CN115861034A - Wireless routing data intelligent management system - Google Patents
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Abstract
The invention relates to the technical field of data encryption, in particular to an intelligent management system for wireless routing data. The data processing module in the system is used for constructing a two-dimensional matrix and a two-dimensional gray matrix image by data information; the threshold value obtaining module is used for screening a plurality of segmentation threshold values from preset threshold values and segmenting the two-dimensional gray matrix image to obtain a segmented image; the threshold screening module is used for selecting an optimal threshold from the segmentation thresholds and segmenting the two-dimensional gray matrix image based on the optimal threshold to obtain a target segmentation image; the encryption storage module is used for scanning pixel points in the target segmentation image based on the optimal scanning rule to obtain a ciphertext, taking the optimal scanning rule and the construction parameters of the two-dimensional matrix as a secret key, and storing data information based on the ciphertext and the secret key. According to the invention, the optimal scanning rule is selected for the obtained optimal target segmentation image for scrambling, so that the effect better than that of the traditional scrambling operation is achieved, and the intelligent management of wireless routing data is completed.
Description
Technical Field
The invention relates to the technical field of data encryption, in particular to an intelligent management system for wireless routing data.
Background
The wireless router is used for user to surf the internet and has a wireless coverage function; the broadband wireless network repeater can be regarded as a repeater, and forwards broadband network signals connected to the wall of a home to nearby wireless network equipment through an antenna; wireless routers are widely used and popularized, which facilitate internet surfing and clothes and eating and housing, but at the same time, a large amount of data information exists in wireless local area networks. Many data relate to a large amount of privacy of company units or individuals, and if effective encryption processing is not performed on data information, the data are easily attacked and stolen by network hackers, so that the privacy and the security of the data are difficult to ensure.
The common method for encrypting data at present is to use scrambling encryption, and encryption is completed by changing the position information of time sequence data, but the data scrambling effect obtained by the method cannot be evaluated; the conditions of large similarity and small difference between the scrambling sequence and the original data sequence exist, and further the privacy and the encryption effect of the data are not ensured.
Disclosure of Invention
In order to solve the technical problem that the privacy and encryption effect of data cannot be guaranteed by using the conventional scrambling encryption, the invention aims to provide an intelligent management system for wireless routing data, which comprises the following modules:
the data processing module is used for converting each data message into a decimal number; constructing a two-dimensional matrix and a corresponding two-dimensional gray matrix image by decimal numbers;
the threshold value obtaining module is used for taking the normalized gray value of each pixel point in the two-dimensional gray matrix image as a preset threshold value; calculating a threshold selection standard according to the difference between a preset threshold and a preset gray value; selecting a preferred preset threshold according to a threshold selection standard; selecting preset thresholds with adjacent sizes by taking the optimal preset threshold as a starting point, respectively taking the preset thresholds as segmentation thresholds, and segmenting the two-dimensional gray matrix image to obtain at least two segmented images;
the threshold value screening module is used for acquiring the number of connected domains formed by pixel points with different pixel values in the segmented image; calculating the approximation degree of the segmented image according to the difference between the numbers of connected domains corresponding to the pixel points with different pixel values; calculating a segmentation standard according to the number of connected domains in the segmented image and the approximation degree; based on the segmentation standard, selecting an optimal threshold value from segmentation threshold values; dividing the two-dimensional gray matrix image based on the optimal threshold value to obtain a target divided image;
the encryption storage module is used for scanning the target segmentation image by adopting different scanning rules and screening out the optimal scanning rule; based on the optimal scanning rule, scanning pixel points in the target segmentation image, and extracting decimal numbers corresponding to the pixel points as ciphertext; and taking the optimal scanning rule and the construction parameters of the two-dimensional matrix as a key, and storing data information based on the ciphertext and the key.
Preferably, the constructing a two-dimensional matrix and a corresponding two-dimensional gray matrix image from decimal numbers includes:
placing decimal numbers corresponding to all data information in a two-dimensional matrix according to the sequence of the data information, wherein the two-dimensional matrix is a two-dimensional matrix constructed by the decimal numbers; the construction parameters of the two-dimensional matrix are the length and the width of the two-dimensional matrix;
and taking each element in the two-dimensional matrix as a pixel value of a pixel point on the image to construct a two-dimensional gray matrix image corresponding to the two-dimensional matrix.
Preferably, the calculating the threshold selection criterion according to the difference between the preset threshold and the preset gray value includes:
calculating an absolute value of a difference value between a preset threshold value and a preset gray value as a first absolute value; and carrying out negative correlation mapping on the first absolute value, and taking the obtained result value as a threshold selection standard.
Preferably, the acquiring the number of connected components formed by the pixel points with different pixel values in the segmented image includes:
the segmentation image is a binary image; the number of connected domains comprises a first number of connected domains and a second number of connected domains;
acquiring the number of connected domains formed by pixel points with pixel values of 1 in the segmented image as the number of first connected domains;
and acquiring the number of connected domains formed by pixel points with pixel values of 0 in the segmented image as the number of second connected domains.
