CN116541858A - Data cloud storage method and system based on big data and edge calculation - Google Patents
Data cloud storage method and system based on big data and edge calculation Download PDFInfo
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Abstract
The invention provides a data cloud storage method and a data cloud storage system based on big data and edge calculation, wherein the method comprises the following steps: acquiring data information, and preprocessing the data information to obtain encryption information; extracting the encryption information characteristics and generating an encryption characteristic value; the encryption characteristic value and a preset encryption characteristic value are used for obtaining a characteristic difference value; judging whether the characteristic difference value is larger than or equal to a preset threshold value or not; if the encryption information is greater than or equal to the encryption information, generating a correction coefficient, and correcting the encryption information according to the correction coefficient; if the data information is smaller than the preset value, encrypting and storing the data information; by encrypting the data information, the storage security of the data information is improved.
Description
Technical Field
The application relates to the field of data storage, in particular to a data cloud storage method and system based on big data and edge calculation.
Background
With the rapid development of cloud computing, cloud services are favored by a wide range of users. The user can conveniently and rapidly acquire the required resources from the cloud resource sharing pool at any time and any place, and can easily store own data in the cloud. The service provided by the cloud computing can reduce cost and save expenditure, can increase the energy storage capacity, and has higher flexibility. However, the storage form, position and other information of the cloud data are unknown to the user, so that the data needs to be stored safely, the data is prevented from being stolen, the residual data in the cloud may be utilized by illegal users, the security of sensitive data of the user is seriously threatened, and therefore, a comprehensive and efficient scheme is required to destroy the data in the cloud, recover the destroyed data when necessary, and improve the security of data storage.
Disclosure of Invention
The invention aims to provide a data cloud storage method and a data cloud storage system based on big data and edge calculation, which can improve the storage safety of data information by carrying out encryption processing on the data information.
The embodiment of the application also provides a data cloud storage method based on big data and edge calculation, which comprises the following steps:
acquiring data information, and preprocessing the data information to obtain encryption information;
extracting the encryption information characteristics and generating an encryption characteristic value;
the encryption characteristic value and a preset encryption characteristic value are used for obtaining a characteristic difference value;
judging whether the characteristic difference value is larger than or equal to a preset threshold value or not;
if the encryption information is greater than or equal to the encryption information, generating a correction coefficient, and correcting the encryption information according to the correction coefficient;
and if the data information is smaller than the data information, encrypting and storing the data information.
Optionally, in the data cloud storage method based on big data and edge calculation according to the embodiment of the present application, the obtaining data information performs preprocessing on the data information to obtain encrypted information; comprising the following steps:
acquiring data information, and carrying out marginalization processing on the data information to obtain edge data;
dividing the edge data to generate metadata and residual data;
separately encrypting the metadata and the residual data to obtain first encryption information and second encryption information;
encoding and blocking the metadata according to the first encryption information to obtain a plurality of partition data;
calculating weight coefficients of the plurality of partition data, and performing sub-encryption processing on the plurality of partition data according to the weight coefficients to obtain sub-encryption information;
and independently encrypting the partition data according to the sub-encryption information.
Optionally, in the data cloud storage method based on big data and edge calculation according to the embodiment of the present application, if the data cloud storage method is smaller than the big data, encrypting and storing the data information includes:
acquiring data information and generating a hash value;
judging the hash value and a preset hash value threshold;
if the data offset information is larger than the data offset information, generating the data offset information, and judging deviation information of the data information according to the data offset information;
if the data information is smaller than the data information, a storage strategy is generated, and the data information is stored in an encrypted mode according to the storage strategy.
Optionally, in the data cloud storage method based on big data and edge calculation according to the embodiment of the present application, after the encrypting and storing the data information if the data information is smaller than the big data, the method further includes:
acquiring calling information, and calling the data according to the calling information;
carrying out encryption analysis on the data of different partitions according to the sub encryption information, and generating decryption data;
matching the decrypted data with the encrypted information to obtain matching degree;
judging whether the matching degree is larger than or equal to a matching degree threshold value or not;
if the matching degree threshold value is greater than or equal to the matching degree threshold value, decrypting the partition data and generating a decryption percentage;
judging whether the decryption percentage is larger than or equal to a preset percentage;
if the proportion is larger than or equal to the preset proportion, splicing the partition data and the residual data to obtain decryption information;
if the matching degree is smaller than the matching degree threshold value, generating a matching degree correction parameter, and correcting the decrypted data according to the matching degree correction parameter;
if the partition data is smaller than the preset percentage, the partition data is continuously called.
