CN115795517A - Asset data storage method and device - Google Patents
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Abstract
The invention relates to the technical field of asset data management, in particular to an asset data storage method and device, which comprises the following steps: dividing asset data to be stored into
Individual asset data block, pair
Performing importance classification on each asset data block to obtain asset data blocks with different importance, generating a coding coefficient according to the importance of each asset data block, receiving a data description text of each asset data block, extracting text features of the data description text to obtain a data description vector, and sequencing each asset data block according to the coding coefficient and the data description vector of the asset data block to obtain a plurality of encrypted data blocksA chain for storing the encrypted data chain to the distributed storage system, wherein the chain number of the encrypted data chain is not more than the storage node number of the distributed storage system
And storing the asset data blocks which are not included in any encrypted data chain into a pre-constructed local storage system. The invention can reduce the leakage risk after the asset data is stored.
Description
Technical Field
The present invention relates to the field of asset data management technologies, and in particular, to an asset data storage method and apparatus, an electronic device, and a computer-readable storage medium.
Background
Asset data refers to data resources that are physically or electronically recorded, owned or controlled by an individual or business, and that can bring future economic benefits to the business. Common asset data includes massive pictures, internal customer information files, and the like.
The asset data has high value, and therefore, the asset data has important significance for storage of the asset data. The traditional asset data storage mainly depends on a distributed storage system, asset data is divided into a plurality of asset data blocks, and each asset data block is stored in different distributed storage nodes, so that the asset data storage is completed.
The asset data storage relying on the distributed storage system has certain practicability, but the asset data is directly split into a plurality of asset data blocks and then stored in the distributed storage nodes, and because connection logic among the asset data blocks is not considered, the storage logic is relatively simple, and secondary leakage of the asset data is easily caused.
Disclosure of Invention
The invention provides an asset data storage method, an asset data storage device and a computer readable storage medium, and mainly aims to reduce the leakage risk after asset data is stored.
In order to achieve the above object, the present invention provides an asset data storage method, including: receiving an asset data storage instruction, acquiring asset data to be stored according to the asset data storage instruction and determining a distributed storage system of the asset data to be stored, wherein the distributed storage system comprises
Each storage node consists of a plurality of storage nodes; dividing the asset data to be stored into
Individual asset data block, pair
Performing importance classification on each asset data block to obtain asset data blocks with different importance; generating a coding coefficient according to the importance of each asset data block; receiving a data description text of each asset data block, and extracting text features of the data description text to obtain a data description vector; sequencing each asset data block according to the coding coefficient and the data description vector of the asset data block to obtain a plurality of asset data chains; calculating the data leakage rate of each asset data chain, and encrypting each corresponding asset data chain based on the data leakage rate to obtain an encrypted data chain; storing the encrypted data chain to the distributed storage system, wherein the chain number of the encrypted data chain is not more than the storage node number of the distributed storage system
And storing the asset data block which is not included in any encrypted data chain to a pre-constructed local storage system to finish asset data storage.
Optionally, dividing the asset data to be stored into
An asset data block comprising: performing encryption operation on the asset data to be stored to obtain encrypted asset data, wherein the encryption operation adopts a symmetric encryption mode; splitting the encrypted asset data according to data generation time or data importance to obtain
An asset data block.
Optionally, the pair
Individual asset data block execution importanceAnd (3) performing sex classification to obtain asset data blocks with different importance, wherein the sex classification comprises the following steps: receiving the set importance level types, and sequentially sending each asset data block to a pre-constructed auditing platform; and performing importance classification on each asset data block by using the auditing platform, wherein the importance classification level belongs to an importance level category until the importance classification of each asset data block is completed to obtain the asset data blocks with different importance.
Optionally, the generating an encoding coefficient according to the importance of each asset data block includes: counting all asset data blocks belonging to the same class of importance levels, and calculating coding coefficients corresponding to the same class of importance levels according to the following method: generating a corresponding weight coefficient for each asset data block belonging to the same class of importance level, wherein the value of the weight coefficient of the asset data block of the high importance level is higher than that of the asset data block of the low importance level; generating coding coefficients corresponding to the importance levels of the same class according to the following formula:
, wherein ,
denotes the first
The coding coefficients corresponding to the class importance levels,
is shown as
Class importance level of
The data of each of the asset data blocks,
is shown as
Class importance level the first
The weight coefficients of the individual asset data blocks.
Optionally, the extracting text features of the data description text to obtain a data description vector includes: carrying out preprocessing operation including word segmentation and abnormal word removal on the data description text to obtain a primary description text; performing word vector conversion on each word of the primary description text to obtain a plurality of groups of description word vectors; calculating the similarity of each description word vector and other description word vectors, and combining the description word vectors with the similarity greater than or equal to a similarity threshold; and combining all the description word vectors according to the text sequence of the primary description text to obtain the data description vector.