Preferably, the calculating the approximation degree of the segmented image according to the difference between the numbers of connected domains corresponding to the pixel points of different pixel values includes:
calculating the ratio of the number of the first connected domains to the number of the second connected domains as the ratio of the number of the connected domains;
calculating the absolute value of the difference value between the connected domain quantity ratio and a preset first threshold value as the difference degree; and carrying out negative correlation mapping on the difference degree, and taking the obtained result value as the approximate degree of the segmented image.
Preferably, the calculating the segmentation criteria according to the number of connected components in the segmented image and the approximation degree includes:
and carrying out weighting and averaging on the normalized number of the connected domains and the approximation degree, and taking the obtained result value as a segmentation standard.
Preferably, the scanning the target segmentation image by using different scanning rules to screen out an optimal scanning rule includes:
scanning a target segmentation image by using a raster scanning rule to obtain a corresponding coding sequence as a first coding sequence;
respectively scanning the target segmentation image by using at least two scanning rules except the raster scanning rule to obtain corresponding coding sequences as second coding sequences;
obtaining a scanning rule value according to the difference of the coding numerical values at the same position in the first coding sequence and the second coding sequence; and taking the scanning rule corresponding to the maximum scanning rule value as the optimal scanning rule.
Preferably, said obtaining a scanning rule value based on the difference of the coded numerical values at the same position in said first code sequence and said second code sequence comprises:
calculating the absolute value of the difference of the coded values at the same position in the first coded sequence and the second coded sequence as a second absolute value; the sum of the second absolute values corresponding to the coding numerical values is used as an initial rule value; and taking the normalized initial rule value as a scanning rule value.
Preferably, the selecting the preferred preset threshold according to the threshold selection criterion includes:
and selecting a preset threshold corresponding to the maximum threshold selection standard as a preferred preset threshold.
Preferably, the selecting an optimal threshold from the segmentation thresholds based on the segmentation criteria includes:
and acquiring a corresponding segmentation threshold value when the segmentation standard is closest to a preset second threshold value as an optimal threshold value.
The embodiment of the invention at least has the following beneficial effects:
the data processing module in the system is used for converting each data information into a decimal number, constructing a two-dimensional matrix and a corresponding two-dimensional gray matrix image, converting the data information into the two-dimensional gray matrix image, and further selecting a proper threshold value to segment the image by analyzing the image data to complete the subsequent encryption process. The threshold acquisition module is used for selecting an optimal preset threshold from preset thresholds; the optimal threshold value is obtained through subsequent adjustment, and compared with a method of directly taking all the preset threshold values as the segmentation threshold values, a large amount of calculation amount is reduced. The threshold value screening module is used for acquiring the number of connected domains in the segmented image; calculating the approximation degree of the segmented image according to the difference between the numbers of connected domains corresponding to the pixel points with different pixel values; calculating a segmentation standard according to the number of connected domains in the segmented image and the approximation degree; based on the segmentation standard, an optimal threshold is selected from the segmentation thresholds, the segmentation standard is obtained according to the number characteristics of connected domains in the segmented image and the difference characteristics between the number of connected domains formed by the pixel points with different pixel values, the optimal threshold is screened out, the purpose of obtaining the best segmentation effect more accurately is achieved, the connected domains are dispersed as much as possible, the number of the connected domains of different types is similar as much as possible, and the better segmentation effect is achieved. The two-dimensional gray matrix image is segmented based on the optimal threshold value to obtain a target segmentation image, the segmentation effect of the target segmentation image is better compared with other segmentation images, and the different data can be selected in an individualized and self-adaptive manner by analyzing the data and selecting the appropriate optimal threshold value for segmentation; the encryption storage module is used for scanning the target segmentation image by adopting different scanning rules and screening out the optimal scanning rule; based on the optimal scanning rule, scanning pixel points in the target segmentation image, and extracting decimal numbers corresponding to the pixel points as ciphertext; the optimal scanning rule and the construction parameters of the two-dimensional matrix are used as keys, data information is stored based on the ciphertext and the keys, the optimal scanning rule is selected to scan the target segmentation image, the ciphertext is extracted based on the target segmentation image with the best segmentation effect, the keys are obtained, scrambling stability is stronger, and the difference before and after encryption and the encryption effect are ensured. According to the method, the wireless routing data is subjected to scale conversion and pretreatment, the numerical value of the wireless routing data is converted into a gray scale interval so as to construct a two-dimensional gray matrix image of the data, and a proper threshold value is selected for segmentation through analyzing the data; and an optimal scanning rule is selected for the target segmentation image obtained by segmentation to carry out scrambling, the maximum difference between the processed matrix and the original two-dimensional matrix is ensured to the greatest extent, the effect better than that of the traditional scrambling operation is achieved, and intelligent management of wireless routing data is completed.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of 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 other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a system block diagram of an intelligent management system for wireless routing data according to an embodiment of the present invention;
fig. 2 is a two-dimensional grayscale matrix image converted from a two-dimensional matrix to a grayscale image according to an embodiment of the present invention.
