CN112100277A - Method, system, equipment and product for realizing enterprise data chaining prediction machine - Google Patents
Method, system, equipment and product for realizing enterprise data chaining prediction machine Download PDFInfo
- Publication number
- CN112100277A CN112100277A CN202010961426.9A CN202010961426A CN112100277A CN 112100277 A CN112100277 A CN 112100277A CN 202010961426 A CN202010961426 A CN 202010961426A CN 112100277 A CN112100277 A CN 112100277A
- Authority
- CN
- China
- Prior art keywords
- data
- enterprise
- enterprise data
- chain
- intelligent contract
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
- G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
- G06F21/6227—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database where protection concerns the structure of data, e.g. records, types, queries
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/64—Protecting data integrity, e.g. using checksums, certificates or signatures
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Computer Security & Cryptography (AREA)
- Business, Economics & Management (AREA)
- Databases & Information Systems (AREA)
- General Engineering & Computer Science (AREA)
- Computer Hardware Design (AREA)
- Accounting & Taxation (AREA)
- Bioethics (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Software Systems (AREA)
- Finance (AREA)
- Data Mining & Analysis (AREA)
- Computing Systems (AREA)
- Development Economics (AREA)
- Economics (AREA)
- Marketing (AREA)
- Strategic Management (AREA)
- Technology Law (AREA)
- General Business, Economics & Management (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a method, a system, equipment and a product for realizing an enterprise data uplink prediction machine, wherein the method comprises the following steps: the standardization of enterprise data is realized by combining metadata and XBRL standard; calling a pre-talker service plug-in integrated in an enterprise application development platform to store data changed in the enterprise data storage and modification process into a message queue; the predictive engine service consumes data from the message queue and invokes an on-chain predictive engine intelligent contract to implement enterprise data chaining. And when the intelligent contracts of other users have the requirement for acquiring the data, calling the intelligent contracts of the enterprise data service, and performing data request and data response through the XBRL standard to acquire the uplink enterprise data. The method utilizes the XBRL to standardize the enterprise data, solves the problems of enterprise application, prediction machines and on-chain intelligent contract data communication standard by utilizing the XBRL standard, and realizes the universal enterprise data uplink prediction machine based on the XBRL standardization.
Description
Technical Field
The invention relates to an enterprise data XBRL standardization, an enterprise intelligent development platform based on a block chain, an enterprise data uplink and XBRL labeling data chain storage, in particular to an enterprise data uplink prediction machine implementation method based on the XBRL standardization.
Background
The key core technology of the blockchain relates to various complex technical researches such as decentralization, distrust, collective maintenance, reliable databases, timestamps, consensus algorithm, reliable P2P communication, asymmetric encryption and the like, and the technical complexity influences the application and the rapid popularization of the blockchain. Enterprise informatization projects are various, used technical development frames are not uniform, and the integration of different projects and block chains needs to be repeated, thereby wasting enterprise resources, increasing enterprise cost and influencing the wide application of the block chains in enterprises.
The decentralized account book and the intelligent contract of the block chain solve the trust problem of P2P interaction for the current society, and no centralized mechanism is needed for trust endorsement, which is a great innovation of the human social trust system. However, the current intelligent contract cannot actively acquire information outside the chain, so that the intelligent contract can only execute tasks in a closed and isolated environment and cannot be communicated with the outside world. An oracle is an on-chain and off-chain mechanism that breaks through a blockchain, i.e., a mechanism that writes information outside the blockchain into the blockchain. At present, according to the classification of business organization forms, one of the common language prediction machines is a centralized single-speaker mechanism, i.e. a centralized language prediction machine (such as Oraclize), and the other is an decentralized multi-speaker mechanism, i.e. an decentralized language prediction machine (such as Chainlink, DOS Network, etc.). The language prediction machines represented by Oraclelize and Chainlink are all language prediction machines surrounding public chain services, and are not suitable for application of enterprise blockchain services. The eXtensible Business Reporting Language (XBRL) is an XML-based markup Language. The formulation and management of the XBRL standard is governed by the XBRL International Association (XBRL International). The XBRL is mainly used for providing management and management information of enterprise decision makers.
With the deep advancement of large data, the data capitalization is becoming an increasingly obvious trend. More and more enterprises are considering data as assets, but due to the lack of unified planning of enterprise information system construction, the format and definition of data in different systems are inconsistent, and the problems of low data quality, difficult data garbage processing, low data conversion efficiency and the like exist, so that information transmission between systems cannot be performed, and a data standard system based on a business activity process cannot be formed to realize direct exchange and transmission of data between cross-platforms.
