Digital information encryption method and system based on cloud computing

Abstract

The invention relates to the technical field of digital information encryption, in particular to a digital information encryption method and system based on cloud computing, and the method comprises the following steps: carrying out the data flow capture of a cloud service platform through a sensor, analyzing the CPU utilization rate of a server and the network bandwidth utilization condition, carrying out the statistics of the resource utilization condition of the platform, and generating a real-time resource utilization graph. According to the method, the data flow of the cloud service platform is monitored in real time, and the CPU utilization rate and the network bandwidth condition of the server are accurately analyzed, so that the resource utilization condition of the platform is subjected to deep statistics, the resource management mode is optimized, and the resource utilization rate is improved by generating the prediction resource demand graph and predicting the future resource utilization. The dynamic encryption task distribution plan is adjusted according to the actual network and server state, the timely response of the encryption service and the data security are ensured, the encryption strength and speed are optimized through the real-time adjustment of the encryption parameters, and the security and protection capability of data transmission are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of digital information encryption, and in particular to a digital information encryption method and system based on cloud computing. Background Art

[0002] The field of digital information encryption technology focuses on the design and implementation of security algorithms to protect the privacy and integrity of data during transmission. Use mathematics and computational theory to transform information so that it can only be accessed by those who hold a specific key. Encryption technology is divided into two categories: symmetric encryption and asymmetric encryption. Symmetric encryption uses the same key for encryption and decryption, while asymmetric encryption uses a pair of public and private keys. Modern encryption technology also includes hash functions and digital signatures to verify the integrity of information and the credibility of the source. Encryption technology is widely used in network security, e-commerce, data protection and other fields to prevent data leakage and unauthorized access.

[0003] Among them, the digital information encryption method refers to the use of cloud platform resources and capabilities to achieve data encryption processing. Cloud services provide computing power for encryption processing, allowing users to upload data to the cloud for encryption or decryption without the need to deploy complex hardware and manage encryption software locally. Its main purpose is to improve data security and accessibility. In a multi-user environment, it can effectively manage and protect sensitive information stored in the cloud. Even if it is intercepted during data transmission, the data content cannot be deciphered without the corresponding key, ensuring data security and privacy.

[0004] Although existing digital information encryption technology provides basic data protection functions, its application in cloud computing environments faces limitations, mainly manifested in the static nature of resource allocation and insufficient predictability. The elastic resource management advantages of cloud platforms are often not fully utilized, resulting in insufficient resource response during high-demand periods, affecting service efficiency and user experience. In the case of unpredicted high traffic, fixed resource allocation may not be able to handle the sudden amount of data, causing processing delays or service interruptions. The lack of effective real-time resource monitoring and adaptive adjustment mechanisms limits the adaptability of encryption services in the face of network fluctuations and load changes, and increases the security risks of data during transmission. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a digital information encryption method and system based on cloud computing.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a digital information encryption method based on cloud computing, comprising the following steps:

[0007] S1: Capture data streams from the cloud service platform through sensors, analyze server CPU usage and network bandwidth usage, count platform resource utilization, and generate real-time resource utilization graphs;

[0008] S2: Using the real-time resource utilization graph, predict resource demand, conduct quantitative analysis on future resource usage of the platform, analyze resource usage trends, and obtain a predicted resource demand graph;

[0009] S3: Analyze the predicted resource demand graph, evaluate the resource matching of the cloud service platform, optimize the task scheduling scheme, dynamically allocate encryption tasks to the cloud platform, and generate a dynamic encryption task allocation plan;

[0010] S4: Based on the dynamic encryption task allocation plan, the encryption parameters of the cloud platform, including the execution rate and the key length, are adjusted according to the current network status and server load, and an encryption adjustment plan is generated;

[0011] S5: Apply the encryption adjustment scheme to adjust the encryption rate in real time, adjust the key strength and encryption speed according to network delay and CPU load, and generate an encryption efficiency and security report;

[0012] S6: Use the encryption efficiency and security report to monitor encryption operations, track key usage, verify the continuity and integrity of encryption tasks through system logs and monitoring data, and generate encryption operation monitoring records.

[0013] As a further solution of the present invention, the real-time resource utilization graph includes CPU utilization index, bandwidth occupancy ratio and resource usage frequency, the predicted resource demand graph includes resource demand peak, resource consumption trend analysis results and expected resource gap, the dynamic encryption task allocation plan includes task allocation list, resource matching degree and optimization scheduling parameters, the encryption adjustment scheme includes rate adjustment parameters, key length option records and adaptive network response results, the encryption efficiency and security report includes efficiency rating, security assessment results and key strength verification results, and the encryption operation monitoring record includes key activity log, performance monitoring data and integrity check results.

