Satellite data storage safety monitoring method, system, equipment and medium

Abstract

The invention discloses a satellite data storage safety monitoring method, system and device and a medium, and belongs to the technical field of safety monitoring, and the method comprises the steps: collecting satellite data, preprocessing the satellite data, generating a chaotic mapping initial seed according to the preprocessed satellite data, and storing the chaotic mapping initial seed in a database; setting a mapping parameter based on the chaotic mapping initial seed and generating a key, and encrypting an access path by using the key; storing the satellite data and the encrypted access path to a database, calculating a stability index based on the encrypted access path, and performing abnormality judgment according to the stability index; feedback data in the safety monitoring process are collected, the mapping parameters are adjusted based on the feedback data, and the adjusted safety monitoring result is displayed through a visual interface. According to the method, the security of data encryption is improved, the capability of detecting and responding to abnormal behaviors in the storage process is enhanced, and the anti-cracking capability of the encryption method is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of safety monitoring, in particular to a satellite data storage safety monitoring method, system, device and medium. BACKGROUND

[0002] With the rapid development of satellite technology, satellite data collection, storage and security monitoring play an increasingly important role in various application fields. Satellite data covers a large amount of environmental monitoring information, meteorological data, communication data, etc. The accuracy and security of these data are directly related to the stability and reliability of various application systems. Satellite data collection and storage technology has gradually developed, and progress has been made in processing capacity, transmission rate and data storage capacity. With the sharp increase in satellite data storage and transmission, how to ensure the security of these massive data and prevent potential security threats has become an important problem in current technology development.

[0003] The existing satellite data storage security monitoring technology has some deficiencies. The current satellite data security technology mainly relies on traditional cryptography encryption technology, which is not enough when dealing with unexpected risks that may occur during satellite data storage. The existing security monitoring method often ignores the changes and nonlinear characteristics in the satellite data storage process, resulting in a large limitation of traditional methods in data security analysis. SUMMARY

[0004] In view of the above problems, the present application is proposed.

[0005] Therefore, the technical problem solved by the present application is: how to provide a satellite data storage security monitoring method, system, device and medium, which solves the problem that the current satellite data security technology mainly relies on traditional cryptography encryption technology, which is not enough when dealing with unexpected risks that may occur during satellite data storage. The existing security monitoring method often ignores the changes and nonlinear characteristics in the satellite data storage process, resulting in a large limitation of traditional methods in data security analysis.

[0006] To solve the above technical problems, the present application provides the following technical solutions: a satellite data storage security monitoring method, comprising: collecting satellite data, preprocessing the satellite data, generating a chaotic mapping initial seed according to the preprocessed satellite data, setting a mapping parameter based on the chaotic mapping initial seed and generating a key, and encrypting an access path using the key; storing the satellite data and the encrypted access path to a database, calculating a stability index based on the encrypted access path, and performing abnormality judgment according to the stability index; collecting feedback data during the security monitoring process, adjusting the mapping parameter based on the feedback data, and displaying the adjusted security monitoring result through a visual interface.

[0007] As a preferred scheme of the satellite data storage security monitoring method, the method comprises the following steps: constructing a time series matrix according to the preprocessed satellite data; calculating an information matrix based on the time series matrix; calculating a curvature tensor according to the information matrix; and generating a chaotic mapping initial seed using the curvature tensor.

[0008] As a preferred scheme of the satellite data storage security monitoring method, the method comprises the following steps: storing the satellite data and the encrypted access path into a database, including storing the satellite data and the encrypted access path into a central database and setting a secure access measure; performing cloud backup on the stored data using the central database, and regularly performing integrity detection on the stored data and the backup data; and generating an integrity detection record after the detection is completed and synchronously storing the record into the central database.

[0009] As a preferred scheme of the satellite data storage security monitoring method, the method comprises the following steps: calculating a stability index based on the encrypted access path, and performing abnormality judgment according to the stability index, including calculating a deviation degree between adjacent access paths after encryption storage; calculating an access path change rate according to the deviation degree; calculating a stability index based on the access path change rate; setting a judgment threshold, comparing the stability index with the judgment threshold, and determining a behavior judgment result according to a comparison result.