Disclosure of Invention
The invention provides a method, a system, equipment and a product for realizing an enterprise data uplink prediction machine, aiming at the problems that information transmission cannot be carried out between systems and direct data exchange and transmission between cross-platforms cannot be realized by a data standard system based on a business activity process because the construction of an enterprise information system lacks unified planning and the formats and definitions of data in different systems are inconsistent, so that the problems of low data quality, difficult data garbage processing, low data conversion efficiency and the like exist.
The technical scheme of the invention is as follows:
in a first aspect, a technical solution of the present invention provides a method for implementing an enterprise data uplink prediction machine, including the following steps:
the standardization of enterprise data is realized by combining metadata and XBRL standard;
calling a pre-talker service plug-in integrated in an enterprise application development platform to store data changed in the enterprise data storage and modification process into a message queue;
the predictive engine service consumes data from the message queue and invokes an on-chain predictive engine intelligent contract to implement enterprise data chaining.
The XBRL labeling of the enterprise data is realized, the evidence storage on an enterprise data chain is realized, and the connection between the chain and the off-chain enterprise data is realized by calling the off-chain enterprise data by the data of the intelligent contract based on the XBRL standard. A block chain network supporting an intelligent contract is used as an operating environment, and a predictive machine service is provided for the block chain network and can be called by the intelligent contract or application requiring the predictive machine service.
Further, the enterprise data comprises metadata, main data, transaction data, process data and semi-structured/unstructured data of the enterprise; the step of realizing enterprise data standardization by combining metadata and XBRL standard comprises the following steps:
the standardization of the enterprise metadata is realized by combining the mapping relation between the metadata and the XBRL classification standard element information according to a metadata model;
establishing a corresponding relation between main data standard codes defined by XBRL classification standards and enterprise owner data to realize XBRL standardization of the enterprise owner data;
and realizing XBRL standardization of the business data based on the corresponding relation between the business model metadata and the XBRL standard.
In the process of business application development, the labeling of the enterprise data XBRL is completed, and the labeling of the XBRL can be realized by the data generated by the business system after production and application.
Further, the on-chain prediction machine intelligent contract comprises an enterprise data chaining intelligent contract;
the method for realizing enterprise data uplink by calling the intelligent contract of the prediction machine on the chain comprises the following steps:
the prediction machine service consumes data from the message queue and calls an intelligent contract for uplink of enterprise data to calculate a data hash value of the enterprise data; recording the calculated hash value to a block chain;
the prediction machine service monitors the transaction result on the chain;
when a transaction success event is monitored, storing the enterprise data record and the chain transaction information record; meanwhile, the transaction information on the chain is put into a message queue to inform the enterprise of the transaction state on the application chain;
the enterprise application exchanges information from the message queue consumption chain and records the information to the uplink log. And the recording on the enterprise data chain is realized only under the conditions of data hash value change and transaction failure. Avoid repeated uplinking of data without change.
Further, the chain prediction machine intelligent contract also comprises an enterprise data service intelligent contract;
the method further comprises the following steps:
the business intelligent contract on the chain sends a data request for requesting the needed enterprise data by calling the enterprise data service intelligent contract;
after receiving a data request, the enterprise data service intelligent contract dispatches a data request event;
after monitoring the event, the preloader service matches the corresponding metadata and business model according to the XBRL-json description, and queries and returns the corresponding enterprise data.
By defining a uniform XBRL-json data request standard, the universal enterprise data service intelligent contract is realized to provide data service for a business intelligent contract.
Further, in the step of sending a data request for requesting the required enterprise data, parameters of the data request include a caller intelligent contract address and a data request described by XBRL-json.
Further, after monitoring the event, the predicting machine service matches the corresponding metadata and business model according to XBRL-json description, and after the step of querying and returning the corresponding enterprise data, the method further comprises the following steps:
the language predicting machine service inquires corresponding enterprise data and calls an intelligent contract of the enterprise data service;
the enterprise data service intelligent contract carries out hash calculation on the inquired corresponding enterprise data and carries out comparison and verification on the hash value of the data calculated by the enterprise data chaining intelligent contract;
and if the comparison result is consistent, calling back the enterprise data responded by the service intelligent contract.
And the enterprise data service intelligent contract dispatches a data request event, provides an off-link data receiving service interface to receive data, performs hash calculation on the received data and compares the received data with an on-link hash, and calls back the service intelligent contract after the verification is passed. The invention standardizes enterprise data by using the XBRL, solves the problems of enterprise application, a prediction machine and an intelligent contract data communication standard on a chain by using the XBRL standard, and realizes the universal enterprise data uplink prediction machine based on the XBRL standardization.
