CN108829899B

CN108829899B - Data table storage, modification, query and statistical method

Info

Publication number: CN108829899B
Application number: CN201810758558.4A
Authority: CN
Inventors: 张海鹰; 周海燕
Original assignee: Beijing Gupanchuangshi Science And Technology Development Co ltd
Current assignee: Beijing Gupanchuangshi Science And Technology Development Co ltd
Priority date: 2014-07-24
Filing date: 2014-07-24
Publication date: 2021-08-20
Anticipated expiration: 2034-07-24
Also published as: CN105279198A; CN105279198B; CN108829899A

Abstract

The invention relates to the field of data control, in particular to a data table storage method, a data table modification method, a data query method and a data statistical method. The data table storage method comprises the following steps: the data table is split according to the type of the data table, so that the split data table cannot express the meaning of the original data table, and then the split data table is stored to a preset position. According to the data table storage method provided by the invention, the field in one original data table is split to generate a plurality of new data tables, so that the single new data table cannot represent the meaning of the original data table, and each field splitting mode is unique, even if a new data table is obtained by others, the original data table cannot be restored according to the new data table, and the content in the original data table cannot be leaked, so that the safety of the content in the data table is ensured, and the defects in the prior art are further solved.

Description

Data table storage, modification, query and statistical method

The present application is a divisional application with application number 2014103539042 entitled data sheet storage, modification, query and statistical method

Technical Field

The invention relates to the field of data control, in particular to a data table storage method, a data table modification method, a data query method and a data statistical method.

Background

With the continuous development of internet technology, products based on various cloud architectures are in a wide range at present, and carriers of cloud storage technologies are cloud systems, and specifically, the cloud systems include public clouds and private clouds. Private clouds are secure but not convenient for large-scale deployment, and public clouds are physically and psychologically weak, but have relatively good computational, storage, and bandwidth resources. Many users are therefore faced with the dilemma of having their own data stored in public networks, which is subject to security problems.

For example, the existing data such as video, audio, text, database and the like are mostly private data of individuals or public institutions, if the data are singly placed in a service provider, the user considers that the data are out of the control range of the user regardless of the encryption of the provider, so that the psychological insecurity is caused, and the main technology adopted globally at present is to store and encrypt the data of the user by using an encryption means of the user and store the data in a system of the user separately.

It is foreseen that by obtaining the encrypted password or using a brute force cracking tool to calculate the decrypted password in the reverse direction, the desired plaintext information can be obtained from the data stored in the cloud system for direct reading. The data table is used as a main object of data, the data table is a basis of other objects, and if the data table is divulged, the core content in the database has the risk of divulging a secret. Thus, a need exists for a method to address data table compromise in a cloud storage environment.

Disclosure of Invention

The present invention is directed to a data table storage method, a data table modification method, a data query method and a data statistics method, so as to solve the above-mentioned problems.

In an embodiment of the present invention, a data table storage method is provided, including:

acquiring a new data table building instruction, wherein the new data table building instruction comprises a plurality of association modes among the new data tables;

splitting fields in one original data table according to the association mode and the type of the original data table acquired in advance to generate a plurality of new data tables, so that the single new data table cannot represent the meaning of the original data table, and the splitting mode of each field is unique;

and respectively storing the different new data tables into at least two cloud systems according to a preset first storage position.

Preferably, if the type of the original data table is a digital type, splitting a field in one of the original data tables according to the association manner and the type of the original data table acquired in advance to generate a plurality of new data tables includes:

splitting an original field in the original data table to generate a filling field and a balance field, wherein the filling field and the balance field respectively form the new data table, so that the combination of the filling field and the balance field and the original field meet a preset functional relationship.

Preferably, the value of the original field has a positive correlation with the value of the padding field and the value of the balance field, respectively.

Preferably, the splitting the original field in the original data table to generate the padding field and the balancing field includes: and acquiring the filling field from the digital matrix acquired in advance, and calculating by using the filling field and the original field according to a preset functional relation to generate a balance field.

Preferably, the digital matrix is a square matrix with odd-numbered rows and columns, the numbers in the same row in the matrix sequentially increase from front to back, the first number in the row with the larger row number is larger than the last number in the row with the smaller row number, the number of the numbers in the digital matrix is larger than the maximum number, and the maximum number is the number of the numbers generated after each number in the original field is replaced by 9.

Preferably, the obtaining the padding field from the pre-obtained number matrix includes:

determining the value X of the intermediate number according to the digit of the original field,

calculating the digit difference Y between the value of the original field and the intermediate number;

and acquiring the filling field in the digital matrix according to the numerical value of the digit difference value and the intermediate number, wherein the filling field is the numerical value of the X + Y-th element in the digital matrix.

Preferably, after the padding field and the balance field respectively form the new data table, adding a padding field between adjacent fields in the new data table, or adding a padding field between adjacent record values in the new data table.

Preferably, if the type of the original data table is a character type, splitting a field in one of the original data tables according to the association manner and the type of the original data table acquired in advance to generate a plurality of new data tables includes:

splitting character codes of characters in the original data table to obtain a plurality of groups of mutually related split character codes;

and respectively forming a plurality of new data tables by one or more groups of the mutually associated split character codes.

Preferably, after the encoding one or more groups of the mutually associated split characters respectively form a plurality of new data tables, the method further includes: filling codes are added between two adjacent split character codes in a new data table, and the character code corresponding to each character is split in a unique mode.

Preferably, if the type of the original data table is a date type, splitting a field in one of the original data tables according to the association manner and the type of the original data table acquired in advance to generate a plurality of new data tables includes:

generating a plurality of mutually related split fields according to unprocessed date type fields in an original data table, and enabling the plurality of split fields and the unprocessed date type fields to meet a preset functional relationship, wherein the split fields comprise date type fields and/or digital type fields; and forming one or more split date type fields/split number type fields into a new data table.

