CN114143333A

CN114143333A - Method for processing data of prediction machine and centralized prediction machine module

Info

Publication number: CN114143333A
Application number: CN202111470564.8A
Authority: CN
Inventors: 李鹏飞; 张强; 梁智昊; 王舒榕; 周海京; 杨毅
Original assignee: Hebei Xiong'an New Area Management Committee; Industrial and Commercial Bank of China Ltd ICBC; ICBC Technology Co Ltd
Current assignee: Hebei Xiong'an New Area Management Committee; Industrial and Commercial Bank of China Ltd ICBC; ICBC Technology Co Ltd
Priority date: 2021-12-03
Filing date: 2021-12-03
Publication date: 2022-03-04

Abstract

The embodiment of the application provides a prediction machine data processing method and a centralized prediction machine module, which can be used in the technical field of block chains, and the method comprises the following steps: obtaining the predictive machine data corresponding to the predictive machine event of the block chain and used for requesting the target external data from a target data source outside the block chain in the predictive machine system; sending the data of the prediction machine to each target prediction machine node outside the prediction machine system so that each target prediction machine node acquires target external data from a target data source according to the data of the prediction machine; and based on the aggregation strategy corresponding to the target data source prestored in the local, aggregating the target external data sent by each target predicting machine node, and sending the corresponding aggregation result to the block chain. The method and the device can effectively improve the efficiency and the reliability of the data processing process of the prediction machine, and further can effectively improve the efficiency, the reliability and the authenticity of obtaining third-party data from the outside of the block chain.

Description

Method for processing data of prediction machine and centralized prediction machine module

Technical Field

The application relates to the technical field of data processing, in particular to the technical field of block chains, and specifically relates to a predictive speaker data processing method and a centralized predictive speaker module.

Background

The prediction machine solves the problem of impedance mismatch of the world outside the blockchain, expands the range of the blockchain which can obtain information, can search and verify real world data, and submits the information to an intelligent contract in an encrypted mode, and is like a third-party data agent in the blockchain world.

At present, there are two methods for processing the data of the predictive engine, which are generally used to obtain the third-party data outside the blockchain by using the predictive engine technology, one of which is: the data channel for acquiring information is required to acquire third-party data of a data source outside the block chain through the centralized predictive machine system. The second is as follows: the adoption of decentralized propheter systems distributed among different network nodes is realized, and data acquisition and aggregation are carried out from different nodes, which also means that the propheter nodes of the whole network are required to achieve a uniform aggregation result.

However, the common fault of the centralized predictive engine system is poor reliability, and the feedback nodes are all obtained through single information, so that once the system itself is malignant or receives other malicious attacks, the situation that the data conducted to the block chain by the predictive engine system is unreliable may occur. The design scheme of the decentralized prediction machine is to distribute the prediction machines in different nodes in the network for calling, and although this way can improve the reliability of the whole prediction machine system, since the result sets of the third-party data sources obtained by the respective prediction machines need to be aggregated finally, the aggregation efficiency may be correspondingly lower as the number of the prediction machine nodes is larger. That is to say, the existing data processing modes of the predictive speakers, which acquire third-party data outside the block chain through the predictive speakers technology, all have the problem that the requirements on the data processing reliability and efficiency of the predictive speakers cannot be met at the same time.

Disclosure of Invention

Aiming at the problems in the prior art, the application provides a prediction machine data processing method and a centralized prediction machine module, which can effectively improve the efficiency and reliability of the prediction machine data processing process, and further can effectively improve the efficiency, reliability and authenticity of obtaining third-party data from the outside of a block chain.

In order to solve the technical problem, the application provides the following technical scheme:

in a first aspect, the present application provides a data processing method for a prediction machine, including:

obtaining the predictive player data corresponding to the predictive player event of the block chain and used for requesting target external data from a target data source outside the block chain in a predictive player system;

sending the data of the prediction machine to each target prediction machine node outside the prediction machine system so that each target prediction machine node acquires the target external data from the target data source according to the data of the prediction machine;

and based on the aggregation strategy corresponding to the target data source prestored locally, performing aggregation processing on the target external data sent by each target predicting machine node, and sending a corresponding aggregation result to the block chain.

Further, before the aggregating, based on the aggregation policy corresponding to the target data source prestored locally, the method further includes, before the aggregating the target external data sent by each target talker node, the method further includes:

detecting whether each target prediction machine node sends the target external data or not;

and if all the current target prediction machine nodes send the target external data, or if all the current target prediction machine nodes which do not send the target external data are in a timeout state, acquiring an aggregation strategy corresponding to the target data source locally.

Further, the aggregation policy includes: data adoption rules and aggregation algorithms;

correspondingly, the aggregating, based on the aggregation policy corresponding to the target data source prestored locally, the target external data sent by each target talker node is aggregated, including:

judging whether the target external data sent by each target predictive machine node meets the data acquisition rule or not, if so, selecting a plurality of data to be aggregated from each target external data based on the data acquisition rule;

and carrying out aggregation processing on the data to be aggregated based on the aggregation algorithm.

Further, the determining whether the target external data sent by each target talker node meets the data acceptance rule, and if yes, selecting a plurality of data to be aggregated from each target external data based on the data acceptance rule, includes:

and judging whether the total number of the target external data with the same data in the target external data sent by each target predictive machine node is greater than or equal to the total number threshold of the same data specified by the data adoption rule, if so, selecting each target external data with the same data as the data to be aggregated.

Further, the obtaining, inside the predictive speech machine system, predictive speech machine data corresponding to the predictive speech machine event of the blockchain and used for requesting the target external data from the target data source outside the blockchain includes:

if the fact that the block chain generates the prediction machine event at present is monitored, the prediction machine event is stored into the prediction machine system;

analyzing the predicting machine event to obtain a unique identifier of a target data source outside the block chain;

and judging whether the target data source is locally registered or not according to the unique identifier of the target data source, if so, generating the predictive speaker data corresponding to the predictive speaker event and used for requesting target external data from the target data source outside the block chain.

Further, the generating of the predictive engine data corresponding to the predictive engine event and used for requesting target external data from a target data source outside the blockchain includes:

extracting a target variable used for requesting target external data from the target data source from the predicting machine event;

converting the target variable into a corresponding standard protocol based on a preset standard protocol format, wherein the standard protocol is used for storing the corresponding relation among the field names, the field types and the field descriptions in the target variable;

and compressing the standard protocol to obtain the predictive speech machine data corresponding to the predictive speech machine event.