[0010] As a preferred scheme of the satellite data storage security monitoring method, wherein: the feedback data collected in the security monitoring process is based on the feedback data to adjust the mapping parameters, including: collecting feedback data with labels, calculating the false positive rate;According to the false positive rate, adjust the mapping parameters until the false positive rate meets the preset condition.

[0011] As a preferred scheme of the satellite data storage security monitoring method, wherein: the satellite data includes access time stamp, access frequency, device temperature, access path and file size;The pre-processing of the satellite data includes: using network time protocol for time alignment, using data cleaning rules to remove redundant information, using Gaussian filter for denoising, using Z-score analysis method to identify and delete abnormal data, using linear interpolation method to fill in the missing data, and normalizing the satellite data.

[0012] As a preferred scheme of the satellite data storage security monitoring method, wherein: the time series matrix is constructed according to the pre-processed satellite data, including: sorting the pre-processed satellite data in time sequence, constructing time series;Using sliding window method to segment the time series, obtaining time segments;Using statistical analysis method to calculate the logarithmic energy, standard deviation and autocorrelation value of the time segment respectively, defined as feature vector;The feature vector is sorted according to time sequence, and the time series matrix is constructed.

[0013] The application provides a satellite data storage security monitoring system.

[0014] To solve the above technical problems, the application provides the following technical scheme: a satellite data storage security monitoring system, comprising: a collection encryption module for collecting satellite data, preprocessing satellite data, generating a chaotic mapping initial seed according to the preprocessed satellite data, setting a mapping parameter based on the chaotic mapping initial seed and generating a key, and encrypting the access path using the key;A storage monitoring module for storing satellite data and encrypted access path to a database, calculating a stability index based on the encrypted access path, and performing abnormality judgment according to the stability index;An adjustment visualization module for collecting feedback data in the security monitoring process, adjusting the mapping parameters based on the feedback data, and displaying the adjusted security monitoring results through a visualization interface.

[0015] The application provides a computer device, comprising a memory and a processor, the memory stores a computer program, characterized in that the processor executes the computer program to realize the steps of the satellite data storage security monitoring method.

[0016] The application provides a computer readable storage medium, which stores a computer program, and the computer program is used to realize the steps of the satellite data storage security monitoring method when executed by a processor.

[0017] The application has the following beneficial effects: the satellite data is collected by using the APL interface, the satellite data is preprocessed, the partial derivative of the probability distribution is calculated by using the numerical differential method, the elements of the Fisher information matrix are calculated by using the expected gradient outer product method, the Riemann curvature tensor is calculated, the Logistic mapping parameters are set by using the bifurcation rule, and the access path is encrypted by using the XOR operation method; the satellite data generated by collection and analysis is stored, the Lyapunov exponent is calculated, and the stored data is monitored; the security of data encryption is improved, the detection and response capability of abnormal behavior in the storage process is enhanced, and the anti-cracking capability of the encryption method is improved. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the application, and for those skilled in the art, other drawings can also be obtained without creative labor. Among them:

[0019] Figure 1 The flow chart of the satellite data storage security monitoring method provided by one embodiment of the application.

[0020] Figure 2 The structural diagram of the satellite data storage security monitoring system provided by one embodiment of the application. DETAILED DESCRIPTION

[0021] In order to make the above-mentioned purposes, features and advantages of the application more obvious and easy to understand, the specific implementation manner of the application will be described in detail below with reference to the drawings in the specification.

[0022] In the following description, many specific details are set forth in order to provide a thorough understanding of the application, but the application can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without departing from the connotation of the application, therefore the application is not limited to the specific embodiments disclosed below.

[0023] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation manner of the application. "In one embodiment" appearing in different places in the specification does not mean the same embodiment, and is not an independent or selective embodiment which is mutually exclusive with other embodiments.

[0024] Embodiment 1, refer to Figure 1 For an embodiment of the present application, the embodiment provides a satellite data storage security monitoring method, comprising:

[0025] S100: Collect satellite data, pre-process the satellite data, generate a chaotic mapping initial seed according to the pre-processed satellite data, set mapping parameters based on the chaotic mapping initial seed and generate a key, and encrypt the access path using the key.

[0026] S200: Store the satellite data and the encrypted access path to a database, calculate a stability index based on the encrypted access path, and make an abnormality judgment according to the stability index.

[0027] S300: Collect feedback data in the security monitoring process, adjust the mapping parameters based on the feedback data, and display the adjusted security monitoring results through a visual interface.

