CN116185306A - Sewage treatment system data storage method using potamogeton crispus - Google Patents
Sewage treatment system data storage method using potamogeton crispus Download PDFInfo
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- CN116185306A CN116185306A CN202310442819.2A CN202310442819A CN116185306A CN 116185306 A CN116185306 A CN 116185306A CN 202310442819 A CN202310442819 A CN 202310442819A CN 116185306 A CN116185306 A CN 116185306A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
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- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
- G06F3/0601—Interfaces specially adapted for storage systems
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
- G06F3/0601—Interfaces specially adapted for storage systems
- G06F3/0602—Interfaces specially adapted for storage systems specifically adapted to achieve a particular effect
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- 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
- Y02W—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO WASTEWATER TREATMENT OR WASTE MANAGEMENT
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- Y02W10/10—Biological treatment of water, waste water, or sewage
Abstract
The invention relates to the technical field of data compression, in particular to a data storage method of a sewage treatment system by utilizing water caltrop. The method for optimizing the dictionary matrix by acquiring the high-frequency related data avoids the problem of reduced compression efficiency caused by updating each dictionary atom of the dictionary matrix, and improves the compression efficiency of the data of the sewage treatment system by utilizing the water caltrop.
Description
Technical Field
The invention relates to the technical field of data compression, in particular to a data storage method of a sewage treatment system by utilizing water caltrop.
Background
The water caltrop stone buds can adsorb a large amount of particles suspended in a water body in a short time, and sink to the bottom of a lake after adsorbing suspended substances with about one time of the weight of the water caltrop stone buds, and further the adsorbed suspended substances and bottom mud are utilized to promote germination, so that the water caltrop can play a great role in the field of sewage treatment. In the sewage treatment operation process using the water caltrop, a large amount of data can be generated at each stage, the data represent the treatment effect of the sewage treatment process and the existing state information of the sewage, and important basis is provided for the adjustment of the subsequent sewage treatment process and the targeted chemical analysis, so that the data are required to be compressed and stored in time. And since the amount of data generated in the sewage treatment system is large, it is necessary to improve the compression efficiency in the compression process.
In the prior art, when the sewage treatment system data is compressed, a compression method such as encoding compression is generally adopted to compress one of the sewage treatment system data, so that the compressed data occupies smaller space, but the sewage treatment system data has various types, and the corresponding method for compressing one of the sewage treatment system data has lower efficiency. In the prior art, the K-SVD algorithm can simultaneously compress and store various sewage treatment system data, but the dictionary updating process in the K-SVD algorithm needs to update each dictionary atom, is redundant, and the compression efficiency is reduced due to the slow updating speed of the corresponding dictionary.
Disclosure of Invention
In order to solve the technical problems that dictionary atoms are required to be updated in the process of updating a dictionary in a K-SVD algorithm, the dictionary atoms are redundant, and the compression efficiency is reduced due to the fact that the corresponding dictionary is updated slowly, the invention aims to provide a data storage method of a sewage treatment system by utilizing water caltrop, and the adopted technical scheme is as follows:
the invention provides a sewage treatment system data storage method using water caltrop, which comprises the following steps:
acquiring sewage treatment system data utilizing water caltrop through a preset sampling frequency in a preset sampling time period, and constructing a sewage data matrix according to various sewage treatment system data; the sampling time period at least comprises two;
in different sewage data matrixes, according to the distribution trend of data in each sewage treatment system data and the difference distribution condition of adjacent data, a corresponding change trend matrix is obtained, according to the difference of corresponding position data in the change trend matrix between every two sampling time periods, a change trend difference value is obtained, and a marked sewage data matrix is screened according to the distribution condition of all the change trend difference values;
obtaining a marked change trend difference according to the change trend difference value corresponding to each marked sewage data matrix and all other marked sewage data matrixes, and obtaining a trend deviation degree at each moment according to the data trend association degree between each sewage treatment system data and other types of sewage treatment system data in each marked sewage data matrix and the data value distribution deviation characteristic of the data at each moment; obtaining the correlation degree of each moment according to the variation trend difference of the marks in each marked sewage data matrix and the trend deviation degree of each moment;
And screening out high-frequency related data in each marked sewage data matrix according to the correlation degree in each marked sewage data matrix and the variation trend difference value between each marked sewage data matrix and other sewage data matrixes, and optimizing the dictionary matrix according to the high-frequency related data to complete the compression storage of the sewage treatment system data by using the water caltrop.
Further, the method for obtaining the change trend matrix comprises the following steps:
setting different first matrix parameters for different change trends of the sewage treatment system data of the target type in the sewage data matrix; the trend of change includes an increasing trend, a decreasing trend and other trends;
if the change trend is an increasing trend or a decreasing trend, taking the average value of the data value difference between each data and the next data in the sewage treatment system data of the target type as a second matrix parameter, and taking a preset first numerical value as a third matrix parameter;
if the change trend is other trends, setting a second matrix parameter to be a preset second numerical value; taking the average value of the data value difference between each data and the next data in the sewage treatment system data of the target class as a third matrix parameter;
The first matrix parameter, the second matrix parameter and the third matrix parameter form a change trend sequence of the target type; changing the target type to obtain a change trend sequence under all types, and replacing the change trend sequence with the corresponding type of sewage treatment system data in the change trend matrix to obtain the change trend matrix.
Further, the method for obtaining the variation trend difference value comprises the following steps:
and obtaining a variation trend difference value according to the difference accumulation sum of the corresponding position data between every two variation trend matrixes between every two sampling time periods.
Further, the method for acquiring the marked sewage data matrix comprises the following steps:
counting all the variation trend difference values, dividing the variation trend difference values into two sets by adopting a clustering analysis method according to the numerical values of all the variation trend difference values, and marking two sewage data matrixes corresponding to each variation trend difference value in the set with the minimum variation trend difference value as a marked sewage data matrix.
