CN112214617A - Digital file management method and system based on block chain technology - Google Patents

Digital file management method and system based on block chain technology Download PDF

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CN112214617A
CN112214617A CN202011256018.XA CN202011256018A CN112214617A CN 112214617 A CN112214617 A CN 112214617A CN 202011256018 A CN202011256018 A CN 202011256018A CN 112214617 A CN112214617 A CN 112214617A
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CN112214617B (en
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李细主
冯美柱
何强
李婷
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Guangdong Xinhedao Information Technology Co ltd
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Abstract

The invention relates to the technical field of digital file management, in particular to a digital file management method and system based on a block chain technology. The invention respectively modifies the reading levels of the knowledge points in the coding knowledge points of the mining structured and unstructured archive coding data based on the attribution relationship between the mining knowledge map coding knowledge points and the whole knowledge map coding knowledge points in the mining structured and unstructured archive coding data, and updating the configured digital archive pushing management service based on the structured and unstructured archive encoding data, the mined structured archive encoding data and the mined unstructured archive encoding data, so that the associated classification precision of the digital archive pushing management service can be improved, and based on the coding knowledge points of mining the coding data of the structured and unstructured archive coding data, the digital archive push management service can update more accurate configuration parameters, therefore, the reliability of service operation is improved, and the associated classification precision of the digital files is further improved.

Description

Digital file management method and system based on block chain technology
Technical Field
The invention relates to the technical field of digital file management, in particular to a digital file management method and system based on a block chain technology.
Background
In the related art, the generation of the information of the digital archive push management service is usually performed by configuring the digital archive push management service based on only structured and unstructured archive coding data, which does not consider the association condition of knowledge map coding knowledge points, thereby causing the reduction of the association classification precision of the digital archive push management service.
Disclosure of Invention
In order to overcome at least the above-mentioned deficiencies in the prior art, the present invention provides a method and system for managing digital files based on block chain technology, which respectively excavates at least one knowledge map coding knowledge point for structured file coding data and unstructured file coding data, respectively modifies the knowledge point browsing levels in the coding knowledge points of the excavated structured and unstructured file coding data based on the attribution relationship between the mined knowledge map coding knowledge points and the entire knowledge map coding knowledge points in the excavated structured and unstructured file coding data, so that the data distribution of the excavated structured and unstructured file coding data is changed compared with the structured and unstructured file coding data, and the data management method and system for managing digital files based on the structured and unstructured file coding data and the excavated structured and unstructured file coding data, the digital archive push management service obtained by updating configuration can improve the correlation classification precision of the digital archive push management service, and based on the coding knowledge points of mining structured and unstructured archive coding data, the digital archive push management service can update more accurate configuration parameters, thereby improving the reliability of service operation.
In a first aspect, the present invention provides a digital archive management method based on a block chain technique, applied to a digital archive server, where the digital archive server is in communication connection with a plurality of digital archive terminals, and the method includes:
acquiring extended archive coding data corresponding to archive updating information of an archive management object of the digital archive terminal, and acquiring structured and unstructured archive coding data from the extended archive coding data, wherein the structured archive coding data are archive coding data in a structured form, the unstructured archive coding data are archive coding data in an unstructured form, and coding knowledge points of the structured and unstructured archive coding data comprise knowledge point reading levels;
mining at least one knowledge map coding knowledge point of the structured archive coding data and the unstructured archive coding data respectively to obtain mined structured archive coding data corresponding to the structured archive coding data and mined unstructured archive coding data corresponding to the unstructured archive coding data;
respectively adjusting the knowledge point turning levels in the coding knowledge points of the mining structured and unstructured archive coding data stored in a block chain associated in advance based on the attribution relation between the mining knowledge map coding knowledge points and the whole knowledge map coding knowledge points in the mining structured and unstructured archive coding data, wherein the mining knowledge map coding knowledge points in the mining structured and unstructured archive coding data are respectively regarded as unstructured archive coding data content and structured archive coding data content;
and updating and configuring the digital archive push management service based on the structured and unstructured archive encoding data, the mined structured archive encoding data and the mined unstructured archive encoding data to obtain the digital archive push management service with the updated and configured data.
In a possible implementation manner of the first aspect, the mining at least one knowledge graph coding knowledge point for the structured archive coding data and the unstructured archive coding data respectively to obtain mined structured archive coding data corresponding to the structured archive coding data and mined unstructured archive coding data corresponding to the unstructured archive coding data includes:
determining structured archive coding data and unstructured archive coding data of knowledge points needing to be mined for knowledge graph coding;
mining knowledge graph coding knowledge points of at least one same node in the structured archive coding data and the unstructured archive coding data to obtain mined structured archive coding data corresponding to the structured archive coding data and mined unstructured archive coding data corresponding to the unstructured archive coding data;
the step of adjusting the knowledge point turning level in the coding knowledge points of the mining structured and unstructured archive coding data stored in the block chain associated in advance based on the attribution relationship between the mining knowledge map coding knowledge points and the whole knowledge map coding knowledge points in the mining structured and unstructured archive coding data comprises the following steps:
calculating the content affiliation relationship between the non-mined structured archive encoding data content in the mined structured archive encoding data and the structured archive encoding data of the mined structured archive encoding data on the basis of the mined knowledge map encoding knowledge points in the mined structured archive encoding data;
adjusting the knowledge point reading level of the mining structured archive coding data based on the content attribution relationship of the structured archive coding data of the mining structured archive coding data;
calculating the attribution relationship between the mined knowledge map coding knowledge points in the mined unstructured archive coding data and the mined unstructured archive coding data, and taking the attribution relationship as the content attribution relationship of the structured archive coding data of the mined unstructured archive coding data;
and adjusting the knowledge point reading level of the mining unstructured archive coding data based on the content attribution relationship of the structured archive coding data of the mining unstructured archive coding data.
In a possible implementation manner of the first aspect, the mining knowledge graph coding knowledge points of at least one same node in the structured archive coding data and the unstructured archive coding data to obtain mined structured archive coding data corresponding to the structured archive coding data and mined unstructured archive coding data corresponding to the unstructured archive coding data includes:
dividing the structured archive coded data and the unstructured archive coded data into knowledge graph units with the same quantity according to the same division rule;
randomly selecting a file knowledge editing object from a preset service range as a file knowledge editing object mined by knowledge graph units of the structured file encoding data and the unstructured file encoding data;
coding a knowledge graph unit of the data coded by the structured and unstructured archives to obtain a knowledge graph unit coding sequence;
disordering the sequence of codes in the knowledge graph unit coding sequence to obtain the disordered knowledge graph unit coding sequence;
selecting the codes of the mining archive knowledge editing objects of the knowledge graph units as mining codes from the disordered knowledge graph unit coding sequences;
and mining knowledge graph units indicated by the mining codes in the structured and unstructured archive coded data to obtain mining structured archive coded data corresponding to the structured archive coded data and mining unstructured archive coded data corresponding to the unstructured archive coded data.