Preferably, the generating a plurality of correlated processed datelike fields/numerically-like fields from an unprocessed datelike field comprises:

selecting a date displayed by a predetermined date type field as a reference date;

calculating a difference in days between the date of the unprocessed date type field and the reference date;

dividing the day difference into a plurality of day values according to a preset proportion, and enabling the combination of the plurality of day values and the day difference to accord with a preset functional relationship;

and composing a new data table by using the fields of each day number value.

Preferably, before the storing the different new data tables into at least two cloud systems according to a preset first storage location, the method further includes:

storing the new data table building instruction, the association mode and the first storage position in a cache;

after respectively storing different new data tables to at least two cloud systems according to a preset first storage position, the method further comprises the following steps:

and sending the association mode and the first storage position, and deleting the cached new data table building instruction, the association mode and the first storage position.

Preferably, the storing the different new data tables into at least two cloud systems according to a preset first storage location includes storing the different new data tables into at least one public storage system and one private storage system respectively.

Preferably, the storing the different new data tables into at least two cloud systems according to a preset first storage location respectively includes:

and respectively storing different new data tables into at least three cloud systems according to preset new data table storage positions, and storing each new data table into at least two cloud databases.

Preferably, before splitting a field in one of the original data tables according to the association manner and the type of the original data table acquired in advance, the method further includes:

acquiring identity information corresponding to the original data table;

judging whether the identity information of the original data table appears in a pre-acquired execution list or not, wherein the execution list comprises the identity information of the original data table which is to be stored;

and if so, executing the splitting step.

and storing a plurality of new data tables into the public storage system and the private storage system according to a preset storage ratio.

The embodiment of the invention also provides a data table modification method of the non-reduction data table based on the data table storage method, which comprises the following steps:

acquiring data to be written, a writing position and a writing mode, wherein the writing mode comprises deletion, addition and replacement;

according to the writing position and a pre-acquired storage position, searching a new data table to be modified in a cloud database, wherein the storage position comprises a storage address of each new data table;

if the writing mode is deleting, deleting a field corresponding to the writing position in the new data table to be modified according to the writing position and the association mode so as to generate a modified data table;

if the writing mode is addition/replacement, splitting the data to be written according to the type and the association mode, deleting the field corresponding to the writing position in the new data table to be modified, and adding the split data to be written into the new data table to be modified according to the writing position to generate a plurality of modified data tables.

The embodiment of the invention also provides a data table modification method for restoring the data table based on the data table storage method, which comprises the following steps:

acquiring data to be written, a writing position and a writing mode;

reading a plurality of the new data tables corresponding to the write locations;

restoring the new data tables into original data tables according to the association mode, or restoring the new data tables into partial original data tables according to the association mode;

modifying the original data table/partial original data table according to the writing position, the to-be-written mode and the written data to generate a modified original data table;

splitting the modified original data table according to the type of the modified original data table according to the association mode;

and storing the split original data table into different cloud systems according to a preset modified data table storage position.

The embodiment of the invention also provides a data table data query method based on the data table storage method, which comprises the following steps:

acquiring a query keyword;

splitting the keywords according to the association mode to obtain a plurality of new keywords;

inquiring a key data table which is consistent with the new key words in a plurality of cloud systems;

combining a plurality of key data tables into an original data table according to the association mode or combining the key data tables into a partial original data table;

and sending out the original data table or the partial original data table.

The embodiment of the invention also provides a data statistical method based on the data table storage method, which comprises the following steps:

acquiring a statistical mode and a query keyword, wherein the statistical mode comprises a data type and a calculation mode;

inquiring a key data table which is consistent with the new key words in a plurality of cloud systems, and inquiring a plurality of key data in the key data table according to the data types;

acquiring the key data, and combining the key data according to the association mode to obtain data to be counted;

and calculating the data to be counted according to the calculation mode to obtain a counting result.

Preferably, before obtaining the key data and combining the key data according to the association manner to obtain the data to be counted, the method includes: and calculating the key data stored in the same cloud system according to a preset combination mode to generate the key data.

Compared with the prior art in which data to be stored is encrypted and then stored in a database, and a desired plaintext information can be obtained from the data stored in a cloud system by obtaining an encryption password or reversely calculating a decryption password by using a brute force cracking tool, thereby causing data leakage, the data table storage method provided by the embodiment of the invention splits a field in an original data table to generate a plurality of new data tables, so that the new data table alone cannot represent the meaning of the original data table, and each field is split in a unique manner, even if a new data table is obtained by others, the original data table cannot be restored according to the new data table, and the content in the original data table cannot be leaked, thereby ensuring the security of the content in the data table, thereby overcoming the defects in the prior art.

Drawings

FIG. 1 is a flow chart of a data table storage method according to an embodiment of the invention;

FIG. 2 is a flow chart illustrating a method for modifying a data table of a non-reduced data table in accordance with an embodiment of the present invention;

FIG. 3 illustrates a flow chart of a data table modification method of restoring a data table of an embodiment of the present invention;

FIG. 4 shows a flow diagram of a data query method of an embodiment of the invention;

FIG. 5 shows a flow chart of a data statistics method of an embodiment of the invention.

Detailed Description

The present invention will be described in further detail below with reference to specific embodiments and with reference to the attached drawings. The embodiment 1 of the present invention provides a basic scheme of a data table storage method, as shown in fig. 1, including the following steps:

s101, acquiring a new data table constructing instruction, wherein the new data table constructing instruction comprises an association mode among a plurality of new data tables;

s102, splitting fields in one original data table according to the association mode and the type of the original data table acquired in advance to generate a plurality of new data tables, so that the single new data table cannot represent the meaning of the original data table, and the splitting mode of each field is unique;

and S103, storing different new data tables to at least two cloud systems according to a preset first storage position.