[0028] It should be noted that the existing satellite data security technology mainly relies on traditional cryptography encryption technology, which is not capable of dealing with unexpected risks that may occur during the satellite data storage process. The fixedness and predictability of traditional encryption methods make them vulnerable to advanced persistent threat attacks. At the same time, the existing security monitoring methods often ignore the changes and nonlinear characteristics in the satellite data storage process, and cannot effectively capture the evolution law of data access behavior, resulting in delay and misjudgment in identifying abnormal behavior. In addition, the lack of effective feedback adjustment mechanism and real-time visualization display makes it difficult for security monitoring to adapt to the changing threat environment and also fails to provide intuitive data status information for technical personnel.

[0029] Therefore, in view of the above problems of insufficient encryption security, limited abnormal detection capability and lack of intelligent adjustment mechanism, through the steps of S100-S300, the key is generated using the unpredictability and high sensitivity of chaotic mapping, the access path is highly encrypted, the stability index is calculated to accurately identify and timely warn abnormal behavior in the data storage process, and through the collection of feedback data and the adjustment of mapping parameters, combined with the intuitive display of the visual interface, the intelligent, adaptive and visual management of satellite data storage security monitoring is realized.

[0030] Embodiment 2, refer to Figure 1 For an embodiment of the present application, based on the previous embodiment, a satellite data storage security monitoring method is provided, comprising:

[0031] In the present application, the satellite data in step S100 includes access timestamp, access frequency, device temperature, access path and file size.

[0032] It should be noted that in the embodiments of the present application, satellite data collection can be performed using the APL interface for data collection.

[0033] In the embodiments of the present application, the satellite data is preprocessed in step S100, including:

[0034] Time alignment is performed using the Network Time Protocol, redundant information is removed using data cleaning rules, denoising is performed using Gaussian filtering, abnormal data is identified and deleted using the Z-score analysis method, missing data is filled using the linear interpolation method, and the satellite data is normalized.

[0035] Specifically, preprocessing the satellite data means performing time alignment using the Network Time Protocol (NTP) to ensure that the timestamps of the satellite data are uniform and accurate, eliminating data deviations caused by clock drift or time errors, thereby ensuring the consistency of the data time series; the use of Gaussian filtering can effectively remove random noise in the satellite data, enhancing the signal strength of the data; satellite data is often affected by transmission noise and external interference, especially in remote or harsh environments, and denoising helps to reduce the impact of these disturbances on data analysis, thereby obtaining more stable and reliable results; by calculating the standardized value of each data point, it can be determined which data points deviate from the normal range; linear interpolation is used to fill in missing values in the satellite data, ensuring the integrity of the data set; through normalization, data of different dimensions and scales can be unified, thereby improving the efficiency and accuracy of subsequent multivariate analysis, machine learning model training, etc., and improving the accuracy, integrity and consistency of the satellite data, providing high-quality data support for subsequent analysis, modeling and safety monitoring.

[0036] In the embodiments of the present application, the initial seed of chaotic mapping is generated according to the preprocessed satellite data in step S100, including the following steps A1-A4:

[0037] A1: Construct a time series matrix according to the preprocessed satellite data.

[0038] A2: Calculate an information matrix based on the time series matrix.

[0039] A3: Calculate the curvature tensor according to the information matrix.

[0040] A4: Generate the initial seed of chaotic mapping using the curvature tensor.

[0041] Specifically, step A1 constructs a time series matrix according to the preprocessed satellite data, including:

[0042] The preprocessed satellite data is sorted in chronological order to construct a time series.

[0043] The time series is segmented using a sliding window method to obtain time segments.

[0044] The log energy, standard deviation and autocorrelation value of each time segment are calculated using statistical analysis methods, and are defined as a feature vector.

[0045] The feature vectors are sorted in time sequence to construct a time series matrix.

[0046] Specifically, the information matrix is calculated based on the time series matrix in step A2, including steps A21-A23:

[0047] A21: The probability distribution parameters are fitted using maximum likelihood, the bandwidth is set using Silverman rule, and the joint probability density is calculated using kernel density estimation.

[0048] A22: The log probability density of the joint probability density is calculated using log transformation, and the partial derivative of the probability distribution is calculated using numerical differentiation method.