In one possible implementation manner of the first aspect, the digital archive push management service includes an archive entity feature identification unit and an archive entity content prediction unit;
the step of updating and configuring the digital archive push management service based on the structured and unstructured archive coded data, the mined structured archive coded data and the mined unstructured archive coded data to obtain the digital archive push management service with the updated and configured data comprises the following steps:
taking the structured and unstructured archive coding data, and the mining structured archive coding data and the mining unstructured archive coding data as update configuration archive coding data of a digital archive push management service to be updated and configured;
extracting knowledge map characteristic information of the data for updating and configuring the file coded by the file entity characteristic identification unit;
performing classification prediction of archive coding data in a structured form and archive coding data in an unstructured form on the update configuration archive coding data based on the knowledge graph feature information by the archive entity content prediction unit;
calculating a pushing management parameter of the digital archive pushing management service based on the prediction result of the updated configuration archive coding data and the coding knowledge point of the updated configuration archive coding data;
adjusting parameters of the digital archive push management service based on the push management parameters to obtain the digital archive push management service with updated configuration;
wherein the prediction result of updating the encoded data of the configuration profile comprises: updating the predictive knowledge point reading grade and the predictive non-knowledge point reading grade of the coded data of the configuration file;
the step of calculating the push management parameters of the digital archive push management service based on the prediction result of the updated configuration archive encoded data and the encoding knowledge points of the updated configuration archive encoded data comprises:
calculating a first pushing management parameter of the updated configuration file coded data based on a knowledge point turning level in the coding knowledge points of the updated configuration file coded data and a prediction knowledge point turning level in the prediction result;
determining an actual non-knowledge point reading level of the updated configuration file encoding data based on the knowledge point reading level of the updated configuration file encoding data;
calculating a second push management parameter of the updated configuration file coded data based on the actual non-knowledge point reading level of the updated configuration file coded data and the predicted non-knowledge point reading level in the prediction result;
and obtaining the total push management parameters of the digital archive push management service based on the first push management parameters and the second push management parameters of the updated and configured archive coded data.
In a possible implementation manner of the first aspect, the digital archive push management service further includes an archive entity extraction unit connected to the archive entity feature identification unit;
the method further comprises the following steps:
acquiring the structured and unstructured archive coding data, and presetting actual characteristic information of characteristic dimensions of the knowledge graph;
acquiring the mining structured archive coding data and mining unstructured archive coding data, and presetting actual characteristic information of characteristic dimensions of the knowledge graph;
before adjusting the parameters of the digital archive push management service based on the push management parameters to obtain the digital archive push management service with the updated configuration, the method further comprises the following steps:
performing archive entity extraction on the knowledge graph feature information of the updated configuration archive coded data through the archive entity extraction unit to obtain archive entity extraction feature information of the updated configuration archive coded data in the preset knowledge graph feature dimension;
based on the updated configuration file encoding data, extracting feature information from the actual feature information of the preset knowledge graph feature dimension and the file entity to obtain dimension distinguishing information of the digital file pushing management service in the preset knowledge graph feature dimension;
the step of adjusting the parameters of the digital archive push management service based on the push management parameters to obtain the digital archive push management service with the updated configuration, comprises:
and adjusting parameters of the digital archive push management service based on the push management parameters and the dimension distinguishing information to obtain the digital archive push management service with updated configuration.
In a possible implementation manner of the first aspect, the actual feature information of the preset feature dimension of the knowledge graph includes: the actual file semantic relation information of the traceability information dimension, wherein the file entity extraction unit comprises a file index file entity extraction unit;
the obtaining, by the archive entity extraction unit, archive entity extraction of the knowledge graph feature information of the updated configuration archive encoded data at the preset knowledge graph feature dimension of the updated configuration archive encoded data includes:
performing file index file entity extraction on the knowledge graph characteristic information of the updated configuration file coded data through the file index file entity extraction unit to obtain predicted file semantic relation information of the updated configuration file coded data;
the step of obtaining dimension distinguishing information of the digital archive push management service in the preset knowledge graph characteristic dimension based on the updated configuration archive coded data and the actual characteristic information of the preset knowledge graph characteristic dimension and the archive entity extraction characteristic information comprises the following steps:
calculating file semantic relation distinguishing information based on actual file semantic relation information and predicted file semantic relation information of the same updated configuration file coded data;
and determining dimension distinguishing information of the digital archive push management service on the traceability information dimension based on the archive semantic relation distinguishing information.
In a possible implementation manner of the first aspect, the step of obtaining the structured and unstructured archive encoding data and presetting actual feature information of feature dimensions of the knowledge graph includes:
performing archive semantic relation analysis on knowledge graph distribution in the structured archive coding data to obtain actual archive semantic relation information of the structured archive coding data;
setting file semantic relations without file index information as actual file semantic relation information of the unstructured file coded data for the unstructured file coded data;
the step of acquiring the mining structured archive coding data and the mining unstructured archive coding data and presetting the actual feature information of the feature dimension of the knowledge graph comprises the following steps:
determining actual file semantic relation information of the structured file coded data corresponding to the mined structured file coded data as first initial file semantic relation information of the mined structured file coded data;
based on the position of the content of the unstructured archive coding data in the mined structured archive coding data, replacing archive index information located at the same node in the first initial archive semantic relation information with archive index information of the content of the unstructured archive coding data to obtain actual archive semantic relation information of the mined structured archive coding data;
determining actual file semantic relationship information of the unstructured file coded data corresponding to the mined unstructured file coded data as second initial file semantic relationship information of the mined unstructured file coded data;
based on the position of the content of the structured archive coded data in the mined unstructured archive coded data, the archive index information in the same node in the second initial archive semantic relation information is replaced by the archive index information of the content of the structured archive coded data, so that the actual archive semantic relation information of the mined unstructured archive coded data is obtained.
In a possible implementation manner of the first aspect, the step of obtaining structured and unstructured archive encoded data from the extended archive encoded data includes:
after original interest archive coding data are acquired from the extended archive coding data, knowledge graph analysis is carried out on the original interest archive coding data, and knowledge graph distribution in the original interest archive coding data is determined;
in the original interest archive coded data, expanding the knowledge graph distribution by taking the knowledge graph distribution as a reference to obtain expanded knowledge graph distribution;
intercepting archive coded data of the expanded knowledge graph distribution from the original interest archive coded data to serve as unstructured archive coded data;
and acquiring structured archive coding data, wherein the structured archive coding data comprises archive coding data in a structured form.
In a possible implementation manner of the first aspect, the step of obtaining extended archive encoding data corresponding to archive update information of an archive management object for the digital archive terminal includes:
acquiring archive updating information of an archive management object of the digital archive terminal, wherein the archive updating information is knowledge graph data information obtained by carrying out corresponding cloud computing data statistics on archive entity effective parameter items of each target subscription archive entity encoding knowledge point of the archive management object;
performing file entity feature mining on the file updating information to obtain basic file entity features corresponding to the file updating information, and performing file entity feature mining on file updating information of a cloud file metadata framework corresponding to the file updating information to obtain corresponding global file entity features, wherein the cloud file metadata framework is a file metadata framework with feedback effectiveness greater than preset effectiveness in file metadata frameworks of other file management objects similar to the file management object;
calculating a loss profile physical feature between the base profile physical feature and the global profile physical feature;
and mining the physical characteristics of the loss archives based on a preset AI network to obtain extended archive coding data corresponding to the archive updating information, and generating corresponding extended digital archive push management service information based on the extended archive coding data.