Specifically, in step S101, the association relationship among the new data tables is used to combine the new data tables into the original data table according to the association relationship. The association relation may refer to a process of splitting the original data table; the present invention also refers to a functional relationship between corresponding data in a plurality of new data tables (a field or a record value in an original data table is split into a plurality of new fields or new record values, and the plurality of new fields or the plurality of new record values are used to form a field in the original data table, or a record value, a plurality of new fields split from a field in the original data table are referred to as corresponding data, or a plurality of new record values split from a record value in the original data table are referred to as corresponding data, wherein a field is composed of one or more record values, and the split here can be understood as splitting numbers or codes according to a relationship of digits or a relationship of sizes) and corresponding data in the original data table, that is, corresponding data in the original data table can be calculated/restored by corresponding data in a plurality of new data tables.

In step S102, after the instruction for constructing the new data table is obtained, the original data table (which may also be referred to as a to-be-split data table) is also split according to the type of the original data table. The type of the original data table may be determined according to the obtained original data table, or may be obtained from a command for constructing a new data table. The types of data tables include: numeric, date, and character types. And splitting the fields in the data table according to different types of the fields in the data table. The meaning of the original data table cannot be expressed by a single new data table, specifically, if the original field is a number 55, the original field 55 can be divided into two fields of 50 and 60 (the original field cannot be divided into 55 and 0), and then neither 50 nor 60 can reflect the meaning of the original field 55, so that the real meaning of the field in the data table is realized, and meanwhile, 55 can be calculated through 50/2+60/2, so that the hiding function is performed in a dividing mode; for another example, 55 can be split into 5 and 11 (cannot be split into 55 and 1), and 5 × 11 is 55, that is, 55 in the original data table is restored. The "making the new data table alone unable to represent the meaning of the original data table" can also be understood as making the data table alone unable to be restored as the original data table, because the new data table alone cannot be restored as the original data table, the true meaning of the original data table cannot be read. "making the new data table alone unable to represent the meaning of the original data table" may also be understood as: only after the corresponding fields in the new data tables are merged and calculated, the record values in the fields can be completely identical to the record values of the fields in the original data table.

It should be noted that, in step S102, after the original data table is split, all the new data tables obtained after the splitting may be restored to the original data table, or part of the new data tables obtained after the splitting may be restored to the original data table. If the original data table is divided into 5 new data tables, the original data table can be restored by at least 5 new data tables, and the original data table can also be restored by at least 2-4 new data tables.

In addition, the splitting mode of each field is unique, and when the original data table corresponding to each terminal (or the original data table can be classified according to the size, type, source, function, and the like of the data table, and is not limited to classifying the data table according to the terminal), the split results of the same field are completely the same (one-to-one), that is, X1 and X2 are obtained after a split, Y1 and Y2 are obtained after B split, which is different from a, or X1 and Y1 are obtained after B split, or X2 and Y1 are obtained after split, but X1 and X2 are never obtained, so as to ensure effective differentiation.

Step S103, the new data tables obtained after the original data tables are split are respectively stored in different cloud systems (including a private storage system and a public storage system), so that each cloud system cannot obtain enough new data tables capable of being restored into the original data tables. As described in step S102, if the original data table can be restored from the part of the new data table obtained after the splitting, the part of the original data table can be stored in different cloud systems respectively. For example, A, B, C, D and E are obtained by splitting the original data table, 5 new data tables are obtained, wherein any three new data tables can be combined to restore the original data table, and then at most two new data tables can be stored in each cloud system to prevent a certain cloud system from obtaining a sufficient number of new data tables enough to restore the original data table, if the 5 new data tables are respectively stored in the 5 cloud systems, the storage manner may be AB stored in one cloud system, BC stored in one cloud system, CD stored in one cloud system, DE stored in one cloud system, and EA stored in one cloud system.

Even if a plurality of new data tables (that is, enough new data tables capable of restoring the original data table) are stored in the same cloud system, the cloud system does not know the association relationship, and thus the plurality of new data tables cannot be restored to the original data table, and the meaning of the field in the original data table cannot be interpreted. In order to better ensure the security of the data table, the association relationship may be stored only in the terminal that issues the instruction to construct the new data table, or the association relationship may be stored only in the private storage system or the verified public storage system. Of course, for convenience of storing, restoring or using the new data tables, the instruction for constructing the new data tables may carry a storage location, that is, a first storage location, and even if the association relationship is obtained, since the storage location of each new data table is not clear, a sufficient number of new data tables that can be restored to the original data table may not be obtained. Further, the storage location and the association relationship may be stored in different locations to enhance security, or the storage location and the association relationship may be encrypted by using different encryption methods, respectively, to improve security. The storage locations may be carried in the construct new data table instructions or may be generated locally.

According to the data table storage method provided by the embodiment of the invention, the field in one original data table is split to generate a plurality of new data tables, so that the single new data table cannot represent the meaning of the original data table, and each field splitting mode is unique, even if a new data table is obtained by others, the original data table cannot be restored according to the new data table, and the content in the original data table cannot be leaked, so that the safety of the content in the data table is ensured, and the defects in the prior art are further solved.

The embodiment 2 of the invention provides an optimization scheme of a data table storage method, and on the basis of the embodiment 1, the types of the data table comprise a digital type, a date type and a character type.

If the type of the original data table is a digital type, in step S102, splitting a field in one original data table according to the association manner and the type of the original data table acquired in advance to generate a plurality of new data tables includes:

If the field of the original data table is a numeric field, the original field (also called original number) can be divided into a padding field (also called padding number) and a balance field (also called balance number), the padding number and the balance number are combined, and the original number can be formed after calculation. Of course, the padding number and the balance number do not refer to a number, and both the padding number and the balance number refer to a number type, and the padding number refers to a basic number, that is, any number obtained in advance; the balance number is calculated from the pad number and the original number. Further, the padding number and the balance number may each be plural. Specifically, if the original number is 40, the padding number is 4, and if the correlation is based on multiplication, the balance number may be 10, or 5 and 2. If the original number is 50, the obtained padding numbers are 2 and 5, and the correlation is calculated by multiplication, then the balance number should be 5. I.e., 5 (balance number) ÷ 50 (original number) ÷ 2 (filler number) ÷ 5 (filler number); of course, the balance number may be the original number, such as 20 (balance number) 5 (original number) 4 (padding number).