Detailed Description
To further illustrate the technical means and effects of the present invention for achieving the predetermined objects, the following detailed description of the wireless routing data intelligent management system according to the present invention, its specific implementation, structure, features and effects will be given in conjunction with the accompanying drawings and the 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 embodiment of the invention provides a specific implementation method of a wireless routing data intelligent management system, which is suitable for a data intelligent management scene. The encryption of the wireless routing data is realized under the scene. The technical problem that privacy and encryption effect of data cannot be guaranteed when conventional scrambling encryption is used is solved. According to the method, the wireless routing data is subjected to scale conversion and pretreatment, the numerical value of the wireless routing data is converted into a gray scale interval so as to construct a two-dimensional gray matrix image of the data, and a proper threshold value is selected for segmentation through analyzing the data; and an optimal scanning rule is selected for the target segmentation image obtained by segmentation to carry out scrambling, the maximum difference between the processed matrix and the original matrix is ensured to the greatest extent, the effect better than that of the traditional scrambling operation is achieved, and intelligent management of wireless routing data is completed.
The following describes a specific scheme of the wireless routing data intelligent management system provided by the present invention in detail with reference to the accompanying drawings.
Referring to fig. 1, a system block diagram of an intelligent management system for wireless routing data according to an embodiment of the present invention is shown, where the system block diagram includes the following modules:
a data processing module 10 for converting each data information into a decimal number; and constructing a two-dimensional matrix and a corresponding two-dimensional gray matrix image by decimal numbers.
Firstly, preprocessing the wireless routing data information: because the computer can only identify the binary data of 0 and 1 when transmitting data, the conversion operation is needed for the wireless routing data; the numbers in the wireless routing data are directly converted into binary codes, english letters, identification symbols and the like in the wireless routing data are firstly converted into ASC codes by referring to an ASC table, and then the obtained ASC codes are converted into the binary codes.
And carrying out the binary conversion on each character or letter, and correspondingly storing the obtained binary coding subsegment and the original data information.
The binary code lengths corresponding to the ASC code median values of more than or equal to 64 are 7 bits; the binary code length corresponding to the value less than 64 is less than 7 bits; therefore, in order to standardize the length of binary coding after data conversion, the fixed length of the binary coding subsegment of each character is specified to be 7; the method comprises the following steps that (1) a code with a data letter or character ASC code smaller than 7 bits is subjected to 0 complementing operation at the head of the code; for example, if the ASC code of the current character is 60, it is converted into a binary of 111100, i.e. the encoding length is 6, then the number of bits to complement 0 before the binary encoding is 1, i.e. to complement one 0; a 7-bit binary code is obtained: 0111100.
the step realizes that the wireless routing data information is converted into binary code which can be operated and identified by a computer and the segment length is standardized; the coding subsections are required to be processed to enable the numerical values converted into decimal numbers to be in the range of 0-255, so that a two-dimensional gray matrix image is conveniently constructed, and an optimal threshold value is selected according to the subsequently constructed two-dimensional gray matrix image for segmentation to obtain a target segmentation image; the target segmentation image is a binary image, and an optimal scanning rule is selected according to the characteristics of the binary image to perform scrambling operation.
The binary coding sub-segment digit of each data obtained in the above step is 7 bits; the maximum value of the ASC code is 127, so that the binary code converted into decimal value can be uniformly distributed in the range of 0-255, and when the binary code is distributed in the range of 0-255, the gray value ranges from 0-255. Therefore, the bit increasing operation needs to be performed on the obtained coding subsegment: complementing 1 at the tail of the obtained 7-bit code; for example, the original binary-coded sub-segment is 1111111; at this time, the decimal value is converted into 127; after the bit increasing operation, 8-bit binary code is obtained and is 11111111; the decimal value is converted into 255 at this time, and the requirement that the converted decimal value is distributed between 0 and 255 is met.
Respectively carrying out bit increasing operation on the obtained 7-bit binary codes according to the bit increasing operation mode, wherein the coding length of each sub-section is 8; for example, the current binary-coded sub-segment sequences are 0110111, 1011100, 1110111; the sequence of the binary coding subsections after the bit increasing processing is as follows: 01101111, 10111001, 11101111; the sequence after the increase of the bits is converted into decimal values as follows: 111. 185 and 239.
Further, a two-dimensional matrix and a corresponding two-dimensional gray matrix image are constructed by decimal numbers, specifically: and sequentially placing decimal numbers corresponding to all the data information in a two-dimensional matrix according to the sequence of the data information, wherein the two-dimensional matrix is a two-dimensional matrix constructed by the decimal numbers, and constructing a two-dimensional gray matrix image corresponding to the two-dimensional matrix by taking each element in the two-dimensional matrix as a pixel value of a pixel point on the image. Or the decimal number obtained by conversion can be correspondingly stored in the numerical value sequence set according to the sequence of the original binary coding subsegment; sequentially according to the numerical order of the numerical sequence set
A decimal value is taken as a row, and the value in the value sequence set is divided into ^ greater or greater>
A row; is found to be of size>
Of (2) is calculated. The construction parameters of the two-dimensional matrix are the length a and the width b of the two-dimensional matrix. It should be noted that a and b may be equal or different, and the values of a and b are set by the implementer according to specific data and scenes. Furthermore, each element in the two-dimensional matrix corresponds to its binary-coded sub-block, where the element is also a decimal number. The two-dimensional matrix is scanned line by using the traditional raster scanning rule, and an initial decimal value sequence set can be obtained through restoration. Taking each element in the constructed two-dimensional matrix as the gray value; and converting the currently constructed two-dimensional matrix into a two-dimensional gray matrix image. Fig. 2 shows a two-dimensional gray-scale matrix image converted from the constructed two-dimensional matrix into a gray-scale image.