[0014] As a further solution of the present invention, the specific steps of capturing data streams of the cloud service platform through sensors, analyzing server CPU usage and network bandwidth usage, and counting platform resource utilization to generate a real-time resource utilization graph are as follows:

[0015] S101: deploy sensors on the cloud service platform to capture data streams in real time, including CPU usage and network bandwidth usage data, and time-stamp the collected data to obtain the original data stream;

[0016] S102: performing data preprocessing on the original data stream, removing errors and redundant data, and standardizing the format of useful data to obtain screening performance data;

[0017] S103: By filtering the performance data, average CPU and bandwidth utilization are calculated, resource usage trends are analyzed in combination with time series, and converted into visual charts to generate real-time resource utilization charts.

[0018] As a further solution of the present invention, the specific steps of predicting resource demand through the real-time resource utilization graph, quantitatively analyzing the future resource usage of the platform, analyzing the resource usage trend, and obtaining the predicted resource demand graph are as follows:

[0019] S201: Collecting time series data of CPU usage and network bandwidth through the resource utilization graph, analyzing the periodic characteristics of the data, identifying peaks and valleys, and obtaining a resource usage cycle analysis report;

[0020] S202: Based on the resource usage cycle analysis report, an increase and decrease trend analysis is performed, and by calculating the increase and decrease trend of resource demand of the data, a change in resource demand in the short term is predicted to obtain a preliminary resource demand trend analysis result;

[0021] S203: Compare the preliminary resource demand trend analysis results with historical data, adjust prediction parameters to match historical trends, analyze resource demand, and obtain a predicted resource demand graph.

[0022] As a further solution of the present invention, the resource demand increase and decrease trend is according to the formula:

[0023]

[0024] Calculate, where Indicates at time The predicted value of It's in time The actual observed demand data represents the current demand level. It is the forecast value of the previous moment, which is used to reflect the previous demand trend.

[0025] As a further solution of the present invention, the predicted resource demand graph is analyzed, the resource matching of the cloud service platform is evaluated, the task scheduling scheme is optimized, and the cloud platform performs dynamic encryption task allocation. The specific steps of generating a dynamic encryption task allocation plan are as follows:

[0026] S301: Analyze the CPU and bandwidth usage of the server node based on the predicted resource demand graph, identify the imbalanced area in the resource configuration through the data of the peak and valley periods, and obtain the resource configuration imbalance analysis result;

[0027] S302: According to the resource allocation imbalance analysis result, adjust task allocation, reconfigure processing nodes, optimize overall resource utilization efficiency, and obtain a task rescheduling plan;

[0028] S303: Apply the task rescheduling scheme, generate a dynamic encryption task allocation plan based on the server node allocation scheduling strategy dynamics according to the security requirements of the encryption task.

[0029] As a further solution of the present invention, based on the dynamic encryption task allocation plan, the encryption parameters of the cloud platform, including the execution rate and the key length, are adjusted according to the current network status and server load. The specific steps of generating the encryption adjustment plan are:

[0030] S401: Based on the dynamic encryption task allocation plan, the encryption task execution rate and key length setting are collected, and the relationship between the data and the real-time load of the server and the network status is analyzed to obtain the execution parameter adjustment requirements;

[0031] S402: Designing an adjustment strategy according to the execution parameter adjustment requirement, balancing the server load and encryption efficiency, and obtaining a preliminary encryption parameter adjustment plan;

[0032] S403: Implement the preliminary encryption parameter adjustment plan, monitor the execution effect after adjustment, run encryption tasks according to the new parameters, dynamically adjust parameters according to real-time feedback to optimize performance, optimize encryption parameter settings, and generate an encryption adjustment plan.

[0033] As a further solution of the present invention, the encryption adjustment scheme is applied to adjust the encryption rate in real time, adjust the key strength and encryption speed according to network delay and CPU load, and generate an encryption efficiency and security report in the following specific steps:

[0034] S501: Based on the encryption adjustment scheme, monitor the network delay data and server CPU load in real time, analyze the influence of encryption speed and key strength, identify the key points of key and speed adjustment, and obtain adjustment demand analysis results;

[0035] S502: According to the adjustment demand analysis result, adjust the key strength and execution rate of the encryption task to obtain adjusted encryption parameters;

[0036] S503: Apply the adjusted encryption parameters, continuously monitor the execution efficiency and security of the encryption task, collect and analyze the adjusted encryption performance data, and obtain an encryption efficiency and security report.

