Cross-network interaction method and system based on the irreversible and fluctuating properties of light

Abstract

This invention provides a cross-network interaction method and system based on the irreversible and wave properties of light, relating to the field of network security technology. The method includes obtaining modulation sensitivity factors through multi-scale decomposition, using an asymmetric modulation operator for dynamic compensation modulation to generate an optical signal carrying unique wave information, establishing a unidirectional transmission link using an optical isolator, and generating check values ​​through wave feature sequences and feature mapping matrices respectively, comparing matching degrees to determine data integrity. This invention achieves secure and efficient data interaction between heterogeneous networks, effectively preventing data leakage and tampering, and improving the security and reliability of cross-network data transmission.

Description

Technical Field

[0001] This invention relates to the field of network security technology, and in particular to a cross-network interaction method and system based on the irreversible and wave properties of light. Background Technology

[0002] With the rapid development of network technology, the demand for data interaction between different networks is increasing, especially in sensitive fields such as industrial control, financial transactions, and national defense security, which places higher demands on the security and reliability of cross-network interaction. Traditional cross-network interaction technologies mainly rely on electrical signal transmission and software protection measures to achieve data exchange between physically isolated networks. Optical communication, as a high-bandwidth, low-latency transmission method, offers a new approach to building secure cross-network communication due to its irreversible and fluctuating properties. Based on the physical characteristics of light, unidirectional data flow between networks can be realized, effectively preventing data backhaul and network intrusion, and providing physical-level security for critical information infrastructure.

[0003] Current cross-network interaction technologies still have several shortcomings. Although traditional electronic isolation technology can achieve physical isolation, it lacks a dynamic verification mechanism for the integrity of data transmission, making it difficult to cope with advanced persistent threats and data tampering attacks. Existing photoelectric conversion technologies mostly use fixed modulation methods, which cannot be adaptively modulated according to data characteristics, resulting in unstable signal quality in complex electromagnetic environments and making it difficult to guarantee high-reliability transmission. Existing cross-network communication solutions generally lack multi-dimensional data verification mechanisms, relying only on a single feature for data verification, which cannot fully capture the fluctuation characteristics and energy changes of the signal during transmission, resulting in verification blind spots and security risks.

[0004] With the increasing complexity and precision of cybersecurity threats, there is an urgent need for a technology that can utilize the physical properties of light to achieve highly reliable and irreversible cross-network interaction. This technology should ensure the unidirectional flow of data while guaranteeing the security and integrity of cross-network data transmission through multi-dimensional feature extraction and verification. Summary of the Invention

[0005] This invention provides a cross-network interaction method and system based on the irreversible and wave properties of light, which can solve the problems in the prior art.

[0006] A first aspect of this invention provides a cross-network interaction method based on the irreversible and wave properties of light, comprising:

[0007] Obtain the original electrical signal data to be transmitted from the source network;

[0008] Based on the modulation sensitive factor obtained from the original electrical signal data through multi-scale decomposition, an asymmetric modulation operator is used to dynamically compensate and modulate the modulation sensitive factor to generate an optical signal carrying unique fluctuation information.

[0009] The optical signal is transmitted through a unidirectional optical transmission channel configured with an optical isolator, forming a unidirectional transmission link from the source network to the target network;

[0010] The spatial and phase modulation characteristics of the optical signal are collected using a photodetector, the wave feature sequence is extracted, and a first verification value is generated based on the wave feature sequence and unique wave information.

[0011] The optical signal is restored to the target electrical signal data, and a multi-level sampling sequence is obtained through adaptive hierarchical sampling. Signal abruptness features and energy accumulation features are extracted from the multi-level sampling sequence, and a feature mapping matrix is ​​constructed to generate a second verification value.

[0012] Calculate the matching degree between the first check value and the second check value to determine the integrity of the target electrical signal data.

[0013] In one optional embodiment, the modulation sensitivity factor of the original electrical signal data is obtained based on multi-scale decomposition, and an asymmetric modulation operator is used to dynamically compensate and modulate the modulation sensitivity factor to generate an optical signal carrying unique fluctuation information, including:

[0014] The original electrical signal data is decomposed into multiple scales to obtain the corresponding fundamental frequency component and harmonic component. The correlation strength between the fundamental frequency component and the harmonic component is calculated, a feature correlation matrix is ​​constructed, and modulation sensitive factors are identified based on the feature correlation matrix.

[0015] The modulation sensitive factor is divided into multiple discrete intervals. Initial differential mapping weights are generated based on the data fluctuation range of the discrete intervals. The initial differential mapping weights are dynamically iteratively optimized using a chaotic sequence to generate an iterative optimization sequence.

[0016] An asymmetric modulation operator is constructed based on the iterative optimization sequence and decomposed into amplitude modulation component, phase modulation component, and frequency modulation component. The cross-entropy value is calculated to construct the coupling degree matrix, and the nonlinear coupling relationship is obtained through a nonlinear function.

[0017] A compensation modulation mechanism is established based on the aforementioned nonlinear coupling relationship. When the amplitude modulation component is distorted, distortion correction is achieved through the linkage compensation of the phase modulation component and the frequency modulation component, thereby generating dynamic compensation parameters.

[0018] The dynamic compensation parameters are combined with the asymmetric modulation operator to form a composite modulation strategy, generating unique fluctuation information;

[0019] The unique fluctuation information is modulated onto an optical signal carrier, and an optical signal carrying the unique fluctuation information is output.

[0020] In one optional embodiment, the modulation sensitivity factor is divided into multiple discrete intervals, and initial differential mapping weights are generated based on the data fluctuation range of the discrete intervals. A chaotic sequence is then used to dynamically iteratively optimize the initial differential mapping weights, generating the iterative optimization sequence, which includes:

[0021] Calculate the data distribution density of the modulation sensitive factor, determine the interval segmentation threshold, and divide the modulation sensitive factor into multiple discrete intervals based on the interval segmentation threshold;

[0022] The weight benchmark is calculated based on the data fluctuation range of each discrete interval, and the weight benchmark is normalized to generate the initial difference mapping weight.

[0023] Dynamic iterative calculation is performed on the initial difference mapping weights to obtain stability parameters, and bifurcation intervals are determined based on the stability parameters; chaotic mapping parameters are set within the bifurcation intervals to generate an initial chaotic sequence; the initial chaotic sequence is mapped point-to-point with the initial difference mapping weights to generate an iterative initial sequence.

[0024] Using the initial iteration sequence as input, the iteration begins, the fluctuation variance of the current iteration sequence is calculated, the iteration step size is adaptively adjusted, the adjusted iteration step size is multiplied by the current chaotic value, and the current iteration sequence is nonlinearly modulated; the modulated sequence is normalized to generate the next iteration sequence; when the difference between two adjacent iteration sequences is less than a preset convergence threshold, the current iteration sequence is determined as the optimized difference mapping weight;

[0025] The optimized difference mapping weights are recombined and matched according to the distribution characteristics of the discrete intervals to generate an iterative optimization sequence.

[0026] In one optional embodiment, the spatial and phase modulation characteristics of the optical signal are collected using a photodetector, a wave feature sequence is extracted, and a first verification value is generated based on the wave feature sequence and unique wave information, including:

[0027] The unidirectional optical transmission channel is divided into multiple detection areas, and a photodetector is set in each detection area to collect optical signals; the optical signals in each detection area are hierarchically encoded, the modulation depth of the optical signal as it changes with spatial position is extracted, and a spatial modulation feature map is established.

[0028] A tuning grating is set in each detection area, and the diffraction angle of the light signal is adjusted by changing the grating period to obtain the light intensity distribution at different diffraction angles; the phase difference of the light intensity distribution at each diffraction angle is calculated to extract the phase modulation characteristics of the light signal.

[0029] The modulation sensitivity of the sampling points is calculated based on the spatial modulation feature map and the phase modulation feature. The reference point is determined by the modulation sensitivity and correlation analysis is performed to generate the wave feature sequence.

[0030] The standard modulation parameters are demodulated from the unique fluctuation information, and the correlation between the fluctuation feature sequence and the standard modulation parameters is calculated. The first verification value is generated based on the correlation.

[0031] In one optional embodiment, the modulation sensitivity of the sampling points is calculated based on the spatial modulation feature map and phase modulation features. A reference point is determined using the modulation sensitivity, and correlation analysis is performed to generate a wave feature sequence, including:

[0032] Calculate the light intensity difference between adjacent detection regions in the spatial modulation feature map to obtain the light intensity gradient matrix, and extract the set of regions whose light intensity change rate is greater than a preset change threshold from the light intensity gradient matrix;

[0033] Based on the sampling points determined by the set of regional locations, the phase modulation feature values ​​at each sampling point are extracted to generate a light intensity-phase correspondence curve, the modulation change of the light signal at different locations is determined, and the modulation sensitivity of each sampling point is calculated based on the modulation change.

[0034] The modulation sensitivities are sorted in descending order, and multiple sampling points with the highest modulation sensitivities are selected according to a preset number to determine the reference points. The correlation coefficients between the remaining sampling points and the reference points are calculated.

[0035] The correlation coefficient is used as a weighting factor and weighted and superimposed with the modulation change of the corresponding sampling point to obtain the fluctuation feature sequence.

