A high-security quantum random number generator system based on wavelet transform

Abstract

The application relates to the field of quantum random number generators, and provides a high-security quantum random number generator system based on wavelet transformation, which comprises an original random sequence generation module, a security monitoring module connected with the original random sequence generation module, and a post-processing and randomness detection module; the original random sequence generation module is used for generating an original random sequence; the security monitoring module is used for processing the original random sequence based on wavelet transformation to determine whether the system is abnormal; and the post-processing and randomness detection module is used for post-processing and randomness detection of the original random sequence when the system is not abnormal. Through real-time monitoring and abnormality detection, the application significantly improves the identification ability of signal abnormality and potential attacks, and utilizes wavelet transformation for time-frequency analysis, so that the system can accurately capture local changes of signals, and the security is enhanced.

Description

Technical Field

[0001] This invention relates to the field of quantum random number generators, and more specifically, to a high-security quantum random number generator system based on wavelet transform. Background Technology

[0002] Random numbers play an indispensable role in modern information technology, particularly in fields such as cryptography, statistics, and scientific simulations. For example, in cryptography, secure communication protocols (such as encryption, digital signatures, and authentication) heavily rely on high-quality random numbers to ensure security. However, traditional pseudo-random number generators (PRNGs) generate random numbers based on algorithms and initial seeds. These random numbers can be predicted under certain conditions, leading to potential security risks.

[0003] A quantum random number generator (QRNG) extracts quantum random numbers by observing quantum physical processes. Unlike classical pseudo-random number generation methods, QRNG generates random numbers based on the inherent uncertainty of quantum mechanics. Theoretically, its results are unpredictable, making it the closest random number generation technique to ideal true randomness to date. Therefore, it has significant advantages in applications with high security requirements.

[0004] A typical QRNG consists of a Quantum Entropy Source (QES), sampling, post-processing, and randomness detection modules. The QES comprises a quantum light source module and a detection module. The quantum light source module generates a random substate signal, which is then converted into a random electrical signal by the detection module. Since classical noise is unavoidable in practical systems—for example, the random electrical signal output by the detection module often contains electrical noise, especially at low frequencies—a filtering circuit is typically designed before sampling to remove low-frequency classical noise and improve the quantum-classical noise signal-to-noise ratio. The sampling module then discretizes the filtered electrical signal to obtain the original random sequence. The post-processing module processes the generated original random sequence to obtain the final random sequence. The randomness detection module performs statistical analysis on the generated final random number to verify whether it meets the randomness criteria.

[0005] In practical QRNG systems, filtering circuits are widely used to remove classical noise in the low-frequency band to improve the signal-to-noise ratio of quantum signals and the randomness quality of the system output. However, as a key component of the QRNG system, the filtering module can also become a potential attack surface, especially vulnerable to spectral attacks in the frequency domain. Spectral attacks typically refer to attackers interfering with the frequency selectivity of the filter, affecting the frequency components of the filtered output signal, and thus impacting the security of the random numbers generated by the system.

[0006] While traditional models often assume attackers can directly manipulate the internal parameters of filter circuits, the feasibility of such physical access attacks is low in most real-world deployment scenarios. In contrast, more realistic attack methods involve non-contact means, such as applying external electromagnetic signals of specific frequencies or utilizing power supply-side channel interference, to indirectly manipulate the system's spectral response. The following are two typical forms of spectral attacks:

[0007] The first scenario involves affecting the effective frequency response range of the filter through external interference signals. An attacker can apply precisely designed electromagnetic perturbations to the spectrum, causing the filter to exhibit bandwidth expansion or contraction: bandwidth expansion may allow more low-frequency classical noise to penetrate the filter and enter the system, reducing the proportion of quantum signals in the output sequence; bandwidth contraction may truncate some effective quantum signal frequency components. Such perturbations can disrupt the spectral structure of the system's output sequence, thereby inducing random degradation or pattern enhancement, reducing the system's credibility in high-security fields such as cryptography.