Optionally, in the data cloud storage method based on big data and edge calculation according to the embodiment of the present application, if the matching degree threshold is greater than or equal to the matching degree threshold, the decrypting process is performed on the partition data, and further includes:
acquiring calling information, and calling the data according to the calling information;
carrying out encryption analysis on the data of different partitions according to the sub encryption information, and generating decryption data;
matching the decrypted data with the encrypted information to obtain matching degree;
judging whether the matching degree is larger than or equal to a matching degree threshold value or not;
if the matching degree threshold value is greater than or equal to the matching degree threshold value, decrypting the partition data;
if the matching degree difference value is smaller than the matching degree threshold value, a matching degree difference value is generated;
calculating whether the matching degree difference is larger than a preset difference;
if the residual error data is larger than the residual error data, generating a self-destruction program, and destroying the residual error data according to the self-destruction program.
Optionally, in the data cloud storage method based on big data and edge calculation according to the embodiment of the present application, if the data cloud storage method is greater than the big data, a self-destruction program is generated, and residual data is destroyed according to the self-destruction program, including:
acquiring a self-destruction program, configuring and recovering partitions of the self-destruction program, and storing recovery information;
judging the times that the matching degree is smaller than the matching degree threshold value in the matching process of the decrypted data and the encrypted information, and generating cracking times;
if the number of times of cracking is greater than a preset number threshold, locking information is generated, and the partition data is locked through the locking information;
and if the number of times of cracking is smaller than a preset number threshold, recovering the self-destruction data through a recovery secret key.
In a second aspect, an embodiment of the present application provides a data cloud storage system based on big data, where the system includes: the system comprises a memory and a processor, wherein the memory comprises a program of a data cloud storage method based on big data and edge calculation, and the program of the data cloud storage method based on the big data and the edge calculation realizes the following steps when being executed by the processor:
acquiring data information, and preprocessing the data information to obtain encryption information;
extracting the encryption information characteristics and generating an encryption characteristic value;
the encryption characteristic value and a preset encryption characteristic value are used for obtaining a characteristic difference value;
judging whether the characteristic difference value is larger than or equal to a preset threshold value or not;
if the encryption information is greater than or equal to the encryption information, generating a correction coefficient, and correcting the encryption information according to the correction coefficient;
and if the data information is smaller than the data information, encrypting and storing the data information.
Optionally, in the big data based data cloud storage system described in the embodiments of the present application, the obtaining data information performs preprocessing on the data information to obtain encrypted information; comprising the following steps:
acquiring data information, and carrying out marginalization processing on the data information to obtain edge data;
dividing the edge data to generate metadata and residual data;
separately encrypting the metadata and the residual data to obtain first encryption information and second encryption information;
encoding and blocking the metadata according to the first encryption information to obtain a plurality of partition data;
calculating weight coefficients of the plurality of partition data, and performing sub-encryption processing on the plurality of partition data according to the weight coefficients to obtain sub-encryption information;
and independently encrypting the partition data according to the sub-encryption information.
Optionally, in the big data based data cloud storage system according to the embodiment of the present application, if the data information is smaller than the big data, encrypting and storing the data information includes:
acquiring data information and generating a hash value;
judging the hash value and a preset hash value threshold;
if the data offset information is larger than the data offset information, generating the data offset information, and judging deviation information of the data information according to the data offset information;
if the data information is smaller than the data information, a storage strategy is generated, and the data information is stored in an encrypted mode according to the storage strategy.
In a third aspect, an embodiment of the present application further provides a computer readable storage medium, where the computer readable storage medium includes a data cloud storage method program based on big data and edge calculation, where when the data cloud storage method program based on big data and edge calculation is executed by a processor, the steps of the data cloud storage method based on big data and edge calculation according to any one of the foregoing embodiments are implemented.