Further, the method for acquiring the association degree of the data trend comprises the following steps:
screening out all kinds of sewage treatment system data at the moment of the target sewage treatment system data in the marked sewage data matrix, and marking the matrix formed by the residual data as a contrast data matrix;
In the comparison data matrix, calculating gray correlation degrees of data between the data types of the sewage treatment system corresponding to the data of the target sewage treatment system and the data types of other sewage treatment systems, counting all gray correlation degrees corresponding to the data of the target sewage treatment system, screening out gray correlation degrees larger than a threshold value by adopting a maximum inter-class variance method, and obtaining data trend correlation degrees corresponding to the data of the target sewage treatment system according to the average value of the gray correlation degrees larger than the threshold value;
and changing the target sewage treatment system data to obtain the data trend association degree corresponding to the sewage treatment system data in the marked sewage data matrix.
Further, the method for acquiring the data value distribution offset feature comprises the following steps:
in the marked sewage data matrix, fitting is carried out by adopting a least square method according to the data value of each sewage treatment system data to obtain a corresponding fitting straight line, and the data value distribution deviation characteristic of the data value at each moment is obtained according to the difference average value between each sewage treatment system data in each sewage treatment system data and the corresponding fitting value on the corresponding fitting straight line.
Further, the method for obtaining the trend deviation degree comprises the following steps:
In the marked sewage data matrix, calculating the average value of the pearson correlation coefficient between the data corresponding to each moment and other moments, and obtaining the linear correlation degree at each moment;
calculating the product of the accumulated sum of the corresponding linear correlation degree at each moment and the data trend correlation degree of the data of each sewage treatment system at the corresponding moment; and taking the ratio of the product to the corresponding data value distribution offset characteristic at each moment as the trend deviation degree at each moment.
Further, the method for obtaining the correlation degree comprises the following steps:
obtaining a change trend difference corresponding to a target marked sewage data matrix, and obtaining the correlation degree of each moment in the target marked sewage data matrix according to the trend deviation degree under each moment and the ratio of the change trend difference;
and changing the target marked sewage data matrix to obtain the correlation degree of each moment in all marked sewage data matrices.
Further, the method for acquiring the high-frequency related data comprises the following steps:
counting all variation trend difference values between the target marked sewage data matrix and other sewage data matrixes, and sequentially arranging all variation trend difference values and the relativity of all sewage treatment system data at each moment into a vector to obtain a relativity vector of the sewage treatment system data at each moment;
Obtaining a correlation vector corresponding to each marked sewage data matrix, dividing all the correlation vectors into two types of correlation vector sets by adopting a Fisher criterion, calculating the average value of each correlation in each type of correlation vector set to obtain a corresponding correlation average value, and recording the sewage treatment system data at each moment corresponding to each correlation vector in the correlation set with the largest correlation average value as high-frequency correlation data.
Further, the method for acquiring the variation trend difference of the mark comprises the following steps:
obtaining the marked variation trend difference corresponding to the target marked sewage data matrix according to the average value of variation trend difference values between the target marked sewage data matrix and other marked sewage data matrices;
and changing the target marked sewage data matrix to obtain marked variation trend differences of all marked sewage data matrices.
The invention has the following beneficial effects:
in consideration of the fact that indexes contained in the sewage treatment system data are fixed, indexes acquired at different time have certain memory and have larger correlation with sewage treatment degree, the numerical values of the corresponding sewage treatment system data generally show ascending or descending regularity characteristics in different time periods, the marked sewage data matrix is screened out according to the change trend difference value by obtaining the similarity degree of the change trend between the change trend difference values representing different sewage data matrixes, redundant data information in the corresponding marked sewage data matrix is less, and the marked sewage data matrix is further analyzed, so that compression efficiency is improved. Further, the method and the device obtain the corresponding correlation degree according to the data trend correlation degree, the data value distribution condition and the marking change trend difference of each sewage treatment system data in the marking sewage data matrix on the basis of the marking sewage data matrix, further screen the data of the marking sewage data matrix according to the correlation degree information and the change trend difference value to obtain high-frequency correlation data, optimize the dictionary through the high-frequency correlation data, avoid the problem that the compression efficiency is reduced due to the fact that dictionary atoms are required to be updated in the dictionary updating process in the K-SVD algorithm while the follow-up sparse matrix is not influenced, further improve the updating speed of the dictionary, and further improve the compression efficiency of the sewage treatment system data using the potamogeton crispus.
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 storing data in a sewage treatment system using water pondweed 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 is a detailed description of a specific implementation, structure, characteristics and effects of a sewage treatment system data storage method using water caltrop according to the invention with reference to the accompanying drawings and preferred embodiments. 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 following specifically describes a specific scheme of a data storage method of a sewage treatment system using water pondweed according to the present invention.
Referring to fig. 1, a flowchart of a data storage method of a sewage treatment system using water curls according to an embodiment of the present invention is shown, the method includes:
step S1: acquiring sewage treatment system data using water caltrop through a preset sampling frequency in a preset sampling time period, and constructing a sewage data matrix according to various sewage treatment system data; the sampling period includes at least two.
The invention aims to provide a data storage method of a sewage treatment system by utilizing water caltrop, which is used for compressing and storing the sewage treatment system data by utilizing the water caltrop according to the characteristics of fixed indexes contained in the sewage treatment system data, certain memory of indexes acquired at different time and high correlation with sewage treatment degree, thereby improving compression efficiency. It is therefore necessary to first acquire the sewage treatment system data to be compressed.
A sewage treatment system typically has a plurality of stations, each station having a corresponding sewage monitoring index, monitoring interval, and index acquisition means. For example, during a biological treatment stage, wastewater monitoring metrics include: MLSS, MLVSS, SV, SOUR characterizing activated sludge, etc.; DO, ORP, pH for characterizing the living environment of microorganisms; SRT, residual sludge quantity and external reflux quantity for representing the working condition of the activated sludge; ammonia nitrogen, nitrate nitrogen, TP and the like of each flow section representing the treatment effect of the process. It should be noted that, the sewage monitoring indexes include, but are not limited to, all the sewage detection indexes described above, and different monitoring indexes have the characteristics of fixed indexes, having certain memory of indexes obtained at different times and having a relatively large correlation with the sewage treatment degree in the sewage treatment process, so any sewage monitoring index is suitable for the data storage method of the sewage treatment system provided by the embodiment of the present invention, and the sewage monitoring indexes and the obtaining methods thereof are well known to those skilled in the art, and will not be further described herein.
