CN110928880A - Data processing method, device, terminal and medium based on block chain - Google Patents
Data processing method, device, terminal and medium based on block chain Download PDFInfo
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Abstract
The embodiment of the invention discloses a data processing method, a device, a terminal and a medium based on a block chain, wherein the method comprises the following steps: acquiring a data acquisition request generated based on an intelligent contract, and acquiring reference data from each data source object according to the data acquisition request to obtain a reference data set; if the similarity between the reference data in the reference data set is greater than the preset similarity, selecting one reference data from the reference data as the target data; target data is applied to the intelligent contract. By implementing the method, the intelligent contract can directly acquire data from the node, so that the consensus verification process is simplified, and the efficiency of acquiring the data by the intelligent contract is improved.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a data processing method and apparatus based on a block chain, a terminal, and a medium.
Background
The block chain is a decentralized database in nature, namely a series of data blocks which are generated by using a cryptographic method in a correlation manner, wherein each data block comprises a batch of transaction information and is used for verifying the validity of the transaction information and generating a next block. However, the blockchain is a closed environment, and real world data outside the chain cannot be actively acquired on the chain. This is mainly because the blockchain cannot actively initiate a network call, and the intelligent contract on the chain passively receives data. Secondly, the intelligent contract is not "intelligent" in nature, and it only reaches the program in the trigger state when the corresponding conditions are met. And the intelligent contract can judge whether the corresponding conditions are met currently or not only by acquiring external data.
When the existing intelligent contract needs to acquire data, an acquisition request needs to be sent to a predictive engine contract deployed in a block chain network, after the predictive engine contract takes an access request sent by the intelligent contract, the external network is used for acquiring the data, and the data is uploaded to the block chain network, so that the intelligent contract takes the data from the block chain, and the chaining operation can be completed only by the participation of a plurality of common identification nodes, which causes that the efficiency of the intelligent contract for acquiring the data is low.
Disclosure of Invention
The embodiment of the invention provides a data processing method, a data processing device, a data processing terminal and a data processing medium based on a block chain, which can enable an intelligent contract to directly acquire data from a node, thereby simplifying the process of consensus verification and improving the efficiency of acquiring data by the intelligent contract.
In a first aspect, an embodiment of the present invention provides a data processing method based on a blockchain, where the method is executed by any one first node in a blockchain network, and an intelligent contract is deployed in the blockchain network, and the method includes:
acquiring a data acquisition request generated based on the intelligent contract, wherein the data acquisition request is used for acquiring target data meeting preset requirements, the data acquisition request comprises data source objects, and each data source object is used for providing reference data;
acquiring reference data from each data source object according to the data acquisition request to obtain a reference data set;
if the similarity between the reference data in the reference data set is greater than the preset similarity, selecting one reference data from the reference data as the target data;
applying the target data to the intelligent contract.
In a second aspect, an embodiment of the present invention provides a data processing apparatus based on a block chain, where the apparatus includes:
the acquisition module is used for acquiring a data acquisition request generated based on the intelligent contract, the data acquisition request is used for acquiring target data meeting preset requirements, the data acquisition request comprises data source objects, and each data source object is used for providing reference data;
the obtaining module is further configured to obtain reference data from each data source object according to the data obtaining request, so as to obtain a reference data set;
a selecting module, configured to select one reference data from the reference data sets as the target data if a similarity between the reference data in the reference data set is greater than a preset similarity;
and the application module is used for applying the target data to the intelligent contract.
In a third aspect, an embodiment of the present invention provides a terminal, including a processor, an input interface, an output interface, and a memory, where the processor, the input interface, the output interface, and the memory are connected to each other, where the memory is used to store a computer program, and the computer program includes program instructions, and the processor is configured to call the program instructions to execute the method according to the first aspect.
In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program includes program instructions, which, when executed by a processor, cause the processor to execute the method of the first aspect.
In the embodiment of the invention, a first node acquires a data acquisition request generated based on an intelligent contract, and acquires reference data from each data source object according to the data acquisition request to obtain a reference data set; and if the similarity between the reference data in the reference data set is greater than the preset similarity, the first node selects one reference data from the reference data as target data and applies the target data to the intelligent contract. By implementing the method, the nodes in the block chain network can directly return the data required by the intelligent contract without chaining the data, thereby simplifying the process of common identification verification and improving the efficiency of acquiring the data by the intelligent contract.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, 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 flowchart of a data processing method based on a block chain according to an embodiment of the present invention;
fig. 2 is a schematic flowchart of another data processing method based on a blockchain according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a consensus verification process according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a blockchain network according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a block chain according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of a data processing apparatus based on a block chain according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of a terminal according to an embodiment of the present invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
The block chain is a decentralized database in nature, namely a series of data blocks which are generated by using a cryptographic method in a correlation manner, wherein each data block comprises a batch of transaction information and is used for verifying the validity of the transaction information and generating a next block. However, the blockchain is a closed environment, and real world data outside the chain cannot be actively acquired on the chain. This is mainly because the blockchain cannot actively initiate a network call, and the intelligent contract on the chain passively receives data. Secondly, the intelligent contract is not "intelligent" in nature, and it only reaches the program in the trigger state when the corresponding conditions are met. And the intelligent contract can judge whether the corresponding conditions are met currently or not only by acquiring external data.
