Encryption method and system for guaranteeing credible transmission of data in cloud environment

Abstract

The invention discloses an encryption method and system for guaranteeing credible transmission of data in a cloud environment, and relates to the technical field of data encryption transmission, and the method comprises the steps: constructing an initial transmission information set of a cloud computing platform in combination with historical cloud data transmission records; constructing a multi-dimensional state space; extracting any guarantee data in the multi-dimensional state space to obtain target guarantee data; performing real-time monitoring on dynamic transmission of the target guarantee data to obtain real-time transmission information; obtaining a time delay reasonable threshold value, and carrying out comparative analysis to obtain transmission credibility; and if the transmission credibility does not reach the preset credibility limit value, carrying out credible transmission early warning on any guarantee data. The technical problem that in the prior art, data transmission in the cloud environment lacks an effective credible monitoring means, abnormal conditions are difficult to early warn in time, and data transmission safety and reliability are difficult to guarantee is solved, and the technical effects of credible monitoring and abnormal early warning in the data transmission process in the cloud environment and guarantee of data transmission safety and reliability are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of data encryption transmission, and particularly to an encryption method and system for ensuring the trustworthy transmission of data in a cloud environment. Background Art

[0002] In today's digital age, cloud computing technology has been widely applied in various fields by virtue of its powerful computing power, storage resource sharing, and flexible service mode. Data transmission in a cloud environment involves the interaction and storage of a large amount of data, and the secure and reliable transmission of data is of crucial importance. However, there are many problems with existing cloud data transmission technologies. On the one hand, the monitoring of the data transmission process is not comprehensive and accurate enough, making it difficult to grasp the real transmission status of data in real time; on the other hand, there is a lack of an effective abnormal warning mechanism. When abnormal situations such as data transmission delay, packet loss, or tampering occur, they cannot be detected and measures cannot be taken in a timely manner, resulting in the security and reliability of data transmission being difficult to guarantee, which may cause huge losses to users and enterprises, such as the leakage of business secrets and business interruption.

[0003] The prior art has the technical problem that in a cloud environment, there is a lack of effective and trustworthy monitoring means for data transmission, and it is difficult to give an early warning of abnormal situations in a timely manner, resulting in the security and reliability of data transmission being difficult to guarantee. Summary of the Invention

[0004] This application provides an encryption method and system for ensuring the trustworthy transmission of data in a cloud environment, which is used to solve the technical problem that in the prior art, there is a lack of effective and trustworthy monitoring means for data transmission in a cloud environment, and it is difficult to give an early warning of abnormal situations in a timely manner, resulting in the security and reliability of data transmission being difficult to guarantee.

[0005] In view of the above problems, this application provides an encryption method and system for ensuring the trustworthy transmission of data in a cloud environment.

[0006] In the first aspect of this application, an encryption method for ensuring the trustworthy transmission of data in a cloud environment is provided. The method includes:

[0007] Constructing an initial transmission information set of a cloud computing platform in combination with historical cloud data transmission records; adding a predetermined hidden signal to the initial transmission information set to obtain a target transmission information set, and constructing a multi-dimensional state space according to the target transmission information set; extracting any safeguard data from the multi-dimensional state space, and preprocessing the any safeguard data to obtain target safeguard data; performing real-time monitoring on the dynamic transmission of the target safeguard data to obtain real-time transmission information, where the real-time transmission information includes a real-time transmission delay value; obtaining a reasonable delay threshold, and comparing and analyzing the real-time transmission delay value with the reasonable delay threshold to obtain a transmission credibility; if the transmission credibility does not reach a predetermined credible limit value, then giving a trustworthy transmission warning for the any safeguard data.

[0008] In the second aspect of the present application, an encryption system for ensuring trusted data transmission in a cloud environment is provided. The system includes:

[0009] An information set construction module for constructing an initial transmission information set of the cloud computing platform in combination with historical cloud data transmission records; a multi-dimensional state space construction module for adding a predefined hidden signal to the initial transmission information set to obtain a target transmission information set, and constructing a multi-dimensional state space according to the target transmission information set; a target guaranteed data acquisition module for extracting any guaranteed data from the multi-dimensional state space and preprocessing the any guaranteed data to obtain target guaranteed data; a real-time transmission information acquisition module for monitoring the dynamic transmission of the target guaranteed data in real time to obtain real-time transmission information, where the real-time transmission information includes a real-time transmission delay value; a transmission credibility acquisition module for obtaining a reasonable delay threshold and comparing and analyzing the real-time transmission delay value with the reasonable delay threshold to obtain transmission credibility; and a trusted transmission warning module for performing a trusted transmission warning on the any guaranteed data if the transmission credibility does not reach a predefined trusted limit.

[0010] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0011] Constructing an initial transmission information set of the cloud computing platform in combination with historical cloud data transmission records; a multi-dimensional state space; extracting any guaranteed data from the multi-dimensional state space to obtain target guaranteed data; monitoring the dynamic transmission of the target guaranteed data in real time to obtain real-time transmission information; obtaining a reasonable delay threshold and comparing and analyzing the real-time transmission delay value with the reasonable delay threshold to obtain transmission credibility; and performing a trusted transmission warning on the any guaranteed data if the transmission credibility does not reach a predefined trusted limit. It achieves the technical effects of trusted monitoring and abnormal warning in the data transmission process in a cloud environment, and ensures the safe and reliable data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings required for the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings without creative efforts.

[0013] Figure 1 It is a schematic flowchart of an encryption method for ensuring trusted data transmission in a cloud environment provided by an embodiment of the present application;

[0014] Figure 2 It is a schematic structural diagram of an encryption system for ensuring trusted data transmission in a cloud environment provided by an embodiment of the present application.

[0015] Description of the reference numerals: Information set construction module 10, multi-dimensional state space construction module 20, target guarantee data acquisition module 30, real-time transmission information acquisition module 40, transmission credibility acquisition module 50, trusted transmission warning module 60. Specific embodiments

[0016] This application provides an encryption method and system for ensuring the trusted transmission of data in a cloud environment, aiming to solve the technical problem in the prior art that there is a lack of effective trusted monitoring means for data transmission in a cloud environment, making it difficult to give early warnings in a timely manner for abnormal situations, resulting in the difficulty in ensuring the security and reliability of data transmission.

[0017] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0018] Embodiment 1, as Figure 1 shown, this application provides an encryption method for ensuring the trusted transmission of data in a cloud environment, and the method includes:

[0019] Step S100: Construct an initial transmission information set of the cloud computing platform in combination with historical cloud data transmission records.