Specifically, the value of the original field has positive correlation with the value of the padding field and the value of the balance field, respectively. When the data table is stored, the system will automatically sequence the data table, if the value of the first original field (original number) is larger, the value of the second original field (original number) is smaller, the value of the first balance field and/or the first padding field generated by the first original field is smaller, and the value of the second balance field and/or the second padding field generated by the second original field is larger, the problem of disordered sequencing may be caused, therefore, in order to ensure that after splitting, the balance field and the padding field have the same sequencing order as the corresponding original fields when arranged, that is, the value of the original field has positive correlation with the value of the padding field and the value of the balance field respectively, and further ensure that after the data table formed by the original fields after being sequenced is split, the formed data table carries a plurality of balance fields or a new data table carrying a plurality of padding fields, the arrangement order of the balance field and the filling field corresponding to the original field can not be changed, and confusion in data table statistics can not be caused.

For example, the calculation formula for generating two new data tables is:

since a + B ═ X, X-B ═ a,

since (a) + (B) ═ X, (X) - (B) ═ a,

in the same way, the method for preparing the composite material,

since a-B ═ X, X + B ═ a,

since (a) - (B) ═ X, (X) + (B) ═ a.

Wherein, A is the field in the original data table, B is the filling field, and X is the balance field.

Similarly, the calculation formula for generating the multiple tables is as follows:

since a + B + C ═ X, X-C-B ═ a,

since (a) + (B) + (C) ═ X, (X) - (C) - (B) ═ a)

Further, the splitting the original field in the original data table to generate the padding field and the balancing field includes: and acquiring the filling number from a pre-acquired number matrix, and calculating by using the filling field and the original field according to a preset functional relation to generate a balance field.

The filling numbers are obtained from the pre-generated number matrix, so that the speed of obtaining the filling numbers can be increased, and the obtaining of the filling numbers can be more regular. Furthermore, the filling numbers can be obtained from the number matrix according to a certain rule, so as to ensure that the original numbers of a certain category (interval, range) can correspondingly obtain the filling numbers of a certain range, and the obtaining of the filling numbers is more regular.

Specifically, the digital matrix is a square matrix with odd-numbered rows and columns, the numbers in the same row in the matrix sequentially increase from front to back, the first digit in the row with the larger row number is greater than the last digit in the row with the smaller row number, the number of the digits in the digital matrix is greater than the maximum number, and the maximum number is the numerical value of the digit generated after each digit in the original field is replaced by 9.

Each element position is represented in the matrix, typically by an, m, where n and m represent rows and columns, respectively. When the numbers of the same row in the matrix increase sequentially from front to back, the first digit in the row with larger row number is larger than the last digit in the row with smaller row number, so that the numerical value of each element in the matrix increases column by column according to the sequence of columns (any element in one column is larger than any element in the previous column), and increases row by row according to the sequence of rows.

The digital matrix is set in this way, the digital matrix can be made more regular, and when the padding field is obtained, the obtaining can be performed in the following manner, where the obtaining of the padding field from the pre-obtained digital matrix includes:

By the method for acquiring the filling field, the acquired filling field can be ensured to be present at a later position in the digital matrix when the numerical value of the original field is larger, the acquired filling field is larger when the original field is larger, and the acquired filling field is smaller when the original field is smaller according to a preset rule. Specifically, the numbers in the number matrix are arranged from small to large, and the number of elements in the number matrix is greater than the maximum number, that is, the value of the number generated after each digit in the original field is replaced by 9. If the original field is 345, the corresponding maximum number is 999, the original field is 23, and the corresponding maximum number is 99, it is ensured that the number of elements in the number matrix is greater than the maximum number, and it is ensured that the values of different original fields can all correspond to elements which acquire different values (the value of each element is different). It should be noted that the intermediate number and the maximum number mentioned herein are integers, and the balance field and the padding field may also be integers.

Further, the process of acquiring the padding field can be simplified by determining the intermediate number. The median of the numbers with the same number of bits is the same, e.g. the maximum numbers of 23, 56, 78 are all 99, the median are all 50. Then the digit difference is the value of the original field-the median, then the original field is 23, the median is 50, 23-50-27, then in the digit matrix, the position of the 50 th element is found and the 27 th element is pushed forward, i.e. the 23 th element is the padding field to be fetched. It should be noted that, the first element is obtained by arranging rows and columns in the digital matrix in order, and the previous element of the first element in the next row is the last element in the first row; in the same row, the element is arranged in front and back according to the size relationship of the number of the columns. For example, in a 5 x 5 number matrix (an, m, n are the number of rows and m is the number of columns), the second element before a2,3 is a2, 1; the third element before a2,3 is a1, 3. Of course, the intermediate number may be calculated in a manner that does not require a specific manner, but the original numbers with the same number of digits should have the same intermediate number, such as the intermediate number being the maximum number/B + a/B, where a and B are both natural numbers other than 0.

By the method for acquiring the filling fields, each original field can be ensured to have, only one filling field corresponds to the original number (numerical values of each element in the number matrix are different), and the balance field calculated through the preset functional relationship can also be ensured to have positive correlation with the original field (the original number). Specifically, if the original number is 5 and the padding field is 6, the balance field may be 5+6 — 11; if a calculation rule is used, balance field is original number + padding field X; a balance field (original number) padding field (X); balance field X + padding field Y; the balance field is the original number X + the pad field/Y, where X and Y can each be any natural number or any number that can be expressed on the number axis. The balance field calculated in this way also has a positive correlation with the original number. And it is acquired through a pre-generated number matrix, thereby simplifying and standardizing the acquisition process of the padding field.

In order to increase the complexity and difficult reducibility of the padding field and the balance field, after the padding field and the balance field respectively form the new data table, the method further comprises adding the padding field between adjacent fields in the new data table or adding the padding field between adjacent record values in the new data table.