In a second aspect, a technical solution of the present invention provides a system for implementing an enterprise data uplink prediction machine, including an enterprise data source, an on-chain environment unit, and an off-chain environment unit;
the system comprises a data standardization processing module and a calling module;
the on-chain environment unit is provided with an on-chain prediction machine intelligent contract; the downlink environment unit is provided with a language predicting machine service module;
the data standardization processing module is used for acquiring enterprise data from an enterprise data source and realizing enterprise data standardization by combining metadata and an XBRL standard;
the calling module is used for calling a pre-talker service plug-in integrated in the enterprise application development platform to store the changed data in the enterprise data storage and modification process into a message queue;
and the prediction machine service module is used for consuming data from the message queue and calling an intelligent contract of the prediction machine on the chain to realize the data uplink of the enterprise.
Furthermore, the prediction machine service module is used for consuming data from the message queue and calling an intelligent contract for chaining the enterprise data to calculate a data hash value of the enterprise data; recording the calculated hash value to a block chain; monitoring the transaction result on the chain; when a transaction success event is monitored, storing the enterprise data record and the chain transaction information record; meanwhile, the transaction information on the chain is put into a message queue to inform the enterprise of the transaction state on the application chain; the method is also used for monitoring the data request event of the intelligent contract of the enterprise data service, analyzing the XBRL-json data request, matching metadata and a business model, loading the enterprise data according to the model metadata and calling the intelligent contract of the enterprise data service to write in the data. And when the intelligent contracts of other users have the requirement for acquiring the data, calling the intelligent contracts of the enterprise data service, and performing data request and data response through the XBRL standard to acquire the uplink enterprise data.
In a third aspect, the present invention further provides an electronic device, including a memory and a processor, where the memory and the processor complete communication with each other through a bus; the memory stores program instructions executable by the processor, the processor invoking the program instructions capable of performing the enterprise data uplink prediction machine implementation method of the first aspect.
In a fourth aspect, the present invention also provides a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the method for implementing an enterprise data uplink prediction machine according to the first aspect.
According to the technical scheme, the invention has the following advantages: the method comprises the steps of defining a unified XBRL-json data request standard to realize the intelligent contract of the general enterprise data service and provide data service for a business intelligent contract; and the enterprise data service intelligent contract dispatches a data request event, provides an off-link data receiving service interface to receive data, performs hash calculation on the received data and compares the received data with an on-link hash, and calls back the service intelligent contract after the verification is passed. The invention standardizes enterprise data by using the XBRL, solves the problems of enterprise application, a prediction machine and an intelligent contract data communication standard on a chain by using the XBRL standard, and realizes the universal enterprise data uplink prediction machine based on the XBRL standardization.
In addition, the invention has reliable design principle, simple structure and very wide application prospect.
Therefore, compared with the prior art, the invention has prominent substantive features and remarkable progress, and the beneficial effects of the implementation are also obvious.
Drawings
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
Fig. 1 is a schematic diagram of an enterprise uplink data link according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a prediction machine providing data services according to an embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention, and it is obvious that the described embodiment is only a part of the embodiment of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention. The following explains key terms appearing in the present invention.
The invention provides a method for realizing enterprise data chaining based on XBRL standardization, which realizes XBRL tagging of enterprise data and evidence storage on an enterprise data chain, and realizes connection between the chain and the off-chain enterprise data by calling the off-chain enterprise data based on the XBRL standard by data of an intelligent contract. The invention relies on a block chain network (such as a super book Fabric and an Ethernet) supporting intelligent contracts as an operating environment, provides a language predictive machine service for the block chain network, and can be called by intelligent contracts or applications requiring the language predictive machine service. The invention is executed in a license chain environment by default, and incentive mechanisms such as voting deposit and winning prize are not designed. The method is easy to extend to a public chain execution environment after adding the incentive mechanism.
The embodiment of the invention provides a method for realizing an enterprise data uplink prediction machine, which comprises the following steps:
step 1: the standardization of enterprise data is realized by combining metadata and XBRL standard;
step 2: calling a pre-talker service plug-in integrated in an enterprise application development platform to store data changed in the enterprise data storage and modification process into a message queue;
and step 3: the predictive engine service consumes data from the message queue and invokes an on-chain predictive engine intelligent contract to implement enterprise data chaining.
