CN115952551A - Data processing method for building BIM model - Google Patents
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Abstract
The invention relates to the field of data processing, in particular to a data processing method for building BIM (building information modeling), which comprises the following steps: acquiring a two-dimensional matrix; obtaining a block size according to the two-dimensional matrix, obtaining a plurality of window matrixes according to the block size, determining the regularity significance of each window matrix, and further obtaining a guide template; obtaining the statistical significance of each single Gaussian model according to the regular significance of each window matrix; obtaining a plurality of matrix blocks according to the block size, determining a plurality of guide templates of each matrix block, further obtaining a plurality of optimal single Gaussian models of each matrix block, obtaining a standby single Gaussian model of each matrix block according to the optimal single Gaussian models of the matrix blocks, determining a first weight of the standby single Gaussian model of each matrix block, and encrypting the matrix blocks according to the first weight to obtain a ciphertext matrix; and storing the ciphertext matrix on a server of the building BIM modeling system. The invention can realize data security protection and improve encryption efficiency.
Description
Technical Field
The application relates to the field of data processing, in particular to a data processing method for building BIM model.
Background
With the development of computer technology, building BIM modeling is more widely applied, and data in the building BIM model includes core data of many enterprises, such as building layout of customers and building process of the customers; in order to prevent the loss of enterprise benefits caused by the leakage of data in the BIM, the BIM data needs to be protected safely, and a common data security protection method is data encryption.
The traditional encryption processing method adopts the same encryption method for all data, but the regular characteristics of all data in the BIM model are different, and the regularity of some data is obvious, so that the data can not be covered by the data rule if a simple encryption processing method is adopted, and the data is decrypted by taking the analysis statistical rule as a breakthrough in the data decryption process, so that the data is easy to crack when the data rule is exposed. Some data have less obvious regularity, and the data do not need to be encrypted by adopting a complex encryption method, which increases the complexity of calculation, so that the data need to be encrypted by adopting encryption methods with different complexities by combining the regular characteristics of the data.
In summary, the invention provides a data processing method for a building BIM model, which is designed by analyzing data of the building BIM model, and can improve encryption efficiency while realizing data security protection.
Disclosure of Invention
In order to solve the above technical problem, the present invention provides a data processing method for building a BIM model, the method including:
acquiring a building BIM data sequence, setting a comprehensive key sequence, and forming a two-dimensional matrix according to the building BIM data sequence;
obtaining a block size according to the two-dimensional matrix, obtaining a plurality of window matrixes according to the block size, obtaining regular significance of the window matrixes according to the similarity of each window matrix and other window matrixes, and taking the window matrixes with the regular significance smaller than a regular significance division threshold value as a guide template;
acquiring a Gaussian mixture model of each window matrix, and obtaining the statistical significance of each single Gaussian model according to the Gaussian mixture model and the regular significance of each window matrix; obtaining a plurality of matrix blocks according to the size of blocks, taking the rule significance of a window matrix corresponding to each matrix block as the rule significance of each matrix block, obtaining a key sequence of each matrix block according to a comprehensive key sequence, obtaining the number of guide templates of each matrix block according to the rule significance of each matrix block, obtaining a plurality of guide templates of each matrix block according to the key sequence and the number of guide templates of each matrix block, obtaining a Gaussian mixture model of each matrix block, obtaining a plurality of preferred single Gaussian models of each matrix block according to the guide templates of each matrix block, taking the plurality of preferred single Gaussian models and the single Gaussian models of each matrix block as standby single Gaussian models of each matrix block, obtaining a first weight of the standby single Gaussian model of each matrix block according to the rule significance of each matrix block, the Gaussian mixture model and the statistical significance of each single Gaussian model, and encrypting the matrix blocks according to the first weight of the standby single Gaussian model of each matrix block to obtain a ciphertext matrix; and storing the ciphertext matrix on a server of the building BIM modeling system.
Preferably, the obtaining of the block size according to the two-dimensional matrix includes the specific steps of:
accumulating each row of data in the two-dimensional matrix to obtain a row accumulated sum of each row of data, accumulating each row of data in the two-dimensional matrix to obtain a column accumulated sum of each column of data, arranging the row accumulated sums of all rows of the two-dimensional matrix according to a row sequence to obtain a row accumulated sum sequence, and arranging the column accumulated sums of all columns of the two-dimensional matrix according to a column sequence to obtain a column accumulated sum sequence;
obtaining the height of the block according to the periodicity of the row accumulation sum sequence, including: utilizing time sequence analysis to split a line accumulation sum sequence into a trend sequence, a period sequence and a residual sequence, carrying out Fourier transform on the period sequence to obtain frequency spectrum data of the period sequence, obtaining the frequency corresponding to each nonzero amplitude value in the frequency spectrum data, taking the reciprocal of the frequency of each nonzero amplitude value as the period of each nonzero amplitude value, taking each nonzero amplitude value as a weight, carrying out weighted summation on the periods of all nonzero amplitude values to obtain the period of the line accumulation sum sequence, and taking the period of the line accumulation sum sequence as a block height;
obtaining the width of the block according to the periodicity of the column accumulation sum sequence;
and forming the block height and the block width into a block size.
