CN115801451B - File protection cloud perception management platform double-network isolation method - Google Patents
File protection cloud perception management platform double-network isolation method Download PDFInfo
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Abstract
A dual-network isolation method of a file protection cloud perception management platform relates to the technical field of digital information transmission, and firstly, a file identification system is adopted to identify and collect file information; secondly, controlling network connection by adopting a network control system, and realizing internal and external network connection through an interface connection program; thirdly, an external network management system is adopted to realize the connection between the management platform and the Internet through a second transmitting antenna and a second receiving antenna; thirdly, the intranet management system realizes the connection between the management platform and the internal network through the first transmitting antenna and the first receiving antenna; thirdly, the information processing system realizes network information optimization classification through a neural network-frog-leaping algorithm; finally, the network safety monitoring system monitors the network safety state in real time in the network connection process. The invention greatly improves the archive protection cloud sensing capability and the data communication capability.
Description
Technical Field
The invention relates to the technical field of digital information transmission, in particular to a dual-network isolation method of an archive protection cloud perception management platform.
Background
The dual-network isolation means that the external network and the internal network cannot interfere with each other, namely, the internal network cannot be called when the external network is called, and the external network cannot be influenced, and the internal network is called; the existing double-network isolation technology can be divided into three types, namely double-machine isolation, a double-network isolation card and a double-network switcher, wherein the double-machine isolation is to configure two computers, one is in charge of internal network connection, and the other is in charge of external network connection; the dual-network isolation card is characterized in that a hard disk is additionally configured, an internal network hard disk is in a power-off state when an external network is connected, and the external network hard disk is in the power-off state when the internal network is connected; the dual-network switcher is a network switching device.
In the prior art, a dual-network isolation technology is gradually advanced, and a dual-network isolation implementation method is proposed in a patent CN201510060188.3, wherein a mode switching instruction sent by an embedded controller is used for controlling a logic chip connected with a network, the first logic chip is used for operating a first internal and external network switching state, the second logic chip is used for operating a second internal and external network switching state, the network connection is controlled to be in a dual-network mode, a dual-operating system mode and a conventional mode, the switching instruction is in a high level, the dual-operating system instruction is in a low level, and the network mode is in the conventional mode; the switching instruction is low level, the double operation instruction is high level, and the network mode is a double operation system mode; in the patent CN201120217067.2, a dual-network isolation system is proposed, which is connected to a network through a dual-network isolation hardware switch, wherein a TCM chip stores network configuration data, a dual-network isolation switch is used as a network selector and connected to the TCM chip and integrated in a computer motherboard, the TCM chip is used for storing gateway data and performing encryption processing on the data, a network connection system is selected, and a BIOS chip obtains the gateway configuration data starting system to implement network selection. Both techniques can realize dual-network isolation, but do not have information processing capability, the isolation effect of the dual-network is not obvious, and the network signal processing efficiency is low.
Disclosure of Invention
Aiming at the problems, the invention discloses a double-network isolation method of an archive protection cloud perception management platform, which can realize effective isolation of internal and external networks, efficiently process network data, optimize information and greatly improve network security.
In order to achieve the technical effects, the invention adopts the following technical scheme:
a dual-network isolation method of a file protection cloud perception management platform is characterized by comprising the following steps of: comprising the following steps:
s1: a PDA classifier is adopted to scan two-dimension code labels of the file bags, basic information in the file bags is identified, and the file protection cloud perception management platform automatically synchronizes the basic information in a database through a GPRS network communication unit arranged inside;
s2: the network control system controls the network connection of the archive protection cloud perception management platform, an operator inputs an internal network connection command at a network connection interface, and the management platform realizes internal work network connection through a special data circuit; an operator inputs an external network connection command at a network connection interface, and the management platform is connected with an external network through an optical fiber internet module to realize network information transmission search;
s3: the management platform inquires file information, inputs a secret key inquiry command, a signal generator generates the frequency of an inquiry signal, a first transmitting antenna transmits the frequency signal, a first receiving antenna receives the frequency signal, the frequency signal is converted into an electric signal through a signal conversion circuit, and a signal processing module calibrates and processes the inquiry command of the electric signal and dynamically invokes file information in a database according to the inquiry command;
s4: the management platform is connected with the internet to inquire information, the main server establishes wireless connection with external subnet units through a second transmitting antenna, communication channels are established among the subnet units with the same communication parameters, the second receiving antenna receives returned information of each subnet unit, and the information processing module classifies and processes the returned information; the information processing module adopts a neural network-frog-leaping algorithm to realize the classification processing of the information, and the neural network-frog-leaping algorithm comprises an information optimization model, an improved frog-leaping algorithm model, a frog-leaping classifier and a data transmission relay station.
