Unmanned aerial vehicle inspection traffic control system and method based on chaotic system

Abstract

The invention discloses an unmanned aerial vehicle inspection traffic control system and method based on a chaotic system. The unmanned aerial vehicle inspection traffic control system comprises a sending end, an encryption module and a receiving end, the sending end is used for acquiring an original inspection image acquired in unmanned aerial vehicle inspection; the encryption module performs data interaction with the sending end and is used for encrypting the original inspection image to obtain encrypted image data; the receiving end is used for acquiring the encrypted image data; in the encryption process, using a plaintext association framework of an SHA-256 hash function, and applying the plaintext association framework to the encrypted original image to enhance the sensitivity of the plaintext; moreover, the coded data is better in compatibility, is suitable for being embedded into different structured data formats, guarantees the data integrity, also gives consideration to the protocol friendliness, and has a larger chaotic region, stronger theoretical robustness, a higher engineering fault-tolerant rate, a more flexible system design space and better safety performance.

Description

Technical Field

[0001] The present disclosure relates to the field of drone inspection, and in particular to a drone inspection traffic control system and method based on a chaotic system. Background Art

[0002] Due to the rapid development of the low-altitude economy today, drones are often used in a variety of tasks such as inspection and monitoring, security emergency, surveying and modeling, etc., among which image transmission has become an important part of drone work. At the same time, the security of drone inspection systems when transmitting images and other data has become a focus of attention, especially digital images containing sensitive information in the military, medical and other fields.

[0003] At present, Base64 encoding is mostly used as the main transmission method for images generated during drone inspections. Although this method has certain versatility and convenience, its defects are also very obvious, such as the easy exposure of sensitive information. Therefore, it is necessary to develop an easy-to-use, efficient and secure image encryption system to encrypt digital images containing sensitive information to protect their confidentiality.

[0004] Based on this, the present invention provides a UAV inspection traffic control system for encrypting and transmitting inspection images based on a chaotic system. Summary of the invention

[0005] The purpose of the present disclosure is to provide a UAV inspection traffic control system and method based on a chaotic system to solve at least one technical problem in the prior art.

[0006] The technical solution of the present disclosure is as follows:

[0007] A UAV inspection traffic control system based on a chaotic system, comprising:

[0008] The sending end is used to obtain the original inspection images obtained during the drone inspection;

[0009] An encryption module, which performs data interaction with the transmitting end and is used to encrypt the original inspection image to obtain encrypted image data;

[0010] A receiving end, performing data interaction with the encryption module to obtain the encrypted image data;

[0011] The encryption module encrypts the original inspection image through the following process:

[0012] Collect the original inspection images obtained during the drone inspection, perform hash operations, and generate hash values;

[0013] The hash values ​​are grouped and any group is converted from binary to decimal to obtain two key parameters;

[0014] Setting an initial value, and selecting at least two initial values ​​and an iteration number N0 as keys to be input into the chaotic system for iteration to form a ciphertext;

[0015] The sending end uses the key parameter, initial value and number of iterations N0 as the key of the encryption algorithm and sends the ciphertext to the receiving end.

[0016] The setting of the initial value, and selecting at least two initial values ​​and the number of iterations N0 as keys to be input into the chaotic system for iteration to form a ciphertext, includes:

[0017] Use the chaotic system to iterate any initial value xi for N0+2 times, discard the first N0 iteration results, and obtain at least 2 chaotic sequences Xi;

[0018] All the chaotic sequences are concatenated into a final sequence X, and a permutation sequence Y and a diffusion sequence Z are generated;

[0019] A ciphertext is formed according to the permutation sequence Y and the diffusion sequence Z.

[0020] The method of splicing all the chaotic sequences into a final sequence X and generating a permutation sequence Y and a diffusion sequence Z comprises:

[0021] ;

[0022] ;

[0023] Among them, X is the chaotic sequence; M×N is the size of the image.

[0024] The forming of ciphertext according to the permutation sequence Y and the diffusion sequence Z comprises:

[0025] Use the replacement sequence Y to perform a replacement operation on the preset string b64Str, convert the Base64 code at this time into the corresponding index, and obtain b64index;

[0026] Use diffusion sequence Z to perform a diffusion operation on the b64index to obtain b64diffused;

[0027] The b64diffused is used as the ciphertext.

[0028] The use of the diffusion sequence Z to perform a diffusion operation on the index Index to obtain b64diffused includes:

[0029] b64diffused = b64index ⊕ Z;

[0030] Among them, ⊕ represents the exclusive OR operation.

[0031] The preset string b64Str includes:

[0032] Iteratively generate pseudo-random sequences using chaotic systems;

[0033] Get three basic grayscale images, each of which has a size of M×N;

[0034] The three basic grayscale images are used as red, green and blue channels respectively and combined into an RGB color image;

[0035] The RGB color image is encoded using Base64 encoding into a Base64 string b64Str, whose size is 4×M×N.

