An Encryption Optical Transmission Method and System Based on a Four-Dimensional Memristive Conditionally Symmetric Chaotic System and a Memristive Neural Network

Abstract

The present invention discloses an encrypted optical transmission method and system based on a four-dimensional memristive conditional symmetric chaotic system and a memristive neural network, belonging to the technical field of optical transmission, including: obtaining an original sequence; generating a logistic chaotic sequence through a logistic chaotic model, and using the logistic chaotic sequence to select a certain chaotic sequence or attractor from a pre-constructed key space library through a four-dimensional memristive conditional symmetric chaotic system; inputting the original sequence and the selected chaotic sequence or attractor into the memristive neural network for exclusive-or encryption to obtain encrypted data, modulating the encrypted data and then sending it into a channel for transmission; demodulating and decrypting the received encrypted data to complete optical transmission; realizing multi-layer encryption of data, improving the security performance of optical transmission, and solving the defect that the transmission security is less considered in the prior art; further reducing the overall complexity, and being very suitable for the OFDM-PON scenario.

Description

Technical Field

[0001] The present invention relates to an encrypted optical transmission method and system based on a four-dimensional memristive conditional symmetric chaotic system and a memristive neural network, belonging to the technical field of optical transmission. Background Art

[0002] Since the invention of optical fiber communication technology in the 1960s, due to its unique characteristics, optical fiber communication technology has become the only communication system that can support global communication and has developed rapidly; in today's communication network, optical fiber communication has become an essential part. With people's pursuit of a better life, people's requirements for data services are getting higher and higher, and higher requirements are put forward for the security of information transmission; however, recent research shows that with the continuous improvement of communication technology in recent years, the gradual commercialization of 5G technology, and the continuous development of various multimedia technologies, people's requirements for data services have exceeded the pace of current information technology updates. People's demand for the next-generation communication capacity has reached above bits, but the technical upgrade route for the next-generation communication standard is still an open question; at the same time, due to the continuous increase in the amount of data, data security has also become an essential part of system optimization.

[0003] In recent years, communication technology has been continuously updated, the data capacity has been continuously expanded, and its security has become increasingly prominent. Therefore, data security and confidentiality technology has received special attention in the field of optical communication; to meet the rapidly growing bandwidth and capacity requirements in optical access networks, passive optical networks (PONs) are regarded as the most promising access solutions; among them, PONs based on orthogonal frequency division multiplexing (OFDM) have more advantages, with higher spectral efficiency, stronger dispersion tolerance, more scalable modulation of constellations, and lower commercial costs.

[0004] For the protection of data security, more and more scholars use encryption methods of neural networks or chaotic models to solve the problem. Traditional neural network circuits have limitations. After the circuit is powered off, the circuit weights of the neural network will automatically disappear. Therefore, traditional neural network circuits are not adaptable to new models and new data.

[0005] Currently, in most research on OFDM-PON systems, the system transmission capacity and signal transmission quality are improved through advanced coding modulation and high-performance algorithms, but due to the large amount of data transmission, the security of the system is rarely considered. Summary of the Invention

[0006] The object of the present invention is to provide an encrypted optical transmission method and system based on a four-dimensional memristive conditional symmetric chaotic system and a memristive neural network, which can achieve multi-layer encryption of data, provide a faster information processing function, improve the security performance of optical transmission, and solve the defect that the transmission security is less considered in the prior art; and because the XOR function structure of the memristive neural network is simple, the overall complexity is further reduced, and it is more suitable for the OFDM-PON scenario.

[0007] To achieve the above object, the present invention is implemented by the following technical solutions:

[0008] In the first aspect, the present invention provides an encrypted optical transmission method based on a four-dimensional memristive conditional symmetric chaotic system and a memristive neural network, including:

[0009] Obtain the original sequence;

[0010] Generate a logistic chaotic sequence through the logistic chaotic model, and use the logistic chaotic sequence to select a certain chaotic sequence or attractor from the pre-constructed key space library of the four-dimensional memristive conditional symmetric chaotic system;

[0011] Input the original sequence and the selected chaotic sequence or attractor into the memristive neural network for XOR encryption to obtain encrypted data, modulate the encrypted data and then send it into the channel for transmission;

[0012] Demodulate and decrypt the received encrypted data to complete optical transmission.