S5: the network safety monitoring system carries out safety supervision on the working states of the internal network and the external network of the management platform, the network speed measuring unit calculates the network speed according to the response time, the network data statistics unit calculates the data information in the network nodes and divides the network nodes into stable type, fluctuation type and mutation type according to the characteristics of the data information, the safety management unit carries out safety value statistics on the fluctuation type and mutation type network nodes based on a neural network deep learning algorithm, the safety value is lower than a lowest threshold value, and the network hazard warning unit starts an alarm mode; the neural network deep learning algorithm comprises a BP training model, a function set and a deep statistics module.
As a further technical scheme of the invention, the file bag two-dimension code comprises file numbers, generation time, belonging addresses, file attributes, author information and a two-dimension code history scanning record of the file.
As a further technical scheme of the invention, the file database is stored by adopting a distributed ring topology structure, and files are stored in the distributed ring topology structure in the form of stacks with the same attribute and the same group.
As a further technical scheme of the invention, the archive protection cloud perception management platform queries information through an external network, the information received by the second receiving antenna is classified through a neural network-frog-leaping algorithm, and the method for optimizing information data by the neural network algorithm comprises the following steps:
s41: calculating information energy function
Defining hidden layer information values in a neural network algorithm as
Wherein->
Representing the amount of hidden layer information->
Representing the information value as a vector, the visible layer information value as +.>
Wherein->
The information energy function in the neural network is shown as a formula (1) and represents the information quantity of the visible layers:
in the formula (1), the components are as follows,
representing the number of bits of the hidden layer information value->
Representing implicit coefficients in the neural network, < >>
Representing display coefficients in a neural network, < >>
Representing the number of bits of the information value of the visible layer, +.>
Bias value representing hidden layer information, +.>
Representing the%>
Bias value of bit information value +.>
Representing the%>
Bit information value->
Bias value representing visible layer information, +.>
Representing the +.>
Bias value of bit information value +.>
Representing the +.>
Bit information value->
Information connection matrix representing hidden layer and visible layer, < >>
Representing the%>
Bit information value and +.>
A connection matrix of bit information values;
s42: calculating probability distribution of information values of an implicit layer and a visible layer according to an energy function, wherein the probability distribution is shown as a formula (2):
in the formula (2), the amino acid sequence of the compound,
representing the partitioning factor,/->
Representing the arithmetic constant->
Representing an information energy function representing information values in the hidden layer +.>
And visible layer->
Information energy function, +.>
Representing probability distribution->
Representing hidden layer->
And visible layer->
Probability distribution of information values; fitting the distribution condition of the received network return information in the neural network through the information value distribution probability conditions of the hidden layer and the visible layer;
s43: calculating the relative entropy of information values of an implicit layer and an input layer
Defining hidden layer information value probability distribution as
The probability distribution of the information values of the visible layer is +.>
，
And->
The relative entropy calculation formula of (2) is shown in formula (3):
in the formula (3), the amino acid sequence of the compound,
representing sample space, ++>
Indicating relative distance>
Representing hidden layer->
And visible layer->
Distance of information value distribution->
Representing a spatial relationship, wherein when the relative distance is minimum, the implicit layer information value is closest to the visible layer information value distribution;
s44: reconstructing an information error function
The received information enters an hidden layer for processing, a certain amount of hidden layer information values are selected as training samples, and the calculation formula of the logarithmic loss function of the hidden layer information values is shown as formula (4):
in the formula (4), the amino acid sequence of the compound,
representing the weight connection matrix from visible layer to hidden layer, < ->
Representing loss factor,/->
Representing the%>
Bit information value +.>
Probability distribution (S)/(S)>
Representing a logarithmic loss function, +.>
Representing implicit layer offset value +.>
Visible layer bias value->
Weight connection matrix from visible layer to hidden layer>
A logarithmic loss function of (2);
and continuously optimizing the received information value by a small batch characteristic gradient descent method of the information momentum, and minimizing a reconstruction error according to a logarithmic loss function until the information value error reaches an ideal minimum value.
