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    公开号:ES2879908T9
    申请号:ES18866758T
    申请日:2018-12-29
    公开日:2022-01-21
    发明作者:Ning Xia
    申请人:Advanced New Technologies Co Ltd；
    IPC主号:
    专利说明:

    [0002] Blockchain-based Distributed Open Collaboration of Map Applications
    [0004] BACKGROUND
    [0006] Distributed Ledger Systems (DLS), which can also be called consensus networks and/or blockchain networks, allow participating entities to store data securely and immutably. DLS are commonly known as blockchain networks without reference to a particular user scenario. Example types of blockchain networks may include public blockchain networks, private blockchain networks, and consortium blockchain networks. A public blockchain network is open for all entities to use the DLS and participate in the consensus process. A private blockchain network is provided for a particular entity, which centrally controls read and write permissions. A consortium blockchain network is provided for a select group of entities, which control the consensus process and includes an access control layer.
    [0008] Map data collection is resource intensive for map application providers, and users often complain that the map data collected is not up-to-date enough for their needs. Although conventional map data collection can be used to address these issues, a more efficient solution to address map data collection would be advantageous. US 2011/0246148 A1 describes systems, methods, apparatus, devices, articles and means for updating radio models. An exemplary method for one or more server devices comprises receiving at one or more communication interfaces at least one measurement corresponding to a position of a first mobile device within an indoor environment. The at least one radio model that is stored in one or more memories is updated based, at least in part, on the at least one measurement to produce the at least one updated radio model. The at least one radio model and the at least one updated radio model correspond to the indoor environment. The at least one updated radio model is transmitted to allow a second mobile device to use the at least one updated radio model for positioning within the indoor environment.
    [0010] US 2018/356236 A1 describes a system and method for updating map data for an autonomous vehicle (AV). The method includes collecting, using one or a plurality of AV sensors from a first AV, collecting sensor data, and comparing the collected sensor data to map data to determine the presence of potentially modified data. The method further includes generating a proof-of-work (PoW) block including the potentially modified data and collecting, using one or a plurality of AV sensors of a second AV, first verification sensor data. The potentially modified data is then compared to the first verification sensor data to generate a first verified map block based on the first verification sensor data and add the first verified map block to a first chain of verification blocks. verified map.
    [0012] Randy Meech, “We Have Some Blocksplaining To Do” and the StreeCred team, “4 Days, 500+ Users, 3000+ Places” contain generic descriptions of StreetCred, a blockchain-based marketplace for location data and a prototype application of the same.
    [0014] RESUME
    [0016] The invention is defined by the appended claims.
    [0018] The specification implementations include computer-implemented methods for open, distributed, blockchain-based collaboration. More particularly, the implementations of the specification are directed to providing a blockchain-based distributed open collaboration platform capable of collecting and providing map data.
    [0020] In some implementations, actions include receiving, at a map service provider associated with a blockchain, map data from a data collector; transmit the map data to a data service provider for processing; receive, at the map service provider, distributed non-open source data from a data provider; transmit the distributed non-open source data to the data service provider for processing with the map data; receive a request for processed map data from a service provider; retrieve, from the data service provider, the processed map data that is generated from the map data and the distributed non-open source data; and transmitting the processed map data to the service user in response to the request. Other implementations include corresponding computer systems, apparatus, and programs configured to perform the actions of the methods, encoded in computer storage devices.
    [0022] Each of these and other implementations may optionally include one or more of the following features: A first feature, combinable with any of the following features, wherein the map service provider provides automated data consumption or data authorization for the service user.
    [0024] A second feature, combinable with any of the following features, wherein the map service provider, the data service provider and the data provider are computing devices that participate in the blockchain.
    [0026] A third feature, combinable with any of the following features, further comprising: prior to receiving the map data from the data collector, executing, through the map service provider, a smart contract with the data collector.
    [0028] A fourth feature, combinable with any of the following features, further comprising: before transmitting the map data to the data service provider, executing, through the service provider, a smart contract with the data service provider.
    [0030] A fifth feature, combinable with any of the following features, wherein transmitting the map data to a data service provider for processing includes transmitting the map data to a data service provider to normalize the map data or discard irregular data from the map data.
    [0032] A sixth feature, combinable with any of the following features, wherein the blockchain is a consortium blockchain.
    [0034] A seventh feature, combinable with any of the following features, where the data collector collects the map data using distributed open collaboration.
    [0036] The specification memory also provides a non-transient computer-readable storage medium coupled to one or more computers and having instructions stored therein that are executable by the one or more computers to perform operations in accordance with implementations of the methods provided in the specification. this document.
    [0038] The specification further provides a system for implementing the methods provided herein. The system includes one or more computers and one or more computer-readable memories coupled to the one or more computers and having instructions stored therein that are executable by the one or more computers to perform operations in accordance with implementations of the provided methods. in the present document.
    [0040] It is appreciated that the methods according to the specification may include any combination of the aspects and features described herein. That is, the methods according to the specification are not limited to the combinations of aspects and features specifically described herein, but also include any combination of the aspects and features provided.
    [0041] Details of one or more implementations of the specification are set forth in the accompanying drawings and description below. Other features and advantages of the specification will be apparent from the description and drawings, and from the claims.
