CN115310117B - Remote monitoring operation and maintenance system for central air conditioner - Google Patents

Remote monitoring operation and maintenance system for central air conditioner Download PDF

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CN115310117B
CN115310117B CN202211247937.XA CN202211247937A CN115310117B CN 115310117 B CN115310117 B CN 115310117B CN 202211247937 A CN202211247937 A CN 202211247937A CN 115310117 B CN115310117 B CN 115310117B
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杜国栋
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Jiangsu Taiente Environmental Technology Co ltd
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    • FMECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
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    • F24FAIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
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Abstract

The invention relates to the field of data processing, in particular to a remote monitoring operation and maintenance system of a central air conditioner. The processing system comprises a data collector and a controller, wherein the collector is used for collecting the operation and maintenance data sequence of the central air conditioner in real time, and the controller is used for: the method comprises the steps of acquiring data to obtain a matrix sequence, generating a first sequence formed by directional coefficient vectors, obtaining the directional coefficient vector of each first data according to the matrix sequence, obtaining encryption parameter data of the first data according to the directional coefficient vectors, constructing an encryption model according to the encryption parameter data and the directional coefficient vectors, encrypting the matrix sequence according to the encryption model to obtain a ciphertext matrix sequence, and transmitting the ciphertext matrix sequence to a central air conditioner remote monitoring operation and maintenance system, so that the encryption of the central air conditioner operation and maintenance data is realized, some important data in the central air conditioner operation and maintenance data are effectively prevented from being stolen, and the operation and maintenance safety of the central air conditioner is improved.

Description

Remote monitoring operation and maintenance system for central air conditioner
Technical Field
The invention relates to the technical field of data processing, in particular to a remote monitoring operation and maintenance system of a central air conditioner.
Background
The central air conditioner is an important refrigerating and heating tool, the operation effect of the central air conditioner directly affects the experience of a user, and therefore, a remote monitoring operation and maintenance system is required to be used for monitoring the operation condition of the central air conditioner in real time and regulating and controlling the central air conditioner in real time. The operation and maintenance of a general air conditioner is realized by analyzing operation and maintenance data by a professional to remotely regulate and control the air conditioner, and sensitive data or important control instructions of an operation and maintenance system are easily leaked when the operation and maintenance data are stolen in transmission, so that the safety of the air conditioner is threatened. Therefore, in order to avoid stealing the operation and maintenance data of the air conditioner in the transmission process, encryption processing needs to be performed on the transmitted operation and maintenance.
Some data values in the operation and maintenance data of the central air conditioner may be the same or have similar variation trends, and the operation and maintenance data have certain periodic similarity, so that the data have a certain incidence relation, the incidence relation of the data is difficult to break through by obtaining ciphertext data by a traditional encryption method, or the incidence relation of the data can be broken through paying huge calculation amount, so that the data decryption can be easily completed through analyzing statistical characteristics or breaking violently, or the encryption calculation amount is huge, and the data cannot be used.
In view of the above situation, the invention provides a remote monitoring operation and maintenance system for a central air conditioner, wherein different direction coefficient vectors are distributed to each piece of first data, and the number of items of an encryption model is determined according to the evaluation condition of the first data with the same direction coefficient vectors through analysis, so that the encrypted data is difficult to crack through an anti-decryption mode.
Disclosure of Invention
In order to solve the above technical problems, an object of the present invention is to provide a remote operation and maintenance monitoring system for a central air conditioner, wherein the processing system includes a signal collector and a controller, the signal collector is configured to collect an operation and maintenance data sequence of the central air conditioner in real time, and the controller is configured to:
acquiring a central air conditioner operation and maintenance data sequence, coordinates of each first data in the central air conditioner operation and maintenance data sequence and a first dimension of each first data;
generating a first sequence, wherein each element in the first sequence is a direction coefficient vector, and the direction coefficient vector of each first data is determined in the first sequence according to the coordinate of each first data;
dividing first data with the same directional coefficient vector into a data set to obtain a plurality of data sets, obtaining the value amplitude and the number of approximate data of each first data according to each data set, and obtaining the cracking difficulty of each first data according to the value amplitude and the number of the approximate data of each first data;
constructing an optimal encryption model of each first data according to the cracking difficulty of each first data, the first dimension and the direction coefficient vector of each first data;
taking the solution of the optimal encryption model as ciphertext data of each first data;
and forming ciphertext data sequences by using the ciphertext data of all the first data, and transmitting the ciphertext data sequences as the encryption result of the operation and maintenance data of the central air conditioner.