When the existing intelligent contract needs to acquire data, an acquisition request needs to be sent to a predictive engine contract deployed in a block chain network, after the predictive engine contract takes an access request sent by the intelligent contract, the external network is used for acquiring the data, and the data is uploaded to the block chain network, so that the intelligent contract takes the data from the block chain, and the chaining operation can be completed only by the participation of a plurality of common identification nodes, which causes that the efficiency of the intelligent contract for acquiring the data is low. For example, the specific setting in the intelligent contract is that when the temperature is 20 degrees, a transfers 100 units to B, and the intelligent contract executes the contract only when the temperature is detected to be 20 degrees, so the intelligent contract needs to send a data acquisition request to the predictive engine contract, and after the predictive engine contract acquires the temperature data, the data is sent to each node in the block chain network for consensus verification, and after the verification is passed, the temperature data is uploaded to the block chain network, so that the intelligent contract acquires the temperature data. The above process needs the participation of each common identification node, and the data can be acquired only after the data chaining is intelligently saved, so that the data acquisition efficiency is low.
Based on the above description, an embodiment of the present invention provides a data processing method based on a blockchain, where the data processing method is mainly implemented based on a blockchain technology, where a blockchain is a novel application of distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm, and other computer technologies, and is essentially a decentralized database, that is, a string of data blocks generated by using a cryptography method in association, and each data block contains a batch of transaction information for verifying the validity of the transaction information and generating a next block. The block chain underlying platform can comprise processing modules such as user management, basic service, intelligent contract and operation monitoring. The user management module is responsible for identity information management of all blockchain participants, and the identity information management comprises public and private key generation maintenance (account management), key management, user real identity and blockchain address corresponding relation maintenance (authority management) and the like. The method comprises the specific implementation steps that a first node acquires a data acquisition request generated based on an intelligent contract, and acquires reference data from each data source object according to the data acquisition request to obtain a reference data set; and if the similarity between the reference data in the reference data set is greater than the preset similarity, the first node selects one reference data from the reference data as target data and applies the target data to the intelligent contract. By implementing the method, the first node can directly return data required by the intelligent contract without chaining the data, so that the common identification verification process is simplified, and the efficiency of acquiring the data by the intelligent contract is improved.
In summary, by using the block chain-based data processing method, when an intelligent contract needs to acquire data, the first node directly acquires corresponding data from the outside, judges whether the acquired data is reliable data or not based on the similarity of the data acquired by different data sources, and directly returns the data to the intelligent contract after the reliable data is acquired, so that the intelligent contract determines whether a trigger condition is met or not based on the received data. By implementing the method, the node can directly verify the acquired data and send the data to the intelligent contract after the verification is passed, so that the process of carrying out consensus verification on the data by other nodes is simplified, and the efficiency of acquiring the data by the intelligent contract is improved.
An embodiment of the present invention provides a data processing method based on a block chain, please refer to fig. 1, where the data processing process based on the block chain may include the following steps S101 to S104:
s101, the first node acquires a data acquisition request generated based on the intelligent contract.
In the embodiment of the present invention, the first node may be any node in a blockchain network, and specifically may be a terminal, including a mobile phone, a computer, a tablet computer, and the like, where an intelligent contract is deployed in the blockchain network, the data acquisition request is used to acquire target data meeting a preset requirement, where the preset requirement may be a temperature value requirement, a ball game playing time node requirement, and the like, and specifically may be preset by a user, where the target data is data meeting the preset requirement, such as a temperature value, a ball game playing time node, and the like, the data acquisition request includes data source objects, each data source object is used to provide reference data, the data source objects may be various applications, such as a weather application and a ball game relay application, a background server of each data source object may store corresponding reference data, and in a specific implementation, the data source objects in the data acquisition request may include multiple applications of the same type, each data source object can provide corresponding reference data meeting the preset requirement, the reference data meeting the preset requirement and stored by different data source objects can be the same or different, for example, for the requirement of temperature value, the temperature value provided by the first data source object is 20 degrees, the temperature value provided by the second data source object is 19 degrees, and the temperature value provided by the third data source object is 20 degrees.