    [0043] DESCRIPTION OF THE DRAWINGS
    [0045] FIG. 1 shows an example of an environment that can be used to run specification implementations.
    [0047] FIG. 2 represents an example of a conceptual architecture according to implementations of the specification.
    [0049] FIG. 3 depicts an example of a blockchain-based distributed open collaboration mapping application system environment according to implementations of the specification.
    [0050] FIG. 4 represents an example of a process that can be executed in accordance with implementations of the specification.
    [0052] FIG. 5 depicts exemplary modules of an apparatus 500 in accordance with implementations of this specification.
    [0054] Similar reference symbols in the various drawings indicate similar elements.
    [0055] DETAILED DESCRIPTION
    [0057] Specification implementations include computer-implemented methods for distributed open collaboration service applications based on blockchain technologies. More particularly, the implementations of the specification are directed to providing a blockchain-based distributed open collaboration platform capable of collecting and providing map data.
    [0059] To provide additional context for the specification implementations, and as introduced above, Distributed Ledger Systems (DLS), which may also be referred to as consensus networks (eg, composed of peer-to-peer nodes) and Blockchain networks enable participating entities to securely and immutably transact and store data. Although the term blockchain is generally associated with currency networks, blockchain is used herein to refer generally to a DLS without reference to a particular use case.
    [0061] A blockchain is a data structure that stores transactions in a way that the transactions are immutable. Therefore, transactions recorded on a blockchain are reliable and trustworthy. A blockchain includes one or more blocks. Each of the blocks in the chain is linked to a previous block immediately before it in the chain by including a cryptographic digest of the previous block. Each of the blocks also includes a timestamp, its own cryptographic digest, and one or more transactions. The transactions, which have already been verified by the nodes of the blockchain network, are processed and encoded in a Merkle tree. A Merkle tree is a data structure in which the data at the leaf nodes of the tree is summarized, and all the summaries at each of the branches of the tree are concatenated at the root of the branch. This process continues up the tree to the root of the entire tree, which stores a summary that is representative of all the data in the tree. A summary purporting to be from a transaction stored in the tree can be quickly verified by determining whether it is consistent with the structure of the tree.
    [0063] While a blockchain is a decentralized or at least partially decentralized data structure for storing transactions, a blockchain network is a network of computing nodes that manage, update, and maintain one or more blockchains by broadcasting, verifying and transaction validation, etc. As introduced above, a blockchain network can be provided as a public blockchain network, a private blockchain network, or a consortium blockchain network.
    [0064] In a public blockchain network, the consensus process is controlled by nodes in the consensus network. For example, hundreds, thousands, even millions of entities can cooperate on a public blockchain network, each of which operates at least one node on the public blockchain network. Consequently, the public blockchain network can be considered a public network with respect to the participating entities. In some examples, a majority of entities (nodes) must sign each block for it to be valid and added to the blockchain (distributed ledger) of the blockchain network. Example public blockchain networks include private peer-to-peer payment networks that leverage a distributed ledger, called a blockchain. As noted above, the term blockchain, however, is used to refer generally to distributed ledgers without particular reference to a particular blockchain network.
    [0066] In general, a public blockchain network supports public transactions. A public transaction is shared with all nodes on the public blockchain network and stored on a global blockchain. A global blockchain is a chain of blocks that repeats itself across all nodes. That is, all nodes are in a perfect state of consensus regarding the global blockchain. To achieve a consensus (eg, an agreement to add a block to a blockchain), a consensus protocol is implemented within the public blockchain network. Example consensus protocols include, but are not limited to, Proof-of-Work (POW) (eg, implemented in some forex networks), Proof-of-Stake (POS), and Proof-of-Authority (POA). POW is referred to herein as a non-limiting example.
    [0068] Generally, a private blockchain network is provided for a particular entity, which centrally controls read and write permissions. The entity controls which nodes can participate in the blockchain network. Consequently, private blockchain networks are generally known as permissioned networks that place restrictions on who can participate in the network and on their level of participation (eg, only in certain transactions). Various types of access control mechanisms may be used (eg, existing participants vote on the addition of new entities, a regulatory authority may control admission).
    [0070] In general, a blockchain consortium network is private between the participating entities. In a blockchain consortium network, the consensus process is controlled by an authorized set of nodes, where one or more nodes are operated by a respective entity (eg financial institution, insurance company). For example, a consortium of ten (10) entities (eg, financial institutions, insurance companies) may operate a blockchain consortium network, each of which operates at least one node of the consortium network. block chain. Consequently, the supply chain consortium network blocks can be considered a private network with respect to the participating entities. In some examples, each entity (node) must sign each block for it to be valid and added to the blockchain. In some examples, at least a subset of entities (nodes) (for example, at least 7 entities) must sign each block for it to be valid and added to the blockchain.
    [0072] The specification implementations are described in more detail herein with reference to a consortium blockchain network, or a combination of two or more of a consortium blockchain network and a consortium blockchain network. public or private (hereinafter referred to as a "blockchain network"). However, it is contemplated that the implementations of the specification may be implemented in any appropriate type of blockchain network.
    [0074] The specification implementations are described in more detail herein in view of the above context. More particularly, and as introduced above, the implementations of the specification are directed to providing a blockchain-based distributed open collaboration platform capable of collecting and providing map data.
