CN115987485A - Hydraulic model data processing method - Google Patents
Hydraulic model data processing method Download PDFInfo
- Publication number
- CN115987485A CN115987485A CN202310264820.0A CN202310264820A CN115987485A CN 115987485 A CN115987485 A CN 115987485A CN 202310264820 A CN202310264820 A CN 202310264820A CN 115987485 A CN115987485 A CN 115987485A
- Authority
- CN
- China
- Prior art keywords
- data
- sub
- interval
- class
- parameter
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Abstract
The invention relates to the field of data processing, in particular to a hydraulic model data processing method, which comprises the following steps: acquiring a first data sequence; obtaining each subsection interval according to the first data sequence; all the first-class data and all the second-class data of each sub-section interval are obtained according to each transformation parameter combination of each sub-section interval; obtaining evaluation indexes of all transformation parameter combinations according to all the first-class data and the second-class data in all the subsegment intervals, and further obtaining the optimal transformation parameter combinations of all the subsegment intervals; obtaining the final adjustment frequency of each data according to the optimal transformation parameter combination of each sub-section interval, and further obtaining the index sequences of the ciphertext data and all the second-class data in each sub-section interval; obtaining sub-segment keys of each sub-segment interval so as to obtain each sub-block; obtaining a chaotic sequence according to each sub-block; and storing the chaotic sequence and the ciphertext data. The invention encrypts the data by destroying the frequency information of the data, and is simpler and more reliable.
Description
Technical Field
The invention relates to the field of data processing, in particular to a hydraulic model data processing method.
Background
The hydraulic model is a small-scale model manufactured by simulating dynamic change of water in a complex natural environment, namely a diffusion process of substances in water, in a laboratory, and is used for predicting the change when certain influence is exerted on the environment, such as real-time calculation and simulation of hydraulic information of fluid in all pipelines, such as Reynolds number, flow, pressure drop, flow speed, temperature, pressure of important nodes and the like, and provides a basis for scientific adjustment and management of a central air-conditioning circulating water system; the relevant data of the hydraulic model can effectively integrate the data resources of the central air-conditioning circulating water system, visually display the current parameter data and assist the management personnel of the central air-conditioning circulating water system to analyze and make decisions; therefore, the importance of the relevant data of the hydraulic model to the central air-conditioning circulating water system is self-evident;
in the storage process of hydraulic model data, in order to ensure the safety of the related data of the hydraulic model, the hydraulic model data needs to be encrypted, the traditional encryption mainly adopts an entropy coding encryption mode, namely, the encryption is carried out by analyzing probability distribution information of the data, but the algorithm can also be broken through the regularity of frequency information, so the risk of data exposure and attack is easily generated by using the existing encryption method. In addition, although the frequency information of the data can be changed by using linear transformation, the traditional linear transformation uses the same parameters to process all data, has no pertinence to the hydraulic data acquired in real time, namely the traditional linear transformation can generate unstable changes due to different distribution conditions of the original data;
therefore, it is very important to design an encryption method that can perform a targeted linear transformation according to the frequency information of the data and further destroy the frequency information of the data.
Disclosure of Invention
The invention provides a hydraulic model data processing method, which aims to solve the existing problems.
The hydraulic model data processing method adopts the following technical scheme:
one embodiment of the invention provides a hydraulic model data processing method, which comprises the following steps:
acquiring a first data sequence;
obtaining a statistical histogram according to the first data sequence, and calling the frequency count corresponding to each data in the statistical histogram as the initial frequency count of each data; obtaining a second data sequence according to the statistical histogram; obtaining each subsection interval according to the frequency of each data and adjacent data in the second data sequence;
obtaining a first initial parameter and a second initial parameter of each subsection interval according to the initial frequency of each data in each subsection interval; obtaining the value ranges of the first conversion parameter and the second conversion parameter corresponding to each sub-section interval according to the first initial parameter, the maximum initial frequency and the minimum initial frequency of each sub-section interval; obtaining each transformation parameter combination according to the value ranges of the first transformation parameters and the second transformation parameters of each subsection interval; after each subsection interval is subjected to linear transformation by using each transformation parameter combination, the frequency number corresponding to each data is called as the adjustment frequency number of each data; all the first class data and all the second class data of each subsection interval are obtained according to each transformation parameter combination; obtaining an evaluation index of each transformation parameter combination according to the adjustment frequency and the initial frequency of all the first-class data and the second-class data in each subsection interval; obtaining the optimal transformation parameter combination of each sub-section interval according to the evaluation index of each transformation parameter combination;
obtaining the final adjustment frequency of each data according to the optimal transformation parameter combination of each sub-section interval; obtaining ciphertext data according to the final adjustment frequency count and the initial frequency count of each data, and obtaining index sequences of all the second-class data in each subsection interval; obtaining a sub-segment key of each sub-segment interval according to the optimal transformation parameter combination of each sub-segment interval, the first initial parameter and the second initial parameter; obtaining each sub-block according to the index sequence of all the second-class data corresponding to each sub-block interval and the corresponding sub-block key; obtaining a chaotic sequence according to each sub-block; and storing the chaotic sequence and the ciphertext data.
