Offline file security control method and system based on quantum key and national secret algorithm

Abstract

The invention discloses an offline file security management and control method and system based on a quantum key and a national secret algorithm, and relates to the technical field of file security management and control, and the method comprises the steps: setting a plurality of simulated quantum key updating frequencies, equally dividing the time before updating the quantum key in a simulation process in simulation software, obtaining equal interval security data and key updating performance influence data; constructing a key updating frequency effect evaluation model based on a BP neural network according to the safety data and the key updating performance influence data in the equal division interval; according to the output of the key updating frequency effect evaluation model and the corresponding quantum key updating frequency, drawing a frequency-effect curve graph; and performing data analysis on the frequency-effect curve graph to obtain a screened quantum key update frequency, and applying the screened quantum key update frequency as a final quantum key update frequency to offline file security management and control. According to the invention, the problem of lack of balance in selection of the quantum key updating frequency through experience setting in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of offline file security management and control, and more specifically, to an offline file security management and control method and system based on quantum key and national secret algorithm. Background Art

[0002] In the field of file security management and control, existing technologies mainly rely on symmetric encryption and asymmetric encryption methods to protect the confidentiality of data transmission; these traditional encryption technologies are often insufficient when facing man-in-the-middle attacks and data tampering; in addition, identity authentication mechanisms usually rely on static passwords, which are difficult to effectively prevent unauthorized access; access control mainly adopts role-based strategies and lacks detailed user and device binding management; in terms of data integrity protection, despite the use of hash algorithms, it is still vulnerable to attacks and lacks strong protection measures; at the same time, the key management process is complicated and prone to leakage, and the audit and monitoring mechanisms also lack real-time and comprehensiveness; these shortcomings show the urgent need to improve file security and management efficiency.

[0003] For example, the invention patent announcement with announcement number: CN104715168B discloses a method and system for file security control and traceability based on digital fingerprints, wherein the method includes a file upload process: when a user uploads a file to a file server, the file server starts a natural language analysis program, performs full-text scanning and matching analysis on the file text, finds a position where a digital fingerprint can be inserted, generates a fingerprint feature position table, stores it together with the original file in the file server, and generates an upload and download record table to record the information uploaded this time; file download process: when a user initiates a file download request to the file server, the file server generates a binary random code of the same length according to the length of the fingerprint feature position table, and when the corresponding bit of the random code is 1, inserts a digital fingerprint into the text at the corresponding position, generates a new file and sends it to the user, and the information downloaded this time is recorded in the upload and download record table. The present invention can realize the traceability and security management of text files.

[0004] For example, the invention patent with publication number CN116303293A discloses a file circulation control method based on secure documents. In order to realize the authority control of secure documents in the circulation process, it is necessary to encrypt the document content first, and perform circulation control in terms of authority level. The authorization types of documents are divided into organizations, roles, and users. The authority levels of the three are divided into user>organization>role. The confidentiality levels of documents are divided into: top secret, confidential, secret, and ordinary. In order to prevent the occurrence of high-density and low-flow situations, only users with a confidentiality level greater than or equal to this level can open the document for operation. The present invention can effectively solve the problem of secure document circulation operation, and still retain the convenience of document forwarding and use, and has the advantages of high practicality.

[0005] The above-disclosed technical solution has at least the following technical problems: Although quantum keys provide theoretical security, they may still face new security threats such as side channel attacks and quantum computing attacks. In order to improve security performance, the file management system needs to update the quantum keys. However, too high a frequency of quantum key updates may lead to management burden and resource consumption, while too low a frequency may increase the risk of keys being attacked. The existing technology usually lacks balance in the selection of quantum key update frequency through empirical settings.

[0006] In view of the above problems, the present invention proposes a solution. Summary of the invention

[0007] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide an offline file security management method and system based on quantum keys and national secret algorithms, which analyzes the optimal quantum key update frequency to solve the problem that the prior art usually lacks a balance in the selection of quantum key update frequency through empirical settings.