[0036] The chaotic system comprises:

[0037]

[0038] Where n represents the number of iterations of the chaotic system; is the value of the chaotic system at the nth iteration; r is the control parameter used to adjust the chaotic behavior of the system; is an exponential function, with the base of natural logarithms, e, as the base. is the index; It is the value of the chaotic system at the n-1th iteration, that is, the result of the previous iteration.

[0039] The acquisition of the key parameters includes:

[0040] Performing a SHA-256 hash operation on the original inspection image to generate a 256-bit hash value h;

[0041] Divide the hash value h into 8 groups, each group has 32 bits;

[0042] Convert each group from binary to decimal to obtain 8 parameters R1, R2, …, R8;

[0043] Two key parameters h1 and h2 are generated through XOR operation.

[0044] The acquisition of the key parameters includes:

[0045] ;

[0046] .

[0047] A UAV inspection traffic control method based on a chaotic system, based on the UAV inspection traffic control system, comprises:

[0048] Collect the original inspection images obtained during the drone inspection, perform hash operations, and generate hash values;

[0049] The hash values ​​are grouped and any group is converted from binary to decimal to obtain two key parameters;

[0050] Setting an initial value, and selecting at least two initial values ​​and an iteration number N0 as keys to be input into the chaotic system for iteration to form a ciphertext;

[0051] The sending end uses the key parameter, the initial value and the number of iterations N0 as the key of the encryption algorithm and sends the ciphertext to the receiving end.

[0052] Beneficial effects:

[0053] The beneficial effects of the present disclosure include at least:

[0054] The unmanned aerial vehicle inspection traffic control system based on the chaotic system disclosed in the present invention obtains the original inspection image obtained in the unmanned aerial vehicle inspection through the sending end; the encryption module is used to encrypt the original inspection image to obtain the encrypted image data; and then the receiving end and the encryption module are used to exchange data to obtain the encrypted image data; the encryption module encrypts the original image to form encoded data; because the encoded data has better compatibility, it is suitable for embedding different structured data formats, ensuring data integrity while also taking into account protocol friendliness, reducing errors that may occur during transmission, and improving transmission advantages and high application value in network transmission. Moreover, the chaotic system is used for encryption in the present disclosure, which has more complex dynamic behavior, reduces output unevenness and has a larger chaotic range; at the same time, the initial value and control parameters in the encryption process have higher sensitivity, resist brute force cracking, and improve the advantages of application in the security field; and it has a larger chaotic area, stronger theoretical robustness, higher engineering fault tolerance, more flexible system design space, and more superior safety performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 is a system block diagram described in the present disclosure;

[0056] Figure 2 It is a chaotic characteristic diagram of the present disclosure and other chaotic systems when the control parameters are large;

[0057] Figure 3 It is a chaotic characteristic diagram of the present disclosure and other chaotic systems when the control parameters are relatively small;

[0058] Figure 4 is a sample entropy graph of the present disclosure and other chaotic systems;

[0059] Figure 5 is a 0-1 test K value curve diagram of the present disclosure;

[0060] Figure 6 It is a 0-1 test K value curve diagram of the present disclosure and other chaotic systems;

[0061] Figure 7 is a graph of the initial value sensitivity test of the present disclosure;

[0062] Figure 8 is a control parameter sensitivity test curve diagram of the present disclosure;

[0063] Fig. 9 are the original image, the encrypted image, the correct key decrypted image, and the incorrect key decrypted image in the method embodiment of the present disclosure;

[0064] Fig.10 is a flow chart of the method described in the present disclosure. DETAILED DESCRIPTION

[0065] In the following, various embodiments of the present disclosure will be described more fully. The present disclosure may have various embodiments, and adjustments and changes may be made therein. However, it should be understood that there is no intention to limit the various embodiments of the present disclosure to the specific embodiments disclosed herein, but rather the present disclosure should be understood to cover all adjustments, equivalents and / or alternatives that fall within the spirit and scope of the various embodiments of the present disclosure.

[0066] Hereinafter, the terms "include" or "may include" that may be used in various embodiments of the present disclosure indicate the presence of disclosed functions, operations, or elements, and do not limit the addition of one or more functions, operations, or elements. In addition, as used in various embodiments of the present disclosure, the terms "include", "have", and their cognates are intended only to indicate specific features, numbers, steps, operations, elements, components, or a combination of the foregoing, and should not be understood as first excluding the presence of one or more other features, numbers, steps, operations, elements, components, or a combination of the foregoing or the possibility of adding one or more features, numbers, steps, operations, elements, components, or a combination of the foregoing.