As a further technical scheme of the invention, the information classification is realized by improving the frog-leaping algorithm according to the neural network algorithm, and the realization steps of the improved frog-leaping algorithm are as follows:
randomly selecting optimized information value composition
Individual information frog individuals, the information frog population is
Defining the%>
Only information frog->
Wherein->
Representing information frog>
Characteristic elements representing information frog, +.>
Representing the number of characteristic elements +.>
Representing>
Information about frog->
Characteristic elements for information frog in populationThe step length of the frog exercise with the row information is updated, and the updated calculation formula is shown as the formula (5):
in the formula (5), the amino acid sequence of the compound,
information frog indicating best fitness in the population, < ->
、
Representing +.>
、
Information frog, ->
Representing random real numbers, < >>
Information frog indicating worst fitness in population, < ->
Representing information frog exercise factor->
Step length update of the information frog is represented;
calculating updated information frog according to the updated information frog step length, as shown in a formula (6):
in the formula (6), the amino acid sequence of the compound,
representing the optimized information frog, ++>
、
Information frog indicating worst fitness
A lower limit and an upper limit of the step size allowed to be changed;
through chaotic mapping, the frog-leaping mixing optimization efficiency of the information frog is improved, and the mapping calculation is shown as a formula (7):
in the formula (7), the amino acid sequence of the compound,
representing the mapping coefficient->
Representing the sequence->
The mapped sequence of the information frog is represented, the mapped relational expression and the updated information frog form a new frog sequence, the information frog with the largest sequence number is selected from the frog sequence to be used as a lead frog, the characteristic elements contained in the lead frog are main characteristic elements of the frog group, an information characteristic classification model is established, and the characteristic elements are used as classification standards to realize information value classification.
As a further technical scheme of the invention, the data transmission relay station is used for temporarily storing data received by an external network, and comprises a data collector, a registering unit and a characteristic storage group; the data acquisition device adopts a low-power consumption singlechip to acquire data to be optimized, the registering unit adopts a shift register to temporarily store the optimized data, and the characteristic storage group adopts a capacitance storage to realize classified storage of the classified data.
As a further technical solution of the present invention, a network isolation method includes: the 001-number network channel is adopted to realize the connection between the management platform and the internal network, and the 002-number network channel is adopted to realize the connection between the management platform and the external network; the 001 and 002 network channels adopt parallel circuits to control the on or off of the network channels; the management platform opens the network channel based on the control current and does not communicate with the network channel to realize network isolation.
As a further technical scheme of the invention, the invention comprises the following steps: the depth statistics module is used for realizing sum calculation of the safety values and comprises a data normalization processing module, a DEEP model and an auxiliary module.
The beneficial effects of the invention are as follows:
different from the conventional technology, the invention provides a dual-network isolation method of a file protection cloud perception management platform, a file identification system is used for identifying file information, a network control system controls network connection, internal and external network connection is realized through an interface connection program, an external network management system realizes connection of a management platform and the Internet through a second transmitting antenna and a second receiving antenna, an internal network management system realizes connection of the management platform and an internal network through a first transmitting antenna and a first receiving antenna, an information processing system realizes network information optimization classification through a neural network-frog-leaping algorithm, and a network security monitoring system monitors network security states in real time in the network connection process; compared with other double-network isolation technologies, the double-network isolation method of the archive protection cloud perception management platform can better realize effective isolation of the internal network and the external network, efficiently process network data, optimize information and greatly improve network safety.
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For a clearer description of embodiments of the invention or of solutions in the prior art, the drawings that are necessary for the description of the embodiments or of the prior art will be briefly described, it being apparent that the drawings in the description below are only some embodiments of the invention, from which, without inventive faculty, other drawings can be obtained for a person skilled in the art, in which:
FIG. 1 shows a step diagram of a dual-network isolation method of an archive protection cloud sensing management platform;
FIG. 2 shows a dual-network isolation system diagram of an archive protection cloud sensing management platform of the present invention;
FIG. 3 illustrates a signal optimization classification flow chart of the present invention;
FIG. 4 shows the results of the algorithm classification experiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention;
in a specific embodiment, a dual-network isolation method and implementation process of an archive protection cloud sensing management platform are shown in fig. 1 to fig. 4.
A dual-network isolation method of a file protection cloud perception management platform is characterized by comprising the following steps of: comprising the following steps:
s1: the palm computer (Personal Digital Assistant, PDA) classifier has certain operating system, can scan the file pocket in its 50cm scope, there is software development (Software Development Kit, SDK) system in the interior, have man-machine interface, can realize display and input function, scan the file pocket two-dimensional code label, discern the basic information in the file pocket, the file protects the cloud and perceives the management platform to synchronize the basic information in the database automatically through the internal GPRS network communication unit; wherein the database is integrated in a unit computer hard disk.
S2: the network control system controls the network connection of the archive protection cloud perception management platform by implementing a closed loop circuit set for control and communication through a network, an operator inputs an internal network connection command at a network connection interface, and the management platform realizes internal work network connection through a special data circuit; an operator inputs an external network connection command at a network connection interface, and the management platform is connected with an external network through an optical fiber internet module to realize network searching;
s3: the management platform inquires file information, the network security management platform verifies that a secret key inquiry command needs to be input for identity, the signal generator generates the frequency of an inquiry signal when the command is correct, the first transmitting antenna transmits the frequency signal, the first receiving antenna receives the frequency signal and converts the frequency signal into an electric signal through the signal conversion circuit, and the signal processing module calibrates and processes the inquiry command of the electric signal and dynamically invokes file information in the database according to the inquiry command; the signal processing module adopts DSP and FPGA parallel integration, selects ADSP series multiple DSP coupling digital signal processing module and Xilinx company Spartan3AN series chip, wherein Spartan3AN series chip is as core, multiple DSP coupling digital signal processing module auxiliary integration around the chip, constitutes signal processing module through net gape and circuit.