[0008] The second scenario involves frequency-domain interference injection of pseudo-random or modulated signals. This involves injecting pseudo-random or artificial interference signals into the quantum signal. This type of attack adjusts the frequency response of the filtering circuit, causing the pseudo-random signal to superimpose with the quantum signal, thus masking the inherent randomness of the quantum signal. This injection of pseudo-random components may cause the output random numbers to exhibit certain predictable patterns, disrupting its inherent uniformity and unpredictability. This attack method directly undermines the core characteristics of the quantum random number generator, and especially in applications with high security requirements, it may lead to security vulnerabilities in random number generation, thereby threatening the system's security.

[0009] Furthermore, abnormal states of the quantum entropy source itself can also lead to unreliable outputs from the system, thereby compromising the system's security. The quantum entropy source is the fundamental source of randomness in QRNG systems; therefore, when the quantum entropy source is in an abnormal state, such as due to equipment aging or environmental temperature interference, the quality of the generated random numbers will decrease. Summary of the Invention

[0010] The present invention aims to provide a high-security quantum random number generator system based on wavelet transform to solve the security problems of QRNG systems caused by the vulnerability of filter circuits to attacks and entropy source anomalies.

[0011] The present invention provides a high-security quantum random number generator system based on wavelet transform, comprising an original random sequence generation module, a security monitoring module and a post-processing and randomness detection module connected to the original random sequence generation module;

[0012] The original random sequence generation module is used to generate the original random sequence;

[0013] The security monitoring module is used to process the original random sequence based on wavelet transform in order to determine whether there is an anomaly in the system;

[0014] The post-processing and randomness detection module is used to process the original data when there is no abnormality in the system. In a preferred embodiment, the security monitoring module includes a wavelet transform module, an anomaly detection module, and an alarm and response module connected in sequence.

[0015] The wavelet transform module is used to perform multi-scale discrete wavelet decomposition on the original random sequence based on wavelet transform to obtain wavelet coefficients at each scale level, including approximation coefficients and detail coefficients.

[0016] The anomaly detection module is used to compare the wavelet coefficients obtained by wavelet transform with the statistical characteristics of historical normal operation data to calculate the total anomaly score.

[0017] The alarm and response module is used to determine whether there is an anomaly in the system based on the calculated total anomaly score.

[0018] In a preferred embodiment, the wavelet transform module is implemented using an orthogonal filter bank.

[0019] In a preferred embodiment, the orthogonal filter bank is implemented by a pair of low-pass filters and a high-pass filter.

[0020] In a preferred embodiment, the anomaly detection module is specifically used for:

[0021] Calculate four statistics, including the energy and variance of the approximation coefficients and detail coefficients in the wavelet coefficients obtained by wavelet transform;

[0022] For historical normal operating data, calculate the historical mean and standard deviation of the four corresponding statistics;

[0023] Calculate the standardized deviation of the four statistics using energy, variance, historical mean, and standard deviation;

[0024] The total anomaly score is obtained by summing all the standardized deviations.

[0025] Set a threshold for whether the system is in a normal or abnormal state. If the total abnormal score exceeds the threshold, the system is determined to be in an abnormal state; otherwise, the system is in a normal state.

[0026] In a preferred embodiment, the alarm and response module is specifically used for:

[0027] Set threshold ranges for several abnormal levels when the system is in an abnormal state;

[0028] Based on the threshold range of the total abnormal score, the system is determined to be in a normal or abnormal state.

[0029] In a preferred embodiment, the alarm and response module is further configured to:

[0030] Different response strategies are adopted based on the level of anomaly.

[0031] In a preferred embodiment, the anomaly level includes:

[0032] A minor anomaly indicates a slight fluctuation in the signal. When a minor anomaly occurs, an early warning mechanism is triggered to notify the administrator to monitor and record the anomaly information.

[0033] A moderate anomaly indicates that the signal deviates from the normal range. When a moderate anomaly occurs, the system will pause quantum random number generation and conduct detailed analysis, and will intervene manually if necessary.

[0034] A serious anomaly indicates a significant deviation in the signal. When a serious anomaly occurs, the system will immediately stop generating quantum random numbers, disconnect from external communication, activate the emergency response mechanism, and notify the administrator for emergency handling.

