CN113282957A - Data asset racking processing method and device - Google Patents
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Abstract
The invention provides a racking processing method and device for data assets, wherein the method comprises the following steps: determining a dependency relationship of the data assets; the asset information of the data asset and the dependency relationship are uploaded to a block chain in a log mode through an intelligent contract, the problems that sharing protection and authorization source tracing difficulty of data are great due to data reproducibility in related technologies can be solved, the data asset is put on shelf through introducing a block chain technology, the data can be safely shared through the block chain, the dependency relationship of the data asset is stored while the asset information of the data asset is stored through the block chain, and technical support is provided for data source tracing.
Description
Technical Field
The invention relates to the field of data processing, in particular to a racking processing method and device for data assets.
Background
The data is used as the national basic strategic resources and key production elements and is the basic resources and innovation engine for the development of the economic society. The realization of the optimal configuration of the data assets is a key factor for promoting the industry upgrading. However, the problems of unbalanced distribution, asymmetric information and the like generally exist in the current data, so that the value of the data cannot be fully exerted. The data sharing mechanism breaks through data barriers and monopolies and becomes the trend and direction of the future financial industry development.
Federal data, an emerging data sharing mode, aims to ensure the absolute security of data and support the training of a model under the condition of no damage. The federated learning is used as a distributed machine learning paradigm, modeling can be carried out on data of a plurality of data owners under the condition that the data cannot be out of a domain, and under a federated mechanism, data of all participants are not transferred by using a privacy security computing technology, so that user privacy is not leaked or data specifications are not influenced.
However, due to the reproducibility of data, the difficulty of data sharing protection and right confirmation tracing is very high, which causes problems of uneven benefit distribution, unclear responsibility and the like, and meanwhile, problems of unclear data right boundary, unclear right and benefit distribution rule, potential safety hazard in data sharing and the like are also introduced in data sharing circulation, and these factors hinder the development of data sharing services.
Aiming at the problem that the sharing protection and the authority confirmation tracing difficulty of data are great due to the reproducibility of the data in the related technology, no solution is provided.
Disclosure of Invention
The embodiment of the invention provides a shelf loading processing method and device for data assets, which at least solve the problem that the sharing protection and the right confirmation source tracing difficulty of data are extremely high due to the reproducibility of the data in the related technology.
According to an embodiment of the invention, there is provided a racking processing method of data assets, including:
determining a dependency relationship of the data assets;
and uploading the asset information of the data asset and the dependency relationship to a block chain in a log mode through an intelligent contract.
Optionally, determining the dependency relationship of the data asset comprises:
determining a dependency relationship of a data asset through a directed acyclic graph, wherein each participant node in the directed acyclic graph carries an identity ID of a data asset provider.
Optionally, determining the dependency relationship of the data asset through the directed acyclic graph comprises:
determining, by a ParentOf and a ChildOf, a dependency of the data asset based on the directed acyclic graph, wherein the data asset includes operations, data, and a model, the dependency including at least one of: the data are operated to obtain new data, one or more data are operated to obtain a model, the data and the model are operated to obtain a new model, the one or more models are operated to obtain a new model, and the plurality of operations form new operations.
Optionally, uploading, by the smart contract, the asset information of the data asset and the dependency relationship into the blockchain in the form of a log comprises:
and writing log information into the block chain in a form of executing the event by the intelligent contract, wherein the log information carries the asset information of the data asset and the dependency relationship.
Optionally, writing log information into the block chain in the form of executing the event by the smart contract comprises:
triggering a corresponding Register event by executing the function of interaction between the client defined by the intelligent contract and the block chain;
storing the log information onto the block chain.
Optionally, storing the log information onto the block chain comprises:
storing metadata information for the data assets in the blockchain;
storing, by IPFS, an IPFS address of the data asset into the blockchain;
storing the dependency into the blockchain through ParentOf and ChildOf.
Optionally, the method further comprises:
registering the data asset through an addAsset function;
generating an asset ID of the asset data by hashing the data asset, wherein the asset information comprises metadata, a data description, and the asset ID.