    [0076] FIG. 1 depicts an example of an environment 100 that can be used to run implementations of the specification. In some examples, the example environment 100 allows entities to participate in a blockchain network 102. The example environment 100 includes computing devices 106, 108 and a network 110. In some examples, the network 110 includes a network of local area (LAN), wide area network (WAN), the Internet, or a combination thereof, and connects websites, user devices (eg, computing devices), and server systems. In some examples, network 110 may be accessed through a wired and/or wireless communications link. In some examples, network 110 enables communication with and within blockchain network 102 . In general, network 110 represents one or more communication networks.
    [0078] In the example depicted, the computing systems 106, 108 may each include any appropriate computing system that allows participation as a node in the blockchain network 102 . Example computing devices include, without limitation, a server, a desktop computer, a laptop computer, a tablet computer, and a smartphone. In some examples, computer systems 106, 108 host one or more computer-implemented services for interfacing with blockchain network 102 . For example, computer system 106 may host computer-implemented services of a first entity (eg, Participant A), such as the transaction management system that the first entity uses to manage its transactions with one or more other entities. (eg, other participants). Computer system 108 may host computer-implemented services of a second entity (eg, Participant B), such as a transaction management system that the second entity uses to manage its transactions with one or more other entities (eg, Participant B). g., other participants). In the example of FIG. 1, the blockchain network 102 is depicted as a peer-to-peer network of nodes, and the computing systems 106, 108 provide first entity and second entity nodes, respectively, that participate in the blockchain network 102. blocks.
    [0080] FIG. 2 depicts an example of a conceptual architecture 200 according to specification implementations. Example conceptual architecture 200 includes participant systems 202, 204, 206 corresponding to Participant A, Participant B, and Participant C, respectively. Each of the participants (eg, user, company) participates in a blockchain network 212 provided as a peer-to-peer network that includes a plurality of nodes 214, at least some of which record information electronically. immutable on a 216 chain of blocks. Although a single block chain 216 is schematically represented within the block chain network 212, multiple copies of the block chain 216 are provided and maintained throughout the block chain network 212, as described further below. detail in this document.
    [0082] In the example depicted, each of the participant systems 202, 204, 206 is provided by or on behalf of Participant A, Participant B, and Participant C, respectively, and functions as a respective node 214 within the blockchain network. . As used herein, a node generally refers to an individual system (eg, computer, server) that is connected to the blockchain network 212 and enables a respective participant to participate in the blockchain network. block chain. In the example of FIG. 2, one participant corresponds to each of the nodes 214. It is contemplated, however, that a participant may operate multiple nodes 214 within the blockchain network 212, and/or multiple participants may share a node 214. In some In examples, participating systems 202, 204, 206 communicate with or through the blockchain network 212 using a protocol (eg, hypertext transfer protocol secure (HTTPS)) and/or using calls to remote procedure (RPC).
    [0084] Nodes 214 may have varying degrees of participation within blockchain network 212 . For example, some nodes 214 may participate in the consensus process (eg, as caretaker nodes that add blocks to the blockchain 216), while other nodes 214 do not participate in the consensus process. To give another example, some nodes 214 store a complete copy of the block chain 216, while other nodes 214 only store copies of portions of the block chain 216. For example, data access privileges may limit the blockchain data a respective participant stores within their system. respective. In the example of FIG. 2, the participating systems 202, 204, 206 store respective full copies 216', 216", 216"' of the chain 216 of blocks.
    [0086] A chain of blocks (eg, block chain 216 in FIG. 2) is made up of a chain of blocks, each block storing data. Example data includes transaction data representative of a transaction between two or more participants. While transactions are used herein by way of non-limiting example, it is contemplated that any appropriate data may be stored on a blockchain (eg, documents, images, video, audio). Example transactions may include, without limitation, exchanges of anything of value (eg, assets, products, services, currency). Transaction data is immutably stored within the blockchain. That is, transaction data cannot be changed.
    [0088] Before storing them in a block, the transaction data is summarized. Digest is a process of transforming transaction data (provided as string data) into a fixed-length digest value (also provided as string data). It is not possible to undigest the digest value to get the transaction data. Digest ensures that even a small change in transaction data results in a completely different digest value. Also, and as noted above, the digest value is of fixed length. That is, no matter the size of the transaction data, the length of the digest value is fixed. The digest includes processing the transaction data through a digest function to generate the digest value. An example of a hash function includes, without limitation, the Secure Hash Algorithm (SHA)-256, which generates 256-bit hash values.
    [0090] Transaction data from multiple transactions is summarized and stored in a block. For example, summary values of two transactions are provided and are themselves summarized to provide another summary. This process is repeated until, for all transactions to be stored in a block, a single digest value is provided. This hash value is called the Merkle root hash and is stored in a block header. A change to any of the transactions will result in a change to its summary value, and ultimately a change to the Merkle root summary.
    [0092] Blocks are added to the blockchain through a consensus protocol. Multiple nodes within the blockchain network participate in the consensus protocol and compete for a block to be added to the blockchain. Such nodes are known as miners (or caretaker nodes). The POW, presented above, is used as a non-limiting example.