In a second aspect, an embodiment of the present invention further provides a digital archive management apparatus based on a block chain technology, which is applied to a digital archive server, where the digital archive server is communicatively connected to a plurality of digital archive terminals, and the apparatus includes:
the acquisition module is used for acquiring extended archive coding data corresponding to archive updating information of an archive management object of the digital archive terminal and acquiring structured and unstructured archive coding data from the extended archive coding data, wherein the structured archive coding data are archive coding data in a structured form, the unstructured archive coding data are archive coding data in an unstructured form, and coding knowledge points of the structured and unstructured archive coding data comprise knowledge point reading levels;
the mining module is used for respectively mining at least one knowledge map coding knowledge point of the structured archive coding data and the unstructured archive coding data to obtain mined structured archive coding data corresponding to the structured archive coding data and mined unstructured archive coding data corresponding to the unstructured archive coding data;
the adjusting module is used for respectively adjusting the knowledge point turning levels in the coding knowledge points of the mining structured and unstructured archive coding data stored in a block chain associated in advance based on the attribution relationship between the mining knowledge map coding knowledge points and the whole knowledge map coding knowledge points in the mining structured and unstructured archive coding data, wherein the mining knowledge map coding knowledge points in the mining structured and unstructured archive coding data are respectively regarded as the unstructured archive coding data content and the structured archive coding data content;
and the updating configuration module is used for updating and configuring the digital archive pushing management service based on the structured and unstructured archive coding data, the mined structured archive coding data and the mined unstructured archive coding data to obtain the digital archive pushing management service with the updated and configured data.
In a third aspect, an embodiment of the present invention further provides a digital archive management system based on a blockchain technology, where the digital archive management system based on the blockchain technology includes a digital archive server and a plurality of digital archive terminals communicatively connected to the digital archive server;
the digital archive server is configured to:
acquiring extended archive coding data corresponding to archive updating information of an archive management object of the digital archive terminal, and acquiring structured and unstructured archive coding data from the extended archive coding data, wherein the structured archive coding data are archive coding data in a structured form, the unstructured archive coding data are archive coding data in an unstructured form, and coding knowledge points of the structured and unstructured archive coding data comprise knowledge point reading levels;
mining at least one knowledge map coding knowledge point of the structured archive coding data and the unstructured archive coding data respectively to obtain mined structured archive coding data corresponding to the structured archive coding data and mined unstructured archive coding data corresponding to the unstructured archive coding data;
respectively adjusting the knowledge point turning levels in the coding knowledge points of the mining structured and unstructured archive coding data stored in a block chain associated in advance based on the attribution relation between the mining knowledge map coding knowledge points and the whole knowledge map coding knowledge points in the mining structured and unstructured archive coding data, wherein the mining knowledge map coding knowledge points in the mining structured and unstructured archive coding data are respectively regarded as unstructured archive coding data content and structured archive coding data content;
and updating and configuring the digital archive push management service based on the structured and unstructured archive encoding data, the mined structured archive encoding data and the mined unstructured archive encoding data to obtain the digital archive push management service with the updated and configured data.
In a fourth aspect, an embodiment of the present invention further provides a digital archive server, where the digital archive server includes a processor, a machine-readable storage medium, and a network interface, where the machine-readable storage medium, the network interface, and the processor are connected through a bus system, the network interface is configured to be communicatively connected to at least one digital archive terminal, the machine-readable storage medium is configured to store a program, an instruction, or code, and the processor is configured to execute the program, the instruction, or the code in the machine-readable storage medium to perform the block chain technology-based digital archive management method in the first aspect or any one of the possible implementation manners in the first aspect.
In a fifth aspect, an embodiment of the present invention provides a computer-readable storage medium, where instructions are stored, and when executed, cause a computer to perform the method for digital archive management based on the blockchain technique in the first aspect or any one of the possible implementations of the first aspect.
Based on any one of the aspects, mining at least one knowledge map coding knowledge point is respectively carried out on the structured archive coding data and the unstructured archive coding data, so that the mining structured archive coding data and the mining unstructured archive coding data can be obtained, and the knowledge point turning levels in the coding knowledge points of the mining structured archive coding data and the unstructured archive coding data are respectively modified based on the attribution relation between the mined knowledge map coding knowledge points and the whole knowledge map coding knowledge points in the mining structured archive coding data and the unstructured archive coding data, wherein the mined knowledge map coding knowledge points in the mining structured archive coding data and the unstructured archive coding data are respectively regarded as the content of the unstructured archive coding data and the content of the structured archive coding data; therefore, the data distribution is changed compared with structured and unstructured archive coding data when the structured and unstructured archive coding data are mined, the digital archive push management service obtained through configuration is updated based on the structured and unstructured archive coding data, the structured archive coding data and the unstructured archive coding data are mined, the association classification precision of the digital archive push management service can be improved, and more accurate configuration parameters can be updated by the digital archive push management service based on the coding knowledge points of the structured and unstructured archive coding data, so that the reliability of service operation is improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that need to be called in the embodiments are briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention, and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a schematic diagram of an application scenario of a digital archive management system based on a blockchain technique according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a method for managing a digital file based on a blockchain technique according to an embodiment of the present invention;
FIG. 3 is a block chain technology-based functional block diagram of a digital file management apparatus according to an embodiment of the present invention;
FIG. 4 is a block diagram illustrating the components of a digital archive server for implementing the above-mentioned method for managing digital archives based on blockchain technology according to an embodiment of the present invention.
Detailed Description
The present invention is described in detail below with reference to the drawings, and the specific operation methods in the method embodiments can also be applied to the apparatus embodiments or the system embodiments.
FIG. 1 is an interaction diagram of a digital archive management system 10 based on blockchain technology according to an embodiment of the present invention. The digital archive management system 10 based on the blockchain technique may include a digital archive server 100 and a digital archive terminal 200 communicatively coupled to the digital archive server 100. The digital archive management system 10 based on the blockchain technique shown in FIG. 1 is only one possible example, and in other possible embodiments, the digital archive management system 10 based on the blockchain technique may also include only a portion of the components shown in FIG. 1 or may also include other components.
In this embodiment, the digital archive server 100 and the digital archive terminal 200 in the digital archive management system 10 based on the blockchain technology can cooperate to execute the digital archive management method based on the blockchain technology described in the following method embodiments, and the detailed description of the method embodiments can be referred to in the following steps of the digital archive server 100 and the digital archive terminal 200.