Preferably, the constructing an optimal encryption model of each first data according to the cracking difficulty of each first data, the first dimension and the direction coefficient vector of each first data is as follows:
Figure DEST_PATH_IMAGE002
wherein,
Figure DEST_PATH_IMAGE003
preceding the directional coefficient vector of the ith first data
Figure DEST_PATH_IMAGE004
The data of the dimensions is represented by the dimension,
Figure DEST_PATH_IMAGE005
a first dimension representing the ith first data,
Figure DEST_PATH_IMAGE006
indicating the difficulty of cracking the ith first data,
Figure DEST_PATH_IMAGE007
indicating the ith first data.
Preferably, the method for determining the directional coefficient vector of each first data in the first sequence according to the coordinates of each first data comprises:
acquiring the number of direction coefficient vectors contained in the first sequence, and calculating the position of the direction coefficient vector of each first data in the first sequence according to the number and the coordinates of each first data;
the directional coefficient vector at the position in the first sequence is taken as the directional coefficient vector of each first data.
Preferably, the formula for calculating the position of the directional coefficient vector of each first datum in the first sequence is as follows:
Figure DEST_PATH_IMAGE009
wherein,
Figure DEST_PATH_IMAGE010
a line coordinate representing the ith first data,
Figure DEST_PATH_IMAGE011
column coordinates representing the ith first data,
Figure DEST_PATH_IMAGE012
representing the number of vectors containing directional coefficients in the first sequence,
Figure DEST_PATH_IMAGE013
the position of the directional coefficient vector representing the first data in the first sequence.
Preferably, the method for obtaining the value amplitude and the number of the approximate data of each first data according to each data set includes:
taking the information entropy of all data in the data set as the value amplitude of each first data in the data set;
any first data in the data set is recorded as second data, other data except the second data in the data set is recorded as third data, the second data and each third data are subjected to difference to obtain a difference value, the third data with the difference value absolute value smaller than a first preset threshold value are used as approximate data of the second data, and the number of all the approximate data of the second data is used as the number of the approximate data of the second data, namely the number of the approximate data of each first data.
Preferably, the formula for calculating the cracking difficulty of each first data is as follows:
Figure DEST_PATH_IMAGE014
wherein,
Figure 100002_DEST_PATH_IMAGE015
indicating the amplitude of the ith first data,
Figure DEST_PATH_IMAGE016
indicates the number of approximate data of the ith first data,
Figure 100002_DEST_PATH_IMAGE017
is shown as
Figure DEST_PATH_IMAGE018
A first dataThe difficulty of the cracking of the concrete well pipe,
Figure DEST_PATH_IMAGE019
is a pair of
Figure DEST_PATH_IMAGE020
And carrying out downward rounding processing.
Preferably, the method for acquiring the central air-conditioning operation and maintenance data sequence, the coordinates of each first data in the central air-conditioning operation and maintenance data sequence, and the first dimension of each first data includes:
acquiring an air conditioner operation and maintenance data sequence, wherein the air conditioner operation and maintenance data sequence is composed of a plurality of first data;
dividing each air conditioner operation and maintenance data sequence into a plurality of subsequences, converting each subsequence into a two-dimensional matrix of each subsequence, and forming the two-dimensional matrix of all the subsequences into a two-dimensional matrix sequence;
and acquiring the coordinate of each first data in the two-dimensional matrix, and acquiring the position of the two-dimensional matrix where each first data is located in the two-dimensional matrix sequence as the first dimension of each first data.