Because the sewage treatment process is longer, and meanwhile, all the sewage treatment system data are analyzed and processed, the corresponding calculation is complex, and the characteristics of the sewage treatment system data, namely, the sewage treatment system data change along with the change of time and have a certain rule, the trend of the sewage treatment system data in different time periods has a certain similarity, and a plurality of sewage treatment system data exist in the sewage treatment system data by utilizing the water caltrop. According to the embodiment of the invention, the sewage treatment system data utilizing the water caltrop is obtained through the preset sampling frequency in the preset sampling time period, and a sewage data matrix is constructed according to various sewage treatment system data; the sampling period includes at least two.
In order to make the change of the sewage treatment system data with time more obvious, the embodiment of the invention sets the preset sampling frequency to be 1 time per minute, and each preset sampling time period is set to be 1 hour. It should be noted that, the implementer may set the preset sampling frequency and the preset sampling time period according to the specific implementation environment, which will not be further described herein. Considering that the size of the sewage data matrix can affect the calculated amount, in the embodiment of the invention, the length of each preset time period is set to be 1 hour, and each sewage data matrix is set to contain 20 kinds of sewage treatment system data; and constructing a sewage data matrix by taking time as rows and the types of sewage treatment system data as columns, wherein each row of the sewage data matrix comprises 60 sewage treatment system data, each column comprises 20 sewage treatment system data, each row of the data is each sewage treatment system data which is arranged from left to right according to the sequence of sampling moments in a preset time period, and each column of the data is different types of sewage treatment system data at the same moment. It should be noted that, the preset time period length and the type of the sewage treatment system data included in each sewage data matrix may be set according to the specific implementation situation of the practitioner, which will not be further described herein.
The matrix of the corresponding sewage data matrix is expressed as:
wherein, the liquid crystal display device comprises a liquid crystal display device,
representing a sewage data matrix
，
Data of a sewage treatment system;
for the number of data of each sewage treatment system acquired through a preset sampling frequency over a preset period of time, in the embodiment of the present invention,
set to 60;
for the number of types of wastewater treatment system data in each wastewater data matrix, in an embodiment of the present invention,
set to 20;
represent the first
Seed sewage treatment System data
Data values corresponding to the data of the sewage treatment system, wherein
Less than or equal to
，
Less than or equal to
And (2) and
and
are all positive integers.
Step S2: in different sewage data matrixes, a corresponding change trend matrix is obtained according to the distribution trend of data in each sewage treatment system data and the difference distribution condition of adjacent data, a change trend difference value is obtained according to the difference of corresponding position data in the change trend matrix between every two sampling time periods, and a marked sewage data matrix is screened according to the distribution condition of all the change trend difference values.
And (3) acquiring a sewage data matrix corresponding to the sewage treatment system data by utilizing the water caltrop through the step (S1). In the process of compressing the sewage data matrix by adopting the traditional K-SVD algorithm, a corresponding dictionary matrix is required to be obtained according to the sewage data matrix, and each dictionary atom in the dictionary matrix is updated, so that the compressed storage of the sewage data graph is further realized. However, the dictionary matrix directly obtained through the sewage data matrix according to the conventional K-SVD algorithm is usually an overcomplete dictionary, that is, a dictionary containing a large amount of redundant information data, which causes a large number of repeated updating steps when obtaining the corresponding coefficient matrix in the subsequent updating process of the dictionary matrix, thereby further reducing the compression efficiency. Therefore, if the corresponding dictionary matrix can be obtained according to the characteristics of the data in the sewage data matrix and the information appearing at high frequency, the repeated updating process of the dictionary matrix can be reduced, and the compression efficiency is further improved.
Considering that the indexes contained in the sewage treatment system data are fixed, the indexes acquired at different time periods have certain memory and have larger correlation with the sewage treatment degree, and different sewage data matrixes can represent the change condition of the sewage treatment system data at different time periods, so that the different sewage data matrixes acquired at different time periods have stronger similarity and tendency. It is necessary to analyze the consistency of the trend of variation among the various matrices of wastewater data. According to the embodiment of the invention, firstly, in different sewage data matrixes, a corresponding change trend matrix is obtained according to the distribution trend of data in each sewage treatment system data and the difference distribution condition of adjacent data.
Since different sewage index data change with the change of sewage treatment progress, that is, the numerical value of different kinds of sewage treatment system data may show a certain change trend with the progress of time, the change trend may be larger or smaller. For example, the MLSS is the amount of suspended solids in the mixed solution of sewage and activated sludge in the aeration tank, and the higher the value of the MLSS in a reasonable range in the aerobic nitrification process, the better the corresponding treatment effect. And SV30 is the volume ratio of settled sludge and mixed liquor in mixed liquor after sewage and activated sludge in an aeration tank, and the lower the numerical value of SV30 in a reasonable range is, the better the corresponding treatment effect is.
Preferably, the method for acquiring the change trend matrix includes:
setting different first matrix parameters for different change trends of the sewage treatment system data of the target type in the sewage data matrix; the trend of change includes an increasing trend, a decreasing trend, and other trends. The first matrix parameters characterize the trend of each sewage treatment system data in time sequence, and each sewage treatment system data has only three possible data distribution trends: an ascending sequence in time order, a descending sequence in time order, and a non-ascending and non-descending sequence in time order. Namely, the data distribution trend of each sewage treatment system can be clearly embodied according to the first matrix parameters of the change trend matrix. In the embodiment of the invention, when the change trend of the target type of sewage treatment system data is an increasing trend, setting the corresponding first matrix parameter to be 1; when the change trend of the target type sewage treatment system data is a decreasing trend, setting the corresponding first matrix parameter to be-1; when the change trend of the sewage treatment system data of the target type is other trend, the corresponding first matrix parameter is set to 0. It should be noted that, in the embodiment of the present invention, the purpose of setting the first matrix parameters to 1, -1, and 0 is to facilitate subsequent calculation, and an implementer may set the corresponding values of the first matrix parameters according to the specific real-time environment and the change trend.