Specifically, when a user sets an intelligent contract, a contract triggering condition can be set in the intelligent contract, the contract triggering condition can be that the temperature is 20 degrees, the ball game is carried out to section 4, and the like, when the intelligent contract detects that the triggering condition is met, a task corresponding to the intelligent contract is executed, the task can be that the A transfers 100 yuan to the B, and the B sends goods to the A, and the like. However, the intelligent contract needs to acquire external target data (such as the current temperature or the running condition of the ball game) to determine whether the contract triggering condition is currently met, so that a data acquisition request needs to be generated based on the intelligent contract and acquired by a first node in the blockchain.
In a specific implementation, each node in the blockchain network provided by the embodiment of the present invention may be deployed with a predictive contract for acquiring a data acquisition request generated based on an intelligent contract, where a specific manner for a first node to acquire the data acquisition request generated based on the intelligent contract may be that a blockchain network periodically generates a data acquisition request based on the intelligent contract and sends the data acquisition request to the predictive contract of the first node, so that the first node periodically acquires the data acquisition request generated based on the intelligent contract, or the blockchain network may also generate a data acquisition request based on the intelligent contract and send the data acquisition request to the predictive contract of the first node when a trigger event is detected, where the trigger event may be that a transaction occurs between nodes of the blockchain network, a node in the network newly increases or exits, and the like, specifically, the user may set in advance in an intelligent contract in the blockchain network. Or, a plurality of data acquisition nodes for data acquisition may be set in the blockchain network in advance, the data acquisition nodes periodically acquire the data acquisition request generated based on the intelligent contract from the blockchain network, and the first node is any one of the data acquisition nodes.
S102, the first node acquires reference data from each data source object according to the data acquisition request to obtain a reference data set.
In the embodiment of the invention, after the first node acquires the data acquisition request generated based on the intelligent contract, reference data is acquired from each data source object according to the data acquisition request to obtain a reference data set. For example, the data obtaining request is used to obtain a current outdoor temperature value, the data source object includes a first weather application program, a second weather application program, and a third weather application program, the first node may obtain that the current outdoor temperature value is a first temperature value from the first weather application program, obtain that the current outdoor temperature value is a second temperature value from the second weather application program, and obtain that the current outdoor temperature value is a third temperature value from the third weather application program, and the first node determines the first temperature value, the second temperature value, and the third temperature value as reference data and determines the 3 pieces of reference data as a reference data set.
In one implementation manner, a preplanning machine contract is pre-deployed in a first node, the preplanning machine contract includes a data acquisition code, the first node acquires reference data from each data source object according to a data acquisition request, and a specific manner of acquiring a reference data set may be that the first node adds information of each data source object to the data acquisition code in a form of a parameter to obtain a target data acquisition code; the first node executes a target data acquisition code in a prediction machine contract to send a data acquisition request to each data source object; and receiving the reference data returned by each data source object to obtain a reference data set. The information of the data source object may be a name or an access address of the data source object, that is, the first node acquires the corresponding reference data specifically according to the language predictive engine contract deployed by the first node.
S103, if the similarity between the reference data in the reference data set is greater than the preset similarity, the first node selects one reference data from the reference data as target data.
In the embodiment of the invention, after the first node acquires the reference data from each data source object and obtains the reference data set, the similarity between each reference data in the reference data set is calculated.
In a specific implementation, the similarity between the reference data in the reference data set may be calculated in a specific manner, where the first node determines a first number corresponding to the same reference data in the reference data set and a second number corresponding to all reference data in the reference data set; and determining a similarity between the respective reference data in the reference data set from a ratio between the first number and the second number. It should be noted that the same reference data may specifically be mode data in the reference data set, that is, reference data with the highest occurrence frequency, for example, if the reference data set includes temperature values returned by 5 data source objects, including "20, and 20", it is determined that the first quantity corresponding to the same reference data is 5, the second quantity corresponding to all the reference data is 5, and a ratio between the first quantity and the second quantity is 100%. The first node determines that the similarity between the reference data in the reference data set is 100%, and if the reference data set includes temperature values returned by 5 data source objects, including "20, 19, 20, and 20", it determines that the first quantity corresponding to the same reference data is 4, the second quantities corresponding to all the reference data are 5, and the ratio between the first quantity and the second quantity is 80%. The first node determines that the similarity between the respective reference data in the reference data set is 80%.
Further, if the similarity between the reference data in the reference data set is greater than the preset similarity, the first node selects one reference data from the reference data as the target data.
Specifically, a specific manner of selecting one reference data from the reference data sets as the target data by the first node may be that the first node determines the same M reference data from the reference data sets, and determines any one of the M reference data as the target data, where M is a positive integer, and the same M reference data may specifically be the M reference data with the highest occurrence frequency. For example, if the reference data set includes 5 temperature values returned by the data source objects, including "20, 19, 20, and 20", the first node determines that the similarity between the reference data in the reference data set is 80%, and the preset similarity is 70%, the first node determines that the similarity between the reference data is greater than the preset similarity, and the same reference data is "20", and the first node may determine "20" as the target data.