According to still another embodiment of the present invention, there is also provided an racking processing apparatus of a data asset, including:
a determining module for determining a dependency relationship of the data assets;
and the uploading module is used for uploading the asset information of the data asset and the dependency relationship to the block chain in a log mode through the intelligent contract.
Optionally, the determining module includes:
and the determining submodule is used for determining the dependency relationship of the data assets through a directed acyclic graph, wherein each participant node in the directed acyclic graph carries the identity ID of a data asset provider.
Optionally, the determination submodule is further used for
Determining, by a ParentOf and a ChildOf, a dependency of the data asset based on the directed acyclic graph, wherein the data asset includes operations, data, and a model, the dependency including at least one of: the data are operated to obtain new data, one or more data are operated to obtain a model, the data and the model are operated to obtain a new model, the one or more models are operated to obtain a new model, and the plurality of operations form new operations.
Optionally, the uploading module includes:
and the writing submodule is used for writing the log information into the block chain in a form of executing the event by the intelligent contract, wherein the log information carries the asset information of the data asset and the dependency relationship.
Optionally, the write submodule includes:
the triggering unit is used for triggering a corresponding registration event Register event by executing the interactive function between the client defined by the intelligent contract and the block chain;
and the storage unit is used for storing the log information to the block chain.
Optionally, the memory cell is also used for
Storing metadata information for the data assets in the blockchain;
storing, by IPFS, an IPFS address of the data asset into the blockchain;
storing the dependency into the blockchain through ParentOf and ChildOf.
Optionally, the apparatus further comprises:
the registration module is used for registering the data asset through an addAssset function;
and the generation module is used for generating the asset ID of the asset data by carrying out hash processing on the data asset, wherein the asset information comprises metadata, data description and the asset ID.
According to a further embodiment of the present invention, a computer-readable storage medium is also provided, in which a computer program is stored, wherein the computer program is configured to perform the steps of any of the above-described method embodiments when executed.
According to yet another embodiment of the present invention, there is also provided an electronic device, including a memory in which a computer program is stored and a processor configured to execute the computer program to perform the steps in any of the above method embodiments.
By the method, the dependency relationship of the data assets is determined; the asset information of the data assets and the dependency relationship are uploaded to a block chain in a log mode through an intelligent contract, the problems that sharing protection and authorization source tracing difficulty of data are high due to data reproducibility in related technologies can be solved, the data assets are put on shelf through introducing a block chain technology, the data can be safely shared through the block chain, the dependency relationship of the data assets is stored while the asset information of the data assets is stored through the block chain, and technical support is provided for data source tracing.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a block diagram of a hardware configuration of a mobile terminal of a racking method of a data asset of an embodiment of the present invention;
FIG. 2 is a flow diagram of a method of racking data assets according to an embodiment of the present invention;
FIG. 3 is a first diagram illustrating dependencies of data assets, according to an embodiment of the invention;
FIG. 4 is a second schematic diagram of the dependency of a data asset according to an embodiment of the present invention;
FIG. 5 is a third schematic diagram of the dependency of a data asset according to an embodiment of the present invention;
FIG. 6 is a fourth schematic diagram of the dependency of a data asset according to an embodiment of the present invention;
FIG. 7 is a fifth schematic diagram of the dependency of data assets, according to an embodiment of the invention;
FIG. 8 is a block diagram of an on-shelf processing device for data assets according to an embodiment of the invention.
Detailed Description
The invention will be described in detail hereinafter with reference to the accompanying drawings in conjunction with embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
Example 1
The method provided by the first embodiment of the present application may be executed in a mobile terminal, a computer terminal, or a similar computing device. Taking a mobile terminal as an example, fig. 1 is a hardware structure block diagram of a mobile terminal of the on-shelf processing method of data assets according to an embodiment of the present invention, and as shown in fig. 1, the mobile terminal may include one or more processors 102 (only one is shown in fig. 1) (the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA, etc.), and a memory 104 for storing data, and optionally, the mobile terminal may further include a transmission device 106 for communication function and an input/output device 108. It will be understood by those skilled in the art that the structure shown in fig. 1 is only an illustration, and does not limit the structure of the mobile terminal. For example, the mobile terminal may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.