If the change trend is an increasing trend or a decreasing trend, taking the average value of the data value difference between each data and the next data in the sewage treatment system data of the target type as a second matrix parameter, and taking a preset first numerical value as a third matrix parameter. If the change trend is other trends, setting a second matrix parameter as a preset second numerical value; taking the average value of the data value difference between each data and the next data in the sewage treatment system data of the target class as a third matrix parameter. In the embodiment of the present invention, the preset first value and the preset second value are both set to 0. The purpose of setting the preset first numerical value and the preset second numerical value to 0 in the embodiment of the invention is to enable the second matrix parameter to only represent the data increasing and decreasing degree of the sequence with the increasing trend and the decreasing trend in the finally obtained change trend matrix, and the third matrix parameter to only represent the data swinging degree of the non-increasing trend and the non-decreasing trend, so that the influence on subsequent calculation is reduced. It should be noted that the first value and the second value may be preset by the implementer according to the implementation environment, and further description is omitted herein.
The second matrix parameters represent the increasing and decreasing degrees of the data types of the sewage treatment system with increasing trend and decreasing trend, and the average value of the data value difference between each data and the next data represents the second matrix parameters in various sewage treatment coefficient data with increasing trend and decreasing trend, so that the changing degree of the data values of each adjacent position can be reflected, namely the change of the data of the sewage treatment system of each sewage treatment system is reflected, and the larger the data value difference between each data and the next data is, the larger the corresponding second matrix parameters are.
The third matrix parameter represents the swinging degree of the data of the sewage treatment system data types with non-increasing trend and non-decreasing trend, and the method for acquiring the third matrix parameter is the same as the method for acquiring the second matrix parameter, and is represented by the average value of the data value difference between each data and the next data. Similarly, the third matrix parameters can reflect the change degree between the data values of the adjacent positions, namely, the change of the sewage treatment system data of each sewage treatment system is reflected, and the larger the data value difference between each data and the next data is, the larger the corresponding third matrix parameters are.
The aim of the embodiment of the invention to separately calculate the second matrix parameter and the third matrix parameter is that: in the process of comparing the change trend matrixes subsequently, the degree of difference between sequences of different change trends is increased, and subsequent comparison analysis is facilitated. It should be noted that when the second matrix parameter whose variation trend is an increasing trend or a decreasing trend and the third matrix parameter whose variation trend is another trend are calculated, the average value of the data value differences may be added and replaced, and both parameters may represent the distribution characteristics of the data value differences in the whole.
In the embodiment of the present invention, the method for acquiring the first matrix parameter, the second matrix parameter and the third matrix parameter is expressed as follows in terms of formulas:
wherein, the liquid crystal display device comprises a liquid crystal display device,
is the first sewage data matrix
A first matrix parameter for the sewage treatment system data,
is the first sewage data matrix
A second matrix parameter for the sewage treatment system data,
is the first sewage data matrix
A third matrix parameter for the sewage treatment system data,
is the first sewage data matrix
A data sequence corresponding to the sewage treatment system data in time sequence,
is the first sewage data matrix
Seed sewage treatment system data are time-sequential
The data of the sewage treatment system,
is the first sewage data matrix
Seed sewage treatment system data are time-sequential
The data of the sewage treatment system,
is the first sewage data matrix
The amount of data of the sewage treatment system, in the embodiment of the invention
60. It should be noted that, in the embodiment of the present invention, the values of the first matrix parameters are set to be
Is intended to facilitate subsequent calculations and is not limited to
The specific numerical values can be set according to the specific implementation conditions of the implementers,and will not be further described herein.
The change trend matrix corresponding to each sewage data matrix is expressed as:
Wherein, the liquid crystal display device comprises a liquid crystal display device,
is a sewage data matrix
A corresponding matrix of the trend of the change,
is a sewage data matrix
Middle (f)
A first matrix parameter corresponding to the sewage treatment system data,
is a sewage data matrix
Middle (f)
A second matrix parameter corresponding to the sewage treatment system data,
is a sewage data matrix
Middle (f)
A third matrix parameter corresponding to the sewage treatment system data;
is a sewage data matrix
Type data of the sewage treatment system data, in the embodiment of the present invention,
set to 20.
The aim of calculating the change trend matrix corresponding to each sewage data matrix is to analyze the consistency of the change trend among the sewage data matrices. Thus, a difference in the corresponding trend matrix between each sewage data matrix and the other sewage data matrices is required. According to the embodiment of the invention, the variation trend difference value is obtained according to the difference of the corresponding position data in the variation trend matrix between every two sampling time periods.
Preferably, the method for acquiring the variation trend difference value includes:
and obtaining a variation trend difference value according to the difference accumulation sum of the corresponding position data between every two variation trend matrixes between every two sampling time periods.
Specific: obtaining a change trend matrix 
And a change trend matrix
Wherein
And
to distinguish the letters of the change trend matrix; calculating a change trend matrix
And a change trend matrix
Numerical value differences among the data of corresponding positions in the model are added according to the numerical value differences to obtain a change trend matrix
And a change trend matrix
A variation trend difference value between the two; according to the change trend matrix
And a change trend matrix
And obtaining the variation trend difference values between every two variation trend matrixes by using the variation trend difference value obtaining method.
Because the information of the difference representation between the single data between the corresponding positions of each change trend matrix is not much and has little meaning, the embodiment of the invention takes the accumulated sum of the numerical differences between the corresponding positions of each change trend matrix as the change trend difference value between each change trend matrix, namely the change trend difference value between two change trend matrices is represented by the integral data difference of the change trend matrix, and the change trend difference value can represent the similarity of the change trends between the two sewage data matrices corresponding to the two change trend matrices. When the change trend matrixes corresponding to the two sewage data matrixes are similar, the corresponding change trend difference values are smaller, so that the two sewage data matrixes are similar.