S4: the management platform queries information on the internet, the main server establishes wireless connection with external subnet units through a second transmitting antenna, communication channels are established among the subnet units with the same communication parameters, the second receiving antenna receives returned information of each subnet unit, the information processing module classifies and processes the returned information, and the subnet units correspond to datagram mechanisms of the packet switched network. The network connection first establishes two new datagrams with a maximum capacity of 64 bits, the first datagram length is set to insert data, the more-flag is set to 1, the offset is unchanged, the second datagram length is set to insert data, the more-flag is set to 0, and the offset is set to one eighth of the first datagram length. The information processing module adopts a neural network-frog-leaping algorithm to realize the classification processing of the information, and the neural network-frog-leaping algorithm comprises an information optimization model, an improved frog-leaping algorithm model, a frog-leaping classifier and a data transmission relay station.
S5: the network safety monitoring system carries out safety supervision on the working states of the internal network and the external network of the management platform, the network speed measuring unit calculates network speed according to response time, the network data counting unit calculates data information in network nodes and divides the network nodes into stable type, fluctuation type and mutation type according to the characteristics of the data information, the safety management unit carries out safety value statistics on the fluctuation type and mutation type network nodes based on a neural network deep learning algorithm, the safety value is lower than a minimum threshold value, and the network hazard warning unit starts an alarm mode. The neural network deep learning algorithm comprises a BP training model, a function set and a deep statistics module.
In a specific embodiment, the two-dimensional code of the file bag includes a file number, a generation time, an address, a file attribute, author information and a history record of the two-dimensional code.
In a specific embodiment, the archive database storage adopts a distributed ring topology structure, and the archives are stored in the distributed ring topology structure in the form of stacks with the same attribute and the same group.
In a specific embodiment, the archive protection cloud perception management platform queries information through an external network, information received by the second receiving antenna is classified through a neural network-frog-leaping algorithm, and the method for optimizing information data by the neural network algorithm comprises the following steps:
s41: calculating information energy function
Defining hidden layer information values in a neural network algorithm as
Wherein->
Representing the amount of hidden layer information->
Representing the information value as a vector, the visible layer information value as +.>
Wherein->
The information energy function in the neural network is shown as a formula (1) and represents the information quantity of the visible layers:
in the formula (1), the components are as follows,
representing the number of bits of the hidden layer information value->
Representing implicit coefficients in the neural network, < >>
Representing display coefficients in a neural network, < >>
Representing the number of bits of the information value of the visible layer, +.>
Bias value representing hidden layer information, +.>
Representing the%>
Bias value of bit information value +.>
Representing the%>
Bit information value->
Bias value representing visible layer information, +.>
Representing the +.>
Bias value of bit information value +.>
Representing the +.>
Bit information value->
Information connection matrix representing hidden layer and visible layer, < >>
Representing the%>
Bit information value and +.>
A connection matrix of bit information values;
among the parameters mentioned above, the above-mentioned parameters,
implicit layer->
Bias value of bit information value for adjusting given value of hidden layer,
Visible layer->
The offset value of the bit information value is used to adjust the visible layer set point,/->
The information connection matrix is used for adjusting the sensing accuracy in the system, < + >>
The larger the implicit coefficient is, the higher the information content is, the higher the possibility that the information value is utilized in the optimization process is, and the received information value can be standardized and unified through the parameter calculation, so that the following processing is facilitated.
S42: calculating probability distribution of information values of an implicit layer and a visible layer according to an energy function, wherein the probability distribution is shown as a formula (2):
in the formula (2), the amino acid sequence of the compound,
representing the partitioning factor,/->
Representing the arithmetic constant->
Representing an information energy function representing information values in the hidden layer +.>
And visible layer->
Information energy function, +.>
Representing probability distribution->
Representing hidden layer->
And visible layer->
Probability distribution of information values; fitting the distribution condition of the received network return information in the neural network through the information value distribution probability conditions of the hidden layer and the visible layer;
among the parameters mentioned above, the above-mentioned parameters,
the distribution factors carry out statistical calculation on information values of different layers, so that the operation efficiency is improved, and the user is added with the information values of the different layers>
The value range of the operation constant is [0,1 ]]And through the parameter calculation, the information value is subjected to unsupervised model training, so that the following processing is facilitated.