[0008] To achieve the above object, the present invention provides the following technical solutions: The offline file security control method and system based on quantum key and national secret algorithm include the following steps: obtaining historical data of quantum key distribution and reception during offline file security control, and simulating the process of quantum key distribution and reception during offline file security control based on historical data and simulation software; setting several simulated quantum key update frequencies, dividing the time before the quantum key update of the simulation process in the simulation software into equal parts, and obtaining security data of the equally divided intervals and key update performance impact data; constructing a key update frequency effect evaluation model based on the BP neural network according to the security data of the equally divided intervals and the key update performance impact data; drawing a frequency-effect curve diagram according to the output of the key update frequency effect evaluation model and the corresponding quantum key update frequency; performing data analysis on the frequency-effect curve diagram to obtain a screened quantum key update frequency, and applying it as the final quantum key update frequency to offline file security control.

[0009] In a preferred embodiment, the historical data of quantum key distribution and reception during offline file security management and control includes the number of quantum key distributions, channel noise level, signal strength, time synchronization error, historical security events, user behavior data and environmental factors; the equally divided interval security data includes multi-period cracking risk coefficients; the key update performance influencing data includes the update load influence coefficient and the key validity coefficient.

[0010] In a preferred embodiment, the specific method for obtaining the multi-period cracking risk coefficient is as follows: obtain the time required for key distribution, the time for the key to be stored in the file management system, the time required for the user to access the key, and the file management interaction time related to the key, and establish a quantum key life cycle model; simulate the process in which the cracking risk gradually increases with time based on the exponential growth function to obtain a cracking probability function; perform data analysis based on the quantum key life cycle model and the cracking probability function and introduce a preset dynamically adjusted attack capability coefficient to calculate the cracking risk coefficient between each equal partition; add the cracking risk coefficients between all equal partitions to obtain the multi-period cracking risk coefficient.

[0011] In a preferred embodiment, the specific method for obtaining the update load influence coefficient is as follows: real-time acquisition of the computing resource consumption rate and key update delay time corresponding to the interval E time period within P time when key distribution and reception are performed during offline file security management, and numbering the computing resource consumption rate and key update delay time corresponding to the interval E time period within P time when key distribution and reception are performed during offline file security management; calculating the update load standard deviation and the update load average value through the computing resource consumption rate and key update delay time corresponding to the interval E time period within P time when key distribution and reception are performed during offline file security management; calculating the update load variation coefficient based on the update load standard deviation and the update load average value; calculating the update load influence coefficient based on the update load variation coefficient.

[0012] In a preferred embodiment, the specific method for obtaining the key validity coefficient is as follows: perform quantum bit error rate measurement in a simulation scenario to obtain quantum bit error rate intensity data for each frequency range, perform spectrum analysis on the measured quantum bit error rate data, identify the frequency range in which the channel noise generated by the system is located, and calculate the channel noise density; integrate the channel noise density in all frequency ranges to obtain the total channel noise density; calculate the noise interference power based on the total channel noise density combined with the area around the system; measure the voltage and current of the simulation scenario to calculate the input power, and calculate the key validity coefficient by combining the input power with the noise interference power.

[0013] In a preferred embodiment, the key update frequency effect evaluation model is constructed based on the BP neural network according to the equally divided interval security data and the key update performance impact data, specifically: the obtained multi-period cracking risk coefficient, update load impact coefficient and key validity coefficient are used to construct a line key update frequency effect evaluation model to generate a key update frequency effect evaluation coefficient.

[0014] In a preferred embodiment, the frequency-effect curve diagram is drawn according to the output of the key update frequency effect evaluation model and the corresponding quantum key update frequency, specifically: obtaining the key update frequency effect evaluation coefficient of the output under different key update frequency settings from the key update frequency effect evaluation model; establishing a rectangular coordinate system, marking the horizontal axis as the line key update frequency value, and the vertical axis as the key update frequency evaluation coefficient; marking the key update frequency evaluation coefficient corresponding to each key update frequency value on the rectangular coordinate system to form data points; connecting the data points to form a frequency-effect curve diagram.

[0015] In a preferred embodiment, the frequency-effect curve is subjected to data analysis to obtain a screened quantum key update frequency, specifically by obtaining several peak values ​​of the frequency-effect curve, performing a change slope analysis on each peak value, and taking the key update frequency corresponding to the peak value whose change slope is closest to 1 as the optimal key update frequency, and using it as the final quantum key update frequency for offline file security management and control.

