CN120915575A - Traffic data safety transmission method and system based on block chain - Google Patents
Traffic data safety transmission method and system based on block chainInfo
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- CN120915575A CN120915575A CN202511214996.0A CN202511214996A CN120915575A CN 120915575 A CN120915575 A CN 120915575A CN 202511214996 A CN202511214996 A CN 202511214996A CN 120915575 A CN120915575 A CN 120915575A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/10—Network architectures or network communication protocols for network security for controlling access to devices or network resources
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/12—Applying verification of the received information
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/32—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials
- H04L9/3247—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials involving digital signatures
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/40—Network security protocols
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/50—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols using hash chains, e.g. blockchains or hash trees
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/50—Reducing energy consumption in communication networks in wire-line communication networks, e.g. low power modes or reduced link rate
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- General Engineering & Computer Science (AREA)
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Abstract
The invention discloses a traffic data safety transmission method and system based on a block chain, and relates to the technical field of block chain application, wherein the method comprises the steps of connecting a traffic data monitoring network to acquire an acquisition data set, and establishing an association relation between the acquisition data set and traffic events; the method comprises the steps of configuring rule partitions according to the rule partitions, obtaining a block chain processing area and a mapping area, locating data in the processing area, extracting target data, carrying out hash processing and signing, recording uplink such as abstracts and signatures, establishing downlink mapping, and transmitting a data set based on a block chain network and a mapping relation. The invention solves the technical problems of the traditional traffic data transmission method, such as data encryption, authority management, tamper resistance, traceability and the like, and achieves the technical effects of improving the safety, the integrity and the traceability of data transmission.
Description
Technical Field
The invention relates to the technical field of blockchain application, in particular to a blockchain-based traffic data secure transmission method and system.
Background
In intelligent traffic systems, secure transmission of traffic data is critical to the development of traffic safety, management and related applications. In the prior art, the traditional traffic data transmission method has the defects in the aspects of data encryption, authority management, tamper resistance, traceability and the like.
Along with the development of intelligent traffic, traffic data has the characteristics of large data volume, complex types (including vehicle positions, speeds, road identifications and the like), strong real-time performance, privacy safety and the like. The traditional mode adopts centralized storage, is easy to attack and has high single-point fault risk, encryption and verification processes are split, the data integrity can not be ensured, and the authority management is distributed and lacks a dynamic verification and traceability mechanism.
Disclosure of Invention
The application provides a traffic data safety transmission method and system based on a blockchain, which are used for solving the technical problems of the traditional traffic data transmission method, such as data encryption, authority management, tamper resistance, traceability and the like.
The application provides a traffic data safety transmission method based on a blockchain, which comprises the steps of connecting a traffic data monitoring network to obtain a traffic collection data set, establishing an incidence relation between the traffic collection data set and traffic events, configuring an encryption data partitioning rule according to the incidence relation, identifying and partitioning the traffic collection data set to obtain a blockchain processing area and a blockchain mapping area, carrying out data positioning on the traffic collection data set based on the blockchain processing area, extracting target collection data to carry out hash processing, generating a data abstract, carrying out digital signature by a private key used by a data source node, submitting the data abstract, the digital signature, a data index and access authority information to a blockchain network to carry out on-chain recording, establishing a mapping relation under a blockchain, and carrying out traffic collection data set transmission based on the blockchain network and the mapping relation under the blockchain.