Preferably, the method for acquiring each sub-segment interval is as follows:
regarding the nth data in the second data sequence, recording the difference between the n +1 th data and the initial frequency count of the nth data as a first difference of the nth data; a difference value between the nth data and the (n-1) th data is referred to as a second difference value of the nth data; when the first difference and the second difference of the nth data are both 0, the nth data is not a segmentation point; otherwise, the nth data is a segmentation point; judging each data in the second data sequence to obtain each segmentation point; all data between two adjacent segmentation points form a subsegment interval, and each subsegment interval is obtained.
Preferably, the method for acquiring the first initial parameter and the second initial parameter of each sub-segment interval is as follows:
and performing linear fitting on each data in each subsection interval and the initial frequency of each data to obtain a function expression of each subsection interval, wherein a constant term of the function expression is called as a second initial parameter of each subsection interval, and a coefficient of the function expression is called as a second initial parameter of each subsection interval.
Preferably, the first transformation parameter and the second transformation parameter corresponding to each sub-segment interval refer to: when the targeted linear transformation is performed on each subsection interval, a constant term of a function expression of the targeted linear transformation is referred to as a second transformation parameter, and a coefficient of the function expression of the targeted linear transformation is referred to as a first transformation parameter.
Preferably, the method for acquiring all the first-class data and all the second-class data of each sub-segment interval is as follows:
for each conversion parameter combination, after each conversion parameter combination is used, all data with the adjustment frequency number being greater than or equal to the initial frequency number in each sub-section interval are called all class-one data of each sub-section interval, and all data with the adjustment frequency number being smaller than the initial frequency number in each sub-section interval are called all class-two data of each sub-section interval.
Preferably, the expression for obtaining the evaluation index of each transformation parameter combination is:
in the above-mentioned formula, the compound has the following structure,
an evaluation index of the t-th transformation parameter combination; />
The number of the first class data in the ith subsection interval after the t-th transformation parameter combination is used is shown; />
The number of the second class data in the ith subsection interval after the t-th transformation parameter combination is used is shown; />
The adjustment frequency number of the u-th class data in the ith subsection interval is represented; />
The initial frequency number of the u-th data in the i-th subsection interval is represented; />
Indicating the adjustment frequency of the v second class data in the i subsection interval; />
Indicating the initial frequency count of the v second class data in the i subsection interval.
Preferably, the ciphertext data acquiring step includes:
for the u-th data in a subsection interval, inserting the u-th data before the position where the u-th data appears for the first time in the first data sequence, wherein the number of the inserted u-th data is equal to the difference between the final adjustment frequency number and the initial frequency number of the u-th data; for the v-th class data in a subsection interval, deleting the v-th class data from the position where the v-th class data first appears in the first data sequence, wherein the deleted number is equal to the difference between the initial frequency count and the final adjustment frequency count of the v-th class data;
and processing each class of data and each class of second data in each sub-section interval, and calling the processed first data sequence as ciphertext data.
Preferably, the method for obtaining the index sequences of all the second-class data in each sub-segment interval is as follows: and forming the index sequence of each second type data by the position sequence number of each deleted second type data in the first data sequence.
Preferably, the method for acquiring the sub-segment key of each sub-segment interval is as follows:
the first transformation parameter in the optimal transformation parameter combination of each sub-section interval is called as the first optimal transformation parameter of each sub-section interval; recording the difference value between the first optimal transformation parameter and the first initial parameter of each subsection interval as a first parameter difference; and recording the difference value between the second optimal transformation parameter and the second initial parameter of each sub-section interval as a second parameter difference, and taking the first parameter difference and the second parameter difference as the sub-section key of each sub-section interval.