[0016] The system of the offline file security control method based on quantum key and national secret algorithm is characterized in that it includes a process simulation module, a data acquisition module, a data analysis module, a frequency analysis module and a frequency screening module, and there is a connection between the modules; the process simulation module is used to obtain the historical data of quantum key distribution and reception during offline file security control, and simulate the process of quantum key distribution and reception during offline file security control based on the historical data and simulation software; the data acquisition module is used to set a number of simulated quantum key update frequencies, divide the time before the quantum key update of the simulation process in the simulation software into equal parts, and obtain the security data of the equal-divided intervals and the key update performance impact data; the data analysis module is used to construct a key update frequency effect evaluation model based on the BP neural network according to the security data of the equal-divided intervals and the key update performance impact data; the frequency analysis module is used to draw a frequency-effect curve diagram according to the output of the key update frequency effect evaluation model and the corresponding quantum key update frequency; the frequency screening module is used to perform data analysis on the frequency-effect curve diagram to obtain the screened quantum key update frequency, and use it as the final quantum key update frequency for offline file security control.

[0017] The technical effects and advantages of the offline file security management method and system based on quantum key and national secret algorithm of the present invention are as follows: 1. The present invention simulates the quantum key distribution and reception process, effectively optimizes the update frequency of the quantum key, and improves the security and performance of the system. Through the analysis of historical data and the use of simulation software, the present invention can quantify the cracking risk coefficient, update load impact coefficient and key validity coefficient in different time periods, and realize dynamic adjustment based on these coefficients to ensure the accuracy and timeliness of key updates. At the same time, the present invention has significant advantages in reducing cracking risks, optimizing resource utilization, and improving system responsiveness, effectively balancing security and performance requirements, making key updates more intelligent and efficient. In addition, by monitoring the system load and key validity, the present invention can achieve optimized management of the key life cycle, avoid security risks or performance bottlenecks caused by premature or late updates, thereby providing strong technical support and guarantee for the security management and control of offline files.

[0018] 2. The key update frequency effect evaluation model constructed by the present invention based on the BP neural network realizes the scientific evaluation and optimization of the key update frequency. The model comprehensively considers the multi-period cracking risk coefficient, the update load impact coefficient and the key validity coefficient, and effectively generates the key update frequency effect evaluation coefficient, thereby providing data support for key updates; specifically, by drawing a frequency-effect curve, the effect evaluation under different key update frequency settings can be intuitively displayed, thereby identifying the optimal quantum key update frequency. Finally, through the analysis of the frequency-effect curve, the peak value with the change slope closest to 1 is selected as the optimal update frequency, ensuring the security management and control of offline files. This method not only improves the efficiency and security of key updates, but also reduces the potential risks caused by frequent updates or untimely updates, and has significant application value and practical significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a structural diagram of the offline file security management and control method based on quantum key and national secret algorithm of the present invention.

[0020] Figure 2 This is a schematic diagram of the structure of the offline file security management and control system based on quantum key and national secret algorithm of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] Embodiment 1, Figure 1The present invention provides an offline file security management method based on quantum key and national secret algorithm, which includes the following steps: S1, obtain the historical data of quantum key distribution and reception during offline file security management and control, and simulate the process of quantum key distribution and reception during offline file security management and control based on the historical data and simulation software.

[0023] In this embodiment, the historical data of quantum key distribution and acceptance during offline file security management includes but is not limited to the number of quantum key distributions, channel noise level, signal strength, time synchronization error, historical security events, user behavior data and environmental factors.

[0024] It should be noted that the number of quantum key distributions is: the number of successful and failed distributions in each time period; Channel noise level: The channel noise intensity measured during the key distribution process; Signal strength: Signal strength data during distribution to assess channel quality; Time synchronization error: The time synchronization deviation between devices affects the timeliness of key distribution; User behavior data: frequency and pattern of user key requests; Historical security events: Records of security events related to key distribution, including attack attempts and failures; Environmental factors: the impact of environmental changes such as temperature and humidity on channel performance.

[0025] In this embodiment, the process of quantum key distribution and reception during offline file security management and control is simulated based on historical data and simulation software, specifically: Simulation software is software that can process variables such as signal strength, noise level and time synchronization accuracy to optimize the frequency of quantum key updates, such as QuTiP, NetSquid, GMSim, etc.