Optionally, the calculating the similarity between each description word vector and other description word vectors includes: the similarity is calculated by adopting the following method: the similarity between the dialogue features can be calculated by adopting the following similarity calculation method:
, wherein ,
representation description word vector
And description word vector
The degree of similarity between the two images is determined,
to represent
And other descriptive word vectors
The weight coefficient of (a) is,
for the description included in the data description textThe total number of data for the word vector,
is a bias coefficient, and
。
optionally, the sorting each asset data block according to the coding coefficient and the data description vector of the asset data block to obtain multiple asset data chains includes: acquire all of different importance
Each asset data block is coded according to different coding coefficients
Each asset data block is divided into
An asset data group of which
The same number of categories as the importance level category; acquiring an asset data group with the maximum coding coefficient, and sequentially extracting each asset data block in the asset data group with the maximum coding coefficient to obtain an asset data head; the following operations are performed for each asset header: receiving a set maximum number of chains of asset data chainshWherein the maximum number of chainshAt least 2, and the maximum number of chainshNo greater than the number of storage nodes of the distributed storage system
From all over
Selecting other asset data blocks with the highest similarity with the data description vector of the asset data head from the asset data blocks as connected data blocks; performing end-to-end connection on the connected data blocks and the asset data head to obtain an asset data branch chain, wherein the asset data branch chain comprises 2 asset data blocks in total, and the head of the asset data branch chainThe data head is the asset data head, and the tail is a connected data block; judging the quantity of the asset data blocks of the asset data branch chain and the maximum chain quantityhThe magnitude relationship of (1); if the number of the asset data blocks of the asset data branch chain is less than the maximum chain numberhContinuously calculating other asset data blocks with the highest similarity with the connected data blocks, and continuously executing head-to-tail connection operation to obtain an asset data branch chain, wherein the asset data branch chain comprises 3 asset data blocks in total, and the head of the asset data branch chain is still the head of the asset data; until the number of asset data blocks of the asset data sub-chain equals the maximum chain numbernAnd directly terminating the end-to-end connection to obtain the asset data chain comprising the asset data blocks.
Optionally, the calculating a data leakage rate of each asset data chain includes: all asset data blocks included in the asset data chain are counted, and a leakage risk probability value of each included asset data block in a historical storage record is retrieved; and calculating the data leakage rate of each asset data chain based on the leakage risk probability value.
Optionally, the calculating, based on the leakage risk probability value, a data leakage rate of each asset data chain includes: and calculating to obtain the data leakage rate by adopting the following formula:
, wherein ,
denotes the first
A chain of asset data is formed,
denotes the first
The total number of asset data blocks included in the asset data chain,
is shown as
The data leakage rate of a strip asset data chain,
denotes the first
Item in the asset data chain
The leak risk probability values of the asset data blocks in the history log,
is shown as
First in the asset data chain
Data description vector and the second of the asset data block
The data of each asset data block describes a similarity value of the vectors.
In order to solve the above problems, the present invention also provides an asset data storage device, comprising: the asset data splitting module is used for receiving an asset data storage instruction, acquiring asset data to be stored according to the asset data storage instruction and determining a distributed storage system of the asset data to be stored, wherein the distributed storage system consists of n storage nodes, dividing the asset data to be stored into k asset data blocks, and performing importance classification on the k asset data blocks to obtain asset data blocks with different importance; the coding coefficient calculation module is used for generating a coding coefficient according to the importance of each asset data block; the asset data chain building module is used for receiving the data description text of each asset data block, extracting text features of the data description text to obtain data description vectors, and sequencing each asset data block according to the coding coefficient and the data description vectors of the asset data block to obtain a plurality of asset data chains; the encryption module is used for calculating the data leakage rate of each asset data chain and encrypting each corresponding asset data chain based on the data leakage rate to obtain an encrypted data chain; and the asset data storage module is used for storing the encrypted data chains to the distributed storage system, wherein the number of the chains of the encrypted data chains is not more than the number n of the storage nodes of the distributed storage system, and storing the asset data blocks which are not included in any encrypted data chain to a pre-constructed local storage system to finish asset data storage.
In order to solve the above problem, the present invention also provides an electronic device, including: a memory storing at least one instruction; and the processor executes the instructions stored in the memory to realize the asset data storage method.
In order to solve the above problem, the present invention also provides a computer-readable storage medium having at least one instruction stored therein, the at least one instruction being executed by a processor in an electronic device to implement the asset data storage method described above.