    [0094] Mining nodes run the consensus process to add transactions to the blockchain. Although multiple mining nodes participate in the consensus process, only one mining node can write the block to the blockchain. That is, mining nodes compete in the consensus process for their block to be added to the blockchain. In more detail, a mining node periodically collects pending transactions from a pool of transactions (eg, up to a predefined limit on the number of transactions that can be included in a block, if any). The transaction pool includes transaction messages from participants in the blockchain network. The mining node builds a block and adds the transactions to the block. Before adding the transactions to the block, the mining node checks if any of the transactions are already included in a block on the blockchain. If a transaction is already included in another block, the transaction is discarded.
    [0096] The mining node generates a block header, digests all transactions in the block, and combines the digest value in pairs to generate more digest values until a single digest value is provided for all transactions in the block (the root digest of Merkle). This summary is added to the block header. The miner also determines the digest value of the most recent block in the blockchain (ie, the last block added to the blockchain). The mining node also adds a one-time random number value and timestamp to the block header. In a mining process, the mining node attempts to find a digest value that meets the required parameters. The mining node keeps changing the one-time random number value until it finds a digest value that meets the required parameters.
    [0097] Each of the miners on the blockchain network tries to find a digest value that meets the required parameters and thus compete with each other. Finally, one of the mining nodes finds a digest value that meets the required parameters and announces it to all other mining nodes on the blockchain network. The other mining nodes check the digest value, and if it is found to be correct, they check each of the transactions in the block, accept the block, and add the block to their copy of the blockchain. In this way, a global blockchain state is consistent across all mining nodes within the blockchain network. The process described above is the POW consensus protocol.
    [0099] With reference to FIG. 2, a non-limiting example is provided. In this example, Participant A wants to send a currency amount to Participant B. Participant A generates a transaction message (eg, including From, To, and Value fields) and sends the transaction message to the network block chain, which adds the transaction message to a group of transactions. Each of the mining nodes in the blockchain network creates a block and takes all the transactions from the transaction pool (i.e., up to a predefined limit on the number of transactions that can be added to a block, if any), and adds the transactions to the block. In this way, the transaction published by Participant A is added to the blocks of the mining nodes.
    [0101] In some blockchain networks, cryptography is implemented to keep transactions private. For example, if two nodes want to keep a transaction private, so that other nodes on the blockchain network cannot discern the details of the transaction, the nodes can encrypt the transaction data. Example cryptography includes, without limitation, symmetric encryption and asymmetric encryption. Symmetric encryption refers to an encryption process that uses a single key for both encryption (generating ciphertext from plaintext) and decryption (generating plaintext from ciphertext). In symmetric encryption, the same key is available to multiple nodes, so each of the nodes can encrypt/decrypt transaction data.
    [0103] Asymmetric encryption uses key pairs, each of which includes a private key and a public key, with the private key being known only to a respective node and the public key being known to any or all other nodes in the chain network. of blocks. One node can use another node's public key to encrypt data, and the encrypted data can be decrypted using another node's private key. For example, and referring again to FIG. 2, Participant A can use Participant B's public key to encrypt the data and send the encrypted data to Participant B. Participant B can use its private key to decrypt the encrypted data (ciphertext) and extract the original data (text). flat). Messages encrypted with a node's public key can only be decrypted using the node's private key.
    [0105] Asymmetric encryption is used to provide digital signatures, which allow participants in a transaction to confirm to other participants in the transaction, as well as the validity of the transaction. For example, one node can digitally sign a message and another node can confirm that the message was sent by the node based on Participant A's digital signature. Digital signatures can also be used to ensure that messages are not tampered with in transit. For example, and referring again to FIG. 2, Participant A must send a message to Participant B. Participant A generates a message digest and then, using its private key, encrypts the digest to provide a digital signature as an encrypted digest. Participant A adds the digital signature to the message and sends the message with the digital signature to Participant B. Participant B decrypts the digital signature using Participant A's public key and extracts the digest. Participant B summarizes the message and compares the summaries. If the digests are the same, Participant B can confirm that the message was indeed from Participant A and was not tampered with.
    [0107] The specification implementations are described in more detail herein in view of the above context. More particularly, and as presented above, the specification implementations are directed to blockchain-based distributed open collaboration service applications. For ease of description, the specification uses a map service as an example by way of illustration. However, the described subject matter can also be applied to other types of services, for example, a weather service or a supermarket product price inquiry service.
    [0109] Conventional map applications have been and are used in many aspects related to people's daily lives. For example, map applications can be used to plan trips, check traffic conditions, and locate a point of interest (such as a restaurant or hospital). However, map data collection is one of the main challenges facing map application providers, creating difficulties for both map data providers and map service users. For example, traditional map service providers that collect data centrally must spend a large amount of resources (eg, staff and computing resources) to collect and update map data. Meanwhile, map service users constantly complain that map data in their particular geographic regions is not timely or complete. For example, map data generally does not reflect a real-time road condition, especially when there are traffic accidents, road maintenance, or other road-related situations or emergencies. Additionally, due to a lack of timely/complete collection capability, map data providers are generally unable to provide map data related to closed areas (such as an indoor mall) with detailed subdivisions.