[0026] Furthermore, the process of quantum key distribution and reception during offline file security management and control is simulated, specifically including the following aspects: Quantum channel model: simulate the characteristics of quantum channels, including channel noise, signal attenuation and interference, and analyze their impact on quantum key distribution; Quantum state transmission: simulate the generation, transmission and measurement process of quantum states, and evaluate the fidelity and security of quantum states under different conditions; Key generation rate: Based on the characteristics of the quantum channel and the transmission efficiency of the quantum state, the key generation rate in different scenarios is simulated to select the optimal parameters; Security analysis: Simulate potential attack methods (such as eavesdropping, channel interference, etc.) in the quantum key distribution process to evaluate the system's anti-attack capabilities; Update strategy evaluation: Based on historical data and simulation results, evaluate the impact of different key update frequencies on system security and performance, and provide data support for subsequent frequency optimization; Through simulations in these aspects, we can fully understand the performance of quantum key distribution in practical applications and optimize the security management strategy of offline files. I will not go into details here.

[0027] S2, setting several simulated quantum key update frequencies, dividing the time before the quantum key update of the simulation process in the simulation software into equal parts, and obtaining security data and key update performance impact data of the divided intervals.

[0028] In this embodiment, the equally divided interval security data includes multi-period cracking risk coefficients; the key update performance impact data includes an update load impact coefficient and a key validity coefficient.

[0029] The multi-period cracking risk coefficient is used to evaluate the attack risk of existing keys in different time periods, mainly analyzing the attacker's ability to guess and crack the key. This coefficient reveals the time when the key may be cracked by monitoring the security changes of the key at different stages, thereby providing data support for the optimization of the frequency of quantum key updates. By comprehensively considering these risks, the probability of the system being attacked can be effectively reduced, ensuring the best balance between the frequency of key updates and security requirements.

[0030] Analyzing the risk factor of cracking in multiple time periods has the following advantages for evaluating the system effect and selecting the optimal quantum key update frequency: Time-specific: By segmenting different time periods, the vulnerability of keys in a specific period can be clearly identified, helping decision makers to update encryption in high-risk periods; Quantify attack risk: Provide specific cracking risk coefficients, so that security assessment has a quantitative basis, which is convenient for comparison with historical data and optimization of update frequency; Performance optimization: Evaluate the performance of the key in actual operation to ensure that the update frequency does not cause system delays or insufficient bandwidth due to excessive frequency; Lifecycle management: By monitoring the validity and usage cycle of keys, it is possible to accurately determine when to update and prevent expired keys from being used; Attack pattern recognition: Analyze attacker behavior patterns to help identify possible attack strategies, thereby adjusting the update frequency to respond to potential threats; Data-driven decision-making: Based on multi-period data, the decision-making process is supported, so that the update strategy does not only rely on experience, but is based on actual data and risk assessment; Flexibility and adaptability: Based on real-time risk assessment results, the update frequency can be quickly adjusted to adapt to the ever-changing attack environment and technology; In summary, analyzing the cracking risk coefficient in multiple time periods is of great significance for evaluating the system effect and selecting the optimal quantum key update frequency.

[0031] The specific method for obtaining the multi-period cracking risk coefficient is as follows: The time required for key distribution, the time the key is stored in the file management system, the time required for the user to access the key, and the file management interaction time related to the key are obtained to establish a quantum key life cycle model; Based on the exponential growth function, the cracking risk is simulated to increase gradually with time, and the cracking probability function is obtained; According to the quantum key life cycle model and cracking probability function, a preset dynamically adjusted attack capability coefficient is introduced to perform data analysis and calculate the cracking risk coefficient between each equal partition; The cracking risk coefficients of all equal partitions are added together to obtain the multi-period cracking risk coefficient.

[0032] The specific calculation formula of the quantum key life cycle model is as follows:

[0033] The specific calculation formula of the cracking probability function is as follows:

[0034] The specific calculation formula of the attack capability coefficient is as follows:

[0035] The specific calculation formula of the multi-period cracking risk coefficient is as follows:

[0036] In the formula, is the key life cycle, is the time the key is stored in the file management system, The time required for the user to access the key. is the time required for key distribution, Manage interaction time for key-related files, is the probability that the key is cracked at time t, is the attack capability coefficient, is the preset initial attack capability. is the attacker’s technology iteration rate, For computing power, is the strength of quantum key protocol, To crack the risk factor in multiple time periods, is the key lifecycle between the ith equal partitions, is the total number of equally divided intervals, is the attack capability coefficient between the i-th equal partitions, is the label between equal partitions.