S43: calculating the relative entropy of information values of an implicit layer and an input layer
Defining hidden layer information value probability distribution as
The probability distribution of the information values of the visible layer is +.>
，
And->
The relative entropy calculation formula of (2) is shown in formula (3):
in the formula (3), the amino acid sequence of the compound,
representing sample space, ++>
Indicating relative distance>
Representing hidden layer->
And visible layer->
Distance of information value distribution->
Representing a spatial relationship, wherein the relative distance is minimum, and the implicit layer information value is closest to the visible layer information value distribution;
among the parameters mentioned above, the above-mentioned parameters,
the sample space comprises an input layer, an implicit layer, a visible layer and an output layer;
The relative distance is expressed in the sample space, and the probability distribution of the information value of the hidden layer is +.>
Probability distribution with visible layer information value is +.>
And the distribution distance is calculated through the parameters, and the approximate value of the information value is solved, so that the following processing is facilitated.
S44: reconstructing an information error function
The received information enters an hidden layer for processing, a certain amount of hidden layer information values are selected as training samples, and the calculation formula of the logarithmic loss function of the hidden layer information values is shown as formula (4):
in the formula (4), the amino acid sequence of the compound,
representing the weight connection matrix from visible layer to hidden layer, < ->
Representing loss factor,/->
Representing the%>
Bit information value +.>
Probability distribution (S)/(S)>
Representing a logarithmic loss function, +.>
Representing implicit layer offset value +.>
Visible layer bias value->
Weight connection matrix from visible layer to hidden layer>
A logarithmic loss function of (2);
in the above parameters, the visible layer-to-hidden layer weight connection matrix
Representation->
And (3) connecting the matrixes of the connection matrixes, continuously optimizing the received information value by a small batch characteristic gradient descent method of the information momentum, and minimizing a reconstruction error according to a logarithmic loss function until the information value error reaches an ideal minimum value.
In a specific embodiment, the information classification is realized by improving the frog-leaping algorithm according to the neural network algorithm, and the implementation steps of the improved frog-leaping algorithm are as follows:
randomly selecting optimized information value composition
Individual information frog individuals, the information frog population is
Defining the%>
Only information frog->
Wherein->
Representing information frog>
Characteristic elements representing information frog, +.>
Representing the number of characteristic elements +.>
Representing>
Information about frog->
And (3) carrying out information frog movement step length updating on the information frog in the population by the characteristic elements, wherein an updating calculation formula is shown in a formula (5):
in the formula (5), the amino acid sequence of the compound,
information frog indicating best fitness in the population, < ->
、
Representing +.>
、
Information frog, ->
Representing random real numbers, < >>
Information frog indicating worst fitness in population, < ->
Representing information frog exercise factor->
Step length update of the information frog is represented;
among the parameters mentioned above, the above-mentioned parameters,
the random real numbers are generated by uniformly distributing (0, 1), when the fitness of the information frog is fuzzy, namely +.>
Information frog and +.>
And when the information frog with the worst adaptability in the population approaches, reselecting the frog population. Through the parameter calculation, the input information frog can be screened, and the following processing is facilitated.
Calculating updated information frog according to the updated information frog step length, as shown in a formula (6):
in the formula (6), the amino acid sequence of the compound,
representing the optimized information frog, ++>
、
Information frog indicating worst fitness
A lower limit and an upper limit of the step size allowed to be changed; />
Among the parameters mentioned above, the above-mentioned parameters,
the optimized information frog is better than the information frog with worst adaptability in the original population
Replacing information frog in the population, otherwise, reselecting frog population, and re-optimizing information frog with worst adaptability in the population>
Continuously optimizing updating information frog->
Until the global convergence reaches the maximum number of times.
Through chaotic mapping, the frog-leaping mixing optimization efficiency of the information frog is improved, and the mapping calculation is shown as a formula (7):
in the formula (7), the amino acid sequence of the compound,
representing the mapping coefficient->
Representing the sequence->
The mapped sequence of the information frog is represented, the mapped relational expression and the updated information frog form a new frog sequence, the information frog with the largest sequence number is selected from the frog sequence to be used as a lead frog, the characteristic elements contained in the lead frog are main characteristic elements of the frog group, an information characteristic classification model is established, and the characteristic elements are used as classification standards to realize information value classification.
In a specific embodiment, the data transmission relay station is used for temporarily storing data received by the external network, and comprises a data collector, a register unit and a feature storage group; the data acquisition device adopts a low-power consumption singlechip to acquire data to be optimized, the registering unit adopts a shift register to temporarily store the optimized data, and the characteristic storage group adopts a capacitance storage to realize classified storage of the classified data.
In a specific embodiment, the network isolation method includes: the 001-number network channel is adopted to realize the connection between the management platform and the internal network, and the 002-number network channel is adopted to realize the connection between the management platform and the external network; the 001 and 002 network channels adopt parallel circuits to control the on or off of the network channels; the management platform opens the network channel based on the control current and does not communicate with the network channel to realize network isolation.