The XBRL labeling of the enterprise data is realized, the evidence storage on an enterprise data chain is realized, and the connection between the chain and the off-chain enterprise data is realized by calling the off-chain enterprise data by the data of the intelligent contract based on the XBRL standard. A block chain network supporting an intelligent contract is used as an operating environment, and a predictive machine service is provided for the block chain network and can be called by the intelligent contract or application requiring the predictive machine service.
It should be noted that the enterprise data includes metadata (data self-description information) of the enterprise, main data (stable data), transaction data (volatile data), process data (log, internal control data), and semi-structured/unstructured data (document, image, volume data, streaming data, etc.); the step of realizing enterprise data standardization by combining metadata and XBRL standard comprises the following steps:
the standardization of the enterprise metadata is realized by combining the mapping relation between the metadata and the XBRL classification standard element information according to a metadata model; establishing a corresponding relation between main data standard codes defined by XBRL classification standards and enterprise owner data to realize XBRL standardization of the enterprise owner data; and realizing XBRL standardization of the transaction data based on the corresponding relation between the business model metadata and the XBRL standard. In the process of business application development, the labeling of the enterprise data XBRL is completed, and the labeling of the XBRL can be realized by the data generated by the business system after production and application.
In some embodiments, the on-chain predictive machine intelligent contract comprises an enterprise data chaining intelligent contract; in step 3, the predicting machine service consumes data from the message queue, and the step of calling the intelligent contract of the predicting machine on the chain to realize the data uplink of the enterprise comprises the following steps:
step 31: the prediction machine service consumes data from the message queue and calls an intelligent contract for uplink of enterprise data to calculate a data hash value of the enterprise data; recording the calculated hash value to a block chain;
step 32: the prediction machine service monitors the transaction result on the chain;
step 33: when a transaction success event is monitored, storing the enterprise data record and the chain transaction information record; meanwhile, the transaction information on the chain is put into a message queue to inform the enterprise of the transaction state on the application chain;
step 34: the enterprise application exchanges information from the message queue consumption chain and records the information to the uplink log. And the recording on the enterprise data chain is realized only under the conditions of data hash value change and transaction failure. Avoid repeated uplinking of data without change.
In some embodiments, the on-chain predictive machine intelligence contract further comprises an enterprise data services intelligence contract; the method further comprises the following steps:
step 41: the business intelligent contract on the chain sends a data request for requesting the needed enterprise data by calling the enterprise data service intelligent contract;
step 42: after receiving a data request, the enterprise data service intelligent contract dispatches a data request event; parameters of the data request comprise an intelligent contract address of a calling party and the data request described by XBRL-json;
step 43: after monitoring the event, the preloader service matches the corresponding metadata and business model according to the XBRL-json description, and queries and returns the corresponding enterprise data.
By defining a uniform XBRL-json data request standard, the universal enterprise data service intelligent contract is realized to provide data service for a business intelligent contract.
In step 43, the step of querying and returning the corresponding enterprise data further includes:
the language predicting machine service inquires corresponding enterprise data and calls an intelligent contract of the enterprise data service;
the enterprise data service intelligent contract carries out hash calculation on the inquired corresponding enterprise data and carries out comparison and verification on the hash value of the data calculated by the enterprise data chaining intelligent contract;
and if the comparison result is consistent, calling back the enterprise data responded by the service intelligent contract.
And the enterprise data service intelligent contract dispatches a data request event, provides an off-link data receiving service interface to receive data, performs hash calculation on the received data and compares the received data with an on-link hash, and calls back the service intelligent contract after the verification is passed. The invention standardizes enterprise data by using the XBRL, solves the problems of enterprise application, a prediction machine and an intelligent contract data communication standard on a chain by using the XBRL standard, and realizes the universal enterprise data uplink prediction machine based on the XBRL standardization.
The method provided by the invention is specifically described as follows as shown in figures 1 and 2:
1. an enterprise develops a business application system through an application development platform, and defines metadata (data self-description information) of a data structure required by the business system according to requirements and design, a main data defining model, a business application defining model, a business input form and a function defining menu; and according to the enterprise XBRL classification standard, defining the corresponding relation between the data structure column and the XBRL classification standard element in the data object metadata, and defining the corresponding relation between the main data standard code and the XBRL classification standard code. In the process of business application development, the labeling of the enterprise data XBRL is completed, and the labeling of the XBRL can be realized by the data generated by the business system after production and application.
2. The service plug-in for calling the prediction machine is integrated in the data storage and modification service processes of metadata, main data, transaction data, process data and semi-structural/unstructured data, puts the changed data into a message queue and records logs locally in an application.