[0013] Combined with the first aspect, further, the expression of the four-dimensional memristive conditional symmetric chaotic system is:

[0014]

[0015] Among them, x, y, z, and u are all independent variables, a, d, c, and b are all constants, and W(u) is the magnetic flux control module.

[0016] Combined with the first aspect, further, generating a logistic chaotic sequence through the logistic chaotic model includes:

[0017] x n+1 = μx n (1 - x n )

[0018] Among them, n is the number of iterations, μ is the control parameter, x n and x n+1 respectively represent the logistic chaotic sequences obtained from the nth iteration and the (n + 1)th iteration of the logistic chaotic model.

[0019] In combination with the first aspect, further, selecting a certain chaotic sequence or attractor from the key space library using a logistic chaotic sequence includes:

[0020] Mapping the tenths, hundredths, and thousandths digits of the logistic chaotic sequence into a three - bit binary number according to a preset mapping rule, and selecting a certain chaotic sequence or attractor from the key space library according to the three - bit binary number.

[0021] In combination with the first aspect, further, the mapping rule is: mapping even numbers to 0 and odd numbers to 1.

[0022] In combination with the first aspect, further, modulating the encrypted data includes:

[0023] Performing 16QAM constellation mapping on the encrypted data, then sending the serial encrypted data into the OFDM modulation module for modulation, and then performing serial - to - parallel conversion.

[0024] In combination with the first aspect, further, demodulating and decrypting the received encrypted data includes:

[0025] Performing parallel - to - serial conversion on the received encrypted data, then sending it into the OFDM demodulation module for demodulation, then performing the demapping process of QAM, and finally performing serial - to - parallel conversion, and performing XOR decryption in the memristive neural network to obtain the original sequence, completing optical transmission.

[0026] In combination with the first aspect, further, the key space library is obtained by the following method:

[0027] Generating four groups of chaotic sequences through a four - dimensional memristive conditional symmetric chaotic system, selecting the initial values of different chaotic sequences to map out symmetric attractors, and forming the key space library from the chaotic sequences and attractors.

[0028] In the second aspect, the present invention also provides an encrypted optical transmission system based on a four - dimensional memristive conditional symmetric chaotic system and a memristive neural network, including:

[0029] An acquisition module: used to acquire the original sequence;

[0030] A sequence selection module: used to generate a logistic chaotic sequence through a logistic chaotic model, and select a certain chaotic sequence or attractor from the key space library using the logistic chaotic sequence;

[0031] An encryption modulation module: used to input the original sequence and the selected chaotic sequence or attractor into the memristive neural network for XOR encryption to obtain encrypted data, modulate the encrypted data and send it into the channel for transmission;

[0032] Decryption and demodulation module: used to demodulate and decrypt the received encrypted data to complete optical transmission.

[0033] Combined with the second aspect, further, it also includes a key space library acquisition module:

[0034] Used to generate four groups of chaotic sequences through a four-dimensional memristive conditional symmetric chaotic system, select the initial values of different chaotic sequences to map out symmetric attractors, and form a key space library from the chaotic sequences and attractors.

[0035] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0036] The present invention provides an encrypted optical transmission method and system based on a four-dimensional memristive conditional symmetric chaotic system and a memristive neural network. The key space library is pre-constructed through a four-dimensional memristive conditional symmetric chaotic system, and a variety of key space libraries are generated by using the four-dimensional memristive conditional symmetric chaotic system for encryption to improve the encryption performance; a logistic chaotic sequence is generated through a logistic chaotic model, and the unpredictability of generating chaotic sequences by using the chaotic model is used to improve the anti-interference ability of optical transmission, and the selection of the key space library is realized by using the logistic chaotic sequence; the original sequence and the selected chaotic sequence or attractor are input into the memristive neural network for exclusive-or encryption, and the exclusive-or encryption function of the memristive neural network is used to further encrypt the data. Moreover, with the great development of future integrated circuits, the memristive neural network can also realize algorithm engraving on an embedded chip to achieve hardware encryption, further realizing the security protection of large-capacity data communication; in summary, the solution of the present invention can realize multi-layer encryption of data, provide a faster information processing function, improve the security performance of optical transmission, and since the exclusive-or function structure of the memristive neural network is simple, the overall complexity is further reduced, which is very suitable for the OFDM-PON scenario. Description of the Drawings