A threshold obtaining module 20, configured to use the normalized gray value of each pixel in the two-dimensional gray matrix image as a preset threshold; calculating a threshold selection standard according to the difference between a preset threshold and a preset gray value; selecting a preferred preset threshold according to a threshold selection standard; and selecting the preset threshold values with adjacent sizes by taking the optimal preset threshold value as a starting point, and dividing the two-dimensional gray matrix image to obtain at least two divided images by respectively taking the preset threshold values as the division threshold values.
Further, pixel gray value analysis is carried out on the two-dimensional gray matrix image obtained by the data processing module, and an optimal threshold value is selected to carry out segmentation on the two-dimensional gray matrix image. The step only aims at the optimal threshold selection standard of the data encryption scene, so that the number of the numerical blocks on two sides in the two-dimensional gray matrix image which is segmented by the optimal threshold is close to the maximum, and the subsequent encryption effect is ensured. Therefore, for the method of adaptive threshold segmentation or threshold selection by using a histogram commonly used in image processing, the segmentation purpose is different, and the method is not suitable for the scene in the embodiment of the present invention, and the obtained segmented image effect is different according to different images, and cannot be comprehensively evaluated. For example, the main purpose of threshold segmentation in image processing is to obtain a region of interest; the foreground area and the background area are divided, and the threshold division is mainly used for data encryption, so that the discreteness of the divided two parts of areas is large, the quantity difference is small, the subsequent scanning scrambling operation is convenient, and the scrambling effect is better. Therefore, the optimal threshold value is screened out by analyzing the pixel points in the two-dimensional gray matrix image.
Firstly, the normalized gray value of each pixel point in the two-dimensional gray matrix image is used as a preset threshold value. Each pixel point corresponds to a preset threshold, and each two-dimensional gray matrix image corresponds to a plurality of preset thresholds.
The preset threshold value is calculated according to the formula:
wherein the content of the first and second substances,
a preset threshold corresponding to the pixel point i; />
The gray value of the pixel point i is obtained; />
Is the maximum gray value in the two-dimensional gray matrix image; />
Is the minimum gray value in the two-dimensional gray matrix image.
The calculation formula of the preset threshold is a normalized gray value corresponding to the pixel point, and is not described herein again.
And calculating the normalized gray value of each pixel point in the two-dimensional gray matrix image, and performing the following calculation on the obtained preset threshold. Calculating a threshold selection standard according to the difference between a preset threshold and a preset gray value, specifically: calculating an absolute value of a difference value between a preset threshold value and a preset gray value as a first absolute value; and carrying out negative correlation mapping on the first absolute value, and taking the obtained result value as a threshold selection standard. In the embodiment of the invention, the first absolute value is realized by an exponential function taking a negative first absolute value as an exponent and taking a natural constant as a base numberNegative correlation mapping of values. In the embodiment of the present invention, the value of the preset gray value is 0.5, and in other embodiments, the value is adjusted by an implementer according to the actual situation. It should be noted that the preset gradation value is set to 0.5 because
The normalized gray value is in the range of 0-1, the optimal threshold corresponding to the two-dimensional gray matrix image is centered as much as possible, and the difference between the two divided areas is small. />
The threshold selection criterion is calculated by the formula:
wherein the content of the first and second substances,
selecting a criterion for the threshold; />
Is an exponential function with a natural constant as a base number; />
A preset threshold corresponding to the pixel point i; />
Is a preset grey scale value; />
Is the first absolute value.
Wherein exp (-x) in the calculation formula of the threshold selection standard also realizes the negative correlation mapping of the first absolute value; satisfy when the first absolute value
The closer to 0 the value of (a), the larger the corresponding threshold selection criterion, the closer to 1 the value of the threshold selection criterion, and the inverse relation between the first absolute value and the threshold selection criterion.
Calculating threshold selection standards of preset thresholds corresponding to pixel points in the two-dimensional gray matrix image, and selecting an optimal preset threshold from the preset thresholds according to the threshold selection standards, specifically: and selecting a preset threshold corresponding to the maximum threshold selection standard as a preferred preset threshold. The optimal preset threshold value only represents a value with a moderate gray value in all gray values existing in the current two-dimensional gray matrix image, and the probability that the number of connected domains of black and white parts of a binary image obtained by segmentation with the optimal preset threshold value as the threshold value is similar is theoretically large; therefore, the segmentation experiment was performed as a preset segmentation threshold.