[0067] In various embodiments of the present disclosure, the expression "or" or "at least one of A or / and B" includes any combination or all combinations of the words listed at the same time. For example, the expression "A or B" or "at least one of A or / and B" may include A, may include B, or may include both A and B.

[0068] The expressions (such as "first", "second", etc.) used in the various embodiments of the present disclosure may modify the various constituent elements in the various embodiments, but may not limit the corresponding constituent elements. For example, the above expressions do not limit the order and / or importance of the elements. The above expressions are only used for the purpose of distinguishing one element from other elements. For example, the first user device and the second user device indicate different user devices, although both are user devices. For example, without departing from the scope of the various embodiments of the present disclosure, the first element may be referred to as the second element, and similarly, the second element may also be referred to as the first element.

[0069] It should be noted that if it is described that one component element is “connected” to another component element, the first component element may be directly connected to the second component element, and a third component element may be “connected” between the first component element and the second component element. Conversely, when one component element is “directly connected” to another component element, it can be understood that there is no third component element between the first component element and the second component element.

[0070] The term “user” used in various embodiments of the present disclosure may indicate a person using an electronic device or a device (eg, an artificial intelligence electronic device) using the electronic device.

[0071] The terms used in the various embodiments of the present disclosure are only used for the purpose of describing specific embodiments and are not intended to limit the various embodiments of the present disclosure. As used herein, the singular form is intended to also include the plural form, unless the context clearly indicates otherwise. Unless otherwise defined, all terms (including technical terms and scientific terms) used herein have the same meaning as the meanings commonly understood by ordinary technicians in the field to which the various embodiments of the present disclosure belong. The terms (such as the terms defined in the generally used dictionary) will be interpreted as having the same meaning as the contextual meaning in the relevant technical field and will not be interpreted as having an idealized meaning or an overly formal meaning, unless clearly defined in the various embodiments of the present disclosure.

[0072] The defects of the existing UAV inspection system image encryption algorithm are as follows:

[0073] 1. Encryption efficiency issues and insufficient support for multi-image encryption technology:

[0074] Some existing image encryption algorithms are inefficient when processing large-scale image data. For example, some encryption algorithms based on complex mathematical transformations (such as compressed sensing) have high security, but they require a large number of matrix operations during the encryption process, resulting in a long encryption time.

[0075] Many existing image encryption algorithms are mainly used to encrypt a single image, and there is a lack of effective integrated solutions for the encryption of multiple images. In practical applications, drone image data transmission often requires the simultaneous transmission of multiple images, and most existing encryption algorithms require the encryption of each image separately, which increases the computational complexity and data transmission overhead.

[0076] 2. Intrinsic safety loss:

[0077] Base64 encoding is essentially a way of converting binary data into printable ASCII characters. It was not originally designed for data encryption, but only to ensure that data can be transmitted smoothly under specific circumstances. This means that once image data transmitted using Base64 encoding is received, anyone with basic decoding tools can easily restore the original data, making sensitive information obtained by drone inspections, such as details of key infrastructure and images of confidential areas, completely unsafe, and easily causing data leakage, posing a serious threat to relevant units and national security. As an encoding mechanism, it only realizes data format conversion and lacks real encryption protection.

[0078] 3. Data expansion causes transmission bottleneck:

[0079] Base64 encoding causes the data volume to expand by 33%. The measured data shows that in a 4G network environment, the transmission delay of 1080P inspection images increases by 40-60ms. When performing high-definition 3D modeling, the data transmission time of a single task is extended by more than 12 minutes. The present invention adopts a new image encryption system to encrypt images according to their characteristics. Compared with the traditional scheme of encoding images into text and then encrypting them, it is more efficient.

[0080] 4. Poor adaptability to network transmission

[0081] Most of the existing image encryption algorithms in drone inspection systems do not fully consider the characteristics of network transmission. During the transmission process, image data may be affected by factors such as network protocols and data formats. For example, image data encrypted by some algorithms may not be suitable for direct transmission in the network and requires additional encoding or format conversion, which increases the complexity of transmission and the possibility of errors.

[0082] A chaotic system is a nonlinear dynamic system. Due to its pseudo-randomness, sensitivity to initial conditions, non-periodicity, and long-term unpredictability, a chaotic system is very suitable for image encryption with large amounts of data and strong correlation. Applying a chaotic system to image encryption is safer and more effective than a classical cryptographic system. The defects of existing chaotic systems are as follows:

[0083] 1. Insufficient behavioral complexity:

[0084] Existing chaotic systems, such as the Logistic chaotic system, have certain chaotic characteristics, but may show lower dynamic complexity within certain parameter ranges. For example, when the parameter of the Logistic chaotic system is less than 3.56, its output sequence will appear periodic, rather than a truly chaotic state. This makes the encryption algorithm based on such chaotic systems less secure in the face of complex attacks.