[0037] Figure 1 It is a flowchart of an encrypted optical transmission method based on a four-dimensional memristive conditional symmetric chaotic system and a memristive neural network provided by an embodiment of the present invention;

[0038] Figure 2 It is a phase diagram of a four-dimensional memristive conditional symmetric chaotic system provided by an embodiment of the present invention;

[0039] Figure 3 It is a bifurcation diagram of a logistic chaotic model provided by an embodiment of the present invention;

[0040] Figure 4 It is a model diagram of a memristive neural network provided by an embodiment of the present invention;

[0041] Figure 5It is the algorithm flowchart of the memristive neural network provided by the embodiments of the present invention. Detailed implementation manners

[0042] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and cannot be used to limit the protection scope of the present invention.

[0043] Embodiment 1

[0044] As Figure 1 shown, the present invention provides an encrypted optical transmission method based on a four-dimensional memristive conditional symmetric chaotic system and a memristive neural network, including:

[0045] S1. Obtain the original sequence.

[0046] Obtain the bit information to be sent to obtain the original sequence.

[0047] S2. Generate a logistic chaotic sequence through the logistic chaotic model, and use the logistic chaotic sequence to select a certain chaotic sequence or attractor in the key space library pre-established through the four-dimensional memristive conditional symmetric chaotic system.

[0048] Previously, generate four groups of chaotic sequences through the four-dimensional memristive conditional symmetric chaotic system, select the initial values of different chaotic sequences to map out symmetric attractors, and form a key space library from the chaotic sequences and attractors.

[0049] Generate four groups of chaotic sequences by using the four-dimensional memristive conditional symmetric chaotic system. The expression of the four-dimensional memristive conditional symmetric chaotic system is as follows:

[0050]

[0051] where x, y, z, and u are all independent variables, and a, d, c, and b are all constants, which are 3.55, 0, 3, and 0.5 respectively in this embodiment, and W(i) is the magnetic flux control module.

[0052] The magnetic flux control module is described as:

[0053]

[0054] where i is the current value in the memristor, and α and β are constants, which are 0.1 and 0.3 respectively in this embodiment.

[0055] In this embodiment, the initial values of the chaotic sequences are set to (-3, 0, -1, 0.1) and (3, 0, 1, 0.1) respectively, and the fourth-order Runge-Kutta method is used to solve the partial differential equation in the expression of the four-dimensional memristive conditional symmetric chaotic system. The phase diagrams of the four-dimensional memristive conditional symmetric chaotic system in different phase planes are asFigure 2 as shown; from Figure 2 it can be seen that the four-dimensional memristive conditional symmetric chaotic system exhibits complex bifurcation dynamic characteristics and chaotic properties with high security performance.

[0056] The four-dimensional memristive conditional symmetric chaotic system can generate x1, y1, z1, u1, x2, y2, z2, u2 sequences respectively. The initial values corresponding to x1, y1, z1, u1 are (-3, 0, -1, 0.1), and the initial values corresponding to x2, y2, z2, u2 are (3, 0, 1, 0.1); according to different initial values, symmetric phase diagrams are generated, and eight chaotic sequences (the initial four groups of chaotic sequences and their mapped symmetric attractors) are set as a key space library.

[0057] Generate a logistic chaotic sequence through the logistic chaotic model. The mapping formula of the logistic chaotic model is as follows:

[0058] x n+1 = μx n (1 - x n )

[0059] where n is the number of iterations, μ is the control parameter, x n and x n+1 respectively represent the logistic chaotic sequences obtained from the nth iteration and the (n + 1)th iteration of the logistic chaotic model; the bifurcation diagram of the logistic chaotic model is as Figure 3 shown.

[0060] Use the logistic chaotic sequence to select a certain chaotic sequence or attractor from the key space library pre-constructed by the four-dimensional memristive conditional symmetric chaotic system. Each value of the logistic chaotic sequence has tenths, hundredths, and thousandths. Use these three values to map to the corresponding sequence; the mapping rule is to map even numbers to 0 and odd numbers to 1, then there are exactly 8 combinations, corresponding to the above 8 sequences.

