transformation sparsification treatment on wavelet coefficients in the wavelet coefficient sequence to obtain a wavelet reconstruction sequence; and compressing wavelet coefficients in the wavelet reconstruction sequence after differential encoding to obtain compressed data corresponding to the temperature history data sequence.
2. The blockchain-based chemical production line data optimization processing method of claim 1, wherein the calculation formula of the fluctuation degree of the wavelet coefficient is as follows:
wherein,the fluctuation degree of the wavelet coefficient of the jth wavelet coefficient in the ith temperature history data sequence; n is the number of temperature history data sequences under the quality index of the same chemical product; />Similarity of energy distribution structure vectors for the i-th temperature history data sequence and the i+1th temperature history data sequence; />The absolute value of the difference value of the jth wavelet coefficient in the wavelet coefficient sequence corresponding to the ith temperature history data sequence and the jth wavelet coefficient in the wavelet coefficient sequence corresponding to the (i+1) th temperature history data sequence is obtained.
3. The blockchain-based chemical production line data optimization processing method of claim 2, wherein the energy distribution structure vector obtaining method is as follows:
and taking a vector formed by the modes of wavelet coefficients in the wavelet coefficient sequence corresponding to the temperature history data sequence as an energy distribution structure vector.
4. The blockchain-based chemical production line data optimization processing method of claim 2, wherein the similarity of the energy distribution structure vectors of the two temperature history data sequences is: cosine similarity of energy distribution structure vectors of two temperature history data sequences.
5. The blockchain-based chemical production line data optimization processing method of claim 1, wherein the confidence coefficient of the wavelet coefficient is calculated according to the following formula:
wherein,confidence level of wavelet coefficient under current frequency band; />A normalized value of a modulus of a wavelet coefficient of the temperature history data sequence under the current frequency band under the overall frequency band; />The fluctuation degree of the wavelet coefficient corresponding to the temperature history data sequence under the current frequency band is obtained; />The average value of the fluctuation degree of the wavelet coefficient corresponding to all the temperature history data sequences in the current frequency band.
6. The blockchain-based chemical production line data optimization processing method of claim 1, wherein the performing wavelet transform sparsification processing on wavelet coefficients in a wavelet coefficient sequence based on confidence coefficient to obtain a wavelet reconstruction sequence comprises:
and reserving the wavelet coefficients which are larger than or equal to a preset dividing threshold value in the wavelet coefficient sequence, deleting the wavelet coefficients which are smaller than the preset dividing threshold value in the wavelet coefficient sequence, and constructing a wavelet reconstruction sequence by the reserved wavelet coefficients.
7. The blockchain-based chemical production line data optimization processing method of claim 1, wherein the compressing wavelet coefficients in the wavelet reconstruction sequence after differential encoding to obtain compressed data corresponding to a temperature history data sequence comprises:
and carrying out differential encoding on the wavelet coefficients in the wavelet reconstruction sequence, and storing the wavelet coefficients subjected to differential encoding as compressed data corresponding to the temperature history data sequence.
8. The blockchain-based chemical production line data optimization processing method of claim 1, wherein the performing wavelet decomposition on the temperature history data sequence to obtain a wavelet coefficient sequence comprises:
and carrying out wavelet decomposition on the temperature history data sequence to obtain a wavelet coefficient sequence with low-to-high frequency arrangement.
9. The blockchain-based chemical production line data optimization processing method according to claim 1, wherein the wavelet coefficient sequence is composed of wavelet coefficients obtained by wavelet decomposition.
10. The blockchain-based chemical production line data optimization processing method according to claim 1, wherein after the wavelet coefficients in the wavelet reconstruction sequence after the compression differential encoding are obtained, the method further comprises:
the compressed data is converted back into a temperature history data sequence by inverse wavelet transform.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311353792.6A CN117094450B (en) | 2023-10-19 | 2023-10-19 | Block chain-based chemical production line data optimization processing method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311353792.6A CN117094450B (en) | 2023-10-19 | 2023-10-19 | Block chain-based chemical production line data optimization processing method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117094450A true CN117094450A (en) | 2023-11-21 |
| CN117094450B CN117094450B (en) | 2024-01-09 |
Family
ID=88775536
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311353792.6A Active CN117094450B (en) | 2023-10-19 | 2023-10-19 | Block chain-based chemical production line data optimization processing method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117094450B (en) |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110055222A1 (en) * | 2009-08-26 | 2011-03-03 | Lakshminarayn Choudur | Energy-based wavelet thresholding |
| US20120014575A1 (en) * | 2010-07-13 | 2012-01-19 | Empire Technology Development Llc | Hybrid 2-D ECG Data Compression Based on Wavelet Transforms |
| EP2618269A1 (en) * | 2012-01-17 | 2013-07-24 | Siemens Aktiengesellschaft | A method for processing of measurements from several sensors |