Preferably, the obtaining of the regular significance of the window matrix according to the similarity between each window matrix and other window matrices includes the following specific steps:
the number of the window matrixes is called as window data, the frequency of the window data is obtained by counting the occurrence frequency of the window data in each window matrix, and the frequency of the window data is arranged according to the sequence of the window data from small to large to obtain a window frequency sequence of each window matrix;
setting the row direction as a walking direction, and forming a window data pair by each window data and right adjacent data; setting the column direction as a walking direction, forming window data pairs by using each window data and the next adjacent data, counting the occurrence frequency of each window data pair to obtain the frequency of each window data pair, obtaining the accumulated sum of two data in the window data pairs as the accumulated sum of the window data pairs, and arranging the frequency of each window data pair according to the sequence of the accumulated sum of the window data from small to large to obtain the gray level co-occurrence sequence of each window matrix;
calculating cosine similarity of the window frequency sequence of each window matrix and the window frequency sequences of other window matrices respectively to obtain first similarity of each window matrix and other window matrices; calculating cosine similarity of the gray level co-occurrence sequence of each window matrix and the gray level co-occurrence sequences of other window matrices to obtain second similarity of each window matrix and other window matrices; taking the mean value of the first similarity and the second similarity of each window matrix and other window matrices as the third similarity of each window matrix and other window matrices; and taking the third similarity mean value of each window matrix and all other window matrices as the regularity significance of each window matrix.
Preferably, the obtaining of the statistical significance of each single gaussian model according to the gaussian mixture model and the regular significance of each window matrix includes the following specific steps:
acquiring each single Gaussian model from the Gaussian mixture model of each window matrix and marking as the single Gaussian model of each window matrix; and acquiring the weight of the single Gaussian model of each window matrix, taking the regular significance of each window matrix as the weight, and performing weighted summation on the weights of the same single Gaussian model of all the window matrices to obtain the statistical significance of each single Gaussian model.
Preferably, the obtaining of the number of the guide templates of each matrix block according to the regularity significance of each matrix block includes the following specific steps:
and acquiring the number of the guide templates, and taking a down-rounded value of the product of the regularity significance of each matrix block and the number of the guide templates as the number of the guide templates of each matrix block.
Preferably, the obtaining of the plurality of pilot templates of each matrix block according to the key sequence and the number of pilot templates of each matrix block includes the specific steps of:
sequencing all the guide templates in an ascending manner according to the regular significance to obtain a guide template sequence;
obtaining each key value in the key sequence of each matrix block
Taken a ^ th or greater in the guide template sequence>
The plurality of guide templates are used as guide templates of each key value of each matrix block, and a plurality of guide templates are obtained by a plurality of key values in the key sequence of each matrix block.
Preferably, the obtaining of the plurality of optimal single gaussian models of each matrix block according to the guide template of each matrix block includes the specific steps of:
and acquiring a single Gaussian model with the maximum weight of each guide template as a preferred single Gaussian model of each guide template, obtaining a plurality of preferred single Gaussian models by the plurality of guide templates, and taking the preferred single Gaussian models corresponding to the plurality of guide templates of each matrix block as the preferred Gaussian models of each matrix block.
Preferably, the obtaining of the first weight of the standby single gaussian model of each matrix block according to the regularity significance of each matrix block, the gaussian mixture model and the statistical significance of each single gaussian model includes the following specific steps:
the standby single Gaussian models of each matrix block comprise a single Gaussian model and an optimal single Gaussian model;
determining a first weight of each optimal single Gaussian model of each matrix block:
obtaining the weight of each optimized single Gaussian model of each matrix block, and obtaining the weight of each optimized single Gaussian model of each matrix block according to the weight, statistical significance and regular significance of each optimized single Gaussian model of each matrix block:
determining a first weight of each single Gaussian model of each matrix block:
obtaining each single Gaussian model in the Gaussian mixture model of each matrix block as the single Gaussian model of each matrix block, obtaining the weight of each single Gaussian model of each matrix block, and obtaining the first weight of each single Gaussian model of each matrix block according to the rule significance of each matrix block, the weight and the statistical significance of each single Gaussian model of each matrix block:
Preferably, the encrypting the matrix blocks according to the first weight of the standby single gaussian model of each matrix block to obtain the ciphertext matrix includes the following specific steps:
taking the first weight of each standby single Gaussian model as a weight, carrying out weighted summation on all standby single Gaussian models of each matrix block to obtain a first Gaussian mixture model of each matrix block, determining first data of each position in each matrix block by using the first Gaussian mixture model of each matrix block, and calling the matrix block formed by the first data of each matrix block as a ciphertext matrix block; and splicing all the ciphertext matrix blocks together to obtain a ciphertext matrix according to the positions of the matrix blocks corresponding to the ciphertext matrix blocks.
The embodiment of the invention at least has the following beneficial effects: the two-dimensional matrix is obtained, and because the two-dimensional matrix comprises some data with poor regularity and some data with strong regularity, the value law of the data with strong regularity can be covered by using the data characteristics with poor regularity, the regularity significance of each window matrix needs to be obtained by analyzing the data in each area of the two-dimensional matrix, and the window matrix with poor regularity significance is selected as a guide template to encrypt the data in the two-dimensional matrix.