In an implementation manner, the above steps may be specifically executed by a predictive engine contract deployed in the first node, for example, a similarity determination condition is preset in the predictive engine contract, and a target data selection manner is set in advance, so that the predictive engine contract selects one reference data from each reference data as the target data.
And S104, the first node applies the target data to the intelligent contract.
In the embodiment of the invention, after the first node determines the target data, the target data can be directly returned to the intelligent contract, so that the intelligent contract applies the target data, namely, whether the task triggering condition is met currently is judged according to the target data. Specifically, the first node may return to the intelligent contract through a president contract deployed in the first node. By the method, the nodes can judge the reliability of the data based on the similarity of the data acquired from different data sources, and if the similarity of the data returned by the data sources is high enough, the data can be directly applied to the intelligent contract in the block chain without being sent to other nodes for consensus verification, so that the process of data verification is simplified, and the efficiency of acquiring the data by the intelligent contract is improved.
In the embodiment of the invention, a first node acquires a data acquisition request generated based on an intelligent contract, and acquires reference data from each data source object according to the data acquisition request to obtain a reference data set; and if the similarity between the reference data in the reference data set is greater than the preset similarity, the first node selects one reference data from the reference data as target data and applies the target data to the intelligent contract. By implementing the method, the node can directly return the data required by the intelligent contract without chaining the data, thereby simplifying the process of common identification verification and improving the efficiency of acquiring the data by the intelligent contract.
An embodiment of the present invention provides a data processing method based on a block chain, please refer to fig. 2, where the data processing process based on the block chain may include the following steps S201 to S208:
s201, the first node acquires a data acquisition request generated based on the intelligent contract.
In the embodiment of the present invention, the first node may be any node in the blockchain network, and may specifically be a terminal, including a mobile phone, a computer, a tablet computer, and the like, where an intelligent contract is deployed in the blockchain network, the data acquisition request is used to acquire target data meeting a preset requirement, the data acquisition request includes data source objects, each data source object is used to provide reference data, each data source object may specifically be each application program, and specifically, the first node may acquire the data acquisition request generated based on the intelligent contract from the blockchain network at regular time through a predictive contract configured in the first node, and a user may configure interval duration of the regular acquisition in the predictive contract in advance.
S202, the first node acquires reference data from each data source object according to the data acquisition request to obtain a reference data set.
In the embodiment of the present invention, each data source object may specifically be each application program, after acquiring a data acquisition request generated based on an intelligent contract, a first node may detect whether or not it has an access right for each application program in the data acquisition request, and if there is a target application program that does not have an access right, the first node feeds back to a blockchain network that it does not have an access right for accessing the target application program, that is, cannot acquire reference data provided by the target application program, in which case, the blockchain network may automatically modify the data acquisition request generated based on the intelligent contract, that is, the target application program is removed from the data acquisition request including the data source object, and send a new data acquisition request to the first node again, where the new data acquisition request does not include the target application program, and the first node acquires the reference data from each data source object according to the new data acquisition request, or the first node sends the data acquisition request to other nodes with the access right of the target application program in the block chain network, so that the other nodes acquire the reference data from the target application program, and the first node acquires the reference data provided by the target application program from the other nodes.
If the first node has the access right aiming at each application program in the data acquisition request, the first node acquires the reference data from each application program according to the data acquisition request to obtain a reference data set. Specifically, the first node sends an access request to each application program and receives reference data returned by each application program. In one implementation manner, a preplanning machine contract is pre-deployed in a first node, the preplanning machine contract includes a data acquisition code, the first node acquires reference data from each data source object according to a data acquisition request, and a specific manner of acquiring a reference data set may be that the first node adds an access address of each application program to the data acquisition code in a parameter form to obtain a target data acquisition code; the first node executes a target data acquisition code in a prediction machine contract to send a data acquisition request to each data source object; and receiving the reference data returned by each data source object to obtain a reference data set.
S203, the first node checks whether signature information carried by each reference data in the reference data set exists in a preset database.
In the embodiment of the invention, each reference data in the reference data set also carries signature information, the first node acquires the reference data from each data source object according to the data acquisition request, and after the reference data set is obtained, whether the signature information carried in each reference data in the reference data set exists in a preset database or not is detected, wherein a legal signature information set is stored in the preset database in advance, and the legal signature information set comprises a plurality of legal signature information. That is, after the first node acquires each reference data, the legitimacy of the source of each reference data is detected, specifically, the validity of the reference data can be verified through signature information carried in the reference data, if the signature information carried in the reference data is stored in a preset database, it is determined that the reference data is valid and can be used, and step S204 is executed, if the tag information carried in the reference data is not stored in the preset database, the first node can discard the reference data. The signature information may be a digital key, a fingerprint password, and the like, and is not limited specifically. By the method, the legality of the reference data source can be detected, and the information from an illegal data source object is prevented from being acquired. In particular, the first node may perform the above steps using a predictive engine contract deployed in the first node.
