Cloud platform data protection method and system based on big data

Abstract

The invention discloses a cloud platform data protection method and system based on big data, and relates to the technical field of data protection, the system comprises a data detection end, a data anti-counterfeiting end and a protection cooperation end, the data detection end is used for collecting multi-channel data of a cloud platform in real time and carrying out real-time noise detection on the multi-channel data, and the data anti-counterfeiting end is used for protecting the multi-channel data; the data anti-counterfeiting end is used for judging whether noise of data processing is affected or not, the data anti-counterfeiting end is used for carrying out anti-counterfeiting identification on data received by the multi-channel cloud platform in real time by establishing a multi-scale time-varying data feature model, and the protection cooperation end is used for receiving an anti-counterfeiting result by receiving the data noise in real time and the multi-channel data of the cloud platform. And a cloud platform data processing setting password and a reverse password are subjected to dual-protection collaborative generation. According to the cloud platform data protection method and system based on big data, the accuracy of cloud platform data receiving is improved, anti-fake data is effectively identified, and unauthorized access and data leakage are prevented.

Description

Technical Field

[0001] The present invention relates to the field of data protection technology, and in particular to a cloud platform data protection method and system based on big data. Background Art

[0002] Cloud platform data protection is a comprehensive set of measures and strategies designed to ensure the security, integrity, and availability of data stored on the cloud platform. The physical center of the cloud platform is usually located in a facility with a high-level physical security system, including site selection considerations, internal zoning, fire alarm systems, etc. to ensure the physical security of the data center. The cloud platform prevents unauthorized network access and attacks by deploying security measures such as advanced firewalls and intrusion detection systems, as well as implementing strict access control policies.

[0003] Application number CN201911346810.1 discloses a method and system for protecting OpenStack cloud hosts. This system uses the Ceph API to take a snapshot of a storage image, exports the snapshot as a data file, and then uploads the exported data file to a backup server for storage. Advantages: With simple configuration, online data protection can be achieved for cloud hosts powered by OpenStack images and cloud hard drives, without modifying cloud platform parameters. This improves overall backup efficiency and reduces operational and maintenance costs.

[0004] After searching the above patents, it was found that there are still some shortcomings in cloud platform data protection: 1. The cloud platform relies on multiple components and services in the supply chain, but there are loopholes or malicious behaviors in any link of the supply chain. Especially when processing cloud platform data, the existing data noise will cause data reception abnormalities, which may directly threaten the security of the cloud platform; 2. When the cloud platform receives data, it is easy to cause the data to receive false data, resulting in the cloud platform data processing being unable to effectively identify anti-counterfeiting data, affecting the cloud platform data protection effect; 3. The identity authentication mechanism of some cloud platforms is imperfect, or the access control policy is improperly configured, which may lead to unauthorized access and easy data leakage, affecting the security and reliability of cloud platform data protection.

[0005] Therefore, a cloud platform data protection method and system based on big data are proposed to solve the above problems. Summary of the Invention

[0006] The main purpose of the present invention is to provide a cloud platform data protection method and system based on big data to solve the problems raised in the above background.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is: a cloud platform data protection method and system based on big data, including a data detection terminal, a data anti-counterfeiting terminal and a protection coordination terminal;

[0008] The data detection terminal is used to collect multi-channel data from the cloud platform in real time and perform real-time noise detection on the multi-channel data to determine whether the noise of data processing is affected. When the data processing noise is abnormal, the data detection terminal analyzes the cause of the noise and monitors the data protection status in real time based on environmental parameters and makes corrections.

[0009] The data anti-counterfeiting terminal is used to perform anti-counterfeiting identification on data received by the multi-channel cloud platform in real time by establishing a multi-scale time-varying data feature model, and to track and crush fake data received by the multi-channel cloud platform in real time, as well as reversely identify fake data received by the multi-channel cloud platform;

[0010] The protection collaboration terminal is used to receive anti-counterfeiting results by receiving data noise and multi-channel data of the cloud platform in real time, and collaboratively generate double protection by setting passwords and reverse passwords for cloud platform data processing, thereby realizing collaborative protection and multiple password protection of cloud platform data.