To solve the technical problems in the background art, fig. 2 is a flowchart illustrating a digital file management method based on a blockchain technique according to an embodiment of the present invention, which can be executed by the digital file server 100 shown in fig. 1, and the digital file management method based on a blockchain technique is described in detail below.
Step S110, obtaining extended archive encoding data corresponding to the archive updating information of the archive management object of the digital archive terminal, and obtaining structured and unstructured archive encoding data from the extended archive encoding data.
In this embodiment, the extended archive encoded data may refer to archive encoded data that can be extended in the archive entity requirements.
In this embodiment, the structured archive coding data may be understood as archive coding data in a structured form, the unstructured archive coding data may be understood as archive coding data in an unstructured form, and the coding knowledge points of the structured and unstructured archive coding data include knowledge point reading levels, where the knowledge point reading levels may be understood as confidence intervals of reading knowledge points generated based on big data behaviors of a knowledge graph.
Step S120, mining at least one knowledge map coding knowledge point for the structured archive coding data and the unstructured archive coding data respectively to obtain mined structured archive coding data corresponding to the structured archive coding data and mined unstructured archive coding data corresponding to the unstructured archive coding data.
In this embodiment, the knowledge-graph encoded knowledge points may be used to represent classified encoded knowledge points included in the attribute of a knowledge requirement in the digital archive push management service.
Step S130, based on the attribution relationship between the mined knowledge map coding knowledge points and the whole knowledge map coding knowledge points in the mining structured and unstructured archive coding data, respectively adjusting the knowledge point browsing levels in the coding knowledge points of the mining structured and unstructured archive coding data stored in the pre-associated block chain.
In this embodiment, the mined knowledge map coding knowledge points in the structured archive coding data and the unstructured archive coding data can be regarded as the unstructured archive coding data content and the structured archive coding data content, respectively.
Step S140, updating and configuring the digital archive push management service based on the structured and unstructured archive coding data, and mining the structured archive coding data and the unstructured archive coding data, so as to obtain the digital archive push management service with updated and configured data.
Based on the above steps, this embodiment respectively performs mining of at least one knowledge map coding knowledge point on the structured archive coding data and the unstructured archive coding data, respectively modifies the knowledge point turning levels in the coding knowledge points of the mined structured archive coding data and the unstructured archive coding data based on the attribution relationship between the mined knowledge map coding knowledge points and the overall knowledge map coding knowledge points in the mined structured archive coding data and the unstructured archive coding data, so that the data distribution of the mined structured archive coding data and the unstructured archive coding data is changed compared with the structured archive coding data and the unstructured archive coding data, and updates the configured digital archive push management service based on the structured archive coding data, the mined structured archive coding data and the mined unstructured archive coding data, and the associated classification precision of the digital archive push management service can be improved, and based on the coding knowledge points of the data coded by mining the structured and unstructured archives, the digital archive push management service can update more accurate configuration parameters, thereby improving the reliability of service operation.
In one possible implementation manner, for step S120, in the process of mining at least one knowledge graph coding knowledge point for each of the structured archive coded data and the unstructured archive coded data to obtain mined structured archive coded data corresponding to the structured archive coded data and mined unstructured archive coded data corresponding to the unstructured archive coded data, the mining may be implemented by the following exemplary sub-steps, which are described in detail below.
Substep S121, determining structured archive coding data and unstructured archive coding data requiring knowledge point mining for knowledge graph coding.
And a substep S122, mining knowledge graph coding knowledge points of at least one same node in the structured archive coding data and the unstructured archive coding data to obtain mining structured archive coding data corresponding to the structured archive coding data and mining unstructured archive coding data corresponding to the unstructured archive coding data.
In one possible implementation manner, for step S130, in the process of respectively adjusting the knowledge point flipping levels in the coding knowledge points of the mined structured and unstructured archive coding data stored in the pre-associated block chain based on the attribution relationship between the mined knowledge map coding knowledge points and the overall knowledge map coding knowledge points in the mined structured and unstructured archive coding data, the following exemplary sub-steps can be implemented, which are described in detail below.
And a substep S131, calculating the content affiliation relationship between the un-mined structured archive coded data content in the mined structured archive coded data and the structured archive coded data in the mined structured archive coded data based on the mined knowledge map coding knowledge points in the mined structured archive coded data.
And a substep S132 of adjusting the knowledge point browsing level of the mining structured archive coded data based on the content attribution relationship of the structured archive coded data of the mining structured archive coded data.
And a substep S133, calculating the attribution relationship between the mined knowledge map coding knowledge points in the mined unstructured archive coding data and the mined unstructured archive coding data, and taking the attribution relationship as the content attribution relationship of the structured archive coding data of the mined unstructured archive coding data.
And a substep S134, adjusting the knowledge point reading level of the mining unstructured archive coding data based on the content attribution relationship of the structured archive coding data of the mining unstructured archive coding data.
Further, in one possible implementation, for sub-step S122, it may be implemented by the following exemplary embodiments.
(1) And dividing the structured archive coded data and the unstructured archive coded data into the same number of knowledge graph units according to the same division rule.
(2) Randomly selecting a file knowledge editing object from the preset service range as a file knowledge editing object mined by the knowledge map units of the structured file encoding data and the unstructured file encoding data.
(3) And coding the knowledge graph units of the data coded by the structured and unstructured archives to obtain a knowledge graph unit coding sequence.
(4) And (4) disordering the sequence of codes in the knowledge map unit coding sequence to obtain the disordered knowledge map unit coding sequence.
(5) And selecting the codes of the mining archive knowledge editing objects of the knowledge graph units as mining codes from the disordered knowledge graph unit coding sequences.
(6) And mining knowledge graph units indicated by mining codes in the structured and unstructured archive coded data to obtain mined structured archive coded data corresponding to the structured archive coded data and mined unstructured archive coded data corresponding to the unstructured archive coded data.
In one possible implementation manner, for step S140, the digital archive push management service specifically includes an archive entity feature identification unit and an archive entity content prediction unit.
Based on this, in the process of updating and configuring the digital archive push management service based on the structured and unstructured archive coding data, and mining the structured archive coding data and the unstructured archive coding data to obtain the digital archive push management service with the updated configuration completed, the following exemplary substeps can be implemented.
The substep S141 is to use the structured and unstructured archive coding data, and the mined structured archive coding data and the mined unstructured archive coding data as the update configuration archive coding data of the digital archive push management service to be configured to be updated.
And a substep S142, extracting the knowledge graph characteristic information of the updated and configured archive coded data through the archive entity characteristic identification unit.
And a substep S143, performing classification prediction of the archive coded data in the structured form and the archive coded data in the unstructured form on the update configuration archive coded data by the archive entity content prediction unit based on the knowledge graph characteristic information.
In the sub-step S144, based on the prediction result of the updated configuration file encoded data and the encoding knowledge point of the updated configuration file encoded data, the push management parameters of the digital file push management service are calculated.
And a substep S145, adjusting parameters of the digital file pushing management service based on the pushing management parameters, and obtaining the digital file pushing management service with updated configuration.