[0035] In a preferred embodiment, the security monitoring module further includes a monitoring switch that connects the security monitoring module to the original random sequence generation module;

[0036] The monitoring switch is used to control the activation of the safety monitoring module.

[0037] In a preferred embodiment, the monitoring switch includes three operating modes:

[0038] (1) Startup Enabled Mode: In startup enabled mode, the security monitoring module will automatically start when the system starts;

[0039] (2) On-demand activation mode: In on-demand activation mode, the monitoring function is activated by external conditions or changes in the internal state of the system;

[0040] (3) Periodic activation mode: In periodic activation mode, the monitoring function will start periodically according to the set time interval.

[0041] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0042] 1. This invention significantly improves the ability to identify signal anomalies and potential attacks through real-time monitoring and anomaly detection, and utilizes wavelet transform for time-frequency analysis, enabling the system to accurately capture local changes in signals and enhance security.

[0043] 2. This invention adopts corresponding emergency response measures according to different levels of anomalies to ensure flexible response to various threats.

[0044] Therefore, overall, the present invention improves the security, stability and random number quality of the system, making it suitable for applications with high security requirements. Attached Figure Description

[0045] Figure 1 The image shows a signal whose frequency changes abruptly at 0.5s, along with the results of performing Fourier transform and wavelet transform on the signal.

[0046] Figure 2 This is a schematic diagram of a high-security quantum random number generator system based on wavelet transform, provided as an embodiment of the present invention. Detailed Implementation

[0047] 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. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0048] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0049] Example

[0050] To effectively prevent spectrum attacks, the signal spectrum can be monitored and analyzed in real time. Accurately understanding the spectral characteristics and dynamic changes of a signal is fundamental to spectrum attack protection. In the field of signal processing, the Fourier Transform (FT) is a commonly used spectral analysis method. The Fourier Transform can transform a time-domain signal into the frequency domain, revealing the frequency components and their intensity. For continuous signals, the mathematical expression of the Fourier Transform is as follows:

[0051]

[0052] in, It is a signal function. It's frequency. t It's time.

[0053] However, the Fourier transform also has significant limitations. It assumes the signal is stationary, meaning it cannot capture how the signal spectrum changes over time, making it unsuitable for analyzing transient or non-stationary signals. For example, when faced with dynamic spectral attacks (such as frequency jumps or signal abrupt changes), the Fourier transform cannot provide dynamic information about the signal spectrum over time, limiting its application in real-time spectrum monitoring.

[0054] To address this problem, wavelet transform (WT) emerged as a time-frequency analysis tool. Unlike the Fourier transform (FT), wavelet transform can simultaneously acquire both time and frequency domain information of a signal, enabling detailed observation of the signal's spectrum changes over time. By using basis functions (wavelet functions) at different scales and locations, wavelet transform can flexibly analyze the characteristics of a signal at different time points and within different frequency ranges. Wavelet transform provides multi-resolution analysis of signals, simultaneously offering localized information in both time and frequency. Discrete wavelet transform (DWT) is a commonly used form, and its mathematical expression is as follows:

[0055]

[0056] in, These are wavelet basis functions. It is a scale. b It is a translation. This characteristic is particularly useful for analyzing non-stationary signals or signals with transient characteristics.

[0057] like Figure 1The image shows a signal whose frequency abruptly changes at 0.5 seconds, and both Fourier transform and wavelet transform are performed on this signal. The results show that we can only obtain overall frequency domain information from the Fourier transform, while the Fourier transform can reveal the signal's time-varying characteristics. The Fourier transform is primarily suitable for stationary signals, providing global frequency information, while the wavelet transform, by simultaneously providing localized analysis of time and frequency, is advantageous for analyzing non-stationary signals. The Fourier transform provides a global frequency representation but cannot capture the signal's time-varying characteristics or instantaneous changes, making it suitable for stationary signals but unsuitable for non-stationary signals or signals with localized characteristics. Conversely, the wavelet transform provides a flexible time-frequency representation, capable of varying resolution at different scales.