The memory 104 may be used for storing computer programs, for example, software programs and modules of application software, such as computer programs corresponding to the method for processing data assets on shelves in the embodiment of the present invention, and the processor 102 executes various functional applications and data processing by running the computer programs stored in the memory 104, so as to implement the method. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to the mobile terminal over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 106 is used for receiving or transmitting data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a Network adapter (NIC), which can be connected to other Network devices through a base station so as to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.
In this embodiment, a method for processing a data asset on shelf, which is operated in the mobile terminal or the network architecture, is provided, and fig. 2 is a flowchart of a method for processing a data asset on shelf according to an embodiment of the present invention, as shown in fig. 2, the flowchart includes the following steps:
step S202, determining the dependency relationship of the data assets;
in an embodiment of the present invention, the step S202 may specifically include: determining a dependency relationship of a data asset through a directed acyclic graph, wherein each participant node in the directed acyclic graph carries an identity ID of a data asset provider. Further, determining a dependency relationship of the data asset through a ParentOf and a ChildOf based on the directed acyclic graph, wherein the data asset comprises an operation, data, and a model, and the dependency relationship comprises at least one of: the data are operated to obtain new data, one or more data are operated to obtain a model, the data and the model are operated to obtain a new model, the one or more models are operated to obtain a new model, and the plurality of operations form new operations.
And step S204, uploading the asset information of the data asset and the dependency relationship to a block chain in a log mode through an intelligent contract.
In an embodiment of the present invention, the step S204 may specifically include: and writing log information into the block chain in a form of executing the event by the intelligent contract, wherein the log information carries the asset information of the data asset and the dependency relationship. Further, triggering a corresponding Register event by executing the function of interaction between the client defined by the intelligent contract and the block chain; storing the log information into the blockchain, and specifically, storing the metadata information of the data asset into the blockchain; storing, by IPFS, an IPFS address of the data asset into the blockchain; storing the dependency into the blockchain through ParentOf and ChildOf.
Determining the dependency relationship of the data assets through the steps S202 to S204; the asset information of the data assets and the dependency relationship are uploaded to a block chain in a log mode through an intelligent contract, the problems that sharing protection and authorization source tracing difficulty of data are high due to data reproducibility in related technologies can be solved, the data assets are put on shelf through introducing a block chain technology, the data can be safely shared through the block chain, the dependency relationship of the data assets is stored while the asset information of the data assets is stored through the block chain, and technical support is provided for data source tracing.
In an alternative embodiment, the data asset is registered via an addAsset function; generating an asset ID of the asset data by hashing the data asset, wherein the asset information comprises metadata, a data description, and the asset ID.
The embodiment provides a traceability model, which can support full life cycle tracking of a development process, including data transformation, data and data interaction, data and model interaction, model transformation, and the like. Meanwhile, a Directed Acyclic Graph (DAG) is introduced to assist a tracing model in tracking and process exhibition.
The traceability model merges artificial intelligence assets into three categories: operation, dataset, model, then two relationships are defined: ParentOf and ChildOf to express dependencies between assets. The construction of the DAG graph is realized by taking the artificial intelligence assets as nodes and taking the relationships as edges. Operation refers to any executable algorithm, including data and model conversion, machine learning, deep learning, and the like. FIG. 3 is a first diagram illustrating the dependency relationship of data assets, and as shown in FIG. 3, Dataset refers to any digital information, and data can be formed into new data through a series of operations. FIG. 4 is a second schematic diagram of the dependency of data assets, according to an embodiment of the invention, as shown in FIG. 4, a model is trained from one or more data; fig. 5 is a third schematic diagram of a dependency relationship of a data asset according to an embodiment of the present invention, as shown in fig. 5, a new model may also be obtained based on an existing model and data, fig. 6 is a fourth schematic diagram of a dependency relationship of a data asset according to an embodiment of the present invention, as shown in fig. 6, a new model may also be obtained by a combination of different models being operated, and fig. 7 is a fifth schematic diagram of a dependency relationship of a data asset according to an embodiment of the present invention, as shown in fig. 7, a new operation may be formed by different operations, and the like. To facilitate tracking of the people involved in each phase, to transparently enforce limits, each node of a direct Acyclic Directed Graph (DAG) will also derive the provider or operator ID information.