[0036] In an optional embodiment, the optical signal is restored to target electrical signal data, and a multi-level sampling sequence is obtained through adaptive hierarchical sampling. Signal abrupt change features and energy accumulation features are extracted from the multi-level sampling sequence, and a feature mapping matrix is ​​constructed to generate a second verification value, including:

[0037] The target electrical signal data is sampled in layers according to an adaptive sampling interval to obtain a multi-layer sampling sequence, and the signal difference between adjacent sampling layers is calculated to obtain an inter-layer difference sequence.

[0038] The amplitude jump degree and jump position are obtained from the interlayer differential sequence to determine the amplitude jump point; the phase change rate of the interlayer differential sequence is calculated to obtain the phase change point where the phase change exceeds a preset phase threshold, and the signal change feature is determined based on the temporal position information of the amplitude jump point and the phase change point.

[0039] Wavelet packet decomposition is performed on the multi-layer sampling sequence to obtain energy coefficients for multiple frequency bands. The energy density distribution in each frequency band is calculated, and regions with energy density greater than a preset density threshold are extracted as energy accumulation areas. The energy concentration and energy diffusion coefficient of the energy accumulation areas are calculated to obtain energy accumulation characteristics.

[0040] The temporal location information of the signal abrupt change feature is constructed into a feature temporal matrix, and the energy distribution information of the energy accumulation feature is constructed into an energy distribution matrix; tensor decomposition is performed on the feature temporal matrix and the energy distribution matrix to obtain the feature mapping matrix;

[0041] The feature mapping matrix is ​​transformed to obtain a feature projection sequence, and a second verification value is generated based on the feature projection sequence.

[0042] In one optional embodiment, calculating the matching degree between the first check value and the second check value to determine the integrity of the target electrical signal data includes:

[0043] Extract the optical signal fluctuation correlation feature from the first verification value and the feature mapping projection feature from the second verification value;

[0044] A wave feature vector is constructed based on the wave correlation characteristics of optical signals, and a mapping feature vector is constructed based on the feature mapping projection characteristics. The wave feature vector and the mapping feature vector are reconstructed in the same feature space to generate the photoelectric signal feature correspondence matrix.

[0045] The matching degree is obtained by calculating the feature similarity of the matrix corresponding to the photoelectric signal features, and the integrity of the target electrical signal data after transmission through the unidirectional optical transmission channel is determined based on the matching degree.

[0046] A second aspect of this invention provides a cross-network interaction system based on the irreversible and wave properties of light, comprising:

[0047] The first unit is used to obtain the original electrical signal data to be transmitted from the source network;

[0048] The second unit is used to obtain the modulation sensitive factor of the original electrical signal data based on multi-scale decomposition, and to dynamically compensate and modulate the modulation sensitive factor using an asymmetric modulation operator to generate an optical signal carrying unique fluctuation information.

[0049] The third unit is used to transmit the optical signal through a unidirectional optical transmission channel configured with an optical isolator, forming a unidirectional transmission link from the source network to the target network;

[0050] The fourth unit is used to collect the spatial and phase modulation characteristics of the optical signal using a photodetector, extract the wave feature sequence, and generate a first verification value based on the wave feature sequence and unique wave information.

[0051] The fifth unit is used to restore the optical signal to the target electrical signal data, obtain a multi-layer sampling sequence through adaptive hierarchical sampling, extract signal abrupt change features and energy accumulation features from the multi-layer sampling sequence, and construct a feature mapping matrix to generate a second verification value.

[0052] The sixth unit is used to calculate the matching degree between the first check value and the second check value to determine the integrity of the target electrical signal data.

[0053] A third aspect of the present invention provides an electronic device, comprising:

[0054] processor;

[0055] Memory used to store processor-executable instructions;

[0056] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0057] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0058] In this embodiment of the invention, a cross-network interaction method based on the irreversible and wave properties of light is used to achieve secure one-way data transmission between networks, effectively preventing the risk of reverse information theft and attacks, and ensuring the security and integrity of data interaction. The use of asymmetric modulation and a one-way optical transmission channel, combined with wave feature extraction and a dual verification mechanism, ensures that data is not tampered with during transmission, improving the reliability and stability of cross-network data exchange. Furthermore, the use of multi-scale decomposition and adaptive hierarchical sampling technology effectively identifies and processes signal mutations and energy accumulation characteristics, enhancing the system's ability to verify the integrity of electrical signal data, making it suitable for high-security network isolation environments. Attached Figure Description

[0059] Figure 1 This is a flowchart illustrating the cross-network interaction method based on the irreversible and wave properties of light according to an embodiment of the present invention.

[0060] Figure 2 This is a flowchart of adaptive modulation and compensation for optical signals. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0063] Figure 1 This is a flowchart illustrating the cross-network interaction method based on the irreversible and wave properties of light according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0064] Obtain the original electrical signal data to be transmitted from the source network;

[0065] Based on the modulation sensitive factor obtained from the original electrical signal data through multi-scale decomposition, an asymmetric modulation operator is used to dynamically compensate and modulate the modulation sensitive factor to generate an optical signal carrying unique fluctuation information.

[0066] The optical signal is transmitted through a unidirectional optical transmission channel configured with an optical isolator, forming a unidirectional transmission link from the source network to the target network;

[0067] The spatial and phase modulation characteristics of the optical signal are collected using a photodetector, the wave feature sequence is extracted, and a first verification value is generated based on the wave feature sequence and unique wave information.

[0068] The optical signal is restored to the target electrical signal data, and a multi-level sampling sequence is obtained through adaptive hierarchical sampling. Signal abruptness features and energy accumulation features are extracted from the multi-level sampling sequence, and a feature mapping matrix is ​​constructed to generate a second verification value.

[0069] Calculate the matching degree between the first check value and the second check value to determine the integrity of the target electrical signal data.

[0070] In one optional implementation, the modulation sensitivity factor of the original electrical signal data is obtained based on multi-scale decomposition, and an asymmetric modulation operator is used to dynamically compensate and modulate the modulation sensitivity factor to generate an optical signal carrying unique fluctuation information, including:

[0071] The original electrical signal data is decomposed into multiple scales to obtain the corresponding fundamental frequency component and harmonic component. The correlation strength between the fundamental frequency component and the harmonic component is calculated, a feature correlation matrix is ​​constructed, and modulation sensitive factors are identified based on the feature correlation matrix.

[0072] The modulation sensitive factor is divided into multiple discrete intervals. Initial differential mapping weights are generated based on the data fluctuation range of the discrete intervals. The initial differential mapping weights are dynamically iteratively optimized using a chaotic sequence to generate an iterative optimization sequence.

[0073] An asymmetric modulation operator is constructed based on the iterative optimization sequence and decomposed into amplitude modulation component, phase modulation component, and frequency modulation component. The cross-entropy value is calculated to construct the coupling degree matrix, and the nonlinear coupling relationship is obtained through a nonlinear function.

[0074] A compensation modulation mechanism is established based on the aforementioned nonlinear coupling relationship. When the amplitude modulation component is distorted, distortion correction is achieved through the linkage compensation of the phase modulation component and the frequency modulation component, thereby generating dynamic compensation parameters.

[0075] The dynamic compensation parameters are combined with the asymmetric modulation operator to form a composite modulation strategy, generating unique fluctuation information;

[0076] The unique fluctuation information is modulated onto an optical signal carrier, and an optical signal carrying the unique fluctuation information is output.

[0077] In one specific implementation, when performing multi-scale decomposition on the original electrical signal data, a wavelet transform method is used to decompose the original electrical signal into sub-signals of different frequency bands. The Daubechies wavelet basis is selected to perform a 5-level decomposition of the signal, resulting in one low-frequency component and five high-frequency components. The low-frequency component represents the fundamental frequency component of the signal, while the high-frequency components correspond to harmonic components. Taking an audio signal with a sampling frequency of 20kHz as an example, after wavelet decomposition, a fundamental frequency component of 0-625Hz and harmonic components in four frequency ranges—625-1250Hz, 1250-2500Hz, 2500-5000Hz, and 5000-10000Hz—can be obtained.

[0078] When calculating the correlation strength between the fundamental frequency component and the harmonic components, a cross-correlation coefficient is calculated for each pair of components. The cross-correlation calculation uses a sliding window method with a window length of 512 sampling points and a sliding step size of 128 sampling points. For each window position, the cross-correlation coefficient between the two signal components is calculated, with values ​​ranging from -1 to 1; a larger absolute value indicates a stronger correlation. Taking the fundamental frequency component and the first harmonic component as an example, a cross-correlation coefficient of 0.78 can be obtained in a typical signal, indicating a strong positive correlation between the two.

[0079] When constructing the eigencorrelation matrix, the pairwise cross-correlation coefficients of all components are arranged in matrix form. For five components, a 5×5 symmetric matrix is ​​formed. The diagonal elements of the matrix are 1, representing the autocorrelation coefficient. By analyzing the magnitude of the elements in the eigencorrelation matrix, modulation-sensitive factors, i.e., those components that are strongly correlated with other components, can be identified. For example, if the average absolute value of the off-diagonal elements in a row or column of the matrix is ​​greater than 0.6, the corresponding component is identified as a modulation-sensitive factor.