[0020] Specifically, by collecting a large number of past historical cloud data transmission records of the cloud computing platform, covering various aspects such as data transmission time, path, size, node information, and network status, and then deeply analyzing these records. Key information related to data transmission characteristics is screened out, such as those related to data transmission security and efficiency. Then, according to the requirements of subsequent operations such as constructing a multi-dimensional state space and evaluating transmission credibility, these key information are classified, summarized, and integrated according to specific rules and logics, and finally an initial transmission information set of the cloud computing platform is constructed.

[0021] Step S200: Add a predefined hidden signal to the initial transmission information set to obtain a target transmission information set, and construct a multi-dimensional state space according to the target transmission information set.

[0022] Specifically, in the encryption process of ensuring the trusted transmission of data in the cloud environment, a predefined hidden signal is added to the initially constructed transmission information set. The function of this hidden signal is similar to an enhancer in data mining. In complex cloud data transmission scenarios, it can make the useful information originally hidden in the data more prominent, enhancing the data activity. After adding the hidden signal, a target transmission information set is formed, which contains more key information helpful for ensuring the trusted transmission of data. Then, based on the various characteristics and dimensions of this target transmission information set, a multi-dimensional state space is constructed from multiple dimensions such as the time characteristics of the data, the spatial location, and the attributes of the data itself, which is of great significance for realizing the trusted transmission of data in the cloud environment.

[0023] Step S300: Extract any data for ensuring data from the multi-dimensional state space, and preprocess the any data for ensuring data to obtain target data for ensuring data.

[0024] Specifically, from the constructed multi-dimensional state space, select any data for ensuring data that needs to be transmitted and ensured. For these data, first, the wavelet threshold denoising method is used to remove the noise mixed in the data during the acquisition, transmission, or storage process, improving the data quality to obtain denoised data for ensuring data. Then, the massive parallel Bayesian factorization method is used to output a time series scheduling strategy. According to this strategy, analyze the data feature set of the denoised data for ensuring data, including the data importance coefficient and the data sensitivity coefficient. Then, according to the data index obtained from the data feature set, use the symmetric encryption algorithm to encrypt the data, and at the same time use the asymmetric encryption algorithm to encrypt the transmission encryption key to complete the encryption scheduling, and finally obtain the target data for ensuring data. These processes effectively enhance the data security and order, providing a high-quality data basis for subsequent real-time monitoring and evaluation of the transmission credibility.

[0025] Step S400: Perform real-time monitoring on the dynamic transmission of the target data for ensuring data to obtain real-time transmission information, where the real-time transmission information includes a real-time transmission delay value.

[0026] Specifically, after obtaining the target security data, to ensure the security and reliability of the data during the dynamic transmission process, it is necessary to conduct a full-range real-time monitoring on it. With the help of monitoring technology, continuously track the entire transmission process of the target security data from the sender to the receiver. During the monitoring process, a series of rich information will be collected, and among them, the real-time transmission delay value is particularly important. The real-time transmission delay value reflects the actual time delay experienced by the data during the transmission process and is one of the key indicators for measuring the transmission quality. In addition, the real-time transmission information also covers other key data such as real-time phase space coordinates and real-time transmission content (including real-time message digest). These real-time transmission information together constitute the basis for evaluating the data transmission status, not only providing basic data for obtaining the transmission credibility subsequently, but also providing strong support for judging whether the data transmission is normal and whether corresponding measures (such as retransmission, etc.) need to be taken.

[0027] Step S500: Obtain a reasonable delay threshold, and compare and analyze the real-time transmission delay value with the reasonable delay threshold to obtain the transmission credibility.

[0028] Specifically, in the cloud environment data trusted transmission encryption process, to accurately judge the credibility of the target security data transmission, it is necessary to first obtain a reasonable delay threshold. The determination of this threshold needs to consider multiple factors, including obtaining the initial phase space coordinates of any security data, combining the predicted phase space coordinates to obtain a fixed delay value, at the same time referring to the predetermined single-node forwarding reasonable threshold, and determining it according to the predicted transmission path determined by the initial and predicted phase space coordinates. After obtaining the reasonable delay threshold, compare it with the real-time transmission delay value obtained by real-time monitoring. If the real-time transmission delay value is within the reasonable threshold range, it indicates that the transmission condition is relatively ideal and the transmission credibility is high; otherwise, the credibility is low. In addition, the predicted phase space coordinates can also be obtained by analyzing in the multi-dimensional state space, calculate the real-time space distance value in combination with the real-time phase space coordinates, and generate a time series of space distance values. Conduct a trend analysis on it based on the predetermined transmission duration to obtain the predicted space distance value, and use the normalized value as the weight coefficient to further adjust the transmission credibility, so as to realize a more accurate and comprehensive evaluation of the data transmission credibility.

[0029] Step S600: If the transmission credibility does not reach the predetermined credible limit value, give a trusted transmission warning for the any security data.

[0030] Specifically, after calculating and evaluating the transmission credibility, it is compared with a pre-set predetermined credibility limit. This predetermined credibility limit is comprehensively determined based on various factors such as the security requirements of data transmission, business standards, and past experience, and is a key indicator for measuring the credibility of data transmission. Once the transmission credibility fails to reach this predetermined credibility limit, it means that there may be risks during data transmission, such as unstable transmission paths, possible data tampering, etc. At this time, quickly activate the warning mechanism for ensuring the credible transmission of any data. This warning will notify relevant personnel or system modules in a specific manner, such as issuing an alarm prompt, generating a warning report, etc. After receiving the warning, relevant personnel or systems can take corresponding measures in a timely manner, such as checking the transmission path, re-encrypting the data, starting the data retransmission process, etc., so as to reduce the data transmission risk, ensure the integrity and security of the data, and ensure that the data transmission in the cloud environment meets the credible requirements.

[0031] In a possible implementation manner, step S500 further includes:

[0032] Step S510: Analyze and obtain the predicted phase space coordinates of the any guaranteed data in the multi-dimensional state space.

[0033] Step S520: Extract the real-time phase space coordinates from the real-time transmission information.

[0034] Step S530: Calculate the real-time spatial distance value between the real-time phase space coordinates and the predicted phase space coordinates.

[0035] Step S540: Generate a time series of spatial distance values according to the correspondence between the real-time spatial distance value and the real time.

[0036] Step S550: Obtain a predetermined transmission duration, and perform a trend analysis on the time series of spatial distance values in combination with the predetermined transmission duration to obtain a predicted spatial distance value.

[0037] Step S560: Adjust the transmission credibility with the normalized predicted spatial distance value as the weight coefficient.