[0037] The update load impact coefficient is used to evaluate the performance load of the system during the quantum key update process to determine the optimal update frequency. This coefficient can quantify the consumption of system resources (such as computing power, network bandwidth, and processing time) by key updates, especially the performance bottlenecks that may be caused when updates are too frequent, such as increased latency or reduced bandwidth utilization. By analyzing the update load impact coefficient, the system can find the balance between update frequency and system performance while ensuring security, and avoid updates that are too frequent or too sparse, which affect system stability and security.

[0038] Analyzing the update load impact coefficient has the following advantages for evaluating system effects and selecting the optimal quantum key update frequency: Balance security and performance: By analyzing the update load impact coefficient, it is possible to avoid system performance degradation caused by too frequent updates, such as increased latency or excessive bandwidth usage, while ensuring that keys are updated in a timely manner to respond to security threats; Optimize resource utilization: This coefficient helps the system evaluate resource consumption during key updates, thereby optimizing the utilization of computing, storage, and network bandwidth and avoiding wasting system resources on unnecessary frequencies. Improve system responsiveness: By controlling the update load impact coefficient, the system can maintain a low performance burden during the key update process, thereby improving the system response speed under high load or emergency conditions and maintaining good performance; Preventing performance bottlenecks: Analyzing this factor can help identify the update frequency that may lead to system performance bottlenecks, avoid overly frequent updates when the system load is high, and ensure the overall stability and continuity of the system; Dynamic adaptability: Based on the changes in system load in different environments, updating the load impact coefficient can help the system dynamically adjust the frequency of quantum key updates, thereby flexibly adapting to needs in different usage scenarios and ensuring the best key update strategy; In summary, the update load impact coefficient is of great significance for evaluating the system effect and selecting the optimal quantum key update frequency.

[0039] The specific method for obtaining the update load influence coefficient is as follows: Obtain in real time the computing resource consumption rate and key update delay time corresponding to the interval of time E within P time when the key is distributed and accepted during offline file security management and control, and number the computing resource consumption rate and key update delay time corresponding to the interval of time E within P time when the key is distributed and accepted during offline file security management and control; The update load standard deviation and update load average are calculated by calculating the resource consumption rate and key update delay time corresponding to the interval E time period during key distribution and reception during offline file security management and control within the P time period; Calculate the update load variation coefficient based on the update load standard deviation and the update load average; The update load influence coefficient is calculated based on the update load variation coefficient.

[0040] The specific calculation formula for the updated load standard deviation is as follows:

[0041] The specific calculation formula for updating the load average value is as follows:

[0042] The specific calculation formula for the update load influence coefficient is as follows:

[0043] In the formula, To update the load standard deviation, To update the load average, The total number of numbers for calculating resource consumption rate and key update delay time, The number for calculating resource consumption rate and key update delay time, To calculate the resource consumption rate, is the key update delay time, To update the load influence coefficient.

[0044] The key validity coefficient is used to evaluate the effectiveness and security performance of quantum keys throughout their entire life cycle. By monitoring the applicability of keys in different application scenarios and time periods, it ensures that they will not become invalid during use due to reduced security or increased attack risks. This coefficient can reflect the service life of the key in a specific environment and its impact on system security, helping to identify when the key needs to be updated to maintain a high level of security. By comprehensively analyzing the key validity coefficient, the system can determine the optimal quantum key update frequency to avoid overly frequent updates while ensuring key security.

[0045] Analyzing the key validity coefficient has the following advantages for evaluating the system effect and selecting the optimal quantum key update frequency: Dynamic adaptability: By real-time monitoring of the validity of keys in different application scenarios, the key validity coefficient can dynamically reflect the usage status of the key, helping the system to quickly identify when key updates are needed, thereby adapting to changes in security requirements in different environments; Optimize key lifecycle: The key validity coefficient helps balance the lifespan and security of the key, avoiding security risks or resource waste caused by updating the key too early or too late, and ensuring that the key update occurs at the most effective time. Preventing security vulnerabilities: By continuously evaluating the validity of keys, the system can detect potential security vulnerabilities or outdated keys in advance and update them in a timely manner, reducing the chances of attackers using old keys to attack, thereby improving the overall security of the system; Reduce unnecessary update overhead: This coefficient can effectively identify whether the key is still secure within a specific period of time, avoiding the burden of the system caused by too frequent updates, thereby improving resource utilization efficiency and reducing system load; Support for multiple scenarios: The key validity coefficient can be adjusted according to different application environments (such as high-security scenarios or low-risk scenarios), providing the system with a more flexible update frequency solution to ensure the best balance between security and performance in multiple application scenarios; In summary, the key validity coefficient is of great significance for evaluating the system effect and selecting the optimal quantum key update frequency.