The invention has the following beneficial effects: according to the method, different direction coefficient vectors are distributed to each piece of data, the number of terms of the encryption equation is determined by analyzing the value conditions of operation and maintenance data corresponding to the same direction coefficient vector, the direction coefficient vector is used as a constant coefficient of the equation, each operation and maintenance data is used as a function value component to output the encryption equation, and therefore the constructed encryption equation can be guaranteed to contain proper number of terms, the calculation amount of encryption is not increased, and meanwhile the encryption equation can be prevented from being solved. A plurality of groups of feasible solutions are obtained by solving the encryption equation, one feasible solution is selected from all the feasible solutions to be used as the encrypted data of the operation and maintenance data, the encrypted data obtained in the mode has no statistical characteristics, the incidence relation among the data is broken, and the encrypted data is difficult to break through statistical analysis.
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 flowchart illustrating the general steps of a central air conditioner remote monitoring operation and maintenance system according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a flowchart of a remote operation and maintenance monitoring system for a central air conditioner according to an embodiment of the present invention is shown, where the method includes:
and S001, acquiring data to construct a two-dimensional matrix and generating a first sequence.
1. And collecting data to construct a two-dimensional matrix.
Acquiring an operation maintenance data sequence of the central air conditioner, wherein the operation maintenance data comprises:
(1) Meteorological data, including indoor temperature, humidity, wind speed;
(2) And the operation data comprises cold source operation data, freezing side hydraulic operation data, cooling side hydraulic operation data, tail end air disc operation data and tail end fan unit operation data.
And splicing the meteorological data and the operation data acquired at each moment together to form an operation and maintenance data subsequence at one moment, and splicing the operation and maintenance data subsequences at all moments together to form a one-dimensional operation and maintenance data sequence.
In order to facilitate subsequent encryption processing, a plurality of two-dimensional matrixes are constructed from the one-dimensional operation and maintenance data sequence. The specific method for obtaining a plurality of two-dimensional matrices according to the operation maintenance data sequence is as follows:
uniformly dividing one-dimensional running data sequence into
Figure DEST_PATH_IMAGE021
Has a length of
Figure DEST_PATH_IMAGE022
Into which each sub-sequence is divided
Figure DEST_PATH_IMAGE023
Each length is
Figure DEST_PATH_IMAGE024
Data segment of will
Figure 458187DEST_PATH_IMAGE023
Has a length of
Figure 93437DEST_PATH_IMAGE024
Is converted into
Figure 100002_DEST_PATH_IMAGE025
The matrix is referred to as a two-dimensional matrix. The sequence formed by all the two-dimensional matrixes is a matrix sequence, and each element in the matrix sequence is a two-dimensional matrix.
2. A first sequence is generated.
(1) Generating an N1-dimensional data sequence by using chaotic mapping, and the scheme
Figure DEST_PATH_IMAGE026
10000 was taken. It should be noted that the function parameters of the chaotic map are well agreed by both parties, so that transmission is not needed.
(2) And obtaining a direction coefficient vector according to the data sequence.
Will be provided with
Figure 469579DEST_PATH_IMAGE021
The dimension data sequence is generated by random permutation and combination
Figure DEST_PATH_IMAGE027
An
Figure DEST_PATH_IMAGE028
Vector of dimensions, each
Figure 255001DEST_PATH_IMAGE028
The vector of dimensions is taken as a directional coefficient vector. Forming a first sequence by all the direction coefficient vectors, wherein each element in the first sequence is each direction coefficient vector; in the scheme
Figure DEST_PATH_IMAGE029
100 is taken.