3. The method comprises the steps that a pre-talker service consumes data from a message queue, an enterprise data uplink intelligent contract is called, a data hash is calculated in the enterprise data uplink intelligent contract, the data hash of the enterprise data is recorded on a block chain through multi-party recognition, the enterprise data record and the link transaction information are recorded in a MongoDB or an IPFS for storage after the pre-talker service monitors a transaction success event, the data stored outside the link is prevented from being tampered through a transaction ID and the link enterprise data hash, and data origination is achieved through recording different versions of the data. Meanwhile, the transaction information on the chain is put into a message queue to inform the enterprise of the transaction state on the application chain.
4. The enterprise application exchanges information from the message queue consumption chain and records the information to the uplink log, so that the recording on the enterprise data chain is realized only under the conditions of data hash change and transaction failure.
5. Other business intelligence contracts on the chain request the needed enterprise data by invoking the enterprise data service intelligence contract. The data request parameters comprise calling party intelligent contract addresses and data requests described by XBRL-json, the enterprise data service intelligent contracts send data request events after receiving the requests, and the prediction machine service matches corresponding metadata and business models according to the XBRL-json description after monitoring the events, queries and returns corresponding business data.
6. The prediction machine inquires corresponding enterprise data, calls an enterprise data service intelligent contract, the enterprise data service intelligent contract carries out hash calculation on the service data, the hash calculation is compared with the data hash on the chain, the service data is transmitted through the background callback service intelligent contract by verification, and the follow-up service logic processing is completed according to the data.
The technical scheme of the invention provides an enterprise data uplink prediction machine implementation system, which comprises an enterprise data source, an on-chain environment unit and an off-chain environment unit;
the system comprises a data standardization processing module and a calling module;
the on-chain environment unit is provided with an on-chain prediction machine intelligent contract; the downlink environment unit is provided with a language predicting machine service module;
the data standardization processing module is used for acquiring enterprise data from an enterprise data source and realizing enterprise data standardization by combining metadata and an XBRL standard;
the method comprises the steps of carrying out abstract modeling on a data structure of an enterprise information system, establishing a database table structure corresponding to data object metadata, carrying out abstract modeling on main data of the enterprise information system, establishing dictionary model metadata describing the main data, carrying out abstract modeling on enterprise business receipts, and establishing business model metadata describing business receipt data. The metadata description of the enterprise data is a hierarchical structure, the business model metadata depends on the data object metadata and the dictionary model metadata, and the dictionary model metadata depends on the data object metadata and other dictionary model metadata referenced by the dictionary model. And by means of the hierarchical metadata model, standardization of enterprise data is further realized by combining with XBRL classification standards. Firstly, mapping data object metadata including column information, main foreign key information, index information, other extended attribute information and XBRL classification standard element information of a data structure;
and then establishing a corresponding relation with main data standard codes defined by the XBRL classification standard and the enterprise owner data to realize XBRL standardization of the enterprise owner data. The XBRL standardization of enterprise business data is further realized, the business model metadata describes a model of an enterprise business form, the content of the business model metadata defines which related data object metadata the business document is composed of, and describes the relationship between the forms. Through the metadata relationship of the business model organization, we can form the mapping of the business model and the XBRL standard. The business data of the enterprise is input from a general source business form or is integrated with a system interface, the business data storage depends on the definition of the business model metadata, and the business data is stored and loaded through the business model. Based on the corresponding relation between the business model metadata and the XBRL standard, the XBRL standardization of the business data can be realized.
The calling module is used for calling a pre-talker service plug-in integrated in the enterprise application development platform to store the changed data in the enterprise data storage and modification process into a message queue;
in order to ensure the safety and reliability of the enterprise data chaining process, the enterprise metadata, the main data, the transaction data, the process data and the semi-structural/unstructured data are chained in real time when being stored and modified by integrating a predictive engine service plug-in an enterprise application development platform. The predictive speech machine service plug-in set is responsible for being connected to the predictive speech machine service node, and data uplink is achieved through calling an intelligent contract on the chain by the predictive speech machine service node.
And the prediction machine service module is used for consuming data from the message queue and calling an intelligent contract of the prediction machine on the chain to realize the data uplink of the enterprise.