In order to better encrypt each area of the two-dimensional matrix, more guide templates need to be distributed to the matrix blocks with stronger regularity and significance. In order to cover the data rule of each area in the two-dimensional matrix by using the value characteristics in the guide template, the main value rules in each two-dimensional matrix need to be analyzed, so that a plurality of single Gaussian models of each window matrix are obtained, the statistical significance of each single Gaussian model is further obtained, and the statistical rule significance degree of each value characteristic is reflected by the statistical significance of each single Gaussian model. In order to protect data in each matrix block, value characteristics in each matrix block need to be reduced, and meanwhile, some value characteristics of a guide template are filled in the matrix blocks, so that each optimal single-Gaussian model is obtained according to the guide template of each matrix, a standby single-Gaussian model of each matrix block is obtained by combining the single-Gaussian models of each matrix block, the weight of each standby single-Gaussian model is adjusted according to the statistical significance of each single-Gaussian model and the regular significance of each matrix block to obtain a first weight of each standby single-Gaussian model of each matrix block, and a first Gaussian mixture model of each matrix block is constructed according to the first weight of each single-Gaussian model. The value characteristics of the guide template are introduced by adjusting the weight of the optimal single Gaussian model from the guide template and the weight of the single Gaussian model of the matrix block per se, and the value law of the matrix block per se can be reduced at the same time, so that the aim of data protection is fulfilled.
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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 flow chart of a data processing method for building a BIM model according to the present invention;
FIG. 2 is a data flow diagram for building a BIM model according to 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 data processing method for building BIM model 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, the different references to "one embodiment" or "another embodiment" do not necessarily refer to 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 describes a specific scheme of the data processing method for building the BIM model in detail with reference to the accompanying drawings.
Referring to fig. 1, a flow chart of steps of a data processing method for building a BIM model according to an embodiment of the present invention is shown, the method includes the following steps:
and S001, acquiring a building BIM data sequence, constructing a two-dimensional matrix by using the building BIM data sequence, and setting a comprehensive key sequence.
Since the data in the building BIM model can contain some technical core data of the client, the leakage of the data can lead to the loss of the client benefits and further lead to the loss of the client; therefore, in order to prevent this, data in the building BIM model needs to be stored in an encrypted manner, so that the data security is protected.
Obtaining a building BIM model data sequence with the length of N1 in a building BIM system, wherein the building BIM comprises a building model and a road model, and structures in the building model and the road model are formed by three-dimensional structure points, so that the building BIM model data mainly comprises all three-dimensional structure point coordinates, and the three-dimensional structure point coordinates of each position in the building model are spliced together to obtain the building BIM model data sequence. And uniformly splitting the building BIM model data sequence with the length of N1 into M building BIM model subsequences with the length of N. It should be noted that, when the last split building BIM model subsequence does not meet the requirement of length N, the length of the subsequence is extended to N by filling zero at the end of the subsequence. In this example
N, M are 10000, 100 and 100 respectively, and in other embodiments, the implementation may be based on actual settings.
And taking each building BIM model subsequence with the length of N as each row of the two-dimensional matrix, wherein M building BIM model subsequences form an M multiplied by N two-dimensional matrix.
Setting a key sequence: generating a chaotic sequence by utilizing a chaotic mapping function, taking the chaotic sequence as a comprehensive key sequence, and calling each element in the comprehensive key sequence as a key value; the constant parameters of the chaotic mapping function determine the chaotic sequence, and the constant parameters of the chaotic mapping function are agreed by both parties in advance, so that the key sequence does not need to be shared.
And S002, obtaining the block size according to the two-dimensional matrix, obtaining a plurality of window matrixes according to the block size, calculating the rule significance of each window matrix, and obtaining a guide template according to the rule significance of each window matrix.
Because the data rule of some areas in the two-dimensional matrix is obvious, the data of the areas can repeatedly appear for many times, the data rule is obvious, the data rule of some areas is poor, the data repeated appearance frequency of the areas is less, and therefore the data in the areas with poor rules can be used as a guide template to encrypt the data in the two-dimensional matrix, and the data rule in the two-dimensional matrix is covered.
1. Obtaining the block size:
and to obtain the guide template in the two-dimensional matrix, a block size needs to be determined, and the data rule of each block area in the size is more obvious, so that the data rule in the two-dimensional matrix is better found, and the data rule is better covered by utilizing the guide template.
When the block size is determined, the periodic rule of row and column data needs to be analyzed, and single-period data which repeatedly appears for many times can be divided into one region according to the periodicity of the row and column data, so that the guide template can be conveniently obtained, and the periodic characteristics of each region can be covered by the guide template.
And accumulating each row of data in the two-dimensional matrix to obtain a row accumulated sum of each row of data, and accumulating each column of data in the two-dimensional matrix to obtain a column accumulated sum of each column of data. And arranging the row accumulation sums of all rows of the two-dimensional matrix according to the row sequence to obtain a row accumulation sum sequence. The column sums of all columns of the two-dimensional matrix are arranged in column order to obtain a column sum sequence.
Obtaining the block height according to the periodicity of the row accumulation sum sequence: the line-sum sequence is split into a trend sequence, a periodic sequence and a residual sequence using a time-series sequence decomposition algorithm (STL). The method comprises the steps of carrying out Fourier transform on a periodic sequence to obtain frequency spectrum data of the periodic sequence, wherein the frequency spectrum data reflects the response amplitude of each frequency data, obtaining the frequency corresponding to each nonzero amplitude in the frequency spectrum data, taking the reciprocal of the frequency of each nonzero amplitude as the period of each nonzero amplitude, taking each nonzero amplitude as a weight, carrying out weighting and averaging on the periods of all nonzero amplitudes to obtain the period of a row accumulation sum sequence, and reflecting the period characteristics of the row accumulation sum sequence, so that the period of the row accumulation sum is taken as the block height H.