[0061] Among them, the role of the tenths place is to select which initial value of the four-dimensional memristive conditional symmetric chaotic system. Even numbers are (-3, 0, -1, 0.1), and odd numbers are (3, 0, 1, 0.1).

[0062] The specific mapping process is as follows: First, map the tenths, hundredths, and thousandths to 000, 001, 010, 011, 100, 101, 110, 111 according to their preset mapping rules, and then map the three-bit binary numbers to the x1, y1, z1, u1, x2, y2, z2, u2 sequences respectively. The specific mapping rules are shown in Table 1.

[0063] Table 1 Mapping Rules of Logistic Chaotic Sequence

[0064]

[0065]

[0066] S3. Input the original sequence and the selected chaotic sequence or attractor into the memristive neural network for XOR encryption to obtain encrypted data. After modulating the encrypted data, send it into the channel for transmission.

[0067] Definition of XOR: True XOR false is true, false XOR true is true, true XOR true is false, false XOR false is false; that is, when the two values are the same, it is false, otherwise it is true; the XOR truth table is shown in Table 2.

[0068] Table 2 XOR Truth Table

[0069] <![CDATA[x1]]> <![CDATA[x2]]> y 0 0 0 0 1 1 1 0 1 1 1 0

[0070] The neural network model for implementing XOR is the perceptron model, a neural network invented by Frank Rosenblatt at Cornell Aeronautical Laboratory, with input variables x = (x1, x2) T , and the weight matrix is where w ij is the connection weight from input neuron i to input neuron j.

[0071] This perceptron model has a total of three layers. The first layer is the input layer, the second layer is the hidden layer, and the third layer is the output layer; there are hard limit functions in the second and third layers. The output of the hard limit function is called the actual output, which is mainly used for feedback of weights for judgment; the hard limit function in the second layer is i = 1, 2, w i represents the i-th column of the weight matrix; the hard limit function in the third layer is w3 represents the 3rd column of the weight matrix.

[0072] The truth table of the neural network model for implementing XOR is shown in Table 3. Map the input 0 to -0.5 and the input 1 to 0.5. This neural network model has a total of 5 neurons and 6 weights.

[0073] Table 3 XOR Neural Network Truth Table

[0074] <![CDATA[x1]]> <![CDATA[x2]]> y -0.5 -0.5 0 -0.5 0.5 1 0.5 -0.5 1 0.5 0.5 0

[0075] In this embodiment, an XOR encryption function based on a memristive neural network is proposed. The model diagram of the memristive neural network is as Figure 4 shown, Figure 4 The large circles represent neurons, and the small circles represent synapses, that is, weights. There are a total of 4 weight memristors and 2 memristors.

[0076] Among them, a negative resistor is added to the memristor to form a weighted memristor. The reason is that in a memristor, the resistance value of the memristor cannot be a complex number, while the weight value of a neural network must have a complex number. Therefore, a negative resistor is added to the memristor to meet the requirement.

[0077] The input layer \(x=(x_1,x_2)\) T and the hidden layer \(z=(z_1,z_2)\) T are connected by a memristor and a constant negative resistor (referred to as a weighted memristor), and the hidden layer and the output layer \(y\) are connected by a memristor.

[0078] The mathematical expression of the resistance matrix The mathematical model of the weighted memristor is:

[0079]

[0080] Among them, is the weighted memristor, \(R'\) is the added negative resistor, \(R\) on is the memristance value when the whole is the doped region, \(R\) off is the memristance value when the whole is the non-doped region, \(V\) on is the positive threshold voltage, \(V\) off is the negative threshold voltage.

[0081] According to Figure 4 mainly program \(R\) 11 , \(R\) 12 , \(R\) 21 , \(R\) 22 , \(R\) 13 , \(R\) 23 to initialize 6 memristors.

[0082] According to the mathematical model of the weighted memristor, initialize \(R\) 11 , \(R\) 12 , \(R\) 21 , \(R\) 22 Set \(R\) on =2.5, \(R\) off =1.5, \(R'=-2\), \(V\) on =1, \(V\) off =-1, then there is:

[0083]

[0084] Among them, \(x\) i is the voltage of the weighted memristor.