[0011] The data detection terminal includes a data acquisition module, an information monitoring module and a receiving influence module;

[0012] The data acquisition module includes a multi-source acquisition unit and a data arrangement unit;

[0013] The multi-source acquisition unit is used to collect cloud platform data information of the Togo channel through a data monitor and an acquisition chip;

[0014] The data arrangement unit is used to arrange the cloud platform data information monitored in real time by sequence number.

[0015] The information monitoring module includes a noise detection unit and an environment detection unit;

[0016] The noise detection unit is used to calculate the cloud platform data noise at the current moment in real time. The calculation formula is as follows:

[0017]

[0018] Where Z represents the cloud platform data noise at the current moment, x represents the data observation value of the current channel, μ represents the mean, and σ represents the standard deviation;

[0019] The environment detection unit is used to monitor the cloud platform data feature receiving parameters at the current moment in real time through the environment monitoring equipment, and the environment monitoring equipment includes an electromagnetic radiation meter, a temperature sensor, a humidity sensor and a magnetic field sensor.

[0020] The receiving impact module includes a data impact unit, an impact analysis unit and a state correction unit;

[0021] The data impact unit is used to set the standard parameters for receiving cloud platform data features, and calculate the difference between the basic parameters at the current moment and the standard parameters. If the difference is less than or equal to 0.01 and greater than or equal to -0.01, it indicates that the cloud platform data feature reception is normal. If the difference is greater than 0.01 or less than -0.01, it indicates that the cloud platform data feature reception is abnormal, and the reporting system issues a voice broadcast reminder;

[0022] The impact analysis unit is used to continuously calculate the standard parameter threshold and the average value of the basic parameters based on the difference between the basic parameters of the cloud platform data monitored at the current moment and the standard parameters, and finally calculate the deviation value of each basic parameter. The calculation formula is as follows:

[0023]

[0024] Among them, U is the effective value of each basic parameter, N is the sampling basic parameter of different channels, and UK is the instantaneous sampling value of the parameter of each channel;

[0025] The state correction unit is used to calculate the parameter deviation between each basic parameter at the current moment and the standard data protection parameter. The calculation formula is as follows:

[0026]

[0027] The data receiving status is corrected by using the calculated parameter difference. If the parameter deviation is greater than or equal to 0, the parameter value of each basic parameter is increased. If the parameter deviation is less than 0, the parameter value of each basic parameter is reduced and tracking is continued through the data tracker.

[0028] The data anti-counterfeiting terminal includes a feature model module, an anti-counterfeiting identification module and a multi-channel tracking module;

[0029] The feature model module includes a model building unit and a multi-source intercommunication unit;

[0030] The model building unit is used to build a cloud platform data feature receiving model for deep learning and set cloud platform data feature receiving parameters, and multi-channel data is arranged by sequence number;

[0031] The multi-source intercommunication unit is used to realize multi-source intercommunication of multi-channel data in real time through a data receiver and a data converter.

[0032] The anti-counterfeiting identification module includes an anti-counterfeiting identification unit, a tracking and crushing unit, and a reverse identification unit;

[0033] The anti-counterfeiting identification unit is used to determine the feature similarity between current data and historical data through the Pearson correlation coefficient. The calculation formula is as follows:

[0034]

[0035] Among them, C(D S ,D S ′) represents the similarity between the characteristic parameters of the currently received data and the characteristic parameters of the historical data, cov(x,y) represents the covariance of the two received data features x and y, σ x σ y is the product of the standard deviations of the two received data features x and y. If C(D S ,D S ′) is equal to 1, the data is considered valid data. If C(D S ,D S ′) is not equal to 1, the data is judged to be false data;

[0036] The tracking and shredding unit is used to shred the pseudo data in real time through a hard disk data shredder;

[0037] The reverse identification unit is used to reversely calculate the repetition rate of the received data features, specifically by calculating the repetition rate of the historical data features and the features of the data to be received. The calculation formula is as follows:

[0038]

[0039] Among them, D sm is the repetition rate of the received data feature to be checked, sm j is the repetition rate of the received data feature to be checked in the jth channel, L j Query the dataset receiving data features for the j-th channel.