Wherein updating the prediction result of the encoded data of the configuration profile may include: updating the predictive knowledge point reading level and the predictive non-knowledge point reading level of the coded data of the configuration file.
As such, in sub-step S144, it can be realized by the following exemplary embodiments.
(1) A first push management parameter for updating the configuration archive coded data is calculated based on a knowledge point reading level in the coding knowledge points of the configuration archive coded data and a prediction knowledge point reading level in the prediction result.
(2) And determining the actual non-knowledge point reading level of the updated configuration file coded data based on the knowledge point reading level of the updated configuration file coded data.
(3) And calculating a second pushing management parameter of the updated configuration file coded data based on the actual non-knowledge point reading level of the updated configuration file coded data and the predicted non-knowledge point reading level in the prediction result.
(4) And obtaining the total push management parameters of the digital archive push management service based on the first push management parameters and the second push management parameters of the updated configuration archive coding data.
In a possible implementation manner, the digital archive push management service may further include an archive entity extraction unit connected to the archive entity feature identification unit.
Based on this, the embodiment may further obtain structured and unstructured archive coding data, and preset actual feature information of the feature dimension of the knowledge graph, and obtain mined structured archive coding data and mined unstructured archive coding data, and preset actual feature information of the feature dimension of the knowledge graph.
In this way, before the sub-step S145, the profile entity extraction unit may further perform profile entity extraction on the knowledge graph feature information of the updated configuration profile coded data to obtain profile entity extraction feature information of the updated configuration profile coded data in the preset knowledge graph feature dimension.
And then, based on the updated configuration file encoding data, extracting the characteristic information from the actual characteristic information of the preset knowledge graph characteristic dimension and the file entity to obtain dimension distinguishing information of the digital file pushing management service in the preset knowledge graph characteristic dimension.
Thus, in the sub-step S145, the parameters of the digital archive push management service may be adjusted based on the push management parameters and the dimension differentiation information, so as to obtain the digital archive push management service with the updated configuration completed.
In a possible implementation manner, the actual feature information of the preset knowledge graph feature dimension may include actual archive semantic relationship information of a traceability information dimension, and the archive entity extraction unit may include an archive index archive entity extraction unit.
Based on the above, in the process of extracting the characteristic information of the file entity of the updated configuration file coded data by the file entity extracting unit to obtain the file entity extracted characteristic information of the updated configuration file coded data at the preset characteristic dimension of the knowledge graph, the file index file entity extracting unit can be used for extracting the file index file entity from the knowledge graph characteristic information of the updated configuration file coded data to obtain the predicted file semantic relation information of the updated configuration file coded data.
In the process of configuring archive coded data based on updating, extracting characteristic information at the actual characteristic information of the preset knowledge graph characteristic dimension and the archive entity to obtain dimension distinguishing information of the digital archive push management service at the preset knowledge graph characteristic dimension, the archive semantic relationship distinguishing information can be specifically calculated based on the actual archive semantic relationship information and the predicted archive semantic relationship information of the same updated configuration archive coded data, so that the dimension distinguishing information of the digital archive push management service on the traceability information dimension can be determined based on the archive semantic relationship distinguishing information.
In a possible implementation manner, in the above-mentioned process of obtaining structured and unstructured archive coded data, in the process of presetting actual feature information of a feature dimension of a knowledge map, archive semantic relationship analysis can be performed on knowledge map distribution in the structured archive coded data to obtain actual archive semantic relationship information of the structured archive coded data, and then, for the unstructured archive coded data, archive semantic relationships without archive index information are set as actual archive semantic relationship information of the unstructured archive coded data.
In this way, in the process of acquiring mining structured archive encoding data and mining unstructured archive encoding data and presetting actual feature information of feature dimensions of the knowledge graph, the following exemplary embodiments can be implemented.
(1) And determining actual file semantic relation information of the structured file coded data corresponding to the mined structured file coded data as first initial file semantic relation information of the mined structured file coded data.
(2) Based on the position of the content of the unstructured archive coding data in the mined structured archive coding data, the archive index information in the first initial archive semantic relation information, which is positioned at the same node, is replaced by the archive index information of the content of the unstructured archive coding data, so that the actual archive semantic relation information of the mined structured archive coding data is obtained.
(3) And determining the actual file semantic relationship information of the unstructured file coded data corresponding to the mined unstructured file coded data as second initial file semantic relationship information of the mined unstructured file coded data.
(4) Based on the position of the content of the structured archive coded data in the mined unstructured archive coded data, the archive index information in the same node in the second initial archive semantic relation information is replaced by the archive index information of the content of the structured archive coded data, and the actual archive semantic relation information of the mined unstructured archive coded data is obtained.
Further, in a possible implementation manner, in step S110, in the process of obtaining the structured and unstructured archive encoding data from the extended archive encoding data, after obtaining the original interest archive encoding data from the extended archive encoding data, the original interest archive encoding data may be subjected to a knowledge graph analysis to determine a knowledge graph distribution in the original interest archive encoding data.
Based on the above, the knowledge graph distribution can be expanded in the original interest archive coding data by taking the knowledge graph distribution as a reference to obtain expanded knowledge graph distribution, and the archive coding data of the expanded knowledge graph distribution is intercepted from the original interest archive coding data to be used as unstructured archive coding data.
In addition, at the same time, structured archive encoding data is obtained, wherein the structured archive encoding data comprises archive encoding data in a structured form.
In one possible implementation manner, for step S110, in the process of acquiring the extended archive encoding data corresponding to the archive update information of the archive management object for the digital archive terminal, the following exemplary sub-steps may be implemented.
In the substep S111, archive update information for the archive management object of the digital archive terminal 200 is acquired.
And a substep S112, performing archive entity feature mining on the archive update information to obtain a basic archive entity feature corresponding to the archive update information, and performing archive entity feature mining on the archive update information of the cloud archive metadata framework corresponding to the archive update information to obtain a corresponding global archive entity feature.
In substep S113, a lost profile physical attribute between the base profile physical attribute and the global profile physical attribute is calculated.
And a substep S114 of mining the physical characteristics of the lost file based on the preset AI network to obtain the extended file coding data corresponding to the file updating information.
In this embodiment, the archive update information is the data information of the knowledge graph obtained by performing corresponding cloud computing data statistics on archive entity effective parameter items of each target subscription archive entity encoding knowledge point based on the archive management object. The archive entity valid parameter item of the target subscription archive entity encoded knowledge point can be understood as a virtual encoded knowledge point object for the target subscription archive entity encoded knowledge point with statistical significance. Each virtual encoded knowledge point object may comprise virtual encoded knowledge point data represented by at least one archival entity virtual encoded knowledge point.
In this embodiment, the preset AI network may be obtained by performing update configuration on a preset update configuration sample and mining label coding knowledge points corresponding to the update configuration sample by using a conventional deep learning network, where the update configuration sample may refer to a large number of file entity features obtained by manual comparison, and is not a key point in the embodiment of the present invention in a specific update configuration process, and reference is made to a conventional update configuration manner in the prior art, which is not described herein again.