The application provides a traffic data safety transmission system based on a blockchain, which comprises a traffic acquisition data set acquisition module, a blockchain network recording module, a traffic acquisition data set transmission module and a traffic acquisition data set transmission module, wherein the traffic acquisition data set acquisition module is used for connecting a traffic data monitoring network to acquire a traffic acquisition data set and establishing an association relation between the traffic acquisition data set and a traffic event, the traffic acquisition data set partitioning module is used for configuring an encryption data partitioning rule according to the association relation to identify and partition the traffic acquisition data set to acquire a blockchain processing area and a blockchain mapping area, the traffic acquisition data set positioning module is used for carrying out data positioning on the traffic acquisition data set based on the blockchain processing area, extracting target acquisition data to carry out hash processing, generating a data summary and carrying out digital signature by a private key used by a data source node, the blockchain network recording module is used for submitting the data summary, the digital signature and data index and access information to a blockchain network to carry out on-chain recording and establishing a mapping relation under the blockchain with the blockchain mapping area, and the traffic acquisition data set transmission module is used for carrying out traffic acquisition data set transmission based on the blockchain network and the under-chain mapping relation.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
The method comprises the steps of obtaining a traffic collection data set through connecting a traffic data monitoring network, establishing an association relation between the traffic collection data set and traffic events, configuring an encryption data partitioning rule according to the association relation, dividing data into a blockchain processing area and a blockchain mapping area, carrying out hash processing and digital signature on target data of the blockchain processing area, submitting the target data to a blockchain network record, establishing an under-chain mapping relation, and transmitting the data based on the blockchain network and the under-chain mapping relation. The authority management verification is carried out through the intelligent contract, and the data consistency verification is completed by combining the hash on the chain, so that the safety and high-efficiency transmission of traffic data is realized, the defects of the traditional method in aspects of data encryption, authority management, tamper resistance, traceability and the like are overcome, the safety and reliability of traffic data transmission are improved, and the technical effects of improving the safety, the integrity and the traceability of the data transmission are achieved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a traffic data security transmission method based on a blockchain according to an embodiment of the present application.
Fig. 2 is a schematic structural diagram of a traffic data security transmission system based on blockchain according to an embodiment of the present application.
The reference numerals illustrate a traffic collection data set acquisition module 1, a traffic collection data set partitioning module 2, a traffic collection data set positioning module 3, a blockchain network recording module 4 and a traffic collection data set transmission module 5.
Detailed Description
The application provides a traffic data safety transmission method and system based on a blockchain, which are used for solving the technical problems of the traditional traffic data transmission method, such as data encryption, authority management, tamper resistance, traceability and the like.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that, the terms "first," "second," and the like in the description of the present application and the above drawings are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or modules not expressly listed or inherent to such process, method, article, or apparatus.
In a first embodiment, as shown in fig. 1, a traffic data security transmission method based on a blockchain, wherein the method includes:
and step A100, connecting a traffic data monitoring network to obtain a traffic collection data set, and establishing an association relation between the traffic collection data set and traffic events.
In the embodiment of the application, the traffic data monitoring network is a distributed data acquisition system consisting of various sensors, intelligent equipment and communication modules which are deployed in a traffic scene, and is used for acquiring multidimensional data information in the traffic field in real time.
Specifically, first, an acquisition system covering multi-dimensional data is constructed. The method comprises the steps of utilizing a sensor network deployed in road infrastructure (such as traffic lights, cameras and radars) and vehicle-mounted terminals to collect data such as vehicle positions, speeds, directions, road identification information, traffic light states, surrounding vehicle identification information and the like in real time to form a traffic collection data set containing massive real-time information, and the specific steps are described in detail in A110. The data cover key elements such as vehicle dynamics, road environment and the like in the traffic scene, and a rich data source is provided for subsequent analysis.
After data are collected, the association relation between the traffic collection data set and the traffic event is established. The data and the event are split, so that the differentiation processing cannot be carried out according to the event characteristics, and a traffic event library is required to be introduced as an associated bridge. First, the events in the traffic event library are classified according to the influence range, timeliness and related entity class. And then analyzing the core influence data and the auxiliary influence data of each class of event. By establishing the mapping relation between the event and the data set, key data fields corresponding to different event types are defined, a bidirectional index mechanism of the event-data is formed, each piece of collected data can be traced back to a specific traffic event scene, a logic basis is provided for subsequent data partitioning and safety processing, and specific steps are described in detail in A210-A220.
The comprehensive collection of traffic data is realized through the multi-source sensor network, the deep association of the data and the event is established based on the event grade system, a foundation is laid for the subsequent data grading processing according to the event characteristics, and the traffic data can more accurately serve the safety transmission and management requirements.
And step A200, configuring encryption data partitioning rules according to the association relation, and identifying and partitioning the traffic collection data set to obtain a blockchain processing area and a blockchain mapping area.
Optionally, an encryption data partitioning rule is configured according to the association relation between the traffic collection data set and the traffic event, and the core data is partitioned into a blockchain processing area and the auxiliary data is partitioned into a blockchain mapping area according to the partition identification characteristics of the core data and the auxiliary data, and the specific steps are described in detail in A230.
And step A300, carrying out data positioning on the traffic collection data set based on the blockchain processing area, extracting target collection data to carry out hash processing, generating a data abstract, and carrying out digital signature by a private key used by a data source node.