Adding padding fields between adjacent fields, or between adjacent record values within the new data table, can disrupt the order of fields, or records. Furthermore, the person who illegally obtains the data table can not know which are filling fields and which are numbers (fields or record values) which should appear in the data table, so that illegal reading can be prevented. It should be noted that the padding field may be the same as the aforementioned padding field, or may be extracted again from the number matrix, and the method provided in this paragraph is not limited to the value and source of the padding field, and may be any natural number capable of performing the padding function.

Numbers can also be split in various binary systems, such as binary, octal, decimal, hexadecimal,

such as binary number 11101011

Detachable 1110, 1011, rules would record their actual number of bits represented, e.g.

11100000、00001011；

10101010、01000001。

The above method of splitting binary digits is a separation splitting, otherwise known as a packet splitting. That is, binary, octal, decimal and hexadecimal numbers are split according to the difference of digits. In order to ensure that no error occurs in the sequence when the digits are restored to the original digits, 0 or other padding characters (such as numbers, English characters, Greek letters and the like) can be filled between adjacent digits of the new digits so as to replace the digits of the even digits which do not appear in the first new digit with the padding characters or replace the digits of the odd digits which do not appear in the second new digit. Then, when the first new number and the second new number are restored to the original number, the padding character can be removed, and the number (the number not padded with the character) at the corresponding position in the other new number is correspondingly added to restore the new number. Such as splitting 11101011 into 10101010 and 01000001.

The separation splitting is not limited to the splitting position, and the splitting can be performed by separating one bit, or separating by separating two bits (the numbers of the first, fourth, seventh, tenth, etc. digits are taken out to form an array, and the numbers of the rest digits are formed into an array).

Values such as A, B, C in 16-ary may also be split in the same way.

In addition to splitting odd and even bits, a group split may be performed, such as splitting an 8-bit number, splitting the first four bits and the last four bits to form two new groups of digits, and after splitting, padding characters may be added to the removed digits, such as splitting 11101011 into 11100000 and 00001011. That is, a number of adjacent digits is formed into a new number, and the new number is split from an original number.

It should be noted that after splitting the fields of the digital font, each new field (the field obtained after splitting) may be marked to determine where the new field is located in the original field when the plurality of new fields (or the new data table, where the new fields are stored in the new data table) are restored to the original field (or the original data table, where the original fields are stored in the original data table). If 11101011 is split into 11100000 and 00001011, the 11100000 mark is effective with the first four digits, and the padding character is '0'; 00001011 the last four digits are valid and the pad character is "0", then at the time of recovery, the new digit can be recovered simply according to the meaning of the tag. If necessary, the position information of the new number can be recorded so as to be used when the new number is restored to the original number (original data table).

In addition to splitting the digits, the digits of each digit of the new digit can be uniformly operated on the basis of splitting, for example, the digits of each digit are uniformly +1, and then the new digits after +1 are stored. When the data is restored to the original data table, the action of "-1" is performed in a unified way.

The field includes not only a digital type but also a character type, and if the type of the record value is the character type, splitting the field in one original data table according to the association mode and the type of the original data table acquired in advance to generate a plurality of new data tables includes:

splitting character codes of characters in the original data table to obtain a plurality of groups of mutually related split character codes; and respectively forming a plurality of new data tables by one or more groups of the mutually associated split character codes.

In the data table, characters are represented by a set of character strings (code strings) arranged in a predetermined order. Thus, a string of characters, or code string, representing a character may be broken down, such as, for example, a word whose UTF-8 encoding is D4C 64E 91E 4BA91, splitting the word into D4C 64E 91 and E4BA 91. Thus, splitting can be performed according to the sequence of characters in a character string to split the character string into a plurality of groups of character strings, wherein a single split sub-character string cannot represent the meaning of the original character string, and because there are a plurality of characters having sub-character strings (e.g., E4BA 91), that is, there may be many character strings corresponding to the characters comprising the sub-character strings, when a sentence includes a plurality of characters, and each character also corresponds to a complete character string, after each character string is split, it is difficult to know the meaning represented by the non-split character string from the split sub-character strings (the difficulty is that one sub-character string can correspond to a plurality of characters, if there are 10 sub-character strings arranged, that is, a sentence is composed of 2 sub-character strings, each sub-character string corresponds to 10 characters, then the possible combinations are 102, and as the number of strings of a sentence increases, the possible permutation combinations grow in the order of an exponential power, the possible combinations being inefficiently disposable).

Besides, the character string can be split in a sequence order, and the character string can also be split in a position-separated manner, for example, if the UTF-8 code of a certain character is D4C 64E 91E 4BA91, the character string can be split in the sequence of the parity bits of the character string. If D4C6 is split into DC and C6, 4E91 is split into 49 and E1, and E4BA91 is split into EB9 and 4a1, each bit split can also play a role in implying the original character meaning. Further, in addition to the above-mentioned space splitting, splitting may be performed according to a predetermined sequence, for example, taking the first and last characters of each character string, and taking the first and last characters, i.e., D (first character) and 1 (last character) from D4C 64E 91E 4BA91, the two split combinations are D1 and 4C 64E 91E 4BA 9. Splitting may also be performed according to a certain mathematical rule, for example, according to a fibonacci number sequence, that is, taking the numbers at positions 1, 2,3, 5, 8, 13, and 21 in a group of character strings and leaving the other numbers to distinguish the original character string into two groups of substrings, and this predetermined sequence may be a part of the association.

Further, after the encoding one or more groups of the mutually associated split characters respectively form a plurality of new data tables, the method further includes: filling codes are added between two adjacent split character codes in a new data table, and the character code corresponding to each character is split in a unique mode.

Like the numeric data table, after splitting, padding codes are added among a plurality of substrings (split character codes), and the effect of destroying the original meaning of the substrings can also be achieved. When the padding codes are added, the patterns of the padding codes can be recorded, for example, the padding codes can be numbers, English and Greek letters, and other codes of character codes in character strings can be distinguished.

After the character-type new data table is formed, position information of a plurality of fields (or character strings) in the new data table may be recorded (normally, the recording position of a field after splitting does not change, but for convenience, the position of each field of the new field may be recorded), so as to restore the original data table.