[0037] As a further solution of the present invention, the encryption efficiency and security report is used to monitor encryption operations, track key usage, verify the continuity and integrity of encryption tasks through system logs and monitoring data, and generate encryption operation monitoring records in the following specific steps:

[0038] S601: Based on the encryption efficiency and security report, the encryption operation data including key usage frequency and system logs are captured in real time, and the key usage pattern and potential security vulnerabilities are identified through the data to obtain a real-time monitoring and analysis report;

[0039] S602: Analyze the system log according to the real-time monitoring analysis report, verify the execution continuity and data integrity of the encryption task, and obtain the encryption task integrity verification record;

[0040] S603: Summarize the encryption task integrity check records, analyze the execution status and security of the encryption task, and generate encryption operation monitoring records.

[0041] A digital information encryption system based on cloud computing, comprising:

[0042] The data stream capture module deploys sensors on the cloud service platform to capture data streams in real time, timestamp the collected data, perform data preprocessing, calculate the average CPU and bandwidth utilization, and generate a real-time resource utilization graph;

[0043] The resource prediction module analyzes the data periodicity characteristics through the resource utilization graph, predicts the change in resource demand in the short term, adjusts the prediction parameters to match the historical trend, and obtains a predicted resource demand graph;

[0044] The node scheduling module analyzes the CPU and bandwidth usage of the server nodes based on the predicted resource demand graph, reconfigures the processing nodes, and generates a dynamic encryption task allocation plan based on the server node allocation scheduling strategy dynamics;

[0045] The parameter adjustment module analyzes the data and the real-time server load and network status based on the dynamic encryption task allocation plan, balances the server load and encryption efficiency, and obtains a preliminary encryption parameter adjustment plan;

[0046] The performance optimization module implements the preliminary encryption parameter adjustment plan, optimizes encryption parameter settings, monitors network delay data and server CPU load, identifies key points for key and speed adjustment, and obtains adjustment demand analysis results;

[0047] The key management module adjusts the key strength and execution rate of the encryption task according to the adjustment demand analysis result, collects and analyzes the adjusted encryption performance data, and obtains an encryption efficiency and security report;

[0048] Based on the encryption efficiency and security report, the monitoring and verification module captures encryption operation data in real time, verifies the execution continuity and data integrity of encryption tasks, analyzes the execution status and security of encryption tasks, and generates encryption operation monitoring records.

[0049] Compared with the prior art, the advantages and positive effects of the present invention are:

[0050] In the present invention, by real-time monitoring of the data flow of the cloud service platform and accurate analysis of the server's CPU usage and network bandwidth, in-depth statistics on the platform's resource utilization are conducted, the resource management method is optimized, and by generating a predicted resource demand graph, future resource usage is predicted. The dynamic encryption task allocation plan is adjusted according to the actual network and server status, ensuring the timely response of the encryption service and data security. The real-time adjustment of encryption parameters optimizes the encryption strength and speed, and significantly improves the security and protection capabilities of data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a schematic diagram of the steps of the present invention;

[0052] Figure 2 is a flow chart of the steps of S1 of the present invention;

[0053] Figure 3 is a flow chart of the steps of S2 of the present invention;

[0054] Figure 4 is a flow chart of the steps of S3 of the present invention;

[0055] Figure 5 is a flow chart of the steps of S4 of the present invention;

[0056] Figure 6 is a flow chart of the steps of S5 of the present invention;

[0057] Figure 7 is a flow chart of the steps of S6 of the present invention;

[0058] Figure 8 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0060] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0061] See also Figure 1 , a digital information encryption method based on cloud computing, comprising the following steps:

[0062] S1: Capture data streams from the cloud service platform through sensors, analyze server CPU usage and network bandwidth usage, count platform resource utilization, and generate real-time resource utilization graphs;

[0063] S2: Use the real-time resource utilization graph to predict resource demand, conduct quantitative analysis of the platform's future resource usage, analyze resource usage trends, and obtain a predicted resource demand graph;

[0064] S3: Analyze the predicted resource demand graph, evaluate the resource matching of the cloud service platform, optimize the task scheduling scheme, dynamically allocate encryption tasks to the cloud platform, and generate a dynamic encryption task allocation plan;

[0065] S4: Based on the dynamic encryption task allocation plan, adjust the encryption parameters of the cloud platform, including execution rate and key length, according to the current network status and server load, and generate an encryption adjustment plan;

[0066] S5: Apply encryption adjustment scheme to adjust encryption rate in real time, adjust key strength and encryption speed according to network delay and CPU load, and generate encryption efficiency and security report;

[0067] S6: Use encryption efficiency and security reports to monitor encryption operations, track key usage, verify the continuity and integrity of encryption tasks through system logs and monitoring data, and generate encryption operation monitoring records.