[0049] A23: The elements of the Fisher information matrix are calculated using the expected gradient outer product method.

[0050] Specifically, the partial derivative of the probability distribution is calculated using numerical differentiation method in step A22, which can be specifically represented as:

[0051]

[0052] Where, is the partial derivative, logp(S|θ) is the log probability density of observing the time series matrix S given the probability distribution parameters θ, θ i is the i-th probability distribution parameter, S is the time series matrix, and ∈ is a very small number.

[0053] Specifically, the elements of the Fisher information matrix are calculated using the expected gradient outer product method in step A23, which can be specifically represented as:

[0054]

[0055] Where, F ij is the element of the Fisher information matrix of the i-th and j-th probability distribution parameters, which represents the correlation dependence between the i-th and j-th probability distribution parameters, is the expected operation, θ j is the j-th probability distribution parameter.

[0056] Further, the curvature tensor is calculated according to the information matrix in step A3, including steps A31-A32:

[0057] A31: compute the elements of the inverse Fisher information matrix using the inverse operation, compute the partial derivatives of the elements of the Fisher information matrix with respect to the eigenvector coordinates using the forward finite difference method, compute the Christoffel symbols.

[0058] A32: compute the partial derivatives of the Christoffel symbols using symbolic differentiation, compute the second order terms of the Christoffel symbols by computing the difference of products of Christoffel symbols, combine the partial derivatives and the second order terms of the Christoffel symbols using the Riemannian geometry basis method, compute the Riemann curvature tensor.

[0059] In particular, the Christoffel symbols are computed in step A31 and can be represented as:

[0060]

[0061] where, is the Christoffel symbol in the i-th dimension with respect to the j-th and k-th coordinate variables of the eigenvector, F im is the element of the Fisher information inverse matrix, is the element of the Fisher information matrix with respect to the m-th and j-th probability distribution parameters, partial derivative with respect to the k-th coordinate variable of the eigenvector, is the element of the Fisher information matrix with respect to the m-th and k-th probability distribution parameters, partial derivative with respect to the j-th coordinate variable of the eigenvector, is the element of the Fisher information matrix with respect to the j-th and k-th probability distribution parameters, partial derivative with respect to the m-th coordinate variable of the eigenvector.

[0062] In particular, the Riemann curvature tensor is computed in step A32 and can be represented as:

[0063]

[0064] where, is the Riemann curvature tensor in the i-th dimension with respect to the j-th, k-th and l-th coordinate variables of the eigenvector, is the Christoffel symbol in the i-th dimension with respect to the j-th and l-th coordinate variables of the eigenvector partial derivative in the k-th dimension, is the Christoffel symbol in the i-th dimension with respect to the j-th and k-th coordinate variables of the eigenvector partial derivative in the l-th dimension, is the Christoffel symbol in the m-th dimension with respect to the j-th and l-th coordinate variables of the eigenvector, Christoffel symbol of the eigenvector in the i-th dimensional direction with respect to the m-th and k-th coordinate variables, Christoffel symbol of the eigenvector in the m-th dimensional direction with respect to the j-th and k-th coordinate variables, Christoffel symbol of the eigenvector in the i-th dimensional direction with respect to the m-th and l-th coordinate variables.

[0065] Further, the curvature tensor is used in step A4 to generate the initial seed of the chaotic mapping, including:

[0066] The Ricci curvature value is calculated using the shrinkage calculation method.

[0067] The Ricci curvature value is mapped to the interval [0, 1] using the Sigmoid transformation to obtain the initial seed of the chaotic mapping.

[0068] The Logistic mapping parameter is set using the bifurcation rule, the Logistic mapping is iterated to generate a chaotic sequence, and the Log-Scaling mapping formula is used to convert it to an integer key value.

[0069] The access path of the preprocessed satellite data is extracted, and the access path is converted to binary using UTF-8 encoding.

[0070] The 8-bit key value is calculated using the Log-Scaling mapping formula, and the encrypted access path is obtained by using the XOR operation method for encryption.

[0071] In an alternative embodiment, the satellite data is preprocessed in step S100, and the abnormal values and outliers in the satellite data can also be identified by an abnormal detection model based on deep learning. The distribution characteristics of normal data are learned using an autoencoder network, and data deviating from the normal mode is labeled and processed. At the same time, a spatiotemporal correlation analysis method is used to intelligently interpolate missing data, according to the time series characteristics and spatial distribution law of the satellite data, the value of the missing data point is derived, and the integrity and continuity of the data are ensured. Finally, the data of different dimensions are converted to the same scale range through data standardization mapping, providing high-quality input data for subsequent matrix calculation.