If the type of the record value is a date type, splitting a field in one original data table according to the association mode and the type of the original data table acquired in advance to generate a plurality of new data tables includes:

Specifically, the date type field such as 1980/10/2 (arranged according to year/month/day) may be split in the manner of a numeric type field from 1980, 10, and 2, and then the new numeric type field obtained by splitting is formed into a new date type field according to the original sequence, for example, when 1980/10/2 is split into 1000/8/1 and 980/2/1, 1980 (year) is 1000+980, 10 (month) is 8+2, and 2 (day) is 1+ 1. The date type field can be split according to any one of the splitting modes of the digital type field described in the foregoing, and then the split new field is combined into the date type field. When the original field information needs to be read, the two (or more) new fields after splitting are recombined (for example, the addition in this field is performed, and other kinds of operations can be performed), and then the original field information can be directly read.

The date type field includes a number type field, so that the date type field can be generated as a plurality of date type fields or a plurality of number type fields at the time of splitting. The field of the datebook type may be split in particular in the following way. Said generating a plurality of interrelated processed datelike fields/numerically-like fields from an unprocessed datelike field comprises:

and composing a new data table by using the fields of each day number value.

The mode of selecting the reference date to count different date type fields can accelerate the calculation speed of the system and improve the stability of the system.

The formula is that a certain day of a certain month of a year is taken as a starting number 1, a number is added every one day in the original table, a number is reduced every one day, in order to avoid multiplying a decimal by 100, and then multiplying by two or more asymmetric percentages to be a percentage of 100%, the result in each table is obtained, specifically, 1980/10/2, 1980/1/1 day is taken as the starting date 1, the number of 1980/10/2 is 276, 276 is multiplied by 100, and then asymmetric random percentages are multiplied by 73% and 27%, A is taken as 20148, the formula 276 100 73% is 20148, B is taken as 7452, the formula 276 100% 27% is 7452, both tables can represent a specific date, but the single table can not be used for back-pushing the number represented by the original date.

It should be noted that, regardless of whether the data table is digital, character, or date, the data table may be re-split and then encrypted for improving the security of the fields (character, digital, date) in the new data table.

In the flash memory system, in addition to splitting the data table, the method further includes deleting data stored in the cache, that is, before storing the different new data tables to at least two cloud systems according to a preset first storage location:

By deleting the cache file, the workload of the flash memory system can be reduced. The flash memory system needs an external operating system to establish a connection with the flash memory system first, and then subsequent operations (such as sending data and instructions to the flash memory system) can be performed. It should be noted that the module for executing the data table splitting action may be stored in the network or in the terminal for sending the instruction for constructing the new data table, and the terminal is usually controlled by the user, and the module for executing the data table splitting action is placed in the terminal, so as to improve the security of the user. Of course, considering that a terminal will not always perform the data table splitting action, the module for performing the data table splitting action may also be placed at the network end, so as to improve the utilization efficiency and save the network resources.

In specific use, the new data table needs to be stored in different storage systems according to the requirements of users, for example,

the storing of the different new data tables to the at least two cloud systems according to the preset first storage positions respectively comprises storing the different new data tables to at least one public storage system and one private storage system respectively.

If, the storing the different new data tables to at least two cloud systems according to the preset first storage locations respectively includes:

It should be noted that the private storage system has high security but poor access convenience, and the public storage system has low security but fast access convenience, so the storage ratio of the public storage system or the private storage system can be selected according to the property of the data sheet to be split (emphasizing the data sheet security, or emphasizing the convenience of accessing and reading the data sheet). Among them, it is preferable that a plurality of new data sheets are stored in at least one public storage system and at least one private storage system. Of course, according to the different needs of the user (the data sheet is required to have higher security and better readability), a part of the new data sheets can be stored in the public storage system (the part of the new data sheets can be respectively stored in the public storage systems to improve the security of the new data sheets), and the other several new data sheets can be stored in the private storage system (the part of the new data sheets can be respectively stored in the private storage systems to improve the security of the new data sheets), and all the new data sheets stored in the public storage system and the private storage system can be restored to the original data sheets after being combined; or a part of the plurality of new data tables can be combined and then can be restored to the original data table, wherein the part (several data tables of the plurality of data tables) also needs to be stored in at least one public storage system and one private storage system respectively.

In order to ensure that the stored data does not have a problem, the storing different new data tables to at least two cloud systems according to a preset first storage position respectively comprises:

It should be noted that, during storage, a damage of the storage system may be encountered, which may cause a problem that the stored file cannot be read, and the new data tables are associated with each other, which may directly cause that no other data table can be read due to a damage of one new data table. Therefore, each new data table can be stored for multiple times, namely, each new data table is respectively stored in a plurality of storage systems, and when one storage system is damaged, the new data table in the damaged storage system can be obtained from other storage systems. Thereby ensuring the integrity and readability of the data.

Specifically, if the original data table is split into data tables A, B and C, only A, B, C is combined and can be restored into the original data table, and the cloud systems are X, Y and Z. Then at the time of storage, a and B may be stored in X, B and C in Y, and C and a in Z. Thus, if X fails, the original data table can also be restored by storing B, C and C, A in Y and Z, respectively.

Before splitting a field in one original data table according to the association mode and the type of the original data table acquired in advance, the method further includes:

acquiring identity information corresponding to the original data table;

and if so, executing the splitting step.

The system usually includes an authentication system to verify whether a local system needs to execute an instruction sent by a terminal system, one way is to pre-establish a data table, where the data table stores the identity information of the terminal system authorized to execute splitting, and each time the identity information of the terminal system is received (or the identity information corresponding to the original data table is obtained), it needs to verify whether the identity information is authorized, and if the identity information is authorized (the identity information of the original data table appears in a pre-obtained execution list), the subsequent splitting and storing steps are executed.