Change trend matrix
And a change trend matrix
The method for obtaining the variation trend difference value between the two is expressed as the following formula:
wherein, the liquid crystal display device comprises a liquid crystal display device,
is a change trend matrix
And a change trend matrix
The variation trend difference value between the two,
is a change trend matrix
Middle (f)
Seed sewage treatment System data
The parameters of the matrix are set to be,
is a change trend matrix
Middle (f)
Seed sewage treatment System data
Matrix parameters;
as a normalization function, it acts to normalize the content in brackets.
According to the change trend difference values, the similarity of trend between every two sewage data matrixes can be reflected, the association degree between the sewage data matrixes corresponding to the smaller change trend difference values is larger, and the information which appears at higher frequency is corresponding to all the sewage data matrixes. Therefore, the embodiment of the invention screens out the marked sewage data matrix according to the distribution condition of all the variation trend difference values. The sewage data matrix is marked, namely, the information data which corresponds to the higher frequency occurrence.
Preferably, the method for acquiring the marked sewage data matrix comprises the following steps:
counting all the variation trend difference values, dividing the variation trend difference values into two sets by adopting a clustering analysis method according to the numerical values of all the variation trend difference values, and marking two sewage data matrixes corresponding to each variation trend difference value in the set with the minimum variation trend difference value as a marked sewage data matrix. Because the variation trend difference value is obtained by the variation trend matrixes corresponding to the two sewage data matrixes, each variation trend difference value corresponds to the two sewage data matrixes; and each sewage data matrix can obtain a variation trend difference value according to the variation trend difference value among other sewage data matrices, so that each sewage data matrix corresponds to a plurality of variation trend difference values. Therefore, when one variation trend difference value belongs to the set with the minimum variation trend difference value in all variation trend difference values corresponding to the sewage data matrix, the corresponding sewage data matrix is the marked sewage data matrix. The correlation degree between the sewage data matrixes corresponding to the variation trend difference values in the set with the minimum variation trend difference values is larger, namely the trend similarity between the corresponding sewage data matrixes is higher, and the corresponding information data with higher frequency in all the sewage data matrixes is obtained.
In the embodiment of the invention, the K-means algorithm is adopted to perform cluster analysis at k=2. It should be noted that, the method for dividing the variation trend difference value is not limited to the clustering analysis, and the variation trend difference value may be divided into a variation trend difference value set with a larger value and a variation trend difference value set with a smaller value according to the value of the variation trend difference value by the existing data dividing method. For example, a set of change trend difference values equal to or greater than a threshold may be used as a set of change trend difference values having a large value, and a set of change trend difference values smaller than the threshold may be used as a set of change trend difference values according to the maximum inter-class variance method. And the K-means algorithm is well known in the art and will not be further described herein.
Step S3: obtaining a marked change trend difference according to the change trend difference value corresponding to each marked sewage data matrix and all other marked sewage data matrixes, and obtaining a trend deviation degree at each moment according to the data trend association degree between each sewage treatment system data and other types of sewage treatment system data in each marked sewage data matrix and the data value distribution deviation characteristic of the data at each moment; and obtaining the correlation degree of each moment according to the variation trend difference of the marks in each marked sewage data matrix and the trend deviation degree of each moment.
The marked sewage data matrix is obtained through the step S2, but the sewage treatment system data contained in the sewage data matrix is information in a certain time period acquired manually, and the screening process of the marked sewage data matrix is also based on the information, and redundant data still exists in the corresponding sewage treatment system data in each marked sewage data matrix, namely the corresponding marked sewage data matrix cannot be completely used as high-frequency related data, so that the embodiment of the invention needs to further screen on the basis of the marked sewage data matrix.
Considering that when certain data in the marked sewage data matrix is high-frequency data, the degree of correlation between the corresponding data and other types of data is high, and the distribution trend of the corresponding types of sewage treatment system data is more regular, namely the corresponding sewage treatment system data is easier to be represented by other data. Therefore, according to the embodiment of the invention, the trend deviation degree of each moment is obtained according to the data trend association degree between each sewage treatment system data and other kinds of sewage treatment system data in each marked sewage data matrix and the data value distribution deviation characteristic of the data at each moment.
Preferably, the method for acquiring the association degree of the data trend comprises the following steps:
screening out all kinds of sewage treatment system data at the moment of the target sewage treatment system data from the marked sewage data matrix, and marking the matrix formed by the residual data as a contrast data matrix; by screening all the data at the moment when the target sewage treatment system data is, the influence of the data at the same moment can be avoided in the process of calculating the data trend association degree later, and each sewage treatment system data can obtain a corresponding comparison data matrix, so that the subsequent calculation is convenient.
And in the comparison data matrix, calculating gray correlation between the sewage treatment system data type corresponding to the target sewage treatment system data and other sewage treatment system data types. In the embodiment of the invention, all data of the column where the target sewage treatment system data is located in the marked sewage data matrix are screened out, and the rest data are formed into a contrast data matrix. If the degree of correlation between the numerical value of the target type sewage treatment system data and the other type sewage treatment system data is larger, that is, the data value of the target type sewage treatment system data is greatly influenced by the data value of the other type, it is indicated that certain regularity exists between the corresponding type sewage treatment system data and the other data, that is, the easier the target sewage treatment system data is represented by the other sewage treatment system data, that is, the higher the possibility of being high-frequency related data is.
According to trend similarity of different types of sewage treatment system data, the data value of the target sewage treatment system data is calculated to be influenced by other data values through gray correlation, the data correlation of the target sewage treatment system data is further characterized, but if gray correlation calculation is directly carried out in a marked sewage data matrix, when different types of sewage treatment system data are carried out on the target sewage treatment system data at the same moment, the trend similarity calculation of the different types of sewage treatment system data is influenced, so that when gray correlation calculation is carried out on the target sewage treatment system data, different types of sewage treatment system data under the same moment of the target sewage treatment system data need to be screened, and further calculation of the trend correlation of the data is more accurate.