The block width W is obtained from the column accumulation sum sequence periodicity in the same way.
The finally determined block size is thus W × H.
2. Acquiring a plurality of guide templates:
the process obtains the block size, and the area obtained by segmentation under the size can contain complete single-cycle data as far as possible, so that the subsequent data analysis rule is facilitated. The matrix blocks are divided by utilizing the block size, the similarity condition of the data in each matrix block and the data in other matrix blocks in the whole two-dimensional matrix is analyzed to determine the regular significance of the data in each matrix block, and a guide template set is determined according to the regular significance of the data in each matrix block.
And traversing each data in the two-dimensional matrix by taking each data in the two-dimensional matrix as the center of a preset sliding window and utilizing the preset sliding window with the size of W multiplied by H as a block size to obtain a plurality of window matrixes, wherein for convenience of description, the number in each window matrix is called as window data. This results in a window matrix of a two-dimensional matrix, as shown in fig. 2.
Performing the following operations with a certain window matrix:
acquiring all window data in a window matrix, counting the occurrence frequency of each window data to obtain the frequency of each window data, and arranging the frequency of each window data according to the sequence of the window data from small to large to obtain a window frequency sequence of the window matrix;
setting the row direction as a walking direction, and forming a window data pair by each window data and right adjacent data; setting the column direction as a walking direction, and forming a window data pair by each window data and the next adjacent data; counting the occurrence frequency of each window data pair to obtain the frequency count of each window data pair, taking the accumulated sum of two data in each window data pair as the accumulated sum of each window data pair, and arranging the frequency counts of each window data pair according to the sequence from the accumulated sum of the window data pairs to the big to obtain the gray level co-occurrence sequence of the window matrix.
And respectively calculating cosine similarity of the window frequency sequence of the window matrix and the window frequency sequences of other window matrices to obtain first similarity of the window matrix and other window matrices. And calculating cosine similarity of the gray level co-occurrence sequence of the window matrix and the gray level co-occurrence sequences of other window matrices to obtain second similarity of the window matrix and other window matrices. And taking the mean value of the first similarity and the second similarity of the window matrix and other window matrices as the third similarity of the window matrix and other window matrices.
And taking the third similarity mean value of the window matrix and all other window matrices as the regular significance of the window matrix. At this point, the regular significance of the window matrix is obtained, as shown in fig. 2.
Counting the occurrence times of the rule significance of each window matrix to obtain a rule significance histogram, processing the rule significance histogram by using an OSTU algorithm to obtain a rule significance segmentation threshold K, and enabling the rule significance to be smaller than the rule significance segmentation threshold
The window matrix is used as a guide template to obtain a plurality of guide templates; a set of all the guide templates is referred to as a guide template set. The guiding template is generally data with small regularity, so that in order to cover the obvious regularity data in the two-dimensional matrix, the data with small regularity can be used as the template to guide and process the data in the two-dimensional matrix, so that the obvious regularity in the two-dimensional matrix is covered, and the protection of the data in the two-dimensional matrix is realized. This results in a set of guide templates, as shown in FIG. 2.
And obtaining a plurality of guide templates, determining the block size of the two-dimensional matrix by considering the periodic regularity of data in the two-dimensional matrix in the process of obtaining the guide templates, obtaining a plurality of window matrixes according to the block size, and obtaining the window matrix with smaller regularity as the guide template through the similarity characteristics of each window matrix and other window matrixes.
And S003, dividing the matrix blocks according to the block size, determining a plurality of guide templates of each matrix block, obtaining a standby single Gaussian model of each matrix block according to the guide templates of each matrix block, obtaining the statistical significance of each single Gaussian model according to the window matrix, obtaining a first weight of the standby single Gaussian model of each matrix block according to the statistical significance of each single Gaussian model, and encrypting each matrix block by using the first weight of the standby single Gaussian model to obtain a ciphertext matrix.
1. Dividing matrix blocks:
in the process, the guide template is obtained, and the data in the guide template is the data with smaller rules in the two-dimensional matrix, so that the data in the two-dimensional matrix can be guided and encrypted by using the data in the guide template, and the rules of the data in the two-dimensional matrix are covered.
In this embodiment, the encryption processing is performed based on each matrix block, so that the following two-dimensional matrix needs to be divided into a plurality of matrix blocks, specifically as follows:
setting the size of a matrix block to be equal to the size of a block, uniformly dividing a two-dimensional matrix into a plurality of matrix blocks, carrying out matrix block division from left to right and from top to bottom when the division needs to be explained, and expanding boundary areas in a zero filling mode when the remaining boundary areas can not divide the matrix blocks meeting the size requirement, so that the boundary areas can divide the matrix blocks meeting the size requirement. This results in a matrix block as shown in fig. 2.
2. Determining the regular significance of each matrix block:
and (3) the window matrixes with completely overlapped matrix blocks exist in all the window matrixes, the regular significance of the window matrixes is used as the regular significance of the matrix blocks, the regular significance of the matrix blocks is normalized by using a softmax normalization method to obtain the normalized regular significance of the matrix blocks, and the normalized regular significance of the matrix blocks is called as the regular significance of the matrix blocks for convenience of description. The significance of the regularity of each matrix block is obtained, as shown in fig. 2.