In one embodiment of the present application, first, the blockchain processing area is utilized to precisely locate partitioned core traffic data (such as sensitive information of vehicle position, speed, etc.), and the data tag or indexing mechanism is utilized to quickly retrieve target acquisition data from the blockchain processing area. For example, when processing traffic accident related data, the key information to be linked is located through the mapping relation between the event level and the data field (such as the core data of the collision position of the vehicle corresponding to the high-level accident, the identity of the driver and the like).
After locating the target data, the system hashes the target data. In the hash processing process, the system calls an SHA-256 encryption hash algorithm to perform layer-by-layer operation on target data, namely, firstly dividing original data (such as a vehicle speed value) into data blocks with fixed lengths, filling each data block to enable the data blocks to meet algorithm input requirements, and then gradually compressing and integrating information of each data block through complex mathematical transformation (including bit operation, cyclic redundancy check and the like) to finally generate 256-bit hash values (namely, data abstracts). The digests correspond one-to-one to the original data and are irreversible (the original data cannot be back-deduced by the digests) and highly sensitive (any minor modification of the original data results in the digests being completely different). For example, the vehicle speed of 60km/h is processed by SHA-256 to generate a unique 64-bit hexadecimal character string, and if the data is tampered with 65km/h in transmission, the recalculated hash value is completely different from the digest stored on the chain, so that the data integrity is effectively verified.
Finally, the data source node (such as the vehicle-mounted terminal and the traffic sensor) uses the private key to digitally sign the data summary. The private key signing process is realized through an asymmetric encryption technology, and a signing result is bound with the data abstract to ensure the non-repudiation of a data source. For example, a traffic camera is used as a data source node, a private key is used for signing the collected summary of the road identification information, a receiver verifies the validity of the signature through a public key, and the data can be confirmed to be sent out by the node.
Through the steps, encryption pretreatment of the core traffic data is realized, the data integrity is ensured by hash treatment, the data source reliability is ensured by private key signature, the problems that the data is easy to tamper and the responsibility is difficult to trace in the prior art are solved by combining the encryption pretreatment with the private key signature, and finally the effects of realizing the integrity verification and source tracing of the traffic core data based on the blockchain technology and improving the safety and the credibility of data transmission are achieved.
And step A400, submitting the data abstract, the digital signature, the data index and the access authority information to a blockchain network for on-chain recording, and establishing a mapping relation under a chain with the blockchain mapping area.
In an embodiment of the application, the data index is a unique identifier for locating traffic collection data (including on-link core data and off-link auxiliary data). The access right information refers to right rules set for different 1 data access parties, and comprises information related to multi-factor policy matching mechanisms such as access identities, data access rights, access history behaviors and the like.
Specifically, after the hash processing and digital signature of the core traffic data are completed, key information is stored in an uplink manner and a downlink mapping is established. Firstly, the system carries out structured encapsulation on a data abstract (unique identifier generated by a hash algorithm), a digital signature (private key signature result of a data source node), a data index (unique identifier for locating data) and access authority information (role authority of a data access party) to form a transaction data unit conforming to a blockchain storage format. For example, a summary of the vehicle location data, a signature of the corresponding node, an index of the data's storage under the chain, and rights information authorizing access by the traffic police may be integrated into a transaction package.
The transaction package is then submitted to the blockchain network for verification and recording via a consensus mechanism of the blockchain network, such as a workload proof PoW or a rights proof PoS. And the consensus node verifies the validity of the transaction, for example, whether the digital signature is matched or not and whether the access authority is compliant or not is checked, and after the verification is passed, the transaction is written into a new block of the block chain, so that the non-tamperable storage of the data is realized. For example, in a traffic data scenario, sensor nodes at a plurality of intersections serve as consensus nodes, and a transaction packet of certain accident-related data is commonly verified, and the validity of the transaction packet is ensured and then permanently recorded in a chain.
Meanwhile, the system establishes a link down mapping relationship between the block chain processing region and the block chain mapping region. Auxiliary data for the blockchain map area, such as a surrounding vehicle track, is stored in an under-chain distributed database, such as IPFS, by an encryption algorithm (such as AES), and its index information is recorded on the chain, such as database address, file hash, etc., and specific steps are described in detail in a 410. For example, the auxiliary data is stored under the chain after being encrypted, and only the index value of the data is stored on the chain to form a mapping pair of the index on the chain and the data under the chain, so that the resources under the chain can be rapidly positioned through the index when the data is accessed.