Further, still include:

receiving a data source registration request, wherein the data source registration request comprises a unique identifier of a data source outside the blockchain;

and registering the data source to the local part of the predictive language machine system according to the unique identifier of the data source, acquiring an aggregation strategy corresponding to the data source, and storing the aggregation strategy of the data source to the local part of the predictive language machine system.

Further, the sending the predictive speaker data to each target predictive speaker node outside the predictive speaker system so that each target predictive speaker node obtains the target external data from the target data source according to the predictive speaker data includes:

searching each speaker node which is registered in the speaker system and is currently in an available state, and determining each speaker node which is registered in the speaker system and is currently in the available state as a current target speaker node;

respectively sending the predictive speaker data to each target predictive speaker node so that each target predictive speaker node respectively obtains the target external data from the target data source according to the predictive speaker data;

wherein the obtaining the target external data from the target data source according to the predictive speaker data respectively comprises:

analyzing the data of the prediction machine to obtain a unique identifier of the target data source and a unique identifier of the target external data, accessing the target data source according to the unique identifier of the target data source, and extracting a result set containing the target external data from the target data source according to the unique identifier of the target external data;

encrypting and signing the result set to obtain corresponding signature information;

and sending the result set and the signature information to the inside of the language predicting machine system.

Further, before the sending the predictive speaker data to each target predictive speaker node outside the predictive speaker system, the method further includes:

receiving a registration request of a node of a predictive speaker, wherein the registration request of the node of the predictive speaker comprises a unique identifier of service equipment;

and according to the unique identification of the service equipment, registering the service equipment in the local of the talker system so as to determine the service equipment as the talker node, and setting the registered talker node at any position where the local network can be communicated.

In a second aspect, the present application provides a centralized predictive engine module comprising:

the data acquisition module is used for acquiring the predictive speaker data corresponding to the predictive speaker event of the block chain and used for requesting the target external data from the target data source outside the block chain in the predictive speaker system;

the data distribution module is used for sending the predictive speaker data to each target predictive speaker node outside the predictive speaker system so that each target predictive speaker node acquires the target external data from the target data source according to the predictive speaker data;

and the data aggregation module is used for performing aggregation processing on the target external data sent by each target talker node based on an aggregation policy corresponding to the target data source prestored locally, and sending a corresponding aggregation result to the block chain.

In a third aspect, the present application provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the data processing method of the prediction machine when executing the program.

In a fourth aspect, the present application provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of data processing of a prediction machine.

According to the technical scheme, the method for processing the data of the prediction machine and the centralized prediction machine module comprise the following steps: obtaining the predictive player data corresponding to the predictive player event of the block chain and used for requesting target external data from a target data source outside the block chain in a predictive player system; sending the data of the prediction machine to each target prediction machine node outside the prediction machine system so that each target prediction machine node acquires the target external data from the target data source according to the data of the prediction machine; based on an aggregation strategy corresponding to the target data source prestored locally, aggregation processing is performed on the target external data sent by each target predictive machine node, a corresponding aggregation result is sent to the block chain, predictive machine data in a predictive machine event is extracted from the interior of a predictive machine system, and the target external data sent by each predictive machine node is aggregated, so that the predictive machine data can be effectively extracted from the block chain in a centralized manner and the external data aggregation processing can be performed, the efficiency of predictive machine data processing processes such as predictive machine data extraction from the block chain and external data aggregation processing can be effectively improved, the efficiency of third party data acquisition from the exterior of the block chain can be effectively improved, and the operation stability and the timeliness of data processing of the block chain can be improved; the data of the prediction machine is sent to each prediction machine node outside the prediction machine system to achieve the acquisition of third-party data, so that the aggregation after the third-party data is acquired from the outside of the block chain in a decentralized manner for multiple times can be effectively achieved, the reliability of the data processing process of the prediction machine can be effectively improved, and the reliability and the authenticity of the third-party data acquired from the outside of the block chain can be effectively improved; meanwhile, the aggregation strategy corresponding to the target data source prestored in the predictive speech machine system is adopted to aggregate the external data, so that the reliability of the data processing process of the predictive speech machine can be further improved, and the reliability and the authenticity of acquiring third-party data from the outside of the block chain can be further improved.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a schematic diagram of a deployment architecture of a prolog machine system and prolog machine nodes in an embodiment of the present application.

Fig. 2 is a first flowchart of a data processing method of a prediction machine in the embodiment of the present application.

Fig. 3 is a second flowchart of a data processing method of the prediction machine in the embodiment of the present application.

Fig. 4 is a third flowchart of a data processing method of the prediction machine in the embodiment of the present application.

Fig. 5 is a fourth flowchart illustrating a data processing method of the prediction machine in the embodiment of the present application.

Fig. 6 is a fifth flowchart illustrating a data processing method of the prediction machine in the embodiment of the present application.

Fig. 7 is a sixth flowchart illustrating a data processing method of the prediction machine in the embodiment of the present application.

Fig. 8 is a seventh flowchart illustrating a data processing method of the prediction machine in the embodiment of the present application.

Fig. 9 is an eighth flowchart illustrating a data processing method of a prediction machine in the embodiment of the present application.

Fig. 10 is a ninth flowchart illustrating a data processing method of the prediction machine in the embodiment of the present application.

Fig. 11 is a schematic structural diagram of a centralized prediction machine module in the embodiment of the present application.

Fig. 12 is a schematic structural diagram of an electronic device in an embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but 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 application.

It should be noted that the method and apparatus for processing data of a prediction machine disclosed in the present application can be used in the field of block chain technology, and can also be used in any field other than the field of block chain technology.

The blockchain technology can be defined as a social revolution, and the appearance of the blockchain technology nearly redefines the cooperative relationship between people and organizations under the existing market conditions. In the existing block chain system, one of the most core innovation points is an intelligent contract. The intelligent contract can transfer the value without trusting a third party, so that the authenticity and the non-tamper property of the information transmitted on the link can be ensured no matter for the public link network with extremely high decentralization degree like ether houses and bitcoin, and the network of the alliance autonomous system under the alliance link under the system like Fabric.

The intelligent contract is only a user-definable program, the program can be defined according to the service details of the user, and the relevant logic of the intelligent contract can be realized after the intelligent contract is installed on the nodes on the block chain. Since the operations through the intelligent contracts are all completed on the chain, and the data on the chain is visible and trusted to all users of the current chain (the federation chain is visible to all nodes with corresponding rights currently joining the federation chain, and the public chain is visible to all), the logic and data performed by the intelligent contracts are also trusted.