[0038] Specifically, deeply mine the historical data in the multi-dimensional state space, and extract features related to the phase space coordinates of the guarantee data, such as the time series features of data transmission, the variation law of spatial position, and the performance parameters of transmission nodes. Then, select a suitable machine learning model, such as the Recurrent Neural Network (RNN) and its variant Long Short-Term Memory Network (LSTM). Preprocess the extracted feature data and convert it into a format suitable for model input, and then train the model. During the training process, the model will learn the internal change patterns and trends of the data in the multi-dimensional state space. After training, input the relevant features of any current guarantee data into the trained model. By applying the learned patterns and trends, the model outputs the predicted phase space coordinates of the guarantee data at a certain future moment. In addition, combined with the Kalman filter algorithm, utilize its prediction and estimation capabilities for the dynamic system state to optimize and correct the prediction results of the machine learning model, and further improve the accuracy of the predicted phase space coordinates.

[0039] When extracting the real-time phase space coordinates from the real-time transmission information, an algorithm based on sensor data fusion and Kalman filter is adopted. First, deploy sensors at key nodes on the data transmission path. These sensors can collect information related to the data transmission location, such as signal strength, propagation time, etc. Then, use the data fusion algorithm to comprehensively process the data collected by multiple sensors, eliminate the noise and errors in the data, and obtain a relatively accurate estimated value of the transmission location. Since data transmission is dynamically changing, the Kalman filter algorithm is needed to further optimize these estimated values. The Kalman filter algorithm can predict the current state based on the previous state through two steps of prediction and update, and correct the prediction result by combining the latest measurement value. In this process, the position estimated value obtained by sensor data fusion is used as the measurement value and input into the Kalman filter. After being processed by the Kalman filter, the output result is the real-time phase space coordinate, which can relatively accurately reflect the real-time spatial position of the data during transmission.

[0040] The real-time phase space coordinates are accurately extracted from the real-time transmission information and reflect the actual spatial position of the current data transmission; while the predicted phase space coordinates are obtained through analysis in the multi-dimensional state space and represent the theoretical spatial position where the data should be. To calculate the real-time spatial distance value between the two, the Euclidean distance formula is used. In the multi-dimensional space, if the real-time phase space coordinates are (x1, y1, z1) and the predicted phase space coordinates are (x2, y2, z2), then the real-time spatial distance value Through this calculation, the deviation between the actual transmission position and the expected position can be quantified. The larger the deviation value, the higher the degree of deviation of the transmission process from the expectation, providing a key quantitative basis for generating the time series of the spatial distance value and evaluating the transmission credibility later, and helping to comprehensively judge the reliability of data transmission.

[0041] Record the moment when each calculated real-time spatial distance value is obtained in real time, and associate each real-time spatial distance value with the corresponding real-time moment. As the data transmission continues, a series of such corresponding data pairs of real-time spatial distance values and real-time moments will be accumulated. Through the time series generation algorithm, sort and arrange these data pairs in chronological order to form a time series of spatial distance values. In this time series, each data point represents the distance between the actual transmission position and the predicted position of the data at a specific moment. This time series of spatial distance values can clearly show the fluctuation trend of the real-time spatial distance value over time, providing an important data basis for subsequent trend analysis in combination with the predetermined transmission duration, and then more accurately evaluating the credibility of data transmission.

[0042] The predetermined transmission duration is preset according to factors such as the service requirements of data transmission and the characteristics of the network environment, representing the ideal duration required for the normal transmission of data from the sender to the receiver. After obtaining this predetermined transmission duration, conduct in-depth analysis on the already generated time series of spatial distance values. The time series of spatial distance values reflects the change of the distance between the actual transmission position and the predicted position of the data over time. Use mathematical analysis methods such as polynomial regression fitting to model the time series of spatial distance values. Through these methods, the trend of the spatial distance value changing over time can be captured, and the possible magnitude of the spatial distance value at the end of the predetermined transmission duration can be predicted. First, sample the time series of spatial distance values based on the principle of uniform sampling to obtain a representative set of sampling points, perform polynomial regression fitting on these sampling points to generate a fitting curve. This curve can approximately describe the change trend of the spatial distance value. Substitute the predetermined transmission duration into the fitting curve for collaborative analysis to obtain the first predicted distance value. To improve the prediction accuracy, sampling and fitting can be performed again to obtain a second sampling fitting curve, and then the second predicted distance value can be obtained. Finally, take the smaller value of the two as the predicted spatial distance value. This predicted spatial distance value provides an important basis for subsequent adjustment of transmission credibility, and can more accurately reflect the actual situation and potential risks of data transmission.

[0043] The predicted spatial distance value obtained through the previous steps intuitively reflects the deviation degree between the actual data transmission path and the ideal path. However, the value range and magnitude of this value may vary greatly due to different transmission scenarios and data characteristics. In order to enable this value to participate in the adjustment of transmission credibility as a weight coefficient reasonably and effectively, it is first necessary to perform normalization processing on it. The purpose of normalization is to map the predicted spatial distance value to a specific interval, such as [0, 1], which can eliminate the influence brought by numerical differences in different scenarios and ensure that the weight coefficients are compared and applied on a unified scale. The normalized predicted spatial distance value, as a weight coefficient, reflects the influence weight of the degree of deviation of data transmission from the expectation on the transmission credibility. When this weight coefficient is large, it means that the actual situation of data transmission deviates greatly from the expectation, with high uncertainty and risk. At this time, it is necessary to correspondingly reduce the transmission credibility obtained by comparing the real-time transmission delay value and the reasonable delay threshold before to reflect the potential untrustworthy factors in the current transmission process; on the contrary, if the weight coefficient is small, it indicates that the data transmission basically meets the expectation and the transmission status is relatively stable, and the original transmission credibility can be appropriately increased or maintained. In this way, by combining the predicted spatial distance value to adjust the transmission credibility, the final transmission credibility evaluation result can more comprehensively and accurately reflect the actual transmission state of data in the cloud environment.

[0044] In a possible implementation manner, step S550 further includes:

[0045] Step S551: Sampling the time series of the spatial distance value based on the principle of uniform sampling to obtain a first set of sampling points.

[0046] Step S552: Performing polynomial regression fitting on the first set of sampling points to obtain a first sampling fitting curve.

[0047] Step S553: Conducting collaborative analysis on the predetermined transmission duration and the first sampling fitting curve to obtain a first predicted distance value.

[0048] Step S554: Using the first predicted distance value as the predicted spatial distance value.