In order to solve the problems in the background art, the embodiment of the invention firstly divides the asset data to be stored into
Individual asset data block, pair
The method comprises the steps of performing importance classification on asset data blocks to obtain asset data blocks with different importance, splitting asset data to be stored, sequencing the asset data blocks according to the importance, solving the problem of priority of subsequent storage, further generating coding coefficients according to the importance of each asset data block, receiving data description texts of each asset data block, extracting text features of the data description texts to obtain data description vectors, wherein the generation of the coding coefficients is directly related to the importance of the asset data blocks, and the more important asset data blocks correspond to higher coding coefficients, so that the more important asset data blocks correspond to higher coding coefficients, and further the coding coefficients and the data description of the asset data blocksVector, sequencing is carried out on each asset data block to obtain a plurality of asset data chains, and it can be seen that in the embodiment of the present invention, the asset data blocks are not directly stored in different distributed storage nodes, but an asset data chain is constructed, and each corresponding asset data chain is encrypted based on the data leakage rate according to the data leakage rate of each asset data chain to obtain an encrypted data chain.
Drawings
FIG. 1 is a schematic flow chart diagram of an asset data storage method according to an embodiment of the present invention; FIG. 2 is a functional block diagram of an asset data storage device according to an embodiment of the present invention; fig. 3 is a schematic structural diagram of an electronic device implementing the asset data storage method according to an embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the invention.
The embodiment of the application provides an asset data storage method. The execution subject of the asset data storage method includes, but is not limited to, at least one of electronic devices such as a server and a terminal, which can be configured to execute the method provided by the embodiments of the present application. In other words, the asset data storage method may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.
Referring to FIG. 1, an asset is provided according to an embodiment of the present inventionThe flow chart of the data storage method is schematic. In this embodiment, the asset data storage method includes: s1, receiving an asset data storage instruction, acquiring asset data to be stored according to the asset data storage instruction and determining a distributed storage system of the asset data to be stored, wherein the distributed storage system comprises
And each storage node is composed of a plurality of storage nodes.
It is understood that asset data has grown to be an intangible asset to an individual, business or government. For example, there is a picture manufacturing enterprise, which has originally created 1000 picture resources including landscape pictures, character pictures, etc. in the current month, where such picture resources are asset data of the picture manufacturing enterprise, and now the picture manufacturing enterprise wants to plan to store and protect 1000 original pictures, and thus initiate an asset data storage instruction, where 1000 original pictures are asset data to be stored.
In the embodiment of the invention, because the general data volume of the asset data to be stored is huge, if the traditional local storage cannot meet the requirement, the embodiment of the invention adopts a distributed storage system, wherein the distributed storage system comprises
And each storage node is composed of storage nodes.
S2, dividing the asset data to be stored into
Individual asset data block, pair
And carrying out importance classification on the asset data blocks to obtain asset data blocks with different importance.
In detail, the dividing of the asset data to be stored into
An asset data block comprising: performing encryption operation on the asset data to be stored to obtain encrypted asset dataWherein the encryption operation uses a symmetric encryption mode; splitting the encrypted asset data according to data generation time or data importance to obtain
An asset data block.
It can be understood that, in order to improve the security of asset data storage, the embodiment of the present invention further performs an encryption operation on the asset data to be stored before performing splitting, and further, the pair of the asset data to be stored is performed
The method for classifying the importance of each asset data block to obtain the asset data blocks with different importance comprises the following steps: receiving the set importance level types, and sequentially sending each asset data block to a pre-constructed auditing platform; and performing importance classification on each asset data block by using the auditing platform, wherein the level of the importance classification belongs to the category of the importance level until the importance classification of each asset data block is completed, and obtaining the asset data blocks with different importance.
Illustratively, the set importance level categories are three categories, i.e., a category a, a category B and a category C, wherein the asset data blocks of the category a have the highest importance, and the asset data blocks of the category C have the lowest importance, so that each asset data block is classified into one of the three categories a, B and C by the auditing platform. It should be explained that the reference standards for the importance classification are various, such as the above 1000 original pictures, and the importance of each picture can be determined according to whether each original picture wins a prize, the number of prawns in public, and the like.
And S3, generating a coding coefficient according to the importance of each asset data block.
In detail, the generating of the coding coefficient according to the importance of each asset data block includes: counting all asset data blocks belonging to the same class of importance levels, and calculating coding coefficients corresponding to the same class of importance levels according to the following method: generating a corresponding weight coefficient for each asset data block belonging to the same class of importance level, wherein the weight coefficient of the asset data block of the high importance level has a value higher than that of the asset data block of the low importance levelA value of a weight coefficient for a level of an asset data block; generating coding coefficients corresponding to the importance levels of the same class according to the following formula:
, wherein ,
is shown as
The coding coefficients corresponding to the class importance levels,
denotes the first
Class importance level of
The data of each of the asset data blocks,
denotes the first
Class importance level
A weight coefficient for each asset data block.
For example, the 1000 original pictures belong to 200 original pictures of the class a asset data block, so that corresponding weight coefficients are generated for 200 original pictures, and further coding coefficients corresponding to class a importance levels are obtained.
And S4, receiving the data description text of each asset data block, and extracting text features of the data description text to obtain a data description vector.