    [0111] Distributed open collaboration is a sourcing model through which goods and services can be obtained from a large group of people who submit data using, for example, the Internet, social media, or smartphone apps. Distributed open collaboration allows companies to dedicate work to people from anywhere in the world. Compared to outsourcing, the people involved in distributed open collaboration work are less specific and more public. Distributed open collaboration breaks tasks, usually large tasks, into many smaller subtasks that a multitude of people can work on separately. As such, companies that use distributed open collaboration models can save costs in human, material, and financial resources while improving efficiency and quality of work.
    [0112] There are some implementations of distributed open collaboration functionalities. For example, GOOGLE MAP MAKER is a companion product to GOOGLE MAPS that leverages users' geographic contributions to complement existing content on GOOGLE MAPS. People around the world can now edit information in the GOOGLE MAPS app for greater accuracy. Similarly, the geographic information system (GIS) cloud distributed open collaboration application is a project that is presented to the public as an interactive map, through which users can report a problem or observation. Once users submit reports about a location or asset, including photos and comments, via a web or mobile app, a project coordinator can approve or reject the submissions.
    [0113] However, it is often difficult to collect data from a large number of users or members of an organization, and to have all the collected data centralized in one location, much less to be able to organize the data, track the progress of ongoing projects, and to report project status to the public and stakeholders. For example, to collect map data for a financial district under a traditional distributed open collaboration approach, a map service provider must first advertise to the public and recruit a large number of people available to submit the collected data. This approach has some potential problems resulting from difficulties in forming trust relationships between the participants (such as service provider, data collector, data provider, and data user) of the transaction, which can lead to to long transaction times and a lengthy overall process. For example, according to the traditional approach, a service provider must spend a great deal of time searching for people who are capable of collecting data and entering into individual contracts with one or more data providers and data service users to secure business transactions. . This results in the need to spend a lot of time and resources on each of the contracting parties. Furthermore, even if each of the parties agrees to the specified terms of the contract, whenever a dispute occurs (for example, the service provider is not satisfied with the data received from the data collector and refuses to pay for the data ), each of the disputes requires a particular handling unique to the particular dispute. In such dispute scenarios, each party is required to spend valuable time and resources to resolve one or more disputes.
    [0115] Therefore, existing solutions for distributed open collaborative data collection are insufficient for a large number (eg thousands) of data collectors as well as data providers and service providers in a process of data sharing and secure and streamlined business transactions, and using the same simple application for the same project or goal. Therefore, the key challenges in using this type of distributed open collaboration model for data collection is to find an efficient way to facilitate cooperation between data collectors and data service users (as well as other participants), prioritizing tasks, reducing response time, monitoring progress and tracking transaction details.
    [0117] This disclosure describes a distributed open collaboration service application based on blockchain technologies. Entities (such as data collectors, data service users, data providers, and service providers) that participate in transactions are bound by one or more smart contracts, which are computerized agreements designed to disseminate, verify, or enforce data. informative contracts. Smart contracts are open to the public and can be automatically executed once entered, allowing for reliable transactions without the involvement of third parties. In some implementations, smart contract transactions are traceable and irreversible. For example, the service provider can set requirements for the data needed in a smart contract and publish them to the public. Anyone from the public can accept the smart contract. Once a data collector meets all the requirements detailed in the smart contract (for example, sending valid data to the service provider), the data collector can automatically receive a reward promised in the smart contract, leaving no chance for that the service provider breaches the smart contract. As such, business transactions can be carried out between contracting parties automatically and securely, reducing processing time and saving computing resources for each of the entities.
    [0119] For example, when a map service provider needs to collect data associated with a financial district, instead of hiring data collectors from the public and entering into individual contracts with each of the data collectors, the map service provider may form a blockchain and publish a smart contract on the blockchain, asking people who live near the financial district to submit data. Any individual (not required to join the blockchain) who is aware of the smart contract can transact with the map service provider accepting the terms of the smart contract and receive payments automatically once the data is successfully submitted to the map service provider. As such, the entire transaction process is streamlined with a minimal level of human intervention needed to monitor each step of a transaction.
    [0121] FIG. 3 depicts an example of a blockchain-based distributed open collaboration mapping application system environment 300 in accordance with implementations of the specification. Map service provider 302 can be any map service provider that is capable of forming a blockchain and executing smart contracts with individuals and data providers (eg, data collectors and data providers). The formed blockchain may include the data service provider 304 and the data provider 306 . Data service provider 304 provides data services to map service provider 302, such as verifying or storing map data received from map service provider 302. The data provider 306 may provide additional information that cannot be collected through distributed open collaboration to the map service provider 302 . Such data may include, for example, satellite images of a particular geographic region. Map service provider 302 enters into a smart contract with each of data service provider 304 and data provider 306, respectively, at the beginning of the process. Due to the existence of the smart contract, the map service provider 302 can execute a series of transactions, such as data consumption and data authorization with the data service provider 304 and the data provider 306 automatically, reducing the transaction costs for each of the entities.