[0049] Specifically, the time series of the spatial distance value is processed based on the principle of uniform sampling. The time series of the spatial distance value records the real-time spatial distance values between the actual transmission position and the predicted position of the data at different times. By uniform sampling, data points are selected from this time series at a fixed time interval, thereby obtaining a first set of sampling points. This can reduce the data volume while retaining key information and improve the efficiency of subsequent analysis.

[0050] After obtaining the first set of sampling points, these sampling points represent the discrete distribution of spatial distance values at different times. To obtain a continuous curve that can reflect the changing trend of spatial distance values over time, polynomial regression fitting needs to be performed on the first set of sampling points. First, a suitable polynomial degree is selected, which needs to be determined by combining the characteristics of the time series of spatial distance values and the complexity of data changes. If the degree is too low, it may not be able to accurately fit the changing trend of the data; if the degree is too high, overfitting is likely to occur, resulting in the curve being too sensitive to noise and losing its representativeness of the overall trend. After determining the polynomial degree, the least squares method principle is used to solve the coefficients of the polynomial. The core idea of the least squares method is to minimize the sum of the squares of the errors between the polynomial curve and each sampling point, that is, to make the curve as close as possible to all sampling points. Through a series of mathematical calculations, the coefficients of each term of the polynomial are obtained, thereby determining the specific polynomial function. Finally, the curve corresponding to this polynomial function is the first sampling fitting curve. It connects the originally discrete first set of sampling points with a continuous curve, clearly showing the changing trend of spatial distance values over time, providing a key basis for subsequent collaborative analysis in combination with the predetermined transmission duration and obtaining the first predicted distance value.

[0051] After obtaining the first sampling fitting curve, this curve reflects the changing trend of spatial distance values over time. The predetermined transmission duration is preset based on various factors such as the service requirements of data transmission and network performance, representing the ideal transmission time required for data from the sending end to the receiving end. When performing collaborative analysis on the predetermined transmission duration and the first sampling fitting curve, first, the time point corresponding to the predetermined transmission duration is substituted into the polynomial function represented by the first sampling fitting curve. Since the first sampling fitting curve is obtained by performing polynomial regression fitting on the first set of sampling points, its functional relationship contains the changing law of spatial distance values over time. When the time corresponding to the predetermined transmission duration is substituted as the independent variable into the function, according to the calculation rules of the function, a corresponding dependent variable value can be obtained. This dependent variable value is the predicted spatial distance based on the current changing trend of spatial distance values at the end of the predetermined transmission duration, that is, the first predicted distance value. This value comprehensively considers the historical changing trend of spatial distance values and the predetermined transmission duration, providing an important reference basis for more accurately evaluating the credibility of data transmission subsequently.

[0052] The obtained first predicted distance value is directly used as the predicted spatial distance value. This predicted spatial distance value provides an important basis for subsequently adjusting the transmission credibility with the normalized value as the weight coefficient, making the evaluation of data transmission credibility more accurate.

[0053] In a possible implementation manner, step S554 further includes:

[0054] Step S5541: Sample the time series of the spatial distance values based on the principle of uniform sampling to obtain a second set of sampling points.

[0055] Step S5542: Perform polynomial regression fitting on the second set of sampling points to obtain a second sampling fitting curve.

[0056] Step S5543: Conduct collaborative analysis on the predetermined transmission duration and the second sampling fitting curve to obtain a second predicted distance value.

[0057] Step S5544: Select the smaller distance value between the first predicted distance value and the second predicted distance value as the predicted spatial distance value.

[0058] Specifically, when the preliminary sampling of the time series of spatial distance values (obtaining the first set of sampling points) is completed and the first predicted distance value is obtained based on subsequent analysis, in order to obtain a more comprehensive and accurate prediction result, resampling is required. This time, the operation is carried out on the time series of spatial distance values based on the principle of uniform sampling. Although it also follows the basic principle of uniform sampling, there will be differences in the specific implementation compared to obtaining the first set of sampling points. For example, a different starting time point may be selected from the first sampling, or the sampling interval may be adjusted to ensure that the collected data points can reflect the change characteristics of the spatial distance values from different perspectives. Through this targeted sampling method, a series of new data points are selected from the time series of spatial distance values, and these data points together constitute the second set of sampling points.

[0059] After obtaining the second set of sampling points, polynomial regression fitting is performed on these discrete sampling points to obtain a second sampling fitting curve. Since the selection method of the second set of sampling points is different from that of the first set of sampling points, the data characteristics they reflect also vary. Therefore, separate fitting helps to capture the changing trend of the spatial distance values over time from a new perspective. First, according to the distribution characteristics and data fluctuations of the second set of sampling points, an appropriate polynomial degree is selected. This requires comprehensive consideration of the complexity of the data. If the polynomial degree is selected too low, the fitting curve may not accurately fit the data points and cannot effectively reflect the data change trend; if the degree is too high, overfitting is likely to occur, making the curve too sensitive to noise and reducing its representativeness of the overall trend. After determining the polynomial degree, the least squares method principle is used to solve the coefficients of the polynomial. The core of the least squares method is to continuously adjust the coefficients to minimize the sum of the squares of the errors between the fitting curve and each data point in the second set of sampling points, and determine the specific expression of the polynomial. The curve described by this polynomial is the second sampling fitting curve.

[0060] After obtaining the second sampled fitting curve, the curve presents the changing trend of the spatial distance values reflected by the second set of sampling points over time. The predetermined transmission duration is preset according to factors such as data transmission service requirements and network environment, representing the transmission time required for data from the sending end to the receiving end under ideal conditions. When performing collaborative analysis on the predetermined transmission duration and the second sampled fitting curve, the time point corresponding to the predetermined transmission duration is substituted into the polynomial function represented by the second sampled fitting curve. Since the second sampled fitting curve is obtained by performing polynomial regression fitting on the second set of sampling points, its functional relationship contains a unique changing rule of spatial distance values over time. When the time corresponding to the predetermined transmission duration is substituted as the independent variable into this function, according to the operation rules of the function, a corresponding dependent variable value can be calculated. This dependent variable value is the spatial distance predicted based on the second sampled fitting curve at the end of the predetermined transmission duration, that is, the second predicted distance value.

[0061] Compare the first predicted distance value and the second predicted distance value, and select the smaller distance value as the final predicted spatial distance value. The reason for doing this is that a smaller distance value means that the data transmission is closer to the expected path in terms of spatial position, and can better represent a more ideal situation of data transmission. By comprehensively considering the results obtained from two different sets of sampling and fitting in this way, the predicted spatial distance value can be made more reliable and accurate, providing a more solid foundation for adjusting the transmission credibility with this value as the weight coefficient in the subsequent process.