[0085] Initialize \(R\) 13 , \(R\) 23 Set \(R\) on =1, \(R\) off =0, \(V\)on = 1, V off = -1, then we have:

[0086] where z i is the voltage of the memristor.

[0087] As Figure 5 shown, the algorithm of the memristive neural network includes:

[0088] (1) First, initialize the feedback pulses of the input layer and hidden layer neurons as g1 = [0, 0, 0], g2 = [0, 0, 0], g3 = [0, 0, 0];

[0089] (2) Initialize the resistance value of the memristor;

[0090] (3) Given the initial pulse sequences x1, x2, z1, z2, y;

[0091] (4) Calculate the voltage drops across R 11 , R 12 , R 21 , R 22 , R 13 , R 23 , determine whether the corresponding memristor resistance value needs to be changed, and reset the feedback pulses g1, g2, g3 to the initial values;

[0092] (5) Determine whether the pulse y has a pulse response. According to whether it is an input pulse or a learning pulse, compare the obtained different results with the corresponding output pulses. If it is a learning pulse, return to step (3);

[0093] (6) Repeat steps (3) and (4) until the given pulses are completed;

[0094] (7) Repeat steps (3) and (4), and re - give the pulses again until the generated feedback pulse is 0.

[0095] The encrypted data obtained from the memristive neural network is subjected to parallel - to - serial conversion. In this embodiment, the modulation process includes: First, perform 16QAM constellation mapping, then send the serial data into the OFDM modulation module, then perform serial - to - parallel conversion, and send the modulated encrypted data into the channel for transmission.

[0096] S4. Demodulate and decrypt the received encrypted data to complete optical transmission.

[0097] The encrypted data arriving at the receiving end first undergoes parallel - to - serial conversion, then enters the OFDM demodulation module, then undergoes the demapping process of QAM, and finally undergoes serial - to - parallel conversion and performs the XOR function again in the memristive neural network to obtain the original data.

[0098] Example 2

[0099] An encryption optical transmission system based on a four-dimensional memristive conditional symmetric chaotic system and a memristive neural network provided by an embodiment of the present invention includes:

[0100] An acquisition module: used to acquire an original sequence;

[0101] A sequence selection module: used to generate a logistic chaotic sequence through a logistic chaotic model, and select a certain chaotic sequence or attractor in a key space library with the logistic chaotic sequence;

[0102] An encryption modulation module: used to input the original sequence and the selected chaotic sequence or attractor into a memristive neural network for exclusive-or encryption to obtain encrypted data, and modulate the encrypted data and then send it into a channel for transmission;

[0103] A decryption demodulation module: used to demodulate and decrypt the received encrypted data to complete optical transmission.

[0104] It further includes a key space library acquisition module:

[0105] Used to generate four groups of chaotic sequences through a four-dimensional memristive conditional symmetric chaotic system, select the initial values of different chaotic sequences to map out symmetric attractors, and form a key space library from the chaotic sequences and attractors.

[0106] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0107] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of processes and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0108] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means that implement the function specified in one or more processes and / or blocks Figure 1 one or more processes and / or blocks Figure 1 specified in the block or blocks.

[0109] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable apparatus provide steps for implementing the function specified in one or more processes and / or blocks Figure 1 one or more processes and / or blocks Figure 1 specified in the block or blocks.