    [0123] For data that may be collected through distributed open collaboration collection, such as traffic conditions for a specific geographic region, the map service provider 302 may send the data collection task to one or more data collectors 308 . The data collector 308 collects data for the map service provider 302 on the blockchain in exchange for a reward. For example, data collector 308 can be any individual with the ability to collect map data for a specific geographic region. Once a data collector 308 accepts a smart contract offered by the map service provider 302, the data collector 308 collects map data using, for example, a global positioning system (GPS), in-dash cameras, a car, mobile computing devices or drones, and sells the collected data to the map service provider 302. After the data service provider 304 processes (such as verifies and removes (eg, normalizes and removes irregular data) the submitted data, the map service provider 302 may sell the processed data to a service user 310 who you need to buy or rent the corresponding map data for a particular purpose.
    [0125] FIG. 4 depicts an example of a computer implemented method 400 that can be executed in accordance with implementations of the specification. In some implementations, method 400 may be performed using one or more executable computer programs executed using one or more computing devices. For clarity of presentation, the description that follows generally describes method 400 in the context of the other figures in this description. However, it will be understood that method 400 may be performed, for example, by any suitable system, environment, software, hardware, or a combination of systems, environments, software, or hardware, as appropriate. In some implementations, various steps of method 400 may be executed in parallel, in combination, in loops, or in any order.
    [0127] At 402, a map service provider associated with a blockchain receives map data from a plurality of data collectors. In some implementations, the blockchain may be a consortium blockchain. In some implementations, the map service provider is a company (for example, Alibaba) that provides services to customers through software applications running on mobile and non-mobile computing devices. In some implementations, the services provided may include a map service or a weather service. In some implementations, the data collector collects the map data using distributed open collaboration.
    [0129] In some implementations, the map service provider receives data from one or more data collectors through distributed open collaboration. In some implementations, each of the data collectors is a member of the public or a member of a group. In some implementations, data collectors may use GPS, a car dash camera, a mobile computing device, or a drone to collect the data.
    [0131] In some implementations, before receiving map data from the data collectors, the map service provider executes a smart contract with one or more of the data collectors. The smart contract establishes an incentive mechanism whereby the one or more data collectors can receive a reward once they collect and submit relevant data to the map service provider. In some implementations, the data can be validated to ensure that the data meets the preset requirements of the map service provider. For example, whether the data is collected from the actual data collector, whether it is useful, timely, or based on any other criteria. In some implementations, preset requirements can be included in a smart contract. From 402, method 400 passes to 404.
    [0133] At 404, the map data is transmitted to a data service provider for processing. In some implementations, before transmitting the map data to the data service provider, the map service provider executes a smart contract with the data service provider to ensure that transactions can take place between the map service provider and the data service provider automatically.
    [0134] In some implementations, transmitting the map data to a data service provider for processing includes transmitting the map data to a data service provider to normalize the map data or discard irregular data from the map data. For example, after a map service provider receives data batches for a specific geographic region from one or more data collectors, the map service provider can send the data batches to the data service provider, which may normalize data batches or compare each data batch and remove any data batches that contain irregularities (for example, corrupted, incomplete or out of date). In some implementations, the data service provider may also store the data for the map service provider. From 404, method 400 goes to 406.
    [0136] At 406, the map service provider receives distributed non-collaborative data from a data provider associated with the blockchain. Data received that is not distributed open collaboration is data that cannot be collected (or is difficult to collect) through distributed open collaboration. For example, the map service provider may need information such as satellite imagery or aerial imagery of a geographic region targeted by non-data collectors. In some implementations, prior to receiving the non-crowdsourced data from the data provider, the map service provider executes a smart contract with the data provider to ensure that transactions can be performed automatically. From 406, method 400 goes to 408.
    [0138] At 408, the distributed non-open collaboration data is transmitted to the data service provider for processing with the map data. Starting with 408, method 400 goes to 410.
    [0140] At 410, a request for processed map data is received from a user of the service. In some implementations, the service user may be a customer who needs to buy or rent data for a specific purpose, for example, receiving traffic condition updates for a route or requesting a detailed interior or exterior map of a building. Starting with 410, method 400 goes to 412.
    [0142] At 412, the processed map data that is generated from the map data and the non-collaborative data is retrieved from the data service provider. In some implementations, before sending the processed data to the service user, the map service provider executes a smart contract with the service user to ensure that transactions between them can be performed automatically. After 412, method 400 goes to 414.
    [0144] At 414, the processed map data is transmitted to the service user in response to the request. After 414, the method 400 can be stopped.
    [0146] FIG. 5 depicts exemplary modules of an apparatus 500 in accordance with implementations of this specification. Appliance 500 may be an exemplary implementation of an appliance configured to enable blockchain-based distributed open data collaboration (such as, on a consortium network or other type of blockchain). Apparatus 500 may correspond to the implementations described above, and apparatus 500 includes the following: A first receiver or receiving unit 502 for receiving, at a map service provider associated with a blockchain, map data from a data collector; a first transmitter or a first transmission unit 504 for transmitting the map data to a data service provider for processing; a second receiver or a second receiving unit 506 for receiving, at the map service provider, non-collaborative distributed data from a data provider; a second transmitter or a second transmission unit 508 for transmitting the distributed non-collaborative data to the data service provider for processing with the map data; a third receiver or a third receiving unit 510 for receiving a request for processed map data from a user of the service; a retriever or a retrieval unit 512 for retrieving, from the data service provider, the processed map data that is generated from the map data and the distributed non-open source data; and a third transmitter or a third transmission unit 514 for transmitting the processed map data to the service user in response to the request.