Record the significance of regularity of the ith matrix block as
。
3. Determining the statistical significance of each single gaussian model:
and fitting the Gaussian mixture model of each window matrix, and acquiring each single Gaussian model from the Gaussian mixture model of each window matrix and recording the single Gaussian model as the single Gaussian model of each window matrix. The method comprises the steps of obtaining the weight of a single Gaussian model of each window matrix, taking the regular significance of each window matrix as the weight, carrying out weighted summation on the weights of the same single Gaussian models of all the window matrices to obtain the statistical significance of each single Gaussian model, and explaining that when a certain single Gaussian model does not exist in the window matrix, the weight of the single Gaussian model of the window matrix is 0. For example, a total of 4 window matrices are obtained through a two-dimensional matrix, wherein the regular significance of the 1 st window matrix is 2, three single gaussian models of g1, g2 and g3 exist in the window matrix, the corresponding weights of the three single gaussian models are 0.3,0.4,0.3, the regular significance of the 2 nd window matrix is 1, the window matrix exists, three single gaussian models of g1, g2 and g4 exist, the corresponding weights of the three single gaussian models are 0.1,0.6,0.3, the regular significance of the 3 rd window matrix is 3, three single gaussian models of g2, g3 and g5 exist in the window matrix, the corresponding weights of the three single gaussian models are 0.5,0.3,0.2, and the regular significance of the 4 th window matrix is 4, three single-gauss models of g1, g2 and g4 exist in the window matrix, the weights corresponding to the three single-gauss models are 0.8,0.1,0.1, when the statistical significance of the single-gauss model g1 is calculated, the regular significance of each window matrix is used as a weight, the weights of the single-gauss models g1 of all the window matrices are weighted and summed to obtain the statistical significance of the single-gauss model g1, wherein the regular significance of the 1 st, the 2 nd, the 3 rd and the 4 th window matrices are 2,1,3,4 respectively, and the weights of the g1 single-gauss models of the 1 st, the 2 nd, the 3 rd and the 4 th window matrices are 0.3,0.1,0,0.5 respectively, so that the statistical significance of the obtained single-gauss model g1 is 2 × 0.3 × 0.1+3 × 0+4 × 0.5=2.7.
And carrying out normalization processing on the statistical significance of each single Gaussian model by using a softmax normalization method to obtain the normalized statistical significance of each single Gaussian model. For convenience of description, the statistical significance of each normalized single-gaussian model is referred to as the statistical significance of each single-gaussian model. The statistical significance of each single gaussian model is obtained so far, as shown in fig. 2.
4. And encrypting each matrix block according to the statistical significance of each single Gaussian model and a guide template to obtain a ciphertext matrix:
in order to encrypt each matrix block, a plurality of guide templates need to be allocated to each matrix block, then a standby single gaussian model of each matrix block is determined according to the plurality of single gaussian models of the guide templates of each matrix block and the plurality of single gaussian models of each matrix block, then a first weight of the standby single gaussian model of each matrix block is determined according to the rule significance of each matrix block, the statistical significance of each standby single gaussian model and the weight of each standby single gaussian model, and each matrix block is encrypted according to the first weight of the standby single gaussian model of each matrix block to obtain a ciphertext matrix, which is as follows: (1) Determining the number of guide templates of each matrix block according to the regularity significance of each matrix block:
the higher the significance of the regularity of the matrix block is, the more obvious the regularity of the data in the matrix block is, so that in order to cover up the statistical regularity in the matrix block, more data features in the guide template with poor statistical features need to be blended to cover up the regularity in the matrix block, thereby improving the security of the matrix block data.
The calculation formula of the number of the guide templates of each matrix block is as follows:
(2) Determining the key sequence of each matrix block:
truncating the 1 st position to the 1 st position in the sequence of synthetic keys
The sequence between the positions serves as a key sequence for the first matrix block, and the ^ h or greater is intercepted at the second position>
Position to the ^ th->
In the sequence of (A) and (B) as a key sequence of a second matrix, in the combined key sequence a fifth ^ or a fifth ^ is truncated>
Position to the ^ th->
The sequence between the first matrix block and the second matrix block is used as the key sequence of the third matrix block, and so on, and the key sequence of each matrix block is obtained.
Multiplying each element in the key sequence of the ith matrix block by the key sequence of the ith matrix block
Then rounding up and adding one to obtain a replacing element of each element, replacing each element in the key sequence with each replacing element to obtain a new key sequence of the ith matrix block, and for convenience of description, subsequently, referring the new key sequence of the ith matrix block as the key sequence of the ith matrix block. And obtaining the key sequence of each matrix block in the same way.
(3) Determining a plurality of guide templates of each matrix block according to the key sequence of each matrix block:
and (4) arranging all the guide templates in an ascending order according to the regular significance to obtain a guide template sequence.
Obtaining the j key value in the key sequence of each matrix block
Taken a ^ th or greater in the guide template sequence>
The plurality of guide templates are used as guide templates of the jth key value of each matrix block, the guide templates of each key value of each matrix block are obtained in the same way, and a plurality of guide templates are obtained by a plurality of key values in the key sequence of each matrix block.
To this end, a plurality of guiding templates for each matrix block can be obtained, as shown in fig. 2. Each guide template corresponds to one Gaussian mixture model, and each Gaussian mixture model comprises a plurality of single Gaussian models, so that each guide model corresponds to a plurality of single Gaussian models, and each single Gaussian model corresponds to one weight and one statistical significance. Meanwhile, the method can obtain that each guide template corresponds to one regular significance, and each matrix block corresponds to one regular significance.