After the optimal preset threshold is obtained, the preset thresholds with adjacent sizes are selected by taking the optimal preset threshold as a starting point and are respectively used as segmentation thresholds, and the two-dimensional gray matrix image is segmented to obtain at least two segmentation images. Specifically, the method comprises the following steps: and counting the preset threshold corresponding to each pixel point, wherein the same preset threshold is counted only once, and the preset thresholds are sequenced from small to large according to the magnitude sequence of the preset thresholds to obtain a threshold sequence, wherein no repeated numerical value exists in the threshold sequence. Based on the threshold sequence, selecting preset thresholds with adjacent sizes as starting points by taking the optimal preset threshold as a segmentation threshold; and dividing the two-dimensional gray matrix image based on the division threshold value to obtain at least two divided images. Wherein, select its adjacent size preset threshold value, as cutting apart the threshold value respectively, specific: based on the preferred preset threshold and the threshold sequence, selecting a preset number of preset thresholds from the preferred preset threshold to the left as a segmentation threshold, and selecting a preset number of preset thresholds to the right as a segmentation threshold, wherein the preferred preset threshold is also used as a segmentation threshold. In the embodiment of the present invention, the value of the preset number is 10, and in other embodiments, the value is adjusted by an implementer according to an actual situation.
The method comprises the following steps of obtaining an optimal preset threshold, selecting preset thresholds with adjacent sizes by taking the optimal preset threshold as a starting point, and respectively taking the optimal preset threshold as a segmentation threshold, wherein the method has the advantages that compared with the method that all preset thresholds are directly taken as segmentation thresholds: after the optimal preset threshold is obtained, the final optimal threshold is close to the optimal preset threshold, that is, the optimal threshold is obtained by adjusting on the basis of the optimal preset threshold, only the preset threshold adjacent to the optimal preset threshold is required to be used as a segmentation threshold, and compared with the method that all preset thresholds are directly used as segmentation thresholds, a large amount of calculation is reduced.
The two-dimensional gray matrix image is divided based on the division threshold value to obtain at least two divided images, specifically: setting the gray value of a pixel point with the gray value larger than or equal to a segmentation threshold value in the two-dimensional gray matrix image as 1; setting the gray value of a pixel point with the gray value smaller than the segmentation threshold value in the two-dimensional gray matrix image as 0, and setting the gray value of the pixel point as 1 and setting the gray value as 0 to obtain a binary image, namely the corresponding segmentation image.
The segmentation can also be said to be performed according to a segmentation formula:
wherein the content of the first and second substances,
for dividing the image into coordinates>
The gray value of the pixel point; />
Is a coordinate on the two-dimensional gray-scale matrix image of->
The gray value of the pixel point; />
Is a segmentation threshold.
The threshold screening module 30 is configured to obtain the number of connected domains formed by pixel points with different pixel values in the segmented image; calculating the approximation degree of the segmented image according to the difference between the numbers of connected domains corresponding to the pixel points with different pixel values; calculating a segmentation standard according to the number of connected domains in the segmented image and the approximation degree; based on the segmentation standard, selecting an optimal threshold value from segmentation threshold values; and segmenting the two-dimensional gray matrix image based on the optimal threshold value to obtain a target segmentation image.
After a plurality of segmentation images are obtained, each segmentation image is analyzed, and a segmentation threshold corresponding to the segmentation image with the best effect is screened out to be used as an optimal threshold. Specifically, the method comprises the following steps:
for a segmented image, firstly, the number of connected domains formed by pixel points with different pixel values in the segmented image is obtained, specifically: the number of connected domains comprises a first number of connected domains and a second number of connected domains; acquiring the number of connected domains formed by pixel points with pixel values of 1 in the segmented image as the number of first connected domains; and acquiring the number of connected domains formed by pixel points with pixel values of 0 in the segmented image as the second number of connected domains. Namely, the number of connected domains corresponding to the segmented image is the sum of the number of the first connected domains and the number of the second connected domains. The divided image is a binary image.
Further, calculating the approximation degree of the number of connected domains of a white region with a gray value of 1 and a black region with a gray value of 0 in the current segmented image, that is, calculating the approximation degree of the segmented image according to the difference between the number of connected domains corresponding to pixel points with different pixel values, specifically: calculating the ratio of the number of the first connected domains to the number of the second connected domains as the ratio of the number of the connected domains; and calculating the absolute value of the difference value between the connected component quantity ratio and a preset first threshold value as the difference degree. And carrying out negative correlation mapping on the difference degree, and taking the obtained result value as the approximation degree of the segmented image. In the embodiment of the present invention, the value of the first threshold is preset to be 1, and in other embodiments, the value is adjusted by an implementer according to an actual situation.
The calculation formula of the approximation degree is as follows:
wherein the content of the first and second substances,
to an approximate extent; />
Is an exponential function with a natural constant as a base number; />
Is the first number of connection domains; />
A second connected domain number; />
Is the number ratio of connected domains; 1 is a preset first threshold; />
Is the degree of difference.