It should be understood that each asset data block has a corresponding descriptive text, and for example, one panoramic picture describing a 5A-level scenic spot exists in the 200 original pictures, and the panoramic picture is provided with picture shooting time, shooting inspiration, represented connotation and the like, so that the data description text of each asset data block has differences, and therefore, the text features of the data description text are extracted and used for encrypting subsequent data storage, and the security of asset data can be greatly improved.
In detail, the extracting text features of the data description text to obtain a data description vector includes: carrying out preprocessing operations including word segmentation and abnormal word removal on the data description text to obtain a primary description text; performing word vector conversion on each word of the primary description text to obtain a plurality of groups of description word vectors; calculating the similarity of each description word vector and other description word vectors, and combining description word vectors with the similarity greater than or equal to a similarity threshold; and combining all the description word vectors according to the text sequence of the primary description text to obtain the data description vector.
It should be explained that there is no clear separation mark between words in chinese, and if no preprocessing of word segmentation and abnormal word removal is performed before word vector conversion, a phenomenon of redundancy of each description word vector occurs, so that word segmentation and abnormal word removal are performed on a data description text.
In a preferred implementation of the present invention, the word segmentation process may use a jieba word segmentation program based on Python, JAVA, and other programming languages, and after the word segmentation, a plurality of repeated words, punctuation marks, stop words, and other abnormal words may be found, so that the above abnormal words need to be removed to achieve the purpose of conciseness. If the data description text exists, the data description text is as follows: "the photo is taken in the morning of 29.12.2022, and just in the east, the graceful branches and trunks of the pine and cypress appear Qiuqin, and the picture can show that the pine and cypress are full of wind frost, depression, pallor and strong in fighting, and seems to be full of infinite vitality". The method is obtained by processing based on the jieba participle as follows: [ photograph ] [ shooting ] \8230and [ vitality ].
In addition, the word vector conversion may employ a currently disclosed one-hot model, a word2vec model, or the like.
Further, the calculating the similarity of each description word vector with other description word vectors includes: the similarity is calculated by adopting the following method: can be used forAnd calculating the similarity between the conversation features by adopting a calculation method of the following similarity:
, wherein ,
representation description word vector
And describing the word vector
The degree of similarity between the two images,
to represent
And other descriptive word vectors
The weight coefficient of (a) is,
the total number of data describing a word vector included for the data description text,
is a bias coefficient, and
。
and S5, sequencing each asset data block according to the coding coefficient and the data description vector of the asset data block to obtain a plurality of asset data chains.
In detail, the sorting each asset data block according to the coding coefficient and the data description vector of the asset data block to obtain a plurality of asset data chains includes: all of different importance
Each asset data block is coded according to different coding coefficients
Each asset data block is divided into
An asset data group of which
The same number of categories as the importance level category; acquiring an asset data group with the maximum coding coefficient, and sequentially extracting each asset data block in the asset data group with the maximum coding coefficient to obtain an asset data head; the following operations are performed for each asset header: receiving a set maximum number of asset data chainshWherein the maximum number of chainshAt least 2, and the maximum number of chainshNo greater than the number of storage nodes of the distributed storage system
From all over
Selecting other asset data blocks with the highest similarity with the data description vector of the asset data head from the asset data blocks as connected data blocks; performing end-to-end connection on the connected data blocks and the asset data head to obtain an asset data branch chain, wherein the asset data branch chain comprises 2 asset data blocks, the head of the asset data branch chain is the asset data head, and the tail of the asset data branch chain is the connected data block; judging the quantity of the asset data blocks of the asset data branch chain and the maximum chain quantityhThe magnitude relationship of (1); if the number of the asset data blocks of the asset data branch chain is less than the maximum chain numberhContinuously calculating other asset data blocks with the highest similarity with the connected data blocks, and continuously executing head-to-tail connection operation to obtain an asset data branch chain, wherein the asset data branch chain comprises 3 asset data blocks in total, and the head of the asset data branch chain is still the head of the asset data; until the number of asset data blocks of the asset data sub-chain equals the maximum chain numbernAnd directly terminating the end-to-end connection to obtain the asset data chain comprising the asset data blocks.
Exemplary importance level categories are A, B,C, three types, then represent
That is, there are 3 kinds of asset data groups in total, wherein the coding coefficient of the asset data block of class a is the highest, and the coding coefficient of the asset data block of class C is the lowest, so the asset data block of class a, the asset data block of class C, etc. are all called asset data groups, it is necessary to further select asset data headers from the asset data group of class a in turn, and now, assuming that the panoramic photograph describing the 5A-level scenic spot is selected from the asset data group of class a in turn as an asset data header, the data description vector of the panoramic photograph of the 5A-level scenic spot is sequentially calculated, the similarity with the data description vectors of all other asset data blocks is calculated, and the asset data block with the highest data description vector is selected as the connected data block of the panoramic photograph until the asset data block and the largest chain number included in the asset data chain using the panoramic photograph of the 5A-level scenic spot as the asset data headerhAnd if so, completing the construction process of the asset data chain.