[0080] When dividing the modulation sensitivity factor into discrete intervals, a uniform quantization method is used based on the statistical characteristics of the signal. The signal amplitude range is divided into 8 equal intervals, each with a width of 1 / 8 of the signal's peak-to-peak value. For example, for a signal with a peak-to-peak value of 2V, each interval has a width of 0.25V, forming 8 discrete intervals such as [-1, -0.75], [-0.75, -0.5], etc.

[0081] Initial differential mapping weights are generated based on the data fluctuation range of discrete intervals. The standard deviation of data points within each interval is calculated, and the standard deviation is normalized to the range [0, 1] as the initial weight for that interval. For example, for a certain test signal, the initial weights for the 8 intervals may be [0.15, 0.23, 0.42, 0.65, 0.78, 0.56, 0.31, 0.18].

[0082] When using chaotic sequences to dynamically iteratively optimize the initial difference mapping weights, a Logistic mapping is used to generate the chaotic sequence. The initial value is set to x0 = 0.4, and the control parameter is r = 3.9. The chaotic sequence is generated using an iterative formula. In each iteration, the elements of the chaotic sequence are multiplied by the initial weights and then normalized to update the weights. After 100 iterations, the weights converge, forming the final iteratively optimized sequence. For example, the final optimized weight sequence might be [0.21, 0.32, 0.53, 0.72, 0.85, 0.61, 0.42, 0.25].

[0083] When constructing an asymmetric modulation operator based on an iteratively optimized sequence, a piecewise nonlinear function mapping method is employed. The optimized weight sequence is used as the control points of the piecewise function to construct a nonlinear mapping function. This mapping function converts the input signal into a modulated signal, and the mapping process is asymmetric, meaning the forward and reverse mappings are asymmetrical, ensuring the irreversibility of the modulation process.

[0084] When decomposing an asymmetric modulation operator into amplitude modulation, phase modulation, and frequency modulation components, the Hilbert transform method is used to extract the instantaneous amplitude, instantaneous phase, and instantaneous frequency of the signal. For the amplitude modulation component, it is obtained by calculating the signal envelope; for the phase modulation component, it is obtained by calculating the signal phase angle; and for the frequency modulation component, it is obtained by calculating the time derivative of the phase.

[0085] When constructing the coupling matrix by calculating the cross-entropy values, the cross-entropy of each of the three modulation components is calculated pairwise. The smaller the cross-entropy, the higher the similarity between the two components, and the stronger the coupling. A 3×3 coupling matrix is ​​obtained, reflecting the coupling relationship between the three modulation components. For example, for a certain test signal, the resulting coupling matrix values ​​might be: amplitude and phase coupling 0.35, amplitude and frequency coupling 0.58, and phase and frequency coupling 0.27.

[0086] When obtaining nonlinear coupling relationships through nonlinear functions, a multilayer perceptron network is used to establish the mapping relationship between the three modulation components. The network consists of an input layer (3 nodes), two hidden layers (8 and 4 nodes respectively), and an output layer (3 nodes). By optimizing the network parameters using a training dataset, the network can accurately predict how other components should adjust to maintain the stability of signal characteristics when one component changes.

[0087] When establishing a compensation modulation mechanism based on nonlinear coupling, a feedback control system is constructed. When distortion of the amplitude modulation component is detected, the required compensation adjustments for the phase and frequency components are calculated. The compensation calculation is based on the previously established nonlinear coupling model, and the optimal compensation parameters are determined through backpropagation. For example, when the amplitude modulation component decreases by 10%, the phase modulation component may need to be increased by 15 degrees, while the frequency modulation component may need to be increased by 5% to achieve distortion correction.

[0088] When generating dynamic compensation parameters, signal distortion is continuously monitored, and compensation parameters are calculated in real time. These compensation parameters include the phase compensation angle and frequency adjustment ratio, which change dynamically with the degree of signal distortion. To ensure compensation accuracy, a gradient descent method is used to optimize the compensation parameters, minimizing the error between the compensated signal and the ideal signal.

[0089] When dynamic compensation parameters are combined with asymmetric modulation operators to form a composite modulation strategy, a cascaded modulation system is constructed. The signal is first initially modulated by the asymmetric modulation operator, and then secondarily modulated based on the dynamic compensation parameters, ultimately generating information with unique fluctuation characteristics. This composite modulation ensures that the signal has a high degree of unpredictability and non-reproducibility.

[0090] Phase modulation is employed when unique fluctuation information is modulated onto an optical signal carrier. A Mach-Zehnder modulator is used to convert the electrical signal into changes in optical intensity, with a modulation depth of 0.8. A 1550nm laser with an output power of 10mW is used as the light source. The modulated optical signal is transmitted through single-mode optical fiber, with a fiber length of up to 10 kilometers, and signal attenuation is less than 3dB during transmission.

[0091] Existing cross-network interaction technologies primarily rely on digital encryption and authentication mechanisms, typically based on complex mathematical algorithms. However, these face security challenges due to the ever-increasing computing power. Traditional optical communication systems only use light as an information carrier, failing to fully utilize the physical properties of light to enhance security. This embodiment utilizes the irreversibility and wave properties of light, employing techniques such as multi-scale decomposition, dynamic compensation, and composite modulation to embed unique wave information into the optical signal, achieving physical layer information security protection. Compared to existing technologies, this embodiment establishes a nonlinear coupling relationship between modulation components, achieving automatic compensation for signal distortion; introduces chaotic sequences for dynamic iterative optimization, enhancing the unpredictability of the modulation process; and employs the physical properties of light for information protection, avoiding the computational threats faced by purely mathematical encryption algorithms. This results in a more secure and robust cross-network interaction method that maintains information integrity and security even when encountering interference or attacks during signal transmission.

[0092] like Figure 2 The diagram shown illustrates the flowchart of adaptive modulation and compensation for optical signals.

[0093] In one optional implementation, the modulation sensitivity factor is divided into multiple discrete intervals, and initial differential mapping weights are generated based on the data fluctuation range of the discrete intervals. A chaotic sequence is then used to dynamically iteratively optimize the initial differential mapping weights, generating the iterative optimization sequence, which includes:

[0094] Calculate the data distribution density of the modulation sensitive factor, determine the interval segmentation threshold, and divide the modulation sensitive factor into multiple discrete intervals based on the interval segmentation threshold;

[0095] The weight benchmark is calculated based on the data fluctuation range of each discrete interval, and the weight benchmark is normalized to generate the initial difference mapping weight.

[0096] Dynamic iterative calculation is performed on the initial difference mapping weights to obtain stability parameters, and bifurcation intervals are determined based on the stability parameters; chaotic mapping parameters are set within the bifurcation intervals to generate an initial chaotic sequence; the initial chaotic sequence is mapped point-to-point with the initial difference mapping weights to generate an iterative initial sequence.

[0097] Using the initial iteration sequence as input, the iteration begins, the fluctuation variance of the current iteration sequence is calculated, the iteration step size is adaptively adjusted, the adjusted iteration step size is multiplied by the current chaotic value, and the current iteration sequence is nonlinearly modulated; the modulated sequence is normalized to generate the next iteration sequence; when the difference between two adjacent iteration sequences is less than a preset convergence threshold, the current iteration sequence is determined as the optimized difference mapping weight;

[0098] The optimized difference mapping weights are recombined and matched according to the distribution characteristics of the discrete intervals to generate an iterative optimization sequence.

[0099] In one specific implementation, a dataset of modulation-sensitive factors is acquired. This dataset contains a series of continuous values ​​collected from the device, such as signal strength changes collected by a sensor over a period of time, ranging from -20dB to 30dB. Statistical analysis is performed on this data to calculate its probability density function. Specifically, the entire data range is divided into 50 equal intervals. The number of data points in each interval is counted and divided by the total number of data points to obtain the probability density of each interval. Based on the calculated probability density curve, a density threshold of 0.05 is set to determine the key change points in the data distribution as interval segmentation thresholds. For example, four segmentation thresholds are determined: -15dB, -5dB, 5dB, and 15dB, thereby dividing the modulation-sensitive factors into five discrete intervals: [-20dB, -15dB), [-15dB, -5dB), [-5dB, 5dB), [5dB, 15dB), and [15dB, 30dB].

[0100] For each defined discrete interval, a weight benchmark is calculated. For each interval, the data fluctuation range is used as the basis, specifically calculated as the ratio of the difference between the maximum and minimum values ​​within the interval to the interval width. For example, for the interval [-5dB, 5dB), if the maximum value of the actual data within this interval is 4.8dB and the minimum value is -4.6dB, then the data fluctuation range is 9.4dB, the interval width is 10dB, and the weight benchmark is 0.94. The weight benchmarks calculated for the five intervals are 0.83, 0.91, 0.94, 0.88, and 0.76, respectively. These weight benchmarks are then normalized so that their sum is 1, resulting in initial difference mapping weights of 0.19, 0.21, 0.22, 0.20, and 0.18, respectively.