In the calculation formula of the approximation degree, exp (-x) realizes inverse proportion normalization of the difference degree, namely exp (-x) realizes negative correlation mapping of the difference degree. Ratio of number of connected components
Is the ratio of the first number of connected domains to the second number of connected domains, the closer the ratio of the number of connected domains is to 1, the closer the first number of connected domains and the second number of connected domains are, i.e. the degree of difference->
The closer to 0, the more similar the number of the first connected domain and the number of the second connected domain are reflected. Degree of difference->
The smaller the value of (A), the greater the corresponding approximation degree, which is closer to 1, when the degree of difference->
The larger the value of (a), the smaller the corresponding approximation degree, and the closer to 0.
And calculating the number and the approximation degree of the connected domains corresponding to each segmented image.
The number of connected domains in the segmented image, the approximation degree between the first connected domain number and the second connected domain number are all segmentation judgment criteria of the segmented image. The segmentation criteria are calculated based on the number of connected components in the segmented image and the degree of approximation. Specifically, the method comprises the following steps: and weighting and averaging the normalized number of the connected domains and the approximation degree, and taking the obtained result value as a segmentation standard.
The calculation formula of the segmentation standard is as follows:
wherein the content of the first and second substances,
dividing the image into divided images; />
Is preset with a first weight; />
Is preset with a second weight;
the number of connected domains corresponding to the segmented image; />
The normalized number of connected domains; />
Is a normalization function; />
The degree of approximation corresponding to the segmented image.
For scrambling encryption, the influence of the dispersion of the area position and the random diversity on the encryption effect is more important than the numerical ratio, so in the embodiment of the present invention, the value of the first weight is preset to be 0.6, and the value of the second weight is preset to be 0.4, and in other embodiments, the implementer can adjust the values according to actual situations. When the number of connected domains
The greater the value of (a), the normalized value>
The closer to 1 the value of (a) is, the proportional relation is formed with the change of the number of connected domains; the more the number of the connected domains is, the more discrete the position of the connected domain area is reflected, and the normalized number of the connected domains and the segmentation standard are in a direct proportion relation; the degree of approximation reflects the degree of similarity between two regions of a segmented image obtained by segmentation based on a segmentation threshold, and the greater the degree of approximation, the greater the segmentation criterion corresponding to the segmented image, and the greater the degree of approximation is in direct proportion to the segmentation criterion.
Based on the segmentation criteria, an optimal threshold is selected from the segmentation thresholds. Specifically, the method comprises the following steps: and acquiring a corresponding segmentation threshold value when the segmentation standard is closest to a preset second threshold value as an optimal threshold value. In the embodiment of the present invention, the value of the preset second threshold is 1, and in other embodiments, an implementer may adjust the value according to an actual situation. That is, when the segmentation criterion is closest to 1, the current corresponding segmentation threshold is considered as the optimal threshold. Because the segmentation threshold is calculated by the normalized number of the connected domains and the segmentation standard, the two parameters are subjected to weighted summation, the value range of the segmentation standard is within 0-1, and the closer the segmentation standard is to 1, the better the segmentation effect of the segmented image is reflected, so that the segmentation threshold corresponding to the segmentation standard closest to 1 is selected as the optimal threshold.
And the same method for acquiring the segmentation image is used for segmenting the two-dimensional gray matrix image based on the optimal threshold value to obtain the target segmentation image.
The encryption storage module 40 is used for scanning the target segmentation image by adopting different scanning rules and screening out the optimal scanning rule; based on the optimal scanning rule, scanning pixel points in the target segmentation image, and extracting decimal numbers corresponding to the pixel points as ciphertext; and taking the optimal scanning rule and the construction parameters of the two-dimensional matrix as a key, and storing data information based on the ciphertext and the key.
And scanning the target segmentation image by adopting different scanning rules, and screening out the optimal scanning rule. Specifically, the method comprises the following steps: and scanning the target segmentation image by using a raster scanning rule to obtain a corresponding coding sequence as a first coding sequence. The elements in the coding sequence are pixel values corresponding to the pixel points, and the decimal values corresponding to the pixel points are the coding values. After a target segmentation image obtained based on optimal threshold segmentation is obtained, respectively scanning values corresponding to black pixel points and white pixel points in the target segmentation image line by using raster scanning, and storing a scanned value sequence; and storing decimal number scans corresponding to black pixels with the gray value of 0 into a set H, and storing decimal number scans corresponding to white pixels with the gray value of 1 into a set B.
Further, the optimal scanning rule is selected, and the target segmented image is scanned by using at least two scanning rules except the raster scanning rule to obtain a corresponding coding sequence which is used as a second coding sequence.
And obtaining a scanning rule value according to the difference of the coding numerical values at the same position in the first coding sequence and the second coding sequence obtained by the raster scanning rule and other scanning rules, namely comparing the coding sequences obtained under different scanning rules with the coding sequences obtained under the raster scanning rule to obtain the scanning rule value.
The method for acquiring the scanning rule value comprises the following steps: calculating an absolute value of a difference between the code values at the same positions in the first code sequence and the second code sequence as a second absolute value; the sum of the second absolute values corresponding to the coding numerical values is used as an initial rule value; and taking the normalized initial rule value as a scanning rule value.