[0068] The real-time resource utilization graph includes the CPU utilization index, bandwidth occupancy ratio and resource usage frequency. The predicted resource demand graph includes the resource demand peak, resource consumption trend analysis results and expected resource gap. The dynamic encryption task allocation plan includes the task allocation list, resource matching degree and optimization scheduling parameters. The encryption adjustment plan includes rate adjustment parameters, key length option records and adaptive network response results. The encryption efficiency and security report includes efficiency rating, security assessment results and key strength test results. The encryption operation monitoring record includes key activity log, performance monitoring data and integrity check results.

[0069] See also Figure 2 , the specific steps of S1 are:

[0070] S101: deploy sensors on the cloud service platform to capture data streams in real time, including CPU usage and network bandwidth usage data, and time-stamp the collected data to obtain the original data stream;

[0071] First, real-time data transmission is carried out through the connection between the cloud server and multiple terminal devices, which includes the synchronous collection of CPU usage and network bandwidth usage data. Each terminal device sends data to the server according to the preset frequency. These data are quickly processed and forwarded through the routers and distributors in the cloud service. Each piece of data will be automatically timestamped by the system during transmission to ensure the timeliness and accuracy of the data flow. The continuous collection of data flow and the addition of timestamps ensure the integrity and traceability of the original data flow, and obtain the original data flow organized in time series.

[0072] S102: preprocessing the original data stream, removing errors and redundant data, and standardizing the format of useful data to obtain screening performance data;

[0073] First, anomalies in the data are detected and marked through an error recognition algorithm, such as CPU usage and bandwidth data that exceed the normal range. Then, data cleaning tools are used to remove these data that do not meet the requirements. For duplicate data, a deduplication algorithm is used to process it and remove duplicate records within the same timestamp. Subsequently, the retained useful data is formatted, such as standardizing various data into a unified data format and unit to make it suitable for subsequent data analysis and storage. This step not only improves the data processing efficiency, but also provides cleaned screening performance data for data analysis in subsequent steps.

[0074] S103: by filtering the performance data, calculating the average CPU and bandwidth utilization, analyzing the resource usage trend in combination with the time series, converting it into a visual chart, and generating a real-time resource utilization chart;

[0075] According to the formula , calculate the average CPU and bandwidth utilization. Where, Representative The CPU usage of each data point, Representative Network bandwidth usage data for data points, Represents the total number of data points. Consider a specific set of data points. For example, among the five data points, the CPU usage is 30%, 40%, 35%, 50%, and 45%, and the network bandwidth usage data is 20MB / s, 30MB / s, 25MB / s, 35MB / s, and 40MB / s. The calculation process is:

[0076]

[0077]

[0078] The results show that the current system average CPU utilization is 40% and bandwidth utilization is 30MB / s, which indicates that the system is running at a relatively high efficiency. Analyzing this data will help further understand resource usage trends. After converting them into visual charts, it will be easier for managers and technicians to monitor and optimize resource allocation.

[0079] See also Figure 3 , the specific steps of S2 are:

[0080] S201: Collect time series data of CPU usage and network bandwidth through resource utilization graph, analyze data periodicity characteristics, identify peaks and valleys, and obtain resource usage cycle analysis report;

[0081] The data monitoring system automatically records the CPU and network bandwidth usage every minute or hour. Each data point is timestamped to the second to facilitate subsequent periodic analysis. Through these time series data, time series analysis methods, such as autoregressive models, are used to identify periodic features in the data. This includes identifying peaks and troughs in usage through peak detection algorithms. These periodic features help understand the changing patterns of resource usage over a certain period of time. Finally, a resource usage cycle analysis report is compiled to describe in detail the periodic changes and key time points in resource utilization.

[0082] S202: Based on the resource usage cycle analysis report, an increase and decrease trend analysis is performed, and by calculating the increase and decrease trend of resource demand of the data, the change of resource demand in the short term is predicted to obtain a preliminary resource demand trend analysis result;

[0083] The resource demand increase or decrease trend is based on the formula:

[0084]

[0085] Calculate, where Indicates at time The predicted value of It's in time The actual observed demand data represents the current demand level. It is the forecast value of the previous moment, which is used to reflect the previous demand trend.

[0086] Obtained from the data monitoring system For example, the actual demand data is 400 units.

[0087] Get from history , the predicted demand value is 350 units.

[0088] Substitute the specific values ​​into the formula to calculate .

[0089] The formula is as follows:

[0090]

[0091] The results show that after considering the demand at the previous moment and the current actual demand, the adjusted forecast value of resource demand is about 21.36 units. This forecast value reveals the downward trend of resource demand, indicating that the demand for resources may decrease in the short term, and enterprises may need to adjust their resource allocation strategies to adapt to market changes.