The traceability model is realized by intelligent contract technology of a block chain, information is recorded on the chain in a log mode through the intelligent contract, and the information cannot be tampered once being uploaded. The smart contract writes logs to the blockchain in the form of execution events, and the client application may listen for these events. The smart contracts define and disclose functions that the client will interact with, and by performing these functions to initiate the corresponding events, the logs are stored onto the blockchain. Since a general storage space of the blockchain is limited, an InterPlanetary File System (IPFS) is introduced as a storage module. The callable functions provided by the smart contract are described as follows:
addesset, registering a new artificial intelligence asset, the caller of the function will be automatically designated as maintainer;
a transferAsset, the asset maintainer transfers ownership of an asset from a user on the blockchain to another user;
the addIPFS uploads the assets to the IPFS, and provides a corresponding IPFS address;
requestAccess, applying for asset access rights, all users having rights to do so;
grantAccess, where the asset maintainer grants access rights to the applicant;
getAsset, acquiring a certain type of assets, and all users have the authority;
uploading assets of a certain type, and all users have the authority;
getMaintainer, a maintainer who obtains an asset, all users have the right to do this.
Registering a new artificial intelligence asset through an addAssset function, and performing hash processing on the asset to generate a corresponding assetIdentifier; a user can acquire detailed information of assets through meta-information and retrieve the assets through an IPFS address; in addition, each asset will provide corresponding Parent information to obtain all previous circulation information for the asset at that moment.
Once the method is operated, corresponding events are triggered to chain the log information, so that the tracking function of the tracing model is realized, and the specific events comprise:
event Register(asset_id,metadata)
event IPFS(asset_id,IPFS_addr)
event NewMaintainer(asset_id,previous_maintainer)
event ParentOf(asset_id,parent_id)
event ChildOf(asset_id,childOf)
event GetAsset(asset_id,acquisitor，maintainer)
event UploadAsset(assent_id,uploader)
event RequestAccess(asset_id,accessor)
event GrantAccess(asset_id,accessor)
by executing the Register to store the asset's metadata information, the IPFS will store the asset's IPFS address, and ParentOf and ChildOf will ensure the acyclic nature of the DAG. NewMaintainer is only applicable to the case of replacement asset maintainers. GetAsset obtains assets in an encrypted mode, and RequestAccess and GrantAccess exchange information in an encrypted mode to ensure the safety of the information.
Example 2
There is also provided, in accordance with still another embodiment of the present invention, an on-shelf processing apparatus for a data asset, and fig. 8 is a block diagram of an on-shelf processing apparatus for a data asset according to an embodiment of the present invention, as shown in fig. 8, including:
a determination module 82 for determining dependencies of the data assets;
and an uploading module 84, configured to upload the asset information of the data asset and the dependency relationship into the blockchain in the form of a log through the smart contract.
Optionally, the determining module 82 includes:
and the determining submodule is used for determining the dependency relationship of the data assets through a directed acyclic graph, wherein each participant node in the directed acyclic graph carries the identity ID of a data asset provider.
Optionally, the determination submodule is further used for
Determining, by a ParentOf and a ChildOf, a dependency of the data asset based on the directed acyclic graph, wherein the data asset includes operations, data, and a model, the dependency including at least one of: the data are operated to obtain new data, one or more data are operated to obtain a model, the data and the model are operated to obtain a new model, the one or more models are operated to obtain a new model, and the plurality of operations form new operations.
Optionally, the uploading module 84 includes:
and the writing submodule is used for writing the log information into the block chain in a form of executing the event by the intelligent contract, wherein the log information carries the asset information of the data asset and the dependency relationship.