(4) And encrypting each matrix block according to the plurality of guide templates of each matrix block and the statistical significance of each single Gaussian model to obtain a ciphertext matrix:
because the rule of the data in the guide template is poor, the data characteristics in the guide template are utilized to encrypt each matrix block so as to cover the rule of the data in the matrix block.
a. Determining a first weight of the standby Gaussian model of each matrix block:
in order to mask the data law of each matrix block, the weight of the single gaussian model with high statistical significance in each matrix block needs to be adjusted down, and the weight of some single gaussian models with low statistical significance in the guide template needs to be adjusted up, so that the data law in each matrix block can be better masked, and therefore the first weight of each standby gaussian model of each matrix block is determined based on the above, and the specific following is as follows:
and acquiring a single Gaussian model with the maximum weight of each guide template as a preferred single Gaussian model of each guide template, obtaining a plurality of preferred single Gaussian models by the plurality of guide templates, and taking the preferred single Gaussian models corresponding to the plurality of guide templates of each matrix block as the preferred Gaussian models of each matrix block. And acquiring each single Gaussian model of each matrix block, and calling the single Gaussian model and the optimal single Gaussian model of each matrix block as standby single Gaussian models of each matrix block. This results in a spare single gaussian model for each single gaussian model, as shown in fig. 2.
The first weight of the single gaussian model in the standby single gaussian model of each matrix block and the first weight of the preferred single gaussian model are respectively studied as follows:
determining a first weight of each preferable single Gaussian model of each matrix block:
obtaining the weight of the jth optimized single Gaussian model of the ith matrix block in the Gaussian mixture model of the guide template as the weight of the jth optimized single Gaussian model of the ith matrix block
Obtaining statistical significance in a jth preferred single Gaussian model in an ith matrix block>
The regular significance of the i-th matrix block is acquired->
Based on the weight of the jth preferred single Gaussian model of the ith matrix block->
Statistical significance->
And the significance of the regularity of the ith matrix block>
Obtaining a first weight value of a j-th preferred single Gaussian model of the ith matrix block:
Obtaining a first weight of each single Gaussian model of each matrix block:
obtaining the weight value of the jth single Gaussian model of the ith matrix block
Obtaining statistical significance of the jth single Gaussian model of the ith matrix block->
The regular significance of the i-th matrix block is acquired->
According to the significance of the regularity of the ith matrix block
The weight of the jth single Gaussian model of the ith matrix block->
And statistical significance>
Determining a first weight of a jth single Gaussian model of an ith matrix block:
And obtaining the first weight of each single Gaussian model of each matrix block and the first weight of each optimal single Gaussian model in the same way.
And obtaining a first weight of each single Gaussian model in the standby single Gaussian models of each matrix block and a first weight of the optimal single Gaussian model. For convenience of description, the first weight of the jth spare single gaussian model of the ith matrix block is subsequently recorded as the first weight
。
b. Encrypting each matrix block according to each standby single Gaussian model of each matrix block to obtain a ciphertext matrix block:
and taking the first weight of each standby single Gaussian model as a weight, and carrying out weighted summation on all standby single Gaussian models of each matrix block to obtain a first Gaussian mixture model of each matrix block. And determining first data of each position in each matrix block by using the first Gaussian mixture model of each matrix block, and calling the matrix block formed by the first data of each matrix block as a ciphertext matrix block. And splicing all the ciphertext matrix blocks together according to the positions of the matrix blocks corresponding to the ciphertext matrix blocks to obtain a ciphertext matrix.
At this point, the two-dimensional matrix is divided into a plurality of matrix blocks when the two-dimensional matrix is encrypted, and the significance of the rule of each matrix block is determined. Fitting a Gaussian mixture model to each window matrix to obtain a single Gaussian model, analyzing the statistical rule of each single Gaussian model to obtain the statistical significance of each single Gaussian model, and adjusting the weight of the standby single Gaussian model of each matrix block according to the rule significance of each matrix block, the statistical significance of the single Gaussian model of each matrix block and the statistical significance of each single Gaussian model in the guide template to obtain the first weight of the standby single Gaussian model of each matrix block, so that the statistical rule in the matrix block is reduced, and simultaneously, the data characteristics in the guide template can be introduced into the matrix block, so that the encryption processing of each matrix block is realized to obtain a ciphertext matrix.
And storing the ciphertext matrix in a building BIM modeling server.
And step S004, decrypting the ciphertext matrix to obtain a building BIM model data sequence.
And storing the guide template, the parameters of each single Gaussian model, the statistical significance of each single Gaussian model and the regular significance of each matrix block in an encrypted storage area of the BIM modeling server of the building, wherein the encrypted service area is an area which can be accessed by a password. The relevant staff member allows the server to read the corresponding data of the encrypted storage area by entering the correct password.
Acquiring the size of a guide template, dividing a ciphertext matrix into a plurality of ciphertext matrix blocks according to the size of the guide template, fitting a first Gaussian mixture model of each ciphertext matrix block, acquiring a standby single Gaussian model in the first Gaussian model, namely the single Gaussian model of the first Gaussian mixture model, and acquiring a weight of the standby single Gaussian model, namely the first weight of the standby single Gaussian model in the step S003;
and acquiring a key sequence of the ciphertext matrix block, acquiring a guide template of the ciphertext matrix according to the key sequence, and acquiring an optimal single Gaussian model of each ciphertext matrix according to the guide template.