[0092] S203: Compare the preliminary resource demand trend analysis results with historical data, adjust the forecast parameters to match the historical trend, analyze the resource demand, and obtain a forecast resource demand graph;

[0093] First, import the historical data set, and then use the historical trend analysis tool to compare the current trend with past data. The analysis tool evaluates the change of trend by calculating the standard deviation and mean. In addition, the adjustment of prediction parameters is carried out through optimization algorithms, such as gradient descent, to ensure that the prediction model can adapt to the latest dynamics of the data. These adjusted parameters help to more accurately predict future resource requirements, and finally obtain a detailed predicted resource demand map, showing the trend of predicted resource demand changes over time.

[0094] See also Figure 4 , the specific steps of S3 are:

[0095] S301: Analyze the CPU and bandwidth usage of the server node based on the predicted resource demand graph, identify the imbalanced areas in resource allocation through the data of peak and valley periods, and obtain the analysis results of resource allocation imbalance;

[0096] First, we focus on the data during peak and valley periods. The system automatically marks the high and low points of resource usage in each period through resource monitoring tools. These data are used through advanced data analysis techniques, such as peak and valley detection algorithms, to identify resource usage patterns and imbalanced areas. This information is then aggregated to form an analysis result of resource imbalance. This result reflects in detail which nodes may face the risk of resource shortage or overload and when, providing a basis for subsequent resource adjustment and optimization.

[0097] S302: According to the analysis result of resource allocation imbalance, adjust task allocation, reconfigure processing nodes, optimize overall resource utilization efficiency, and obtain a task rescheduling plan;

[0098] According to the formula , reconfigure the processing nodes. Where, Represents the maximum resource capacity of the node, Represents the resource usage of the current node. Assuming that the maximum CPU capacity of a server node is 100% utilization and the current utilization is 80%, the adjustment coefficient is calculated as:

[0099]

[0100] This shows that in order to optimize resource usage, we can consider transferring part of the load to other nodes to reduce the load of this node and achieve a more balanced resource allocation.

[0101] S303: Applying the task rescheduling scheme, according to the security requirements of the encryption task, based on the server node allocation scheduling strategy, generates a dynamic encryption task allocation plan;

[0102] Task allocation is adjusted dynamically according to the real-time resource usage of server nodes. A dynamic scheduling system is developed, which can monitor the resource status of each node in real time and dynamically allocate encryption tasks to different nodes according to preset security standards. This allocation takes into account the resource utilization and security requirements of the nodes to ensure that encryption tasks have sufficient resources and are secure when executed. The final generated dynamic encryption task allocation plan lists in detail when each task is allocated to which node, as well as the basis and expected effect of the allocation.

[0103] See also Figure 5 , the specific steps of S4 are:

[0104] S401: Based on the dynamic encryption task allocation plan, the encryption task execution rate and key length setting are collected, and the relationship between the data and the real-time load of the server and the network status is analyzed to obtain the execution parameter adjustment requirements;

[0105] We focus on monitoring the CPU usage and network bandwidth of each server node in different time periods. Through data analysis tools, we analyze the correlation between these execution parameters and the real-time server load and network conditions. By identifying patterns and anomalies in the data, we evaluate the effectiveness of resource allocation and the performance of encryption operations. These analyses help identify the execution parameters that need to be adjusted, thereby forming an execution parameter adjustment needs report, which points out potential areas for optimizing parameters under different load conditions.

[0106] S402: Design an adjustment strategy based on the execution parameter adjustment requirements, balance the server load and encryption efficiency, and obtain a preliminary encryption parameter adjustment plan;

[0107] According to the formula , design adjustment strategy. In the formula, represents the key length, Represents the task execution time, Represents processing power. Consider a real scenario where the key length is 2048 bits, the task execution time is 0.8 seconds, and the server processing power is 2500 keys / second. The calculation process of the adjustment strategy is:

[0108]

[0109] This result shows that the current encryption parameter settings are effective given the server processing power, but there is still room for optimization, and higher efficiency can be achieved by further reducing the execution time or adjusting the key length.

[0110] S403: Implement the preliminary encryption parameter adjustment plan, monitor the execution effect after the adjustment, run the encryption task according to the new parameters, dynamically adjust the parameters according to real-time feedback to optimize performance, optimize the encryption parameter settings, and generate the encryption adjustment plan;

[0111] Monitoring includes analyzing indicators such as the completion time, error rate, and resource consumption of encryption tasks. Based on these real-time data, parameters are dynamically adjusted to optimize performance. For example, if the load of a certain node is detected to be continuously too high, the system will automatically reduce the task load of that node and increase it to other nodes with lower loads. Such adjustments ensure that the encryption task not only meets security requirements but also runs efficiently. The encryption adjustment plan finally generated lists in detail the optimal value and adjustment record of each parameter, providing a basis for continuous improvement.