[0072] In another alternative embodiment, the initial seed of the chaotic map generated according to the pre-processed satellite data in step S100 can also be obtained by performing multi-scale decomposition on the time series through wavelet transform method, extracting feature information of different frequency components, reducing data dimension and retaining main variation characteristics through principal component analysis method, calculating the correlation matrix between features based on mutual information theory, constructing the geometric structure of data through topological data analysis method, extracting topological invariants of data as initial parameters of the chaotic map through persistent homology theory, introducing entropy theory to measure the uncertainty of data, combining the entropy value with the geometric invariants to generate the chaotic initial seed with high randomness, and ensuring the security and unpredictability of the subsequent encryption process.

[0073] It should be noted that by combining the Logistic map and the XOR operation, the application can effectively encrypt the access path of the satellite data, the chaotic characteristics of the Logistic map make the encryption process highly unpredictable, and the XOR operation further enhances the complexity of the encryption process, ensuring the security of the data during storage and transmission; the Fisher information matrix is calculated by using the numerical differentiation method and the expected gradient outer product method, so that the monitoring of the satellite data is more accurate.

[0074] In the embodiments of the application, the satellite data and the encrypted access path are stored in the database in step S200, including the following steps B1-B3:

[0075] B1: storing the satellite data and the encrypted access path in the central database and setting security access measures.

[0076] B2: performing cloud backup on the stored data by using the central database, and regularly detecting the integrity of the stored data and the backup data.

[0077] B3: generating an integrity detection record after detection and synchronously storing it in the central database.

[0078] It should be noted that in steps B1-B3, the collected satellite data and the encrypted access path are stored in the central database, and security access measures are set, which effectively prevents unauthorized access, reduces the risk of data leakage and tampering, cloud backup provides additional security for satellite data, solves the problem of data loss that may be faced by local storage, the central database centrally manages satellite data, which can effectively classify, store and query large-scale data, simplifies the data processing process, and the integrity detection record generated each time not only helps to evaluate the health status of the current data, but also can trace back the historical records when the data is abnormal, quickly locate the problem and take repair measures.

[0079] In the embodiments of the present application, the step S200 of calculating the stability index based on the encrypted access path, and the step of judging the anomaly according to the stability index, comprises the following steps B4-B7:

[0080] B4: Calculate the deviation degree between the adjacent access paths after encryption storage.

[0081] B5: Calculate the access path change rate according to the deviation degree.

[0082] B6: Calculate the stability index based on the access path change rate.

[0083] B7: Set a judgment threshold, compare the stability index with the judgment threshold, and determine the behavior judgment result according to the comparison result.

[0084] Specifically, the step B4 of calculating the deviation degree between the adjacent access paths after encryption storage refers to using the Euclidean formula to calculate the deviation degree between the adjacent access paths after encryption storage.

[0085] Specifically, the step B5 of calculating the access path change rate according to the deviation degree refers to calculating the access path change rate based on the deviation degrees between the adjacent access paths after encryption storage, which can be specifically represented as:

[0086]

[0087] Wherein, r s is the s-th storage access path change rate, d s is the s-th deviation degree between the adjacent access paths after encryption storage, and d s-1 is the s-th deviation degree between the adjacent access paths after encryption storage.

[0088] Further, the step B6 of calculating the stability index based on the access path change rate refers to using the Lyapunov index formula to calculate the stability index, wherein the stability index is the Lyapunov index.

[0089] Further, the step B7 of setting a judgment threshold, comparing the stability index with the judgment threshold, and determining the behavior judgment result according to the comparison result, comprises:

[0090] Using the percentile to set the judgment threshold, comparing the stability index with the judgment threshold, when the Lyapunov index is greater than the judgment threshold, judging as abnormal behavior, and issuing a warning.

[0091] When the Lyapunov index is less than or equal to the judgment threshold, it is judged as normal behavior, and the monitoring continues.