Furthermore, the prediction machine service module is used for consuming data from the message queue and calling an intelligent contract for chaining the enterprise data to calculate a data hash value of the enterprise data; recording the calculated hash value to a block chain; monitoring the transaction result on the chain; when a transaction success event is monitored, storing the enterprise data record and the chain transaction information record; meanwhile, the transaction information on the chain is put into a message queue to inform the enterprise of the transaction state on the application chain; the method is also used for monitoring the data request event of the intelligent contract of the enterprise data service, analyzing the XBRL-json data request, matching metadata and a business model, loading the enterprise data according to the model metadata and calling the intelligent contract of the enterprise data service to write in the data.
The intelligent contracts of the on-chain prophetic machine are divided into two types of intelligent contracts, wherein one type of intelligent contracts provides enterprise data records to an on-chain book record (after all parties vote for the data to reach a consensus), and the intelligent contracts are called enterprise uplink intelligent contracts; one type provides on-chain services to provide data services for other intelligent contracts with data requirements, which are called enterprise data service intelligent contracts. And the enterprise chaining intelligent contract can carry out hash calculation on enterprise data and realizes storage on an enterprise data chain through endorsement of a plurality of nodes. The intelligent contract of enterprise data service provides data service for the intelligent contract processed by other services of the user, the extralink service node of the forecast machine monitors the event by distributing the data request event, and calls the data acquisition service to return the data to the intelligent contract of data service, and the intelligent contract of enterprise data service returns the intelligent contract of service to transmit response data.
An embodiment of the present invention provides an electronic device, which may include: the system comprises a processor (processor), a communication Interface (communication Interface), a memory (memory) and a bus, wherein the processor, the communication Interface and the memory are communicated with each other through the bus. The bus may be used for information transfer between the electronic device and the sensor. The processor may call logic instructions in memory to perform the following method: step 1: the standardization of enterprise data is realized by combining metadata and XBRL standard; step 2: calling a pre-talker service plug-in integrated in an enterprise application development platform to store data changed in the enterprise data storage and modification process into a message queue; and step 3: the predictive engine service consumes data from the message queue and invokes an on-chain predictive engine intelligent contract to implement enterprise data chaining.
In addition, the logic instructions in the memory may be implemented in the form of software functional units and may be stored in a computer readable storage medium when sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Embodiments of the present invention also provide a computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions that, when executed by a computer, cause the computer to perform the method of the above method embodiments, for example, comprising: step 1: the standardization of enterprise data is realized by combining metadata and XBRL standard; step 2: calling a pre-talker service plug-in integrated in an enterprise application development platform to store data changed in the enterprise data storage and modification process into a message queue; and step 3: the predictive engine service consumes data from the message queue and invokes an on-chain predictive engine intelligent contract to implement enterprise data chaining.
Although the present invention has been described in detail by referring to the drawings in connection with the preferred embodiments, the present invention is not limited thereto. Various equivalent modifications or substitutions can be made on the embodiments of the present invention by those skilled in the art without departing from the spirit and scope of the present invention, and these modifications or substitutions are within the scope of the present invention/any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. An enterprise data uplink prediction machine implementation method is characterized by comprising the following steps:
the standardization of enterprise data is realized by combining metadata and XBRL standard;
calling a pre-talker service plug-in integrated in an enterprise application development platform to store data changed in the enterprise data storage and modification process into a message queue;
the predictive engine service consumes data from the message queue and invokes an on-chain predictive engine intelligent contract to implement enterprise data chaining.
2. The method of claim 1, wherein the enterprise data includes metadata, master data, transaction data, process data, semi-structured/unstructured data of the enterprise; the step of realizing enterprise data standardization by combining metadata and XBRL standard comprises the following steps:
the standardization of the enterprise metadata is realized by combining the mapping relation between the metadata and the XBRL classification standard element information according to a metadata model;
establishing a corresponding relation between main data standard codes defined by XBRL classification standards and enterprise owner data to realize XBRL standardization of the enterprise owner data;
and realizing XBRL standardization of the transaction data based on the corresponding relation between the business model metadata and the XBRL standard.
3. The method of claim 2, wherein the intelligent contract for an enterprise data uplink predictive machine comprises an intelligent contract for an enterprise data uplink;
the method for realizing enterprise data uplink by calling the intelligent contract of the prediction machine on the chain comprises the following steps:
the prediction machine service consumes data from the message queue and calls an intelligent contract for uplink of enterprise data to calculate a data hash value of the enterprise data; recording the calculated hash value to a block chain;
the prediction machine service monitors the transaction result on the chain;
when a transaction success event is monitored, storing the enterprise data record and the chain transaction information record; meanwhile, the transaction information on the chain is put into a message queue to inform the enterprise of the transaction state on the application chain;
the enterprise application exchanges information from the message queue consumption chain and records the information to the uplink log.