[0085] 2. Insufficient sensitivity of initial values ​​and control parameters:

[0086] Some existing chaotic systems are not sensitive enough to initial values ​​and control parameters. In encryption algorithms, sensitivity to initial values ​​and control parameters is an important indicator of algorithm security. If the chaotic system is not sensitive enough to these parameters, then attackers may be able to crack the encryption algorithm with minor adjustments.

[0087] 3. Limited chaos area:

[0088] Existing chaotic systems, such as the Logistic-Sine-Cosine chaotic system, may have a narrow chaotic region in the parameter space. This means that within certain parameter ranges, the chaotic system may not be able to maintain a chaotic state, thus affecting the security of the encryption algorithm. In addition, existing chaotic systems may have a non-chaotic state within certain parameter intervals, which limits the parameter selection of encryption algorithms based on these chaotic systems and makes it easy for attackers to find a suitable parameter range to crack them.

[0089] In view of the above problems, the present disclosure provides the following embodiments. Specific embodiment 1:

[0091] The present disclosure provides an embodiment:

[0092] A UAV inspection traffic control system based on a chaotic system includes: a transmitting end 100, an encryption module 200 and a receiving end 300; the transmitting end 100 is used to obtain an original inspection image obtained during the UAV inspection; the encryption module 200 performs data interaction with the transmitting end 100 to encrypt the original inspection image to obtain encrypted image data; the receiving end 300 performs data interaction with the encryption module 200 to obtain the encrypted image data;

[0093] The encryption module 200 encrypts the original inspection image through the following process:

[0094] The original inspection images obtained during the drone inspection are collected and hashed to generate hash values; the hash values ​​are grouped and any group is converted from binary to decimal to obtain two key parameters; an initial value is set, and at least two initial values ​​and the number of iterations N0 are selected as keys to be input into the chaotic system for iteration to form a ciphertext; the sending end uses the key parameters, the initial value and the number of iterations N0 as the keys of the encryption algorithm and sends the ciphertext to the receiving end.

[0095] Specifically, the chaotic system proposed in the present invention is used to iteratively generate a pseudo-random sequence, and the mathematical expression of the chaotic system is as follows:

[0096] ;

[0097] Among them, r is the control parameter used to regulate the chaotic behavior of the system;

[0098] Input three grayscale images, each of which has a size of M×N;

[0099] The three grayscale images are used as red, green and blue channels respectively, and combined into an RGB color image img (M×N×3);

[0100] Use Base64 encoding to encode the RGB color image into a Base64 string b64Str, whose size is 4×M×N;

[0101] Perform SHA-256 hash operation on the original image to generate a 256-bit hash value h;

[0102] Divide the hash value h into 8 groups, each with 32 bits;

[0103] Convert each group from binary to decimal to obtain 8 parameters R1, R2, …, R8;

[0104] Generate two key parameters h1 and h2 through XOR operation:

[0105] ;

[0106] ;

[0107] Choose 8 initial values ​​x i , i=1,2,…,8, and the number of iterations N0 are input as keys into the chaotic system proposed in the present invention;

[0108] Use the chaotic system to solve each initial value x i Perform N0+2 iterations, discard the results of the first N0 iterations, and obtain 8 chaotic sequences X i ;

[0109] Modify the N0+2th chaos value:

[0110] for : ;

[0111] for: ;

[0112] Continue to iterate 2M×N times to get 8 chaotic sequences X i ;

[0113] The eight chaotic sequences are concatenated into a final sequence X, and two control sequences are generated:

[0114] ;

[0115] .

[0116] Use the substitution sequence Y to perform a substitution operation on the Base64 string b64Str. The substitution operation is as follows:

[0117] Starting from the 1st character, process each character in the Base64 string b64Str one by one until the 4×M×Nth character, which is the end of the Base64 string. Here 4×M×N is the length of the Base64-encoded string, which corresponds to the size of the original image.

[0118] For the character b64Str(i) at the current index i, its value is temporarily stored in the variable temp. This step is to retain the value of the current character in subsequent operations for subsequent exchange operations.

[0119] Replace the character b64Str(i) at index i with the character b64Str(Y(i)) at the corresponding position in the permutation sequence Y.

[0120] Then, the temporarily stored character temp (that is, the original b64Str(i)) is assigned to position Y(i).

[0121] This step actually swaps the characters b64Str(i) and b64Str(Y(i)) to achieve the obfuscation effect based on the permutation sequence Y.