And S6, calculating the data leakage rate of each asset data chain, and encrypting each corresponding asset data chain based on the data leakage rate to obtain an encrypted data chain.
In detail, the calculating the data leakage rate of each asset data chain includes: counting all asset data blocks included in the asset data chain, and retrieving the leakage risk probability value of each included asset data block in a historical storage record; and calculating the data leakage rate of each asset data chain based on the leakage risk probability value.
In detail, the calculating of the data leakage rate of each asset data chain based on the leakage risk probability value includes: and calculating to obtain the data leakage rate by adopting the following formula:
, wherein ,
is shown as
A chain of asset data is run through the asset,
is shown as
The total number of asset data blocks included in a piece of asset data chain,
is shown as
The data leakage rate of a strip asset data chain,
is shown as
First in the asset data chain
The leak risk probability values of the asset data blocks in the history log,
is shown as
First in the asset data chain
Data description vector of each asset data block, and
the data of each asset data block describes a similarity value of the vectors.
It should be explained that, according to the size of the data leakage rate, the embodiment of the present invention selects an algorithm corresponding to the encryption complexity to encrypt the asset data chain, thereby obtaining the encrypted data chain. For example, if the data leakage rate of the 1 st asset data chain is low, an encryption algorithm with relatively low encryption complexity is selected to encrypt the asset data chain, and if the data leakage rate of the 2 nd asset data chain is high, an encryption algorithm with relatively high encryption complexity is selected to encrypt the asset data chain, and so on.
S7, storing the encrypted data chain to the distributed storage system, wherein the chain number of the encrypted data chain is not more than the storage node number of the distributed storage system
And storing the asset data block which is not included in any encrypted data chain to a pre-constructed local storage system to finish the storage of the asset data.
It can be understood that, in the embodiment of the present invention, the obtained encrypted data chains are stored in the distributed storage system, and each encrypted data chain has different essential differences because the asset data headers included in each encrypted data chain are different, and different degrees of encryption are dynamically executed through different data leakage rates of each encrypted data chain, so that security and intelligence during storage are improved.
In addition, according to the steps, the generation process of the encrypted data chain is mainly constructed according to the similarity of the data description vectors of the asset data blocks of the asset data head, namely, part of the asset data blocks possibly belong to any encrypted data chain due to low similarity with other asset data heads or connected data blocks.
In order to solve the problems in the background art, the embodiment of the invention firstly divides the asset data to be stored into
Individual asset data block, pair
The method and the device have the advantages that the importance classification is carried out on the asset data blocks to obtain the asset data blocks with different importance, and the method and the device can sort the asset data blocks according to the importance while splitting the asset data to be stored, so that the problem of priority level inquiry of subsequent storage is solvedThe method comprises the steps of generating a coding coefficient according to the importance of each asset data block, receiving a data description text of each asset data block, extracting text features of the data description text to obtain a data description vector, wherein the generation of the coding coefficient is directly connected with the importance of the asset data block, and the more important asset data block has a higher corresponding coding coefficient.
Fig. 2 is a functional block diagram of an asset data storage device according to an embodiment of the present invention.
The asset data storage device 100 of the present invention may be installed in an electronic device. According to the implemented functions, the asset data storage device 100 may include an asset data splitting module 101, an encoding coefficient calculation module 102, an asset data chain construction module 103, an encryption module 104, and an asset data storage module 105. The module of the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and can perform a fixed function, and are stored in a memory of the electronic device.
The asset data splitting module 101 is configured to receive an asset data storage instruction, obtain asset data to be stored according to the asset data storage instruction, and determine a distributed storage system for the asset data to be stored, where the distributed storage system is composed of n storage nodes, divide the asset data to be stored into k asset data blocks, and perform importance classification on the k asset data blocks to obtain asset data blocks with different importance; the coding coefficient calculation module 102 is configured to generate a coding coefficient according to the importance of each asset data block; the asset data chain building module 103 is configured to calculate a data leakage rate of each asset data chain, and encrypt each corresponding asset data chain based on the data leakage rate to obtain an encrypted data chain; the encryption module 104 is configured to calculate a data leakage rate of each asset data chain, and encrypt each corresponding asset data chain based on the data leakage rate to obtain an encrypted data chain; the asset data storage module 105 is configured to store the encrypted data chains to the distributed storage system, where the number of the chains of the encrypted data chains is not greater than the number n of storage nodes of the distributed storage system, and store the asset data blocks that are not included in any encrypted data chain to a pre-constructed local storage system, so as to complete asset data storage.
In detail, when the modules in the asset data storage device 100 according to the embodiment of the present invention are used, the same technical means as the block chain based product supply chain management method described in fig. 1 above are used, and the same technical effects can be produced, and details are not described here.