S204, the first node calculates the similarity between the reference data in the reference data set.
In the embodiment of the invention, after the first node determines that the acquired reference data are legal data, the similarity between the reference data in the reference data set is calculated.
In a specific implementation, the similarity between the reference data in the reference data set may be calculated in a specific manner, where the first node determines a first number corresponding to the same reference data in the reference data set and a second number corresponding to all reference data in the reference data set; and determining a similarity between the respective reference data in the reference data set from a ratio between the first number and the second number. It should be noted that the same reference data may specifically be mode data in the reference data set, that is, the reference data with the highest occurrence frequency. For example, if the reference data set includes 5 temperature values returned by the data source objects, including "20, 18, and 20", the reference data with the highest occurrence frequency (i.e., the same reference data) is determined to be "20", and the first number corresponding to the same reference data is determined to be 3, the second number corresponding to all the reference data is determined to be 5, and the ratio between the first number and the second number is 60%.
And S205, if the similarity between the reference data in the reference data set is greater than the preset similarity, the first node selects one reference data from the reference data as target data.
In the embodiment of the invention, if the similarity between the reference data in the reference data set is greater than the preset similarity, the first node selects one reference data from the reference data as the target data. Specifically, a specific manner of selecting one reference data from the reference data sets as the target data by the first node may be that the first node determines the same M reference data from the reference data sets, and determines any one of the M reference data as the target data, where M is a positive integer, and the same M reference data may specifically be the M reference data with the highest occurrence frequency.
S206, the first node applies the target data to the intelligent contract.
In the embodiment of the invention, after the first node determines the target data, the target data can be directly returned to the intelligent contract, so that the intelligent contract applies the target data, namely, whether the task triggering condition is met currently is judged according to the target data. Specifically, the first node may return to the intelligent contract through a president contract deployed in the first node.
S207, the first node broadcasts the target data to at least one second node in the block chain network, so that the target data is subjected to consensus verification by the at least one second node.
In the embodiment of the invention, after the first node applies the target data to the intelligent contract, the intelligent contract judges whether the target data meets the contract triggering condition, wherein the contract triggering condition is preset in the intelligent contract, the contract triggering condition can be that the temperature is 20 ℃, the ball game is carried out to the 4 th section, and the like, when the intelligent contract detects that the triggering condition is met, the task corresponding to the intelligent contract is executed, the task can be that the A transfers 100 yuan to the B, and the B sends goods to the A, and the like. Further, when the intelligent contract determines that the target data meets the task triggering condition, the first node is triggered to broadcast the target data and the task executed by the intelligent contract to at least one second node in the block chain network, so that the at least one second node in the block chain network performs consensus check on the target data. Specifically, the process of performing consensus check on the target data by at least one second node may be that each second node also obtains second reference data from the data source object, and detects whether the second target data obtained based on the second reference data is the same as the target data broadcast by the first node, if the second target data obtained based on the second reference data is the same as the target data broadcast by the first node, it is determined that the check result for the target data is a check pass, and step S208 is executed, as shown in fig. 3, the second node includes a node 1, a node 2, a node 3, and a node 4, the data source object carried in the data obtaining request includes the data source object 1, the data source object 2, the data source object 3, and the data source object 4, and the node 1, the node 2, the node 3, and the node 4 may obtain corresponding reference data from each data source object, so as to complete the consensus check on the target data.
S208, if the target data is checked to pass by at least one second node in the block chain network, the first node packs the target data into blocks and adds the blocks into the block chain network.
In the embodiment of the present invention, if at least one second node in the blockchain network passes the check on the target data, the first node packs the target data into blocks and adds the blocks into the blockchain network. By the mode, after the target data acquired from the outside is used by the intelligent contract, chaining is performed on the external data, so that the efficiency of acquiring the data by the intelligent contract is improved, and the common identification check is performed on the target data by the at least one second node based on the data acquired from the data source object, so that the process of the common identification check is simplified, and the efficiency of data chaining is improved.
In the embodiment of the invention, a first node acquires a data acquisition request generated based on an intelligent contract, and acquires reference data from each data source object according to the data acquisition request to obtain a reference data set; if the similarity between the reference data in the reference data set is greater than the preset similarity, the first node selects one reference data from the reference data as target data, the target data is applied to the intelligent contract, and after the target data acquired from the outside is used, the intelligent contract carries out cochain on the external data, so that the efficiency of the intelligent contract for acquiring the data is improved.