The invention has the beneficial effects that: constructing a statistical histogram according to a first data sequence formed by hydraulic model data, and performing self-adaptive segmentation according to the frequency of each data in the statistical histogram; all the first-class data and all the second-class data of each sub-section interval are obtained according to each transformation parameter combination of each sub-section interval; the optimal transformation parameter combination of each subsection interval is obtained according to all the first class data and the second class data in each subsection interval, and the targeted linear transformation is carried out on each subsection interval according to the optimal transformation parameter combination, so that the adjustment frequency corresponding to each data is greatly different from the initial frequency in the first data sequence before and after the linear transformation, the frequency information of each data in the original data is damaged, the cracking difficulty is improved, and the hydraulic model data is safer and more reliable in the storage process.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart illustrating the steps of a hydraulic model data processing method according to the present invention.
Detailed Description
To further illustrate the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description of a hydraulic model data processing method according to the present invention, its specific implementation, structure, features and effects will be given in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" refers to not necessarily the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
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 the hydraulic model data processing method provided by the present invention with reference to the accompanying drawings.
Referring to fig. 1, a flow chart of steps of a hydraulic model data processing method according to an embodiment of the present invention is shown, the method includes the following steps:
step S001: a first data sequence is acquired.
The hydraulic model is used for simulating the dynamic change of water and the diffusion process of substances in the water under various complex conditions indoors and predicting the influence caused by the water dynamic in a certain environment in advance, so that relevant countermeasures are taken to reduce the consequences caused by the influence. For example, the online hydraulic model system integrates static information of a GIS system and dynamic information such as SCADA (supervisory control and data acquisition), revenue and the like, and combines prediction, estimation and distribution of water consumption, hydraulic modeling and simulation calculation are carried out on the central air-conditioning circulating water system according to a hydraulics theory, the hydraulic operation state of the central air-conditioning circulating water system is tracked online, hydraulic information such as Reynolds number, flow, pressure drop, flow rate, temperature and pressure of important nodes of fluid in all pipelines is simulated through real-time calculation, and a basis is provided for scientific adjustment and control of the central air-conditioning circulating water system.
Because the traditional encryption mainly adopts an entropy coding encryption mode, the decoding can be carried out through the regularity of frequency information; although the frequency information of the data can be changed by using linear transformation, the traditional linear transformation uses the same parameters to process all data, has no pertinence on the hydraulic data acquired in real time, namely the traditional linear transformation generates unstable change due to different distribution conditions of the original data, so that the embodiment performs targeted linear transformation according to the frequency information of the data, further destroys the encryption of the frequency information of the data, and further ensures the safety and reliability of the hydraulic model data.
In this embodiment, taking hydraulic data simulated by a hydraulic model, such as pipeline flow data, as an example, hydraulic model data at each time within a preset time is obtained to obtain a data sequence, which is referred to as a first data sequence, at this time, repeated data exists in the first data sequence, and the preset time in this embodiment is 1 hour.
Step S002: and obtaining each subsection interval according to the first data sequence.
Constructing a statistical histogram according to each data in the first data sequence and the frequency number of each data, wherein the horizontal axis of the statistical histogram represents the size of the data, and the vertical axis represents the frequency number of each data, and the frequency number corresponding to each data in the statistical histogram is referred to as the initial frequency number of each data in this embodiment;
a sequence formed by each data on the horizontal axis in the statistical histogram is called a second data sequence; at this point, no duplicate data is present in the second data sequence.
Most of the existing methods for changing data frequency information are linear transformation, but the traditional linear transformation uses the same parameters to process the whole data distribution range to change the frequency information of different data in a statistical histogram, and the method has no pertinence to hydraulic data collected in real time, namely the traditional linear transformation can generate unstable change due to different distribution conditions of original data; therefore, in the embodiment, the statistical histogram is adaptively segmented, and then linear transformation is performed with pertinence according to the change condition of the frequency information corresponding to each data in each obtained sub-segment interval, so that the data in each sub-segment interval can be ensured to have a larger difference with the frequency information of the original data after the linear transformation, and a better encryption effect is obtained. In this embodiment, each segmentation point is obtained according to the frequency count corresponding to each data in the second data sequence and its adjacent data, so as to perform adaptive segmentation on the statistical histogram, and a specific process of performing adaptive segmentation on the statistical histogram is as follows:
taking the nth data in the second data sequence as an example, the relative size between the frequency counts of the nth data and the adjacent data is calculated first, and the relative size is recorded
For the frequency count of the nth data in the statistical histogram, i.e. the initial frequency count of the nth data,/' ->
Is the initial frequency count of the (n + 1) th data, is greater than>
For the initial frequency of the n-1 th data, the n +1 th data and the nth data are processedThe difference between the initial frequency counts of the data->
A first difference, called nth data, based on the number of data bits in the data stream, a difference between the nth data and the initial frequency count of the (n-1) th data being greater than or equal to>
Referred to as the second difference of the nth data, then:
when in use
And/or>
When the data are all 0, the nth data are not considered as segmentation points; when/is>
And with
When a value other than 0 exists, the nth data is considered as a segmentation point; and sequentially processing each data to obtain each segmentation point, wherein all data between two adjacent segmentation points form a subsection interval, so that each subsection interval is obtained, and the self-adaptive segmentation of the statistical histogram is completed. />
Step S003: obtaining all first-class data and all second-class data of each sub-section interval according to each transformation parameter combination of each sub-section interval, and further obtaining an evaluation index of each transformation parameter combination; and obtaining the optimal transformation parameter combination of each sub-section interval according to the evaluation index of each transformation parameter combination.