[0122] The pseudo code for the permutation operation is as follows:

[0123] for i = 1 to 4 × M × N:

[0124] temp = b64Str(i)

[0125] b64Str(i) = b64Str(Y(i))

[0126] b64Str(Y(i)) = temp

[0127] Convert the Base64 encoding to the corresponding index. The Char in the table represents the Base64 character. The table is as follows

[0128]

[0129] Use diffusion sequence Z to perform diffusion operation on index Index to get b64diffused, where ⊕ represents XOR operation

[0130] b64diffused(i) = b64index(i) ⊕ Z(i)

[0131] Re-encode b64diffused into Base64 encoding according to the table, and the encryption algorithm is completed

[0132] The sender uses the key parameters h1 and h2, 8 initial values ​​xi (i=1,2,…,8) and the number of iterations N0 as the encryption algorithm key, and b64diffused as the encrypted ciphertext to the receiver.

[0133] In summary, the system described in this embodiment can have higher encryption efficiency and better encryption effect in a short time, making it more suitable for network transmission; moreover, it can solve the problems of low efficiency and inability to fully utilize the correlation between images when processing multi-image scenes of traditional single-image encryption algorithms, and can encrypt three grayscale images each time, so as to realize multi-image encryption and ensure security, robustness and computational efficiency; the encryption algorithm based on Base64 encoding proposed in this embodiment can adapt well to network transmission, convert image data into ASCII characters, construct a new discrete chaotic system (1D-SLCM), and conduct extensive tests on its chaotic performance.

[0134] By using 1D-SLCM and Base64 encoding, this embodiment constructs a multi-image system. In the work, a new plaintext association framework using the SHA-256 hash function is proposed and applied to encrypt the original image to enhance the sensitivity of the plaintext. The complete simulation experiment proves the security and reliability of the system. The encoded data has better compatibility and is suitable for embedding in different structured data formats, ensuring data integrity while also taking into account protocol friendliness, reducing errors that may occur during transmission, and improving transmission advantages and high application value in network transmission.

[0135] At the same time, for the chaotic system used in this embodiment, in view of the low-complexity dynamic behavior, uneven output, and small chaotic range of traditional chaotic systems, this embodiment has a more complex dynamic behavior, reduces output unevenness, and has a larger chaotic range; moreover, the initial values ​​and control parameters have higher sensitivity, resist brute force cracking, and improve its advantages in application in the security field; at the same time, it has a larger chaotic area and stronger theoretical robustness, which can be directly converted into: higher engineering fault tolerance, more flexible system design space, and better safety performance.

[0136] Verification stage:

[0137] 1. Testing of chaotic system: The chaotic system in this embodiment is called: 1D-SLCM.

[0138] 1. Lyapunov Exponent (LE): By calculating the maximum Lyapunov exponent, it can be determined whether the system has chaotic characteristics. If the maximum exponent is positive, it indicates that the system is extremely sensitive to the initial conditions, the trajectory diverges exponentially, and it is a chaotic system; if it is negative, the system tends to be stable. A positive LE value indicates that the system has chaotic characteristics. The positive and negative values ​​and magnitude of the Lyapunov exponent reflect the stability of the system in different directions of the phase space. Negative values ​​indicate that the disturbance is attenuated (stable), and positive values ​​indicate that the disturbance is growing (unstable). When all exponents are negative, the system may converge to a fixed point or a periodic orbit. The larger the positive Lyapunov exponent value, the more sensitive the system is to the initial conditions and the worse the long-term predictability.

[0139] like Figure 2-Figure 3 Based on the test results of Lyapunov exponent, the 1D-SLCM system shows significant advantages in chaotic characteristics. Through systematic testing of its parameter space, the test results show that 1D-SLCM continues to have larger LE values ​​in a wide range of parameters, and the higher positive LE values ​​indicate that the orbital divergence rate of 1D-SLCM is faster, and the system is more sensitive to small differences in initial conditions, which is much higher than the diffusion amplitude of the comparison system. This characteristic can be converted into higher key sensitivity in encryption systems. Faster divergence speed and higher unpredictability are important for encryption systems. 1D-SLCM shows larger positive LE values ​​in a wider parameter range, indicating that it is highly sensitive to initial conditions and is superior to some other chaotic systems (such as Logistic-Sine-Cosine, 1-DFCS, 1-DSP, etc.). The high positive LE characteristics and parameter adaptability of 1D-SLCM make it an ideal candidate system for the new generation of chaos engineering (such as secure communications and biomedical signal encryption).

[0140] 2. Sample Entropy (SE):

[0141] In the field of time series analysis, sample entropy (SE) is a key analytical indicator with an extremely important testing purpose. Its core is to accurately measure the self-similarity and complexity of time series through rigorous quantitative methods. In principle, sample entropy is mainly calculated based on the degree of repetition of patterns in time series. When the pattern repetition in the time series is low, it means that the sequence is more random and uncertain, and the sample entropy value is higher; conversely, if the pattern repetition is high and the sequence is more regular, the sample entropy value is lower. Therefore, a higher SE value intuitively reflects that the time series contains more complex internal structures and dynamic change characteristics, and is more complex. This test is used to measure the self-similarity and complexity of time series.