As shown in fig. 4, for a block chain network provided in an embodiment of the present invention, referring to the block chain network shown in fig. 4, the block chain network refers to a system for performing data sharing between nodes, the block chain network may include a plurality of nodes 401, and the plurality of nodes 401 may refer to respective terminals in the block chain network. Each node may receive input information during normal operation and maintain shared data within the blockchain network based on the received input information. In order to ensure information intercommunication in the blockchain network, information connection can exist between each node in the blockchain network, and information transmission can be carried out between the nodes through the information connection. For example, when any node in the blockchain network receives input information, other nodes in the blockchain network acquire the input information according to a consensus algorithm, and store the input information as data in shared data, so that the data stored on all nodes in the blockchain network are consistent.
Each node in the blockchain network has a corresponding node identifier, and each node in the blockchain network can store node identifiers of other nodes in the blockchain network, so that the generated block can be broadcast to other nodes in the blockchain network according to the node identifiers of other nodes. Each node may maintain a node identifier list as shown in the following table, and store the node name and the node identifier in the node identifier list correspondingly. The node identifier may be an IP (Internet Protocol) address and any other information that can be used to identify the node, and table 1 only illustrates the IP address as an example.
| Node name | Node identification |
| Node 1 | 117.114.151.174 |
| Node 2 | 117.116.189.145 |
| … | … |
| Node N | 119.123.789.258 |
Each node in the blockchain network stores one identical blockchain. The block chain is composed of a plurality of blocks, referring to fig. 5, the block chain is composed of a plurality of blocks, the starting block includes a block header and a block main body, the block header stores the version number of the input information, the hash value of the previous block and the Merkle root node, and the block main body stores the input information; the next block of the starting block takes the starting block as a parent block, the next block also comprises a block head and a block main body, the block head stores the input information characteristic value of the current block, the version number of the parent block, the hash value of the previous block and the Merkle root node, and the like, so that the block data stored in each block in the block chain is associated with the block data stored in the parent block, and the safety of the input information in the block is ensured. In a specific implementation, revenue information uploaded by each node may be stored in each block.
When each block in the block chain is generated, when a node where the block chain is located receives input information, the input information is verified, after the verification is completed, the input information is stored in a memory pool, and a hash tree used for recording the input information is updated; and then, updating the updating time stamp to the time when the input information is received, trying different random numbers, and calculating the characteristic value for multiple times, so that the calculated characteristic value can meet the following formula:
SHA256(SHA256(version+prev_hash+merkle_root+ntime+nbits+x))<TARGET
wherein, SHA256 is a characteristic value algorithm used for calculating a characteristic value; version is version information of the relevant block protocol in the block chain; prev _ hash is a block head characteristic value of a parent block of the current block; merkle _ root is a characteristic value of the input information; ntime is the update time of the update timestamp; nbits is the current difficulty, is a fixed value within a period of time, and is determined again after exceeding a fixed time period; x is a random number; TARGET is a feature threshold, which can be determined from nbits.
Therefore, when the random number meeting the formula is obtained through calculation, the information can be correspondingly stored, and the block head and the block main body are generated to obtain the current block. And then, the node where the block chain is located respectively sends the newly generated blocks to other nodes in the block chain network where the newly generated blocks are located according to the node identifications of the other nodes in the block chain network, the newly generated blocks are verified by the other nodes, and the newly generated blocks are added to the block chain stored in the newly generated blocks after the verification is completed.
Based on the above description of the embodiment of the data processing method based on the blockchain, the embodiment of the present invention further discloses a data processing apparatus based on the blockchain, where the data processing apparatus based on the blockchain may be a computer program (including a program code) running in the terminal, or may be an entity apparatus included in the terminal. The blockchain based data processing apparatus may perform the method shown in fig. 1 or fig. 2. Referring to fig. 6, the block chain-based data processing apparatus 60 includes: the device comprises an acquisition module 601, a selection module 602, an application module 603, a determination module 604, a detection module 605 and an uploading module 606.
An obtaining module 601, configured to obtain a data obtaining request generated based on the intelligent contract, where the data obtaining request is used to obtain target data meeting preset requirements, the data obtaining request includes data source objects, and each data source object is used to provide reference data;
the obtaining module 601 is further configured to obtain reference data from each data source object according to the data obtaining request, so as to obtain a reference data set;
a selecting module 602, configured to select one reference data from the reference data sets as the target data if a similarity between the reference data in the reference data set is greater than a preset similarity;
an application module 603 configured to apply the target data to the intelligent contract.
In one implementation, the determining module 604 is specifically configured to:
determining a first number corresponding to the same reference data in the reference data set and a second number corresponding to all reference data in the reference data set;
determining a similarity between respective reference data in the reference data set from a ratio between the first number and the second number.
In an implementation manner, the selecting module 602 is specifically configured to:
determining the same M reference data from the reference data set, wherein M is a positive integer;
and determining any one of the M reference data as the target data.