[0101] To optimize the initial difference mapping weights, dynamic iterative calculation is performed on them. Ten consecutive difference operations are performed on the initial difference mapping weight sequence, and the variance of the difference results is calculated to obtain the stability parameter. Assuming the calculated stability parameter is 0.085, and based on empirical thresholds of 0.05 and 0.15, the current bifurcation interval is determined. Within the bifurcation interval, the chaotic mapping parameter is set to 3.82, and an initial chaotic sequence of length 5 is generated using the Logistic mapping, for example, [0.63, 0.88, 0.40, 0.91, 0.31]. This chaotic sequence is then multiplied point-to-point with the initial difference mapping weights to obtain the iterative initial sequence [0.12, 0.18, 0.09, 0.18, 0.06].

[0102] Iterative optimization begins with the initial sequence. In the first iteration, the variance of the current iteration sequence is calculated to be 0.0025. Based on the variance, an adaptive step size adjustment rule is adopted: when the variance is less than 0.001, the step size is 0.05; when the variance is between 0.001 and 0.01, the step size is 0.03; when the variance is greater than 0.01, the step size is 0.01. Therefore, the step size for this iteration is determined to be 0.03. This step size is multiplied by each value in the current chaotic sequence to obtain the modulation coefficients [0.019, 0.026, 0.012, 0.027, 0.009]. These modulation coefficients are added to the corresponding values ​​in the initial sequence to obtain the modulated sequence [0.139, 0.206, 0.102, 0.207, 0.069]. Normalize the sequence so that its sum is 1, and you get the sequence after the first round of iterations [0.192, 0.285, 0.141, 0.287, 0.095].

[0103] Continue with subsequent iterations. In the second iteration, the fluctuation variance is calculated to be 0.0068 in the same way, the step size is determined to be 0.03, new modulation coefficients are generated and modulation and normalization are performed, resulting in the second iteration sequence [0.187, 0.279, 0.145, 0.282, 0.107]. The sum of the absolute values ​​of the differences between the first and second iteration sequences is 0.028, which is greater than the preset convergence threshold of 0.01, so the iteration continues. After multiple iterations, assuming that after the seventh iteration, the sum of the absolute values ​​of the differences between two adjacent iteration sequences is 0.008, which is less than the preset convergence threshold, the resulting iteration sequence [0.176, 0.256, 0.162, 0.261, 0.145] is determined as the optimized difference mapping weights.

[0104] The optimized differential mapping weights are recombined and matched according to the distribution characteristics of the discrete intervals. Considering that the original modulation sensitivity factor's data distribution ratio in the five intervals is [0.15, 0.25, 0.35, 0.20, 0.05], the optimized differential mapping weights are weighted and averaged with these distribution ratios to obtain the final iterative optimization sequence [0.171, 0.252, 0.207, 0.252, 0.118]. This sequence reflects the optimized weights of the modulation sensitivity factor in different discrete intervals and can be used in subsequent signal processing or decision-making algorithms to improve adaptability and processing accuracy for different signal strength ranges.

[0105] In one optional implementation, the spatial and phase modulation characteristics of the optical signal are collected using a photodetector, a wave feature sequence is extracted, and a first check value is generated based on the wave feature sequence and unique wave information, including:

[0106] The unidirectional optical transmission channel is divided into multiple detection areas, and a photodetector is set in each detection area to collect optical signals; the optical signals in each detection area are hierarchically encoded, the modulation depth of the optical signal as it changes with spatial position is extracted, and a spatial modulation feature map is established.

[0107] A tuning grating is set in each detection area, and the diffraction angle of the light signal is adjusted by changing the grating period to obtain the light intensity distribution at different diffraction angles; the phase difference of the light intensity distribution at each diffraction angle is calculated to extract the phase modulation characteristics of the light signal.

[0108] The modulation sensitivity of the sampling points is calculated based on the spatial modulation feature map and the phase modulation feature. The reference point is determined by the modulation sensitivity and correlation analysis is performed to generate the wave feature sequence.

[0109] The standard modulation parameters are demodulated from the unique fluctuation information, and the correlation between the fluctuation feature sequence and the standard modulation parameters is calculated. The first verification value is generated based on the correlation.

[0110] In one specific implementation, in a cross-network interaction method based on the irreversible and wave properties of light, when a unidirectional optical transmission channel is divided into multiple detection areas, physical partitioning can be achieved using an optical fiber layout. The total length of the unidirectional optical transmission channel is 10 meters, evenly divided into 5 detection areas, each 2 meters long. Optical isolation strips with a width of 0.1 meters are set between the detection areas to reduce optical interference between adjacent areas. A high-sensitivity photodetector is installed at the center of each detection area. The detector is made of InGaAs material, with a detection wavelength range of 1000-1650 nm, a responsivity of 0.95 A / W, a dark current of less than 10 nA, and a signal-to-noise ratio greater than 60 dB. Each detector is connected to an independent signal processing unit to achieve parallel acquisition and processing of optical signals.

[0111] When performing hierarchical encoding of the optical signals in each detection area, a multi-resolution analysis method is employed. The optical signal intensity is sampled according to its spatial variation, with 100 sampling points evenly distributed within each area, and a sampling interval of 2 cm. Wavelet transform is performed on the sampled data, and a 5-level decomposition is conducted using the Haar wavelet basis function to obtain optical signal representations at different scales. The low-frequency component reflects the overall trend of the optical signal's variation, while the high-frequency component contains local detailed features. After hierarchical encoding, the optical signal is represented as a combination of different frequency components, each component corresponding to the variation characteristics at different spatial scales.

[0112] When extracting the modulation depth of an optical signal as it varies with spatial location, the ratio of the difference between the maximum and minimum light intensity values ​​to the average value is calculated. In practical applications, the light intensity data of 100 sampling points within each detection region are statistically analyzed to calculate the modulation depth value. For example, the modulation depth of the optical signal in the first detection region is 0.72, in the second region it is 0.68, in the third region it is 0.75, in the fourth region it is 0.63, and in the fifth region it is 0.70. These modulation depth values ​​constitute a modulation depth vector, reflecting the spatial modulation variation characteristics of the optical signal.

[0113] When constructing a spatial modulation feature map, modulation depth values ​​are mapped onto a two-dimensional plane, with the horizontal axis representing the location coordinates of the detection area and the vertical axis representing the modulation depth value. Continuous spatial modulation feature curves are generated using interpolation methods, and then, combined with color mapping techniques, the modulation depth values ​​are mapped to different colors, forming an intuitive spatial modulation feature heatmap. In the feature heatmap, red areas represent regions with high modulation depth, blue areas represent regions with low modulation depth, and color transition areas represent gradual changes in modulation depth. This visualization allows technicians to intuitively understand the spatial distribution of the modulation characteristics of optical signals.

[0114] When setting up a tuning grating in each detection area, electro-controlled liquid crystal grating technology is employed. The grating is made of liquid crystal material; by applying different voltages, the arrangement of liquid crystal molecules can be changed, thereby adjusting the grating period. The grating size is 10mm × 10mm, the initial grating period is 5μm, the adjustable range is 3-8μm, and the adjustment accuracy is 0.01μm. The grating is installed in the optical path of each detection area, placed perpendicular to the photodetector, ensuring that the incident light can pass through the grating normally and be received by the detector.

[0115] When adjusting the diffraction angle of an optical signal by changing the grating period, the grating period is controlled according to the diffraction formula. The grating period was gradually increased from 3 μm to 8 μm in increments of 0.5 μm, and the diffraction states were recorded for 11 different periods. For an optical signal with a wavelength of 1550 nm, the first-order diffraction angle was approximately 31 degrees when the grating period was 3 μm, and approximately 11 degrees when the grating period was 8 μm. By precisely controlling the grating period, a series of uniformly distributed diffraction angles can be obtained, providing a data basis for subsequent phase difference analysis.

[0116] To obtain the light intensity distribution at different diffraction angles, a linear array photodetector was used to record the diffraction patterns. The linear array detector consisted of 1024 photosensitive elements, covering an angle range of -45 degrees to +45 degrees, with an angular resolution of 0.088 degrees. At each grating period setting, a complete diffraction intensity distribution pattern was recorded, including intensity data for zero-order and multiple-order diffraction. For example, when the grating period was 5 μm, the zero-order diffraction intensity was 2.3 mW, the first-order diffraction intensity was 1.2 mW, and the second-order diffraction intensity was 0.5 mW. These intensity data constituted the light intensity distribution curves at different diffraction angles.

[0117] When calculating the phase difference of light intensity distribution at various diffraction angles, the Fourier transform method is used to extract phase information. The light intensity distribution curves at different diffraction angles are subjected to Fourier transform to obtain the complex spectrum, from which phase angle information is extracted. For two adjacent grating period settings, the phase difference of the corresponding diffraction patterns is calculated. For example, when the grating period changes from 3.0 μm to 3.5 μm, the phase difference is 15 degrees; when it changes from 3.5 μm to 4.0 μm, the phase difference is 13 degrees. These phase difference data constitute the phase modulation feature vector of the optical signal.

[0118] When extracting the phase modulation features of an optical signal, the variation of the phase difference with the grating period is analyzed. Ten phase difference data points under 11 grating period settings are fitted into a smooth curve, and the shape parameters of the curve are extracted, including features such as curvature and rate of change of slope. These shape parameters constitute a set of phase modulation features, which can uniquely characterize the phase modulation properties of the optical signal. In typical applications, the phase modulation features can be represented as a 5-dimensional feature vector, with each dimension corresponding to a shape parameter.