[0142] like Figure 4 In this test of time series generated by various chaotic systems, the sample entropy value of the time series generated by 1D-SLCM is significantly higher than that of the time series generated by other chaotic systems. This result fully demonstrates that the time series generated by 1D-SLCM performs well in terms of complexity and is significantly better than other chaotic systems in terms of self-similarity and complexity characteristics.

[0143] 3.0-1 test:

[0144] The chaotic behavior of the system is judged by the 0-1 test. When the K value approaches 1, it indicates that the system is in a chaotic state, showing a high degree of uncertainty, irregularity, and an exponential amplification effect on small disturbances; when the K value approaches 0, it indicates that the system is in a regular or periodic state.

[0145] The calculation method is as follows:

[0146] ;

[0147] X(i) is the sequence of generated iterations.

[0148] like Figure 5-Figure 6 ,According to the test results, the 0-1 test result of 1D-SLCM is close to 1 in a wide range of parameters (r∈(1.4,350)), ,which indicates that the system has good chaotic characteristics in this parameter range.

[0149] 4. Initial value sensitivity and parameter sensitivity test

[0150] The chaotic system is iterated using initial values ​​and parameters that differ by 10^-15.

[0151] Verify that 1D-SLCM is highly sensitive to small changes in the initial value, ensuring that it meets the core requirement of key security in cryptography.

[0152] Figure 7-Figure 8 The red and blue parts in the middle are chaotic sequences generated by iterations of different values, indicating that the chaotic system has good initial value sensitivity and parameter sensitivity.

[0153] 5. NIST SP 800-22 test:

[0154] The randomness of the sequence generated by the chaotic system 1D-SLCM was evaluated through NIST testing to ensure its suitability for image encryption. Fifteen NIST tests were performed to verify whether the sequence met the internationally accepted cryptographic security standards.

[0155] Test results: The sequence generated by 1D-SLCM passed all 15 NIST tests, and all sub-test P values ​​were within the range of (0.001, 1), meeting the passing criteria. This shows that the sequence generated by 1D-SLCM has good randomness, meets the strict requirements of encryption systems for randomness, can resist statistical attacks, can effectively ensure information security, and is suitable for image encryption.

[0156]

[0157] In summary, through the above-mentioned multiple testing methods, the chaotic system proposed in this embodiment performs better than other existing chaotic systems in terms of chaotic characteristics, complexity, randomness, etc.

[0158] 2. Image transmission encryption effect inspection:

[0159] Test environment:

[0160] Platform: Matlab 2021a;

[0161] Hardware configuration: CPU 2.60GHz, 16GB memory, Windows 11 operating system;

[0162] Test images: Use multiple grayscale images (such as Pepper, Baboon, Sailboat, etc.) with a resolution of 512×512;

[0163] Encryption and decryption effects:

[0164] Encrypted image: The encrypted image is completely unrecognizable from the original image content and appears as a noisy image.

[0165] Decrypting an image: Using the correct key can completely restore the original image, while using the wrong key cannot restore any meaningful information, such as Fig. 9 . Fig. 9 (a) is the original image for example; (b) is the encrypted image; (c) is the image decrypted using the correct key; (d) is the image decrypted using the incorrect key.

[0166] 2. Encryption time analysis;

[0167] Purpose: To verify the efficiency of the algorithm.

[0168] Test images: three grayscale images (Pepper, Baboon, Sailboat), with sizes of 512×512 and 256×256 respectively.

[0169] Results: (Average encryption time per grayscale image)

[0170] 512×512 image: 0.3004 seconds;

[0171] 256×256 image: 0.1229 seconds;

[0172] Time complexity: The time complexity of the encryption algorithm proposed in this invention is measured using the Big O time complexity. Taking an M×N image as an example, the time complexity of encoding it into Base64 encoding is O(M×N), the time complexity of the permutation operation is O(4M×N / 3), and the time complexity of the diffusion operation is O(4M×N). There is no operation in the algorithm that exceeds the time complexity of O(MN), so the final algorithm time complexity is O(MN);

[0173] Conclusion: The algorithm has good operating efficiency while ensuring security and is suitable for real-time encryption and large-scale data processing.

[0174] 3. Peak signal-to-noise ratio (PSNR) analysis;

[0175] PSNR is used to measure the difference between two images. The larger the PSNR value, the more similar the two images are. If the two images are exactly the same, the MSE is 0, and the PSNR is infinite. Therefore, when evaluating the difference between the encrypted image and the original image, a low PSNR value indicates a better encryption effect.