[0040] The multi-channel tracking module includes a multi-source tracking unit and a sorting warning unit;

[0041] The multi-source tracking unit is used to track the data receiving characteristics on multiple channels in real time through a data tracker, receive the multi-channel data results through a data receiver in real time, and arrange the multi-channel data results in sequence;

[0042] The sequencing warning unit is used to report the occurrence of false data and abnormal data receiving parameters in the data after the sequence arrangement, and to issue a voice alarm to the reporting system.

[0043] The protection coordination terminal includes a data receiving module, a protection coordination module and a protection tracking module;

[0044] The data receiving module is used to receive the warning results, noise impact results and environmental impact results of the characteristics of the data to be protected in real time through the data receiver, and determine the data protection plan in real time according to the results;

[0045] The protection cooperation module includes a password setting unit and a dual decryption unit;

[0046] The password setting unit is used to establish a user identification protection password model for each channel of the cloud platform, and set the password model to an Arabic numeral mode, specifically: set an Arabic numeral password through the CD4017 chip, and according to the Arabic numeral password set by the user, the Arabic numeral password includes nine digital symbols from 0 to 9, the real-name authentication obtains the user's real identity, obtains the user-defined digital password, and automatically sorts the Arabic numerals, the sorting number is (X1, X2, ..., X n ), which is a one-time password;

[0047] The double decryption unit is used to swap the order of the user-defined digital password and perform a secondary verification of the password according to the sequence number from back to front, that is, to obtain the sequence number (X n 、X n-1 ,…,X1), identify them one by one according to the serial number and complete the double decryption operation.

[0048] The protection tracking module includes a protection self-tracking unit and a data protection evaluation unit;

[0049] The protection self-tracking unit is used to track the dual password recognition results and decryption operations in real time through a data tracker, and if the decryption recognition error occurs, the reporting system will issue a voice alarm;

[0050] The data protection evaluation unit is used to evaluate the multi-channel data reception results of the cloud platform, and to track and evaluate in real time through a data tracker.

[0051] A cloud platform data protection method based on big data includes the following steps:

[0052] Step 1: Configure the IP address information of the data protection remote control area server;

[0053] Step 2: Enter the data detection terminal, collect multi-channel data in real time during the cloud platform processing, and perform real-time noise detection on the multi-channel data to determine the impact of noise on data reception. If there is an impact, analyze the cause of the noise impact, monitor the data reception status in real time based on environmental parameters, and make corrections;

[0054] Step 3: Enter the data anti-counterfeiting terminal and establish a multi-scale time-varying data feature model to conduct real-time anti-counterfeiting identification of multi-channel cloud platform data. It also tracks and destroys fake data received by the cloud platform in real time, and reversely receives features to identify fake data on the cloud platform.

[0055] Step 4: Enter the protection collaboration terminal, receive anti-counterfeiting results by real-time receiving data noise and cloud platform multi-channel data, set passwords and reverse passwords for cloud platform data processing to collaboratively generate double protection, and realize collaborative protection of cloud platform data and multiple password protection.

[0056] The present invention has the following beneficial effects:

[0057] 1. In the present invention, by setting a data detection terminal, in the cloud platform data protection based on big data, by performing real-time noise detection on multi-channel data, it is timely determined whether the data is affected by noise during reception, and the noise and environment are combined to monitor the data reception status in real time and correct it, so as to avoid the cloud platform multi-channel data being affected by noise during reception, and the receiving plan can be adjusted in time according to the noise impact, reducing the deviation existing in the cloud platform multi-channel data reception, improving the accuracy of cloud platform data reception, reducing the loopholes or malicious behaviors existing in cloud platform data processing, especially for cloud platform data processing, avoiding data noise causing data reception abnormalities.

[0058] 2. In the present invention, by setting up a data anti-counterfeiting terminal, in the cloud platform data protection based on big data, anti-counterfeiting identification is performed on the data received by the cloud platform through multiple channels, and the false data received by the cloud platform through multiple channels is reversely identified. When the cloud platform receives the multi-channel data, it undergoes reverse secondary anti-counterfeiting identification at the same time, which can further avoid the occurrence of false data when the data is received, so that the anti-counterfeiting data can be effectively identified when the cloud platform processes the multi-channel data, thereby increasing the accuracy of the cloud platform data processing.