In a specific embodiment, the depth statistics module is used for realizing sum calculation of the security values, and the depth statistics module comprises a data normalization processing module, a DEEP model and an auxiliary module.
In a specific embodiment, a dual-network isolation system of an archive protection cloud sensing management platform is characterized in that: comprising the following steps:
the display management platform realizes matrix addressing control of the liquid crystal light valve through a TET-LCD liquid crystal display screen, a backlight LCD and a large-scale integrated circuit and is used for displaying file information, instructions, network connection information and abnormal information, and the display management platform displays information based on an ARM image processor;
the file identification system is used for reading the basic information of files and comprises a palm computer classifier and a GPRS network communication unit, wherein the palm computer classifier is connected with the GPRS network communication unit;
the network safety monitoring system is used for detecting the safety of a network in the connection and use process and comprises a network speed measuring unit, a network data statistics unit, a network hazard warning unit and a safety management unit, wherein the network speed measuring unit is connected with the network data statistics unit, the network data statistics unit is connected with the network hazard warning unit, and the network hazard warning unit is connected with the safety management unit;
the network speed measuring unit is realized through Eclipse environment codes, and the display management platform displays an interface after network speed measurement. The network data statistics is based on a resting brain function network model to process and count the data in the network, and the processing statistics comprise data format conversion, time point removal, time layer correction, automatic correction space standardization, linear drift removal, covariate removal and the like.
The double-network control system is used for controlling the archive protection cloud perception management platform to be connected with a network and comprises a network selection unit and a connection unit; the network selection unit introduces a software-defined network controller, and the controller controls reporting of network parameter information and analysis of the received network information, and an operator selects or the controller selects a network preferentially.
The signal processing system adopts digital frequency conversion to realize mode conversion, data compression, extraction, transformation operation and filtering matching of received information, and adopts a parallel capturing method to search the phase and frequency of a signal code in the time domain, wherein the signal mixer extracts the center frequency of the signal, and frequency offset in the signal is eliminated by frequency conversion processing, so that the searching and processing of the signal are realized.
The external network management system is used for realizing the connection between the management platform and the Internet and comprises a second transmitting antenna, an external network and a second receiving antenna; the external network connection adopts an MPLS VPN mode network deployment model to bear network connection.
The intranet management system is used for realizing the connection between a management unit and a unit network to which the management unit belongs, and comprises a first transmitting antenna, an external network and a first receiving antenna; and an intranet GRE tunnel is established between the intranet line and each unit exit router, so that the intranet GRE tunnel is suitable for various protocols, and a connection secret key is established to protect the privacy of the intranet.
The display management platform is respectively connected with the signal processing system, the file identification system, the network security monitoring system and the double-network control system, and the signal processing system is respectively connected with the external network management system and the internal network management system.
In a specific embodiment, the signal processing system adopts a ZU9EG series FPGA chip, and a processor of the chip is integrated with the ASIC in a low power consumption manner, so that dual channel processing of internal signal data and external signals is realized.
In a specific embodiment, the network connection unit adopts a JDBC database to realize network connection, and the JDBC database realizes connection of internal and external networks by calling different interface connection programs.
In a specific embodiment, the analysis and verification of the data optimization classification effect are carried out on the neural network-frog-leaping algorithm, and the specific experiment is as follows:
in order to verify the optimized classification effect of the neural network-frog-leaping algorithm on the received network information, a whale-optimized convolution algorithm and a dragonfly-improved data mining algorithm are used for carrying out a comparison experiment with the algorithm in the invention, the neural network-frog-leaping algorithm is set to be in an English mode, NELA is set to be in an English mode, WOCA is set to be in the whale-optimized convolution algorithm, and the dragonfly-improved data mining algorithm is set to be in an English mode and DDMA is set to be in the dragonfly-improved data mining algorithm.
Experimental environment: the Simulink algorithm modeling platform, the signal generator and the data display screen.
Initial parameters of three algorithm models are set, the training frequency is 50, the verification frequency is 10, the iteration frequency is 200, the signal generator sends out signals with 5 periods, and the signal values processed by the algorithms are shown in table 1.
Table 1 algorithm optimization rate
As can be seen from table 1, the optimizing rate of NELA on the signal is the best, and next, the optimizing rate of DDMA and WOCA on the signal is the worst, and the classification results of the three algorithms on the signal are shown in fig. 4.
As can be seen from fig. 4, as the number of iterations increases, the information feature set of each algorithm increases gradually, where the analysis speed of the information feature set of NELA is the fastest, the analyzed information feature set is higher than the analysis results of other algorithms, as the number of iterations increases, the number of information feature sets also increases continuously, and finally the increasing speed tends to be gentle; wherein NELA has an information profiling capability greater than DDMA, which has an information profiling capability greater than WOCA.