[0058] In view of this, and considering the vulnerability of the filter circuit to attack and the security issues of the QRNG system caused by quantum entropy source anomalies, this invention provides a high-security quantum random number generator system based on wavelet transform. By introducing wavelet transform technology to analyze the signal, it determines whether the QRNG system has been attacked or whether the quantum entropy source is in an abnormal state.

[0059] Specifically, by introducing wavelet transform technology, time-frequency analysis of quantum signals can be performed, enabling real-time monitoring of abrupt changes and anomalies in the signal. When the frequency response of the filtering circuit changes due to external interference or attacks, or when the quantum entropy source is in an abnormal state, the QRNG system can accurately capture these abrupt changes through wavelet transform, promptly detecting potential attack behaviors or anomalies. The time-frequency localization capability of wavelet transform can meticulously identify local frequency changes in the signal, thereby effectively reducing the risk of the filtering circuit being manipulated by adversaries. This invention can significantly improve the anti-interference capability and security of quantum random number generators, especially when facing potential attacks and external interference, effectively ensuring that the generated random numbers meet the standards of high-security applications, and is widely applicable to high-security fields such as encrypted communication and digital signatures.

[0060] like Figure 2 As shown in the figure, an embodiment of the present invention provides a high-security quantum random number generator system based on wavelet transform, including an original random sequence generation module, a security monitoring module and a post-processing and randomness detection module connected to the original random sequence generation module;

[0061] The original random sequence generation module is used to generate the original random sequence;

[0062] The security monitoring module is used to process the original random sequence based on wavelet transform in order to determine whether there is an anomaly in the system;

[0063] The post-processing and randomness detection module is used to perform post-processing and randomness detection on the original random sequence when there are no abnormalities in the system.

[0064] The specific implementation and functional principles of each module in the system are as follows:

[0065] Module 1: Original Random Sequence Generation Module

[0066] The original random sequence generation module includes a quantum entropy source, a filtering circuit, and a discretization sampling module connected in sequence. The quantum entropy source includes a quantum light source and a detection module connected in sequence.

[0067] The working principle of the original random sequence generation module is as follows:

[0068] First, the quantum light signal generated by the quantum light source is detected by the detection module, which converts the quantum light signal into a measurable electrical signal.

[0069] Next, the electrical signal output by the detection module is processed by a filtering circuit to remove low-frequency classical noise.

[0070] Finally, the continuous electrical signal processed by the filtering circuit is discretized by the discretization sampling module, which transforms the filtered continuous electrical signal into a discrete original random sequence.

[0071] Module 2, Safety Monitoring Module:

[0072] The safety monitoring module includes a monitoring switch, a wavelet transform module, an anomaly detection module, and an alarm and response module connected in sequence.

[0073] 1. The monitoring switch can be configured as needed to control the activation of the safety monitoring module, ensuring that the system can flexibly start the monitoring function according to actual needs, so as to maximize efficiency and safety.

[0074] In this embodiment, the monitoring switch includes three operating modes: start-up mode, on-demand mode, and periodic mode. The following is a detailed description of these three modes:

[0075] (1) Startup Triggered Mode: In Startup Triggered Mode, the security monitoring module automatically activates upon system startup. That is, the monitoring switch immediately initiates monitoring when the Quantum Random Number Generator (QRNG) system begins operation. This Startup Triggered Mode is suitable for scenarios requiring continuous monitoring of the entire system's security, ensuring that any potential security threats are detected in real time during the initial system operation phase. By activating the monitoring module upon system startup, this Startup Triggered Mode provides immediate response to potential problems in the early stages of system operation.

[0076] (2) On-Demand Triggered Mode: In on-demand mode, the monitoring function is activated by external conditions or changes in the system's internal state. The monitoring module is only activated when the system needs to perform a security check. This on-demand mode is more flexible, enabling monitoring only when specific security needs occur, avoiding unnecessary resource consumption. It is suitable for scenarios where the system does not require constant high-level monitoring during operation, but can respond promptly when anomalies occur.

[0077] (3) Periodic Triggered Mode: In periodic triggered mode, the monitoring function will start periodically according to the set time interval. This periodic triggered mode ensures that the system checks its security status periodically according to the preset cycle during operation, effectively capturing any potential anomalies or attack behaviors. This periodic triggered mode is suitable for application scenarios that require periodic testing of signal stability or security, and can maintain effective monitoring of randomness and security during long-term system operation.