And obtaining a single Gaussian model of each ciphertext matrix block according to the standby single Gaussian model and the optimized single Gaussian model of each ciphertext matrix block, and obtaining the weight of each single Gaussian model of each ciphertext matrix block according to the first weight, the statistical significance and the rule significance of each single Gaussian model of each ciphertext matrix block. And taking the weight of each single Gaussian model as a weight, and carrying out weighted summation on all the single Gaussian models of each ciphertext matrix block to obtain a Gaussian mixture model of each ciphertext matrix block. And obtaining data of each position by using the Gaussian mixture model of each ciphertext matrix block, replacing the first data in the ciphertext matrix with the data of each position to obtain a matrix block, and splicing the matrix blocks together to obtain a two-dimensional matrix.
And splitting the two-dimensional matrix into a building BIM data sequence.
In summary, the embodiments of the present invention provide a data processing method for a building BIM model, in which a two-dimensional matrix is obtained, and since the two-dimensional matrix includes some data with poor regularity and some data with strong regularity, and the value law of the data with strong regularity can be covered by using the data characteristics with poor regularity, the significance of the regularity of each window matrix needs to be obtained by analyzing the data in each area of the two-dimensional matrix, and the window matrix with poor significance of the regularity is selected as a guide template to encrypt the data in the two-dimensional matrix.
In order to better encrypt each area of the two-dimensional matrix, more guide templates need to be distributed to the matrix blocks with stronger regularity and significance. In order to cover the data rule of each area in the two-dimensional matrix by using the value characteristics in the guide template, the main value rules in each two-dimensional matrix need to be analyzed, so that a plurality of single Gaussian models of each window matrix are obtained, the statistical significance of each single Gaussian model is further obtained, and the statistical rule significance degree of each value characteristic is reflected by the statistical significance of each single Gaussian model. In order to protect data in each matrix block, value characteristics in each matrix block need to be reduced, and meanwhile, some value characteristics of a guide template are filled in the matrix blocks, so that each optimal single-Gaussian model is obtained according to the guide template of each matrix, a standby single-Gaussian model of each matrix block is obtained by combining the single-Gaussian models of each matrix block, the weight of each standby single-Gaussian model is adjusted according to the statistical significance of each single-Gaussian model and the regular significance of each matrix block to obtain a first weight of each standby single-Gaussian model of each matrix block, and a first Gaussian mixture model of each matrix block is constructed according to the first weight of each single-Gaussian model. The value characteristics of the guide template are introduced by adjusting the weight of the optimal single Gaussian model from the guide template and the weight of the single Gaussian model of the matrix block per se, and the value law of the matrix block per se can be reduced at the same time, so that the aim of data protection is fulfilled.
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. And specific embodiments thereof have been described above. In addition, 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.
All 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 other embodiments.
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 of the present invention.
Claims (9)
1. A data processing method for building a BIM model, the method comprising:
acquiring a building BIM data sequence, setting a comprehensive key sequence, and forming a two-dimensional matrix according to the building BIM data sequence;
obtaining a block size according to the two-dimensional matrix, obtaining a plurality of window matrixes according to the block size, obtaining the regular significance of the window matrixes according to the similarity of each window matrix and other window matrixes, and taking the window matrixes with the regular significance smaller than a regular significance division threshold value as a guide template;
acquiring a Gaussian mixture model of each window matrix, and obtaining the statistical significance of each single Gaussian model according to the Gaussian mixture model and the regular significance of each window matrix; obtaining a plurality of matrix blocks according to the size of blocks, taking the rule significance of a window matrix corresponding to each matrix block as the rule significance of each matrix block, obtaining a key sequence of each matrix block according to a comprehensive key sequence, obtaining the number of guide templates of each matrix block according to the rule significance of each matrix block, obtaining a plurality of guide templates of each matrix block according to the key sequence and the number of guide templates of each matrix block, obtaining a Gaussian mixture model of each matrix block, obtaining a plurality of preferred single Gaussian models of each matrix block according to the guide templates of each matrix block, taking the plurality of preferred single Gaussian models and the single Gaussian models of each matrix block as standby single Gaussian models of each matrix block, obtaining a first weight of the standby single Gaussian model of each matrix block according to the rule significance of each matrix block, the Gaussian mixture model and the statistical significance of each single Gaussian model, and encrypting the matrix blocks according to the first weight of the standby single Gaussian model of each matrix block to obtain a ciphertext matrix; and storing the ciphertext matrix on a server of the building BIM modeling system.
2. The data processing method for building the BIM model as claimed in claim 1, wherein said obtaining the block size from the two-dimensional matrix comprises the specific steps of:
accumulating each row of data in the two-dimensional matrix to obtain a row accumulated sum of each row of data, accumulating each row of data in the two-dimensional matrix to obtain a column accumulated sum of each column of data, arranging the row accumulated sums of all rows of the two-dimensional matrix according to a row sequence to obtain a row accumulated sum sequence, and arranging the column accumulated sums of all columns of the two-dimensional matrix according to a column sequence to obtain a column accumulated sum sequence;
obtaining the block height according to the periodicity of the row accumulation sum sequence, comprising: utilizing time sequence analysis to split a row accumulation sum sequence into a trend sequence, a period sequence and a residual sequence, carrying out Fourier transform on the period sequence to obtain frequency spectrum data of the period sequence, obtaining the frequency corresponding to each non-zero amplitude value in the frequency spectrum data, taking the reciprocal of the frequency of each non-zero amplitude value as the period of each non-zero amplitude value, taking each non-zero amplitude value as a weight value, carrying out weighted summation on the periods of all non-zero amplitude values to obtain the period of the row accumulation sum sequence, and taking the period of the row accumulation sum sequence as a blocking height;
obtaining the width of the block according to the periodicity of the column accumulation sum sequence;
and forming the block height and the block width into a block size.