The efficient management of the on-chain memory card and the auxiliary data of the core data is realized through the processes of data encapsulation, consensus verification, on-chain recording and under-chain mapping, the performance bottleneck of the full data on-chain belt is avoided while the data security is ensured, and finally the effects of safe transmission and flexible access of traffic data are realized by the efficient cooperation of the core data trusted memory card and the under-chain data based on the block chain.
And step A500, transmitting a traffic collection data set based on the blockchain network and the under-chain mapping relation.
In one embodiment of the present application, first, data preprocessing is accomplished based on the blockchain processing region and the blockchain mapping region. The system generates a unique data abstract by carrying out hash processing on the core data through the block chain processing area, and uses a private key to sign by a data source node to ensure the integrity and source credibility of the core data. For example, core data such as vehicle position, speed and the like are stored in a uplink way, auxiliary data such as surrounding vehicle tracks and the like are stored in a link in an encrypted way, and quick association is realized through indexes.
In the data transmission stage, the system writes the data abstract, the digital signature, the data index and the access right information into the blockchain through a consensus mechanism by utilizing the distributed characteristic of the blockchain network, so as to realize the non-tamperable transmission of the core data. Meanwhile, the auxiliary data under the chain is synchronously transmitted with the data on the chain through an encryption channel, so that the confidentiality of the data in the transmission process is ensured. For example, in traffic event data transmission, hash values and signatures of core data are broadcast to nodes through a blockchain network, auxiliary data are transmitted to a target node through Secure Socket Layer (SSL) protocol encryption, and the two are synchronously associated through a data index.
After the transmission is completed, the system performs rights management and data verification through intelligent contracts deployed in the blockchain network. When a data access party initiates a request, the intelligent contract firstly verifies the identity and the access authority of the data access party, and the identity verification is carried out based on the hash value on the chain by calling the auxiliary data under the chain, so that the data is ensured not to be tampered in the transmission process. For example, when a traffic police department applies to access certain accident data, the intelligent contract firstly checks the authority level, and access is allowed if the intelligent contract is consistent with the hash value of the core data stored on the chain by calling auxiliary data under the chain.
Through the complete flow of data preprocessing, blockchain network transmission and intelligent contract verification, the security of core data is ensured by utilizing the non-tamperable characteristic of a blockchain, the transmission efficiency of auxiliary data is improved by means of an under-chain mapping mechanism, and the effects of realizing efficient collaborative transmission of the core data and the auxiliary data and meeting the dual requirements of an intelligent traffic system on data security and instantaneity are achieved on the premise of ensuring the integrity and credibility of traffic data.
Further, step a500 in the method provided by the embodiment of the present application includes:
a510, performing authority management verification on access, authorization and calling actions of the traffic collection data through intelligent contracts deployed on the blockchain network.
And A520, when the data access party passes the authority management verification, invoking the data under the chain through the data index, performing consistency verification based on the hash on the chain, and acquiring the traffic transmission data after the consistency verification.
In the embodiment of the application, the intelligent contract is an automatic program deployed in the blockchain network and used for performing authority management verification on access, authorization and calling actions of traffic collected data. It contains access identity, data access rights, multi-factor access policy matching mechanism for access history behavior.
Specifically, after the transmission of the traffic collection data set based on the blockchain network and the mapping relation under the chain is completed, the access and the use of the data are required to be safely controlled. When a data access party initiates a data acquisition request, an authority management verification process is triggered through an intelligent contract deployed in a blockchain network, and specific steps are described in detail in A511-A512.
When the access party passes the authority verification, the auxiliary data (such as the surrounding vehicle track and the traffic light state) stored in the under-chain distributed database is called according to the data index (such as the under-chain data path corresponding to the event ID) stored in the chain, and the corresponding core data hash value (such as the hash abstract of the vehicle speed and the position) is obtained from the blockchain. At this time, the system performs consistency verification by comparing the hash value of the auxiliary data under the chain with the hash value of the core data on the chain, and by recalculating the hash value of the data under the chain and matching the hash value with the record on the chain, it is ensured that the data is not tampered in the transmission process. For example, when congestion assistance data (under-link) for a road segment is invoked, the data integrity is verified by comparison with the road segment vehicle speed data hash value stored on the link. And if the comparison is consistent, allowing the access party to acquire complete traffic transmission data, and if the comparison is inconsistent, refusing to access and triggering the abnormal log record.