The method for forming all the direction coefficient vectors into a first sequence comprises the following steps: sorting all direction coefficient vectors according to data of a first dimension in each direction coefficient vector, placing the direction coefficient vector with larger first dimension data in front of the direction coefficient vectors, when the data of the first dimension is the same, sorting the direction coefficient vectors with the same first dimension data according to the size of second dimension data, arranging the direction coefficient vector with larger second dimension data in front of the direction coefficient vectors, and so on, sequentially using the data size of each position to arrange the direction coefficient vectors to obtain the position of each direction coefficient vector, and taking the sequence obtained by arranging all the direction coefficient vectors according to the position as a first sequence.
S002: and constructing an encryption model, and encrypting the matrix sequence by using the encryption model to obtain a ciphertext matrix sequence.
1. And determining the direction coefficient vector of each coordinate position data according to the matrix coordinates.
Recording each data in each two-dimensional matrix in the matrix sequence as a first data, and recording the first data of a certain two-dimensional matrix
Figure 741477DEST_PATH_IMAGE018
The first data is taken as an example to explain a selection method of each first data direction coefficient vector in the two-dimensional matrix, which specifically comprises the following steps:
get the ithRow coordinate of a data
Figure DEST_PATH_IMAGE030
And column coordinates
Figure DEST_PATH_IMAGE031
Obtaining the number of elements included in the first sequence
Figure 954153DEST_PATH_IMAGE012
. Obtaining the position of the direction coefficient vector of the ith first data in the first sequence according to the row and column coordinates of the ith first data as follows:
Figure DEST_PATH_IMAGE033
wherein% represents a remainder symbol by
Figure DEST_PATH_IMAGE034
Obtaining a directional coefficient vector of the ith first data in the two-dimensional matrix,
Figure 345820DEST_PATH_IMAGE013
and representing the dimension of the direction coefficient vector of the ith first data in the two-dimensional matrix in the first sequence.
In the first sequence, obtain
Figure 13562DEST_PATH_IMAGE013
And the direction coefficient vector of each position is the direction coefficient vector of the ith first data.
Similarly, each first data in each two-dimensional matrix can obtain a direction coefficient vector, and because the direction coefficient vectors for encrypting each first data are different, the different direction coefficient vectors have different encryption effects on the first data at each position, so that the encryption diversity is increased, the spatial correlation of the two-dimensional matrix can be effectively broken, and the decryption difficulty can be increased.
2. And encrypting the matrix sequence according to the direction coefficient vector to obtain a ciphertext matrix sequence.
(1) First, a simple encryption model is given for encryption:
for the first in a certain two-dimensional matrix
Figure 137900DEST_PATH_IMAGE018
A first data
Figure 334526DEST_PATH_IMAGE007
For the first data
Figure 557697DEST_PATH_IMAGE007
The specific method for encrypting by using the encryption model is as follows:
first, the
Figure 12818DEST_PATH_IMAGE018
A first data
Figure 739466DEST_PATH_IMAGE007
The corresponding directional coefficient vector is noted as
Figure DEST_PATH_IMAGE035
From the direction vector
Figure DEST_PATH_IMAGE036
To obtain the first 4-dimensional data
Figure DEST_PATH_IMAGE037
Using the 4 data and the first data
Figure 28365DEST_PATH_IMAGE007
Construction of the independent variables
Figure DEST_PATH_IMAGE038
Polynomial of
Figure DEST_PATH_IMAGE039
. The polynomial may have infinite solutions in which one set of solutions is randomly selected, and the randomly selected set of solutions is sequencedIs marked as
Figure DEST_PATH_IMAGE040
Will solve the sequence
Figure 129045DEST_PATH_IMAGE040
Taking the encrypted three-channel value as the ith first data; then, according to the direction coefficient vectors of all the first data, respectively encrypting all the first data in the two-dimensional matrix to obtain a three-channel ciphertext matrix, wherein
Figure DEST_PATH_IMAGE041
The value of the ith data of the first channel ciphertext matrix in the three-channel ciphertext matrix,
Figure DEST_PATH_IMAGE042
the value of the ith data of the second channel ciphertext matrix obtained by the two-dimensional matrix encryption,
Figure DEST_PATH_IMAGE043
and (4) obtaining the value of the ith data of the ciphertext matrix of the third channel by encrypting the two-dimensional matrix. So far, a two-dimensional matrix is encrypted into a three-channel ciphertext matrix by using the encryption method, and the three-channel ciphertext matrices of all the two-dimensional matrices form a ciphertext matrix sequence.