Using least square method to perform straight line fitting to each subsection interval to obtain linear expression of each subsection interval, and recording the function expression of ith subsection interval as
This embodiment will->
And/or>
Referred to as the first initial parameter and the second initial parameter of the ith sub-segment interval.
Performing targeted linear transformation on each sub-segment interval according to each sub-segment interval in the statistical histogram and the initial function parameters thereof, wherein the specific process is as follows:
the functional expression of the pertinence linear transformation is expressed as
A and b are respectively a first transformation parameter and a second transformation parameter, and the embodiment performs targeted linear transformation on each sub-segment interval by changing values of the two transformation parameters a and b; then according to the frequency numbers corresponding to the data after the linear transformation, corresponding data increasing or data deleting is carried out in the first data sequence relative to the change situation between the frequency numbers corresponding to the data in the statistical histogram, so that the frequency information of the data is changed; however, the 'data deleting' process needs to mark the deleting position and the corresponding deleting data, and extra information is added, so that the conversion parameters of linear conversion need to be selected in a self-adaptive manner, and unnecessary data deleting operation is reduced while the frequency information after the linear conversion is ensured to be changed greatly;
in this embodiment, after performing linear transformation, the frequency count corresponding to each data in each sub-segment interval has a larger difference from the initial frequency count of each data, and in brief, for a sub-segment interval, when the frequency count of a certain data is higher, the frequency count of the data is reduced by the linear transformation; when the frequency of the data is low, the frequency of the data is increased through linear transformation; considering that when the frequency change of different data in a sub-segment interval is relatively slow, the difference between the corresponding frequency before and after linear transformation of each data in the sub-segment interval may be relatively small, namely the frequency information change degree of each data is relatively small at the moment, and the sub-segment interval needs to be processed separately at the moment;
and each isThe initial function parameter corresponding to the sub-section interval can represent the frequency change degree of different data in each sub-section interval, so that the change threshold value is set
0.4, then for the ith subsection interval:
when in use
Then, the frequency change of different data in the subsection interval is considered to be gentler, and at the moment, a second transformation parameter corresponding to the subsection interval is set as a first initial parameter->
Setting a second transformation parameter b corresponding to the subsection interval as a random number between the maximum initial frequency and the minimum initial frequency of the statistical histogram;
when the temperature is higher than the set temperature
Then, the value range of the second transformation parameter b is set as->
(ii) a When/is>
Setting the value range corresponding to the first transformation parameter a as ^ 5>
(ii) a When/is>
Then setting the value range corresponding to the first transformation parameter a as->
(ii) a And both transformation parameters a, b are integers, are greater or less than>
And/or>
The maximum initial frequency and the minimum initial frequency of the ith sub-section interval are respectively;
in this embodiment, after performing linear transformation on the ith sub-segment by using different transformation parameter combinations, the frequency count corresponding to each data is referred to as the adjustment frequency count of each data, and after performing linear transformation by using different parameter combinations, the data with the adjustment frequency count being greater than or equal to the initial frequency count is referred to as a type of data; the data with the adjustment frequency number smaller than the initial frequency number is called second-class data;
then, after linear transformation is performed on the ith sub-segment region according to different transformation parameter combinations, the initial frequency count and the adjustment frequency count of each data obtain an evaluation index of each transformation parameter combination, and the evaluation index of the tth transformation parameter combination can be expressed as:
in the above-mentioned formula, the compound has the following structure,
an evaluation index for the t-th transformation parameter combination; />
The number of the first class data in the ith subsection interval after the t-th transformation parameter combination is used is shown; />
The number of the second class data in the ith subsection interval after the t-th transformation parameter combination is used is shown; />
Indicating the adjustment frequency of the u-th class data in the i-th subsection interval; />
The initial frequency number of the u class data in the i subsection interval is represented; />
Representing the v-th secondary number in the i-th subsection intervalAdjusting frequency according to the data; />
Representing the initial frequency of the v second class data in the i subsection interval;
the transformation parameter combination with the largest evaluation index is referred to as an optimal transformation parameter combination.