[0092] In an optional embodiment, the satellite data and the encrypted access path are stored in the database in step S200, and the data can also be stored in multiple nodes in a distributed storage architecture, the data sharding and redundancy mechanism is used to improve the storage reliability, the blockchain technology is introduced to ensure the data tamper-proof, the smart contract mechanism is established to automatically execute the data access permission control, a multi-level permission management system is used to allocate different data access permissions according to the user roles and access requirements, the data security in the transmission and storage process is ensured through the double protection of data encryption transmission and storage, and a real-time monitoring system is established to continuously monitor the database access behavior, and the malicious access behavior is discovered and prevented in time.

[0093] In another optional embodiment, the stability index is calculated based on the encrypted access path in step S200, and the model of the access path can also be established through the time series analysis method, the sliding window technology is used to capture the behavior pattern changes at different time scales, the machine learning algorithm is introduced to train the anomaly detection model, the baseline mode is learned through the historical normal behavior data, the multivariate statistical control chart is established by using the statistical method, the joint monitoring of the multi-dimensional access features is realized, the accuracy of anomaly detection is improved through the fusion of rule matching and statistical analysis, and the threshold adjustment mechanism is introduced to adjust the judgment threshold according to the system running state and environmental changes, and the false alarm and the missed alarm are reduced.

[0094] It should be noted that the stability index can provide dynamic stability analysis, so that the monitoring system can timely discover the abnormal fluctuations that may exist in the access path, and early warn the security risks in the data storage. The stability index has high sensitivity to chaotic systems, and when abnormal fluctuations occur in the data storage process, the stability index will increase, thereby providing timely early warning. By calculating the deviation degree of adjacent access paths based on the Euclidean formula, and in combination with the calculation of the access path change rate, the present application can capture the change trend of the data storage in different time periods, the judgment threshold is set by using the percentile, which has good adaptability and can automatically adjust the threshold according to the distribution of historical data, thereby avoiding the misjudgment problem caused by the fixed threshold, improving the accuracy and robustness of the monitoring system, and through the calculation of the deviation degree and the change rate of adjacent access paths, a large amount of satellite data can be efficiently processed, not only ensuring the safety monitoring of the data, but also having strong scalability. Through the combination of the stability index and the judgment threshold, the present application realizes intelligent anomaly early warning, and can automatically adjust the monitoring strategy according to the change of real-time data.

[0095] In the embodiments of the present application, feedback data in the security monitoring process is collected in step S300, and the mapping parameters are adjusted based on the feedback data, including the following steps C1-C2:

[0096] C1: Collect feedback data with labels, and calculate the false alarm rate.

[0097] C2: adjusting the mapping parameter according to the false positive rate until the false positive rate meets a preset condition.

[0098] Specifically, the false positive rate in step C1 is calculated by using the ROC curve based on the feedback data with labels.

[0099] Specifically, in step C2, the mapping parameter is adjusted according to the false positive rate until the false positive rate meets a preset condition, including:

[0100] The false positive rate threshold is set using the empirical rule, the adjustment coefficient is set using the incremental adjustment method, and the Logistic mapping parameter is adjusted using the dynamic adjustment method until the false positive rate is less than the false positive rate threshold, and the adjustment is stopped.

[0101] It should be noted that by using the ROC curve to calculate the false positive rate, the false positive situation can be accurately evaluated, and by adjusting the Logistic mapping parameter, the occurrence of false positives is gradually reduced. By using the incremental adjustment method and the dynamic adjustment method, the present application can adjust the parameters of the Logistic mapping in real time under different conditions, ensuring that it can adapt to changing input data and actual application requirements. By continuously optimizing the Logistic mapping parameters, the present application can maintain high-precision anomaly detection capabilities, thereby improving user experience and system reliability. The adjustment of the Logistic mapping parameter not only improves the security of the data encryption process, but also enhances the overall stability and robustness. By using feedback data to continuously adjust the parameters and combining the incremental adjustment method, the efficiency of model training can be effectively improved. By quickly adjusting the parameters of the Logistic mapping, suitable model parameters can be found in a short period of time, reducing the time cost in the debugging process and improving the adaptability and accuracy of the model.

[0102] In the embodiment of the present application, the adjusted safety monitoring result in step S300 is displayed through a visual interface, including:

[0103] The visual interface is built using the front-end framework React.js, including a main chart area and a top information bar.

[0104] The safety monitoring result is displayed in the main chart area, and the false positive rate is displayed in the top information bar.