As can be seen from the above, according to the data cloud storage method, system and medium based on big data and edge calculation provided by the embodiment of the application, the data information is preprocessed by obtaining the data information, so as to obtain the encryption information; extracting the encryption information characteristics and generating an encryption characteristic value; the encryption characteristic value and a preset encryption characteristic value are used for obtaining a characteristic difference value; judging whether the characteristic difference value is larger than or equal to a preset threshold value or not; if the encryption information is greater than or equal to the encryption information, generating a correction coefficient, and correcting the encryption information according to the correction coefficient; if the data information is smaller than the preset value, encrypting and storing the data information; by encrypting the data information, the storage security of the data information is improved.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the application embodiments. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a data cloud storage method based on big data and edge calculation according to an embodiment of the present application;
fig. 2 is a block data separate encryption flow chart of a data cloud storage method based on big data and edge calculation according to an embodiment of the present application;
fig. 3 is a data flow chart of calling partition of a data cloud storage method based on big data and edge calculation according to an embodiment of the present application;
fig. 4 is a residual data corruption flowchart of a data cloud storage method based on big data and edge calculation provided in an embodiment of the present application;
fig. 5 is a schematic structural diagram of a data cloud storage system based on big data according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. The components of the embodiments of the present application, which are generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, as provided in the accompanying drawings, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, are intended to be within the scope of the present application.
It should be noted that like reference numerals and letters refer to like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.
Referring to fig. 1, fig. 1 is a flowchart of a data cloud storage method based on big data and edge computation in some embodiments of the present application. The data cloud storage method based on big data and edge calculation is used in terminal equipment and comprises the following steps:
s101, acquiring data information, and preprocessing the data information to obtain encryption information;
s102, extracting the encryption information characteristics and generating an encryption characteristic value;
s103, the encryption characteristic value and a preset encryption characteristic value are subjected to characteristic difference value obtaining;
s104, judging whether the characteristic difference value is larger than or equal to a preset threshold value;
s105, if the encryption information is greater than or equal to the encryption information, generating a correction coefficient, and correcting the encryption information according to the correction coefficient; and if the data information is smaller than the data information, encrypting and storing the data information.
It should be noted that, obtaining data information, performing importance analysis on the data information, extracting data with importance greater than preset importance, storing the data with importance greater than preset importance into a first partition, storing the data with importance less than preset importance into a second partition, and performing encryption processing on the data in the first partition and the second partition in different manners, so as to realize partition encryption of the data and improve the security of the data. It should be noted that all data calculations in the present invention may employ edge calculations.
Referring to fig. 2, fig. 2 is a block diagram of a separate encryption flow chart of partition data of a data cloud storage method based on big data and edge computation according to some embodiments of the present application. According to the embodiment of the invention, the data information is obtained, and the data information is preprocessed to obtain the encryption information; comprising the following steps:
s201, acquiring data information, and carrying out marginalization processing on the data information to obtain edge data;
s202, dividing edge data to generate metadata and residual data;
s203, the metadata and the residual data are respectively and independently encrypted to obtain first encryption information and second encryption information;
s204, coding and blocking the metadata according to the first encryption information to obtain a plurality of partition data;
s205, calculating weight coefficients of the plurality of partition data, and performing sub-encryption processing on the plurality of partition data according to the weight coefficients to obtain sub-encryption information;
s206, the partition data are encrypted independently according to the sub-encryption information.
The method is characterized in that different encryption processing is carried out on the edge data, the encryption security of the partition data is judged according to the weight coefficient of the partition data, multiple times of encryption is carried out on the partition with smaller encryption security, meanwhile, the different partition data is independently encrypted, the higher cracking difficulty in the data cracking process can be ensured, and the data cannot be cracked at one time.
According to an embodiment of the present invention, if the data information is smaller than the predetermined value, encrypting and storing the data information includes:
acquiring data information and generating a hash value;
judging the hash value and a preset hash value threshold;
if the data offset information is larger than the data offset information, generating the data offset information, and judging deviation information of the data information according to the data offset information;
if the data information is smaller than the data information, a storage strategy is generated, and the data information is stored in an encrypted mode according to the storage strategy.