Fig. 3 is a schematic structural diagram of an electronic device implementing an asset data storage method according to an embodiment of the present invention.
The electronic device 1 may comprise a processor 10, a memory 11 and a bus 12, and may further comprise a computer program, such as an asset data storage method program, stored in the memory 11 and operable on the processor 10.
The memory 11 includes at least one type of readable storage medium, which includes flash memory, removable hard disk, multimedia card, card type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only to store application software installed in the electronic device 1 and various types of data, such as codes of an asset data storage method program, etc., but also to temporarily store data that has been output or is to be output.
The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the whole electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device 1 by running or executing programs or modules (e.g., asset data storage method programs, etc.) stored in the memory 11 and calling data stored in the memory 11.
The bus 12 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.
Fig. 3 shows only an electronic device with components, and it will be understood by those skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than those shown, or some components may be combined, or a different arrangement of components.
For example, although not shown, the electronic device 1 may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so as to implement functions of charge management, discharge management, power consumption management, and the like through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device 1 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
Further, the electronic device 1 may further include a network interface, and optionally, the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a bluetooth interface, etc.), which are generally used for establishing a communication connection between the electronic device 1 and other electronic devices.
Optionally, the electronic device 1 may further comprise a user interface, which may be a Display (Display), an input unit (such as a Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-emitting diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable for displaying information processed in the electronic device 1 and for displaying a visualized user interface, among other things.
It is to be understood that the embodiments described are illustrative only and are not to be construed as limiting the scope of the claims.
The asset data storage method program stored in the memory 11 of the electronic device 1 is a combination of instructions that, when executed in the processor 10, may implement: receiving an asset data storage instruction, acquiring asset data to be stored according to the asset data storage instruction and determining a distributed storage system of the asset data to be stored, wherein the distributed storage system comprises
Each storage node is composed of a plurality of storage nodes; dividing the asset data to be stored into
Individual asset data block, pair
Performing importance classification on each asset data block to obtain asset data blocks with different importance; generating a coding coefficient according to the importance of each asset data block; receiving a data description text of each asset data block, and extracting text features of the data description text to obtain a data description vector; sequencing each asset data block according to the coding coefficient and the data description vector of the asset data block to obtain a plurality of asset data chains; calculating the data leakage rate of each asset data chain, and encrypting each corresponding asset data chain based on the data leakage rate to obtain an encrypted data chain; storing the encrypted data chain to the distributed storage system, wherein the chain number of the encrypted data chain is not more than the storage node number of the distributed storage system
And storing the asset data block which is not included in any encrypted data chain to a pre-constructed local storage system to finish the storage of the asset data.
Specifically, the specific implementation method of the processor 10 for the instruction may refer to the description of the relevant steps in the embodiments corresponding to fig. 1 to fig. 3, which is not repeated herein.
Further, the integrated modules/units of the electronic device 1, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. The computer readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM).
The present invention also provides a computer-readable storage medium storing a computer program which, when executed by a processor of an electronic device, implements: receiving an asset data storage instruction, acquiring asset data to be stored according to the asset data storage instruction and determining a distributed storage system of the asset data to be stored, wherein the distributed storage system comprises
Each storage node is composed of a plurality of storage nodes; dividing the asset data to be stored into
Individual asset data block, pair
Performing importance classification on each asset data block to obtain asset data blocks with different importance; generating a coding coefficient according to the importance of each asset data block; receiving a data description text of each asset data block, and extracting text features of the data description texts to obtain data description vectors; sequencing each asset data block according to the coding coefficient and the data description vector of the asset data block to obtain a plurality of asset data chains; calculating the data leakage rate of each asset data chain, and encrypting each corresponding asset data chain based on the data leakage rate to obtain an encrypted data chain; storing the encrypted data chain to the distributed storage system, wherein the chain number of the encrypted data chain is not more than the storage node number of the distributed storage system
And storing the asset data block which is not included in any encrypted data chain to a pre-constructed local storage system to finish asset data storage.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a string of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, which is used for verifying the validity (anti-counterfeiting) of the information and generating a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
Furthermore, it will be obvious that the term "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the present specification may also be implemented by one unit or means through software or hardware. The terms second, etc. are used to denote names, but not to denote any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A method of asset data storage, the method comprising:
receiving an asset data storage instruction, acquiring asset data to be stored according to the asset data storage instruction and determining a distributed storage system of the asset data to be stored, wherein the distributed storage system comprises
Each storage node is composed of a plurality of storage nodes;
dividing the asset data to be stored into
Individual asset data block, pair
Performing importance classification on each asset data block to obtain asset data blocks with different importance;
generating a coding coefficient according to the importance of each asset data block;
receiving a data description text of each asset data block, and extracting text features of the data description texts to obtain data description vectors;
sequencing each asset data block according to the coding coefficient and the data description vector of the asset data block to obtain a plurality of asset data chains;
calculating the data leakage rate of each asset data chain, and encrypting each corresponding asset data chain based on the data leakage rate to obtain an encrypted data chain;
storing the encrypted data chain to the distributed storage system, wherein the chain number of the encrypted data chain is not more than the storage node number of the distributed storage system
And storing the asset data block which is not included in any encrypted data chain to a pre-constructed local storage system to finish asset data storage.