In the whole authority verification and data calling process, the intelligent closing date automatically writes the access behavior (such as access time, data type and operation main body) into the blockchain in a transaction form to form an untampereable audit log. These logs can be used for subsequent data usage traceability, rights compliance review and security event investigation, ensuring transparency and traceability of data access.
The security access and the trusted use of the traffic collection data are realized through the processes of authority verification, data call under the chain, hash comparison on the chain and operation audit, so that the effect of guaranteeing the data integrity through a collaborative verification mechanism under the chain on the premise of ensuring the traffic data access compliance and realizing the whole-course audit of the access behavior through the blockchain storage is achieved, and the data security management level of the intelligent traffic system is improved.
Further, step a100 in the method provided by the embodiment of the present application includes:
the traffic collection data set comprises one or more of vehicle position, vehicle speed, vehicle direction, road identification information, traffic light state and surrounding vehicle identification information.
Optionally, first, sensor devices deployed at each key node of the road, such as a Global Positioning System (GPS) receiver, traffic lights, laser radar, camera, and wireless communication module, capture vehicle dynamic data and road environment information in real time. For example, vehicle position coordinates and driving directions are continuously acquired by using a vehicle-mounted GPS module, vehicle speed is monitored by radar equipment installed on two sides of a road, road identifications and traffic light states are identified by means of cameras at traffic intersections, and identification information of surrounding vehicles, such as license plates, vehicle types and the like, is acquired by using a vehicle-mounted camera or a sensor network. The devices transmit the collected data to a data convergence center in real time through a 5G, wi-Fi internet of things communication protocol to form a traffic collection data set containing multiple types of data.
Taking vehicle speed monitoring as an example, the traditional single-point speed measuring equipment is easy to be interfered by environment and has limited coverage range, and through the cooperative work of a distributed radar network and a vehicle-mounted sensor, the dynamic tracking and multi-source verification of the vehicle speed can be realized, and the reliability of data is ensured. In the aspect of road identification information acquisition, an image captured by a camera can be analyzed in real time based on an image recognition algorithm built in the camera, key information such as speed limit signs, traffic prohibition and the like are extracted, and hysteresis and errors of manual input are avoided. By integrating the data with different dimensions, a three-dimensional traffic scene model can be constructed, and rich information support is provided for subsequent analysis of traffic events and safe data transmission.
Through the collaborative deployment and data fusion technology of the multiple types of sensor equipment, a multidimensional traffic collection data set can be obtained, the integrity and scene restoration capability of the data set are ensured, and a foundation is laid for establishing the association relation between traffic data and events and implementing differentiated data security processing strategies.
Further, step a200 in the method provided by the embodiment of the present application includes:
And A210, obtaining a traffic event library, and classifying traffic events according to the influence range, timeliness and related entity class of the traffic event library.
And A220, analyzing event core influence data and auxiliary influence data aiming at traffic events classified in each grade, and establishing a mapping relation between the traffic events and a traffic collection data set and dividing and identifying characteristics of the core data and the auxiliary data of each traffic event.
A230, setting the encryption data partitioning rule according to the partition identification characteristics of the core data and the auxiliary data, wherein the core data corresponds to a block chain processing area, and the auxiliary data corresponds to a block chain mapping area.
In the embodiment of the application, the traffic event library is a set for storing traffic event data.
Specifically, firstly, a traffic event library is constructed, historical traffic event data (such as accidents, congestion, construction and the like) is integrated in the library, and three-level classification is carried out according to an influence range (such as regional level and city level), timeliness (such as real-time events and historical events) and related matter levels (such as common vehicles and emergency vehicles). For example, urban real-time traffic accidents are classified into high-grade accidents due to wide influence range and the inclusion of emergency vehicles in the accident subject, and temporary congestion of local road sections is classified into low-grade events.
After classification, the core influence data and the auxiliary influence data need to be analyzed for each level of event, the cross relation dimension (the higher the level is, the higher the dimension is) of the core and the auxiliary influence data is set according to the level, and the extraction rules of the two types of data are determined according to the analysis relation, and specific steps are described in detail in A221-A222.