It should be noted that, after hash operation, each data will obtain a hash value, corresponding data is added to the back of the hash value linked list according to the hash value, a hash linked list is formed at the back of each hash value, the position relationship before and after the data link will be determined according to the sequence of the data reaching the memory, the data that arrives first is linked to the front of the hash value linked list, the data that arrives later is linked to the back of the hash value linked list, a null pointer is linked at the back of each hash linked list to identify the end position of the hash value corresponding to the linked list, the size threshold of the hash linked list is set according to the actual memory space of the edge storage node, and when the data quantity reaches the threshold, the data in the hash linked list is written into the disk in sequence.
Referring to fig. 3, fig. 3 is a flow chart of a partition calling data process of a data cloud storage method based on big data and edge computation according to some embodiments of the present application. According to the embodiment of the invention, if the data information is smaller than the data information, the method further comprises the steps of:
s301, acquiring call information, and calling the data according to the call information;
s302, carrying out encryption analysis on different partition data according to the sub encryption information, and generating decryption data;
s303, matching the decrypted data with the encrypted information to obtain matching degree;
s304, judging whether the matching degree is larger than or equal to a matching degree threshold value; if the matching degree threshold value is greater than or equal to the matching degree threshold value, decrypting the partition data and generating a decryption percentage;
s305, judging whether the decryption percentage is larger than or equal to a preset percentage; if the proportion is larger than or equal to the preset proportion, splicing the partition data and the residual data to obtain decryption information;
s306, if the matching degree is smaller than the matching degree threshold value, generating a matching degree correction parameter, and correcting the decrypted data according to the matching degree correction parameter; if the partition data is smaller than the preset percentage, the partition data is continuously called.
When the decryption process is performed on the partitioned data, the decryption degree is analyzed by judging the decryption percentage, and when the decryption percentage is more than 80%, the successful matching of the decrypted data and the encrypted information can be judged, so that the data is decrypted, forced decryption of the data by external invasion is prevented, and the storage safety of the data is improved.
Referring to fig. 4, fig. 4 is a residual data corruption flow chart of a data cloud storage method based on big data and edge computation in some embodiments of the present application. According to the embodiment of the invention, if the matching degree threshold value is greater than or equal to the matching degree threshold value, the decryption processing is performed on the partition data, and the method further comprises the following steps:
s401, acquiring call information, and calling the data according to the call information;
s402, carrying out encryption analysis on different partition data according to the sub encryption information, and generating decryption data;
s403, matching the decrypted data with the encrypted information to obtain matching degree;
s404, judging whether the matching degree is larger than or equal to a matching degree threshold value;
s405, if the matching degree threshold value is greater than or equal to the matching degree threshold value, decrypting the partition data; if the matching degree difference value is smaller than the matching degree threshold value, a matching degree difference value is generated;
s406, calculating whether the matching degree difference value is larger than a preset difference value; if the residual error data is larger than the residual error data, generating a self-destruction program, and destroying the residual error data according to the self-destruction program.
In the process of decrypting the partition data, if the decryption information is not matched, judging an intrusion state, wherein the partition data is in a forced decryption state, and the self-destruction program is required to be started to damage the data, so that the data is damaged not completely but only to a state in which the data cannot be decrypted, and the intrusion of the data is ensured.
According to the embodiment of the invention, if the data is larger than the residual data, a self-destruction program is generated, and the residual data is destroyed according to the self-destruction program, including:
acquiring a self-destruction program, configuring and recovering partitions of the self-destruction program, and storing recovery information;
judging the times that the matching degree is smaller than the matching degree threshold value in the matching process of the decrypted data and the encrypted information, and generating cracking times;
if the number of times of cracking is greater than a preset number threshold, locking information is generated, and the partition data is locked through the locking information;
and if the number of times of cracking is smaller than a preset number threshold, recovering the self-destruction data through a recovery secret key.
When decryption is immersed, multiple times of decryption matching can occur, decryption information cannot be matched in multiple times of decryption processes, data are locked, and after the decryption information is successfully matched with encryption information, the locked data can be called again, so that the data are prevented from being lost, and meanwhile, the storage safety of the data is improved.