The encryption method randomly selects a group of solutions in all solution spaces as ciphertext data, the same plaintext data can be encrypted into different ciphertext data, different plaintext data can be encrypted into the same ciphertext data, therefore, the statistical characteristics in the ciphertext image are different from the statistical characteristics of the plaintext image, an attacker cannot carry out statistical analysis attack on the ciphertext image according to the rule of the statistical characteristics of the ciphertext image, the encryption rule is difficult to obtain through the statistical characteristics, the difficulty of brute force cracking of the ciphertext image is increased, the encryption and decryption operation of the encryption model is really a simple linear model, and the calculation amount and the calculation speed are high.
However, the operation and maintenance data of the central air conditioner has certain periodicity characteristics, so that the operation and maintenance data of the central air conditioner has periodicity numbersAccording to the method, a large amount of data with the same value can exist in the obtained matrix sequence, and when a plurality of data with the same value exist in the matrix sequence and the direction coefficient vectors corresponding to the data are the same, the encryption mode can easily solve the corresponding plaintext data in an inverse solution mode. For example, if there are multiple data values in the matrix sequence that are the same and the corresponding directional coefficient vectors of the multiple data are the same, an equation set is constructed, and each equation in the equation set is
Figure DEST_PATH_IMAGE044
Are the same, while each formula
Figure 395379DEST_PATH_IMAGE037
When the ciphertext matrix is public, at this time
Figure DEST_PATH_IMAGE045
It is known that the unknowns of the equation set at this time are four data in the undisclosed directional coefficient vector
Figure 228730DEST_PATH_IMAGE037
And plaintext data of two-dimensional matrix
Figure 767159DEST_PATH_IMAGE044
And thus plaintext data is obtained by solving the equation set at this time. To address this problem, a complex encryption model is provided below.
(2) Then, an optimal encryption model is given for encryption:
through analysis, it can be known that the number of approximate data corresponding to the same direction coefficient vector in the matrix sequence is more than the number of unknown numbers, so that the number of unknown numbers needs to be further determined according to the number of the same data corresponding to the same direction vector in the matrix sequence, and the approximate data refers to data with similar values. Meanwhile, when the values of the data corresponding to the coefficient vectors in the same direction in the matrix sequence are similar, the plaintext data in the matrix sequence can be solved, so that the values and amplitudes of the data corresponding to the vectors in the same direction need to be analyzed to determine the number of positions, which is specifically as follows:
a. calculating the value amplitude:
dividing first data with the same direction coefficient vector in a matrix sequence into a category set, dividing all the first data in the matrix sequence into a plurality of category sets, obtaining the category set to which the ith first data of the matrix sequence belongs, recording the category set as a first category set, and calculating by utilizing all the first data of the first category to obtain an information entropy value
Figure 964922DEST_PATH_IMAGE015
Entropy of information
Figure 496267DEST_PATH_IMAGE015
As the value amplitude of the category set in which the ith first data is located,
Figure 463086DEST_PATH_IMAGE015
the larger the data value in the first category set is, the more complicated the data value is, that is, the probability that the value in the first category set is close to the ith first data value is smaller, the equation set in (1) is difficult to establish, or the solution result is only approximate, and an accurate plaintext cannot be obtained.