Step S004: obtaining ciphertext data according to the optimal transformation parameter combination of each subsection interval; obtaining each sub-block according to the index sequence of all the second-class data in each sub-block interval and the sub-block key; obtaining a chaotic sequence according to each sub-block; and storing the chaotic sequence and the ciphertext data.
The first transformation parameter and the second transformation parameter in the optimal transformation parameter combination are respectively called as the first optimal transformation parameter and the second optimal transformation parameter of the ith sub-section interval and are respectively marked as the first optimal transformation parameter and the second optimal transformation parameter
、/>
At this time, the current conversion parameter can ensure that the adjustment frequency number corresponding to each data has a larger difference with the initial frequency number, and the number of data needing to be deleted is minimum;
performing linear transformation on each sub-segment interval according to the optimal transformation parameters of each sub-segment interval, and then calling the frequency value obtained after each data in each sub-segment interval is subjected to linear transformation as the final adjustment frequency value of each data, in this embodiment, the final adjustment frequency value corresponding to the jth data in the ith sub-segment interval after being subjected to targeted linear transformation is recorded as the final adjustment frequency value of each data
The frequency number of the jth data appearing in the statistical histogram, namely the initial frequency number of the jth data is->
；
Then when the jth data of the ith sub-section interval is 'class data', the number is the first numberInserted before the position in the sequence where the data first appears
The data is stored; otherwise, namely when the jth data of the ith subsection interval is 'class II data', the data are deleted in turn from the position where the data appear for the first time in the first data sequence>
When the jth data is deleted, all position serial numbers corresponding to each deleted data form an index sequence of the jth data;
processing each data of each sub-segment interval in sequence, and taking the processed first data sequence as ciphertext data encrypted by targeted linear transformation;
recording the difference value between the first optimal transformation parameter and the first initial parameter of each subsection interval as a first parameter difference; recording the difference value between the second optimal transformation parameter and the second initial parameter of each sub-section interval as a second parameter difference, and taking the first parameter difference and the second parameter difference as sub-section keys of each sub-section interval;
storing each subsection interval, the index sequence corresponding to each two types of data in each subsection interval and the first parameter difference and the second parameter difference corresponding to each subsection interval, wherein the subsection key of the ith subsection interval is
，/>
. In this embodiment, the sequence number set corresponding to each class of secondary data in each sub-segment interval and the corresponding sub-segment key are referred to as a sub-block, that is, each sub-segment interval corresponds to a sub-block, thereby obtaining each sub-block;
the sequence formed by the sequence numbers corresponding to the sub-section intervals is recorded asIn a sequential order, then by
Scrambling the chaotic mapping pair to obtain a chaotic sequence, scrambling the positions of each sub-block, and combining the controllable parameters of the chaotic sequence>
Chaotic key as whole ciphertext data, wherein &>
Represents an initial value for generating the chaotic sequence, which value is ≥ er>
A random number in the range, is greater than or equal to>
Is->
Any number within the range; p denotes scrambling from the p-th data of the sequential sequence, and the default value of p in this embodiment is 200.
The chaotic key is independently stored offline, when the hydraulic model data need to be analyzed, ciphertext data and each subblock need to be decrypted, and the specific decryption process is as follows:
firstly, restoring the positions of the sub-blocks according to the chaotic key and the chaotic sequence; then constructing a statistical histogram according to each data in the ciphertext data and the frequency of each data, and segmenting the statistical histogram according to each sub-segment interval corresponding to each sub-block to obtain each sub-segment interval of the statistical histogram;
using least square method to carry out straight line fitting to each subsection interval to obtain linear expression of each subsection interval, and for the ith subsection interval, the slope of the linear expression obtained by the subsection interval is corresponding to that of the ith subsection interval
Constants of the linear expressionTerm is +>
Then, the slope and constant terms of the obtained linear expression are respectively added with the corresponding sub-segment keys to obtain the original ^ greater or greater than or equal to the value between the ith sub-segment region>
、/>
Obtaining a first initial parameter and a second initial parameter of the ith subsegment interval; and then restoring the ciphertext data by combining the index sequences corresponding to the second class of data, thereby obtaining corresponding plaintext information, and ending the decryption process.