According to an embodiment of the present invention, further comprising:
obtaining decryption time, judging that the decryption time is larger than preset decryption time,
if the data is larger than the predetermined value, judging that the external intrusion occurs, and generating secondary encryption information;
re-encrypting the data through the secondary encryption information, and generating a composite encryption key;
and carrying out recombination encryption on the data through the composite encryption key.
When the decryption time of the calling end when the data is called is larger than the preset decryption time, external invasion is judged, secondary encryption information is generated, the data is encrypted again through the secondary encryption information, and the preset decryption time is set by a person skilled in the art according to actual requirements.
According to an embodiment of the present invention, after the data is encrypted by the compound encryption key, the method further includes:
acquiring decryption time of the data subjected to the re-encryption by the calling terminal;
judging whether the decryption time of the data after the re-encryption by the calling terminal is greater than a preset second decryption time threshold value, if so, triggering prompt information and locking the corresponding data;
and sending the prompt information to a preset management end.
After the data generates the composite encryption key, if the calling end continues to decrypt, the corresponding decryption time is recorded, wherein if the decryption time of the data after the repeated encryption by the calling end is greater than a preset second decryption time threshold, the prompting information is triggered, and the corresponding data is locked.
According to an embodiment of the present invention, the calculating of the weight coefficient for the plurality of partition data, and the sub-encrypting the plurality of partition data according to the weight coefficient specifically includes:
extracting keywords in the partition data;
matching the keywords in the partition data with a preset keyword class library to obtain class numbers of the corresponding keywords;
collecting the grade number of the keywords and extracting the highest grade number of the keywords;
matching the highest level number of the keywords with a preset weight coefficient level table to obtain weight coefficients of corresponding partition data;
and carrying out sub-encryption processing on the plurality of partition data according to the weight coefficient.
It should be noted that, the preset keyword class library stores a large number of keywords and class numbers of corresponding keywords, the preset weight coefficient class table stores class numbers of keywords and weight coefficients matched with the class numbers of the keywords, and when the keywords in the partition data do not exist in the preset keyword class library, the keywords in the corresponding partition data are set as the lowest class numbers.
According to an embodiment of the present invention, further comprising:
acquiring the number of times the data is accessed;
judging whether the number of times the data is accessed is greater than a preset second time threshold value, if so, recording the corresponding name of the corresponding data, and sending the data name to a preset common list for storage;
and displaying the data names in the common list in a preset calling window.
It should be noted that when the number of times that the data is accessed reaches a certain number of times, the corresponding data is the common data, for example, the preset second time threshold is 100 times, when the number of times that the data a is accessed exceeds 100 times, the name of the data a is sent to the preset common list to be stored, and the name of the data a is displayed in the preset call window.
Referring to fig. 5, fig. 5 is a schematic structural diagram of a data cloud storage system based on big data according to some embodiments of the present application. In a second aspect, an embodiment of the present application provides a data cloud storage system 5 based on big data, where the system includes: the memory 51 and the processor 52, the memory 51 includes a program of a data cloud storage method based on big data and edge calculation, and the following steps are implemented when the program of the data cloud storage method based on big data and edge calculation is executed by the processor:
acquiring data information, and preprocessing the data information to obtain encryption information;
extracting the encryption information characteristics and generating an encryption characteristic value;
the encryption characteristic value and a preset encryption characteristic value are used for obtaining a characteristic difference value;
judging whether the characteristic difference value is larger than or equal to a preset threshold value;
if the encryption information is greater than or equal to the encryption information, generating a correction coefficient, and correcting the encryption information according to the correction coefficient;
and if the data information is smaller than the data information, encrypting and storing the data information.
It should be noted that, obtaining data information, performing importance analysis on the data information, extracting data with importance greater than preset importance, storing the data with importance greater than preset importance into a first partition, storing the data with importance less than preset importance into a second partition, and performing encryption processing on the data in the first partition and the second partition in different manners, so as to realize partition encryption of the data and improve the security of the data.
According to the embodiment of the invention, the data information is obtained, and the data information is preprocessed to obtain the encryption information; comprising the following steps:
acquiring data information, and carrying out marginalization processing on the data information to obtain edge data;
dividing the edge data to generate metadata and residual data;
separately encrypting the metadata and the residual data to obtain first encryption information and second encryption information;
encoding and blocking the metadata according to the first encryption information to obtain a plurality of partition data;
calculating weight coefficients of the plurality of partition data, and performing sub-encryption processing on the plurality of partition data according to the weight coefficients to obtain sub-encryption information;
and independently encrypting the partition data according to the sub-encryption information.