[0046] The specific method for obtaining the key validity coefficient is as follows: The quantum bit error rate is measured in a simulation scenario to obtain the quantum bit error rate intensity data in each frequency range. The measured quantum bit error rate data is subjected to spectrum analysis to identify the frequency range of the channel noise generated by the system and calculate the channel noise density. Integrate the channel noise density in all frequency ranges to obtain the total channel noise density; Calculate the noise interference power based on the total channel noise density combined with the area around the system; The voltage and current of the simulated scenario are measured to calculate the input power, and the input power is combined with the noise interference power to calculate the key validity coefficient.

[0047] The specific calculation formula of the channel noise density is as follows:

[0048] The specific calculation formula of the key validity coefficient is as follows:

[0049] In the formula, is the channel noise density, is the key validity coefficient, is the maximum frequency, is the minimum frequency, is the channel noise amplitude at frequency f, is the key loss rate detected, is the key usage frequency, is the voltage of the simulated scenario, is the current of the simulated scene.

[0050] This implementation effectively optimizes the update frequency of quantum keys and improves the security and performance of the system by simulating the quantum key distribution and reception process. Through the analysis of historical data and the use of simulation software, the present invention can quantify the cracking risk coefficient, update load impact coefficient and key validity coefficient in different time periods, and realize dynamic adjustment based on these coefficients to ensure the accuracy and timeliness of key updates. At the same time, the present invention has significant advantages in reducing cracking risks, optimizing resource utilization, and improving system responsiveness, effectively balancing security and performance requirements, making key updates more intelligent and efficient. In addition, by monitoring the system load and key validity, the present invention can achieve optimized management of the key life cycle, avoid security risks or performance bottlenecks caused by premature or late updates, and thus provide strong technical support and guarantee for the security management and control of offline files.

[0051] Embodiment 2, S3, constructs a key update frequency effect evaluation model based on a BP neural network according to the equally divided interval security data and the key update performance impact data.

[0052] The key update frequency effect evaluation model is constructed based on the BP neural network according to the equally divided interval security data and the key update performance impact data, specifically: The obtained multi-period cracking risk coefficient, update load impact coefficient and key validity coefficient are used to construct a line key update frequency effect evaluation model to generate a key update frequency effect evaluation coefficient; The specific calculation formula of the key update frequency effect evaluation coefficient is as follows:

[0053] In the formula, is the key update frequency effect evaluation coefficient, It is the preset proportional coefficient for multi-period cracking risk factor. To update the preset proportionality factor of the load influence factor, is the preset proportional coefficient of the key validity coefficient, To crack the risk factor in multiple time periods, To update the load influence factor, is the key validity coefficient S4, draw a frequency-effect curve according to the output of the key update frequency effect evaluation model and the corresponding quantum key update frequency.

[0054] According to the output of the line key update frequency effect evaluation model and the corresponding quantum key update frequency, a frequency-effect curve is drawn, specifically: Obtaining a key update frequency effect evaluation coefficient of an output under different key update frequency settings from a key update frequency effect evaluation model; A rectangular coordinate system is established, with the horizontal axis being represented by the line key update frequency value, and the vertical axis being represented by the key update frequency evaluation coefficient; Mark the key update frequency assessment coefficient corresponding to each key update frequency value on a rectangular coordinate system to form a data point; Connect the data points to form a frequency-effect curve.

[0055] S5, conduct data analysis on the frequency-effect curve to obtain the screened quantum key update frequency, and use it as the final quantum key update frequency for offline file security management and control.

[0056] The frequency-effect curve is analyzed to obtain the selected quantum key update frequency, which is specifically: Obtain several peak values ​​of the frequency-effect curves, analyze the change slope of each peak value, and take the key update frequency corresponding to the peak value with the change slope closest to 1 as the optimal key update frequency, which is used as the final quantum key update frequency for offline file security management and control.