[0112] See also Figure 6 , the specific steps of S5 are:

[0113] S501: Based on the encryption adjustment scheme, the network delay data and the server CPU load are monitored in real time, the influence of the encryption speed and the key strength is analyzed, the key points of the key and speed adjustment are identified, and the adjustment demand analysis results are obtained;

[0114] Advanced monitoring tools are used to capture real-time data on server load changes and network latency every second. Through data analysis software, these monitoring data are used to analyze the impact of encryption speed and key strength on server performance. By building data models, key points for adjusting key length and encryption speed are identified, including resource usage during peak hours and delays in key generation and application. Ultimately, these analyses help form adjustment needs analysis results and provide detailed recommendations for optimizing encryption operations.

[0115] S502: According to the adjustment demand analysis result, the key strength and execution rate of the encryption task are adjusted to obtain adjusted encryption parameters;

[0116] According to the formula Adjust the key strength and execution rate of the encryption task. represents the rate, Represents the data size, represents the delay, assuming the data size is 1000MB, the delay is 0.5 seconds, and the expected rate is 2000MB / s. The calculation process for adjusting the key strength is:

[0117]

[0118] This result shows that in order to process 1000MB of data within 0.5 seconds of latency, the key strength needs to be set to 4,000,000 units, ensuring that the encryption task is both fast and secure.

[0119] S503: Apply the adjusted encryption parameters, continuously monitor the execution efficiency and security of the encryption task, collect and analyze the adjusted encryption performance data, and obtain an encryption efficiency and security report;

[0120] The execution efficiency and security of encryption tasks are continuously tracked through a high-performance monitoring system, which includes detailed records of completion time, error rate and security violations. Based on the collected data, statistical analysis methods and performance evaluation tools are used to analyze the adjusted encryption performance. In this process, the monitored data will be used to dynamically adjust encryption parameters to further optimize performance and security. The final encryption efficiency and security report shows in detail the improvement effects and potential security risks after adjusting the parameters, providing a basis for real-time feedback and continuous improvement.

[0121] See also Figure 7 , the specific steps of S6 are:

[0122] S601: Based on the encryption efficiency and security report, the encryption operation data is captured in real time, including key usage frequency and system logs, and key usage patterns and potential security vulnerabilities are identified through the data to obtain real-time monitoring and analysis reports;

[0123] The system uses advanced monitoring technology to record key usage frequency and system logs in real time. These data are processed by an efficient data analysis platform. The system can automatically identify key usage patterns and potential security vulnerabilities. This real-time monitoring is done by setting specific algorithms to identify abnormal behavior and data leakage risks. Further analysis also includes comparing historical data and patterns to identify any deviations from the norm. Finally, the resulting real-time monitoring analysis report details the current security status and possible improvement measures.

[0124] S602: Analyze the system log according to the real-time monitoring analysis report, verify the execution continuity and data integrity of the encryption task, and obtain the encryption task integrity verification record;

[0125] First, the system automatically extracts key data points in the log, such as the task start and end timestamps. Then, it uses a data consistency verification algorithm to compare these timestamps to ensure that no interruption or data loss occurs. In addition, for each encryption operation, the system will perform an integrity check to ensure that the integrity of the data before and after encryption is not damaged. Through these steps, the integrity check record of the encryption task is obtained, which provides a basis for further security analysis.

[0126] S603: Summarize the encryption task integrity check records, analyze the execution status and security of the encryption task, and generate encryption operation monitoring records;

[0127] According to the formula , generate encryption operation monitoring records. In the formula, Represents the total number of tasks, Represents the number of tasks that have been verified as complete. Assume that there are 100 encrypted tasks in total, of which 95 tasks are confirmed to be complete in the integrity check. The security calculation is:

[0128]

[0129] This result shows that the integrity and security of current encryption operations are very high, indicating that most encryption tasks can maintain data integrity, providing data support for further improving security;

[0130] See also Figure 8 , a digital information encryption system based on cloud computing, comprising:

[0131] The data stream capture module deploys sensors on the cloud service platform to capture data streams in real time, timestamp the collected data, perform data preprocessing, calculate the average CPU and bandwidth utilization, and generate a real-time resource utilization graph;

[0132] The resource forecasting module uses the resource utilization graph to analyze the periodic characteristics of the data, predict the changes in resource demand in the short term, adjust the forecasting parameters to match the historical trend, and obtain the forecast resource demand graph;

[0133] The node scheduling module analyzes the CPU and bandwidth usage of server nodes based on the predicted resource demand graph, reconfigures processing nodes, and generates a dynamic encryption task allocation plan based on the server node allocation scheduling strategy dynamics;