The method is characterized in that different encryption processing is carried out on the edge data, the encryption security of the partition data is judged according to the weight coefficient of the partition data, multiple times of encryption is carried out on the partition with smaller encryption security, meanwhile, the different partition data is independently encrypted, the higher cracking difficulty in the data cracking process can be ensured, and the data cannot be cracked at one time.
According to an embodiment of the present invention, if the data information is smaller than the predetermined value, encrypting and storing the data information includes:
acquiring data information and generating a hash value;
judging the hash value and a preset hash value threshold;
if the data offset information is larger than the data offset information, generating the data offset information, and judging deviation information of the data information according to the data offset information;
if the data information is smaller than the data information, a storage strategy is generated, and the data information is stored in an encrypted mode according to the storage strategy.
It should be noted that, after hash operation, each data will obtain a hash value, corresponding data is added to the back of the hash value linked list according to the hash value, a hash linked list is formed at the back of each hash value, the position relationship before and after the data link will be determined according to the sequence of the data reaching the memory, the data that arrives first is linked to the front of the hash value linked list, the data that arrives later is linked to the back of the hash value linked list, a null pointer is linked at the back of each hash linked list to identify the end position of the hash value corresponding to the linked list, the size threshold of the hash linked list is set according to the actual memory space of the edge storage node, and when the data quantity reaches the threshold, the data in the hash linked list is written into the disk in sequence.
According to the embodiment of the invention, if the data information is smaller than the data information, the method further comprises the steps of:
acquiring calling information, and calling the data according to the calling information;
carrying out encryption analysis on the data of different partitions according to the sub encryption information, and generating decryption data;
matching the decrypted data with the encrypted information to obtain matching degree;
judging whether the matching degree is larger than or equal to a matching degree threshold value;
if the matching degree threshold value is greater than or equal to the matching degree threshold value, decrypting the partition data and generating a decryption percentage;
judging whether the decryption percentage is larger than or equal to a preset percentage;
if the proportion is larger than or equal to the preset proportion, splicing the partition data and the residual data to obtain decryption information;
if the matching degree is smaller than the matching degree threshold value, generating a matching degree correction parameter, and correcting the decrypted data according to the matching degree correction parameter;
if the partition data is smaller than the preset percentage, the partition data is continuously called.
When the decryption process is performed on the partitioned data, the decryption degree is analyzed by judging the decryption percentage, and when the decryption percentage is more than 80%, the successful matching of the decrypted data and the encrypted information can be judged, so that the data is decrypted, forced decryption of the data by external invasion is prevented, and the storage safety of the data is improved.
According to the embodiment of the invention, if the matching degree threshold value is greater than or equal to the matching degree threshold value, the decryption processing is performed on the partition data, and the method further comprises the following steps:
acquiring calling information, and calling the data according to the calling information;
carrying out encryption analysis on the data of different partitions according to the sub encryption information, and generating decryption data;
matching the decrypted data with the encrypted information to obtain matching degree;
judging whether the matching degree is larger than or equal to a matching degree threshold value;
if the matching degree threshold value is greater than or equal to the matching degree threshold value, decrypting the partition data;
if the matching degree difference value is smaller than the matching degree threshold value, a matching degree difference value is generated;
calculating whether the matching degree difference is larger than a preset difference;
if the residual error data is larger than the residual error data, generating a self-destruction program, and destroying the residual error data according to the self-destruction program.
In the process of decrypting the partition data, if the decryption information is not matched, judging an intrusion state, wherein the partition data is in a forced decryption state, and the self-destruction program is required to be started to damage the data, so that the data is damaged not completely but only to a state in which the data cannot be decrypted, and the intrusion of the data is ensured.