Through the steps, the processing of the hydraulic model data is completed.
In the embodiment, a statistical histogram is constructed according to a first data sequence formed by hydraulic model data, and self-adaptive segmentation is carried out according to the frequency of each data in the statistical histogram; all the first-class data and all the second-class data of each subsection interval are obtained according to each transformation parameter combination of each subsection interval; and obtaining an optimal transformation parameter combination of each subsection interval according to all the first-class data and the second-class data in each subsection interval, and performing targeted linear transformation on each subsection interval according to the optimal transformation parameter combination, so that before and after the linear transformation, the adjustment frequency corresponding to each data is greatly different from the initial frequency in the first data sequence, thereby destroying the frequency information of each data in the original data, improving the cracking difficulty and ensuring that the hydraulic model data is safer and more reliable in the storage process.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and should not be taken as limiting the scope of the present invention, which is intended to cover any modifications, equivalents, improvements, etc. within the spirit and scope of the present invention.
Claims (9)
1. A hydraulic model data processing method, characterized in that the method comprises the following steps:
acquiring a first data sequence;
obtaining a statistical histogram according to the first data sequence, and calling the frequency count corresponding to each data in the statistical histogram as the initial frequency count of each data; obtaining a second data sequence according to the statistical histogram; obtaining each subsection interval according to the frequency of each data and adjacent data in the second data sequence;
obtaining a first initial parameter and a second initial parameter of each subsection interval according to the initial frequency of each data in each subsection interval; obtaining the value ranges of the first conversion parameter and the second conversion parameter corresponding to each sub-section interval according to the first initial parameter, the maximum initial frequency and the minimum initial frequency of each sub-section interval; obtaining each transformation parameter combination according to the value ranges of the first transformation parameters and the second transformation parameters of each subsection interval; after each subsection interval is subjected to linear transformation by using each transformation parameter combination, the frequency number corresponding to each data is called the adjustment frequency number of each data; all the first-class data and all the second-class data of each sub-section interval are obtained according to the combination of each transformation parameter; obtaining an evaluation index of each transformation parameter combination according to the adjustment frequency and the initial frequency of all the first-class data and the second-class data in each subsection interval; obtaining the optimal transformation parameter combination of each sub-section interval according to the evaluation index of each transformation parameter combination;
obtaining the final adjustment frequency of each data according to the optimal transformation parameter combination of each subsection interval; obtaining ciphertext data according to the final adjustment frequency count and the initial frequency count of each data, and obtaining index sequences of all the second-class data in each subsection interval; obtaining a sub-segment key of each sub-segment interval according to the optimal transformation parameter combination of each sub-segment interval, the first initial parameter and the second initial parameter; obtaining each sub-block according to the index sequence of all the second-class data corresponding to each sub-block interval and the corresponding sub-block key; obtaining a chaotic sequence according to each sub-block; and storing the chaotic sequence and the ciphertext data.
2. The hydraulic model data processing method according to claim 1, wherein the obtaining method of each sub-segment interval is:
regarding the nth data in the second data sequence, recording the difference between the n +1 th data and the initial frequency count of the nth data as a first difference of the nth data; a difference value between the nth data and the (n-1) th data is called as a second difference value of the nth data; when the first difference and the second difference of the nth data are both 0, the nth data is not a segmentation point; otherwise, the nth data is a segmentation point; judging each data in the second data sequence to obtain each segmentation point; all data between two adjacent segmentation points form a subsegment interval, and each subsegment interval is obtained.
3. A hydraulic model data processing method according to claim 1, wherein the first initial parameter and the second initial parameter of each sub-segment interval are obtained by:
and performing linear fitting on each data in each sub-section interval and the initial frequency of each data to obtain a function expression of each sub-section interval, wherein a constant term of the function expression is called as a second initial parameter of each sub-section interval, and a coefficient of the function expression is called as a second initial parameter of each sub-section interval.
4. The hydraulic model data processing method according to claim 1, wherein the first transformation parameter and the second transformation parameter corresponding to each sub-segment interval are: when performing the targeted linear transformation on each subsection interval, the constant term of the function expression of the targeted linear transformation is referred to as a second transformation parameter, and the coefficient of the function expression of the targeted linear transformation is referred to as a first transformation parameter.