[0119] When calculating the modulation sensitivity of sampling points based on spatial modulation feature maps and phase modulation features, a weighted fusion model is constructed. The spatial and phase modulation features are normalized to make their magnitudes comparable. The weight of the spatial feature is set to 0.6, and the weight of the phase feature is set to 0.4. The two feature components are then weighted and summed to obtain the comprehensive modulation feature. For each sampling point, the local gradient value of the comprehensive feature is calculated; a larger gradient value indicates higher modulation sensitivity. For example, among 500 sampling points in 5 detection regions, the point with the highest modulation sensitivity is located at sampling position 67 in the third region, with a sensitivity value of 0.91; the point with the lowest sensitivity is located at sampling position 22 in the fourth region, with a sensitivity value of 0.23.

[0120] When determining the reference points based on modulation sensitivity, the top 10 sampling points with the highest modulation sensitivity are selected as the reference point set. These reference points are spatially distributed across different detection areas, comprehensively reflecting the fluctuation characteristics of the optical signal. For each reference point, its spatial coordinates, light intensity value, modulation depth value, and phase characteristic value are recorded, forming a reference point feature description set.

[0121] When performing correlation analysis, a correlation coefficient matrix is ​​calculated between the baseline points. For 10 baseline points, a 10×10 correlation coefficient matrix is ​​formed, with matrix element values ​​ranging from -1 to 1. Baseline point pairs with an absolute correlation coefficient greater than 0.7 are considered significantly correlated; the relationships between these pairs constitute the skeletal structure of the fluctuation characteristics. In practical applications, typically 15-20 pairs of significantly correlated baseline points can be found.

[0122] When generating the wave characteristic sequence, a feature graph is constructed based on the correlation structure of reference points. Reference points are connected according to their correlation strength to form a directed graph structure. The graph structure is traversed, and the sequence of eigenvalues ​​of reference points along the traversal path is recorded; this sequence is the wave characteristic sequence. A typical wave characteristic sequence has a length of 30-50 elements, with each element representing the eigenvalue of a reference point. The order of elements in the sequence reflects the inherent structure of the optical signal wave characteristics.

[0123] When demodulating the standard modulation parameters from unique fluctuation information, inverse modulation technology is used. The received optical signal undergoes photoelectric conversion, followed by envelope detection to extract the modulation waveform. Spectral analysis is performed on the modulation waveform to identify the main frequency components and their amplitude and phase information. These frequency components correspond to the modulation parameters at the transmitting end, including modulation frequency, modulation depth, and modulation phase. For example, the demodulated standard modulation parameters include: fundamental frequency f0 = 2kHz, modulation depth m = 0.75, phase offset φ = 45 degrees, and modulation waveform shape factor K = 0.85.

[0124] When calculating the correlation between the wave characteristic sequence and the standard modulation parameters, a mapping function is constructed. The input to the mapping function is the wave characteristic sequence, and the output is the estimated standard modulation parameters. The correlation value is calculated by comparing the difference between the estimated parameters and the actual demodulation parameters. The correlation calculation uses the cosine similarity method, calculating the cosine of the angle between the two sets of parameter vectors. The correlation value ranges from 0 to 1, with a larger value indicating a stronger correlation. In practical applications, the correlation of legitimate signals is usually greater than 0.85, while the correlation of illegitimate signals is generally less than 0.5.

[0125] When generating the first checksum based on correlation, the correlation value is non-linearly transformed to enhance the checksum's discriminative ability. A sigmoid activation function is used to transform the correlation, resulting in a large gradient in the critical region (between 0.7 and 0.8), thus improving discrimination. The transformed checksum ranges from 0 to 1; checksums greater than 0.9 are considered valid, while those less than 0.3 are considered invalid, and values ​​in the middle require further verification. The checksum is represented using 8 bits, with a maximum precision of 1 / 256.

[0126] In one optional implementation, the modulation sensitivity of the sampling points is calculated based on the spatial modulation feature map and phase modulation features. A reference point is determined using the modulation sensitivity, and correlation analysis is performed to generate a wave feature sequence, including:

[0127] Calculate the light intensity difference between adjacent detection regions in the spatial modulation feature map to obtain the light intensity gradient matrix, and extract the set of regions whose light intensity change rate is greater than a preset change threshold from the light intensity gradient matrix;

[0128] Based on the sampling points determined by the set of regional locations, the phase modulation feature values ​​at each sampling point are extracted to generate a light intensity-phase correspondence curve, the modulation change of the light signal at different locations is determined, and the modulation sensitivity of each sampling point is calculated based on the modulation change.

[0129] The modulation sensitivities are sorted in descending order, and multiple sampling points with the highest modulation sensitivities are selected according to a preset number to determine the reference points. The correlation coefficients between the remaining sampling points and the reference points are calculated.

[0130] The correlation coefficient is used as a weighting factor and weighted and superimposed with the modulation change of the corresponding sampling point to obtain the fluctuation feature sequence.

[0131] In one specific implementation, a spatial modulation feature map to be analyzed is acquired. This feature map, obtained through optical acquisition, contains M×N pixels. To calculate the light intensity difference between adjacent detection areas, a 3×3 window is slid across the entire feature map. For each pixel (i, j) in the feature map, the absolute value of the light intensity difference between it and its eight neighboring pixels is calculated, and the average value is taken as the light intensity gradient value G(i, j) of that point. The light intensity gradient values ​​of all pixels are then combined to form a light intensity gradient matrix. For example, for pixel (5, 6) in the feature map, its light intensity value is 125, and the light intensity values ​​of its eight neighboring pixels are 122, 127, 124, 126, 123, 127, 124, and 126, respectively. Then, the light intensity gradient value G(5, 6) of that point is calculated as the average of the absolute values ​​of all differences, which is 1.875.

[0132] A preset threshold for change is set to 60% of the maximum gradient value in the light intensity gradient matrix. The light intensity gradient matrix is ​​traversed, and the locations of regions with a light intensity change rate greater than this threshold are extracted to form a set of region locations. For example, if the maximum gradient value in the light intensity gradient matrix is ​​5.2, the threshold is set to 3.12, and the coordinates of all pixels with gradient values ​​greater than 3.12 are collected into the set of region locations.

[0133] For each sampling point in the regional location set, its corresponding phase modulation feature value is extracted. The phase modulation feature value, obtained through a phase detection device, represents the degree of phase modulation of the optical signal at that point. For example, for sampling point (12, 15) in the regional location set, its corresponding phase modulation feature value might be 0.85 rad. The light intensity values ​​and phase modulation feature values ​​of all sampling points are paired to generate a light intensity-phase correspondence curve. This curve reflects the change in phase modulation under different light intensity conditions.

[0134] The modulation change at each sampling point is calculated by analyzing the slope of the intensity-phase correlation curve. The modulation change represents the degree of phase modulation change caused by the intensity change. For each sampling point, points within a ±2 pixel radius are selected, and the ratio of intensity change to phase change is calculated to obtain the modulation change at that sampling point. For example, if the intensity of sampling point (20, 25) is 150 and the phase is 0.75 rad, while the average intensity of its surrounding points is 145 and the average phase is 0.70 rad, then the modulation change at that point is 0.01 rad / unit intensity.

[0135] The modulation sensitivity of each sampling point is calculated based on the modulation change. The modulation sensitivity is equal to the modulation change multiplied by the light intensity gradient value at that point, representing how sensitive that point is to changes in light intensity. For example, if the modulation change at sampling point (20, 25) is 0.01 rad / unit light intensity and its light intensity gradient value is 4.2, then its modulation sensitivity is 0.042 rad.

[0136] The modulation sensitivity of all sampling points is sorted in descending order, and the top K sampling points with the highest modulation sensitivity are selected as benchmark points. K can be set to 10% of the total number of sampling points. Assuming there are 200 sampling points, the 20 points with the highest modulation sensitivity are selected as benchmark points. For example, if the modulation sensitivity of sampling point (30, 35) is 0.075 rad, ranking 5th among all sampling points, it will be selected as one of the benchmark points.

[0137] Calculate the correlation coefficients between the remaining sampling points and the reference points. For each non-reference sampling point, calculate the Pearson correlation coefficient between its phase change sequence and the phase change sequence of each reference point, and take the maximum value as the correlation coefficient of that sampling point. For example, after calculating the correlation coefficients between sampling point (40, 45) and the 20 reference points, take the maximum value of 0.83 as the correlation coefficient of that sampling point.

[0138] The correlation coefficient is used as a weighting factor and weighted and superimposed with the modulation change of the corresponding sampling point to obtain the fluctuation characteristic sequence. Specifically, for each time point t, the fluctuation characteristic value F(t) is equal to the sum of the modulation change of all sampling points at that time point multiplied by their respective correlation coefficients, and then divided by the sum of the correlation coefficients. For example, at time point t=5, if there are 200 sampling points with modulation changes of d1, d2, ..., d200 and correlation coefficients of r1, r2, ..., r200, then the fluctuation characteristic value F(5) at that time point is (d1×r1+d2×r2+...+d200×r200) / (r1+r2+...+r200).