According to the embodiment of the invention, if the data is larger than the residual data, a self-destruction program is generated, and the residual data is destroyed according to the self-destruction program, including:
acquiring a self-destruction program, configuring and recovering partitions of the self-destruction program, and storing recovery information;
judging the times that the matching degree is smaller than the matching degree threshold value in the matching process of the decrypted data and the encrypted information, and generating cracking times;
if the number of times of cracking is greater than a preset number threshold, locking information is generated, and the partition data is locked through the locking information;
and if the number of times of cracking is smaller than a preset number threshold, recovering the self-destruction data through a recovery secret key.
When decryption is immersed, multiple times of decryption matching can occur, decryption information cannot be matched in multiple times of decryption processes, data are locked, and after the decryption information is successfully matched with encryption information, the locked data can be called again, so that the data are prevented from being lost, and meanwhile, the storage safety of the data is improved.
According to an embodiment of the present invention, further comprising:
obtaining decryption time, judging that the decryption time is larger than preset decryption time,
if the data is larger than the predetermined value, judging that the external intrusion occurs, and generating secondary encryption information;
re-encrypting the data through the secondary encryption information, and generating a composite encryption key;
and carrying out recombination encryption on the data through the composite encryption key.
The third aspect of the present invention provides a computer readable storage medium, where the readable storage medium includes a data cloud storage method program based on big data and edge calculation, and when the data cloud storage method program based on big data and edge calculation is executed by a processor, the steps of the data cloud storage method based on big data and edge calculation according to any one of the above are implemented.
According to the data cloud storage method, the system and the medium based on big data and edge calculation, the data information is preprocessed by acquiring the data information, so that the encryption information is obtained; extracting the encryption information characteristics and generating an encryption characteristic value; the encryption characteristic value and a preset encryption characteristic value are used for obtaining a characteristic difference value; judging whether the characteristic difference value is larger than or equal to a preset threshold value; if the encryption information is greater than or equal to the encryption information, generating a correction coefficient, and correcting the encryption information according to the correction coefficient; if the data information is smaller than the preset value, encrypting and storing the data information; by encrypting the data information, the storage security of the data information is improved. Is a technology of (a).
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of units is only one logical function division, and there may be other divisions in actual implementation, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the above-described integrated units of the present invention may be stored in a readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solution of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
Claims (10)
1. The data cloud storage method based on big data and edge calculation is characterized by comprising the following steps of:
acquiring data information, and preprocessing the data information to obtain encryption information;
extracting the encryption information characteristics and generating an encryption characteristic value;
the encryption characteristic value and a preset encryption characteristic value are used for obtaining a characteristic difference value;
judging whether the characteristic difference value is larger than or equal to a preset threshold value or not;
if the encryption information is greater than or equal to the encryption information, generating a correction coefficient, and correcting the encryption information according to the correction coefficient;
and if the data information is smaller than the data information, encrypting and storing the data information.
2. The cloud data storage method based on big data and edge calculation according to claim 1, wherein the acquiring data information is performed with preprocessing to obtain encryption information; comprising the following steps:
acquiring data information, and carrying out marginalization processing on the data information to obtain edge data;
dividing the edge data to generate metadata and residual data;
separately encrypting the metadata and the residual data to obtain first encryption information and second encryption information;
encoding and blocking the metadata according to the first encryption information to obtain a plurality of partition data;
calculating weight coefficients of the plurality of partition data, and performing sub-encryption processing on the plurality of partition data according to the weight coefficients to obtain sub-encryption information;
and independently encrypting the partition data according to the sub-encryption information.
3. The cloud data storage method based on big data and edge calculation according to claim 2, wherein if the data information is smaller than the big data, the method is characterized by comprising the following steps:
acquiring data information and generating a hash value;
judging the hash value and a preset hash value threshold;
if the data offset information is larger than the data offset information, generating the data offset information, and judging deviation information of the data information according to the data offset information;
if the data information is smaller than the data information, a storage strategy is generated, and the data information is stored in an encrypted mode according to the storage strategy.