Experiments show that the neural network-frog-leaping algorithm adopted by the invention has higher optimizing capability and classifying capability on signals, and the neural network-frog-leaping algorithm adopted by the invention can be used for more efficiently and accurately optimizing and classifying the signals.
While specific embodiments of the present invention have been described above, it will be understood by those skilled in the art that the foregoing detailed description is given by way of example only, and that various omissions, substitutions and changes in the form of the details of the method and system illustrated may be made by those skilled in the art without departing from the spirit and scope of the invention; for example, it is within the scope of the present invention to combine the above-described method steps to perform substantially the same function in substantially the same way to achieve substantially the same result; accordingly, the scope of the invention is limited only by the following claims.
Claims (5)
1. A dual-network isolation method of a file protection cloud perception management platform is characterized by comprising the following steps of: comprising the following steps:
s1: a PDA classifier is adopted to scan two-dimension code labels of the file bags, basic information in the file bags is identified, and the file protection cloud perception management platform automatically synchronizes the basic information in a database through a GPRS network communication unit arranged inside;
s2: the network control system controls the network connection of the archive protection cloud perception management platform, an operator inputs an internal network connection command at a network connection interface, and the management platform realizes internal work network connection through a special data circuit; an operator inputs an external network connection command at a network connection interface, and the management platform is connected with an external network through an optical fiber internet module to realize network information transmission search;
s3: the management platform inquires file information, inputs a secret key inquiry command, a signal generator generates the frequency of an inquiry signal, a first transmitting antenna transmits the frequency signal, a first receiving antenna receives the frequency signal, the frequency signal is converted into an electric signal through a signal conversion circuit, and a signal processing module calibrates and processes the inquiry command of the electric signal and dynamically invokes file information in a database according to the inquiry command;
s4: the management platform is connected with the internet to inquire information, the main server establishes wireless connection with external subnet units through a second transmitting antenna, communication channels are established among the subnet units with the same communication parameters, the second receiving antenna receives returned information of each subnet unit, and the information processing module classifies and processes the returned information; the information processing module adopts a neural network-frog-leaping algorithm to realize the classification processing of the information, and the neural network-frog-leaping algorithm comprises an information optimization model, an improved frog-leaping algorithm model, a frog-leaping classifier and a data transmission relay station;
s5: the network safety monitoring system carries out safety supervision on the working states of the internal network and the external network of the management platform, the network speed measuring unit calculates the network speed according to the response time, the network data statistics unit calculates the data information in the network nodes and divides the network nodes into stable type, fluctuation type and mutation type according to the characteristics of the data information, the safety management unit carries out safety value statistics on the fluctuation type and mutation type network nodes based on a neural network deep learning algorithm, the safety value is lower than a lowest threshold value, and the network hazard warning unit starts an alarm mode; the neural network deep learning algorithm comprises a BP training model, a function set and a deep statistics module;
the archive protection cloud perception management platform queries information through an external network, information received by the second receiving antenna is optimized and classified through a neural network-frog-leaping algorithm, and the method for optimizing information data through the neural network algorithm comprises the following steps:
s41: calculating an information energy function;
defining hidden layer information values in a neural network algorithm as
Wherein->
Representing the amount of hidden layer information->
Representing the information value as a vector, the visible layer information value as +.>
Wherein->
The information energy function in the neural network is shown as a formula (1) and represents the information quantity of the visible layers:
in the formula (1), the components are as follows,
representing the number of bits of the hidden layer information value->
Representing implicit coefficients in the neural network, < >>
Representing display coefficients in a neural network, < >>
Representing the number of bits of the information value of the visible layer, +.>
Bias value representing hidden layer information, +.>
Representing the%>
Bias value of bit information value +.>
Representing the%>
Bit information value->
Bias value representing visible layer information, +.>
Representing the +.>
Bias value of bit information value +.>
Representing the +.>
Bit information value->
An information connection matrix representing the hidden layer and the visible layer,
representing the%>
Bit information value and +.>
A connection matrix of bit information values;
s42: calculating probability distribution of information values of an implicit layer and a visible layer according to an energy function, wherein the probability distribution is shown as a formula (2):
in the formula (2), the amino acid sequence of the compound,
representing the partitioning factor,/->
Representing the arithmetic constant->
Representing information energy functions, representing information values at hidden layers
And visible layer->
Information energy function, +.>
Representing probability distribution->
Representing hidden layer->
And visible layer->
Probability distribution of information values; fitting the distribution condition of the received network return information in the neural network through the information value distribution probability conditions of the hidden layer and the visible layer;