[0078] After the monitoring switch is turned on, the system begins to analyze the quantum signal in real time through the wavelet transform module and the anomaly detection module to ensure the system's security and the signal quality.

[0079] 2. Wavelet Transform Module

[0080] The wavelet transform module is used to perform multi-scale discrete wavelet decomposition on the original random sequence based on wavelet transform, and obtain wavelet coefficients at each scale level, including approximation coefficients and detail coefficients.

[0081] The wavelet transform module performs time-frequency analysis on the input signal to capture instantaneous changes and frequency anomalies. When the input signal is an original random sequence, multi-scale discrete wavelet decomposition is performed using Discrete Wavelet Transform (DWT). The amount of input data directly affects the reliability of the results and the computational complexity. To ensure sufficient signal features are captured and accurate anomaly detection is achieved, the data volume should be large enough to support multi-scale signal decomposition, while avoiding excessive computational resource consumption.

[0082] For length of The original random sequence The Discrete Wavelet Transform (DWT) decomposes a signal into low-frequency (approximate) components and high-frequency (detail) components at multiple scales. Specifically, the signal is decomposed into low-frequency components (approximate parts) and multiple high-frequency components (detail parts), each representing a different frequency range of the signal. The number of these frequency components is related to the number of layers in the wavelet transform. The low-frequency (approximate) components represent the overall profile of the signal, typically containing information about slower changes, similar to the signal's "general trend." The high-frequency components (details) represent the rapid changes in the signal; these high-frequency components are further subdivided into multiple subbands, each representing the signal's variation within a specific frequency range. The more high-frequency components, the richer the detailed information of the signal.

[0083] In this embodiment, orthogonal wavelet function families (such as Daubechies, Haar, Morlet) are used to process the original random sequence. Perform multi-scale discrete wavelet decomposition and determine the maximum number of decomposition levels. (Generally, 6 to 8 layers are chosen). That is, the wavelet transform module is implemented using an orthogonal filter bank, which is the core tool for implementing the wavelet transform. It consists of a low-pass filter and a high-pass filter. The low-pass filter is used to extract the low-frequency information of the signal, and the high-pass filter is used to extract the high-frequency information of the signal. The orthogonal filter bank divides the signal into multiple frequency bands, each containing different frequency information. (The orthogonal filter bank...) With high-pass filter coefficient ), and so on, the wavelet coefficients of each scale layer are obtained. For the , For layers, its approximation coefficient and detail coefficient The calculation is as follows:

[0084]

[0085]

[0086] Initially, there are By repeating the above calculations, until the first... Wavelet coefficient set of layers: approximation coefficients and detail coefficients at various scales ,in, .

[0087] 3. Anomaly Detection Module

[0088] The anomaly detection module relies on the time-frequency analysis results provided by wavelet transform to identify abnormal fluctuations in the signal. After the wavelet transform completes the time-frequency decomposition of the signal, the anomaly detection module identifies any abnormal behavior by analyzing the statistical characteristics of each frequency component. If the change of a certain frequency component exceeds a predetermined threshold range, the detection module identifies the signal as an anomalous signal.

[0089] The core of anomaly detection lies in comparing the wavelet coefficients obtained by wavelet transform with the statistical characteristics of historical normal operation data to calculate the total anomaly score. In this embodiment, specifically:

[0090] (1) Calculate four statistics, including the energy and variance of the approximation coefficients and detail coefficients in the wavelet coefficients obtained by wavelet transform:

[0091] No. k Energy of wavelet approximation coefficients of the layer and variance The calculations are as follows:

[0092]

[0093]

[0094] in, It is the first The mean of the layer wavelet approximation coefficients, It is the first The number of samples for the wavelet approximation coefficients. The energy of the wavelet detail coefficients. and variance The calculation is the same:

[0095]

[0096]

[0097] in, It is the first The mean of the layer wavelet detail coefficients, It is the first Number of samples for layer wavelet detail coefficients.