In this embodiment, the extended archive encoded data may refer to archive encoded data that may be missing from the archive entity requirements.
In this embodiment, the cloud archive metadata structure is an archive metadata structure with an operation stability greater than a preset operation stability in archive metadata structures of other archive management objects similar to the archive management object, and the other archive management objects similar to the archive management object may refer to archive management objects with similar user characteristics.
In this embodiment, in the process of generating corresponding extended digital archive push management service information based on the extended archive encoded data, for example, the extended encoding knowledge point classification corresponding to each extended knowledge graph distribution may be obtained from the extended archive encoded data, and after the extended digital archive push management service information corresponding to the extended encoding knowledge point classification is obtained, the corresponding extended digital archive push management service information is generated.
Based on the above steps, the embodiment first determines the loss archive entity characteristics having the push reference value, so that the loss archive entity characteristics are utilized to perform extended archive coding data mining, thereby enriching the features of the subsequent calling process of the extended digital archive push management service information and avoiding the loss of the effective information amount.
Fig. 3 is a functional module diagram of a digital file management device 300 based on the blockchain technology according to an embodiment of the present disclosure, and this embodiment can divide the functional modules of the digital file management device 300 based on the blockchain technology according to the method embodiment executed by the digital file server 100, that is, the following functional modules corresponding to the digital file management device 300 based on the blockchain technology can be used to execute the method embodiments executed by the digital file server 100. The device 300 for managing digital files based on blockchain technology may include an obtaining module 310, a mining module 320, an adjusting module 330, and an updating and allocating module 340, wherein the functions of the functional modules of the device 300 for managing digital files based on blockchain technology are described in detail below.
The acquiring module 310 is configured to acquire extended archive encoding data corresponding to archive updating information of an archive management object of the digital archive terminal, and acquire structured and unstructured archive encoding data from the extended archive encoding data, where the structured archive encoding data is archive encoding data existing in a structured form, the unstructured archive encoding data is archive encoding data existing in an unstructured form, and encoding knowledge points of the structured and unstructured archive encoding data include knowledge point reading levels. The obtaining module 310 may be configured to perform the step S110, and the detailed implementation of the obtaining module 310 may refer to the detailed description of the step S110.
And the mining module 320 is configured to mine at least one knowledge graph coding knowledge point for the structured archive coding data and the unstructured archive coding data, respectively, to obtain mined structured archive coding data corresponding to the structured archive coding data and mined unstructured archive coding data corresponding to the unstructured archive coding data. The mining module 320 may be configured to perform the step S120, and the detailed implementation of the mining module 320 may refer to the detailed description of the step S120.
The adjusting module 330 is configured to adjust, based on an attribution relationship between the mined knowledge graph coding knowledge points in the mined structured and unstructured archive coding data and the entire knowledge graph coding knowledge points, knowledge point turning levels in the coding knowledge points of the mined structured and unstructured archive coding data stored in the block chain associated in advance, respectively, where the mined knowledge graph coding knowledge points in the mined structured and unstructured archive coding data are regarded as the unstructured archive coding data content and the structured archive coding data content, respectively. The adjusting module 330 may be configured to perform the step S130, and the detailed implementation of the adjusting module 330 may refer to the detailed description of the step S130.
The update configuration module 340 is configured to perform update configuration on the digital archive push management service based on the structured and unstructured archive encoded data, and the mined structured archive encoded data and the mined unstructured archive encoded data, so as to obtain the digital archive push management service with completed update configuration. The update configuration module 340 may be configured to perform the step S140, and the detailed implementation manner of the update configuration module 340 may refer to the detailed description of the step S140.
It should be noted that the division of the modules of the above apparatus is only a logical division, and the actual implementation may be wholly or partially integrated into one physical entity, or may be physically separated. And these modules may all be implemented in software invoked by a processing element. Or may be implemented entirely in hardware. And part of the modules can be realized in the form of calling software by the processing element, and part of the modules can be realized in the form of hardware. For example, the obtaining module 310 may be a processing element separately set up, or may be implemented by being integrated into a chip of the apparatus, or may be stored in a memory of the apparatus in the form of program code, and the processing element of the apparatus calls and executes the functions of the obtaining module 310. Other modules are implemented similarly. In addition, all or part of the modules can be integrated together or can be independently realized. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in the form of software.
For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more Application Specific Integrated Circuits (ASICs), or one or more microprocessors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs), among others. For another example, when some of the above modules are implemented in the form of a processing element scheduler code, the processing element may be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor that can call program code. As another example, these modules may be integrated together, implemented in the form of a system-on-a-chip (SOC).
Fig. 4 is a schematic diagram illustrating a hardware structure of a digital archive server 100 for implementing the digital archive management method based on the blockchain technology according to the embodiment of the present disclosure, and as shown in fig. 4, the digital archive server 100 may include a processor 110, a machine-readable storage medium 120, a bus 130, and a transceiver 140.
In an implementation process, at least one processor 110 executes computer-executable instructions stored in the machine-readable storage medium 120 (for example, the obtaining module 310, the mining module 320, the adjusting module 330, and the updating and configuring module 340 included in the digital archive management apparatus 300 based on the blockchain technology shown in fig. 3), so that the processor 110 can execute the digital archive management method based on the blockchain technology according to the above method embodiment, where the processor 110, the machine-readable storage medium 120, and the transceiver 140 are connected via the bus 130, and the processor 110 can be configured to control the transceiving action of the transceiver 140, so as to perform data transceiving with the digital archive terminal 200.
For the specific implementation process of the processor 110, reference may be made to the above-mentioned embodiments of the method executed by the digital archive server 100, which implement the similar principle and technical effect, and the detailed description of the embodiments is omitted here.
In the embodiment shown in fig. 4, it should be understood that the Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor.
The machine-readable storage medium 120 may comprise high-speed RAM memory and may also include non-volatile storage NVM, such as at least one disk memory.
The bus 130 may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus 130 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, the buses in the figures of the present invention are not limited to only one bus or one type of bus.
In addition, an embodiment of the present invention further provides a readable storage medium, where the readable storage medium stores computer-executable instructions, and when a processor executes the computer-executable instructions, the method for managing a digital archive based on a block chain technique as described above is implemented.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be regarded as illustrative only and not as limiting the present specification. Various modifications, improvements and adaptations to the present description may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present specification and thus fall within the spirit and scope of the exemplary embodiments of the present specification.
Also, the description uses specific words to describe embodiments of the description. Such as "one possible implementation," "one possible example," and/or "exemplary" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification is included. Therefore, it is emphasized and should be noted that two or more references to "one possible implementation," "one possible example," and/or "exemplary" in various words or phrases in this specification are not necessarily referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the specification may be combined as appropriate.
Moreover, those skilled in the art will appreciate that aspects of the present description may be illustrated and described in terms of several patentable species or contexts, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereof. Accordingly, aspects of this description may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present description may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.
The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.
Computer program code required for the operation of various portions of this specification may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or user terminal. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).