s43: calculating the relative entropy of the information values of the hidden layer and the input layer;
defining hidden layer information value probability distribution as
The probability distribution of the information values of the visible layer is +.>
，
And->
The relative entropy calculation formula of (2) is shown in formula (3):
in the formula (3), the amino acid sequence of the compound,
representing sample space, ++>
Indicating relative distance>
Representing hidden layer->
And visible layer->
Distance of information value distribution->
Representing spatial relationships; the relative distance is minimum, and the implicit layer information value is closest to the visible layer information value distribution;
s44: reconstructing an information error function;
the received information enters an hidden layer for processing, a certain amount of hidden layer information values are selected as training samples, and the calculation formula of the logarithmic loss function of the hidden layer information values is shown as formula (4):
in the formula (4), the amino acid sequence of the compound,
representing the weight connection matrix from visible layer to hidden layer, < ->
Representing loss factor,/->
Representing the%>
Bit information value +.>
Probability distribution (S)/(S)>
Representing a logarithmic loss function, +.>
Representing implicit layer offset value +.>
Visible layer bias value->
Weight connection matrix from visible layer to hidden layer>
A logarithmic loss function of (2);
continuously optimizing the received information value by a small batch characteristic gradient descent method of the information momentum, and minimizing a reconstruction error according to a logarithmic loss function until the information value error reaches an ideal minimum value;
the neural network algorithm realizes information classification by improving the frog-leaping algorithm, and the method comprises the following steps of:
randomly selecting optimized information value composition
Individual information frog individuals, the information frog population is
Defining the%>
Only information frog->
Wherein->
Representing information frog>
Characteristic elements representing information frog, +.>
Representing the number of characteristic elements +.>
Representing>
Information about frog->
And (3) carrying out information frog movement step length updating on the information frog in the population by the characteristic elements, wherein an updating calculation formula is shown in a formula (5):
in the formula (5), the amino acid sequence of the compound,
information frog indicating best fitness in the population, < ->
、
Representing +.>
、
Information frog, ->
Representing random real numbers, < >>
Information frog indicating worst fitness in population, < ->
Representing information frog exercise factor->
Step length update of the information frog is represented;
calculating updated information frog according to the updated information frog step length, as shown in a formula (6):
in the formula (6), the amino acid sequence of the compound,
representing the optimized information frog, ++>
、
Information frog representing worst fitness +.>
A lower limit and an upper limit of the step size allowed to be changed;
through chaotic mapping, the frog-leaping mixing optimization efficiency of the information frog is improved, and the mapping calculation is shown as a formula (7):
in the formula (7), the amino acid sequence of the compound,
representing the mapping coefficient->
Representing the sequence->
Representing the mapped sequence of the information frog, forming a new frog sequence by the mapped relational expression and the updated information frog, selecting the information frog with the largest sequence number from the frog sequence as a lead frog, wherein characteristic elements contained in the lead frog are main characteristic elements of frog groups, establishing an information characteristic classification model, and realizing information value classification by taking the characteristic elements as classification standards;
the network isolation method comprises the following steps: the 001-number network channel is adopted to realize the connection between the management platform and the internal network, and the 002-number network channel is adopted to realize the connection between the management platform and the external network; the 001 and 002 network channels adopt parallel circuits to control the on or off of the network channels; the management platform opens the 001 # network channel and the 002 # network channel based on the control current and does not communicate with each other to realize network isolation.
2. The archive protection cloud perception management platform dual-network isolation method according to claim 1, wherein the archive protection cloud perception management platform dual-network isolation method is characterized by comprising the following steps: the file bag two-dimension code comprises a file number, a generation time, an address, a file attribute, author information of the file and a two-dimension code history scanning record of the file.
3. The archive protection cloud perception management platform dual-network isolation method according to claim 1, wherein the archive protection cloud perception management platform dual-network isolation method is characterized by comprising the following steps: the archives are stored in the distributed ring topology structure in the form of stacks with the same attribute and the same group.
4. The archive protection cloud perception management platform dual-network isolation method according to claim 1, wherein the archive protection cloud perception management platform dual-network isolation method is characterized by comprising the following steps: the data transmission relay station is used for temporarily storing data received by the external network, and comprises a data collector, a registering unit and a characteristic storage group; the data acquisition device adopts a low-power consumption singlechip to acquire data to be optimized, the registering unit adopts a shift register to temporarily store the optimized data, and the characteristic storage group adopts a capacitance storage to realize classified storage of the classified data.
5. The archive protection cloud perception management platform dual-network isolation method according to claim 1, wherein the archive protection cloud perception management platform dual-network isolation method is characterized by comprising the following steps: the depth statistics module is used for realizing sum calculation of the safety values and comprises a data normalization processing module, a DEEP model and an auxiliary module.
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