[0139] The resulting wave characteristic sequence reflects the combined wave characteristics of light intensity and phase modulation throughout the entire spatial region. This characteristic sequence can be used for subsequent applications such as signal analysis, pattern recognition, or anomaly detection. For example, a wave characteristic sequence containing 100 time points may exhibit an upward trend followed by a downward trend, with the peak occurring at the 60th time point and an amplitude of 0.12 rad. This shows a high degree of consistency with the original light intensity change trend, but with significantly reduced noise and an approximately 40% improvement in signal quality.

[0140] In one optional implementation, the optical signal is restored to target electrical signal data, and a multi-level sampling sequence is obtained through adaptive hierarchical sampling. Signal abrupt change features and energy accumulation features are extracted from the multi-level sampling sequence, and a feature mapping matrix is ​​constructed to generate a second verification value, including:

[0141] The target electrical signal data is sampled in layers according to an adaptive sampling interval to obtain a multi-layer sampling sequence, and the signal difference between adjacent sampling layers is calculated to obtain an inter-layer difference sequence.

[0142] The amplitude jump degree and jump position are obtained from the interlayer differential sequence to determine the amplitude jump point; the phase change rate of the interlayer differential sequence is calculated to obtain the phase change point where the phase change exceeds a preset phase threshold, and the signal change feature is determined based on the temporal position information of the amplitude jump point and the phase change point.

[0143] Wavelet packet decomposition is performed on the multi-layer sampling sequence to obtain energy coefficients for multiple frequency bands. The energy density distribution in each frequency band is calculated, and regions with energy density greater than a preset density threshold are extracted as energy accumulation areas. The energy concentration and energy diffusion coefficient of the energy accumulation areas are calculated to obtain energy accumulation characteristics.

[0144] The temporal location information of the signal abrupt change feature is constructed into a feature temporal matrix, and the energy distribution information of the energy accumulation feature is constructed into an energy distribution matrix; tensor decomposition is performed on the feature temporal matrix and the energy distribution matrix to obtain the feature mapping matrix;

[0145] The feature mapping matrix is ​​transformed to obtain a feature projection sequence, and a second verification value is generated based on the feature projection sequence.

[0146] In one specific implementation, the target electrical signal data reconstructed from the optical signal is processed using adaptive hierarchical sampling technology to construct a feature mapping matrix and generate a check value, ensuring signal transmission security. Hierarchical sampling of the target electrical signal data according to an adaptive sampling interval is a crucial step. During implementation, time-domain analysis is performed on the target electrical signal data to calculate the signal intensity change rate, and a reference sampling frequency is set to twice the original signal frequency, i.e., 10MHz. The sampling interval is dynamically adjusted according to the signal change rate. In regions of rapid signal change, the sampling interval is reduced to 0.5 times the reference interval, i.e., 50ns; in regions of stable signal, the sampling interval is increased to twice the reference interval, i.e., 200ns. In this way, a five-layer sampling sequence, L1 to L5, is generated, where L1 is the densest sampling layer and L5 is the sparsest sampling layer.

[0147] After obtaining the multi-layer sampling sequence, the signal difference between adjacent layers is calculated. Taking layers L1 and L2 as an example, the amplitude difference at corresponding time points constitutes the inter-layer difference sequence D12. Similarly, D23, D34, and D45 are calculated to form a complete set of inter-layer differences. When extracting signal abrupt change features from the inter-layer difference sequence, the amplitude jump threshold is set to 20% of the average amplitude. When the amplitude of the electrical signal data abruptly changes from 0.5V to 1.2V, the difference of 0.7V exceeds the threshold of 0.1V, and this point is marked as the amplitude jump point P1 (t=1.25ms). At the same time, the phase change rate is calculated. When the phase abruptly changes from 30° to 120° at 2.5ms, the change rate is 36° / μs, which exceeds the preset phase threshold of 30° / μs, and this point is marked as the phase abrupt change point P2 (t=2.5ms). Arrange all mutation points in chronological order to form a signal mutation feature set {P1(1.25ms), P2(2.5ms), P3(3.75ms)}, where each element contains information on the time position, mutation type, and mutation magnitude.

[0148] Wavelet packet decomposition was performed on the multi-level sampling sequence to extract energy clustering features. Using a 5-level wavelet packet decomposition, the signal was divided into 32 frequency bands, ranging from 0 Hz to 5 MHz. For the L3 sampling layer data, the energy density reached 0.85 J / Hz in the 2.2 MHz to 2.8 MHz band, exceeding the preset density threshold of 0.6 J / Hz; this region was marked as energy clustering region E1. Similarly, energy clustering region E2 was identified in the 4.1 MHz to 4.5 MHz band, with an energy density of 0.78 J / Hz. The energy concentration of the energy clustering regions was calculated. Region E1 accounted for 32% of the total energy, with an energy diffusion coefficient of 0.15, indicating a relatively concentrated energy distribution. Region E2 accounted for 28% of the energy, with a diffusion coefficient of 0.22. Thus, the energy clustering feature set {E1(2.2-2.8 MHz, 32%, 0.15), E2(4.1-4.5 MHz, 28%, 0.22)} was obtained.

[0149] In constructing the feature mapping matrix, the temporal location information of signal abrupt change features is represented as a feature time-series matrix T. With time as the horizontal axis and the abrupt change type as the vertical axis, the amplitude of the abrupt change is marked at the corresponding abrupt change point, forming a sparse matrix. For example, in matrix T, the element at position (1.25ms, "amplitude") has a value of 0.7V, indicating that an amplitude abrupt change occurs at 1.25ms with an amplitude of 0.7V. Simultaneously, the energy accumulation feature is constructed as an energy distribution matrix E, with frequency as the horizontal axis and energy concentration as the vertical axis, marking the energy diffusion coefficient in the corresponding energy accumulation region. For example, the element at position (2.5MHz, 32%) in matrix E has a value of 0.15, indicating that the energy concentration at the 2.5MHz frequency band is 32%, and the diffusion coefficient is 0.15.

[0150] Tensor decomposition is performed on the feature time series matrix T and the energy distribution matrix E, using alternating least squares to decompose them into three component matrices A, B, and C. Matrix A represents the temporal features, with a dimension of the number of time points × 10; matrix B represents the mutation type features, with a dimension of the number of mutation types × 10; and matrix C represents the frequency features, with a dimension of the number of frequency points × 10. These three matrices are combined to obtain the feature mapping matrix M, with a dimension of (number of time points + number of mutation types + number of frequency points) × 10. In this case, matrix M contains feature mappings for 500 time points, 2 mutation types, and 32 frequency points, with a total dimension of 534 × 10.

[0151] By applying a Hadamard transform to the feature mapping matrix M, the feature projection sequence S is obtained. This transform preserves the structural relationships of the original features while enhancing the differences between features. The S sequence is divided into 10 equal-length subsequences, and the statistics of each subsequence, including mean, variance, kurtosis, and entropy, are calculated and combined to form an 80-bit feature vector. The SHA-256 hash algorithm is applied to this vector, and the first 64 bits are used as the second checksum V2. This checksum, together with the first checksum generated by other methods, verifies the accuracy and security of the optical signal reconstruction. This multi-level, multi-feature verification mechanism effectively prevents the risk of tampering and forgery during signal transmission.

[0152] In one optional implementation, calculating the matching degree between the first check value and the second check value to determine the integrity of the target electrical signal data includes:

[0153] Extract the optical signal fluctuation correlation feature from the first verification value and the feature mapping projection feature from the second verification value;

[0154] A wave feature vector is constructed based on the wave correlation characteristics of optical signals, and a mapping feature vector is constructed based on the feature mapping projection characteristics. The wave feature vector and the mapping feature vector are reconstructed in the same feature space to generate the photoelectric signal feature correspondence matrix.

[0155] The matching degree is obtained by calculating the feature similarity of the matrix corresponding to the photoelectric signal features, and the integrity of the target electrical signal data after transmission through the unidirectional optical transmission channel is determined based on the matching degree.

[0156] In one specific implementation, when extracting the optical signal fluctuation correlation characteristics from the first verification value, key data needs to be obtained from the aforementioned first verification value generation process. Specifically, this involves starting with 10 benchmark points with the highest modulation sensitivity and their correlation values. These benchmark points include sampling position 67 in the third region (sensitivity 0.91) and sampling position 33 in the first region (sensitivity 0.89), among others. Effective information is extracted from the generated 10×10 correlation coefficient matrix, retaining benchmark point pairs with correlation coefficients greater than 0.7. For example, the correlation coefficient between sampling position 67 in the third region and sampling position 33 in the first region is 0.83, and the correlation coefficient between sampling position 67 and sampling position 78 in the fifth region is 0.79. These values ​​constitute the weight data of the benchmark point relationship network. Correlation characteristics are extracted from the fluctuation feature sequence, which has 45 elements, each representing a feature value of a benchmark point. These 45 feature values ​​are divided into 5 groups of 9 elements each, and the mean, standard deviation, maximum, minimum, and median of each group are calculated to form a 25-dimensional optical signal fluctuation correlation feature. For example, the statistical characteristics of the first group are: mean 0.83, standard deviation 0.06, maximum value 0.91, minimum value 0.75, and median 0.84.