2. The asset data storage method according to claim 1, wherein said dividing said asset data to be stored into
An asset data block comprising:
performing encryption operation on the asset data to be stored to obtain encrypted asset data, wherein the encryption operation adopts a symmetric encryption mode;
splitting the encrypted asset data according to data generation time or data importance to obtain
An asset data block.
3. The asset data storage method of claim 2, wherein said pair
The method for classifying the importance of each asset data block to obtain the asset data blocks with different importance comprises the following steps:
receiving the set importance level types, and sequentially sending each asset data block to a pre-constructed auditing platform;
and performing importance classification on each asset data block by using the auditing platform, wherein the level of the importance classification belongs to the category of the importance level until the importance classification of each asset data block is completed, and obtaining the asset data blocks with different importance.
4. The asset data storage method of claim 3, wherein said generating coding coefficients according to the importance of each asset data block comprises:
counting all asset data blocks belonging to the same class of importance levels, and calculating coding coefficients corresponding to the same class of importance levels according to the following method:
generating a corresponding weight coefficient for each asset data block belonging to the same class of importance level, wherein the value of the weight coefficient of the asset data block of the high importance level is higher than that of the asset data block of the low importance level;
generating coding coefficients corresponding to the importance levels of the same class according to the following formula:
wherein ,
is shown as
The coding coefficients corresponding to the class importance levels,
is shown as
Class importance level of
The data of each of the asset data blocks,
is shown as
Class importance level
The weight coefficients of the individual asset data blocks.
5. The asset data storage method of claim 4, wherein said extracting text features of the data description text to obtain a data description vector comprises:
carrying out preprocessing operations including word segmentation and abnormal word removal on the data description text to obtain a primary description text;
performing word vector conversion on each word of the primary description text to obtain a plurality of groups of description word vectors;
calculating the similarity of each description word vector and other description word vectors, and combining description word vectors with the similarity greater than or equal to a similarity threshold;
and combining all the description word vectors according to the text sequence of the primary description text to obtain the data description vector.
6. The asset data storage method of claim 5, wherein said calculating a similarity of each description term vector to other description term vectors comprises:
the similarity is calculated by adopting the following method:
the similarity between the dialogue features can be calculated by adopting the following similarity calculation method:
wherein ,
representation description word vector
And describing the word vector
The degree of similarity between the two images,
to represent
And other descriptive word vectors
The weight coefficient of (a) is,
the total number of data describing a word vector included for the data description text,
is a bias coefficient, an
。
7. The asset data storage method of claim 6, wherein said sorting each asset data block according to its coding coefficients and data description vector to obtain a plurality of asset data chains comprises:
all of different importance
Each asset data block is coded according to different coding coefficients
Each asset data block is divided into
An asset data group of which
The same number of categories as the importance level category;
acquiring an asset data group with the maximum coding coefficient, and sequentially extracting each asset data block in the asset data group with the maximum coding coefficient to obtain an asset data head;
the following operations are performed for each asset header:
receiving a set maximum number of chains of asset data chainshWherein the maximum number of chainshAt least 2, and the maximum number of chainshNo greater than the number of storage nodes of the distributed storage system
From all of
Selecting other asset data blocks with the highest similarity with the data description vector of the asset data head from the asset data blocks as connected data blocks;
performing end-to-end connection on the connected data blocks and the asset data head to obtain an asset data branch chain, wherein the asset data branch chain comprises 2 asset data blocks, the head of the asset data branch chain is the asset data head, and the tail of the asset data branch chain is the connected data block;
judging the quantity of the asset data blocks of the asset data branch chain and the maximum chain quantityhThe magnitude relationship of (1);
if the number of the asset data blocks of the asset data branch chain is less than the maximum chain numberhContinuously calculating other asset data blocks with the highest similarity with the connected data blocks, and continuously executing head-to-tail connection operation to obtain an asset data branch chain, wherein the asset data branch chain comprises 3 asset data blocks in total, and the head of the asset data branch chain is still the head of the asset data;
until the number of asset data blocks of the asset data subcohain equals the maximum number of chainsnAnd directly terminating the end-to-end connection to obtain the asset data chain comprising the asset data blocks.
8. The asset data storage method of claim 7, wherein said calculating a data leakage rate for each asset data link comprises:
counting all asset data blocks included in the asset data chain, and retrieving the leakage risk probability value of each included asset data block in a historical storage record;
and calculating the data leakage rate of each asset data chain based on the leakage risk probability value.