b. Obtaining the number of approximate data:
respectively arranging each first data in the first category set to the ith first data
Figure 437995DEST_PATH_IMAGE044
Subtracting to obtain difference of the first data, and making the difference be less than first threshold
Figure DEST_PATH_IMAGE046
As approximate data of the ith first data, the first data in the first category is counted
Figure 781776DEST_PATH_IMAGE018
The number of approximate data of the first data
Figure 849569DEST_PATH_IMAGE016
c. And (3) establishing an encryption model according to the number of each value range of the approximate data:
the position of the two-dimensional matrix to which the ith first data belongs in the matrix sequence is acquired and recorded as
Figure 405315DEST_PATH_IMAGE005
Calculating the cracking difficulty of the ith first data
Figure 65972DEST_PATH_IMAGE017
The cracking difficulty is determined by the value range of the category set where the ith first data is located and the number of the approximate data, and the cracking difficulty calculation formula is as follows:
Figure DEST_PATH_IMAGE047
wherein
Figure 644852DEST_PATH_IMAGE019
Pair of representations
Figure 504617DEST_PATH_IMAGE020
And (5) carrying out downward rounding processing.
Obtaining the direction coefficient vector of the ith first data
Figure 180449DEST_PATH_IMAGE004
Data of individual position
Figure 12008DEST_PATH_IMAGE003
Using the front of the directional coefficient vector
Figure 406080DEST_PATH_IMAGE004
Dimension data
Figure 833651DEST_PATH_IMAGE003
An encryption model is constructed as a constant parameter:
Figure DEST_PATH_IMAGE049
wherein in the formula
Figure 816519DEST_PATH_IMAGE005
Is known, therefore
Figure DEST_PATH_IMAGE050
It is known that at the same time
Figure DEST_PATH_IMAGE051
Can obtain first data
Figure 494012DEST_PATH_IMAGE007
As is known, the unknown data in the current formula are only
Figure DEST_PATH_IMAGE052
All feasible solutions of the expression are solved, and one group of solutions is randomly selected from all feasible solutions
Figure DEST_PATH_IMAGE053
Will be
Figure DEST_PATH_IMAGE054
And taking the encrypted three-channel value as the ith first data, and encrypting the two-dimensional matrix to obtain a three-channel ciphertext matrix, wherein the three-channel ciphertext matrix is obtained
Figure 765594DEST_PATH_IMAGE041
The value of the ith data of the ciphertext matrix of the first channel obtained by encrypting the two-dimensional matrix,
Figure 996855DEST_PATH_IMAGE042
the value of the ith data of the ciphertext matrix of the second channel obtained by encrypting the two-dimensional matrix,
Figure 630968DEST_PATH_IMAGE017
and obtaining the value of the ith data of the ciphertext matrix of the third channel obtained by encrypting the two-dimensional matrix.
And encrypting all the two-dimensional matrixes of the matrix sequence to obtain a ciphertext matrix sequence, wherein each element of the ciphertext matrix sequence is a three-channel ciphertext matrix.
The number of constant coefficients in the equation can be determined according to the similar value condition of all data with the same direction coefficient vector in the matrix sequence through the encryption model, and when the number of the constant coefficients is more than the number of the similar data in the matrix sequence, the plaintext data in the matrix sequence is difficult to determine through solving the equation mode.
And transmitting the ciphertext matrix sequence to a central air-conditioning remote monitoring operation and maintenance system.
S003: and decrypting the ciphertext matrix sequence to obtain the matrix sequence.
The following description takes an example of a decryption method of ciphertext data obtained by a complex encryption model, which specifically includes the following steps:
each two-dimensional matrix of the matrix sequence is encrypted into a three-channel ciphertext matrix, so that the matrix sequence can obtain a corresponding ciphertext matrix sequence, and each element of the ciphertext data sequence is a three-channel ciphertext matrix.
Obtaining the row and column coordinates of the ith second data
Figure DEST_PATH_IMAGE055
Obtaining the position of the three-channel ciphertext matrix in which the ith second data is positioned in the ciphertext matrix sequence
Figure 492744DEST_PATH_IMAGE005
And obtaining a direction coefficient vector of the ith second data according to the row and column coordinates where the ith second data is located in the second step. Obtaining the ith second data
Figure DEST_PATH_IMAGE056
Before obtaining in the direction coefficient vector
Figure 579518DEST_PATH_IMAGE004
Data of a person
Figure 348891DEST_PATH_IMAGE003
Will be ahead of
Figure 119401DEST_PATH_IMAGE004
The data is used as a constant coefficient of a complex encryption model, so that the ith second data obtained by decryption is as follows:
Figure DEST_PATH_IMAGE058
the remote monitoring operation and maintenance system decrypts the transmitted ciphertext matrix sequence by using the method in the S003, displays the decrypted matrix sequence data on a monitoring screen, and realizes the operation and maintenance abnormity judgment of the central air conditioner by analyzing the data on the monitoring screen.