[0057] This embodiment realizes the scientific evaluation and optimization of the key update frequency through the key update frequency effect evaluation model constructed based on the BP neural network. The model comprehensively considers the multi-period cracking risk coefficient, the update load impact coefficient and the key validity coefficient, and effectively generates the key update frequency effect evaluation coefficient, thereby providing data support for key update; specifically, by drawing the frequency-effect curve, the effect evaluation under different key update frequency settings can be intuitively displayed, so as to identify the optimal quantum key update frequency. Finally, through the analysis of the frequency-effect curve, the peak value with the change slope closest to 1 is selected as the optimal update frequency, ensuring the security management and control of offline files. This method not only improves the efficiency and security of key updates, but also reduces the potential risks caused by frequent updates or untimely updates, and has significant application value and practical significance.

[0058] Embodiment 3, Figure 2 The present invention provides an offline file security management and control system based on quantum key and national secret algorithm, including a process simulation module, a data acquisition module, a data analysis module, a frequency analysis module and a frequency screening module, and there are connections between the modules; The process simulation module is used to obtain the historical data of quantum key distribution and reception during offline file security management and control, and simulate the process of quantum key distribution and reception during offline file security management and control based on the historical data and simulation software; The data acquisition module is used to set the quantum key update frequency of several simulations, divide the time before the quantum key update of the simulation process in the simulation software into equal parts, and obtain the security data of the divided intervals and the key update performance impact data; The data analysis module is used to construct a key update frequency effect evaluation model based on the BP neural network according to the equally divided interval security data and the key update performance impact data; The frequency analysis module is used to draw a frequency-effect curve diagram according to the output of the key update frequency effect evaluation model and the corresponding quantum key update frequency; The frequency screening module is used to perform data analysis on the frequency-effect curve to obtain the screened quantum key update frequency, which is used as the final quantum key update frequency for offline file security management and control.

[0059] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0060] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0061] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0062] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0063] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0064] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An offline file security management and control method based on quantum key and national secret algorithm, characterized in that: The steps include: Obtain historical data on quantum key distribution and reception during offline file security management and control, and simulate the process of quantum key distribution and reception during offline file security management and control based on historical data and simulation software; Set the quantum key update frequency of several simulations, divide the time before the quantum key update of the simulation process in the simulation software into equal parts, and obtain the security data of the divided intervals and the key update performance impact data; According to the equally divided interval security data and key update performance impact data, a key update frequency effect evaluation model is constructed based on the BP neural network; Draw a frequency-effect curve based on the output of the key update frequency effect evaluation model and the corresponding quantum key update frequency; Data analysis is performed on the frequency-effect curve to obtain the screened quantum key update frequency, which is used as the final quantum key update frequency for offline file security management and control.

2. The offline file security management and control method based on quantum key and national secret algorithm according to claim 1 is characterized in that: The historical data of quantum key distribution and reception during offline file security management and control includes the number of quantum key distributions, channel noise level, signal strength, time synchronization error, historical security events, user behavior data and environmental factors; the equally divided interval security data includes multi-period cracking risk coefficients; the key update performance impact data includes the update load impact coefficient and the key validity coefficient.

3. The offline file security management and control method based on quantum key and national secret algorithm according to claim 2 is characterized in that: The specific method for obtaining the multi-period cracking risk coefficient is as follows: The time required for key distribution, the time the key is stored in the file management system, the time required for the user to access the key, and the file management interaction time related to the key are obtained to establish a quantum key life cycle model; Based on the exponential growth function, the cracking risk is simulated to increase gradually with time, and the cracking probability function is obtained; According to the quantum key life cycle model and cracking probability function, a preset dynamically adjusted attack capability coefficient is introduced to perform data analysis and calculate the cracking risk coefficient between each equal partition; The cracking risk coefficients of all equal partitions are added together to obtain the multi-period cracking risk coefficient.

4. The offline file security management and control method based on quantum key and national secret algorithm according to claim 3 is characterized in that: The specific method for obtaining the update load influence coefficient is as follows: Obtain in real time the computing resource consumption rate and key update delay time corresponding to the interval of time E within P time when the key is distributed and accepted during offline file security management and control, and number the computing resource consumption rate and key update delay time corresponding to the interval of time E within P time when the key is distributed and accepted during offline file security management and control; The update load standard deviation and update load average are calculated by calculating the resource consumption rate and key update delay time corresponding to the interval E time period during key distribution and reception during offline file security management and control within the P time period; Calculate the update load variation coefficient based on the update load standard deviation and the update load average; The update load influence coefficient is calculated based on the update load variation coefficient.