And counting all gray correlation degrees corresponding to the target sewage treatment system data, screening out gray correlation degrees larger than a threshold value by adopting a maximum inter-class variance method, obtaining the data trend correlation degree corresponding to the target sewage treatment system data according to the average value of the gray correlation degrees larger than the threshold value, and changing the target sewage treatment system data to obtain the data trend correlation degree corresponding to the sewage treatment system data in the marked sewage data matrix. The gray correlation degree with larger values can be screened out through the maximum inter-class variance method, namely, the type of the sewage treatment system data with higher influence degree on the target sewage treatment system data is screened out, and the average value of the gray correlation degree with larger values corresponding to the target sewage treatment system data is further used as the data trend correlation degree of the target sewage treatment system data, so that the influence degree of other data on the target sewage treatment system data is accurately represented while the calculation error of the data trend correlation degree is reduced. The higher the grey correlation degree of the corresponding target sewage treatment system data and the sewage treatment system data corresponding to other types is, the higher the influence degree of the other sewage treatment system data is, and the higher the data trend correlation degree of the corresponding target sewage treatment system data is. It should be noted that, the gray correlation and the maximum inter-class variance method are well known in the art, and are not further defined and described herein.
Preferably, the method for acquiring the data value distribution offset feature comprises the following steps:
in the marked sewage data matrix, fitting is carried out by adopting a least square method according to the data value of each sewage treatment system data to obtain a corresponding fitting straight line, and the data value distribution deviation characteristic of the data value at each moment is obtained according to the difference average value between each sewage treatment system data in each sewage treatment system data and the corresponding fitting value on the corresponding fitting straight line. The difference mean value can represent the regularity of each sewage treatment system data in each sewage treatment system data, when the data distribution in certain sewage treatment system data is more regular, the corresponding difference mean value is smaller, and the data in the sewage treatment system data in the corresponding category is indicated to be easier to use in the representation of other types of sewage treatment system data.
Preferably, the method for acquiring the trend deviation degree includes:
and in the marked sewage data matrix, calculating the average value of the pearson correlation coefficient between the data corresponding to each moment and other moments, and obtaining the linear correlation degree at each moment. The pearson correlation coefficient of different kinds of sewage treatment system data at two moments can be calculated to represent the correlation between the sewage treatment system data at two moments, and the larger the corresponding pearson correlation coefficient is, the more relevant the sewage treatment system data at two moments is. According to the embodiment of the invention, the average value of the Pearson correlation coefficient between the data corresponding to each moment and other moments is calculated, so that the correlation degree of different kinds of sewage treatment system data at each moment and different kinds of sewage treatment system data at other moments in the whole can be represented. It should be noted that, the calculation of the pearson correlation coefficient is well known in the art, and is not further limited and described herein.
Calculating the product of the accumulated sum of the corresponding linear correlation degree at each moment and the data trend correlation degree of the data of each sewage treatment system at the corresponding moment; and taking the ratio of the product to the corresponding data value distribution offset characteristic at each moment as the trend deviation degree at each moment. It should be noted that, by calculating the trend deviation degree by the ratio, the calculation method provided by an embodiment of the present invention is only required to ensure that the product of the linear correlation degree corresponding to each type of sewage treatment system data and the accumulated sum of the data correlation degrees is in direct proportion to the trend deviation degree, and the data trend correlation degree is in inverse proportion to the trend deviation degree when an implementer calculates the specific trend deviation degree. For example, the same calculation effect as the embodiment of the present invention can be achieved by subtracting the degree of correlation of the trend data from the product of the linear correlation degree corresponding to the data of each sewage treatment system and the accumulated sum of the data correlations.
In the embodiment of the invention, the calculation method of the trend deviation degree is expressed as the following formula:
wherein, the liquid crystal display device comprises a liquid crystal display device,
to mark the first in the sewage data matrix
The degree of deviation of the trend corresponding to the moment,
to mark the first in the sewage data matrix 
Time at first
The degree of correlation of the data trend corresponding to the data of the sewage treatment system,
to mark the first in the sewage data matrix
The corresponding degree of linear correlation at the moment in time,
to mark the first in the sewage data matrix
The data of the seed sewage treatment system is shown in the first step
A data value of sewage treatment system data at a moment,
to mark the first in the sewage data matrix
The data of the seed sewage treatment system is shown in the first step
Fitting values corresponding to the sewage treatment system data at the moment;
in order to mark the type number of the sewage treatment system data in the sewage data matrix, in the embodiment of the invention
Setting up20.
Because the marked sewage data matrixes are information data which occur at a higher frequency, the marked sewage data matrixes can be further screened according to the corresponding variation trend difference values among the marked sewage data matrixes, and further analyzed according to the distribution condition of the variation trend difference values among each marked sewage data matrix and other marked sewage data matrixes, so that more accurate high-frequency related data are obtained. According to the embodiment of the invention, the marked variation trend difference is obtained according to the variation trend difference value corresponding to each marked sewage data matrix and all other marked sewage data matrixes. And the trend similarity distribution condition of each marked sewage data matrix and other marked sewage data matrices is represented by marked change trend differences.
Preferably, the method for acquiring the variation trend difference of the mark includes:
obtaining the marked variation trend difference corresponding to the target marked sewage data matrix according to the average value of variation trend difference values between the target marked sewage data matrix and other marked sewage data matrices; and changing the target marked sewage data matrix to obtain marked variation trend differences of all marked sewage data matrices. The distribution condition of the variation trend difference values of the target marked sewage data matrix and other marked sewage data matrices in the whole numerical value can be reflected through the mean value, so that the trend similarity characterization of the marked variation trend difference values on the target marked sewage data matrix is more accurate. And the smaller the difference value of the marking change trend is, the higher the similarity of the corresponding target marking data matrix and other marking sewage data matrices in trend is, which shows that the higher the correlation of the target marking data matrix and other marking data matrices is, the higher the corresponding high frequency occurrence possibility is.
And further obtaining the correlation degree of each moment according to the variation trend difference of the marks in each marked sewage data matrix and the trend deviation degree of each moment.
Preferably, the method for acquiring the correlation degree comprises the following steps:
Obtaining a change trend difference corresponding to the target marked sewage data matrix, and obtaining the correlation degree of each sewage treatment system data according to the trend deviation degree corresponding to each sewage treatment system data and the ratio of the change trend difference in the target marked sewage data matrix; and changing the target marked sewage data matrix to obtain the relativity of each sewage treatment system data in all the marked sewage data matrices.