3. The data processing method for building the BIM model as claimed in claim 1, wherein the obtaining of the regular significance of the window matrix according to the similarity of each window matrix and other window matrices comprises the specific steps of:
the number of the window matrixes is called as window data, the frequency of the window data is obtained by counting the occurrence frequency of the window data in each window matrix, and the frequency of the window data is arranged according to the sequence of the window data from small to large to obtain a window frequency sequence of each window matrix;
setting the row direction as a walking direction, and forming a window data pair by each window data and right adjacent data; setting the column direction as a walking direction, forming window data pairs by using each window data and the next adjacent data, counting the occurrence frequency of each window data pair to obtain the frequency of each window data pair, obtaining the accumulated sum of two data in the window data pairs as the accumulated sum of the window data pairs, and arranging the frequency of each window data pair according to the sequence of the accumulated sum of the window data from small to large to obtain the gray level co-occurrence sequence of each window matrix;
calculating cosine similarity of the window frequency sequence of each window matrix and the window frequency sequences of other window matrices respectively to obtain first similarity of each window matrix and other window matrices; calculating cosine similarity of the gray level co-occurrence sequence of each window matrix and the gray level co-occurrence sequences of other window matrices to obtain second similarity of each window matrix and other window matrices; taking the mean value of the first similarity and the second similarity of each window matrix and other window matrices as the third similarity of each window matrix and other window matrices; and taking the third similarity mean value of each window matrix and all other window matrices as the regularity significance of each window matrix.
4. The data processing method for building the BIM model as claimed in claim 1, wherein said obtaining the statistical significance of each single gaussian model according to the gaussian mixture model and the regular significance of each window matrix comprises the specific steps of:
acquiring each single Gaussian model from the Gaussian mixture model of each window matrix and marking as the single Gaussian model of each window matrix; and acquiring the weight of the single Gaussian model of each window matrix, taking the regular significance of each window matrix as the weight, and performing weighted summation on the weights of the same single Gaussian model of all the window matrices to obtain the statistical significance of each single Gaussian model.
5. The data processing method for building the BIM model as claimed in claim 1, wherein said obtaining the number of the guide templates of each matrix block according to the regular significance of each matrix block comprises the specific steps of:
and acquiring the number of the guide templates, and taking a down-rounded value of the product of the regularity significance of each matrix block and the number of the guide templates as the number of the guide templates of each matrix block.
6. The data processing method for building the BIM model as claimed in claim 1, wherein the obtaining of the plurality of guide templates for each matrix block according to the key sequence and the number of guide templates for each matrix block comprises the specific steps of:
performing ascending arrangement on all the guide templates according to regular significance to obtain a guide template sequence;
obtaining each key value in the key sequence of each matrix block
Taken a ^ th or greater in the guide template sequence>
The plurality of guide templates are used as guide templates of each key value of each matrix block, and a plurality of guide templates are obtained by a plurality of key values in the key sequence of each matrix block.
7. The data processing method for building the BIM model as claimed in claim 1, wherein the obtaining of the plurality of preferred gaussian models of each matrix block according to the guiding template of each matrix block comprises the specific steps of:
and acquiring a single Gaussian model with the maximum weight of each guide template as a preferred single Gaussian model of each guide template, acquiring a plurality of preferred single Gaussian models by the plurality of guide templates, and taking the preferred single Gaussian models corresponding to the plurality of guide templates of each matrix block as the preferred Gaussian model of each matrix block.
8. The data processing method for building the BIM model as claimed in claim 1, wherein said obtaining the first weight of the spare single gaussian model of each matrix block according to the regular significance of each matrix block, the gaussian mixture model and the statistical significance of each single gaussian model comprises the specific steps of:
the standby single Gaussian models of each matrix block comprise a single Gaussian model and an optimal single Gaussian model;
determining a first weight of each preferred single Gaussian model of each matrix block:
obtaining the weight of each optimized single Gaussian model of each matrix block, and obtaining the weight of each optimized single Gaussian model of each matrix block according to the weight, statistical significance and regular significance of each optimized single Gaussian model of each matrix block:
determining a first weight of each single Gaussian model of each matrix block:
obtaining each single Gaussian model from the Gaussian mixture model of each matrix block as the single Gaussian model of each matrix block, obtaining the weight of each single Gaussian model of each matrix block, and obtaining a first weight of each single Gaussian model of each matrix block according to the rule significance of each matrix block, the weight and the statistical significance of each single Gaussian model of each matrix block:
9. The data processing method for building the BIM model as claimed in claim 1, wherein the encrypting the matrix blocks according to the first weight of the spare single gaussian model of each matrix block to obtain the ciphertext matrix comprises the specific steps of:
taking the first weight of each standby single Gaussian model as a weight, carrying out weighted summation on all standby single Gaussian models of each matrix block to obtain a first Gaussian mixture model of each matrix block, determining first data of each position in each matrix block by using the first Gaussian mixture model of each matrix block, and calling the matrix block formed by the first data of each matrix block as a ciphertext matrix block; and splicing all the ciphertext matrix blocks together according to the positions of the matrix blocks corresponding to the ciphertext matrix blocks to obtain a ciphertext matrix.
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