    [0148] The system, apparatus, module, or unit illustrated in the above implementations may be implemented using a computer chip or entity, or may be implemented using a product having a certain function. A typical deployment device is a computer, and the computer can be a personal computer, a laptop, a mobile phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, a e-mail receiving and sending device, a game console, a tablet computer, a wearable device, or any combination of these devices.
    [0150] For a process of implementing functions and roles of each of the units in the apparatus, references may be made to a process of implementing the corresponding steps in the above method. Details are omitted here for simplicity.
    [0152] Since an apparatus implementation basically corresponds to a method implementation, for related parts, references may be made to related descriptions in the method implementation. The apparatus implementation described above is merely an example. Units described as separate parts may or may not be physically separate, and parts shown as units may or may not be physical units, may be located in one location, or may be distributed across multiple network units. Some or all of the modules can be selected based on the actual demands to achieve the objectives of the solutions of the specification. A person skilled in the art can understand and put into practice the embodiments of the present application without creative efforts.
    [0154] Referring again to FIG. 5, can be interpreted as an illustration of an internal functional module and structure of an apparatus for enabling blockchain-based distributed open data collaboration. The execution appliance may be an example of an appliance configured to enable blockchain-based distributed open data collaboration.
    [0156] The subject matter implementations and actions and operations described in this specification may be implemented in digital electronic circuitry, in tangibly embodied computer software or firmware, in computer hardware, including the structures described in this specification and their structural equivalents, or in combinations of one or more of them. Implementations of the subject matter described in this specification may be implemented as one or more computer programs, e.g. eg, one or more modules of computer program instructions, encoded on a computer program carrier, for execution by, or for controlling the operation of, data processing apparatus. The carrier may be a non-transient tangible computer storage medium. Alternatively or additionally, the carrier may be an artificially generated propagated signal, for example, a machine-generated electrical, optical or electromagnetic signal that is generated to encode information for transmission to a suitable receiving apparatus for execution by processing apparatus. of data. The computer storage medium may be or be part of a machine-readable storage device, a machine-readable storage substrate, a serial or random access memory device, or a combination of one or more of these. A computer storage medium is not a propagated signal.
    [0158] The term "data processing apparatus" encompasses all types of apparatus, devices, and machines for processing data, including, but not limited to, a programmable processor, a computer, or multiple processors or computers. The data processing apparatus may include special purpose logic circuitry, e.g. An FPGA (Field Programmable Gate Array), an ASIC (Application Specific Integrated Circuit), or a GPU (Graphics Processing Unit). The apparatus may also include, in addition to hardware, code that creates an execution environment for computer programs, e.g. eg, code that makes up processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more.
    [0160] A computer program, which may also be called or described as a program, software, software application, app, module, software module, engine, script, or code, may be written in any form of language programming, including compiled or interpreted languages, or declarative or procedural languages; and may be deployed in any form, including as a stand-alone program or as a module, component, engine, subroutine, or other unit suitable for execution in a computing environment, which environment may include one or more computers interconnected by a data communications network in one or more locations.
    [0162] A computer program may, but need not, correspond to a file in a file system. A computer program may be stored in a portion of a file that contains other programs or data, e.g. eg, one or more scripts stored in a markup language document, in a single file dedicated to the program in question, or in multiple coordinated files, e.g. eg, files that store one or more modules, subprograms, or pieces of code.
    [0164] The processes and logic flows described in this specification may be performed by one or more computers executing one or more computer programs to perform operations by operating on input data and generating output. Logic processes and flows can also be realized by special purpose logic circuitry, e.g. an FPGa, an ASIC, or a GPU, or by a combination of special-purpose logic circuitry and one or more programmed computers.
    [0166] Computers suitable for executing a computer program may be based on general purpose or special purpose microprocessors or both, or any other type of central processing unit. Generally, a central processing unit will receive instructions and data from read only memory or random access memory or both. The elements of a computer may include a central processing unit to execute instructions and one or more memory devices to store instructions and data. The central processing unit and memory may be supplemented or incorporated into special purpose logic circuitry.
    [0168] Generally, a computer will also include, or be operatively coupled to receive data from, or transfer data to, one or more mass storage devices. Mass storage devices can be, for example, magnetic, magneto-optical, or optical disks, or solid-state drives. However, a computer does not need to have such devices. Also, a computer can be integrated into another device, e.g. e.g. a mobile phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver or portable storage device, e.g. g., a Universal Serial Bus (USB) flash drive, to name just a few.
    [0170] To facilitate interaction with a user, implementations of the subject matter described in this specification may be implemented or configured to communicate with a computer having a display device, e.g. an LCD (liquid crystal display) monitor, to display information to the user, and an input device through which the user can provide input to the computer, e.g. a keyboard and a pointing device, e.g. eg, a mouse, trackball, or trackpad. Other types of devices may also be used to provide interaction with a user; for example, the feedback provided to the user can be any form of sensory feedback, e.g. eg, visual feedback, auditory feedback, or tactile feedback; and user input can be received in any form, including acoustic, voice, or touch input. Also, a computer can interact with a user by sending documents to and receiving documents from a device that is used by the user; for example, by sending web pages to a web browser on a user's device in response to requests received from the web browser, or by interacting with an app running on a user's device, e.g. e.g., a smartphone or electronic tablet. Additionally, a computer may interact with a user by sending text messages or other forms of messaging to a personal device, e.g. e.g., a smartphone running a messaging app, and receiving back messages from the user.