The technical scheme of the existing prediction machine comprises the following steps:

1) centralizing the speaker system solution. The centralized predictive speaker system means that the predictive speaker system belongs to a centralized system, namely, all data channels for acquiring information need to acquire data of an external data source through the centralized predictive speaker system.

2) A decentralized prophetic system solution. The decentralized talker systems are distributed among different network nodes, and data acquisition and aggregation need to be performed from different nodes, which also means that the talker nodes in the whole network need to achieve a uniform aggregation result.

However, the common fault of the centralized predictive engine system is poor reliability, and the feedback nodes are all obtained through single information, once the system itself is malignant or receives other malicious attacks, the situation that the data conducted to the block chain by the predictive engine system is unreliable may occur, and the unreliable system design runs counter to the technical essence of the block chain itself.

In the existing design scheme of the decentralized oracle, the oracle is distributed at different nodes in the network to be called, and although this way can improve the reliability of the whole oracle system, since the result sets of the third-party data sources obtained by the oracle need to be aggregated finally, the aggregation efficiency may be correspondingly lower as the number of the oracle nodes is larger.

Based on this, aiming at the problem that the existing propheter data processing modes for acquiring third-party data outside a block chain through a propheter technology cannot simultaneously meet the requirements of data processing reliability and efficiency of the propheter, the embodiment of the application provides a propheter data processing method, wherein the propheter data corresponding to a propheter event of the block chain and used for requesting target external data from a target data source outside the block chain are acquired inside a propheter system; sending the data of the prediction machine to each target prediction machine node outside the prediction machine system so that each target prediction machine node acquires the target external data from the target data source according to the data of the prediction machine; based on an aggregation strategy corresponding to the target data source prestored locally, aggregation processing is performed on the target external data sent by each target predictive machine node, a corresponding aggregation result is sent to the block chain, predictive machine data in a predictive machine event is extracted from the interior of a predictive machine system, and the target external data sent by each predictive machine node is aggregated, so that the predictive machine data can be effectively extracted from the block chain in a centralized manner and the external data aggregation processing can be performed, the efficiency of predictive machine data processing processes such as predictive machine data extraction from the block chain and external data aggregation processing can be effectively improved, the efficiency of third party data acquisition from the exterior of the block chain can be effectively improved, and the operation stability and the timeliness of data processing of the block chain can be improved; the data of the prediction machine is sent to each prediction machine node outside the prediction machine system to achieve the acquisition of third-party data, so that the aggregation after the third-party data is acquired from the outside of the block chain in a decentralized manner for multiple times can be effectively achieved, the reliability of the data processing process of the prediction machine can be effectively improved, and the reliability and the authenticity of the third-party data acquired from the outside of the block chain can be effectively improved; meanwhile, the aggregation strategy corresponding to the target data source prestored in the predictive speech machine system is adopted to aggregate the external data, so that the reliability of the data processing process of the predictive speech machine can be further improved, and the reliability and the authenticity of acquiring third-party data from the outside of the block chain can be further improved.

In one or more embodiments of the present application, a predictive engine is a system that can enable an intelligent contract to have an ability to acquire external data, the predictive engine system has been deployed and operated in advance, and a chain module of the predictive engine system has been associated with a node on a chain, that is, the chain module may initiate a call request to the intelligent contract deployed on the node through the chain node.

In one or more embodiments of the present application, a propheter node refers to: the predictive engine system requires the ability to acquire external data sources, and such ability needs to be implemented with a relatively independent system. The primary capability of such systems is to enable distributed operation, i.e., the nodes deployed in such systems are distributed among various servers accessible by different companies or enterprises or other public networks. The deployed propheter node can call an external data source and obtain corresponding return data through an instruction sent by the propheter system.

Based on the above, the present application further provides a centralized predictive speech machine module for implementing the predictive speech machine data processing method provided in one or more embodiments of the present application, where the centralized predictive speech machine module may be implemented as a server, and in a specific example, referring to fig. 1, the centralized predictive speech machine module may be disposed inside a centralized predictive speech machine system; the centralized predictive speaker module is respectively and sequentially communicated with all the predictive speaker nodes outside the predictive speaker system; each of the nodes includes, for example, a talker node a, a talker node B to a talker node N, and N is a positive integer greater than 2. Each propheter node is in communication connection with a third-party data source (the data source or the target data source mentioned in the embodiment of the application); the third-party data source includes, for example, a data source a and a data source B, and the data source a and the data source B respectively include a plurality of interfaces, for example, an interface API1, an interface API2, and an interface API 3; the centralized predictive speaker module may be connected to and correspond to at least one blockchain, where each blockchain includes a plurality of blockchain nodes corresponding to enterprises, for example, node 1, node 2 to node N, where N is a positive integer greater than 2.

In addition, a user can send a blockchain data processing request with an external data acquisition requirement to a blockchain through a client device held by the user, so that the centralized prediction machine module acquires a target data source corresponding to the external data acquisition requirement from the blockchain data processing request.

It is understood that the client device may include any mobile device capable of loading applications, such as a smart phone, a tablet electronic device, a network set-top box, a portable computer, a Personal Digital Assistant (PDA), a vehicle-mounted device, a smart wearable device, and the like. Wherein, intelligence wearing equipment can include intelligent glasses, intelligent wrist-watch, intelligent bracelet etc..

The mobile terminal may have a communication module (i.e., a communication unit) that can perform a communication connection with a remote target block chain to implement data transmission with the target block chain.

The following embodiments and application examples are specifically and individually described in detail.

In order to solve the problem that the existing propheter data processing modes for acquiring third-party data outside a block chain through a propheter technology cannot simultaneously meet the requirements of data processing reliability and efficiency of the propheter, the application provides an embodiment of a propheter data processing method, and referring to fig. 2, the propheter data processing method executed based on a centralized propheter module specifically includes the following contents:

step 100: and acquiring predictive machine data corresponding to the predictive machine event of the block chain and used for requesting target external data from a target data source outside the block chain in the predictive machine system.

It can be understood that the centralized predictive engine module of the predictive engine system is deployed in a certain central server, and a plurality of decentralized modules are deployed in servers with different network addresses which can be connected.