5. The hydraulic model data processing method according to claim 1, wherein all the first type data and all the second type data of each sub-segment interval are obtained by:
for each conversion parameter combination, after each conversion parameter combination is used, all data with the adjustment frequency number being larger than or equal to the initial frequency number in each sub-section interval are called all class data of each sub-section interval, and all data with the adjustment frequency number being smaller than the initial frequency number in each sub-section interval are called all class two data of each sub-section interval.
6. The hydraulic model data processing method according to claim 1, wherein the expression for obtaining the evaluation index of each transformation parameter combination is:
in the above-mentioned formula, the compound has the following structure,
an evaluation index of the t-th transformation parameter combination; />
The number of the first class data in the ith subsection interval after the t-th transformation parameter combination is used is shown; />
The number of the second class data in the ith subsection interval after the t-th transformation parameter combination is used is shown; />
The adjustment frequency number of the u-th class data in the ith subsection interval is represented; />
The initial frequency number of the u class data in the i subsection interval is represented; />
Indicating the adjustment frequency of the v second class data in the i subsection interval; />
Indicating the initial frequency of the v second class data in the i subsection interval.
7. The hydraulic model data processing method according to claim 1, wherein the acquiring of the ciphertext data comprises:
for the u-th data in a sub-section interval, inserting the u-th data before the position where the u-th data first appears in the first data sequence, wherein the number of the insertion is equal to the difference between the final adjustment frequency number and the initial frequency number of the u-th data; for the v-th class data in a subsection interval, deleting the v-th class data from the position where the v-th class data first appears in the first data sequence, wherein the deleted number is equal to the difference between the initial frequency count and the final adjustment frequency count of the v-th class data;
and processing each class of data and each class of second data in each sub-section interval, and calling the processed first data sequence as ciphertext data.
8. The hydraulic model data processing method according to claim 1, wherein the index sequence of all the two types of data in each sub-segment interval is obtained by: and forming the index sequence of each class II data by the position sequence number of each deleted class II data in the first data sequence.
9. The hydraulic model data processing method according to claim 1, wherein the sub-segment key of each sub-segment interval is obtained by:
the first transformation parameter in the optimal transformation parameter combination of each sub-section interval is called as the first optimal transformation parameter of each sub-section interval; recording the difference value between the first optimal transformation parameter and the first initial parameter of each subsection interval as a first parameter difference; and recording the difference value between the second optimal transformation parameter and the second initial parameter of each sub-section interval as a second parameter difference, and taking the first parameter difference and the second parameter difference as the sub-section key of each sub-section interval.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310264820.0A CN115987485B (en) | 2023-03-20 | 2023-03-20 | Hydraulic model data processing method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310264820.0A CN115987485B (en) | 2023-03-20 | 2023-03-20 | Hydraulic model data processing method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115987485A true CN115987485A (en) | 2023-04-18 |
| CN115987485B CN115987485B (en) | 2023-05-23 |
Family
ID=85970841
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310264820.0A Active CN115987485B (en) | 2023-03-20 | 2023-03-20 | Hydraulic model data processing method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115987485B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116346676A (en) * | 2023-05-30 | 2023-06-27 | 温州华嘉电器有限公司 | Internet circuit breaker health state evaluation method and system based on data driving |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000115538A (en) * | 1998-09-23 | 2000-04-21 | Xerox Corp | Method for generating real time tone reproduction curve to improve copied image |
| WO2004081719A2 (en) * | 2003-03-07 | 2004-09-23 | Chaoticom, Inc. | Methods and systems for digital rights management of protected content |
| CN106230579A (en) * | 2016-09-18 | 2016-12-14 | 广东省自动化研究所 | A kind of pseudo-random signal method for generation based on chaos and generator |
| US9554738B1 (en) * | 2016-03-30 | 2017-01-31 | Zyomed Corp. | Spectroscopic tomography systems and methods for noninvasive detection and measurement of analytes using collision computing |