The calculation formula of the scanning rule value is as follows:
wherein the content of the first and second substances,
is a scan rule value; />
Is a normalization function; />
The coded value at the ith position in the second coded sequence; />
The coded value at the ith position in the first coding sequence; />
Is the length of the coding sequence; a is the width of the two-dimensional gray matrix image; b is the length of the two-dimensional gray matrix image; />
For coding a value>
And &>
The corresponding second absolute value; />
Is the initial rule value.
The summation to obtain the initial rule value is to obtain the sum of absolute values of differences corresponding to the coded values at the positions on the first coded sequence and the second coded sequence obtained by the two scanning rules, the larger the initial rule value is, the larger the difference between the first coded sequence and the second coded sequence is, th is a hyperbolic tangent function, where the normalization of the initial rule value is shown, so that the initial rule value before the normalization and the scanning rule value obtained after the normalization are in a direct proportion relation, and when the initial rule value is larger, the corresponding scanning rule value is closer to 1.
And respectively calculating the rule values of at least two scanning rules except the raster scanning rule, and taking the scanning rule corresponding to the maximum scanning rule value as the optimal scanning rule.
And scanning the target segmentation image by using an optimal scanning rule, scanning pixel points in the target segmentation image, extracting decimal numbers corresponding to the pixel points as a ciphertext, namely storing the coded sequence obtained after scanning, correspondingly replacing 0 and 1 in the coded sequence by the coded values in the set H and the set B, and using a sequence formed by the coded values as the ciphertext. The coded number here is also a decimal number. If the obtained coding sequence after scanning is 11010100; the sequences in set H are: 23. 15, 56, 20; the sequences in set B are 155, 147, 221, 105. And (3) sequentially replacing 0 in the scanned coding sequence by the numerical value in the set H, and sequentially replacing 1 in the scanned coding sequence by the numerical value in the set B, so as to obtain a sequence consisting of the coding numerical values: 155. 147, 23, 221, 15, 105, 56, 20. The sequence is saved as a ciphertext.
And storing the obtained ciphertext, and storing the optimal scanning rule and the construction parameters of the two-dimensional matrix as a secret key. Data information is stored based on the ciphertext and the key. After obtaining the ciphertext and the key, an embodiment of the present invention provides a decryption method: firstly, establishing parameters according to a two-dimensional matrix, converting a ciphertext sequence into a storage format of the two-dimensional matrix, then restoring a numerical value block in the current two-dimensional matrix to an original position according to an optimal scanning rule, namely restoring elements in the current two-dimensional matrix to the original position according to the optimal scanning rule, further reading a numerical value in the two-dimensional matrix by using a raster scanning mode, and converting the numerical value into a one-dimensional sequence; and carrying out binary code conversion on the decimal in each sequence, carrying out the bit reduction operation of the binary code, corresponding to the bit increasing operation in the data processing module, and converting the binary code after bit reduction into a decimal value again to obtain a plaintext.
In summary, the present invention relates to the field of data encryption technology. The system comprises: the device comprises a data processing module, a threshold value acquisition module, a threshold value screening module and an encryption storage module. The data processing module is used for converting each data information into a decimal number and constructing a two-dimensional matrix and a corresponding two-dimensional gray matrix image; the threshold acquisition module is used for taking the normalized gray value of each pixel point in the two-dimensional gray matrix image as a preset threshold; selecting a preferred preset threshold from preset thresholds; selecting preset thresholds with adjacent sizes by taking the optimal preset threshold as a starting point, respectively taking the preset thresholds as segmentation thresholds, and segmenting the two-dimensional gray matrix image to obtain at least two segmented images; the threshold value screening module is used for acquiring the number of connected domains in the segmented image; calculating the approximation degree of the segmented image according to the difference between the connected domain numbers corresponding to the pixel points with different pixel values; calculating a segmentation standard according to the number of connected domains in the segmented image and the approximation degree; based on the segmentation standard, selecting an optimal threshold value from segmentation threshold values; dividing the two-dimensional gray matrix image based on the optimal threshold value to obtain a target divided image; the encryption storage module is used for scanning the target segmentation image by adopting different scanning rules and screening out the optimal scanning rule; based on the optimal scanning rule, scanning pixel points in the target segmentation image, and extracting decimal numbers corresponding to the pixel points as ciphertext; and taking the optimal scanning rule and the construction parameters of the two-dimensional matrix as a key, and storing data information based on the ciphertext and the key. According to the method, the wireless routing data is subjected to binary conversion and pretreatment, the numerical value of the wireless routing data is converted into a gray scale interval so as to construct a two-dimensional gray matrix image of the data, and a proper threshold value is selected for segmentation through analyzing the data; and selecting an optimal scanning rule for scrambling the binary image obtained by segmentation, so that the maximum difference between the processed matrix and the original matrix is ensured to the greatest extent, a better effect than the traditional scrambling operation is achieved, and the intelligent management of the wireless routing data is completed.