[0134] The parameter adjustment module is based on the dynamic encryption task allocation plan, analyzes the data and real-time server load and network status, balances the server load and encryption efficiency, and obtains a preliminary encryption parameter adjustment plan;

[0135] The performance optimization module implements the preliminary encryption parameter adjustment plan, optimizes encryption parameter settings, monitors network delay data and server CPU load, identifies key points for key and speed adjustment, and obtains adjustment demand analysis results;

[0136] The key management module adjusts the key strength and execution rate of the encryption task according to the adjustment demand analysis results, collects and analyzes the adjusted encryption performance data, and obtains the encryption efficiency and security report;

[0137] The monitoring and verification module captures encryption operation data in real time based on encryption efficiency and security reports, verifies the execution continuity and data integrity of encryption tasks, analyzes the execution status and security of encryption tasks, and generates encryption operation monitoring records.

[0138] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A digital information encryption method based on cloud computing, characterized in that: The following steps are involved: Through sensors, the cloud service platform can capture data flow, analyze server CPU usage and network bandwidth usage, count platform resource utilization, and generate real-time resource utilization graphs; Through the real-time resource utilization graph, resource demand forecasting is performed, future resource usage of the platform is quantitatively analyzed, resource usage trends are analyzed, and a forecast resource demand graph is obtained; Analyze the predicted resource demand graph, evaluate the resource matching of the cloud service platform, optimize the task scheduling scheme, dynamically allocate encryption tasks to the cloud platform, and generate a dynamic encryption task allocation plan; Based on the dynamic encryption task allocation plan, the encryption parameters of the cloud platform, including the execution rate and the key length, are adjusted according to the current network status and server load to generate an encryption adjustment plan; Apply the encryption adjustment scheme to adjust the encryption rate in real time, adjust the key strength and encryption speed according to network delay and CPU load, and generate encryption efficiency and security reports; The encryption efficiency and security report is used to monitor encryption operations, track key usage, verify the continuity and integrity of encryption tasks through system logs and monitoring data, and generate encryption operation monitoring records.

2. The digital information encryption method based on cloud computing according to claim 1, characterized in that: The real-time resource utilization graph includes the CPU utilization index, bandwidth occupancy ratio and resource usage frequency; the predicted resource demand graph includes resource demand peak, resource consumption trend analysis results and expected resource gap; the dynamic encryption task allocation plan includes task allocation list, resource matching degree and optimization scheduling parameters; the encryption adjustment scheme includes rate adjustment parameters, key length option records and adaptive network response results; the encryption efficiency and security report includes efficiency rating, security assessment results and key strength test results; the encryption operation monitoring record includes key activity log, performance monitoring data and integrity check results.

3. The digital information encryption method based on cloud computing according to claim 1, characterized in that: The specific steps of capturing data flow of the cloud service platform through sensors, analyzing server CPU usage and network bandwidth usage, and counting platform resource utilization to generate a real-time resource utilization graph are as follows: Deploy sensors on the cloud service platform to capture data streams in real time, including CPU usage and network bandwidth usage data, and time-stamp the collected data to obtain the original data stream; Preprocessing the original data stream, removing errors and redundant data, and standardizing the format of useful data to obtain screening performance data; By filtering the performance data, the average CPU and bandwidth utilization is calculated, and the resource usage trend is analyzed in combination with the time series, converted into a visual chart, and a real-time resource utilization chart is generated.

4. The method for encrypting digital information based on cloud computing according to claim 1, characterized in that: The specific steps of predicting resource demand through the real-time resource utilization graph, quantitatively analyzing the future resource usage of the platform, analyzing the resource usage trend, and obtaining the predicted resource demand graph are as follows: Through the resource utilization graph, the time series data of CPU usage and network bandwidth are collected, the periodic characteristics of the data are analyzed, the peaks and valleys are identified, and a resource usage cycle analysis report is obtained; Based on the resource usage cycle analysis report, an analysis of the increase and decrease trend is performed, and by calculating the increase and decrease trend of resource demand of the data, the change of resource demand in the short term is predicted to obtain preliminary resource demand trend analysis results; The preliminary resource demand trend analysis results are compared with historical data, the forecast parameters are adjusted to match the historical trend, the resource demand is analyzed, and a forecast resource demand graph is obtained.

5. The method for encrypting digital information based on cloud computing according to claim 4, characterized in that: The resource demand increase and decrease trend is according to the formula: Calculate, where Indicates at time The predicted value of It's in time Actual observed demand data, is the predicted value at the previous moment.