| CN110704805A (en) * | 2019-09-13 | 2020-01-17 | 东南大学 | Pre-stressed concrete beam bridge cracking early warning method based on live load strain |
| CN111915472A (en) * | 2019-05-10 | 2020-11-10 | 浙江大学 | Watermark embedding method, watermark extracting method and watermark extracting device |
| CN112422268A (en) * | 2020-11-10 | 2021-02-26 | 郑州轻工业大学 | Image encryption method based on block scrambling and state conversion |
| CN115314600A (en) * | 2022-10-12 | 2022-11-08 | 山东沃尔美肥业有限公司 | Chemical fertilizer production monitoring data management method |
| CN115719300A (en) * | 2023-01-09 | 2023-02-28 | 浙江出海云技术有限公司 | Personnel information management method for big data |
-
2023
- 2023-03-20 CN CN202310264820.0A patent/CN115987485B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2000115538A (en) * | 1998-09-23 | 2000-04-21 | Xerox Corp | Method for generating real time tone reproduction curve to improve copied image |
| WO2004081719A2 (en) * | 2003-03-07 | 2004-09-23 | Chaoticom, Inc. | Methods and systems for digital rights management of protected content |
| US9554738B1 (en) * | 2016-03-30 | 2017-01-31 | Zyomed Corp. | Spectroscopic tomography systems and methods for noninvasive detection and measurement of analytes using collision computing |
| CN106230579A (en) * | 2016-09-18 | 2016-12-14 | 广东省自动化研究所 | A kind of pseudo-random signal method for generation based on chaos and generator |
| CN111915472A (en) * | 2019-05-10 | 2020-11-10 | 浙江大学 | Watermark embedding method, watermark extracting method and watermark extracting device |
| CN110704805A (en) * | 2019-09-13 | 2020-01-17 | 东南大学 | Pre-stressed concrete beam bridge cracking early warning method based on live load strain |
| CN112422268A (en) * | 2020-11-10 | 2021-02-26 | 郑州轻工业大学 | Image encryption method based on block scrambling and state conversion |
| CN115314600A (en) * | 2022-10-12 | 2022-11-08 | 山东沃尔美肥业有限公司 | Chemical fertilizer production monitoring data management method |
| CN115719300A (en) * | 2023-01-09 | 2023-02-28 | 浙江出海云技术有限公司 | Personnel information management method for big data |
Non-Patent Citations (1)
| Title |
|---|
| 刘巧斌: "工程车辆车桥位移谱统计分布建模及分步参数识别", 农业工程学报 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116346676A (en) * | 2023-05-30 | 2023-06-27 | 温州华嘉电器有限公司 | Internet circuit breaker health state evaluation method and system based on data driving |
| CN116346676B (en) * | 2023-05-30 | 2023-08-04 | 温州华嘉电器有限公司 | Internet circuit breaker health state evaluation method and system based on data driving |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115987485B (en) | 2023-05-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Wang et al. | A chaotic image encryption algorithm based on perceptron model | |
| CN103138917B (en) | The Hamming distance model being input as basis with S box carries out SM4 cryptographic algorithm side channel energy analytical method | |
| CN109104544B (en) | Chaotic image encryption method based on complex network synchronization | |
| CN110324147B (en) | GAN game countermeasure encryption method based on chaos model | |
| CN112597519B (en) | Non-key decryption method based on convolutional neural network in OFDM encryption system | |
| CN103745482B (en) | A kind of Dual-threshold image segmentation method based on bat algorithm optimization fuzzy entropy | |
| CN115987485B (en) | Hydraulic model data processing method | |
| CN111130748A (en) | Quantum chaotic parallel image encryption method | |
| CN106375079A (en) | Chaotic encryption method for voice information | |
| Hurst et al. | Big data analysis techniques for cyber-threat detection in critical infrastructures | |
| CN114021168B (en) | Subway foundation pit excavation risk identification method and device based on federal learning | |
| CN113378160A (en) | Graph neural network model defense method and device based on generative confrontation network | |
| CN108683658B (en) | Industrial control network flow abnormity identification method based on multi-RBM network construction reference model | |
| CN116582349A (en) | Attack path prediction model generation method and device based on network attack graph | |
| CN112039859A (en) | Clustering method of complex graph network under variable-length time window | |
| CN109309536B (en) | Method for reducing approximation complexity of Nakagami inverse CDF function | |
| Aslantas et al. | A novel fragile watermarking based on particle swarm optimization | |
| Kang et al. | ROI image encryption using YOLO and chaotic systems | |
| CN113962286A (en) | Decentralized logistic regression classification prediction method based on piecewise function | |
| CN116582249B (en) | Network security analysis method and system based on big data | |
| CN113098897B (en) | Asymmetric encryption transmission method and system for data in networked control system | |
| CN117579382B (en) | Intelligent real-time sharing method for ecological monitoring station data | |
| CN117560663B (en) | Information interaction method and system based on 5G message | |
| CN114118289B (en) | Method and system for identifying operation condition of finished oil pipeline based on data driving | |
| CN117540441B (en) | Transaction data secure storage method for hall type cloud terminal |
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 |