[0059] 3. In the present invention, by setting up a protection collaboration terminal, in the cloud platform data protection based on big data, by receiving data noise and anti-counterfeiting results of multi-channel data reception in real time, setting cloud platform multi-channel password recognition, and setting decryption procedures of forward passwords and reverse passwords, double password protection is achieved, the perfection of the identity authentication mechanism of some cloud platforms is increased, unauthorized access and data leakage are prevented, and the security and reliability of cloud platform data protection are increased. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 This is a schematic diagram of the system architecture of a cloud platform data protection method and system based on big data of the present invention;

[0061] Figure 2 This is a schematic diagram of the architecture of a big data storage module of a cloud platform data protection method and system based on big data of the present invention;

[0062] Figure 3 This is a schematic diagram of the architecture of a data processing module of a cloud platform data protection method and system based on big data of the present invention;

[0063] Figure 4 This is a schematic diagram of the architecture of an advertisement identification module of a cloud platform data protection method and system based on big data of the present invention. DETAILED DESCRIPTION

[0064] In order to make the technical means, creative features, objectives and effects achieved by the present invention easier to understand, the present invention is further described below in conjunction with specific implementation methods.

[0065] Example 1

[0066] Please refer to Figure 1-2 As shown: A cloud platform data protection method and system based on big data, including a data detection end, a data anti-counterfeiting end and a protection coordination end;

[0067] The data detection terminal is used to collect multi-channel data from the cloud platform in real time and perform real-time noise detection on the multi-channel data to determine whether the noise of data processing is affected. If the data processing noise is abnormal, it analyzes the cause of the noise impact and monitors the data protection status in real time based on environmental parameters and makes corrections.

[0068] The data anti-counterfeiting terminal is used to perform anti-counterfeiting identification on data received by the multi-channel cloud platform in real time by establishing a multi-scale time-varying data feature model, and to track and destroy fake data received by the cloud platform through multiple channels in real time, as well as reversely identify fake data received by the cloud platform through multiple channels;

[0069] The protection collaboration terminal is used to receive anti-counterfeiting results by receiving data noise and multi-channel data of the cloud platform in real time, and collaboratively generate passwords and reverse passwords for double protection of cloud platform data processing, thereby realizing collaborative protection and multiple password protection of cloud platform data.

[0070] The data detection end includes a data acquisition module, an information monitoring module, and a receiving impact module;

[0071] The data acquisition module includes a multi-source acquisition unit and a data arrangement unit;

[0072] The multi-source acquisition unit is used to collect cloud platform data information of the Togo channel through data monitors and acquisition chips;

[0073] The data arrangement unit is used to arrange the cloud platform data information monitored in real time in sequence.

[0074] The information monitoring module includes a noise detection unit and an environment detection unit;

[0075] The noise detection unit is used to calculate the cloud platform data noise at the current moment in real time. The calculation formula is as follows:

[0076] Where Z represents the cloud platform data noise at the current moment, x represents the data observation value of the current channel, μ represents the mean, and σ represents the standard deviation;

[0077] The environmental detection unit is used to monitor the cloud platform data characteristic reception parameters at the current moment in real time through environmental monitoring equipment. The environmental monitoring equipment includes an electromagnetic radiation meter, a temperature sensor, a humidity sensor, and a magnetic field sensor. It can adjust the reception plan in time according to the influence of noise, reduce the deviation in the multi-channel data reception of the cloud platform, improve the accuracy of the cloud platform data reception, and reduce the loopholes or malicious behaviors in the cloud platform data processing.

[0078] The receiving impact module includes a data impact unit, an impact analysis unit and a state correction unit;

[0079] The data impact unit is used to set the standard parameters for receiving cloud platform data features. It calculates the difference between the current basic parameters and the standard parameters. If the difference is less than or equal to 0.01 and greater than or equal to -0.01, it indicates that the cloud platform data feature reception is normal. If the difference is greater than 0.01 or less than -0.01, it indicates that the cloud platform data feature reception is abnormal, and the reporting system will issue a voice broadcast reminder.