[0176] The calculation is as follows:

[0177]

[0178] Where P is the original image, C is the ciphertext image, M and N represent the size of the image;

[0179] The test results are as follows:

[0180] Pepper image: 8.8260 dB;

[0181] Baboon image: 9.5098 dB;

[0182] Sailboat image: 8.2186 dB.

[0183] 4. Plaintext sensitivity test

[0184] Encrypt two images that differ by only one pixel and calculate the difference between the encrypted images. This verifies the algorithm's sensitivity to plaintext and resists differential attacks and chosen plaintext attacks.

[0185] Calculation method:

[0186]

[0187] Test results:

[0188] The average NPCR value of the standard test image is 99.6072% (the theoretical optimal value is approximately equal to 99.6094%) and the average UACI value is 33.4634% (the theoretical optimal value is approximately equal to 33.4635). This shows that the algorithm is highly sensitive to single-pixel differences and is close to the theoretical limit.

[0189] In the extreme image test, the encryption results of pure white and pure black images showed that the ciphertext was completely randomized, and both the NPCR and UACI values ​​met the standards, proving that the algorithm can still resist attacks under extreme conditions.

[0190]

[0191] In summary, the system described in this embodiment adopts a new one-dimensional chaotic system (1D-SLCM), which has been verified by 0–1 test and initial value sensitivity and parameter sensitivity test to have good chaotic characteristics, higher dynamic complexity, initial value and parameter sensitivity, and the pseudo-random sequence generated by 1D-SLCM is combined with SHA-256 hash operation and permutation diffusion operation, so that small changes can be diffused to the entire image, ensuring the unpredictability and security of the key, significantly enhancing the anti-attack capability of the encryption algorithm, and thus effectively resisting the data integrity threat when the drone transmits image data and protecting sensitive information.

[0192] Moreover, this embodiment uses Base64 encoding to convert image data into ASCII characters, which improves the adaptability of encrypted images in network transmission, avoids errors that may occur during transmission, and does not require additional encoding or format conversion, thereby reducing transmission complexity and error probability. Compared with traditional block cipher algorithms such as AES, the stream cipher characteristics of chaotic encryption are more suitable for high-throughput data stream processing in drone scenarios, while avoiding the complexity of key management. It is worth mentioning that this technical solution has passed the NIST SP800-22 randomness test, confirming the cryptographic strength of its key stream, and providing technical support for the compliant deployment of drone inspection systems. In the plaintext sensitivity test, the average NPCR value of the standard test image is 99.6072% (the theoretical optimal value is approximately equal to 99.6094%), and the average UACI value is 33.4634% (the theoretical optimal value is approximately equal to 33.4635), which is close to the theoretical limit; in the extreme image test, the encryption results of pure white and pure black images show that the ciphertext is completely randomized, and both NPCR and UACI values ​​meet the standards. This shows that the algorithm still has good attack resistance capabilities under extreme conditions, and can efficiently and securely encrypt drone inspection images containing sensitive information to protect their confidentiality. Specific embodiment 2:

[0194] The present disclosure also provides an embodiment:

[0195] like Fig.10 , a UAV inspection traffic control method based on a chaotic system, based on the UAV inspection traffic control system described in specific embodiment 1, including: collecting original inspection images obtained during UAV inspection, and performing hash operations to generate hash values; grouping the hash values, and converting any group from binary to decimal to obtain two key parameters; setting initial values, and selecting at least 2 initial values ​​and the number of iterations N0 as keys to input into the chaotic system for iteration to form ciphertext; using the sending end to send the key parameters, initial values ​​and the number of iterations N0 as the keys of the encryption algorithm, and the ciphertext to the receiving end. Specific embodiment 3:

[0197] The present disclosure also provides an embodiment:

[0198] An electronic device includes: a storage medium and a processing unit; wherein the storage medium is used to store a computer program; the processing unit exchanges data with the storage medium, and is used to execute the computer program through the processing unit when transmitting drone inspection images, and perform the steps of the drone inspection traffic control method based on a chaotic system as described in specific embodiment 2.

[0199] The CPU can perform various appropriate actions and processes according to the program stored in the storage medium. The electronic device also includes the following peripherals, including input parts such as keyboards and mice, and output parts such as cathode ray tubes (CRTs), liquid crystal displays (LCDs), and speakers.

[0200] The present disclosure also provides an embodiment:

[0201] A readable storage medium: the readable storage medium stores a computer program; when the computer program is running, the steps of the UAV inspection traffic control method based on a chaotic system as described in specific embodiment 2 are executed.

[0202] In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in combination with an instruction execution system, device or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0203] The above disclosures are only several specific implementation scenarios of the present disclosure, but the present disclosure is not limited thereto, and any changes that can be thought of by a person skilled in the art should fall within the protection scope of the present disclosure. The above disclosure serial numbers are only for description and do not represent the advantages and disadvantages of the implementation scenarios.