[0157] When extracting the feature mapping projection features from the second verification value, we start with the feature time series matrix T and the energy distribution matrix E. Matrix T records the time position and amplitude of the abrupt change, for example, the element value at position (1.25ms, "amplitude") is 0.7V, and the element value at position (2.5ms, "phase") is 90°. Matrix E records the frequency position and diffusion coefficient of the energy accumulation region, for example, the element value at position (2.5MHz, 32%) is 0.15. Data is extracted from the feature mapping matrix M obtained by tensor decomposition of these two matrices. The dimension of matrix M is 534×10. From the feature projection sequence S obtained by Hadamard transform of matrix M, the statistics of the 10 equal-length subsequences calculated above are extracted, including the mean, variance, kurtosis, and entropy of each subsequence, forming a 40-dimensional initial feature vector. For example, the means of the first three subsequences are 0.42, 0.35, and 0.61, and the variances are 0.09, 0.12, and 0.07, respectively. To enhance feature representation, the difference values ​​of statistics between adjacent subsequences are calculated to form a difference feature vector. The initial feature vector and the difference feature vector are then merged to form a feature mapping projection feature vector containing 80 elements.

[0158] When constructing a fluctuation feature vector based on the fluctuation correlation characteristics of optical signals, the 25-dimensional fluctuation correlation characteristics of optical signals are enhanced through processing. For each group of statistical features, the mean is assigned a weight of 0.3, the standard deviation a weight of 0.2, the maximum a weight of 0.2, the minimum a weight of 0.15, and the median a weight of 0.15. A weighted sum is calculated to generate five comprehensive feature values. For example, the comprehensive feature value of the first group is 0.83×0.3 + 0.06×0.2 + 0.91×0.2 + 0.75×0.15 + 0.84×0.15 = 0.834. These five comprehensive feature values ​​are concatenated with the original 25-dimensional features to form a 30-dimensional enhanced feature vector. Considering the temporal nature of the fluctuation characteristics, the rate of change of features between adjacent groups is calculated, resulting in four rate of change features. The enhanced feature vector is merged with the rate of change features to form a 34-dimensional fluctuation feature vector, comprehensively expressing the fluctuation correlation characteristics of optical signals.

[0159] When constructing the mapped feature vector based on the feature mapping projection features, the 80-dimensional feature mapping projection feature vector is reduced in dimensionality and enhanced. Principal component analysis is used to reduce the dimensionality of the features, retaining the top 30 principal components with a contribution rate of 85%, forming the dimensionality-reduced feature vector. To maintain a dimension close to that of the wave feature vector and facilitate subsequent processing, four comprehensive features are further generated through linear combination, achieving a total dimension of 34. In the generated mapped feature vector, the first five elements may be 0.58, 0.42, 0.76, 0.39, and 0.65. These values ​​characterize the main time-frequency characteristics of the electrical signal, especially the energy distribution and abrupt change characteristics. The mapped feature vector and the wave feature vector have the same dimension, facilitating comparison and fusion within the same feature space.

[0160] When reconstructing features from wave eigenvectors and mapped eigenvectors in the same feature space, it is necessary to address the issue that the two sets of features have different origins and physical meanings. Canonical correlation analysis is used to find the direction of maximum correlation between the two sets of features. The cross-covariance matrix between the two eigenvectors is calculated, and singular value decomposition is performed on the matrix to obtain the mapping matrix. The two eigenvectors are projected onto a common feature space using the mapping matrix, resulting in two new sets of eigenvectors with the same dimension and corresponding meanings. In the specific implementation, the mapping matrix obtained from the cross-covariance matrix is ​​34×34 in size. The same mapping transformation is applied to both the wave eigenvectors and the mapped eigenvectors to generate eigenvector pairs with a unified expression. For example, the first three elements of the mapped wave eigenvector are 0.72, 0.61, and 0.53, while the corresponding elements of the mapped eigenvector are 0.68, 0.57, and 0.49. The two sets of values ​​can be directly compared under the same dimensions.

[0161] When generating the photoelectric signal feature correspondence matrix, the correspondence between the two mapped feature vectors is calculated. The 34-dimensional features are rearranged into a 6×6 matrix (discarding the last two elements), with rows representing time scales and columns representing frequency scales. The similarity of corresponding elements in the wave feature matrix and the mapped feature matrix is ​​calculated using the cosine similarity method to generate the 6×6 photoelectric signal feature correspondence matrix. Each element value in the matrix represents the degree of matching between the optical and electrical signal features at a specific time-frequency location, with values ​​ranging from 0 to 1; a larger value indicates a higher degree of matching. For example, the value 0.87 in the second row and third column indicates a high degree of matching between the optical and electrical signal features at the second time scale and the third frequency scale. The feature correspondence matrix visually demonstrates the correspondence between photoelectric signals at different time-frequency locations, helping to identify distortion or tampering locations during signal transmission.

[0162] When calculating the feature similarity of the feature correspondence matrix of photoelectric signals to obtain the matching degree, multiple indicators are considered comprehensively. The mean of the elements of the feature correspondence matrix is ​​calculated to obtain the overall matching degree index, for example, 0.76. The standard deviation of the matrix elements is calculated to obtain the matching consistency index, for example, 0.12; the smaller the value, the more balanced the matching degree at each time-frequency position. The ratio of the trace (sum of diagonal elements) of the matrix to the total sum is calculated to obtain the main feature matching index, for example, 0.25; the closer the value is to 1 / 6, the more uniform the matching. The three indicators are weighted and combined with weights of 0.5, 0.3, and 0.2 respectively, to calculate the comprehensive matching degree of 0.73. The matching degree value ranges from 0 to 1; the larger the value, the better the correspondence between photoelectric signals and the higher the signal integrity.

[0163] When determining the integrity of target electrical signal data based on the matching degree, a judgment standard is set. When the matching degree is greater than 0.8, the signal is judged to be completely intact; when the matching degree is between 0.6 and 0.8, the signal is judged to be basically intact, but there may be slight distortion; when the matching degree is less than 0.6, the signal is judged to be incomplete, with serious distortion or tampering. For the case with a matching degree of 0.73, the judgment result is that the signal is basically intact, with slight distortion. To improve the reliability of the judgment, further analysis is performed in conjunction with the distribution of the feature correspondence matrix. If most of the element values ​​in the matrix are high (>0.7) and a few element values ​​are low (<0.4), it indicates that there is local distortion in the signal at a specific time-frequency position. By locating the distortion position, targeted signal repair or retransmission can be performed. For example, in the case, the element values ​​in the 5th row, 2nd column and the 6th row, 1st column are low (0.35 and 0.38 respectively), indicating that there is distortion in the high-frequency short-time region, which may be caused by high-frequency attenuation in the transmission channel.

[0164] The above method enables accurate determination of the integrity of target electrical signal data transmitted through a unidirectional optical transmission channel. By integrating the wave correlation characteristics of optical signals and the feature mapping projection characteristics of electrical signals, a unified feature space is constructed for comparison, effectively identifying various distortions and tampering that may occur during signal transmission. Compared to single verification methods, this method considers the correspondence between photoelectric signals at multiple time-frequency scales, improving the accuracy and reliability of integrity determination. In practical applications, the judgment threshold and weight parameters can be adjusted according to specific scenarios to meet the requirements of different security levels. This method provides an important security mechanism for cross-network interaction based on the irreversible and wave properties of light.

[0165] The cross-network interaction system based on the irreversible and wave properties of light in this invention includes:

[0166] The first unit is used to obtain the original electrical signal data to be transmitted from the source network;

[0167] The second unit is used to obtain the modulation sensitive factor of the original electrical signal data based on multi-scale decomposition, and to dynamically compensate and modulate the modulation sensitive factor using an asymmetric modulation operator to generate an optical signal carrying unique fluctuation information.

[0168] The third unit is used to transmit the optical signal through a unidirectional optical transmission channel configured with an optical isolator, forming a unidirectional transmission link from the source network to the target network;

[0169] The fourth unit is used to collect the spatial and phase modulation characteristics of the optical signal using a photodetector, extract the wave feature sequence, and generate a first verification value based on the wave feature sequence and unique wave information.

[0170] The fifth unit is used to restore the optical signal to the target electrical signal data, obtain a multi-layer sampling sequence through adaptive hierarchical sampling, extract signal abrupt change features and energy accumulation features from the multi-layer sampling sequence, and construct a feature mapping matrix to generate a second verification value.

[0171] The sixth unit is used to calculate the matching degree between the first check value and the second check value to determine the integrity of the target electrical signal data.

[0172] A third aspect of the present invention provides an electronic device, comprising:

[0173] processor;

[0174] Memory used to store processor-executable instructions;

[0175] The processor is configured to invoke instructions stored in the memory to execute the aforementioned method.