[0098] (2) For historical normal operating data, calculate the historical mean and standard deviation of the corresponding four statistics; a reference statistical model can be established using past normal QRNG sequences to calculate the above four statistics: , , , The historical mean and standard deviation. In particular, if historical data is lacking in the scenario of initial system startup, a set of reference statistics can be constructed using simulation data during the design phase as the default standard for the initial stage of system deployment.

[0099] (3) Using energy, variance, historical mean, and standard deviation, calculate the standardized deviation (z-score) of the four statistics. The calculation formula is as follows:

[0100]

[0101] in, This represents the current energy or variance. It corresponds to the historical average. That is the corresponding historical standard deviation.

[0102] (4) Sum all the standardized deviations to obtain the total anomaly score. The calculation formula is as follows:

[0103]

[0104] Where M represents the total score for anomalies; the larger the total score for anomalies, the more abnormal the data.

[0105] (5) Set a threshold a for whether the system is in a normal or abnormal state. If the total abnormal score exceeds the threshold, the system is determined to be in an abnormal state; otherwise, the system is in a normal state.

[0106] 4. Alarm and Response Module:

[0107] The alarm and response module is activated after the anomaly detection module detects an abnormal system state. It is responsible for classifying and assessing the anomaly and taking corresponding emergency response measures. These anomalies may be caused by various reasons, including system failures, environmental changes, or attacks on the filtering circuit by adversaries. Therefore, the alarm and response module is specifically used for:

[0108] Set threshold ranges for several abnormal levels when the system is in an abnormal state;

[0109] Based on the threshold range of the total abnormal score, the system is determined to be in a normal or abnormal state.

[0110] Different response strategies are adopted based on the level of anomaly.

[0111] In this embodiment, the anomaly level is divided into three categories based on the degree of signal deviation: minor anomaly, moderate anomaly, and severe anomaly. Combining this with the aforementioned determination of the normal state, the system, during actual operation, classifies the system state into the following four categories based on the total anomaly score M corresponding to the current data:

[0112] (1) If M When the system is in a normal state, it is determined that the system is in a normal state.

[0113] (2) If When the system is in a slight anomaly; a slight anomaly usually indicates a small fluctuation in the signal, which may be caused by natural noise, environmental changes, or other non-malicious factors. Such anomalies will trigger an early warning mechanism to notify the administrator to monitor and record the anomaly information.

[0114] (3) If When the system is in a moderate anomaly; a moderate anomaly indicates that the signal deviates from the normal range, which may be caused by an adversary's interference with the filtering circuit or other systematic problems. At this time, the system will suspend the random number generation and conduct a detailed analysis, and manual intervention may be necessary.

[0115] (4) If When the system is in a severe anomaly. A severe anomaly indicates that the signal has a significant deviation, which may be caused by a complex attack on the filtering circuit by an adversary or other serious problems. At this time, the system will immediately stop the random number generation, disconnect the connection with external communication, activate the emergency response mechanism, and notify the administrator for emergency handling.

[0116] Among them, the parameters a, b, and c are the demarcation points used to divide the anomaly levels, and a < b < c. The specific values of these parameters need to be determined according to the actual operating requirements and security objectives of the system.

[0117] Module Three: Post-Processing and Randomness Detection Module:

[0118] The post-processing and randomness detection module is used to perform post-processing and randomness detection on the original random sequence when there is no anomaly in the system to ensure that the finally output random numbers have high-quality randomness and security, and specifically includes a post-processing module and a randomness detection module.

[0119] First, the post-processing module will perform post-processing on the original random sequence, mainly including steps such as removing possible biases, correcting non-uniform distributions, and removing the correlations between data. This process optimizes the statistical characteristics of the data to ensure that the finally output random numbers meet strict randomness criteria, such as uniformity and independence. Commonly used post-processing methods include: m-LSB method, XOR method, Toeplitz matrix method, etc.