The data table modification method is further divided into a data table modification method of a non-reduced data table and a data table modification method of a reduced data table, embodiment 3 of the present invention provides a data table modification method of a non-reduced data table based on a data table storage method, and on the basis of embodiment 1, as shown in fig. 2, the method includes the following steps:

s201, acquiring data to be written, a writing position and a writing mode;

s202, searching new data tables to be modified in a cloud database according to the writing position and a pre-acquired first storage position, wherein the storage position comprises a storage address of each new data table;

s203, if the writing mode is deleting, deleting the field corresponding to the writing position in the new data table to be modified according to the writing position and the correlation mode to generate a modified data table;

s204, if the writing mode is addition/replacement, splitting the data to be written according to the type and the association mode of the data, deleting a field corresponding to the writing position in the new data table to be modified, and adding the split data to be written into the new data table to be modified according to the writing position to generate a plurality of modified data tables.

Specifically, in S201, the data to be written may be data that needs to be replaced, data that needs to be added, or "empty," that is, when the writing manner is deleting, the original data is replaced with the empty data. The writing mode includes addition, or replacement, or deletion. The writing position refers to a position in the original data table where writing is performed, and it should be noted that the writing position may be determined by a key of the original data table. If the writing position is: the name (Wang Li) + the mark (payroll), so that the payroll quantity corresponding to the name can be found out correspondingly according to the name, and then the number can be modified according to a writing mode. Fields between keyword a and keyword B may also be found. The writing location may also be described in terms of length, or other units of measure, such as X lengths after key a) fields need to be modified.

In S202, a new data table to be modified is searched from a plurality of new data tables stored in the cloud system, and as described in the previous paragraph, the new data table may be searched in a manner of a keyword. It should be noted that, as can be seen from embodiment 1, the result of splitting each kind of number, date, and character is the same, and then the splitting manners of the corresponding different kinds of keywords are different, that is, the splitting results are different, the keywords can be split at present, and then the split keywords are used to perform retrieval in the cloud database to determine the new data table corresponding to the writing position. The pre-fetched storage locations indicate the storage addresses of the data tables, and the addresses stored in each new data table may be determined by address lookup, and the write locations may be used to determine which new data table needs to be modified.

In S203, if the writing mode is deleting, the field corresponding to the writing position in the new data table may be deleted to complete the modification. As described above, the writing mode can be searched according to the keywords, and the location corresponding to each keyword can be determined if the split new keyword is unique. And deleting the field corresponding to the writing position to generate a modified data table, and then storing the modified data table, wherein the specific storage position can be stored in a replacement mode according to the original position, and can also be stored in other positions without changing the original data table.

S204, the same as the modification, for example, if the writing manner is addition or replacement, then, on the basis of deletion, the written data is also split according to an associated manner, it has been described above that the splitting manner of each keyword (field) is unique, then the splitting manner of each data to be written is also unique, after the data to be written is split, two or more new data tables are correspondingly added, and the field corresponding to the writing position in the new data table is deleted, so that the modification that the writing manner is addition or replacement is completed.

Embodiment 4 of the present invention provides a data table modification method for restoring a data table based on a data table storage method, which includes, on the basis of embodiment 1, as shown in fig. 3:

s301, acquiring data to be written, a writing position and a writing mode;

s302, reading a plurality of new data tables corresponding to the writing positions;

s303, restoring the new data tables into original data tables according to the association mode, or restoring the new data tables into partial original data tables according to the association mode;

s304, modifying part of the original data table according to the writing position, the mode to be written and the written data to generate a modified original data table;

s305, splitting the modified original data table according to the type of the original data table according to the association mode;

and S306, storing the split original data table into different cloud systems according to the preset storage position of the modified data table.

The data to be written, the writing position and the writing mode are all required to be obtained in a manner similar to the modification mode of the non-restored data table, and the data to be written, the writing position and the writing mode in the modification mode of the data to be written, the writing position and the writing mode in the non-restored data table are the same. In step S302, it is also necessary to read a plurality of new data tables corresponding to the writing locations, where the writing locations may be composed of keywords, so as to determine which new data table needs to be modified. In step S303, the new data tables corresponding to the writing positions are restored to the original data table, where the original data table is a portion to be modified, and the modified portion may not be all of the original data table. In step S304, which part needs to be modified may be determined according to the key word of the writing position, similarly to the modification method of the non-restored data table. If the writing position identifies two keywords and the content to be modified is a field between the two keywords, the data to be written can be added into the original data table in a replacement or addition manner (if the writing manner is deletion, the field in the original data table can be replaced by the "empty" data to be written). If the writing position can also be: the name (Wang Li) + the mark (payroll), so that the payroll quantity corresponding to the name can be found out correspondingly according to the name, and then the number can be modified according to a writing mode. Fields between keyword a and keyword B may also be found. The writing location may also be described in terms of length, or other units of measure, such as X lengths after key a) fields need to be modified.

In step S305, similar to the data table storage manner, the data table needs to be split twice, and the splitting process may be the same as that described in embodiment 1, and is not described herein again. In step S306, the original data table after splitting needs to be stored again. The storage process is the same as step S103, and is not described again.

Embodiment 5 of the present invention provides a data query method based on a data table storage method, and on the basis of embodiment 1, as shown in fig. 4, the method includes:

s401, acquiring a query keyword;

s402, splitting the keywords according to an association mode to obtain a plurality of new keywords;

s403, inquiring key data tables which are consistent with the new keywords in a plurality of cloud systems;

s404, combining a plurality of key data tables into an original data table or a partial original data table according to an association mode;

s405, sending out the original data table or a part of the original data table.

When a data table containing specified keywords needs to be queried, the acquired keywords (or referred to as keywords) need to be split, and as described above, the splitting mode of each keyword is unique, so that a new data table queried according to the split keywords is also corresponding. And combining the inquired key data tables according to a correlation mode to form an original data table or a partial original data table (only a part of data is needed possibly, so that a complete original data table is not needed), and sending the original data table to a specified terminal.