[0062] In a possible implementation manner, step S300 further includes:

[0063] Step S310: Optimize and denoise the arbitrary safeguard data by using the wavelet threshold denoising method to obtain denoised safeguard data.

[0064] Step S320: Output a time series scheduling strategy by combining the massive parallel Bayesian factorization method, and perform encrypted scheduling on the denoised safeguard data according to the time series scheduling strategy to obtain encrypted scheduled safeguard data.

[0065] Step S330: Denote the encrypted scheduled safeguard data as the target safeguard data.

[0066] Specifically, the wavelet threshold denoising method is adopted to optimize and denoise any safeguard data. Wavelet threshold denoising utilizes the characteristics of wavelet transform to decompose the data into different frequency sub-bands, and then processes the wavelet coefficients according to a preset threshold. For wavelet coefficients with amplitudes lower than the threshold, it is considered that they mainly contain noise and are set to zero; while wavelet coefficients higher than the threshold are retained or subjected to a certain shrinkage process. Through such operations, the noise components in the data are removed, and purer denoised safeguard data is obtained, improving the data quality and laying a foundation for subsequent processing.

[0067] By means of the massive parallel Bayesian factorization method, the potential laws and optimal transmission modes of the data in the time dimension are mined from a large amount of historical information related to data transmission, as well as multi-source data such as the current network environment and data characteristics, and then an accurate time series scheduling strategy is output. This strategy fully considers the characteristics and transmission requirements of different data, and plans a reasonable time arrangement for data transmission. Based on this time series scheduling strategy, the encrypted scheduling work is carried out on the denoised safeguard data. When specifically implemented, first deeply analyze the data feature set of the denoised safeguard data, which includes the data importance coefficient and the data sensitivity coefficient. These coefficients intuitively reflect the value and sensitivity of each piece of data. According to the analyzed data index, a symmetric encryption algorithm is selected to encrypt the data to ensure the confidentiality of the data content during transmission. At the same time, in order to further ensure the security of the encryption key, an asymmetric encryption algorithm is used to encrypt and transmit the encryption key. Through this series of operations, the encrypted scheduling of the denoised safeguard data is completed, and finally the encrypted scheduling safeguard data is obtained. These encrypted scheduling safeguard data are not only transmitted according to a reasonable time series, greatly improving the transmission efficiency, but also effectively preventing the data from being stolen or tampered with during transmission under the protection of encryption technology, strongly guaranteeing the security and integrity of the data.

[0068] At this time, the obtained encrypted scheduling safeguard data already has the characteristics to meet the requirements of trustworthy data transmission in the cloud environment. This part of the data is clearly recorded as the target safeguard data, which means that it becomes the core object in subsequent data transmission monitoring, evaluation and other links. This definition enables each module in the entire trustworthy data transmission system to clearly identify and process this data, providing a unified and clear data basis for operations such as real-time monitoring of its dynamic transmission, obtaining real-time transmission information, and evaluating transmission credibility, and ensuring the coherence and accuracy of the entire trustworthy data transmission process.

[0069] In a possible implementation manner, step S320 further includes:

[0070] Step S321: Analyze the data feature set of the denoised safeguard data according to the time series scheduling strategy.

[0071] Step S322: Based on the data index obtained by analyzing the data feature set, use a symmetric encryption algorithm to encrypt the data, and encrypt and transmit the encryption key through an asymmetric encryption algorithm to obtain the encrypted scheduling guarantee data; wherein, the data feature set includes a data importance coefficient and a data sensitivity coefficient.

[0072] Specifically, after the massive parallel Bayesian factorization method outputs the time series scheduling strategy, conduct in-depth analysis on the noise reduction guarantee data to obtain the data feature set. Analyze the noise reduction guarantee data from multiple dimensions according to the processing requirements of the data at different time points, transmission priorities, etc. in the scheduling strategy. For the determination of the data importance coefficient, the position of the data in the entire business process will be considered. For example, if a certain data is the key information to support the normal operation of the core business function, such as the account balance data in financial transactions, it has a high importance coefficient; if it is only auxiliary or reference data, the importance coefficient is relatively low. The evaluation of the data sensitivity coefficient is carried out around the sensitivity of the information contained in the data. If the data involves privacy content such as the user's ID number and bank card password, or the company's unpublicized business secrets and technical patent details, the sensitivity coefficient of such data will be very high; on the contrary, data that is publicly transparent and does not involve sensitive information has a lower sensitivity coefficient. By comprehensively considering and analyzing various aspects, accurately determine the importance coefficient and sensitivity coefficient corresponding to each noise reduction guarantee data, and finally integrate them to form the data feature set.

[0073] After obtaining the data feature set containing the data importance coefficient and the data sensitivity coefficient, comprehensive operations are performed on these coefficients to obtain the data index. Based on the obtained data index, the noise reduction guarantee data is encrypted using a symmetric encryption algorithm. The symmetric encryption algorithm, by virtue of the characteristic of using the same key for encryption and decryption, has the advantages of fast encryption speed and high efficiency, and can quickly convert the original data into ciphertext form, effectively preventing the data from being directly stolen and interpreted during the transmission process. For example, when encrypting a large amount of regular business data, the symmetric encryption algorithm can efficiently complete the encryption task and ensure the data transmission efficiency. However, the security of the key in the symmetric encryption algorithm is crucial. Once the key is leaked, the confidentiality of the data cannot be guaranteed. Therefore, to ensure the security of the encryption key during the transmission process, an asymmetric encryption algorithm is used to encrypt and transmit the encryption key. The asymmetric encryption algorithm has a pair of keys, namely the public key and the private key. The public key can be made public and is used to encrypt data; the private key is strictly confidential and is used to decrypt. When transmitting the encryption key, the sender uses the public key of the receiver to encrypt the key of the symmetric encryption algorithm. After receiving the ciphertext, the receiver uses its own private key to decrypt it, thereby obtaining the key of the symmetric encryption algorithm. This method greatly enhances the security of the encryption key transmission. Even if the ciphertext of the encryption key is intercepted during the transmission process, the real key cannot be cracked without the private key. After a series of operations such as encrypting the data using the symmetric encryption algorithm and encrypting and transmitting the encryption key using the asymmetric encryption algorithm, the encrypted scheduling guarantee data is finally generated. These data not only achieve efficient encryption to protect the data content but also ensure the security of the encryption key transmission.

[0074] In a possible implementation manner, step S500 further includes:

[0075] Step S570: Obtain the initial phase space coordinates of the arbitrary guarantee data.

[0076] Step S580: Combine the initial phase space coordinates with the predicted phase space coordinates to obtain a fixed time delay value.