In step 100, the centralized predictive engine module of the predictive engine system obtains a request of an external data source which a user needs to delegate to invoke by listening to the transaction of the predictive engine event on the chain, and such a request can be defined as the predictive engine event in the present invention. The user may be an actual operation user of an application system on an upper layer of the block chain, a program of the application system interfacing with the block chain interface, or an intelligent contract itself.

Step 200: and sending the data of the prediction machine to each target prediction machine node outside the prediction machine system, so that each target prediction machine node acquires the target external data from the target data source according to the data of the prediction machine.

In step 200, the decentralized prolog node may receive a document in a yaml and json format according to the standard protocol data structure definition, then parse the document according to the standard document, and perform assembly of url, method, parameter type, etc. of the API corresponding to the data source described in the document, and then initiate a call like a third-party data source.

Step 300: and based on the aggregation strategy corresponding to the target data source prestored locally, performing aggregation processing on the target external data sent by each target predicting machine node, and sending a corresponding aggregation result to the block chain.

In step 300, the aggregation policy is determined when the data source is registered with the centralized predictive engine module, such as by using a "consensus aggregation policy" when registering an API of the data source.

As can be seen from the above description, in the data processing method for the prolog machine provided in the embodiment of the present application, prolog machine data in a prolog machine event is extracted from the prolog machine system, and aggregation processing is performed on the target external data sent by each prolog machine node, so that prolog machine data can be effectively extracted from a block chain and external data aggregation processing can be effectively performed in a centralized manner, efficiency of prolog machine data processing processes such as prolog machine data extraction from the block chain and external data aggregation processing can be effectively improved, efficiency of obtaining third party data from outside the block chain can be effectively improved, and operation stability of the block chain and timeliness of data processing can be improved; the data of the prediction machine is sent to each prediction machine node outside the prediction machine system to achieve the acquisition of third-party data, so that the aggregation after the third-party data is acquired from the outside of the block chain in a decentralized manner for multiple times can be effectively achieved, the reliability of the data processing process of the prediction machine can be effectively improved, and the reliability and the authenticity of the third-party data acquired from the outside of the block chain can be effectively improved; meanwhile, the aggregation strategy corresponding to the target data source prestored in the predictive speech machine system is adopted to aggregate the external data, so that the reliability of the data processing process of the predictive speech machine can be further improved, and the reliability and the authenticity of acquiring third-party data from the outside of the block chain can be further improved.

In order to further improve the reliability and effectiveness of the aggregation processing on the target external data sent by each target talker node, referring to fig. 3, in an embodiment of the talker data processing method provided by the present application, the following is further specifically included between step 200 and step 300 of the talker data processing method:

step 001: and detecting whether each target prediction machine node sends the target external data or not.

Step 002: and if all the current target prediction machine nodes send the target external data, or if all the current target prediction machine nodes which do not send the target external data are in a timeout state, acquiring an aggregation strategy corresponding to the target data source locally.

For example, the centralized predictive engine system module receives the return data from the decentralized predictive engine node in different time periods, and when the return data of all the predictive engine nodes are received (here, the predictive engine nodes with timeout condition, such as the predictive engine node A, B, C, D, where ABD normally returns, are also included, and at this time, the centralized predictive engine system module waits until C times out and then enters the next step), the aggregation processing of all the result sets is started.

As can be seen from the above description, in the data processing method of the predictive speech machine provided in the embodiment of the present application, if each current target predictive speech machine node has sent the target external data, or if all target predictive speech machine nodes that have not sent the target external data are in a timeout state, the aggregation policy corresponding to the target data source is locally obtained, so that reliability and effectiveness of aggregation processing performed on the target external data sent by each target predictive speech machine node can be effectively improved, and efficiency of aggregation processing can be effectively improved on the basis of ensuring reliability and effectiveness of aggregation processing, thereby further improving reliability and efficiency of data processing of the predictive speech machine.

In order to further improve the reliability and accuracy of the aggregation processing performed on the target external data, in an embodiment of the method for processing the predictive engine data provided by the present application, the aggregation policy includes: data adoption rules and aggregation algorithms; referring to fig. 4, step 300 of the data processing method of the prediction machine specifically includes the following contents:

step 310: judging whether the target external data sent by each target predictive machine node meets the data acquisition rule, if so, executing step 320; if not, the determination process of step 310 is executed again after waiting for the preset time.

Step 320: selecting a plurality of data to be aggregated from each target external data based on the data adoption rule;

step 330: and carrying out aggregation processing on the data to be aggregated based on the aggregation algorithm.

As can be seen from the above description, the method for processing the data of the predictive speech machine provided in the embodiment of the present application performs aggregation processing on the target external data by using the data adoption rule and the aggregation algorithm, so that the reliability and accuracy of the aggregation processing on the target external data can be further improved, the reliability and effectiveness of the data processing of the predictive speech machine can be further improved, and further the authenticity and reliability of the data of the aggregation result subsequently sent to the block chain can be effectively improved.

In order to improve the reliability of determining whether the target external data meets the data adoption rule, in an embodiment of the data processing method of the predictive speaker provided in the present application, referring to fig. 5, step 310 of the data processing method of the predictive speaker specifically includes the following steps:

step 311: judging whether the total number of the target external data with the same data in the target external data sent by each target predictive machine node is greater than or equal to the total number threshold of the same data specified by the data adoption rule or not; if yes, go to step 320;

referring to fig. 5, the step 320 specifically includes the following steps:

step 321: and selecting the target external data with the same data as the data to be aggregated.

For example, the aggregation policy is determined when the data source is registered in the centralized predictive engine module described in the present invention, such as when a certain API of the data source is registered, the consensus aggregation policy is adopted, that is, the data result of the same returned data set exceeding 1/2 is considered to be correct, and other values inconsistent with the result are not adopted. Therefore, the result set aggregated by the plurality of decentralized prolog nodes can be regarded as a credible data set, and the data of the external data source obtained on the chain can be credible and correct values as long as the number of rogue nodes does not exceed 1/2 of the number of the overall prolog nodes.

As can be seen from the above description, in the data processing method of the predictive speech machine provided in the embodiment of the present application, by using the same data total threshold and the same data total threshold to determine whether the target external data sent by each target predictive speech machine node meets the data acceptance rule, the reliability of determining whether the target external data meets the data acceptance rule can be effectively improved, the reliability of data processing of the predictive speech machine can be further improved, and the authenticity and reliability of data of an aggregation result subsequently sent to a block chain can be further improved.