5. The offline file security management and control method based on quantum key and national secret algorithm according to claim 4 is characterized in that: The specific method for obtaining the key validity coefficient is as follows: The quantum bit error rate is measured in a simulation scenario to obtain the quantum bit error rate intensity data in each frequency range. The measured quantum bit error rate data is subjected to spectrum analysis to identify the frequency range of the channel noise generated by the system and calculate the channel noise density. Integrate the channel noise density in all frequency ranges to obtain the total channel noise density; Calculate the noise interference power based on the total channel noise density combined with the area around the system; The voltage and current of the simulated scenario are measured to calculate the input power, and the input power is combined with the noise interference power to calculate the key validity coefficient.

6. The offline file security management and control method based on quantum key and national secret algorithm according to claim 5 is characterized in that: The key update frequency effect evaluation model is constructed based on the BP neural network according to the equally divided interval security data and the key update performance impact data, specifically: The obtained multi-period cracking risk coefficient, update load impact coefficient and key validity coefficient are used to construct a line key update frequency effect evaluation model to generate a key update frequency effect evaluation coefficient.

7. The offline file security management and control method based on quantum key and national secret algorithm according to claim 6 is characterized in that: According to the output of the line key update frequency effect evaluation model and the corresponding quantum key update frequency, a frequency-effect curve is drawn, specifically: Obtaining a key update frequency effect evaluation coefficient of an output under different key update frequency settings from a key update frequency effect evaluation model; A rectangular coordinate system is established, with the horizontal axis being represented by the line key update frequency value, and the vertical axis being represented by the key update frequency evaluation coefficient; Mark the key update frequency assessment coefficient corresponding to each key update frequency value on a rectangular coordinate system to form a data point; Connect the data points to form a frequency-effect curve.

8. The offline file security management and control method based on quantum key and national secret algorithm according to claim 7 is characterized in that: The frequency-effect curve is analyzed to obtain the selected quantum key update frequency, which is specifically: Obtain several peak values ​​of the frequency-effect curves, analyze the change slope of each peak value, and take the key update frequency corresponding to the peak value with the change slope closest to 1 as the optimal key update frequency, which is used as the final quantum key update frequency for offline file security management and control.

9. The offline file security management and control method based on quantum key and national secret algorithm according to claim 8 is characterized in that: The specific calculation formula of the quantum key life cycle model is as follows: The specific calculation formula of the cracking probability function is as follows: The specific calculation formula of the attack capability coefficient is as follows: The specific calculation formula of the multi-period cracking risk coefficient is as follows: In the formula, is the key life cycle, is the time the key is stored in the file management system, The time required for the user to access the key. is the time required for key distribution, Manage interaction time for key-related files, is the probability that the key is cracked at time t, is the attack capability coefficient, is the preset initial attack capability. is the attacker’s technology iteration rate, For computing power, is the strength of quantum key protocol, To crack the risk factor in multiple time periods, is the key lifecycle between the ith equal partitions, is the total number of equally divided intervals, is the attack capability coefficient between the i-th equal partitions, is the label between equal partitions.

10. A system using the offline file security management method based on quantum key and national secret algorithm as described in any one of claims 1 to 9, characterized in that: It includes a process simulation module, a data acquisition module, a data analysis module, a frequency analysis module and a frequency screening module, and there are connections between the modules; The process simulation module is used to obtain the historical data of quantum key distribution and reception during offline file security management and control, and simulate the process of quantum key distribution and reception during offline file security management and control based on the historical data and simulation software; The data acquisition module is used to set the quantum key update frequency of several simulations, divide the time before the quantum key update of the simulation process in the simulation software into equal parts, and obtain the security data of the divided intervals and the key update performance impact data; The data analysis module is used to construct a key update frequency effect evaluation model based on the BP neural network according to the equally divided interval security data and the key update performance impact data; The frequency analysis module is used to draw a frequency-effect curve diagram according to the output of the key update frequency effect evaluation model and the corresponding quantum key update frequency; The frequency screening module is used to perform data analysis on the frequency-effect curve to obtain the screened quantum key update frequency, which is used as the final quantum key update frequency for offline file security management and control.

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