6. The method for encrypting digital information based on cloud computing according to claim 1, characterized in that: The specific steps of analyzing the predicted resource demand graph, evaluating the resource matching of the cloud service platform, optimizing the task scheduling scheme, dynamically allocating encryption tasks to the cloud platform, and generating a dynamic encryption task allocation plan are as follows: Based on the predicted resource demand graph, analyze the CPU and bandwidth usage of the server nodes, identify the imbalanced areas in resource allocation through the data of peak and trough periods, and obtain the analysis results of resource allocation imbalance; According to the analysis result of imbalanced resource allocation, the task allocation is adjusted, the processing nodes are reconfigured, the overall resource utilization efficiency is optimized, and a task rescheduling plan is obtained; The task rescheduling scheme is applied to generate a dynamic encryption task allocation plan based on the server node allocation scheduling strategy in view of the security requirements of the encryption task.

7. The digital information encryption method based on cloud computing according to claim 1, characterized in that: Based on the dynamic encryption task allocation plan, the encryption parameters of the cloud platform are adjusted, including the execution rate and key length, according to the current network status and server load. The specific steps of generating the encryption adjustment plan are as follows: Based on the dynamic encryption task allocation plan, the encryption task execution rate and key length setting are collected, and the relationship between the data and the real-time server load and network status is analyzed to obtain the execution parameter adjustment requirements; According to the execution parameter adjustment requirements, an adjustment strategy is designed to balance server load and encryption efficiency, and a preliminary encryption parameter adjustment plan is obtained; Implement the preliminary encryption parameter adjustment plan, monitor the execution effect after adjustment, run encryption tasks according to the new parameters, dynamically adjust parameters according to real-time feedback to optimize performance, optimize encryption parameter settings, and generate encryption adjustment plans.

8. The method for encrypting digital information based on cloud computing according to claim 1, characterized in that: The specific steps of applying the encryption adjustment scheme to adjust the encryption rate in real time, adjusting the key strength and encryption speed according to network delay and CPU load, and generating an encryption efficiency and security report are as follows: Based on the encryption adjustment scheme, the network delay data and server CPU load are monitored in real time, the influence of encryption speed and key strength is analyzed, the key points of key and speed adjustment are identified, and the adjustment demand analysis results are obtained; According to the adjustment demand analysis result, the key strength and execution rate of the encryption task are adjusted to obtain adjusted encryption parameters; Apply the adjusted encryption parameters, continuously monitor the execution efficiency and security of encryption tasks, collect and analyze the adjusted encryption performance data, and obtain an encryption efficiency and security report.

9. The method for encrypting digital information based on cloud computing according to claim 1, characterized in that: The encryption efficiency and security report is used to monitor encryption operations, track key usage, and verify the continuity and integrity of encryption tasks through system logs and monitoring data. The specific steps for generating encryption operation monitoring records are as follows: Based on the encryption efficiency and security report, the encryption operation data, including key usage frequency and system logs, is captured in real time, and key usage patterns and potential security vulnerabilities are identified through the data to obtain a real-time monitoring and analysis report; Analyze the system log according to the real-time monitoring and analysis report, verify the execution continuity and data integrity of the encryption task, and obtain the encryption task integrity verification record; Summarize the encryption task integrity check records, analyze the execution status and security of the encryption tasks, and generate encryption operation monitoring records.

10. A digital information encryption system based on cloud computing, characterized in that: According to a cloud computing-based digital information encryption method according to any one of claims 1 to 9, the system comprises: The data stream capture module deploys sensors on the cloud service platform to capture data streams in real time, timestamp the collected data, perform data preprocessing, calculate the average CPU and bandwidth utilization, and generate a real-time resource utilization graph; The resource prediction module analyzes the data periodicity characteristics through the resource utilization graph, predicts the change in resource demand in the short term, adjusts the prediction parameters to match the historical trend, and obtains a predicted resource demand graph; The node scheduling module analyzes the CPU and bandwidth usage of the server nodes based on the predicted resource demand graph, reconfigures the processing nodes, and generates a dynamic encryption task allocation plan based on the server node allocation scheduling strategy dynamics; The parameter adjustment module analyzes the data and the real-time server load and network status based on the dynamic encryption task allocation plan, balances the server load and encryption efficiency, and obtains a preliminary encryption parameter adjustment plan; The performance optimization module implements the preliminary encryption parameter adjustment plan, optimizes encryption parameter settings, monitors network delay data and server CPU load, identifies key points for key and speed adjustment, and obtains adjustment demand analysis results; The key management module adjusts the key strength and execution rate of the encryption task according to the adjustment demand analysis result, collects and analyzes the adjusted encryption performance data, and obtains an encryption efficiency and security report; Based on the encryption efficiency and security report, the monitoring and verification module captures encryption operation data in real time, verifies the execution continuity and data integrity of encryption tasks, analyzes the execution status and security of encryption tasks, and generates encryption operation monitoring records.

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