Optionally, the write submodule includes:
the triggering unit is used for triggering a corresponding registration event Register event by executing the interactive function between the client defined by the intelligent contract and the block chain;
and the storage unit is used for storing the log information to the block chain.
Optionally, the memory cell is also used for
Storing metadata information for the data assets in the blockchain;
storing, by IPFS, an IPFS address of the data asset into the blockchain;
storing the dependency into the blockchain through ParentOf and ChildOf.
Optionally, the apparatus further comprises:
the registration module is used for registering the data asset through an addAssset function;
and the generation module is used for generating the asset ID of the asset data by carrying out hash processing on the data asset, wherein the asset information comprises metadata, data description and the asset ID.
It should be noted that, the above modules may be implemented by software or hardware, and for the latter, the following may be implemented, but not limited to: the modules are all positioned in the same processor; alternatively, the modules are respectively located in different processors in any combination.
Example 3
Embodiments of the present invention also provide a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to perform the steps of any of the above method embodiments when executed.
Alternatively, in the present embodiment, the storage medium may be configured to store a computer program for executing the steps of:
s1, determining the dependency relationship of the data assets;
and S2, uploading the asset information of the data asset and the dependency relationship to a block chain in a log mode through an intelligent contract.
Optionally, in this embodiment, the storage medium may include, but is not limited to: various media capable of storing computer programs, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.
Example 4
Embodiments of the present invention also provide an electronic device comprising a memory having a computer program stored therein and a processor arranged to run the computer program to perform the steps of any of the above method embodiments.
Optionally, the electronic apparatus may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.
Optionally, in this embodiment, the processor may be configured to execute the following steps by a computer program:
s1, determining the dependency relationship of the data assets;
and S2, uploading the asset information of the data asset and the dependency relationship to a block chain in a log mode through an intelligent contract.
Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation manners, and this embodiment is not described herein again.
It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for shelving data assets, comprising:
determining a dependency relationship of the data assets;
and uploading the asset information of the data asset and the dependency relationship to a block chain in a log mode through an intelligent contract.
2. The method of claim 1, wherein determining the dependency of the data asset comprises:
determining a dependency relationship of a data asset through a directed acyclic graph, wherein each participant node in the directed acyclic graph carries an identity ID of a data asset provider.
3. The method of claim 2, wherein determining the dependencies of the data assets via the directed acyclic graph comprises:
determining, by a ParentOf and a ChildOf, a dependency of the data asset based on the directed acyclic graph, wherein the data asset includes operations, data, and a model, the dependency including at least one of: the data are operated to obtain new data, one or more data are operated to obtain a model, the data and the model are operated to obtain a new model, the one or more models are operated to obtain a new model, and the plurality of operations form new operations.
4. The method of claim 1, wherein uploading the asset information of the data asset and the dependency in the form of a log into a blockchain via a smart contract comprises:
and writing log information into the block chain in a form of executing the event by the intelligent contract, wherein the log information carries the asset information of the data asset and the dependency relationship.
5. The method of claim 4, wherein writing log information to the block chain in the form of executing an event by the smart contract comprises:
triggering a corresponding Register event by executing the function of interaction between the client defined by the intelligent contract and the block chain;
storing the log information onto the block chain.
6. The method of claim 5, wherein storing the log information onto the blockchain comprises:
storing metadata information for the data assets in the blockchain;
storing, by IPFS, an IPFS address of the data asset into the blockchain;
storing the dependency into the blockchain through ParentOf and ChildOf.
7. The method according to any one of claims 1 to 6, further comprising:
registering the data asset through an addAsset function;
generating an asset ID of the asset data by hashing the data asset, wherein the asset information comprises metadata, a data description, and the asset ID.
8. An on-shelf processing apparatus for data assets, comprising:
a determining module for determining a dependency relationship of the data assets;
and the uploading module is used for uploading the asset information of the data asset and the dependency relationship to the block chain in a log mode through the intelligent contract.
9. A computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to carry out the method of any one of claims 1 to 7 when executed.
10. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, and wherein the processor is arranged to execute the computer program to perform the method of any of claims 1 to 7.
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