[0110] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. An encryption optical transmission method based on a four-dimensional memristive conditional symmetric chaotic system and a memristive neural network, characterized in that, Including: Obtain the original sequence; Generate a logistic chaotic sequence through the logistic chaotic model, and use the logistic chaotic sequence to select a certain chaotic sequence or attractor in the key space library pre-constructed by the four-dimensional memristive conditional symmetric chaotic system; Input the original sequence and the selected chaotic sequence or attractor into the memristive neural network for XOR encryption to obtain encrypted data, modulate the encrypted data and then send it into the channel for transmission; Demodulate and decrypt the received encrypted data to complete optical transmission; The expression of the four-dimensional memristive conditional symmetric chaotic system is: Wherein, x, y, z, and u are all independent variables, a, d, c, and b are all constants, and W(u) is a function used to express the operation relationship of the magnetic flux control module; The magnetic flux control module is described as: Wherein, i is the current value in the memristor, and α and β are constants; Using the logistic chaotic sequence to select a certain chaotic sequence or attractor in the key space library includes: Map the tenths, hundredths, and thousandths of the logistic chaotic sequence into a three-bit binary number according to the preset mapping rule, and select a certain chaotic sequence or attractor in the key space library according to the three-bit binary number; The mapping rule is: map even numbers to 0 and odd numbers to 1; Generating a logistic chaotic sequence through the logistic chaotic model includes: x n+1 = μx n (1 - x n ) where n is the number of iterations, μ is the control parameter, and x n and x n+1 represent the logistic chaotic sequences obtained from the n-th iteration and the (n + 1)-th iteration of the logistic chaotic model, respectively.

2. The encryption optical transmission method based on a four-dimensional memristive conditional symmetric chaotic system and a memristive neural network according to claim 1, characterized in that, Modulating the encrypted data includes: Perform 16QAM constellation mapping on the encrypted data, then send the serial encrypted data into the OFDM modulation module for modulation, and then perform serial / parallel conversion.

3. A method for encrypted optical transmission based on a four-dimensional memristive conditionally symmetric chaotic system and a memristive neural network according to claim 2, characterized in that, Demodulating and decrypting the received encrypted data includes: Perform parallel / serial conversion on the received encrypted data, then send it into the OFDM demodulation module for demodulation, then perform the demapping process of 16QAM, and finally perform serial / parallel conversion, and perform XOR decryption in the memristive neural network to obtain the original sequence and complete optical transmission.

4. A method for encrypted optical transmission based on a four-dimensional memristive conditionally symmetric chaotic system and a memristive neural network according to claim 1, characterized in that The key space library is obtained by the following method: Generate four groups of chaotic sequences through the four-dimensional memristive conditional symmetric chaotic system, select the initial values of different chaotic sequences to map out symmetric attractors, and form the key space library from the chaotic sequences and attractors.

5. An encrypted optical transmission system based on a four-dimensional memristive conditional symmetric chaotic system and a memristive neural network, characterized in that, Including: Obtaining module: used to obtain the original sequence; Sequence selection module: used to generate a logistic chaotic sequence through the logistic chaotic model, and use the logistic chaotic sequence to select a certain chaotic sequence or attractor in the key space library; Encryption and modulation module: used to input the original sequence and the selected chaotic sequence or attractor into the memristive neural network for XOR encryption to obtain encrypted data, modulate the encrypted data and then send it into the channel for transmission; Decryption and demodulation module: used to demodulate and decrypt the received encrypted data to complete optical transmission; Wherein, the expression of the four-dimensional memristive conditional symmetric chaotic system is: Wherein, x, y, z, and u are all independent variables, a, d, c, and b are all constants, and W(u) is a function used to express the operation relationship of the magnetic flux control module; The magnetic flux control module is described as: Wherein, i is the current value in the memristor, and α and β are constants; Select a certain chaotic sequence or attractor in the key space library using the logistic chaotic sequence, including: Map the tenths, hundredths, and thousandths digits of the logistic chaotic sequence into a three-bit binary number according to a preset mapping rule, and select a certain chaotic sequence or attractor in the key space library according to the three-bit binary number; The mapping rule is: map even numbers to 0 and odd numbers to 1; Generate a logistic chaotic sequence through the logistic chaotic model, including: x n+1 = μx n (1 - x n ) where n is the number of iterations, μ is the control parameter, and x n and x n+1 represent the logistic chaotic sequences obtained from the n-th iteration and the (n + 1)-th iteration of the logistic chaotic model, respectively.

6. The encryption optical transmission system based on a four-dimensional memristive conditional symmetric chaotic system and a memristive neural network according to claim 5, characterized in that, It also includes a key space library acquisition module: Used to generate four groups of chaotic sequences through a four-dimensional memristive conditional symmetric chaotic system, select the initial values of different chaotic sequences to map out symmetric attractors, and form a key space library from the chaotic sequences and attractors.

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