[0120] Subsequently, the data after post-processing needs to pass the strict inspection of the randomness detection module. Randomness detection usually uses a series of statistical tests (such as NIST-STS, TestU01, GM / T 0005-2021, etc.) to evaluate the quality of the random numbers and verify whether the random numbers meet the high-security requirements. If the data passes the randomness detection, it can be finally output; if it fails the detection, the data will be discarded, and the system will restart the random number generation process.

[0121] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A high-security quantum random number generator system based on wavelet transform, characterized in that, It includes a raw random sequence generation module, a security monitoring module connected to the raw random sequence generation module, and a post-processing and randomness detection module; The original random sequence generation module is used to generate the original random sequence; The security monitoring module is used to process the original random sequence based on wavelet transform in order to determine whether there is an anomaly in the system; The post-processing and randomness detection module is used to perform post-processing and randomness detection on the original random sequence when there are no abnormalities in the system. The safety monitoring module includes a wavelet transform module, an anomaly detection module, and an alarm and response module connected in sequence. The wavelet transform module is used to perform multi-scale discrete wavelet decomposition on the original random sequence based on wavelet transform to obtain wavelet coefficients at each scale level, including approximation coefficients and detail coefficients. The anomaly detection module is used to compare the wavelet coefficients obtained by wavelet transform with the statistical characteristics of historical normal operation data to calculate the total anomaly score. The alarm and response module is used to determine whether there is an anomaly in the system based on the calculated total anomaly score.

2. The high-security quantum random number generator system based on wavelet transform according to claim 1, characterized in that, The wavelet transform module is implemented using an orthogonal filter bank.

3. The high-security quantum random number generator system based on wavelet transform according to claim 2, characterized in that, The orthogonal filter bank is implemented by a pair of low-pass filters and a high-pass filter.

4. The high-security quantum random number generator system based on wavelet transform according to claim 1, characterized in that, The anomaly detection module is specifically used for: Calculate four statistics, including the energy and variance of the approximation coefficients and detail coefficients in the wavelet coefficients obtained by wavelet transform; For historical normal operating data, calculate the historical mean and standard deviation of the four corresponding statistics; Calculate the standardized deviation of the four statistics using energy, variance, historical mean, and standard deviation; The total anomaly score is obtained by summing all the standardized deviations. Set a threshold for whether the system is in a normal or abnormal state. If the total abnormal score exceeds the threshold, the system is determined to be in an abnormal state; otherwise, the system is in a normal state.

5. The high-security quantum random number generator system based on wavelet transform according to claim 4, characterized in that, The alarm and response module is specifically used for: Set threshold ranges for several abnormal levels when the system is in an abnormal state; Based on the threshold range of the total abnormal score, the system is determined to be in a normal or abnormal state.

6. The high-security quantum random number generator system based on wavelet transform according to claim 5, characterized in that, The alarm and response module is also used for: Different response strategies are adopted based on the level of anomaly.

7. The high-security quantum random number generator system based on wavelet transform according to claim 6, characterized in that, The anomaly levels include: A minor anomaly indicates a slight fluctuation in the signal. When a minor anomaly occurs, an early warning mechanism is triggered to notify the administrator to monitor and record the anomaly information. A moderate anomaly indicates that the signal deviates from the normal range. When a moderate anomaly occurs, the system will pause quantum random number generation and conduct detailed analysis, and will intervene manually if necessary. A serious anomaly indicates a significant deviation in the signal. When a serious anomaly occurs, the system will immediately stop generating quantum random numbers, disconnect from external communication, activate the emergency response mechanism, and notify the administrator for emergency handling.

8. The high-security quantum random number generator system based on wavelet transform according to any one of claims 1-7, characterized in that, The safety monitoring module also includes a monitoring switch, which connects the safety monitoring module to the original random sequence generation module. The monitoring switch is used to control the activation of the safety monitoring module.

9. The high-security quantum random number generator system based on wavelet transform according to claim 8, characterized in that, The monitoring switch includes three operating modes: (1) Startup Enabled Mode: In startup enabled mode, the security monitoring module will automatically start when the system starts; (2) On-demand activation mode: In on-demand activation mode, the monitoring function is activated by external conditions or changes in the internal state of the system; (3) Periodic activation mode: In periodic activation mode, the monitoring function will start periodically according to the set time interval.

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