CN111145019A

CN111145019A - Method and system for acquiring data outside block chain

Info

Publication number: CN111145019A
Application number: CN201811306441.9A
Authority: CN
Inventors: 梁朝东; 陈复星
Original assignee: Beijing Caiqiu Century Technology Co ltd
Current assignee: Beijing Caiqiu Century Technology Co ltd
Priority date: 2018-11-05
Filing date: 2018-11-05
Publication date: 2020-05-12

Abstract

The block chain is a distributed decentralized public account book and has the characteristics of being irreversible and not being tampered. It adopts intelligent contract and consensus mechanism to store protocol and data in nodes. The method establishes a credible, safe, neutral and autonomous decentralized platform through algorithm self-constraint. However, the blockchain protocol has its own limitations, so that the applications or intelligent contracts built on the blockchain protocol cannot simply and efficiently interact with external content. The invention provides reliable and effective guarantee for triggering and executing the intelligent contract by introducing a predictive machine mechanism into the block chain and carrying out multi-tenant feeding management and scheduling on the predictive machine.

Description

Method and system for acquiring data outside block chain

Technical Field

The invention relates to the technical field of internet, in particular to a realization mode of a block chain for acquiring data outside a chain.

Background

With the development of blockchain technology, blockchains are becoming a decentralized trust broker. The characteristics of transparent and non-falsifiable block chain information make the block chain increasingly become an operation platform for decentralized markets. The intelligent contract is implemented by making the blockchain have the computation capability of programmable dynamic decision results. However, the blockchain protocol has its own limitations, so that the applications or intelligent contracts built on the blockchain protocol cannot simply and efficiently interact with external content. The invention provides reliable and effective guarantee for triggering and executing the intelligent contract by introducing a predictive machine mechanism into the block chain and carrying out multi-tenant feeding management and scheduling on the predictive machine.

Disclosure of Invention

The embodiment of the invention provides a mode for acquiring the data outside the chain on the block chain and the block chain link points, thereby greatly improving the speed, accuracy and reliability of acquiring the data outside the chain on the block chain.

One aspect of the embodiments of the present invention is to provide a method for acquiring data outside a block chain, including:

intelligent contracts in the block chain nodes are divided into user intelligent contracts and prediction machine intelligent contracts;

providing accurate data for a speaker intelligent contract using an independent speaker engine;

the preloader engine performs trust management on the data provider;

the predictive engine tenders the data request to a plurality of data providers;

submitting the data result to a data request party when the data result reaches a sufficient trust value;

the data of each data point is provided by one or more data providers;

the trust value of the data provider is calculated according to the accuracy of data provided by the data provider in history;

and the intelligent contract of the prediction machine transmits the data result to the intelligent contract party of the user in a service callback mode.

It is another aspect of an embodiment of the present invention to provide a block link point, including:

the preloader engine performs trust management on the data provider;

the data of each data point is provided by one or more data providers;

Compared with the prior art, the invention has the beneficial effects that:

according to the block chain preplan method and the block chain system provided by the embodiment of the invention, the data request is bid for a plurality of data providers, and the data providers are subjected to trust evaluation and management, so that the data outside the chain can be more accurately and effectively input to the chain, and the intelligent contract is driven to be more accurately and effectively executed.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.

Fig. 1 is a block chain prediction machine information processing method structure diagram provided by the embodiment of the invention.

Fig. 2 is a flowchart of a method for processing information of a blockchain predictor according to an embodiment of the present invention.

Fig. 3 is a schematic diagram of a block link point provided by an embodiment of the present invention.

Detailed Description

In order to make the objects, contents and advantages of the present invention clearer, the following detailed description of the embodiments of the present invention will be made in conjunction with the accompanying drawings and examples. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.

First, the key features for explaining the technical scheme of the invention are as follows:

the block chain system acquires data outside the chain, and is divided into several parts as shown in fig. 1: distributed application 101, user intelligent contract 102, language predictive machine intelligent contract 103, language predictive machine engine module 104, data providing module 105. The distributed application 101 and the user intelligent contract 102 are user applications; the intelligent contract 103 of the language predictive machine and the engine module 104 of the language predictive machine serve as an intermediary. The data providing module 105 corresponds to a third party interface.

When a user contract 102 initiates a request for off-link Data, it first calls the prediction machine intelligent contract 103 Query _ Data interface to send a request for Data. The data request includes a description of the data to be retrieved outside the chain.