4. The method of claim 3, wherein the on-chain predictive engine smart contract further comprises an enterprise data services smart contract;
the method further comprises the following steps:
the business intelligent contract on the chain sends a data request for requesting the needed enterprise data by calling the enterprise data service intelligent contract;
after receiving a data request, the enterprise data service intelligent contract dispatches a data request event;
after monitoring the event, the preloader service matches the corresponding metadata and business model according to the XBRL-json description, and queries and returns the corresponding enterprise data.
5. The method of claim 4, wherein the sending of the data request for requesting the desired enterprise data includes parameters of the data request including a caller intelligent contract address and a data request described by XBRL-json.
6. The method of claim 4, wherein after the predictive agent service monitors events, the step of matching corresponding metadata and business models according to XBRL-json descriptions and querying and returning corresponding enterprise data further comprises:
the language predicting machine service inquires corresponding enterprise data and calls an intelligent contract of the enterprise data service;
the enterprise data service intelligent contract carries out hash calculation on the inquired corresponding enterprise data and carries out comparison and verification on the hash value of the data calculated by the enterprise data chaining intelligent contract;
and if the comparison result is consistent, calling back the enterprise data responded by the service intelligent contract.
7. An enterprise data uplink prediction machine implementation system is characterized by comprising an enterprise data source, an on-chain environment unit and an off-chain environment unit;
the system comprises a data standardization processing module and a calling module;
the on-chain environment unit is provided with an on-chain prediction machine intelligent contract; the downlink environment unit is provided with a language predicting machine service module;
the data standardization processing module is used for acquiring enterprise data from an enterprise data source and realizing enterprise data standardization by combining metadata and an XBRL standard;
the calling module is used for calling a pre-talker service plug-in integrated in the enterprise application development platform to store the changed data in the enterprise data storage and modification process into a message queue;
and the prediction machine service module is used for consuming data from the message queue and calling an intelligent contract of the prediction machine on the chain to realize the data uplink of the enterprise.
8. The system of claim 7, wherein the predictive engine services module is configured to consume data from the message queue, invoke an enterprise data uplink smart contract to calculate a data hash value of the enterprise data; recording the calculated hash value to a block chain; monitoring the transaction result on the chain; when a transaction success event is monitored, storing the enterprise data record and the chain transaction information record; meanwhile, the transaction information on the chain is put into a message queue to inform the enterprise of the transaction state on the application chain; the method is also used for monitoring the data request event of the intelligent contract of the enterprise data service, analyzing the XBRL-json data request, matching metadata and a business model, loading the enterprise data according to the model metadata and calling the intelligent contract of the enterprise data service to write in the data.
9. An electronic device is characterized by comprising a memory and a processor, wherein the memory and the processor are communicated with each other through a bus; the memory stores program instructions executable by the processor, the program instructions being invoked by the processor to perform the enterprise data uplink prediction machine implementation method of any of claims 1 to 6.
10. A computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions that, when executed by a computer, cause the computer to perform the enterprise data uplink prediction machine-implemented method of any of claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010961426.9A CN112100277A (en) | 2020-09-14 | 2020-09-14 | Method, system, equipment and product for realizing enterprise data chaining prediction machine |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010961426.9A CN112100277A (en) | 2020-09-14 | 2020-09-14 | Method, system, equipment and product for realizing enterprise data chaining prediction machine |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112100277A true CN112100277A (en) | 2020-12-18 |
Family
ID=73751527
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010961426.9A Withdrawn CN112100277A (en) | 2020-09-14 | 2020-09-14 | Method, system, equipment and product for realizing enterprise data chaining prediction machine |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112100277A (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113010561A (en) * | 2021-03-31 | 2021-06-22 | 工银科技有限公司 | Data acquisition method and device based on super account book and computer system |