[0077] Step S590: Obtain a predetermined single-node forwarding reasonable threshold, and combine the predicted transmission path of the initial phase space coordinates and the predicted phase space coordinates to obtain the time delay reasonable threshold.

[0078] Specifically, in a multi-dimensional state space, data has unique spatial position attributes since its generation, and this coordinate precisely depicts the spatial state of the data at the starting moment of transmission. Through various sensors and monitoring modules deployed at the data sending end, multi-dimensional information generated instantaneously by the data is collected, such as the node positions in the network topology, the physical addresses of the data generation devices, etc. Using a spatial mapping algorithm, this information is transformed into coordinate values in the corresponding coordinate system. At the same time, by combining the position identification information contained in the data metadata, through cross-validation and data fusion techniques, redundant and incorrect data are removed, and finally, accurate and unique initial phase space coordinates are obtained.

[0079] After obtaining the initial phase space coordinates of any safeguard data, and at the same time based on the already obtained predicted phase space coordinates. The initial phase space coordinates represent the spatial position where the data is located at the starting moment of transmission, while the predicted phase space coordinates are the spatial positions that the data is estimated to reach at a certain future moment according to factors such as the data transmission law and network conditions. To obtain the fixed delay value, first, the spatial distance between these two coordinates needs to be determined. With the help of the distance calculation formula in multi-dimensional space, considering factors such as the network topology structure and the physical path of data transmission, the actual distance from the initial phase space coordinates to the predicted phase space coordinates is calculated. Then, combined with the average speed of network transmission, which is obtained through comprehensive evaluation of statistical analysis of historical transmission data and various factors such as the current network bandwidth and device performance. Dividing the calculated spatial distance by the average speed gives the time required for the data to be transmitted from the initial position to the predicted position under ideal conditions, and this time is the fixed delay value. This fixed delay value excludes the influence of accidental factors such as sudden interference and device failures in the network and is a theoretical data transmission time benchmark, providing an important reference basis for subsequent judgment of whether the actual delay of data transmission is reasonable.

[0080] Retrieve the reasonable threshold for single-node forwarding of the reservation order from the network operation and maintenance database. This threshold is obtained based on the long-term monitoring of the processing performance, caching capacity of network devices (such as switches and routers), and historical data statistical analysis. For example, the average duration for a certain model of router to process data packets under normal load is set to 50 ms after statistics. At the same time, according to the initial phase space coordinates and the predicted phase space coordinates, use graph theory algorithms (such as Dijkstra's algorithm) to calculate the predicted transmission path in the network topology diagram, and clarify the sequence of nodes that the data will pass through in turn. Then, according to the number of nodes on the predicted transmission path, accumulate the reasonable threshold for single-node forwarding of the reservation order to obtain the basic delay value. For example, if the path contains 10 nodes, the basic delay value is 500 ms (50 ms × 10). In addition, real-time collect network status parameters such as network bandwidth utilization rate and packet loss rate, and establish a Bayesian network model in combination with historical data to calculate the dynamic correction coefficient. For example, the correction coefficient is 1.2 when the network is slightly congested. Finally, multiply the basic delay value by the correction coefficient, that is, 500 ms × 1.2 = 600 ms, so as to obtain the final reasonable threshold for delay. This threshold fully considers the actual operating conditions of the network and provides an accurate reference standard for the evaluation of transmission credibility.

[0081] In a possible implementation manner, step S400 further includes:

[0082] Step S410: Extract the real-time transmission content in the real-time transmission information, where the real-time transmission content includes a real-time message digest.

[0083] Step S420: Obtain the integrity of the real-time message digest by comparing the real-time message digest with the predetermined message digest.

[0084] Step S430: If the integrity of the real-time message digest does not meet the predetermined constraint, send a retransmission signal.

[0085] Step S440: Perform retransmission of the target guaranteed data based on the retransmission signal.

[0086] Specifically, real-time transmission information on the network interface is captured through a network packet capture tool (such as Wireshark), and this information exists in the form of network data packets. Then, according to the protocol used for data transmission (such as TCP / IP, UDP, etc.), the header structure of the data packet is parsed, and the protocol parser is used to identify the data payload part, thereby extracting the real-time transmission content. For the extraction of the real-time message digest, a hash algorithm (such as SHA-256, MD5, etc.) is used to calculate the real-time transmission content, mapping the original data to a fixed-length digest string. For example, by calling the hash function interface in the OpenSSL library, the extracted real-time transmission content is operated on, and finally the data fingerprint that uniquely identifies this transmission content, that is, the real-time message digest, is obtained, providing a key basis for subsequent data integrity verification.

[0087] After the real-time message digest is extracted, it is compared with a pre-set predefined message digest. The predefined message digest is calculated using the same hash algorithm (such as SHA-256, etc.) as that for generating the real-time message digest on the original data before data transmission, and it represents the original characteristics and integrity standard of the data. To obtain the integrity degree of the real-time message digest, the real-time message digest and the predefined message digest are compared bit by bit. First, the two digests are checked bit by bit in binary form, and the number of identical bits is counted. Then, the number of identical bits is divided by the total number of bits of the digest, and the resulting ratio is the integrity degree of the real-time message digest. This integrity degree is presented in the form of a percentage, intuitively reflecting the degree of conformity of the real-time transmitted data with the original data in terms of content. For example, if the total number of bits of the real-time message digest and the predefined message digest is 256 bits, and 250 bits are the same, then the integrity degree of the real-time message digest is 250÷256×100%≈97.66%. Through such comparison and calculation, the integrity status of the data during transmission can be quickly and accurately evaluated, providing an important basis for subsequent judgment on whether data needs to be retransmitted.

[0088] The obtained integrity degree of the real-time message digest is compared with a predefined constraint. The predefined constraint is a standard value set in advance according to the reliability requirements of data transmission. For example, it is required that the integrity degree of the message digest reaches more than 95%. If the integrity degree of the real-time message digest does not meet this predefined constraint, it indicates that the data may be lost, damaged, etc. during transmission, and a retransmission signal will be immediately sent. This signal serves as an instruction for data retransmission, providing a basis for the next operation.

[0089] Based on the retransmission signal, the retransmission of the target guaranteed data is executed. After receiving the retransmission signal, the data sender will reorganize the target guaranteed data and transmit it again according to the established transmission protocol and path to ensure that the data can reach the receiver accurately and without error, thereby guaranteeing the reliability and integrity of the entire data transmission process.

[0090] In a possible implementation manner, step S420 further includes:

[0091] Step S421: Obtain the collaborative transmission message of the target safeguard data.