The predictive engine module 104 is a service module that listens on a blockchain network. When the predictive engine module 104 listens for a request to write data on the chain by the predictive intelligence contract 103, it calls Get _ Authorized _ DataSources to Get the data provider that can provide this data and broadcasts this request to the multiple data provider modules 105.

After receiving the Data request, the Data providing module 105 acquires the Data outside the link, and calls the Feed _ Data interface of the predictive engine module 104 to submit the Data to the predictive engine module 104. The Feed _ Data interface will call the internal Check _ Authorization function to Check the Authorization of the Data provider.

The results of the data submission by data supply module 105 are not immediately sent to the predictive engine intelligence contract 103, which simply causes the weighting of the data results in the predictive engine module 104 to increase. When the weight of the Data result increases beyond the threshold, that is, enough trust is achieved, the Data result is submitted to the prolog smart contract 103 by the prolog engine module 104 calling the Feed _ Final _ Data interface of the prolog smart contract 103.

The Feed _ Final _ Data interface of the intelligent contract 103 of the predicting machine triggers the intelligent contract 103 of the predicting machine to call a callback interface in the intelligent contract 102 of the user, and the calling or clearing of the intelligent contract 102 of the user based on the result of the requested Data is completed. To this end, an asynchronous, out-of-chain data request is completed in closed loop.

In the Feed _ Data call shown in fig. 1110, the degree of increase in the weight of the Data result depends on the degree of trust recorded in the prolog engine module 104 for the Data providing module 105. Each time the data providing module 105 provides the correct data, a trust reward is given in the prophetic engine module 104. Conversely, an incorrect data supply would reduce trust to the data provider to 0.

Fig. 2 is a schematic depiction of the above process.

Fig. 3 depicts the internal structural approach and system of a blockchain node. The block chain node consists of a transaction ledger 301, an intelligent contract virtual machine 302 and a chain state library 303. In this structure and system, the block link node receives the block pushed by the block node through the network, stores the block in the transaction book 301, and simultaneously invokes the intelligent contract virtual machine 302 to sequentially execute transactions in the block, and stores the execution result in the chain state library 303.

In the embodiment of the invention, the method greatly improves the accuracy of the data, prevents instability caused by single-point data, prevents error results caused by inaccurate data provided by certain data providing modules, and greatly improves the accuracy and reliability of the whole block chain network system for acquiring data outside the chain.

It is obvious to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional modules is merely used as an example, and in practical applications, the above functions may be distributed by different functional modules according to needs, that is, the internal structure of the system is divided into different functional modules to complete all or part of the above described functions. For the specific working process of the system described above, reference may be made to the corresponding process in the foregoing method embodiment, which is not described herein again.

Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. The method for acquiring the data outside the block chain is characterized by comprising the step of jointly managing and coordinating the acquisition of the data outside the block chain by a user intelligent contract, a language predictive machine engine and a plurality of data providing modules.

2. The method of claim 1, wherein the predictive engine tenders received data requests outside of the chain to a plurality of data providers.

3. The method of claim 1 or 2, wherein the predictive engine uses a dynamic credit value for each data provider to measure its trustworthiness in order to ensure the accuracy of the data.

4. The method of claim 3, wherein the reliability of the predictive engine on the data provider is computationally generated from the accuracy with which its history provides data.

5. A method according to claim 1 or 2, wherein the predictive engine weights and defers the data provided by the plurality of data providers according to their trust level, and the out-of-chain result data is sent to the user smart contract via the predictive intelligence contract if and only if the total trust value of the data results meets the trust threshold required by the data request.

6. A block chain link point is characterized in that block chain acquisition of data outside a chain is managed and coordinated by a user intelligent contract, a prediction machine engine and a plurality of data providing modules.

7. The method of claim 6, wherein the predictive engine tenders received data requests outside of the chain to a plurality of data providers.

8. The method of claim 6 or 7, wherein the predictive engine uses a dynamic credit value for each data provider to measure its trustworthiness in order to ensure the accuracy of the data.

9. The method of claim 8, wherein the reliability of the predictive engine on the data provider is computationally generated from the accuracy with which its history provides data.

10. A method according to claim 6 or 7, wherein the prediction engine weights and defers the data provided by the plurality of data providers according to their trust level, and the extra-chain result data is sent to the user smart contract via the prediction engine smart contract if and only if the total trust value of the data results reaches the trust threshold required by the data request.