In summary, in the embodiments of the present invention, different direction coefficient vectors are allocated to each data, the number of terms of the encryption equation is determined by analyzing the value taking condition of the operation and maintenance data corresponding to the same direction coefficient vector, the direction coefficient vector is used as a constant coefficient of the equation, and each operation and maintenance data is used as a function value component to generate the encryption equation, so that the constructed encryption equation can be ensured to contain an appropriate number of terms, the calculation amount of encryption is not increased, and the solution can be prevented. A plurality of groups of feasible solutions are obtained by solving the encryption equation, one feasible solution is selected from all the feasible solutions to be used as the encrypted data of the operation and maintenance data, and the encrypted data obtained in the mode has no statistical characteristics and is difficult to crack through statistical analysis.
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.
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 and scope of the present invention.

Claims (4)

1. The utility model provides a central air conditioning remote monitoring operation and maintenance system which characterized in that, operation and maintenance system includes signal collector and controller, signal collector is used for gathering central air conditioning operation and maintenance data sequence in real time, the controller is used for:
acquiring a central air conditioner operation and maintenance data sequence, coordinates of each first data in the central air conditioner operation and maintenance data sequence and a first dimension of each first data;
generating a first sequence, wherein each element in the first sequence is a direction coefficient vector, and the direction coefficient vector of each first data is determined in the first sequence according to the coordinate of each first data;
dividing first data with the same directional coefficient vector into a data set to obtain a plurality of data sets, obtaining the value amplitude and the number of approximate data of each first data according to each data set, and obtaining the cracking difficulty of each first data according to the value amplitude and the number of approximate data of each first data;
constructing an optimal encryption model of each first data according to the cracking difficulty of each first data, the first dimensionality and the direction coefficient vector of each first data;
taking the solution of the optimal encryption model as ciphertext data of each first data;
the ciphertext data of all the first data form a ciphertext data sequence, and the ciphertext data sequence is used as an encryption result of the central air-conditioning operation and maintenance data for transmission; the method for constructing the optimal encryption model of each first data according to the cracking difficulty of each first data, the first dimension and the direction coefficient vector of each first data comprises the following steps:
Figure DEST_PATH_IMAGE001
wherein,
Figure 546320DEST_PATH_IMAGE002
preceding the directional coefficient vector of the ith first data
Figure 713865DEST_PATH_IMAGE003
The data of the dimensions is represented by the dimension,
Figure 21350DEST_PATH_IMAGE004
a first dimension representing the ith first data,
Figure 302158DEST_PATH_IMAGE005
indicating the difficulty of cracking the ith first data,
Figure 757935DEST_PATH_IMAGE006
representing the ith first data;
all feasible solutions of the corresponding expression of the optimal encryption model are solved, and one group of solutions is randomly selected from all feasible solutions
Figure 773295DEST_PATH_IMAGE007
Will be
Figure 602580DEST_PATH_IMAGE008
The encrypted three-channel value is used as the encrypted three-channel value of the ith first data, and the first data is encrypted to obtain a three-channel ciphertext matrix, wherein
Figure 190425DEST_PATH_IMAGE009
Taking the value of the ith data of the ciphertext matrix of the first channel obtained by encrypting the first data,
Figure 754786DEST_PATH_IMAGE010
obtaining ith data of ciphertext matrix of second channel obtained by encrypting first dataValue of,
Figure 444393DEST_PATH_IMAGE005
taking the value of the ith data of the third channel ciphertext matrix obtained by encrypting the first data, and taking the three channel ciphertext matrix as the ciphertext data of the first data;