| CN107016656A (en) * | 2017-04-01 | 2017-08-04 | 中国科学院光电技术研究所 | The sparse base optimization method of small echo in image reconstruction based on compressed sensing |
| CN107291897A (en) * | 2017-06-21 | 2017-10-24 | 浙江工商大学 | A kind of time series data stream clustering method based on small wave attenuation summary tree |
| US20190259041A1 (en) * | 2018-02-20 | 2019-08-22 | James R Jackson | Systems and methods for generating a relationship among a plurality of datasets to generate a desired attribute value |
| CN110688964A (en) * | 2019-09-30 | 2020-01-14 | 哈尔滨工程大学 | Wavelet threshold and EMD combined denoising method based on sparse decomposition |
| US20200382286A1 (en) * | 2018-01-10 | 2020-12-03 | The Trustees Of Princeton University | System and method for smart, secure, energy-efficient iot sensors |
| CN112380934A (en) * | 2020-11-02 | 2021-02-19 | 国网陕西省电力公司电力科学研究院 | Cable partial discharge signal self-adaptive wavelet denoising method based on wavelet entropy and sparsity |
| US20230176550A1 (en) * | 2021-05-06 | 2023-06-08 | Strong Force Iot Portfolio 2016, Llc | Quantum, biological, computer vision, and neural network systems for industrial internet of things |
-
2023
- 2023-10-19 CN CN202311353792.6A patent/CN117094450B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110055222A1 (en) * | 2009-08-26 | 2011-03-03 | Lakshminarayn Choudur | Energy-based wavelet thresholding |
| US20120014575A1 (en) * | 2010-07-13 | 2012-01-19 | Empire Technology Development Llc | Hybrid 2-D ECG Data Compression Based on Wavelet Transforms |
| EP2618269A1 (en) * | 2012-01-17 | 2013-07-24 | Siemens Aktiengesellschaft | A method for processing of measurements from several sensors |
| CN107016656A (en) * | 2017-04-01 | 2017-08-04 | 中国科学院光电技术研究所 | The sparse base optimization method of small echo in image reconstruction based on compressed sensing |
| CN107291897A (en) * | 2017-06-21 | 2017-10-24 | 浙江工商大学 | A kind of time series data stream clustering method based on small wave attenuation summary tree |
| US20200382286A1 (en) * | 2018-01-10 | 2020-12-03 | The Trustees Of Princeton University | System and method for smart, secure, energy-efficient iot sensors |
| US20190259041A1 (en) * | 2018-02-20 | 2019-08-22 | James R Jackson | Systems and methods for generating a relationship among a plurality of datasets to generate a desired attribute value |
| CN110688964A (en) * | 2019-09-30 | 2020-01-14 | 哈尔滨工程大学 | Wavelet threshold and EMD combined denoising method based on sparse decomposition |
| CN112380934A (en) * | 2020-11-02 | 2021-02-19 | 国网陕西省电力公司电力科学研究院 | Cable partial discharge signal self-adaptive wavelet denoising method based on wavelet entropy and sparsity |
| US20230176550A1 (en) * | 2021-05-06 | 2023-06-08 | Strong Force Iot Portfolio 2016, Llc | Quantum, biological, computer vision, and neural network systems for industrial internet of things |
Non-Patent Citations (3)
| Title |
|---|
| LI, YC: "A Novel Image Compression Algorithm Using the Second Generation of Curvelet Transform and SVM", 《PROCEEDINGS OF THE 2009 WRI GLOBAL CONGRESS ON INTELLIGENT SYSTEMS》, vol. 3, pages 117 - 121, XP031516076 * |
| 蒋鹏, 黄清波, 王智, 孙优贤: "一种新的化工过程历史数据压缩方法研究", 浙江大学学报(工学版), no. 06 * |
| 陈春生;刘奎;: "基于多尺度积的磁共振图像增强算法", 安庆师范学院学报(自然科学版), no. 03 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117094450B (en) | 2024-01-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110210658B (en) | Prophet and Gaussian process user network flow prediction method based on wavelet transformation | |
| AU2020214409A1 (en) | Structural damage identification method based on ensemble empirical mode decomposition and convolution neural network | |
| CN115882868B (en) | Intelligent storage method for gas monitoring data | |
| Okarma | Combined image similarity index | |
| CN116418882B (en) | Memory data compression method based on HPLC dual-mode carrier communication | |
| CN117094450B (en) | Block chain-based chemical production line data optimization processing method | |
| CN114640355A (en) | Lossy compression and decompression method, system, storage medium and equipment of time sequence database | |
| CN115987294A (en) | Multidimensional data processing method of Internet of things | |
| Liu et al. | Dynamic quality metric oriented error bounded lossy compression for scientific datasets | |
| CN113381766A (en) | Method, terminal and storage medium for electrocardiogram data compression | |
| CN113381767B (en) | Method, terminal and storage medium for electrocardiogram data compression | |
| CN117316301B (en) | Intelligent compression processing method for gene detection data | |
| CA2490266A1 (en) | Measurement and signature intelligence analysis and reduction technique | |
| CN117411947B (en) | Cloud edge cooperation-based water service data rapid transmission method | |
| Gnutti et al. | A wavelet filter comparison on multiple datasets for signal compression and denoising | |
| CN109286817B (en) | Method for processing quantization distortion information of DCT (discrete cosine transformation) coefficient in video coding | |
| Guo et al. | Wavelet packet and neural network basis medical image compression | |
| US7630564B2 (en) | Decoding apparatus, dequantizing method, and program thereof | |
| CN110705373B (en) | Tank voltage signal classification method based on wavelet packet analysis and residual error network | |
| Sharma et al. | Predictor based block adaptive near-lossless coding technique for magnetic resonance image sequence | |
| CN117312255B (en) | Electronic document splitting optimization management method and system | |
| CN116567269A (en) | Spectrum monitoring data compression method based on signal-to-noise separation | |
| CN117692012B (en) | Remote monitoring and transmitting method for temperature data of intelligent sleeping bag | |
| CN112926442B (en) | Construction method for image target data set balance completion | |
| CN118014988B (en) | Intelligent gallium arsenide substrate wafer defect detection method |
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 |