In order to improve the security and the effectiveness of generating the predictive engine data corresponding to the predictive engine event and used for requesting the target external data from the target data source outside the blockchain, in an embodiment of the predictive engine data processing method provided by the present application, referring to fig. 6, step 100 of the predictive engine data processing method specifically includes the following contents:

step 110: and if the fact that the block chain generates the predictive machine event currently is monitored, storing the predictive machine event into the predictive machine system.

Step 120: and analyzing the predicting machine event to obtain the unique identifier of the target data source outside the block chain.

Step 130: and judging whether the target data source is locally registered or not according to the unique identifier of the target data source, if so, generating the predictive speaker data corresponding to the predictive speaker event and used for requesting target external data from the target data source outside the block chain.

For example, the centralized predictive engine module of the predictive engine system stores the predictive engine events in the monitored on-chain transaction in the database of the centralized system, and then the related processing module of the centralized system processes the predictive engine events and analyzes the third-party data source required to be called by the user from the predictive engine events. The third-party data source needing to be called, which is analyzed here, needs to be a data source which can be supported by the current centralized system. And if the data source is not supported by the centralized system, returning corresponding error information.

As can be seen from the above description, the data processing method of the predictive speech machine provided in the embodiment of the present application can effectively improve the security and the validity of the predictive speech machine data for generating the predictive speech machine event corresponding to the predictive speech machine event and requesting the target external data from the target data source outside the blockchain, and further improve the reliability, the security and the validity of the data processing process of the predictive speech machine.

In order to increase the data normalization degree of each target predictive terminal node acquiring the target external data from the target data source according to the predictive terminal data, in an embodiment of the predictive terminal data processing method provided by the present application, referring to fig. 7, step 130 of the predictive terminal data processing method specifically includes the following contents:

step 131: and extracting target variables for requesting target external data from the target data source from the predicting machine event.

Step 132: and converting the target variable into a corresponding standard protocol based on a preset standard protocol format, wherein the standard protocol is used for storing the corresponding relation among the field names, the field types and the field descriptions in the target variable.

Step 133: and compressing the standard protocol to obtain the predictive speech machine data corresponding to the predictive speech machine event.

For example, the centralized predictive engine module of the predictive engine system fills variables transmitted by a user according to a preset standard protocol format template and a document format which can be analyzed by a decentralized predictive engine node, fills the contents of { { variable name } } with related fields defined in a predictive engine event, and the filled contents are a complete version of standard protocol which can be called by the decentralized predictive engine node. And the decentralized prolog module can accept documents from the standard protocol definition.

As can be seen from the above description, in the data processing method of the predictive speech machine provided in the embodiment of the present application, the target variable is converted into the corresponding standard protocol based on the preset standard protocol format, so that the data normalization degree of each target predictive speech machine node that acquires the target external data from the target data source according to the predictive speech machine data can be effectively improved, and further, the efficiency and reliability of the data processing process of the predictive speech machine can be further improved.

In order to improve the effectiveness and reliability of determining whether the target data source is locally registered, in an embodiment of the talker data processing method provided by the present application, referring to fig. 8, before step 100, or when or after each step is executed, the talker data processing method may specifically include the following:

step 011: receiving a data source registration request, wherein the data source registration request includes a unique identification of a data source outside the blockchain.

Step 012: and registering the data source to the local part of the predictive language machine system according to the unique identifier of the data source, acquiring an aggregation strategy corresponding to the data source, and storing the aggregation strategy of the data source to the local part of the predictive language machine system.

As can be seen from the above description, the method for processing data of a predictive speech machine according to the embodiment of the present application can effectively improve the validity and reliability of determining whether the target data source is locally registered by registering the data source in advance and storing the aggregation policy corresponding to the data source, and can provide an accurate and effective data basis for subsequently aggregating the target external data sent by each of the target predictive speech machine nodes, so as to further improve the reliability, validity, and authenticity of the data processing process of the predictive speech machine.

In order to improve the reliability and effectiveness of sending the data of the predictive speaker to each target predictive speaker node outside the predictive speaker system, in an embodiment of the data processing method of the predictive speaker provided by the present application, referring to fig. 9, step 200 of the data processing method of the predictive speaker specifically includes the following contents:

step 210: searching each speaker node which is registered in the speaker system and is currently in an available state, and determining each speaker node which is registered in the speaker system and is currently in the available state as a current target speaker node;

step 220: and respectively sending the predictive speech machine data to each target predictive speech machine node so that each target predictive speech machine node respectively acquires the target external data from the target data source according to the predictive speech machine data.

1) analyzing the data of the prediction machine to obtain a unique identifier of the target data source and a unique identifier of the target external data, accessing the target data source according to the unique identifier of the target data source, and extracting a result set containing the target external data from the target data source according to the unique identifier of the target external data;

2) encrypting and signing the result set to obtain corresponding signature information;

3) and sending the result set and the signature information to the inside of the language predicting machine system.

For example, in the call request sent by the centralized predictive engine module to all the decentralized predictive engine nodes, all the decentralized predictive engine nodes initiate a call to a third-party data source through the rules of the standard protocol to obtain corresponding return result sets of the external data source, the result sets encrypt and sign the decentralized predictive engine nodes to ensure the traceability of the data result sets, and then the signature information and the original data are returned to the centralized predictive engine system together. Since the predictive engine nodes can be distributed at various organizations, network nodes, etc., their main capability is to acquire data from external data sources and return result sets to the centralized predictive engine system.

As can be seen from the above description, the method for processing the data of the predictive speech machine provided in the embodiment of the present application can effectively improve the reliability and effectiveness of sending the data of the predictive speech machine to each target node of the predictive speech machine outside the system by searching each node of the predictive speech machine that is registered in the system and currently in an available state, so as to further improve the reliability, effectiveness and authenticity of the data processing process of the predictive speech machine.

In order to further improve the efficiency and reliability of sending the predictive speaker data to each target predictive speaker node outside the predictive speaker system, referring to fig. 10, in an embodiment of the predictive speaker data processing method provided in the present application, before, or after step 200 of the predictive speaker data processing method is executed, the following contents may be specifically included:

step 021: and receiving a registration request of the nodes of the talker, wherein the registration request of the nodes of the talker contains the unique identifier of the service device.

Step 022: and according to the unique identification of the service equipment, registering the service equipment in the local of the talker system so as to determine the service equipment as the talker node, and setting the registered talker node at any position where the local network can be communicated.