[0080] The impact analysis unit is used to continuously calculate the difference between the basic parameters of the cloud platform data monitored at the current moment and the standard parameters, the standard parameter threshold and the average value of the basic parameters, and finally calculate the deviation value of each basic parameter. The calculation formula is as follows:

[0081]

[0082] Among them, U is the effective value of each basic parameter, N is the sampling basic parameter of different channels, and UK is the instantaneous sampling value of the parameter of each channel;

[0083] The state correction unit is used to calculate the parameter deviation between the current basic parameters and the standard data protection parameters. The calculation formula is as follows:

[0084]

[0085] The data reception status is corrected by using the calculated parameter difference. If the parameter deviation is greater than or equal to 0, the parameter value of each basic parameter is increased. If the parameter deviation is less than 0, the parameter value of each basic parameter is reduced and the data is tracked continuously through the data tracker. When processing cloud platform data, data noise is avoided to avoid data reception abnormalities.

[0086] Example 2

[0087] Please refer to Figure 3 As shown in the figure: Based on the first embodiment, the data anti-counterfeiting terminal includes a feature model module, an anti-counterfeiting identification module and a multi-channel tracking module;

[0088] The feature model module includes a model building unit and a multi-source intercommunication unit;

[0089] The model building unit is used to build a cloud platform data feature receiving model for deep learning, and set cloud platform data feature receiving parameters, and arrange multi-channel data by sequence number;

[0090] The multi-source intercommunication unit is used to realize multi-source intercommunication of multi-channel data in real time through data receivers and data converters, and to perform anti-counterfeiting identification on the multi-channel received data of the cloud platform, and reversely identify the pseudo data received by the multi-channel received data of the cloud platform, so as to further avoid the occurrence of false data when the multi-channel data of the cloud platform is received.

[0091] The anti-counterfeiting identification module includes an anti-counterfeiting identification unit, a tracking and crushing unit, and a reverse identification unit;

[0092] The anti-counterfeiting identification unit is used to determine the feature similarity between current data and historical data through the Pearson correlation coefficient. The calculation formula is as follows:

[0093]

[0094] Among them, C(D S ,D S ′) represents the similarity between the characteristic parameters of the currently received data and the characteristic parameters of the historical data, cov(x,y) represents the covariance of the two received data features x and y, σ x σ y is the product of the standard deviations of the two received data features x and y. If C(D S ,D S ′) is equal to 1, the data is considered valid data. If C(D S ,D S ′) is not equal to 1, the data is judged to be false data;

[0095] The tracking shredding unit is used to shred the pseudo data in real time through the hard disk data shredder;

[0096] The reverse identification unit is used to reversely calculate the repetition rate of the received data features. Specifically, the repetition rate of the historical data features and the features of the data to be received is calculated. The calculation formula is as follows:

[0097]

[0098] Among them, D sm is the repetition rate of the received data feature to be checked, sm j is the repetition rate of the received data feature to be checked in the jth channel, L j Query the dataset receiving data features for the j-th channel.

[0099] The multi-channel tracking module includes a multi-source tracking unit and a sorting warning unit;

[0100] The multi-source tracking unit is used to track the data receiving characteristics on multiple channels in real time through a data tracker, receive the multi-channel data results through a data receiver in real time, and arrange the multi-channel data results in sequence;

[0101] The sorting warning unit is used to issue a voice alarm to the reporting system when it detects false data and abnormal data receiving parameters in the data after serial arrangement. After reverse secondary anti-counterfeiting identification, it can further avoid the occurrence of false data when receiving data, so that the cloud platform can effectively identify anti-counterfeiting data during multi-channel data processing, thereby increasing the accuracy of cloud platform data processing.

[0102] Example 3

[0103] Please refer to Figure 4 As shown: Based on the first embodiment, the protection coordination terminal includes a data receiving module, a protection coordination module and a protection tracking module;

[0104] The data receiving module is used to receive the early warning results, noise impact results and environmental impact results of the characteristics of the data to be protected in real time through the data receiver, and determine the data protection plan in real time based on the results;

[0105] The protection cooperation module includes a password setting unit and a dual decryption unit;

[0106] The password setting unit is used to establish the user identification protection password model of each channel of the cloud platform, and set the password model to Arabic numeral mode, specifically: set the Arabic numeral password through the CD4017 chip, and according to the Arabic numeral password set by the user, the Arabic numeral password includes nine digital symbols from 0 to 9, the real-name authentication obtains the user's real identity, obtains the user-defined digital password, and automatically sorts the Arabic numerals, the sorting number is (X1, X2, ..., X n ), which is a one-time password;

[0107] The double decryption unit is used to swap the order of the user-defined digital password and perform a secondary verification of the password according to the sequence number from back to front, that is, the sequence number is (X n 、X n-1 ,…,X1), identify them one by one according to the serial number, complete the double decryption operation, set the cloud platform multi-channel password recognition by real-time receiving data noise and multi-channel data reception anti-counterfeiting results, and set the forward password and reverse password decryption procedures to achieve double password protection.