It should be noted that: the precedence order of the above embodiments of the present invention is only for description, and does not represent the merits of the embodiments. The processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
Claims (10)
1. An intelligent management system for wireless routing data is characterized by comprising the following modules:
the data processing module is used for converting each data message into a decimal number; constructing a two-dimensional matrix and a corresponding two-dimensional gray matrix image by decimal numbers;
the threshold acquisition module is used for taking the normalized gray value of each pixel point in the two-dimensional gray matrix image as a preset threshold; calculating a threshold selection standard according to the difference between a preset threshold and a preset gray value; selecting a preferred preset threshold according to a threshold selection standard; selecting preset thresholds with adjacent sizes by taking the optimal preset threshold as a starting point, respectively taking the preset thresholds as segmentation thresholds, and segmenting the two-dimensional gray matrix image to obtain at least two segmented images;
the threshold value screening module is used for acquiring the number of connected domains formed by pixel points with different pixel values in the segmented image; calculating the approximation degree of the segmented image according to the difference between the numbers of connected domains corresponding to the pixel points with different pixel values; calculating a segmentation standard according to the number of connected domains in the segmented image and the approximation degree; based on the segmentation standard, selecting an optimal threshold from segmentation thresholds; dividing the two-dimensional gray matrix image based on the optimal threshold value to obtain a target divided image;
the encryption storage module is used for scanning the target segmentation image by adopting different scanning rules and screening out the optimal scanning rule; based on the optimal scanning rule, scanning pixel points in the target segmentation image, and extracting decimal numbers corresponding to the pixel points as ciphertext; and taking the optimal scanning rule and the construction parameters of the two-dimensional matrix as a key, and storing data information based on the ciphertext and the key.
2. The intelligent management system for wireless routing data of claim 1, wherein the constructing of the two-dimensional matrix and the corresponding two-dimensional grayscale matrix image from decimal numbers comprises:
placing decimal numbers corresponding to all data information in a two-dimensional matrix according to the sequence of the data information, wherein the two-dimensional matrix is a two-dimensional matrix constructed by the decimal numbers; the construction parameters of the two-dimensional matrix are the length and the width of the two-dimensional matrix;
and taking each element in the two-dimensional matrix as a pixel value of a pixel point on the image to construct a two-dimensional gray matrix image corresponding to the two-dimensional matrix.
3. The system of claim 1, wherein the calculating the threshold selection criteria based on the difference between the preset threshold and the preset gray-level value comprises:
calculating an absolute value of a difference value between a preset threshold value and a preset gray value as a first absolute value; and carrying out negative correlation mapping on the first absolute value, and taking the obtained result value as a threshold selection standard.
4. The system according to claim 1, wherein the obtaining of the number of connected components formed by pixels with different pixel values in the segmented image comprises:
the segmentation image is a binary image, and the number of connected domains comprises a first number of connected domains and a second number of connected domains;
acquiring the number of connected domains formed by pixel points with pixel values of 1 in the segmented image as the number of first connected domains;
and acquiring the number of connected domains formed by pixel points with pixel values of 0 in the segmented image as the number of second connected domains.
5. The system for intelligently managing wireless routing data according to claim 4, wherein the calculating the approximation degree of the segmented image according to the difference between the connected domain numbers corresponding to the pixel points with different pixel values comprises:
calculating the ratio of the number of the first connected domains to the number of the second connected domains as the ratio of the number of the connected domains;
calculating the absolute value of the difference value between the connected domain quantity ratio and a preset first threshold value as the difference degree; and carrying out negative correlation mapping on the difference degree, and taking the obtained result value as the approximate degree of the segmented image.
6. The system for intelligently managing wireless routing data according to claim 1, wherein the calculating the segmentation criteria according to the number of connected components in the segmented image and the degree of approximation comprises:
and carrying out weighting and averaging on the normalized number of the connected domains and the approximation degree, and taking the obtained result value as a segmentation standard.
7. The system according to claim 1, wherein the scanning of the target segmented image using different scanning rules to screen out the optimal scanning rule comprises:
scanning a target segmentation image by using a raster scanning rule to obtain a corresponding coding sequence as a first coding sequence;
respectively scanning the target segmentation image by using at least two scanning rules except the raster scanning rule to obtain corresponding coding sequences as second coding sequences;
obtaining a scanning rule value according to the difference of the coding numerical values at the same position in the first coding sequence and the second coding sequence; and taking the scanning rule corresponding to the maximum scanning rule value as the optimal scanning rule.
8. The system for intelligent management of wireless routing data according to claim 7, wherein said deriving a scan rule value based on a difference between encoded values at a same position in said first encoded sequence and said second encoded sequence comprises:
calculating the absolute value of the difference of the coded values at the same position in the first coded sequence and the second coded sequence as a second absolute value; the sum of the second absolute values corresponding to the coding numerical values is used as an initial rule value; and taking the normalized initial rule value as a scanning rule value.
9. The system for intelligent management of wireless routing data according to claim 1, wherein said selecting a preferred preset threshold value according to a threshold selection criterion comprises:
and selecting a preset threshold corresponding to the maximum threshold selection standard as a preferred preset threshold.
10. The system for intelligent management of wireless routing data according to claim 1, wherein said selecting optimal threshold values from among the segmentation threshold values based on the segmentation criteria comprises:
and acquiring a corresponding segmentation threshold value when the segmentation standard is closest to a preset second threshold value as an optimal threshold value.
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