Embodiment 6 of the present invention provides a data statistics method based on a data table storage method, and on the basis of embodiment 1, as shown in fig. 5, the method includes:

s501, acquiring a statistical mode and a query keyword, wherein the statistical mode comprises a data type and a calculation mode;

s502, splitting the keywords according to an association mode to obtain a plurality of new keywords;

s503, inquiring a key data table which is consistent with the new key words in the plurality of cloud systems, and inquiring a plurality of key data in the key data table according to the data types;

s504, key data are obtained and combined according to a correlation mode to obtain data to be counted;

and S505, calculating the data to be counted according to a calculation mode to obtain a counting result.

Specifically, the query keyword (or query keyword) in step S501 is the same as the keyword in embodiment 5, and further, the keywords obtained in step S502 are the same as the keywords in step S402, and the split new keywords corresponding to each keyword are unique.

In step S503, a key data table corresponding to the new keyword is queried in the plurality of cloud systems, where the new keyword is understood as each new keyword. And querying a plurality of key data in the key data table according to the data type. Data types as used herein, such as payroll, age, etc., are attributes of the object being queried that are described by the data type, i.e., key data. Since the key data is already split, in order to enable direct reading, it is necessary to acquire all of the associated key data, and combine the key data in an associated manner to combine the key data (such as wages, ages, and the like) into data to be counted (the key data may be multiple groups, that is, wages or ages of multiple persons), then calculate the data to be counted in a manner listed in the calculation manner to obtain a statistical result, and finally, send the statistical result to a designated terminal to complete data counting.

Before obtaining key data and combining the key data according to a correlation mode to obtain data to be counted, the method comprises the following steps: and calculating the key data stored in the same cloud system according to a preset combination mode to generate the key data.

That is, data statistics may be divided into two times, and data stored in the cloud system that has been split is merged, where it should be noted that the merging mode is obtained in advance, for example, all original data are split in an addition mode, where the original data a is B + C, and the original data D is E + F; when the values of a and D need to be counted, B, C, E obtained after splitting can be directly added, further, B and E are stored in one cloud system, and C and F are stored in another cloud system, so during counting, B and E can be summed, C and F can be summed, and finally, the two summed results are added. However, the splitting modes of a and D are the same, that is, both are summation, or both are subtraction, multiplication, division, and the like. Naturally, the cloud may be known in advance, and direct calculation may be performed specifically, for example, when the original data a is equal to B + C, the original data D is equal to E-F, B and E are stored in one cloud system, and C and F are stored in another cloud system, the sum of a and B may be summed by B + E, the difference may be calculated by C-F (primary calculation), and the results obtained by the two calculation equations may be summed (secondary calculation). Therefore, the probability of errors can be reduced by counting the data twice, the first calculation can be completed by the cloud system, and the second calculation can be completed by the terminal, so that the problem of data leakage is reduced.

It will be apparent to those skilled in the art that the steps of the present invention described above may be implemented by a general purpose computing device, centralized on a single computing device or distributed across a network of computing devices, or alternatively, may be implemented by program code executable by a computing device, such that the program code may be stored in a memory device and executed by a computing device, or may be implemented by individual integrated circuit modules, or by a plurality of modules or steps within the memory device as a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.

The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for storing a data table, comprising:

respectively storing different new data tables into at least two cloud systems according to a preset first storage position;

if the type of the original data table is a digital type, splitting a field in one original data table according to the association mode and the type of the original data table acquired in advance to generate a plurality of new data tables comprises:

splitting an original field in the original data table to generate a filling field and a balance field, wherein the filling field and the balance field respectively form the new data table, so that the combination of the filling field and the balance field and the original field meet a preset functional relationship;

the splitting the original field in the original data table to generate the padding field and the balancing field includes:

acquiring the filling field from a pre-acquired digital matrix;

the method specifically comprises the following steps:

2. The method of claim 1, wherein the value of the original field has a positive correlation with the value of the padding field and the value of the balance field, respectively.

3. The data table storage method of claim 2, wherein the splitting the original field in the original data table to generate the padding field and the balancing field comprises: and acquiring the filling field from the digital matrix acquired in advance, and calculating by using the filling field and the original field according to a preset functional relation to generate a balance field.

4. A method as claimed in claim 3, wherein the number matrix is a square matrix with odd number of rows and columns, and the numbers in the same row in the matrix increase sequentially from front to back, the first number in the row with larger number of rows is greater than the last number in the row with smaller number of rows, the number in the number matrix is greater than the maximum number, and the maximum number is the number of the number generated after each number in the original field is replaced by 9.

5. The data table storage method according to claim 1, further comprising adding a padding field between adjacent fields in the new data table or adding a padding field between adjacent record values in the new data table after the padding field and the balance field respectively form the new data table.

6. The method according to claim 1, wherein before storing the different new data tables into at least two cloud systems according to a preset first storage location, the method further comprises:

7. The method according to claim 1, wherein before splitting a field in one of the original data tables according to the association method and the type of the original data table obtained in advance, the method further comprises:

acquiring identity information corresponding to the original data table;

and if so, executing the step of splitting the field in the original data table to generate a plurality of new data tables.

8. A method for modifying a data table based on a non-reduced data table according to any one of claims 1 to 7, comprising:

if the writing mode is addition or replacement, splitting the data to be written according to the type and the association mode, deleting a field corresponding to the writing position in the new data table to be modified, and adding the split data to be written into the new data table to be modified according to the writing position to generate a plurality of modified data tables.

9. A method for modifying a data table for restoring a data table based on the method for storing a data table according to any one of claims 1 to 7, comprising:

acquiring data to be written, a writing position and a writing mode;

modifying the original data table or a part of the original data table according to the writing position, the to-be-written mode and the written data to generate a modified original data table;

10. The data query method based on the data table storage method according to any one of claims 1 to 7, comprising:

acquiring a query keyword;

and sending out the original data table or the partial original data table.

11. A method of data statistics based on the method of data table storage according to any of claims 1-7, comprising:

respectively inquiring a key data table which is consistent with each new keyword in a plurality of cloud systems, and inquiring a plurality of key data in the key data table according to the data types;