In one implementation, the data source object includes an application, and the detection module 605 is specifically configured to:
detecting whether the first node has access rights aiming at each application program in the data acquisition request;
and if so, executing the step of acquiring the reference data from each data source object according to the data acquisition request.
In an implementation manner, a language predictive contract is pre-deployed in the first node, where the language predictive contract includes a data obtaining code, and the obtaining module 601 is specifically configured to:
adding the information of each data source object into the data acquisition code in a parameter form to obtain a target data acquisition code;
executing a target data acquisition code in the language predictive machine contract, and sending the data acquisition request to each data source object;
and receiving the reference data returned by each data source object to obtain a reference data set.
In an implementation manner, each reference data carries signature information, and the detection module 605 is specifically configured to:
checking whether signature information carried by each reference data in the reference data set exists in a preset database, wherein a legal signature information set is stored in the preset database in advance;
and if so, executing the step of selecting one reference data from the reference data sets as the target data if the similarity between the reference data in the reference data sets is greater than a preset similarity.
In an implementation manner, each reference data carries signature information, and the uploading module 606 is specifically configured to:
broadcasting the target data to at least one second node in the blockchain network so that the at least one second node performs consensus checking on the target data;
and if the target data passes the check of at least one second node in the block chain network, packaging the target data into a block, and adding the block into the block chain network.
In the embodiment of the present invention, an obtaining module 601 obtains a data obtaining request generated based on an intelligent contract, and obtains reference data from each data source object according to the data obtaining request to obtain a reference data set; if the similarity between the reference data in the reference data set is greater than the preset similarity, the selecting module 602 selects one reference data from the reference data as the target data, and the applying module 603 applies the target data to the intelligent contract. By implementing the method, the nodes in the block chain network can directly return the data required by the intelligent contract without chaining the data, thereby simplifying the process of common identification verification and improving the efficiency of acquiring the data by the intelligent contract.
Fig. 7 is a schematic structural diagram of a terminal according to an embodiment of the present invention. As shown in fig. 7, the terminal includes: at least one processor 701, an input device 703, an output device 704, a memory 705, at least one communication bus 702. Wherein a communication bus 702 is used to enable connective communication between these components. The memory 705 may be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one disk memory. The memory 705 may optionally be at least one memory device located remotely from the processor 701. Wherein the processor 701 may be combined with the apparatus described in fig. 6, the memory 705 stores a set of program codes, and the processor 701, the input device 703 and the output device 704 call the program codes stored in the memory 705 to perform the following operations:
a processor 701, configured to obtain a data obtaining request generated based on the intelligent contract, where the data obtaining request is used to obtain target data meeting preset requirements, the data obtaining request includes data source objects, and each data source object is used to provide reference data;
the processor 701 is configured to acquire reference data from each data source object according to the data acquisition request to obtain a reference data set;
a processor 701, configured to select one reference data from the reference data sets as the target data if a similarity between the reference data in the reference data set is greater than a preset similarity;
a processor 701 configured to apply the target data to the intelligent contract.
In one implementation, the processor 701 is specifically configured to:
determining a first number corresponding to the same reference data in the reference data set and a second number corresponding to all reference data in the reference data set;
determining a similarity between respective reference data in the reference data set from a ratio between the first number and the second number.
In one implementation, the processor 701 is specifically configured to:
determining the same M reference data from the reference data set, wherein M is a positive integer;
and determining any one of the M reference data as the target data.
In one implementation, the data source object includes an application program, and the processor 701 is specifically configured to:
detecting whether the first node has access rights aiming at each application program in the data acquisition request;
and if so, executing the step of acquiring the reference data from each data source object according to the data acquisition request.
In an implementation manner, a presupposition machine contract is pre-deployed in the first node, where the presupposition machine contract includes a data acquisition code, and the processor 701 is specifically configured to:
adding the information of each data source object into the data acquisition code in a parameter form to obtain a target data acquisition code;
executing a target data acquisition code in the language predictive machine contract, and sending the data acquisition request to each data source object;
and receiving the reference data returned by each data source object to obtain a reference data set.
In an implementation manner, each reference data carries signature information, and the processor 701 is specifically configured to:
checking whether signature information carried by each reference data in the reference data set exists in a preset database, wherein a legal signature information set is stored in the preset database in advance;
and if so, executing the step of selecting one reference data from the reference data sets as the target data if the similarity between the reference data in the reference data sets is greater than a preset similarity.
In an implementation manner, each reference data carries signature information, and the processor 701 is specifically configured to:
broadcasting the target data to at least one second node in the blockchain network so that the at least one second node performs consensus checking on the target data;
and if the target data passes the check of at least one second node in the block chain network, packaging the target data into a block, and adding the block into the block chain network.