[0108] The protection tracking module includes a protection self-tracking unit and a data protection evaluation unit;

[0109] The protection self-tracking unit is used to track the dual password recognition results and decryption operations in real time through the data tracker. If the decryption recognition error occurs, the reporting system will issue a voice alarm;

[0110] The data protection evaluation unit is used to evaluate the multi-channel data reception results of the cloud platform, and conducts real-time tracking and evaluation through a data tracker to improve the perfection of the authentication mechanism of some cloud platforms, prevent unauthorized access and data leakage, and increase the security and reliability of cloud platform data protection.

[0111] In the present invention, a cloud platform data protection method and system based on big data is provided. When the system is in operation, it collects multi-channel data of the cloud platform in real time, and performs real-time noise detection on the multi-channel data to determine whether the noise of data processing is affected. When the noise of data processing is abnormal, the cause of the noise is analyzed, and the data protection status is monitored and corrected in real time based on environmental parameters. In the cloud platform data protection based on big data, by performing real-time noise detection on multi-channel data, it is timely determined whether the data is affected by noise when receiving, and the noise and environment are combined to monitor the data receiving status in real time and correct it, so as to avoid the cloud platform multi-channel data being affected by noise when receiving, and the receiving plan can be adjusted in time according to the noise impact, reducing the deviation existing in the cloud platform multi-channel data reception, improving the accuracy of cloud platform data reception, reducing the loopholes or malicious behaviors existing in cloud platform data processing, especially for cloud platform data processing, avoiding data noise causing data reception abnormality; by establishing a multi-scale time-varying data feature model, anti-counterfeiting identification is performed on the multi-channel cloud platform received data in real time, and the cloud platform multi-channel reception is tracked and crushed in real time. The invention relates to a method for preventing counterfeit data received by a multi-channel cloud platform and reversely identifying counterfeit data received by a multi-channel cloud platform. In the data protection of a cloud platform based on big data, anti-counterfeiting identification is performed on the multi-channel data received by the cloud platform and reverse identification is performed on the counterfeit data received by the multi-channel cloud platform. When the multi-channel data is received by the cloud platform, reverse secondary anti-counterfeiting identification is performed at the same time. This can further avoid the occurrence of counterfeit data when receiving the data, so that the anti-counterfeiting data can be effectively identified when the multi-channel data is processed by the cloud platform, thereby increasing the accuracy of the data processing of the cloud platform. By establishing a multi-scale time-varying data feature model, anti-counterfeiting identification is performed on the multi-channel cloud platform received data in real time, and counterfeit data received by the multi-channel cloud platform is tracked and crushed in real time, and reverse identification is performed on the multi-channel cloud platform received counterfeit data. In the data protection of a cloud platform based on big data, anti-counterfeiting results of data noise and multi-channel data reception are received in real time, cloud platform multi-channel password identification is set, and decryption procedures of forward password and reverse password are set to achieve double password protection, thereby increasing the perfection of the authentication mechanism of some cloud platforms, preventing unauthorized access and data leakage, and increasing the security and reliability of cloud platform data protection.

[0112] A cloud platform data protection method based on big data includes the following steps:

[0113] Step 1: Configure the IP address information of the data protection remote control area server;

[0114] Step 2: Enter the data detection terminal, collect multi-channel data in real time during the cloud platform processing, and perform real-time noise detection on the multi-channel data to determine the impact of noise on data reception. If there is an impact, analyze the cause of the noise impact, monitor the data reception status in real time based on environmental parameters, and make corrections;

[0115] Step 3: Enter the data anti-counterfeiting terminal and establish a multi-scale time-varying data feature model to conduct real-time anti-counterfeiting identification of multi-channel cloud platform data. It also tracks and destroys fake data received by the cloud platform in real time, and reversely receives features to identify fake data on the cloud platform.