the decryption method is as follows: obtaining the row-column coordinate of the ith first data
Figure 329566DEST_PATH_IMAGE011
Obtaining the position of the three-channel ciphertext matrix where the ith first data is located in the ciphertext data sequence
Figure 53809DEST_PATH_IMAGE004
Obtaining the direction coefficient vector of the ith first data according to the row and column coordinates of the ith first data, and obtaining the ith first data
Figure 582879DEST_PATH_IMAGE012
Before obtaining in the direction coefficient vector
Figure 290941DEST_PATH_IMAGE003
Data of a person
Figure 414360DEST_PATH_IMAGE013
Will be preceded by
Figure 55425DEST_PATH_IMAGE003
Taking the data as a constant coefficient of a complex encryption model, and decrypting to obtain the ith first data as follows:
Figure 771709DEST_PATH_IMAGE014
the formula for calculating the cracking difficulty of each first data is as follows:
Figure DEST_PATH_IMAGE015
wherein,
Figure 888438DEST_PATH_IMAGE016
indicating the amplitude of the ith first data,
Figure DEST_PATH_IMAGE017
indicating the number of approximation data of the ith first data,
Figure 803829DEST_PATH_IMAGE018
denotes the first
Figure 846871DEST_PATH_IMAGE019
The difficulty of cracking the first data,
Figure 780061DEST_PATH_IMAGE020
is a pair of
Figure 275765DEST_PATH_IMAGE021
Carrying out downward rounding treatment;
the method for obtaining the value amplitude and the number of the approximate data of each first data according to each data set comprises the following steps:
taking the information entropy of all data in the data set as the value amplitude of each first data in the data set;
any first data in the data set is recorded as second data, other data except the second data in the data set is recorded as third data, the second data and each third data are subjected to difference to obtain a difference value, the third data with the difference value absolute value smaller than a first preset threshold value are used as approximate data of the second data, and the number of all the approximate data of the second data is used as the number of the approximate data of the second data, namely the number of the approximate data of each first data.
2. The remote operation and maintenance monitoring system for the central air conditioner as claimed in claim 1, wherein the method for determining the direction coefficient vector of each first data in the first sequence according to the coordinate of each first data comprises:
acquiring the number of direction coefficient vectors contained in the first sequence, and calculating the position of the direction coefficient vector of each first data in the first sequence according to the number and the coordinates of each first data;
the directional coefficient vector at the position in the first sequence is taken as the directional coefficient vector of each first data.
3. The remote operation and maintenance monitoring system for central air conditioner according to claim 2, wherein the formula for calculating the position of the directional coefficient vector of each first datum in the first sequence is as follows:
Figure 319813DEST_PATH_IMAGE023
wherein,
Figure 686203DEST_PATH_IMAGE024
a line coordinate representing the ith first data,
Figure DEST_PATH_IMAGE025
column coordinates representing the ith first data,
Figure 727977DEST_PATH_IMAGE026
representing the number of vectors containing directional coefficients in the first sequence,
Figure 228753DEST_PATH_IMAGE027
the position of the directional coefficient vector representing the first data in the first sequence.
4. The system as claimed in claim 1, wherein the method for obtaining the operation and maintenance data sequence of the central air conditioner, the coordinates of each first data in the operation and maintenance data sequence of the central air conditioner and the first dimension of each first data comprises:
acquiring an air conditioner operation and maintenance data sequence, wherein the air conditioner operation and maintenance data sequence is composed of a plurality of first data;
dividing each air conditioner operation and maintenance data sequence into a plurality of subsequences, converting each subsequence into a two-dimensional matrix of each subsequence, and forming the two-dimensional matrix of all subsequences into a two-dimensional matrix sequence;
and acquiring the coordinate of each first data in the two-dimensional matrix, and acquiring the position of the two-dimensional matrix where each first data is located in the two-dimensional matrix sequence as the first dimension of each first data.
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