[0092] Step S422: Calculate the message digest of the collaborative transmission message by using a hash algorithm, and record it as the predetermined message digest.

[0093] Specifically, through a pre-set data call protocol, locate the target safeguard data in the encrypted storage module. These target safeguard data have undergone pre-processing such as wavelet threshold denoising and encrypted scheduling based on massive parallel Bayesian factorization decomposition, and already have relatively high data quality and security. Subsequently, according to the network transmission protocol specifications, format encapsulate the target safeguard data, add metadata such as transmission identifiers, timestamps, and check codes to the data header, and append integrity check information to the tail, and finally combine to generate a collaborative transmission message. This message not only contains the original data content but also integrates various control information required for the transmission process, and is transmitted in the network in the form of data packets. Its integrity will be used as the core basis for evaluating the subsequent data transmission quality.

[0094] Build a benchmark for data integrity verification through a hash algorithm. Call a mature hash algorithm library (such as the hash function module in OpenSSL), and algorithms such as SHA-256 and MD5 can be selected. Use the collaborative transmission message as the input data. The hash algorithm will perform operations on each byte of the message, and through bit operations, loop iterations, etc., compress and map the collaborative transmission message of any length into a fixed-length string. For example, when using the SHA-256 algorithm, regardless of the size of the collaborative transmission message, a 256-bit hash value will be generated finally. Since the hash algorithm is one-way, the original message cannot be deduced backward from the digest, and the same message input will definitely generate the same digest, while different messages will probably generate different digests. Use the calculated hash value as the message digest and mark it as the predetermined message digest. This digest will be used as the digital fingerprint for data transmission integrity verification and will be compared with the real-time message digest in the real-time transmission information later to determine whether data is lost, tampered with, etc. during the transmission process.

[0095] Embodiment 2, based on the same inventive concept as the encryption method for ensuring trusted transmission of safeguard data in the cloud environment in the foregoing embodiment, as Figure 2 shown, the present application provides an encryption system for ensuring trusted transmission of safeguard data in the cloud environment. The system in the embodiments of the present application and the method embodiments are based on the same inventive concept. Among them, the system includes:

[0096] An information set construction module 10, configured to construct an initial transmission information set of the cloud computing platform by combining historical cloud data transmission records.

[0097] The multi-dimensional state space construction module 20 is used to add a predefined hidden signal to the initial transmission information set to obtain a target transmission information set, and construct a multi-dimensional state space according to the target transmission information set.

[0098] The target guarantee data acquisition module 30 is used to extract any guarantee data from the multi-dimensional state space, and preprocess the any guarantee data to obtain target guarantee data.

[0099] The real-time transmission information acquisition module 40 is used to monitor the dynamic transmission of the target guarantee data in real time to obtain real-time transmission information, where the real-time transmission information includes a real-time transmission delay value.

[0100] The transmission credibility acquisition module 50 is used to obtain a reasonable delay threshold, and compare and analyze the real-time transmission delay value with the reasonable delay threshold to obtain transmission credibility.

[0101] The reliable transmission warning module 60 is used to perform a reliable transmission warning on the any guarantee data if the transmission credibility does not reach a predetermined reliable limit value.

[0102] Furthermore, the system is also used for the following functions:

[0103] Analyze in the multi-dimensional state space to obtain the predicted phase space coordinates of the any guarantee data; extract the real-time phase space coordinates from the real-time transmission information; calculate the real-time space distance value between the real-time phase space coordinates and the predicted phase space coordinates; generate a time series of space distance values according to the corresponding relationship between the real-time space distance value and the real-time moment; obtain a predetermined transmission duration, and perform a trend analysis on the time series of space distance values in combination with the predetermined transmission duration to obtain a predicted space distance value; use the normalized predicted space distance value as a weight coefficient to adjust the transmission credibility.

[0104] Furthermore, the system is also used for the following functions:

[0105] Sample the time series of space distance values based on the uniform sampling principle to obtain a first sampling point set; perform polynomial regression fitting on the first sampling point set to obtain a first sampling fitting curve; perform collaborative analysis on the predetermined transmission duration and the first sampling fitting curve to obtain a first predicted distance value; use the first predicted distance value as the predicted space distance value.

[0106] Furthermore, the system is also used for the following functions:

[0107] Sampling the time series of the spatial distance values based on the uniform sampling principle to obtain a second set of sampling points; performing polynomial regression fitting on the second set of sampling points to obtain a second sampling fitting curve; performing collaborative analysis on the predetermined transmission duration and the second sampling fitting curve to obtain a second predicted distance value; taking the smaller distance value between the first predicted distance value and the second predicted distance value as the predicted spatial distance value.

[0108] Further, the system is also used for the following functions:

[0109] Using the wavelet threshold denoising method to optimize and denoise the arbitrary safeguard data to obtain denoised safeguard data; combining the massive parallel Bayesian factorization method to output a time series scheduling strategy, and encrypting and scheduling the denoised safeguard data according to the time series scheduling strategy to obtain encrypted scheduled safeguard data; denoting the encrypted scheduled safeguard data as the target safeguard data.

[0110] Further, the system is also used for the following functions:

[0111] Analyzing the data feature set of the denoised safeguard data according to the time series scheduling strategy; encrypting the data using a symmetric encryption algorithm based on the data index obtained by analyzing the data feature set, and encrypting and transmitting the encryption key using an asymmetric encryption algorithm to obtain the encrypted scheduled safeguard data; wherein the data feature set includes a data importance coefficient and a data sensitivity coefficient.

[0112] Further, the system is also used for the following functions:

[0113] Obtaining the initial phase space coordinates of the arbitrary safeguard data; combining the initial phase space coordinates and the predicted phase space coordinates to obtain a fixed time delay value; obtaining a predetermined single-node forwarding reasonable threshold, and combining the predicted transmission path of the initial phase space coordinates and the predicted phase space coordinates to obtain the time delay reasonable threshold.

[0114] Further, the system is also used for the following functions:

[0115] Extracting the real-time transmission content in the real-time transmission information, wherein the real-time transmission content includes a real-time message digest; obtaining the real-time message digest integrity by comparing the real-time message digest with a predetermined message digest; if the real-time message digest integrity does not meet the predetermined constraint, sending a retransmission signal; performing retransmission execution on the target safeguard data based on the retransmission signal.

[0116] Further, the system is also used for the following functions:

[0117] Obtain the collaborative transmission message of the target guarantee data; calculate the message digest of the collaborative transmission message by using a hash algorithm, and record it as the predetermined message digest.