Because the trend deviation degree of the data of different sewage treatment systems is in direct proportion to the correlation degree, and the mark change trend difference of the corresponding mark data matrix is in inverse proportion to the correlation degree, the embodiment of the invention takes the ratio of the trend deviation degree and the mark change trend difference as the corresponding correlation degree. It should be noted that, as long as the trend deviation degree of the data of different sewage treatment systems is in direct proportion to the correlation degree, the variation trend difference of the marks corresponding to the mark data matrix is in inverse proportion to the correlation degree, and the correlation degree can be obtained by adopting other expression forms except the ratio, for example, normalization is performed after the variation trend deviation degree and the variation trend difference of the marks are made, and an implementer can set up by himself according to specific real-time conditions.
In the embodiment of the invention, the method for acquiring the correlation is expressed as the following formula:
wherein, the liquid crystal display device comprises a liquid crystal display device,
to mark the first in the sewage data matrix
The degree of correlation corresponding to the moment of time,
to mark the first in the sewage data matrix
The degree of deviation of the trend corresponding to the moment,
the variation trend difference of the marks corresponding to the marked sewage data matrix.
Step S4: and screening out high-frequency related data in each marked sewage data matrix according to the correlation degree in each marked sewage data matrix and the variation trend difference value between each marked sewage data matrix and other sewage data matrixes, and optimizing the dictionary matrix according to the high-frequency related data to complete the compression storage of the sewage treatment system data by using the water caltrop.
So far, the correlation degree corresponding to all the data at each moment in each marked sewage data matrix is obtained through the step S3, but the correlation degree can only represent the correlation degree corresponding to all the data among the marked sewage data matrices, so that the data of other sewage data matrices except the marked sewage data matrices are further required to be introduced to further screen out the high-frequency correlation data. According to the embodiment of the invention, the high-frequency related data in each marked sewage data matrix is screened out according to the correlation degree in each marked sewage data matrix and the variation trend difference value between each marked sewage data matrix and other sewage data matrixes.
Preferably, the method for acquiring high-frequency related data includes:
and counting all variation trend difference values between the target marked sewage data matrix and other sewage data matrixes, and sequentially arranging all variation trend difference values and the relativity of all sewage treatment system data at each moment into a vector to obtain a relativity vector of the sewage treatment system data at each moment. And (3) representing the relativity of other sewage data matrixes and the target marked sewage data matrix by calculating all variation trend difference values corresponding to the target marked sewage data matrix, so that the obtained relativity vector is introduced into the other sewage data matrixes on the basis of relativity, and the subsequent screening of final high-frequency related data is more accurate.
Obtaining a correlation vector corresponding to each marked sewage data matrix, dividing all the correlation vectors into two types of correlation vector sets by adopting a Fisher criterion, calculating the average value of each correlation in each type of correlation vector set to obtain a corresponding correlation average value, and recording the sewage treatment system data at each moment corresponding to each correlation vector in the correlation set with the largest correlation average value as high-frequency correlation data. The Fisher criterion can ensure that the intra-class dispersion of the separated different sets is minimum and the inter-class dispersion is maximum, so that the degree of distinction between the vector sets of the correlation degree is screened out by the Fisher criterion, namely the difference of the corresponding data between the vector sets on the high-frequency correlation is larger, and the numerical characteristics of the high-frequency correlation corresponding to the different sets are further determined according to the average value of the correlation degree between each set. And taking the sewage treatment system data at each time in the vector set with high correlation as high-frequency related data, namely the data with high occurrence frequency and high correlation. It should be noted that the fisher criterion is well known in the art, and is not further defined and described herein.
And further optimizing the dictionary matrix according to the high-frequency related data to complete the compression storage of the sewage treatment system data using the water caltrop, and obtaining the corresponding dictionary matrix by adopting an MOD optimal direction method for the obtained high-frequency related data. The data in the dictionary matrix obtained by the embodiment of the invention is high-frequency related data, namely the corresponding data in the dictionary matrix has higher use frequency, so that the subsequent updating process of the dictionary matrix only updates the dictionary atoms corresponding to the data with higher use frequency, and the compression efficiency is higher.
And further updating the dictionary matrix to obtain a corresponding coefficient matrix, compressing the coefficient matrix through Huffman coding to obtain compressed data, and storing the compressed sewage treatment system data by utilizing the water pondweed. According to the embodiment of the invention, the dictionary with higher use frequency is obtained by acquiring the dictionary matrix corresponding to the high-frequency related data, so that the redundancy degree of subsequent dictionary updating is reduced, and the efficiency of data compression and storage is improved. It should be noted that, the MOD optimal direction method and the hough coding compression are well known in the art, and are not further defined and described herein.
The present invention has been completed.
In summary, the sewage data matrix is constructed by utilizing the water pondweed sewage treatment system data, the marked sewage data matrix is screened according to the trend similarity among the sewage data matrices, the relativity of the data at each moment is obtained according to the trend association deviation degree among the marked sewage data matrices, the high-frequency related data is classified and screened, and the compressed storage of the water pondweed sewage treatment system data is completed by optimizing the dictionary matrix through the high-frequency related data. The method for optimizing the dictionary matrix by acquiring the high-frequency related data avoids the problem of reduced compression efficiency caused by updating each dictionary atom of the dictionary matrix, and improves the compression efficiency of the data of the sewage treatment system by utilizing the water caltrop.