9. The asset data storage method of claim 8, wherein said calculating a data leakage rate for each asset data chain based on said leakage risk probability values comprises:
and calculating to obtain the data leakage rate by adopting the following formula:
wherein ,
is shown as
A chain of asset data is run through the asset,
denotes the first
The total number of asset data blocks included in the asset data chain,
is shown as
The data leakage rate of the asset data chain,
is shown as
Item in the asset data chain
Leakage risk probability values for individual asset data blocks in the history store,
is shown as
First in the asset data chain
Data description vector and the second of each asset data block
The data of each asset data block describes a similarity value of the vectors.
10. An asset data storage device, the device comprising:
the asset data splitting module is used for receiving an asset data storage instruction, acquiring asset data to be stored according to the asset data storage instruction and determining a distributed storage system of the asset data to be stored, wherein the distributed storage system comprisesnEach storage node is composed of a plurality of storage nodes, and the asset data to be stored is divided intokIndividual asset data block, pairkPerforming importance classification on each asset data block to obtain asset data blocks with different importance;
the coding coefficient calculation module is used for generating a coding coefficient according to the importance of each asset data block;
the asset data chain building module is used for receiving the data description text of each asset data block, extracting the text features of the data description text to obtain data description vectors, and sequencing each asset data block according to the coding coefficient and the data description vector of the asset data block to obtain a plurality of asset data chains;
the encryption module is used for calculating the data leakage rate of each asset data chain and encrypting each corresponding asset data chain based on the data leakage rate to obtain an encrypted data chain;
the asset data storage module is used for storing the encrypted data chain to the distributed storage system, wherein the chain number of the encrypted data chain is not more than the storage node number of the distributed storage systemnAnd storing the asset data block which is not included in any encrypted data chain to a pre-constructed local storage system to finish asset data storage.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116484437A (en) * | 2023-06-20 | 2023-07-25 | 矩阵纵横设计股份有限公司 | Asset information management method and device and computer equipment |
| CN117235773A (en) * | 2023-11-15 | 2023-12-15 | 山东顺国电子科技有限公司 | Intelligent data classification management method, system and storage medium for big data center |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180145829A1 (en) * | 2016-11-24 | 2018-05-24 | Samsung Electronics Co, Ltd | Data management method |
| CN110830520A (en) * | 2020-01-13 | 2020-02-21 | 中国人民解放军国防科技大学 | Robust and reliable edge storage method and system for Internet of things |
| CN113157648A (en) * | 2021-04-28 | 2021-07-23 | 中国工商银行股份有限公司 | Block chain based distributed data storage method, device, node and system |
| CN113222595A (en) * | 2021-04-23 | 2021-08-06 | 上海和数软件有限公司 | Electric power data storage and query method and system based on block chain technology |
| CN113268615A (en) * | 2021-05-25 | 2021-08-17 | 平安银行股份有限公司 | Resource label generation method and device, electronic equipment and storage medium |
| CN113657545A (en) * | 2021-08-30 | 2021-11-16 | 平安医疗健康管理股份有限公司 | Method, device and equipment for processing user service data and storage medium |
-
2023
- 2023-01-28 CN CN202310042927.0A patent/CN115795517B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180145829A1 (en) * | 2016-11-24 | 2018-05-24 | Samsung Electronics Co, Ltd | Data management method |
| CN110830520A (en) * | 2020-01-13 | 2020-02-21 | 中国人民解放军国防科技大学 | Robust and reliable edge storage method and system for Internet of things |
| CN113222595A (en) * | 2021-04-23 | 2021-08-06 | 上海和数软件有限公司 | Electric power data storage and query method and system based on block chain technology |
| CN113157648A (en) * | 2021-04-28 | 2021-07-23 | 中国工商银行股份有限公司 | Block chain based distributed data storage method, device, node and system |
| CN113268615A (en) * | 2021-05-25 | 2021-08-17 | 平安银行股份有限公司 | Resource label generation method and device, electronic equipment and storage medium |
| CN113657545A (en) * | 2021-08-30 | 2021-11-16 | 平安医疗健康管理股份有限公司 | Method, device and equipment for processing user service data and storage medium |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116484437A (en) * | 2023-06-20 | 2023-07-25 | 矩阵纵横设计股份有限公司 | Asset information management method and device and computer equipment |
| CN116484437B (en) * | 2023-06-20 | 2024-03-29 | 矩阵纵横设计股份有限公司 | Asset information management method and device and computer equipment |
| CN117235773A (en) * | 2023-11-15 | 2023-12-15 | 山东顺国电子科技有限公司 | Intelligent data classification management method, system and storage medium for big data center |
| CN117235773B (en) * | 2023-11-15 | 2024-02-02 | 山东顺国电子科技有限公司 | Intelligent data classification management method, system and storage medium for big data center |
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