[0118] It should be noted that the above sequence of embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above specific embodiments of this specification have been described. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0119] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0120] This specification and the drawings are only exemplary descriptions of the present application and are considered to have covered any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications.

Claims

1. An encryption method for ensuring trusted data transmission in a cloud environment, characterized in that, Including: Construct an initial transmission information set of the cloud computing platform by combining historical cloud data transmission records; Add a predefined hidden signal to the initial transmission information set to obtain a target transmission information set, and construct a multi-dimensional state space based on the target transmission information set; Extract any safeguard data from the multi-dimensional state space, and preprocess the any safeguard data to obtain target safeguard data; Perform real-time monitoring on the dynamic transmission of the target safeguard data to obtain real-time transmission information, where the real-time transmission information includes a real-time transmission delay value; Obtain a reasonable delay threshold, and compare and analyze the real-time transmission delay value with the reasonable delay threshold to obtain a transmission credibility; If the transmission credibility does not reach a predefined credible limit value, then issue a credible transmission warning for the any safeguard data.

2. The encryption method for ensuring trusted data transmission in a cloud environment according to claim 1, wherein It also includes: Analyze in the multi-dimensional state space to obtain the predicted phase space coordinates of the any safeguard data; Extract the real-time phase space coordinates from the real-time transmission information; Calculate the real-time space distance value between the real-time phase space coordinates and the predicted phase space coordinates; Generate a time series of space distance values according to the correspondence between the real-time space distance value and the real-time moment; Obtain a predefined transmission duration, and perform a trend analysis on the time series of space distance values in combination with the predefined transmission duration to obtain a predicted space distance value; Use the normalized predicted space distance value as a weight coefficient to adjust the transmission credibility.

3. The encryption method for ensuring trusted data transmission in a cloud environment according to claim 2, wherein, Obtain a predefined transmission duration, and perform a trend analysis on the time series of space distance values in combination with the predefined transmission duration to obtain a predicted space distance value, including: Sample the time series of space distance values based on the uniform sampling principle to obtain a first sampling point set; Perform polynomial regression fitting on the first sampling point set to obtain a first sampling fitting curve; Perform collaborative analysis on the predefined transmission duration and the first sampling fitting curve to obtain a first predicted distance value; Take the first predicted distance value as the predicted space distance value.

4. The encryption method for ensuring trusted data transmission in a cloud environment according to claim 3, wherein After taking the first predicted distance value as the predicted space distance value, it also includes: Sample the time series of space distance values based on the uniform sampling principle to obtain a second sampling point set; Perform polynomial regression fitting on the second sampling point set to obtain a second sampling fitting curve; Perform collaborative analysis on the predefined transmission duration and the second sampling fitting curve to obtain a second predicted distance value; Take the smaller distance value between the first predicted distance value and the second predicted distance value as the predicted space distance value.

5. The encryption method for ensuring trusted data transmission in a cloud environment according to claim 1, characterized in that, Extract any safeguard data from the multi-dimensional state space, and preprocess the any safeguard data to obtain target safeguard data, including: Use the wavelet threshold denoising method to perform optimized denoising processing on the any safeguard data to obtain denoised safeguard data; Output a time series scheduling strategy by combining the massive parallel Bayesian factorization method, and perform encrypted scheduling on the denoised safeguard data according to the time series scheduling strategy to obtain encrypted scheduled safeguard data; Record the encrypted scheduled safeguard data as the target safeguard data.

6. The encryption method for ensuring trusted data transmission in a cloud environment as described in claim 5, characterized in that, Output a time series scheduling strategy in combination with a massive parallel Bayesian factorization method, and encrypt and schedule the noise reduction guarantee data according to the time series scheduling strategy to obtain encrypted scheduling guarantee data, including: Analyze the data feature set of the noise reduction guarantee data according to the time series scheduling strategy; Based on the data exponent obtained by analyzing the data feature set, use a symmetric encryption algorithm to encrypt the data, and encrypt and transmit the encryption key through an asymmetric encryption algorithm to obtain the encrypted scheduling guarantee data; Among them, the data feature set includes a data importance coefficient and a data sensitivity coefficient.

7. The encryption method for ensuring trusted data transmission in the cloud environment according to claim 2, wherein Obtain a reasonable delay threshold, including: Obtain the initial phase space coordinates of the arbitrary guarantee data; Combine the initial phase space coordinates with the predicted phase space coordinates to obtain a fixed delay value; Obtain a reasonable threshold for single-node forwarding, and combine the predicted transmission path of the initial phase space coordinates and the predicted phase space coordinates to obtain the reasonable delay threshold.

8. The encryption method for ensuring trusted data transmission in a cloud environment according to claim 1, characterized in that, After real-time monitoring of the dynamic transmission of the target guarantee data to obtain real-time transmission information, it further includes: Extract the real-time transmission content in the real-time transmission information, where the real-time transmission content includes a real-time message digest; Obtain the integrity of the real-time message digest by comparing the real-time message digest with a predetermined message digest; If the integrity of the real-time message digest does not meet the predetermined constraint, send a retransmission signal; Perform retransmission of the target guarantee data based on the retransmission signal.

9. The encryption method for ensuring trusted data transmission in the cloud environment according to claim 8, characterized in that, Obtain the integrity of the real-time message digest by comparing the real-time message digest with a predetermined message digest, including: Obtain the collaborative transmission message of the target guarantee data; Use a hash algorithm to calculate the message digest of the collaborative transmission message and record it as the predetermined message digest.

10. An encryption system for ensuring trusted data transmission in a cloud environment, characterized in that, The system is used to implement the encryption method for ensuring the trusted transmission of guarantee data in a cloud environment according to any one of claims 1-9. The system includes: An information set construction module for constructing an initial transmission information set of a cloud computing platform in combination with historical cloud data transmission records; A multi-dimensional state space construction module for adding a predefined hidden signal to the initial transmission information set to obtain a target transmission information set, and constructing a multi-dimensional state space according to the target transmission information set; A target guarantee data acquisition module for extracting any guarantee data from the multi-dimensional state space and preprocessing the any guarantee data to obtain target guarantee data; A real-time transmission information acquisition module for real-time monitoring of the dynamic transmission of the target guarantee data to obtain real-time transmission information, where the real-time transmission information includes a real-time transmission delay value; A transmission credibility acquisition module for obtaining a reasonable delay threshold and comparing and analyzing the real-time transmission delay value with the reasonable delay threshold to obtain transmission credibility; A trusted transmission warning module for warning of trusted transmission of the arbitrary guarantee data if the transmission credibility does not reach a predetermined trusted limit.

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