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 method for storing data in a sewage treatment system using curly pondweed, the method comprising:
acquiring sewage treatment system data utilizing water caltrop through a preset sampling frequency in a preset sampling time period, and constructing a sewage data matrix according to various sewage treatment system data; the sampling time period at least comprises two;
in different sewage data matrixes, according to the distribution trend of data in each sewage treatment system data and the difference distribution condition of adjacent data, a corresponding change trend matrix is obtained, according to the difference of corresponding position data in the change trend matrix between every two sampling time periods, a change trend difference value is obtained, and a marked sewage data matrix is screened according to the distribution condition of all the change trend difference values;
obtaining a marked change trend difference according to the change trend difference value corresponding to each marked sewage data matrix and all other marked sewage data matrixes, and obtaining a trend deviation degree at each moment according to the data trend association degree between each sewage treatment system data and other types of sewage treatment system data in each marked sewage data matrix and the data value distribution deviation characteristic of the data at each moment; obtaining the correlation degree of each moment according to the variation trend difference of the marks in each marked sewage data matrix and the trend deviation degree of each moment;
And screening out high-frequency related data in each marked sewage data matrix according to the correlation degree in each marked sewage data matrix and the variation trend difference value between each marked sewage data matrix and other sewage data matrixes, and optimizing the dictionary matrix according to the high-frequency related data to complete the compression storage of the sewage treatment system data by using the water caltrop.
2. The method for storing data in a sewage treatment system using water curly pondweed as claimed in claim 1, wherein said method for obtaining said trend matrix comprises:
setting different first matrix parameters for different change trends of the sewage treatment system data of the target type in the sewage data matrix; the trend of change includes an increasing trend, a decreasing trend and other trends;
if the change trend is an increasing trend or a decreasing trend, taking the average value of the data value difference between each data and the next data in the sewage treatment system data of the target type as a second matrix parameter, and taking a preset first numerical value as a third matrix parameter;
if the change trend is other trends, setting a second matrix parameter to be a preset second numerical value; taking the average value of the data value difference between each data and the next data in the sewage treatment system data of the target class as a third matrix parameter;
The first matrix parameter, the second matrix parameter and the third matrix parameter form a change trend sequence of the target type; changing the target type to obtain a change trend sequence under all types, and replacing the change trend sequence with the corresponding type of sewage treatment system data in the change trend matrix to obtain the change trend matrix.
3. The method for storing data in a sewage treatment system using water pondweed according to claim 1, wherein the method for obtaining the variation trend difference value comprises:
and obtaining a variation trend difference value according to the difference accumulation sum of the corresponding position data between every two variation trend matrixes between every two sampling time periods.
4. The method for storing data in a sewage treatment system using water curly pondweed as claimed in claim 1, wherein said method for obtaining the marked sewage data matrix comprises:
counting all the variation trend difference values, dividing the variation trend difference values into two sets by adopting a clustering analysis method according to the numerical values of all the variation trend difference values, and marking two sewage data matrixes corresponding to each variation trend difference value in the set with the minimum variation trend difference value as a marked sewage data matrix.
5. The method for storing data in a sewage treatment system using water curls according to claim 1, wherein the method for obtaining the degree of correlation of data trend comprises:
screening out all kinds of sewage treatment system data at the moment of the target sewage treatment system data in the marked sewage data matrix, and marking the matrix formed by the residual data as a contrast data matrix;
in the comparison data matrix, calculating gray correlation degrees of data between the data types of the sewage treatment system corresponding to the data of the target sewage treatment system and the data types of other sewage treatment systems, counting all gray correlation degrees corresponding to the data of the target sewage treatment system, screening out gray correlation degrees larger than a threshold value by adopting a maximum inter-class variance method, and obtaining data trend correlation degrees corresponding to the data of the target sewage treatment system according to the average value of the gray correlation degrees larger than the threshold value;
and changing the target sewage treatment system data to obtain the data trend association degree corresponding to the sewage treatment system data in the marked sewage data matrix.
6. The method for storing data in a sewage treatment system using water curly pondweed as claimed in claim 1, wherein said data value distribution offset characteristic obtaining method comprises:
In the marked sewage data matrix, fitting is carried out by adopting a least square method according to the data value of each sewage treatment system data to obtain a corresponding fitting straight line, and the data value distribution deviation characteristic of the data value at each moment is obtained according to the difference average value between each sewage treatment system data in each sewage treatment system data and the corresponding fitting value on the corresponding fitting straight line.
7. The method for storing data in a sewage treatment system using water pondweed according to claim 1, wherein the trend deviation degree obtaining method comprises:
in the marked sewage data matrix, calculating the average value of the pearson correlation coefficient between the data corresponding to each moment and other moments, and obtaining the linear correlation degree at each moment;
calculating the product of the accumulated sum of the corresponding linear correlation degree at each moment and the data trend correlation degree of the data of each sewage treatment system at the corresponding moment; and taking the ratio of the product to the corresponding data value distribution offset characteristic at each moment as the trend deviation degree at each moment.
8. The method for storing data in a sewage treatment system using water pondweed according to claim 1, wherein the correlation obtaining method comprises:
Obtaining a change trend difference corresponding to a target marked sewage data matrix, and obtaining the correlation degree of each moment in the target marked sewage data matrix according to the trend deviation degree under each moment and the ratio of the change trend difference;
and changing the target marked sewage data matrix to obtain the correlation degree of each moment in all marked sewage data matrices.
9. The method for storing data in a sewage treatment system using water curly pondweed as claimed in claim 1, wherein said method for obtaining high frequency related data comprises:
counting all variation trend difference values between the target marked sewage data matrix and other sewage data matrixes, and sequentially arranging all variation trend difference values and the relativity of all sewage treatment system data at each moment into a vector to obtain a relativity vector of the sewage treatment system data at each moment;
obtaining a correlation vector corresponding to each marked sewage data matrix, dividing all the correlation vectors into two types of correlation vector sets by adopting a Fisher criterion, calculating the average value of each correlation in each type of correlation vector set to obtain a corresponding correlation average value, and recording the sewage treatment system data at each moment corresponding to each correlation vector in the correlation set with the largest correlation average value as high-frequency correlation data.
10. The method for storing data in a sewage treatment system using water curls as claimed in claim 1, wherein said method for obtaining the difference in the mark variation trend comprises:
obtaining the marked variation trend difference corresponding to the target marked sewage data matrix according to the average value of variation trend difference values between the target marked sewage data matrix and other marked sewage data matrices;
and changing the target marked sewage data matrix to obtain marked variation trend differences of all marked sewage data matrices.
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| CN117041359B (en) * | 2023-10-10 | 2023-12-22 | 北京安视华业科技有限责任公司 | Efficient compression transmission method for information data |
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