For example, the decentralized nodes of the predictive speaker need to be registered in the centralized predictive speaker system, and these registered nodes of the predictive speaker can be distributed at any position where the centralized predictive speaker module network can be connected, and theoretically the nodes of the predictive speaker can be infinitely increased.

As can be seen from the foregoing description, in the method for processing data of a predictive speech machine provided in the embodiment of the present application, the service device is registered in the local area of the predictive speech machine system in advance, so that the service device is determined as the node of the predictive speech machine, and the registered node of the predictive speech machine is set at any position where the local network can be connected, so that efficiency and reliability of sending the data of the predictive speech machine to each target node of the predictive speech machine outside the predictive speech machine system can be further improved, and reliability and efficiency of a data processing process of the predictive speech machine can be further improved.

In terms of software, in order to solve the problem that the existing data processing methods of the predictive speakers, which acquire third-party data outside a block chain by using a predictive speaker technology, cannot simultaneously meet the requirements of reliability and efficiency of data processing of the predictive speakers, the present application provides an embodiment of a centralized predictive speaker module for executing all or part of the contents in the data processing method of the predictive speakers, and referring to fig. 11, the centralized predictive speaker module specifically includes the following contents:

a data obtaining module 10, configured to obtain, inside a predictive speech machine system, predictive speech machine data corresponding to a predictive speech machine event of a blockchain and used for requesting target external data from a target data source outside the blockchain;

the data distribution module 20 is configured to send the predictive teller data to each target predictive teller node outside the predictive teller system, so that each target predictive teller node obtains the target external data from the target data source according to the predictive teller data;

and the data aggregation module 30 is configured to aggregate, based on an aggregation policy corresponding to the target data source prestored locally, the target external data sent by each target talker node, and send a corresponding aggregation result to the block chain.

The embodiment of the centralized predictive speech machine module provided in the present application may be specifically configured to execute the processing flow of the embodiment of the data processing method of the predictive speech machine in the foregoing embodiment, and the functions of the processing flow are not described herein again, and reference may be made to the detailed description of the embodiment of the method.

As can be seen from the above description, the centralized predictive phone module provided in this embodiment of the present application can effectively extract predictive phone data from a block chain and perform external data aggregation processing through extracting predictive phone data in a predictive phone event inside a predictive phone system and performing aggregation processing on the target external data sent by each of the predictive phone nodes, and can effectively improve the efficiency of predictive phone data processing processes such as extracting predictive phone data from the block chain and performing external data aggregation processing, thereby effectively improving the efficiency of obtaining third-party data from outside the block chain, and improving the operational stability of the block chain and the timeliness of data processing; the data of the prediction machine is sent to each prediction machine node outside the prediction machine system to achieve the acquisition of third-party data, so that the aggregation after the third-party data is acquired from the outside of the block chain in a decentralized manner for multiple times can be effectively achieved, the reliability of the data processing process of the prediction machine can be effectively improved, and the reliability and the authenticity of the third-party data acquired from the outside of the block chain can be effectively improved; meanwhile, the aggregation strategy corresponding to the target data source prestored in the predictive speech machine system is adopted to aggregate the external data, so that the reliability of the data processing process of the predictive speech machine can be further improved, and the reliability and the authenticity of acquiring third-party data from the outside of the block chain can be further improved.

In order to further explain the scheme, the application example of the application relates to a scheme for designing a predictive speaker node system for calling external data of a predictive speaker system which is in butt joint with a alliance chain or a public chain, and designs a set of technical scheme for designing the predictive speaker system which combines the advantages of centralization and decentralized reliability, and is specifically embodied as a data processing method of the predictive speaker; the application example of the application is used for solving the problem that the intelligent contract can acquire data of an external data source through the predictive engine system, the predictive engine system is divided into two parts, one part is a centralized module, the centralized predictive engine module is mainly used for processing the requirement of the acquisition chain for the external data, then the data of the external data source is acquired through decentralized nodes, aggregation is carried out on the centralized predictive engine system module, and finally the data is returned to the chain.

Specifically, the data processing method of the prediction machine provided by the application example of the application includes the following contents:

step 1: the centralized predictive machine module of the predictive machine system is deployed in a certain central server, and a plurality of decentralized modules are deployed in servers with different network addresses which can be communicated.

Step 2: the centralized predictive machine module of the predictive machine system obtains the request of the external data source which the user needs to delegate and invoke by monitoring the transaction of the predictive machine event on the chain, and the request can be defined as the predictive machine event in the application example of the application. The user may be an actual operation user of an application system on an upper layer of the block chain, a program of the application system interfacing with the block chain interface, or an intelligent contract itself.

And step 3: and a centralized predictive machine module of the predictive machine system stores the predictive machine events in the monitored chain transaction in a database of the centralized system, and then a related processing module of the centralized system processes the predictive machine events and analyzes a third-party data source required to be called by the user from the predictive machine events. The third-party data source needing to be called, which is analyzed here, needs to be a data source which can be supported by the current centralized system. And if the data source is not supported by the centralized system, returning corresponding error information.

And 4, step 4: the centralized predictive engine module of the predictive engine system in the application example of the application example fills variables transmitted by a user according to a document format which can be analyzed by a decentralized predictive engine node according to the following template, fills the contents of { { variable name } } with related fields defined in a predictive engine event, and the filled contents are a complete version of standard protocol which can be called by the decentralized predictive engine node. The specific protocol content can be viewed at a subsequent step.

And 5: the decentralized prolog module in the application example of the application can receive documents defined by a standard protocol, and the decentralized prolog module is defined as a prolog node in the application example of the application. The format definitions of the standard documents that can be invoked by the propheter node are shown in tables 1 to 4.

TABLE 1 Standard protocol fields

TABLE 2 Request Object data Structure in common data Structure definition

TABLE 3 Response Object data Structure in common data Structure definition

TABLE 4 Body Object data Structure in the common data Structure definition

The above data structure can be used to define the third-party data source that the user needs to invoke and the input variables to be filled, and the filling process is performed in a centralized module, if the filling template is in the Yaml format, the contents are shown in table 5:

TABLE 5 examples of fill variables in fill template

The contents of { { location } }, { { language } }, { { unit } } in table 5 above are all referred to as being replaceable by the variable values imported from the client in the predicted machine event. For example, in a predictive engine event, the variables that the user has entered are: chinese, language, unit 1, the contents of { { location } }, { { language } }, { { unit } } are replaced with the values of chinese, 1, respectively.