Claims

1. A UAV inspection traffic control system based on a chaotic system, characterized in that: include: The sending end is used to obtain the original inspection images obtained during the drone inspection; An encryption module, which performs data interaction with the transmitting end and is used to encrypt the original inspection image to obtain encrypted image data; A receiving end, performing data interaction with the encryption module to obtain the encrypted image data; The encryption module encrypts the original inspection image through the following process: Collect the original inspection images obtained during the drone inspection, perform hash operations, and generate hash values; The hash values ​​are grouped and any group is converted from binary to decimal to obtain two key parameters; Setting an initial value, and selecting at least two initial values ​​and an iteration number N0 as keys to be input into the chaotic system for iteration to form a ciphertext; The sending end uses the key parameter, initial value and number of iterations N0 as the key of the encryption algorithm and sends the ciphertext to the receiving end.

2. The unmanned aerial vehicle inspection traffic control system based on a chaotic system according to claim 1 is characterized in that: The setting of the initial value, and selecting at least two initial values ​​and the number of iterations N0 as keys to be input into the chaotic system for iteration to form a ciphertext, includes: Use the chaotic system for any initial value x i Perform N0+2 iterations, discard the results of the first N0 iterations, and obtain at least 2 chaotic sequences X i ; All the chaotic sequences are concatenated into a final sequence X, and a permutation sequence Y and a diffusion sequence Z are generated; A ciphertext is formed according to the permutation sequence Y and the diffusion sequence Z.

3. The unmanned aerial vehicle inspection traffic control system based on chaotic system according to claim 2 is characterized in that: The method of splicing all the chaotic sequences into a final sequence X and generating a permutation sequence Y and a diffusion sequence Z comprises: ； ； Among them, X is the chaotic sequence; M×N is the size of the image.

4. The unmanned aerial vehicle inspection traffic control system based on a chaotic system according to claim 2 is characterized in that: The forming of ciphertext according to the permutation sequence Y and the diffusion sequence Z comprises: Use the replacement sequence Y to perform a replacement operation on the preset string b64Str, convert the Base64 code at this time into the corresponding index, and obtain b64index; Use diffusion sequence Z to perform a diffusion operation on the b64index to obtain b64diffused; The b64diffused is used as the ciphertext.

5. The unmanned aerial vehicle inspection traffic control system based on a chaotic system according to claim 4 is characterized in that: The use of the diffusion sequence Z to perform a diffusion operation on the index Index to obtain b64diffused includes: b64diffused = b64index ⊕ Z; Among them, ⊕ represents the exclusive OR operation.

6. The unmanned aerial vehicle inspection traffic control system based on a chaotic system according to claim 4 is characterized in that: The preset string b64Str includes: Iteratively generate pseudo-random sequences using chaotic systems; Get three basic grayscale images, each of which has a size of M×N; The three basic grayscale images are used as red, green and blue channels respectively and combined into an RGB color image; The RGB color image is encoded using Base64 encoding into a Base64 string b64Str, whose size is 4×M×N.

7. The UAV inspection traffic control system based on chaotic system according to claim 1, 2 or 6, characterized in that: The chaotic system comprises: ; Where n represents the number of iterations of the chaotic system; is the value of the chaotic system at the nth iteration; r is the control parameter used to adjust the chaotic behavior of the system; is an exponential function, with the base of natural logarithms, e, as the base. is the index; It is the value of the chaotic system at the n-1th iteration, that is, the result of the previous iteration.

8. The unmanned aerial vehicle inspection traffic control system based on chaotic system according to claim 1 is characterized in that: The acquisition of the key parameters includes: Performing a SHA-256 hash operation on the original inspection image to generate a 256-bit hash value h; Divide the hash value h into 8 groups, each group has 32 bits; Convert each group from binary to decimal to obtain 8 parameters R1, R2, …, R8; Two key parameters h1 and h2 are generated through XOR operation.

9. The unmanned aerial vehicle inspection traffic control system based on a chaotic system according to claim 8 is characterized in that: The acquisition of the key parameters includes: ； 。 10. A method for controlling unmanned aerial vehicle inspection traffic based on a chaotic system, based on the unmanned aerial vehicle inspection traffic control system according to claim 1, characterized in that: include: Collect the original inspection images obtained during the drone inspection, perform hash operations, and generate hash values; The hash values ​​are grouped and any group is converted from binary to decimal to obtain two key parameters; Setting an initial value, and selecting at least two initial values ​​and an iteration number N0 as keys to be input into the chaotic system for iteration to form a ciphertext; The sending end uses the key parameter, the initial value and the number of iterations N0 as the key of the encryption algorithm and sends the ciphertext to the receiving end.

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