Additionally, the order in which the elements and lists are processed, the use of alphanumeric characters, or other designations in this specification is not intended to limit the order in which the processes and methods of this specification are performed, unless otherwise specified in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by interactive services, they may also be implemented by software-only solutions, such as installing the described system on an existing user terminal or mobile device.
Similarly, it should be noted that in the preceding description of embodiments of the present specification, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to imply that more features than are expressly recited in a claim. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.
It is to be understood that the descriptions, definitions and/or uses of terms in the accompanying materials of this specification shall control if they are inconsistent or contrary to the descriptions and/or uses of terms in this specification.
Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present disclosure. Other variations are also possible within the scope of the present description. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the specification can be considered consistent with the teachings of the specification. Accordingly, the embodiments of the present description are not limited to only those embodiments explicitly described and depicted herein.

Claims (10)

1. A digital archive management method based on block chain technology is applied to a digital archive server, and the digital archive server is in communication connection with a plurality of digital archive terminals, and the method comprises the following steps:
acquiring extended archive coding data corresponding to archive updating information of an archive management object of the digital archive terminal, and acquiring structured and unstructured archive coding data from the extended archive coding data, wherein the structured archive coding data are archive coding data in a structured form, the unstructured archive coding data are archive coding data in an unstructured form, and coding knowledge points of the structured and unstructured archive coding data comprise knowledge point reading levels;
mining at least one knowledge map coding knowledge point of the structured archive coding data and the unstructured archive coding data respectively to obtain mined structured archive coding data corresponding to the structured archive coding data and mined unstructured archive coding data corresponding to the unstructured archive coding data;
respectively adjusting the knowledge point turning levels in the coding knowledge points of the mining structured and unstructured archive coding data stored in a block chain associated in advance based on the attribution relation between the mining knowledge map coding knowledge points and the whole knowledge map coding knowledge points in the mining structured and unstructured archive coding data, wherein the mining knowledge map coding knowledge points in the mining structured and unstructured archive coding data are respectively regarded as unstructured archive coding data content and structured archive coding data content;
and updating and configuring the digital archive push management service based on the structured and unstructured archive encoding data, the mined structured archive encoding data and the mined unstructured archive encoding data to obtain the digital archive push management service with the updated and configured data.
2. The method of claim 1, wherein the mining of at least one knowledge-graph coding knowledge point for the structured archive coding data and the unstructured archive coding data to obtain mined structured archive coding data corresponding to the structured archive coding data and mined unstructured archive coding data corresponding to the unstructured archive coding data comprises:
determining structured archive coding data and unstructured archive coding data of knowledge points needing to be mined for knowledge graph coding;
mining knowledge graph coding knowledge points of at least one same node in the structured archive coding data and the unstructured archive coding data to obtain mined structured archive coding data corresponding to the structured archive coding data and mined unstructured archive coding data corresponding to the unstructured archive coding data;
the step of adjusting the knowledge point turning level in the coding knowledge points of the mining structured and unstructured archive coding data stored in the block chain associated in advance based on the attribution relationship between the mining knowledge map coding knowledge points and the whole knowledge map coding knowledge points in the mining structured and unstructured archive coding data comprises the following steps:
calculating the content affiliation relationship between the non-mined structured archive encoding data content in the mined structured archive encoding data and the structured archive encoding data of the mined structured archive encoding data on the basis of the mined knowledge map encoding knowledge points in the mined structured archive encoding data;
adjusting the knowledge point reading level of the mining structured archive coding data based on the content attribution relationship of the structured archive coding data of the mining structured archive coding data;
calculating the attribution relationship between the mined knowledge map coding knowledge points in the mined unstructured archive coding data and the mined unstructured archive coding data, and taking the attribution relationship as the content attribution relationship of the structured archive coding data of the mined unstructured archive coding data;
and adjusting the knowledge point reading level of the mining unstructured archive coding data based on the content attribution relationship of the structured archive coding data of the mining unstructured archive coding data.
3. The method of claim 2, wherein the mining of knowledge-graph coding knowledge points of at least one same node in the structured archive coding data and the unstructured archive coding data to obtain mined structured archive coding data corresponding to the structured archive coding data and mined unstructured archive coding data corresponding to the unstructured archive coding data comprises:
dividing the structured archive coded data and the unstructured archive coded data into knowledge graph units with the same quantity according to the same division rule;
randomly selecting a file knowledge editing object from a preset service range as a file knowledge editing object mined by knowledge graph units of the structured file encoding data and the unstructured file encoding data;
coding a knowledge graph unit of the data coded by the structured and unstructured archives to obtain a knowledge graph unit coding sequence;
disordering the sequence of codes in the knowledge graph unit coding sequence to obtain the disordered knowledge graph unit coding sequence;
selecting the codes of the mining archive knowledge editing objects of the knowledge graph units as mining codes from the disordered knowledge graph unit coding sequences;
and mining knowledge graph units indicated by the mining codes in the structured and unstructured archive coded data to obtain mining structured archive coded data corresponding to the structured archive coded data and mining unstructured archive coded data corresponding to the unstructured archive coded data.
4. The method for digital archive management based on block chain technology of any of claims 1-3, characterized in that the digital archive push management service comprises an archive entity feature identification unit and an archive entity content prediction unit;
the step of updating and configuring the digital archive push management service based on the structured and unstructured archive coded data, the mined structured archive coded data and the mined unstructured archive coded data to obtain the digital archive push management service with the updated and configured data comprises the following steps:
taking the structured and unstructured archive coding data, and the mining structured archive coding data and the mining unstructured archive coding data as update configuration archive coding data of a digital archive push management service to be updated and configured;
extracting knowledge map characteristic information of the data for updating and configuring the file coded by the file entity characteristic identification unit;
performing classification prediction of archive coding data in a structured form and archive coding data in an unstructured form on the update configuration archive coding data based on the knowledge graph feature information by the archive entity content prediction unit;
calculating a pushing management parameter of the digital archive pushing management service based on the prediction result of the updated configuration archive coding data and the coding knowledge point of the updated configuration archive coding data;
adjusting parameters of the digital archive push management service based on the push management parameters to obtain the digital archive push management service with updated configuration;
wherein the prediction result of updating the encoded data of the configuration profile comprises: updating the predictive knowledge point reading grade and the predictive non-knowledge point reading grade of the coded data of the configuration file;
the step of calculating the push management parameters of the digital archive push management service based on the prediction result of the updated configuration archive encoded data and the encoding knowledge points of the updated configuration archive encoded data comprises:
calculating a first pushing management parameter of the updated configuration file coded data based on a knowledge point turning level in the coding knowledge points of the updated configuration file coded data and a prediction knowledge point turning level in the prediction result;
determining an actual non-knowledge point reading level of the updated configuration file encoding data based on the knowledge point reading level of the updated configuration file encoding data;
calculating a second push management parameter of the updated configuration file coded data based on the actual non-knowledge point reading level of the updated configuration file coded data and the predicted non-knowledge point reading level in the prediction result;
and obtaining the total push management parameters of the digital archive push management service based on the first push management parameters and the second push management parameters of the updated and configured archive coded data.