In the embodiment of the invention, a processor 701 acquires a data acquisition request generated based on an intelligent contract, and acquires reference data from each data source object according to the data acquisition request to obtain a reference data set; if the similarity between the reference data in the reference data set is greater than the preset similarity, the processor 701 selects one reference data from the reference data as target data, and the processor 701 applies the target data to the intelligent contract. By implementing the method, the nodes in the block chain network can directly return the data required by the intelligent contract without chaining the data, thereby simplifying the process of common identification verification and improving the efficiency of acquiring the data by the intelligent contract.
The module in the embodiment of the present invention may be implemented by a general-purpose integrated circuit, such as a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC).
It should be understood that, in the embodiment of the present invention, the Processor 701 may be a Central Processing Unit (CPU), and the Processor may also be other general processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, and the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The bus 702 may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Enhanced ISA (EISA) bus, or the like, and the bus 702 may be divided into an address bus, a data bus, a control bus, or the like, where fig. 7 illustrates only one bold line for ease of illustration, but does not illustrate only one bus or one type of bus.
It will be understood by those skilled in the art that all or part of the processes of the methods of the above embodiments may be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The computer-readable storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it is therefore to be understood that the invention is not limited by the scope of the appended claims.
Claims (10)
1. A data processing method based on a blockchain is applied to a blockchain network, the method is executed by a first node in the blockchain network, and an intelligent contract is deployed in the blockchain network, and the method is characterized by comprising the following steps:
acquiring a data acquisition request generated based on the intelligent contract, wherein the data acquisition request is used for acquiring target data meeting preset requirements, the data acquisition request comprises data source objects, and each data source object is used for providing reference data;
acquiring reference data from each data source object according to the data acquisition request to obtain a reference data set;
if the similarity between the reference data in the reference data set is greater than the preset similarity, selecting one reference data from the reference data as the target data;
applying the target data to the intelligent contract.
2. The method according to claim 1, wherein after obtaining the reference data from each data source object according to the data obtaining request, and obtaining a reference data set, the method further comprises:
determining a first number corresponding to the same reference data in the reference data set and a second number corresponding to all reference data in the reference data set;
determining a similarity between respective reference data in the reference data set from a ratio between the first number and the second number.
3. The method according to claim 1, wherein the selecting one of the reference data as the target data comprises:
determining the same M reference data from the reference data set, wherein M is a positive integer;
and determining any one of the M reference data as the target data.
4. The method according to claim 1, wherein the data source objects comprise applications, and the obtaining reference data from each data source object according to the data obtaining request to obtain a reference data set comprises:
detecting whether the first node has access rights aiming at each application program in the data acquisition request;
and if so, executing the step of acquiring the reference data from each data source object according to the data acquisition request.
5. The method according to claim 1, wherein a predictive engine contract is pre-deployed in the first node, the predictive engine contract includes a data acquisition code, and acquiring reference data from each data source object according to the data acquisition request to obtain a reference data set includes:
adding the information of each data source object into the data acquisition code in a parameter form to obtain a target data acquisition code;
executing a target data acquisition code in the language predictive machine contract, and sending the data acquisition request to each data source object;
and receiving the reference data returned by each data source object to obtain a reference data set.
6. The method according to claim 1, wherein each reference data carries signature information, and after obtaining the reference data from each data source object according to the data obtaining request and obtaining a reference data set, the method further comprises:
checking whether signature information carried by each reference data in the reference data set exists in a preset database, wherein a legal signature information set is stored in the preset database in advance;
and if so, executing the step of selecting one reference data from the reference data sets as the target data if the similarity between the reference data in the reference data sets is greater than a preset similarity.
7. The method of claim 1, wherein after applying the target data in the smart contract, the method further comprises:
broadcasting the target data to at least one second node in the blockchain network so that the at least one second node performs consensus checking on the target data;
and if the target data passes the check of at least one second node in the block chain network, packaging the target data into a block, and adding the block into the block chain network.
8. A blockchain-based data processing apparatus, the method being performed by any first node in a blockchain network in which an intelligent contract is deployed, the apparatus comprising:
the acquisition module is used for acquiring a data acquisition request generated based on the intelligent contract, the data acquisition request is used for acquiring target data meeting preset requirements, the data acquisition request comprises data source objects, and each data source object is used for providing reference data;
the obtaining module is further configured to obtain reference data from each data source object according to the data obtaining request, so as to obtain a reference data set;
a selecting module, configured to select one reference data from the reference data sets as the target data if a similarity between the reference data in the reference data set is greater than a preset similarity;
and the application module is used for applying the target data to the intelligent contract.
9. A terminal, comprising a processor, an input interface, an output interface, and a memory, the processor, the input interface, the output interface, and the memory being interconnected, wherein the memory is configured to store a computer program comprising program instructions, the processor being configured to invoke the program instructions to perform the method of any of claims 1-7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program comprising program instructions that, when executed by a processor, cause the processor to carry out the method according to any one of claims 1-7.
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