[0105] Users who pass real-name verification are allowed to view.

[0106] In an optional embodiment, the adjustment of the mapping parameters based on the feedback data in step S300 can also be achieved by establishing a parameter optimization model through a reinforcement learning algorithm, modeling the mapping parameter adjustment process as a Markov decision process, automatically finding the optimal parameter combination through a reward function design guide algorithm, using a deep Q network or a policy gradient method to adjust the parameters, and introducing a multi-objective optimization algorithm to balance system performance and resource consumption while reducing false positive rates, finding a balance point between multiple objectives through Pareto frontier analysis, and establishing a parameter adjustment history record and effect evaluation mechanism to learn the best parameter adjustment strategy from historical data, and achieving adaptive parameter optimization.

[0107] In another optional embodiment, the adjusted security monitoring results in step S300 can be displayed through a visual interface, and three-dimensional data visualization can be achieved through WebGL technology to build a virtual reality display environment that allows users to immerse themselves in monitoring data, use an adaptive interface to adjust the display content according to user roles and permissions, introduce a data analysis assistant to provide intelligent data interpretation and suggestions, integrate a mobile application to support multi-platform access, send abnormal warning information to relevant personnel in a timely manner through a push notification mechanism, and establish data export and report generation functions to support data output in multiple formats and automatic report generation to meet the individual needs of different users.

[0108] It should be noted that by displaying the visual interface, users can quickly obtain security monitoring results through intuitive charts and information bars, and analyze the data in detail. By displaying the security monitoring results in the main chart area and the false positive rate in the top information bar, users can obtain real-time information about the operation of the monitoring system and identify whether there are false positives or abnormal conditions in a timely manner. In addition, users can not only understand the current security status, but also see real-time indicators such as false positive rates. The component-based structure of React.js makes it easier and more flexible to extend and maintain the system, making it easier to extend or optimize the interface in the future. By simply viewing the monitoring data, the need for data analysis is reduced, further improving the ease of use and accuracy of the monitoring system.

[0109] In summary, the present application uses APL interfaces to collect satellite data, pre-processes the satellite data, uses numerical differentiation to calculate the partial derivative of the probability distribution, uses the expected gradient outer product method to calculate the elements of the Fisher information matrix, calculates the Riemann curvature tensor, uses the bifurcation rule to set the Logistic mapping parameters, and uses the XOR operation method to encrypt the access path. The satellite data collected and analyzed is stored, and the Lyapunov exponent is calculated to monitor the stored data. Not only does this improve the security of data encryption, but it also enhances the detection and response capabilities of abnormal behavior during storage, and improves the anti-cracking ability of the encryption method.

[0110] Embodiment 3, refer to Figure 2 For an embodiment of the present application, the embodiment provides a satellite data storage security monitoring system, comprising: a collection encryption module, configured to collect satellite data, pre-process the satellite data, generate a chaotic mapping initial seed according to the pre-processed satellite data, set a mapping parameter based on the chaotic mapping initial seed and generate a key, and encrypt an access path using the key; a storage monitoring module, configured to store the satellite data and the encrypted access path to a database, calculate a stability index based on the encrypted access path, and perform an abnormality judgment according to the stability index; and an adjustment visualization module, configured to collect feedback data in a security monitoring process, adjust the mapping parameter based on the feedback data, and display an adjusted security monitoring result through a visualization interface.

[0111] Embodiment 4, which is different from the previous three embodiments, is that the function, if realized in the form of a software function unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present application or the part of the technical solution that essentially contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device) to execute all or part of the steps of the method described in the embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0112] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered a list of executable instructions for implementing logic functions, and can be specifically embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device, such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, apparatus, or device and execute the instructions, or in conjunction with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport programs for use by an instruction execution system, apparatus, or device, or in conjunction with these instruction execution systems, apparatus, or devices.

[0113] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example, via an optical scanner, then compiled, interpreted, or otherwise processed, and stored in a computer memory in a form that is then reproducible into a computer readable medium.

[0114] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the embodiments described above, various steps or methods can be implemented, in part, or in whole, in software, or firmware that is stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any of the techniques described herein can be implemented with or without the use of the following technologies, which are well known in the art: discrete logic circuitry having logic gates for implementing logic functions upon an application of data signals; application specific integrated circuits having logic gates, application specific integrated circuits having logic gates, programmable logic arrays (PLAs), field programmable gate arrays (FPGAs), and other implementations that are known in the art.