The content defined by the standard protocol field in step 5 may be further changed and changed according to the actual situation, and is not necessarily defined according to the data structure of the standard protocol described in the present embodiment. The main purpose of the standard protocol is to enable the decentralized prolog-speaker node to analyze the call information of the interface which needs to call the third-party data source, so that any data structure definition can be theoretically performed according to the actual situation as long as the decentralized prolog-speaker node can analyze the content.

Step 6: prior to step 6, the decentralized nodes of the predictive engines need to be registered in the centralized predictive engine system, and these registered nodes of the predictive engines can be distributed at any position where the centralized predictive engine module network can be connected, and theoretically the nodes of the predictive engines can grow infinitely.

In step 5, the centralized talker system module packages the processed standard protocols, finds all registered talker nodes of the decentralized architecture available in the system, and sends the populated standard protocols to all the talker nodes.

The decentralized prolog node can receive the documents in the yaml and json formats which conform to the standard protocol data structure definition, then analyze the documents according to the standard documents, execute the url, the method, the parameter types and the like of the API corresponding to the data source described by the documents, assemble the documents and then initiate the call like a third-party data source.

Such as: the third-party data source calls a certain weather network, and an API under the weather network is named as "acquire current real-time weather information data" (the API under the data source supported by the scheme described in the application example of the present application needs to be defined and registered in advance in the centralized predictive speaker module of the application example of the present application), and then the predictive speaker node initiates an http call to the parsed standard protocol according to the call rule of the API under the data source named as "acquire current trial weather information data" (the call protocol is defined in the standard protocol, and the content defined by the standard protocol needs to meet the call rule of the API of the external data source).

And 7: in step 5, in the call request sent by the centralized language predictive machine module to all the decentralized language predictive machine nodes, all the decentralized language predictive machine nodes initiate a call to a third-party data source through a rule of a standard protocol to obtain corresponding return result sets of the external data source, the result sets encrypt and sign the decentralized language predictive machine nodes to ensure traceability of the data result sets, and then the signature information and the original data are returned to the centralized language predictive machine system together. Since the predictive engine nodes can be distributed at various organizations, network nodes, etc., their main capability is to acquire data from external data sources and return result sets to the centralized predictive engine system.

And 8: the centralized predictive machine system module receives the return data from the decentralized predictive machine node in different time periods, and when the return data of all the predictive machine nodes are received (the predictive machine nodes with the timeout condition, such as the predictive machine node A, B, C, D, wherein the ABD normally return, at this time, the centralized predictive machine system module for receiving the data set waits until the timeout occurs, and then the next step is carried out), all the result sets are aggregated. The aggregation policy is determined when the data source is registered in the centralized predictive engine module described in the application example of the present application, for example, when a certain API of the data source is registered, a "consensus aggregation policy" is adopted, that is, the data result of the same returned data set exceeding 1/2 is considered to be correct, and other values inconsistent with the result are not adopted. Therefore, the result set aggregated by the plurality of decentralized prolog nodes can be regarded as a credible data set, and the data of the external data source obtained on the chain can be credible and correct values as long as the number of rogue nodes does not exceed 1/2 of the number of the overall prolog nodes.

And step 9: after the centralized predictive engine system module completes the aggregation process of the data sets returned by the decentralized language and the nodes based on the step 8, the centralized predictive engine system module sends the data to the chain, and then completes the uplink process of the credible data acquired from the external data source under the chain.

The data processing method of the prediction machine provided by the application example of the application can enable a user to acquire data information outside a chain through an intelligent contract. Without a prediction engine system, the blockchain itself cannot obtain external real-time information data, which also causes the blockchain world to be split from the real world. The design of the predictive engine system described in the application example of the present application provides such a predictive engine system that combines the advantages associated with a centralized and decentralized architecture. The centralized predictive teller module in the scheme design described in the scheme not only ensures the credibility of the external data source (the data source needs to be registered in the centralized predictive teller system) to a certain extent, but also improves the efficiency of data aggregation processing, and different types of aggregation schemes can be registered according to the characteristics of the data source.

The decentralized talker node described in the talker data processing method is mainly responsible for receiving contents defined by a standard protocol and initiating a call to an API interface of a third-party data source. Since the predictive node can be anywhere in the network, the predictive node can be deployed in any segment and location of a participating enterprise or personal server on the blockchain. Because the nodes of the prediction machines are completely separated from the centralized prediction machine system, the nodes of the prediction machines are only responsible for calling according to the content described by the protocol, the centralized prediction machine system is greatly ensured not to have additional interference on the nodes and the autonomy of the nodes of the prediction machines, and the basic requirements of decentralization are met.

And finally, acquiring data of an external data source by the decentralized talkback nodes, signing and returning the data to the centralized talkback system module, and performing aggregation processing in the centralized system module. In the whole process, the problem that a large number of speaker nodes must participate in aggregation to cause low system efficiency is avoided, and meanwhile corresponding malicious nodes can be eliminated from data returned by the speaker nodes.

In terms of hardware, in order to solve the problem that the existing methods for processing the data of the predictive speaker that obtains the third-party data outside the blockchain by using the predictive speaker technology cannot meet the requirements of reliability and efficiency of processing the data of the predictive speaker at the same time, the present application provides an embodiment of an electronic device for implementing all or part of the contents in the method for processing the data of the predictive speaker, where the electronic device specifically includes the following contents:

fig. 12 is a schematic block diagram of a system configuration of an electronic device 9600 according to an embodiment of the present application. As shown in fig. 12, the electronic device 9600 can include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. Notably, this fig. 12 is exemplary; other types of structures may also be used in addition to or in place of the structure to implement telecommunications or other functions.