[0116] Step 4: Enter the protection collaboration terminal, receive anti-counterfeiting results by real-time receiving data noise and cloud platform multi-channel data, set passwords and reverse passwords for cloud platform data processing to collaboratively generate double protection, and realize collaborative protection of cloud platform data and multiple password protection.

[0117] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A cloud platform data protection system based on big data, characterized in that: The system includes a data detection terminal, a data anti-counterfeiting terminal and a protection coordination terminal; The data detection terminal is used to collect multi-channel data from the cloud platform in real time and perform real-time noise detection on the multi-channel data to determine whether the noise of data processing is affected. When the data processing noise is abnormal, the data detection terminal analyzes the cause of the noise and monitors the data protection status in real time based on environmental parameters and makes corrections. The data anti-counterfeiting terminal is used to perform anti-counterfeiting identification on data received by the multi-channel cloud platform in real time by establishing a multi-scale time-varying data feature model, and to track and crush fake data received by the multi-channel cloud platform in real time, as well as reversely identify fake data received by the multi-channel cloud platform; The protection collaboration terminal is used to receive anti-counterfeiting results by receiving data noise and multi-channel data of the cloud platform in real time, and collaboratively generate double protection by setting passwords and reverse passwords for cloud platform data processing, thereby realizing collaborative protection and multiple password protection of cloud platform data.

2. The system according to claim 1, wherein: The data detection terminal includes a data acquisition module, an information monitoring module and a receiving influence module; The data acquisition module includes a multi-source acquisition unit and a data arrangement unit; The multi-source acquisition unit is used to collect cloud platform data information of the Togo channel through a data monitor and an acquisition chip; The data arrangement unit is used to arrange the cloud platform data information monitored in real time by sequence number.

3. The system according to claim 2, characterized in that: The information monitoring module includes a noise detection unit and an environment detection unit; The noise detection unit is used to calculate the cloud platform data noise at the current moment in real time. The calculation formula is as follows: Where Z represents the cloud platform data noise at the current moment, x represents the data observation value of the current channel, μ represents the mean, and σ represents the standard deviation; The environment detection unit is used to monitor the cloud platform data feature receiving parameters at the current moment in real time through the environment monitoring equipment, and the environment monitoring equipment includes an electromagnetic radiation meter, a temperature sensor, a humidity sensor and a magnetic field sensor.

4. The system according to claim 1, wherein: The receiving impact module includes a data impact unit, an impact analysis unit and a state correction unit; The data impact unit is used to set the standard parameters for receiving cloud platform data features, and calculate the difference between the basic parameters at the current moment and the standard parameters. If the difference is less than or equal to 0.01 and greater than or equal to -0.01, it indicates that the cloud platform data feature reception is normal. If the difference is greater than 0.01 or less than -0.01, it indicates that the cloud platform data feature reception is abnormal, and the reporting system issues a voice broadcast reminder; The impact analysis unit is used to continuously calculate the standard parameter threshold and the average value of the basic parameters based on the difference between the basic parameters of the cloud platform data monitored at the current moment and the standard parameters, and finally calculate the deviation value of each basic parameter. The calculation formula is as follows: Among them, U is the effective value of each basic parameter, N is the sampling basic parameter of different channels, and UK is the instantaneous sampling value of the parameter of each channel; The state correction unit is used to calculate the parameter deviation between each basic parameter at the current moment and the standard data protection parameter. The calculation formula is as follows: The data receiving status is corrected by using the calculated parameter difference. If the parameter deviation is greater than or equal to 0, the parameter value of each basic parameter is increased. If the parameter deviation is less than 0, the parameter value of each basic parameter is reduced and tracking is continued through the data tracker.

5. The system according to claim 4, characterized in that: The data anti-counterfeiting terminal includes a feature model module, an anti-counterfeiting identification module and a multi-channel tracking module; The feature model module includes a model building unit and a multi-source intercommunication unit; The model building unit is used to build a cloud platform data feature receiving model for deep learning and set cloud platform data feature receiving parameters, and multi-channel data is arranged by sequence number; The multi-source intercommunication unit is used to realize multi-source intercommunication of multi-channel data in real time through a data receiver and a data converter.