4. The cloud data storage method based on big data and edge calculation according to claim 3, wherein after the data information is stored in an encrypted manner if the data information is smaller than the data information, the method further comprises:
acquiring calling information, and calling the data according to the calling information;
carrying out encryption analysis on the data of different partitions according to the sub encryption information, and generating decryption data;
matching the decrypted data with the encrypted information to obtain matching degree;
judging whether the matching degree is larger than or equal to a matching degree threshold value or not;
if the matching degree threshold value is greater than or equal to the matching degree threshold value, decrypting the partition data and generating a decryption percentage;
judging whether the decryption percentage is larger than or equal to a preset percentage;
if the proportion is larger than or equal to the preset proportion, splicing the partition data and the residual data to obtain decryption information;
if the matching degree is smaller than the matching degree threshold value, generating a matching degree correction parameter, and correcting the decrypted data according to the matching degree correction parameter;
if the partition data is smaller than the preset percentage, the partition data is continuously called.
5. The cloud data storage method based on big data and edge calculation according to claim 4, wherein if the matching degree threshold is greater than or equal to the matching degree threshold, the method further comprises:
acquiring calling information, and calling the data according to the calling information;
carrying out encryption analysis on the data of different partitions according to the sub encryption information, and generating decryption data;
matching the decrypted data with the encrypted information to obtain matching degree;
judging whether the matching degree is larger than or equal to a matching degree threshold value or not;
if the matching degree threshold value is greater than or equal to the matching degree threshold value, decrypting the partition data;
if the matching degree difference value is smaller than the matching degree threshold value, a matching degree difference value is generated;
calculating whether the matching degree difference is larger than a preset difference;
if the residual error data is larger than the residual error data, generating a self-destruction program, and destroying the residual error data according to the self-destruction program.
6. The cloud data storage method based on big data and edge calculation according to claim 5, wherein if the cloud data is larger than the cloud data, generating a self-destruction program, and destroying residual data according to the self-destruction program, comprising:
acquiring a self-destruction program, configuring and recovering partitions of the self-destruction program, and storing recovery information;
judging the times that the matching degree is smaller than the matching degree threshold value in the matching process of the decrypted data and the encrypted information, and generating cracking times;
if the number of times of cracking is greater than a preset number threshold, locking information is generated, and the partition data is locked through the locking information;
and if the number of times of cracking is smaller than a preset number threshold, recovering the self-destruction data through a recovery secret key.
7. A big data based data cloud storage system, the system comprising: the system comprises a memory and a processor, wherein the memory comprises a program of a data cloud storage method based on big data and edge calculation, and the program of the data cloud storage method based on the big data and the edge calculation realizes the following steps when being executed by the processor:
acquiring data information, and preprocessing the data information to obtain encryption information;
extracting the encryption information characteristics and generating an encryption characteristic value;
the encryption characteristic value and a preset encryption characteristic value are used for obtaining a characteristic difference value;
judging whether the characteristic difference value is larger than or equal to a preset threshold value or not;
if the encryption information is greater than or equal to the encryption information, generating a correction coefficient, and correcting the encryption information according to the correction coefficient;
and if the data information is smaller than the data information, encrypting and storing the data information.
8. The big data-based data cloud storage system of claim 7, wherein the acquiring data information performs preprocessing on the data information to obtain encrypted information; comprising the following steps:
acquiring data information, and carrying out marginalization processing on the data information to obtain edge data;
dividing the edge data to generate metadata and residual data;
separately encrypting the metadata and the residual data to obtain first encryption information and second encryption information;
encoding and blocking the metadata according to the first encryption information to obtain a plurality of partition data;
calculating weight coefficients of the plurality of partition data, and performing sub-encryption processing on the plurality of partition data according to the weight coefficients to obtain sub-encryption information;
and independently encrypting the partition data according to the sub-encryption information.
9. The big data based data cloud storage system of claim 8, wherein if the data information is smaller than the data information, the encrypting and storing the data information includes:
acquiring data information and generating a hash value;
judging the hash value and a preset hash value threshold;
if the data offset information is larger than the data offset information, generating the data offset information, and judging deviation information of the data information according to the data offset information;
if the data information is smaller than the data information, a storage strategy is generated, and the data information is stored in an encrypted mode according to the storage strategy.
10. A computer readable storage medium, wherein the computer readable storage medium includes a data cloud storage method program based on big data and edge calculation, and the data cloud storage method program based on big data and edge calculation implements the steps of the data cloud storage method based on big data and edge calculation according to any one of claims 1 to 6 when the data cloud storage method program based on big data and edge calculation is executed by a processor.
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