In one embodiment, the predictive processor data processing functions may be integrated into a central processor. Wherein the central processor may be configured to control:

As can be seen from the above description, according to the electronic device provided in the embodiment of the present application, by extracting the talker data in the talker event in the talker system and performing aggregation processing on the target external data sent by each talker node, centralized extraction of the talker data from the block chain and external data aggregation processing can be effectively achieved, and the efficiency of processing processes of extracting the talker data from the block chain and performing external data aggregation processing on the talker data can be effectively improved, so that the efficiency of obtaining third-party data from the outside of the block chain can be effectively improved, and the operation stability of the block chain and the timeliness of data processing can be improved; the data of the prediction machine is sent to each prediction machine node outside the prediction machine system to achieve the acquisition of third-party data, so that the aggregation after the third-party data is acquired from the outside of the block chain in a decentralized manner for multiple times can be effectively achieved, the reliability of the data processing process of the prediction machine can be effectively improved, and the reliability and the authenticity of the third-party data acquired from the outside of the block chain can be effectively improved; meanwhile, the aggregation strategy corresponding to the target data source prestored in the predictive speech machine system is adopted to aggregate the external data, so that the reliability of the data processing process of the predictive speech machine can be further improved, and the reliability and the authenticity of acquiring third-party data from the outside of the block chain can be further improved.

In another embodiment, the centralized predictive speech machine module may be configured separately from the central processor 9100, for example, the centralized predictive speech machine module may be configured as a chip connected to the central processor 9100, and the data processing function of the predictive speech machine is realized by the control of the central processor.

As shown in fig. 12, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is noted that the electronic device 9600 also does not necessarily include all of the components shown in fig. 12; further, the electronic device 9600 may further include components not shown in fig. 12, which can be referred to in the related art.

As shown in fig. 12, a central processor 9100, sometimes referred to as a controller or operational control, can include a microprocessor or other processor device and/or logic device, which central processor 9100 receives input and controls the operation of the various components of the electronic device 9600.

The memory 9140 can be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information relating to the failure may be stored, and a program for executing the information may be stored. And the central processing unit 9100 can execute the program stored in the memory 9140 to realize information storage or processing, or the like.

The input unit 9120 provides input to the central processor 9100. The input unit 9120 is, for example, a key or a touch input device. Power supply 9170 is used to provide power to electronic device 9600. The display 9160 is used for displaying display objects such as images and characters. The display may be, for example, an LCD display, but is not limited thereto.

The memory 9140 can be a solid state memory, e.g., Read Only Memory (ROM), Random Access Memory (RAM), a SIM card, or the like. There may also be a memory that holds information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes called an EPROM or the like. The memory 9140 could also be some other type of device. Memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application/function storage portion 9142, the application/function storage portion 9142 being used for storing application programs and function programs or for executing a flow of operations of the electronic device 9600 by the central processor 9100.

The memory 9140 can also include a data store 9143, the data store 9143 being used to store data, such as contacts, digital data, pictures, sounds, and/or any other data used by an electronic device. The driver storage portion 9144 of the memory 9140 may include various drivers for the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging applications, contact book applications, etc.).

The communication module 9110 is a transmitter/receiver 9110 that transmits and receives signals via an antenna 9111. The communication module (transmitter/receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as in the case of a conventional mobile communication terminal.

Based on different communication technologies, a plurality of communication modules 9110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, may be provided in the same electronic device. The communication module (transmitter/receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby implementing ordinary telecommunications functions. The audio processor 9130 may include any suitable buffers, decoders, amplifiers and so forth. In addition, the audio processor 9130 is also coupled to the central processor 9100, thereby enabling recording locally through the microphone 9132 and enabling locally stored sounds to be played through the speaker 9131.

An embodiment of the present application further provides a computer-readable storage medium capable of implementing all the steps in the predictive speaker data processing method in the foregoing embodiment, where the computer-readable storage medium stores a computer program, and the computer program, when executed by a processor, implements all the steps of the predictive speaker data processing method in the foregoing embodiment, where the execution subject of the computer program is a server or a client, for example, the processor implements the following steps when executing the computer program:

As can be seen from the above description, the computer-readable storage medium provided in this embodiment of the present application, by extracting the talker data in the talker event from the inside of the talker system and performing aggregation processing on the target external data sent by each talker node, it is possible to effectively extract the talker data from the block chain and perform external data aggregation processing in a centralized manner, and it is possible to effectively improve the efficiency of processing the talker data, such as extracting the talker data from the block chain and performing external data aggregation processing, and further effectively improve the efficiency of obtaining third-party data from outside the block chain, and improve the operation stability of the block chain and the timeliness of data processing; the data of the prediction machine is sent to each prediction machine node outside the prediction machine system to achieve the acquisition of third-party data, so that the aggregation after the third-party data is acquired from the outside of the block chain in a decentralized manner for multiple times can be effectively achieved, the reliability of the data processing process of the prediction machine can be effectively improved, and the reliability and the authenticity of the third-party data acquired from the outside of the block chain can be effectively improved; meanwhile, the aggregation strategy corresponding to the target data source prestored in the predictive speech machine system is adopted to aggregate the external data, so that the reliability of the data processing process of the predictive speech machine can be further improved, and the reliability and the authenticity of acquiring third-party data from the outside of the block chain can be further improved.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.

Claims

1. A method for processing data of a prediction machine, comprising:

2. The method for processing data of a talker according to claim 1, wherein before performing aggregation processing on the target external data sent by each target talker node based on an aggregation policy corresponding to the target data source pre-stored locally, the method further comprises:

3. The prediction machine data processing method according to claim 1 or 2, wherein the aggregation policy comprises: data adoption rules and aggregation algorithms;

4. The method according to claim 3, wherein the determining whether the target external data sent by each of the target predictive machine nodes meets the data acceptance rule, and if yes, selecting a plurality of pieces of data to be aggregated from each of the target external data based on the data acceptance rule includes:

5. The method for processing the predictive speech machine data according to claim 1, wherein the obtaining, inside the predictive speech machine system, predictive speech machine data corresponding to a predictive speech machine event of a blockchain for requesting target external data from a target data source outside the blockchain comprises:

6. The method for processing the predictive engine data according to claim 5, wherein the generating the predictive engine data corresponding to the predictive engine event for requesting target external data from a target data source outside the blockchain comprises:

7. The prediction machine data processing method according to claim 5, further comprising:

8. The method for processing the data of the language predictive machine according to claim 1, wherein the sending the data of the language predictive machine to each target language predictive machine node outside the language predictive machine system so that each target language predictive machine node obtains the data of the target language from the target data source according to the data of the language predictive machine comprises:

9. The prolog machine data processing method according to claim 8, further comprising, before said sending the prolog machine data to respective target prolog machine nodes outside the prolog machine system:

10. A centralized predictive engine module, comprising:

11. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the predictive processor data processing method of any of claims 1 to 9 when executing the computer program.

12. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the predictive-machine data processing method of any one of claims 1 to 9.