5. The method as claimed in claim 4, wherein the digital archive push management service further comprises an archive entity extraction unit connected to the archive entity feature identification unit;
the method further comprises the following steps:
acquiring the structured and unstructured archive coding data, and presetting actual characteristic information of characteristic dimensions of the knowledge graph;
acquiring the mining structured archive coding data and mining unstructured archive coding data, and presetting actual characteristic information of characteristic dimensions of the knowledge graph;
before adjusting the parameters of the digital archive push management service based on the push management parameters to obtain the digital archive push management service with the updated configuration, the method further comprises the following steps:
performing archive entity extraction on the knowledge graph feature information of the updated configuration archive coded data through the archive entity extraction unit to obtain archive entity extraction feature information of the updated configuration archive coded data in the preset knowledge graph feature dimension;
based on the updated configuration file encoding data, extracting feature information from the actual feature information of the preset knowledge graph feature dimension and the file entity to obtain dimension distinguishing information of the digital file pushing management service in the preset knowledge graph feature dimension;
the step of adjusting the parameters of the digital archive push management service based on the push management parameters to obtain the digital archive push management service with the updated configuration, comprises:
and adjusting parameters of the digital archive push management service based on the push management parameters and the dimension distinguishing information to obtain the digital archive push management service with updated configuration.
6. The method of claim 5, wherein the actual feature information of the predetermined feature dimension of the knowledge-graph comprises: the actual file semantic relation information of the traceability information dimension, wherein the file entity extraction unit comprises a file index file entity extraction unit;
the obtaining, by the archive entity extraction unit, archive entity extraction of the knowledge graph feature information of the updated configuration archive encoded data at the preset knowledge graph feature dimension of the updated configuration archive encoded data includes:
performing file index file entity extraction on the knowledge graph characteristic information of the updated configuration file coded data through the file index file entity extraction unit to obtain predicted file semantic relation information of the updated configuration file coded data;
the step of obtaining dimension distinguishing information of the digital archive push management service in the preset knowledge graph characteristic dimension based on the updated configuration archive coded data and the actual characteristic information of the preset knowledge graph characteristic dimension and the archive entity extraction characteristic information comprises the following steps:
calculating file semantic relation distinguishing information based on actual file semantic relation information and predicted file semantic relation information of the same updated configuration file coded data;
and determining dimension distinguishing information of the digital archive push management service on the traceability information dimension based on the archive semantic relation distinguishing information.
7. The method of claim 6, wherein the step of obtaining the actual feature information of the encoded data of the structured and unstructured archive in the predetermined feature dimension comprises:
performing archive semantic relation analysis on knowledge graph distribution in the structured archive coding data to obtain actual archive semantic relation information of the structured archive coding data;
setting file semantic relations without file index information as actual file semantic relation information of the unstructured file coded data for the unstructured file coded data;
the step of acquiring the mining structured archive coding data and the mining unstructured archive coding data and presetting the actual feature information of the feature dimension of the knowledge graph comprises the following steps:
determining actual file semantic relation information of the structured file coded data corresponding to the mined structured file coded data as first initial file semantic relation information of the mined structured file coded data;
based on the position of the content of the unstructured archive coding data in the mined structured archive coding data, replacing archive index information located at the same node in the first initial archive semantic relation information with archive index information of the content of the unstructured archive coding data to obtain actual archive semantic relation information of the mined structured archive coding data;
determining actual file semantic relationship information of the unstructured file coded data corresponding to the mined unstructured file coded data as second initial file semantic relationship information of the mined unstructured file coded data;
based on the position of the content of the structured archive coded data in the mined unstructured archive coded data, the archive index information in the same node in the second initial archive semantic relation information is replaced by the archive index information of the content of the structured archive coded data, so that the actual archive semantic relation information of the mined unstructured archive coded data is obtained.
8. The method of claim 1, wherein the step of obtaining structured and unstructured archive encoding data from the extended archive encoding data comprises:
after original interest archive coding data are acquired from the extended archive coding data, knowledge graph analysis is carried out on the original interest archive coding data, and knowledge graph distribution in the original interest archive coding data is determined;
in the original interest archive coded data, expanding the knowledge graph distribution by taking the knowledge graph distribution as a reference to obtain expanded knowledge graph distribution;
intercepting archive coded data of the expanded knowledge graph distribution from the original interest archive coded data to serve as unstructured archive coded data;
and acquiring structured archive coding data, wherein the structured archive coding data comprises archive coding data in a structured form.
9. The method for digital archive management based on blockchain technology according to any one of claims 1 to 8, wherein the step of obtaining the extended archive encoding data corresponding to the archive update information of the archive management object for the digital archive terminal includes:
acquiring archive updating information of an archive management object of the digital archive terminal, wherein the archive updating information is knowledge graph data information obtained by carrying out corresponding cloud computing data statistics on archive entity effective parameter items of each target subscription archive entity encoding knowledge point of the archive management object;
performing file entity feature mining on the file updating information to obtain basic file entity features corresponding to the file updating information, and performing file entity feature mining on file updating information of a cloud file metadata framework corresponding to the file updating information to obtain corresponding global file entity features, wherein the cloud file metadata framework is a file metadata framework with feedback effectiveness greater than preset effectiveness in file metadata frameworks of other file management objects similar to the file management object;
calculating a loss profile physical feature between the base profile physical feature and the global profile physical feature;
and mining the physical characteristics of the loss archives based on a preset AI network to obtain extended archive coding data corresponding to the archive updating information, and generating corresponding extended digital archive push management service information based on the extended archive coding data.
10. A digital file management system based on a block chain technology is characterized in that the digital file management system based on the block chain technology comprises a digital file server and a plurality of digital file terminals which are in communication connection with the digital file server;
the digital archive server is configured to:
acquiring extended archive coding data corresponding to archive updating information of an archive management object of the digital archive terminal, and acquiring structured and unstructured archive coding data from the extended archive coding data, wherein the structured archive coding data are archive coding data in a structured form, the unstructured archive coding data are archive coding data in an unstructured form, and coding knowledge points of the structured and unstructured archive coding data comprise knowledge point reading levels;
mining at least one knowledge map coding knowledge point of the structured archive coding data and the unstructured archive coding data respectively to obtain mined structured archive coding data corresponding to the structured archive coding data and mined unstructured archive coding data corresponding to the unstructured archive coding data;
respectively adjusting the knowledge point turning levels in the coding knowledge points of the mining structured and unstructured archive coding data stored in a block chain associated in advance based on the attribution relation between the mining knowledge map coding knowledge points and the whole knowledge map coding knowledge points in the mining structured and unstructured archive coding data, wherein the mining knowledge map coding knowledge points in the mining structured and unstructured archive coding data are respectively regarded as unstructured archive coding data content and structured archive coding data content;
and updating and configuring the digital archive push management service based on the structured and unstructured archive encoding data, the mined structured archive encoding data and the mined unstructured archive encoding data to obtain the digital archive push management service with the updated and configured data.
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