[0115] It should be noted that the above-mentioned embodiments illustrate rather than limit the application, since the scope of the application is indicated by the appended claims rather than by the examples that are described above. Although specific terms are employed herein, they are used in a generic and descriptive sense only and not for purposes of limitation, as it will be obvious to those skilled in the art that modifications can be made without departing from the spirit and scope of the application.

Claims

1. A method for monitoring the security of satellite data storage, characterized in that: include, Collect satellite data, preprocess the satellite data, generate an initial seed for chaotic mapping based on the preprocessed satellite data, set mapping parameters and generate a key based on the initial seed for chaotic mapping, and use the key to encrypt the access path; The satellite data and the encrypted access path are stored in the database, a stability index is calculated based on the encrypted access path, and anomaly detection is performed based on the stability index. Collect feedback data during the safety monitoring process, adjust the mapping parameters based on the feedback data, and display the adjusted safety monitoring results through a visualization interface.

2. The satellite data storage security monitoring method as described in claim 1, characterized in that: The step of generating an initial seed for chaotic mapping based on preprocessed satellite data includes: A time series matrix is ​​constructed based on the preprocessed satellite data; Calculate the information matrix based on the time series matrix; Calculate the curvature tensor based on the information matrix; The curvature tensor is used to generate an initial seed for the chaotic map.

3. The satellite data storage security monitoring method as described in claim 2, characterized in that: The satellite data and the encrypted access path are stored in the database, including: The satellite data and encrypted access paths are stored in a central database, and secure access measures are set. The stored data is backed up to the cloud using a central database, and the integrity of the stored data and backup data is checked regularly. After the inspection is completed, an integrity inspection record is generated and synchronously stored in the central database.

4. The satellite data storage security monitoring method as described in claim 3, characterized in that: A stability index is calculated based on the encrypted access path, and anomaly detection is performed based on the stability index, including: Calculate the deviation between adjacent access paths after encrypted storage; Calculate the access path change rate based on the deviation; Calculate the stability index based on the access path change rate; A judgment threshold is set, the stability index is compared with the judgment threshold, and the behavior judgment result is determined based on the comparison result.

5. A satellite data storage security monitoring method as described in claim 4, characterized in that: The process of collecting feedback data during security monitoring and adjusting the mapping parameters based on the feedback data includes: Collect tagged feedback data and calculate the false alarm rate; The mapping parameters are adjusted according to the false alarm rate until the false alarm rate meets a preset condition.

6. The satellite data storage security monitoring method as described in claim 5, characterized in that: The satellite data includes access timestamps, access frequency, device temperature, access path, and file size; Preprocessing the satellite data includes: The satellite data is aligned using the Network Time Protocol, redundant information is removed using data cleaning rules, noise is denoised using Gaussian filtering, outlier data is identified and removed using Z-score analysis, missing data is filled using linear interpolation, and the data is normalized.

7. A satellite data storage security monitoring method as described in claim 6, characterized in that: The construction of a time series matrix based on the preprocessed satellite data includes: The preprocessed satellite data is sorted in chronological order to construct a time series. The time series is segmented using the sliding window method to obtain time segments; The logarithmic energy, standard deviation, and autocorrelation value of each time segment are calculated using statistical analysis methods and defined as eigenvectors. The feature vectors are sorted in chronological order to construct a time series matrix.

8. A satellite data storage security monitoring system, employing the satellite data storage security monitoring method as described in any one of claims 1 to 7, characterized in that, include: The collection encryption module is used to collect satellite data, preprocess the satellite data, generate an initial seed for chaotic mapping based on the preprocessed satellite data, set mapping parameters and generate a key based on the initial seed for chaotic mapping, and use the key to encrypt the access path. The storage monitoring module is used to store satellite data and encrypted access paths to the database, calculate the stability index based on the encrypted access paths, and make anomaly judgments based on the stability index. The visualization module is adjusted to collect feedback data during the safety monitoring process. Based on the feedback data, the mapping parameters are adjusted, and the adjusted safety monitoring results are displayed through the visualization interface.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the satellite data storage security monitoring method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the satellite data storage security monitoring method according to any one of claims 1 to 7.

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