    [0172] This specification uses the term "configured for" in connection with computer software systems, apparatus, and components. For a system of one or more computers to be configured to perform particular operations or actions, it means that the system has installed software, firmware, hardware, or a combination of them that, in operation, cause the system to perform the operations or actions. That one or more computer programs are configured to perform particular operations or actions means that the program(s) include instructions that, when executed by the data processing apparatus, cause the apparatus to perform the operations or actions. That a special purpose logic circuit is configured to perform particular operations or actions means that the circuit has electronic logic that performs the operations or actions.
    [0174] While this specification contains many implementation-specific details, these should not be construed as limitations on the scope of what is claimed, which is defined by the claims themselves, but rather as descriptions of features that may be specific to particular implementations. Certain features that are described in this specification in the context of separate implementations may also be realized in combination in a single implementation. Conversely, various features that are described in the context of a single implementation may also be realized in multiple implementations separately or in any suitable sub-combination. Furthermore, although features may be described above as operating in certain combinations and even initially claimed as such, one or more features of a claimed combination may in some cases be removed from the combination, and the claim may be directed to a sub-combination or variation of a subcombination.
    [0176] Similarly, while the operations are illustrated in the drawings and recited in the claims in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations are performed, to achieve desirable results. Under certain circumstances, multitasking and parallel processing can be advantageous. Furthermore, the separation of various modules and system components in the implementations described above should not be understood to require such separation in all implementations, and it should be understood that the program components and systems described can generally be integrated together in a single software product. or packaged into multiple software products.
    [0178] Particular implementations of the subject matter have been described. For example, the actions recited in the claims can be performed in a different order and still achieve desirable results. As an example, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or the sequential order, to achieve desirable results. In some cases, multitasking and parallel processing can be advantageous.
    权利要求:
    Claims (7)
    [1]
    1. A computer implemented method (400), the method comprising:
    executing, by a map service provider (302) associated with a blockchain, a first smart contract on the blockchain with a data collector (308), said first smart contract enabling the data collector to receive a reward once it collects and sends relevant data to the map service provider, where the map service provider and the data collector are computing devices that participate in the blockchain, and then
    receiving (402), at the map service provider, map data from the data collector; executing, via the map service provider, a second smart contract on the blockchain with a data service provider (304), where the data service provider is a computing device participating in the blockchain, and then
    transmit (404) the map data to the data service provider for processing; executing, via the map service provider, a third smart contract on the blockchain with a data provider (306), where the data provider is a computing device participating in the blockchain, and then
    receiving (406), at the map service provider, distributed non-open source data from the data provider;
    transmitting (408) the distributed non-open source data to the data service provider for processing with the map data;
    receiving (410) a request for processed map data from a user of the service;
    retrieving (412), from the data service provider, the processed map data that is generated from the map data and the distributed non-open source data;
    execute, via the map service provider, a fourth smart contract on the blockchain with the user of the service, and then
    transmitting (414) the processed map data to the service user in response to the request.
    [2]
    The computer-implemented method (400) of claim 1, wherein the map service provider (302) provides automated data consumption or data authorization for the service user.
    [3]
    3. The computer-implemented method (400) of claim 1, wherein transmitting (404) the map data to the data service provider (304) for processing includes transmitting the map data to the data service provider for processing. normalize the map data or discard irregular data from the map data.
    [4]
    The computer implemented method (400) of claim 1, wherein the blockchain is a consortium blockchain.
    [5]
    5. The computer-implemented method (400) of claim 1, wherein the map data is collected by the data collector (308) using distributed open collaboration.
    [6]
    6. One or more computer-readable storage media encoded with instructions that, when executed by one or more processors, cause the one or more processors to perform the method (400) according to any one of claims 1 to 5.
    [7]
    7. A system comprising:
    one or more processors; Y
    one or more computer-readable storage media encoded with instructions that, when executed by one or more processors, cause the one or more processors to perform the method (400) according to any one of claims 1 to 5.
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    同族专利:
    公开号 | 公开日
    WO2019072311A3|2019-10-24|
    US20190301883A1|2019-10-03|
    PH12019501794A1|2020-03-09|
    US10677607B2|2020-06-09|
    KR20200083935A|2020-07-09|
    EP3566418A4|2020-03-04|
    KR102151903B1|2020-09-03|
    TWI712982B|2020-12-11|
    ES2879908T3|2021-11-23|
    CN110383791A|2019-10-25|
    EP3566418A2|2019-11-13|
    WO2019072311A2|2019-04-18|
    PL3566418T3|2021-11-15|
    TW202032488A|2020-09-01|
    SG11201907107WA|2019-08-27|
    JP6763094B2|2020-09-30|
    EP3566418B1|2021-04-21|
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    法律状态:
    优先权:
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    PCT/CN2018/125637|WO2019072311A2|2018-12-29|2018-12-29|Blockchain-based crowdsourcing of map applications|
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