| CN113052721A (en) * | 2021-03-18 | 2021-06-29 | 国网北京市电力公司 | Electric power data processing method and device |
| CN113064902A (en) * | 2021-04-06 | 2021-07-02 | 北京瑞卓喜投科技发展有限公司 | Method and device for retrieving transaction data on chain and electronic equipment |
| CN113114744A (en) * | 2021-03-30 | 2021-07-13 | 清华大学 | Block chain system supporting cross-chain transaction under data lake architecture |
| CN113205416A (en) * | 2021-04-19 | 2021-08-03 | 杭州溪塔科技有限公司 | Service processing method and system based on block chain prediction machine |
| CN113762900A (en) * | 2021-11-08 | 2021-12-07 | 江苏荣泽信息科技股份有限公司 | Supply chain management system and method based on block chain prediction machine |
| CN114328132A (en) * | 2022-03-15 | 2022-04-12 | 北京百度网讯科技有限公司 | Method, device, equipment and medium for monitoring state of external data source |
| CN115577386A (en) * | 2022-10-09 | 2023-01-06 | 湖北普联东文信息科技有限公司 | Enterprise credit increase service method and system based on XBRL and block chain |
| CN116029729A (en) * | 2023-03-22 | 2023-04-28 | 安徽省大数据中心 | Cross-link method and system based on dynamic access application link management contract mode |
-
2020
- 2020-09-14 CN CN202010961426.9A patent/CN112100277A/en not_active Withdrawn
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113052721A (en) * | 2021-03-18 | 2021-06-29 | 国网北京市电力公司 | Electric power data processing method and device |
| CN113052721B (en) * | 2021-03-18 | 2024-04-30 | 国网北京市电力公司 | Power data processing method and device |
| CN113114744A (en) * | 2021-03-30 | 2021-07-13 | 清华大学 | Block chain system supporting cross-chain transaction under data lake architecture |
| CN113010561A (en) * | 2021-03-31 | 2021-06-22 | 工银科技有限公司 | Data acquisition method and device based on super account book and computer system |
| CN113064902A (en) * | 2021-04-06 | 2021-07-02 | 北京瑞卓喜投科技发展有限公司 | Method and device for retrieving transaction data on chain and electronic equipment |
| CN113205416A (en) * | 2021-04-19 | 2021-08-03 | 杭州溪塔科技有限公司 | Service processing method and system based on block chain prediction machine |
| CN113762900A (en) * | 2021-11-08 | 2021-12-07 | 江苏荣泽信息科技股份有限公司 | Supply chain management system and method based on block chain prediction machine |
| CN114328132A (en) * | 2022-03-15 | 2022-04-12 | 北京百度网讯科技有限公司 | Method, device, equipment and medium for monitoring state of external data source |
| CN115577386A (en) * | 2022-10-09 | 2023-01-06 | 湖北普联东文信息科技有限公司 | Enterprise credit increase service method and system based on XBRL and block chain |
| CN116029729A (en) * | 2023-03-22 | 2023-04-28 | 安徽省大数据中心 | Cross-link method and system based on dynamic access application link management contract mode |
| CN116029729B (en) * | 2023-03-22 | 2023-06-06 | 安徽省大数据中心 | Cross-link method and system based on dynamic access application link management contract mode |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112100277A (en) | Method, system, equipment and product for realizing enterprise data chaining prediction machine | |
| US10878379B2 (en) | Processing events generated by internet of things (IoT) | |
| US20130332240A1 (en) | System for integrating event-driven information in the oil and gas fields | |
| CN107656818A (en) | The flow implementation method and related system of a kind of bill business | |
| KR20040024865A (en) | System and method for identifying and generating business events | |
| CA2533167A1 (en) | Information access using ontologies | |
| CN111247550B (en) | Computerized transaction management module for blockchain networks | |
| US20210191957A1 (en) | Data integration and curation | |
| KR20040024864A (en) | System and method for generating and propagating business events | |
| KR20200124267A (en) | Semantic actions and inference support through distributed semantic data | |
| CN111160658B (en) | Collaborative manufacturing resource optimization method, system and platform | |
| CN101873334A (en) | State-driven executable service flow execution method | |
| CN102880683A (en) | Automatic network generation system for feasibility study report and generation method thereof | |
| US20230096523A1 (en) | Rule evaluation for real-time data stream | |
| CN113297433A (en) | Method and system for accessing graph database | |
| CN113326148A (en) | Data interaction system based on micro-service | |
| CN101315632A (en) | Intelligent portal system based on context perception | |
| Krinkin et al. | Architecture of a telecommunications network monitoring system based on a knowledge graph | |
| US20020116354A1 (en) | Method and system for transforming session data | |
| CN111782481B (en) | Universal data interface monitoring system and monitoring method | |
| US20210406931A1 (en) | Contextual marketing system based on predictive modeling of users of a system and/or service | |
| Murth et al. | Knowledge-based interaction patterns for semantic spaces | |
| US20230229461A1 (en) | Correlation engine and policy manager (cpe), method and computer program product | |
| CN114969722A (en) | Government affair data privacy calculation system supporting multiple data types | |
| Lv | Applications of the small and medium enterprise management system using the clustering algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20201218 |
|
| WW01 | Invention patent application withdrawn after publication |