[0176] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0177] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0178] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A cross-network interaction method based on the irreversible and wave properties of light, characterized in that, include: Obtain the original electrical signal data to be transmitted from the source network; Based on the modulation sensitivity factor obtained from the original electrical signal data through multi-scale decomposition, an asymmetric modulation operator is used to dynamically compensate and modulate the modulation sensitivity factor to generate an optical signal carrying unique fluctuation information, including: The original electrical signal data is decomposed into multiple scales to obtain the corresponding fundamental frequency component and harmonic component. The correlation strength between the fundamental frequency component and the harmonic component is calculated, a feature correlation matrix is ​​constructed, and modulation sensitive factors are identified based on the feature correlation matrix. The modulation sensitive factor is divided into multiple discrete intervals. Initial differential mapping weights are generated based on the data fluctuation range of the discrete intervals. The initial differential mapping weights are dynamically iteratively optimized using a chaotic sequence to generate an iterative optimization sequence. An asymmetric modulation operator is constructed based on the iterative optimization sequence and decomposed into amplitude modulation component, phase modulation component, and frequency modulation component. The cross-entropy value is calculated to construct the coupling degree matrix, and the nonlinear coupling relationship is obtained through a nonlinear function. A compensation modulation mechanism is established based on the aforementioned nonlinear coupling relationship. When the amplitude modulation component is distorted, distortion correction is achieved through the linkage compensation of the phase modulation component and the frequency modulation component, thereby generating dynamic compensation parameters. The dynamic compensation parameters are combined with the asymmetric modulation operator to form a composite modulation strategy, generating unique fluctuation information; The unique fluctuation information is modulated onto an optical signal carrier, and an optical signal carrying the unique fluctuation information is output. Specifically, the modulation sensitivity factor is divided into multiple discrete intervals. Initial differential mapping weights are generated based on the data fluctuation range of the discrete intervals. A chaotic sequence is used to dynamically iteratively optimize the initial differential mapping weights, generating an iterative optimization sequence including: Calculate the data distribution density of the modulation sensitive factor, determine the interval segmentation threshold, and divide the modulation sensitive factor into multiple discrete intervals based on the interval segmentation threshold; The weight benchmark is calculated based on the data fluctuation range of each discrete interval, and the weight benchmark is normalized to generate the initial difference mapping weight. Dynamic iterative calculation is performed on the initial difference mapping weights to obtain stability parameters, and bifurcation intervals are determined based on the stability parameters; chaotic mapping parameters are set within the bifurcation intervals to generate an initial chaotic sequence; the initial chaotic sequence is mapped point-to-point with the initial difference mapping weights to generate an iterative initial sequence. Using the initial iteration sequence as input, the iteration begins, the fluctuation variance of the current iteration sequence is calculated, the iteration step size is adaptively adjusted, the adjusted iteration step size is multiplied by the current chaotic value, and the current iteration sequence is nonlinearly modulated; the modulated sequence is normalized to generate the next iteration sequence; when the difference between two adjacent iteration sequences is less than a preset convergence threshold, the current iteration sequence is determined as the optimized difference mapping weight; The optimized difference mapping weights are recombined and matched according to the distribution characteristics of the discrete interval to generate an iterative optimization sequence; The optical signal is transmitted through a unidirectional optical transmission channel configured with an optical isolator, forming a unidirectional transmission link from the source network to the target network; The spatial and phase modulation characteristics of the optical signal are collected using a photodetector, the wave feature sequence is extracted, and a first verification value is generated based on the wave feature sequence and unique wave information. The optical signal is restored to the target electrical signal data, and a multi-level sampling sequence is obtained through adaptive hierarchical sampling. Signal abruptness features and energy accumulation features are extracted from the multi-level sampling sequence, and a feature mapping matrix is ​​constructed to generate a second verification value. Calculate the matching degree between the first check value and the second check value to determine the integrity of the target electrical signal data.

2. The method according to claim 1, characterized in that, The process involves using a photodetector to collect the spatial and phase modulation characteristics of an optical signal, extracting a wave feature sequence, and generating a first verification value based on the wave feature sequence and unique wave information, including: The unidirectional optical transmission channel is divided into multiple detection areas, and a photodetector is set in each detection area to collect optical signals; the optical signals in each detection area are hierarchically encoded, the modulation depth of the optical signal as it changes with spatial position is extracted, and a spatial modulation feature map is established. A tuning grating is set in each detection area, and the diffraction angle of the light signal is adjusted by changing the grating period to obtain the light intensity distribution at different diffraction angles; the phase difference of the light intensity distribution at each diffraction angle is calculated to extract the phase modulation characteristics of the light signal. The modulation sensitivity of the sampling points is calculated based on the spatial modulation feature map and the phase modulation feature. The reference point is determined by the modulation sensitivity and correlation analysis is performed to generate the wave feature sequence. The standard modulation parameters are demodulated from the unique fluctuation information, and the correlation between the fluctuation feature sequence and the standard modulation parameters is calculated. The first verification value is generated based on the correlation.

3. The method according to claim 2, characterized in that, Based on spatial modulation feature maps and phase modulation features, the modulation sensitivity of sampling points is calculated. Reference points are determined using the modulation sensitivity, and correlation analysis is performed to generate a wave characteristic sequence, including: Calculate the light intensity difference between adjacent detection regions in the spatial modulation feature map to obtain the light intensity gradient matrix, and extract the set of regions whose light intensity change rate is greater than a preset change threshold from the light intensity gradient matrix; Based on the sampling points determined by the set of regional locations, the phase modulation feature values ​​at each sampling point are extracted to generate a light intensity-phase correspondence curve, the modulation change of the light signal at different locations is determined, and the modulation sensitivity of each sampling point is calculated based on the modulation change. The modulation sensitivities are sorted in descending order, and multiple sampling points with the highest modulation sensitivities are selected according to a preset number to determine the reference points. The correlation coefficients between the remaining sampling points and the reference points are calculated. The correlation coefficient is used as a weighting factor and weighted and superimposed with the modulation change of the corresponding sampling point to obtain the fluctuation feature sequence.

4. The method according to claim 1, characterized in that, The optical signal is restored to the target electrical signal data. A multi-layer sampling sequence is obtained through adaptive hierarchical sampling. Signal abrupt changes and energy accumulation features are extracted from the multi-layer sampling sequence. A feature mapping matrix is ​​constructed to generate a second verification value, including: The target electrical signal data is sampled in layers according to an adaptive sampling interval to obtain a multi-layer sampling sequence, and the signal difference between adjacent sampling layers is calculated to obtain an inter-layer difference sequence. The amplitude jump degree and jump position are obtained from the interlayer differential sequence to determine the amplitude jump point; the phase change rate of the interlayer differential sequence is calculated to obtain the phase change point where the phase change exceeds a preset phase threshold, and the signal change feature is determined based on the temporal position information of the amplitude jump point and the phase change point. Wavelet packet decomposition is performed on the multi-layer sampling sequence to obtain energy coefficients for multiple frequency bands. The energy density distribution in each frequency band is calculated, and regions with energy density greater than a preset density threshold are extracted as energy accumulation areas. The energy concentration and energy diffusion coefficient of the energy accumulation areas are calculated to obtain energy accumulation characteristics. The temporal location information of the signal abrupt change feature is constructed into a feature temporal matrix, and the energy distribution information of the energy accumulation feature is constructed into an energy distribution matrix; tensor decomposition is performed on the feature temporal matrix and the energy distribution matrix to obtain the feature mapping matrix; The feature mapping matrix is ​​transformed to obtain a feature projection sequence, and a second verification value is generated based on the feature projection sequence.

5. The method according to claim 1, characterized in that, Calculating the matching degree between the first check value and the second check value to determine the integrity of the target electrical signal data includes: Extract the optical signal fluctuation correlation feature from the first verification value and the feature mapping projection feature from the second verification value; A wave feature vector is constructed based on the wave correlation characteristics of optical signals, and a mapping feature vector is constructed based on the feature mapping projection characteristics. The wave feature vector and the mapping feature vector are reconstructed in the same feature space to generate the photoelectric signal feature correspondence matrix. The matching degree is obtained by calculating the feature similarity of the matrix corresponding to the photoelectric signal features, and the integrity of the target electrical signal data after transmission through the unidirectional optical transmission channel is determined based on the matching degree.

6. A cross-network interaction system based on the irreversible and wave properties of light, used to implement the method of any one of claims 1-5, characterized in that, include: The first unit is used to obtain the original electrical signal data to be transmitted from the source network; The second unit is used to obtain the modulation sensitive factor of the original electrical signal data based on multi-scale decomposition, and to dynamically compensate and modulate the modulation sensitive factor using an asymmetric modulation operator to generate an optical signal carrying unique fluctuation information. The third unit is used to transmit the optical signal through a unidirectional optical transmission channel configured with an optical isolator, forming a unidirectional transmission link from the source network to the target network; The fourth unit is used to collect the spatial and phase modulation characteristics of the optical signal using a photodetector, extract the wave feature sequence, and generate a first verification value based on the wave feature sequence and unique wave information. The fifth unit is used to restore the optical signal to the target electrical signal data, obtain a multi-layer sampling sequence through adaptive hierarchical sampling, extract signal abrupt change features and energy accumulation features from the multi-layer sampling sequence, and construct a feature mapping matrix to generate a second verification value. The sixth unit is used to calculate the matching degree between the first check value and the second check value to determine the integrity of the target electrical signal data.

7. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to invoke instructions stored in the memory to execute the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 5.

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