6. The system according to claim 5, characterized in that: The anti-counterfeiting identification module includes an anti-counterfeiting identification unit, a tracking and crushing unit, and a reverse identification unit; The anti-counterfeiting identification unit is used to determine the feature similarity between current data and historical data through the Pearson correlation coefficient. The calculation formula is as follows: Among them, C(D S ,D S ′) represents the similarity between the characteristic parameters of the currently received data and the characteristic parameters of the historical data, cov(x,y) represents the covariance of the two received data features x and y, σ x σ y is the product of the standard deviations of the two received data features x and y. If C(D S ,D S ′) is equal to 1, the data is considered valid data. If C(D S ,D S ′) is not equal to 1, the data is judged to be false data; The tracking and shredding unit is used to shred the pseudo data in real time through a hard disk data shredder; The reverse identification unit is used to reversely calculate the repetition rate of the received data features, specifically by calculating the repetition rate of the historical data features and the features of the data to be received. The calculation formula is as follows: Among them, D sm is the repetition rate of the received data feature to be checked, sm j is the repetition rate of the received data feature to be checked in the jth channel, L j Query the dataset receiving data features for the j-th channel.

7. The system according to claim 6, characterized in that: The multi-channel tracking module includes a multi-source tracking unit and a sorting warning unit; The multi-source tracking unit is used to track the data receiving characteristics on multiple channels in real time through a data tracker, receive the multi-channel data results through a data receiver in real time, and arrange the multi-channel data results in sequence; The sequencing warning unit is used to report the occurrence of false data and abnormal data receiving parameters in the data after the sequence arrangement, and to issue a voice alarm to the reporting system.

8. The system according to claim 1, wherein: The protection coordination terminal includes a data receiving module, a protection coordination module and a protection tracking module; The data receiving module is used to receive the warning results, noise impact results and environmental impact results of the characteristics of the data to be protected in real time through the data receiver, and determine the data protection plan in real time according to the results; The protection cooperation module includes a password setting unit and a dual decryption unit; The password setting unit is used to establish a user identification protection password model for each channel of the cloud platform, and set the password model to an Arabic numeral mode, specifically: set an Arabic numeral password through the CD4017 chip, and according to the Arabic numeral password set by the user, the Arabic numeral password includes nine digital symbols from 0 to 9, the real-name authentication obtains the user's real identity, obtains the user-defined digital password, and automatically sorts the Arabic numerals, the sorting number is (X1, X2, ..., X n ), which is a one-time password; The double decryption unit is used to swap the order of the user-defined digital password and perform a secondary verification of the password according to the sequence number from back to front, that is, to obtain the sequence number (X n 、X n-1 ,…,X1), identify them one by one according to the serial number and complete the double decryption operation.

9. The system according to claim 8, characterized in that: The protection tracking module includes a protection self-tracking unit and a data protection evaluation unit; The protection self-tracking unit is used to track the dual password recognition results and decryption operations in real time through a data tracker, and if the decryption recognition error occurs, the reporting system will issue a voice alarm; The data protection evaluation unit is used to evaluate the multi-channel data reception results of the cloud platform, and to track and evaluate in real time through a data tracker.

10. A cloud platform data protection method based on big data, referring to a cloud platform data protection system based on big data according to any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: Configure the IP address information of the data protection remote control area server; Step 2: Enter the data detection terminal, collect multi-channel data in real time during the cloud platform processing, and perform real-time noise detection on the multi-channel data to determine the impact of noise on data reception. If there is an impact, analyze the cause of the noise impact, monitor the data reception status in real time based on environmental parameters, and make corrections; Step 3: Enter the data anti-counterfeiting terminal and establish a multi-scale time-varying data feature model to conduct real-time anti-counterfeiting identification of multi-channel cloud platform data. It also tracks and destroys fake data received by the cloud platform in real time, and reversely receives features to identify fake data on the cloud platform. Step 4: Enter the protection collaboration terminal, receive anti-counterfeiting results by real-time receiving data noise and cloud platform multi-channel data, set passwords and reverse passwords for cloud platform data processing to collaboratively generate double protection, and realize collaborative protection of cloud platform data and multiple password protection.

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