Then, a mapping relation between the event and the data set is established, key attributes (such as an influence range, timeliness and related entity level) of each level event are extracted from the traffic event library, and then the attributes are associated and matched with specific fields in the traffic collection data set. Taking accident level as an example, the low-level accident is only related to basic data fields such as vehicle speed, position and the like, the medium-level accident is further related to fields such as the type of the involved vehicle, collision angle and the like, and the high-level accident is required to be related to more fields such as driver identity identification, vehicle tracks in 500 meters around and the like. By expanding the corresponding relation between event attributes and data fields layer by layer, sensitive features (such as geographic positions and identity marks) and auxiliary data expansion features (such as environment states and non-sensitive tracks) contained in the core data are identified, and finally, clear standards for dividing the core data and the auxiliary data according to event levels are formed, so that basis is provided for setting the partitioning rules of the follow-up encrypted data.
Based on the association relation and the characteristics, the system configures an encrypted data partitioning rule, namely, the core data is divided into a blockchain processing area, a hash algorithm is adopted to generate a data abstract and carry out private key signature, so that the integrity and source traceability of the data on the chain are ensured, auxiliary data is divided into a blockchain mapping area, encrypted and stored in an under-chain distributed database, and quick calling is realized through the index on the chain. For example, the real-time location of the emergency vehicle is stored as core data up-link, while the model information of the surrounding ordinary vehicle is stored as auxiliary data down-link, and is accessed through the index only after the authority verification.
The accurate mapping from traffic event characteristics to data security policies is realized through the processes of event library construction, class classification, data analysis and partition configuration, the process guarantees the security of core data through a block chain by means of a data classification mechanism driven by event classes, the processing efficiency of auxiliary data is improved by using under-chain storage, and the effects of differentiated data security management and efficient transmission based on the event characteristics are finally achieved.
Further, step a220 in the method provided by the embodiment of the present application includes:
And A221, setting the cross relation dimension of the core influence data and the auxiliary influence data according to the grade, wherein the cross relation dimension is higher as the traffic event grade is higher.
A222, according to the cross relation dimension, analyzing the relation of the core influence data and the auxiliary influence data of the traffic event, and determining the extraction rule of the core influence data and the auxiliary influence data.
In the embodiment of the application, the cross relation dimension refers to the number of association levels between the core influence data and the auxiliary influence data, which are set according to the traffic event level, and is used for reflecting the analysis complexity of the two types of data.
Specifically, first, the cross relation dimension of the core influence data and the auxiliary influence data is preset according to the grade (such as low, medium and high) of the traffic event. For example, a low-level event (such as temporary congestion of a local road section) is provided with 1-2 cross dimensions, only the speed and congestion position of a vehicle are required to be used as core data, surrounding road marks are required to be used as auxiliary data, a medium-level event (such as traffic accidents of an urban arterial road) is lifted to 2-3 cross dimensions, the type of the accident vehicle is required to be related and analyzed with the driving direction of the surrounding vehicle except the position and speed of the accident vehicle, a high-level event (such as full-city-level traffic control related to an emergency vehicle) is provided with 3-5 cross dimensions, and the cross relation of multi-source data such as an emergency vehicle route, a real-time traffic light state, a multi-vehicle track in 500 m surrounding is required to be integrated.
In the parsing process, hierarchical processing is carried out on the data by utilizing the cross relation dimension. Taking a high-level event as an example, firstly extracting the position of an emergency vehicle as core data, then cross-verifying the avoidance track of surrounding common vehicles as auxiliary data with the core data based on the dimension of the type and the running direction of the vehicle, and then analyzing the expansion effect of adjusting the influence range of the event when the intersection signal lamp is analyzed through the dimension of the traffic light state and the event timeliness to form second-layer auxiliary data. Through multi-dimensional cross analysis, the core data and the auxiliary data are not divided by a simple dichotomy, but form a net-shaped association structure, so that the data integrity and scene restoration degree of the high-level event are ensured. Low-level events are quickly divided by simple dimensions.
The method has the advantages that the fine analysis of event data of different levels is realized by dynamically adjusting the cross relation dimension, the waste of data processing resources is avoided by concise dimension setting for low-level events, the comprehensiveness of core data and the relevance of auxiliary data are ensured by multi-dimensional cross analysis for high-level events, and finally the effects of dynamically adjusting the data analysis depth based on event levels and improving the relevance of the high-level event data and the low-level event processing efficiency are achieved.
Further, step a400 in the method provided by the embodiment of the present application includes:
a410, storing the auxiliary data in the under-chain distributed database in an encryption mode, and establishing a unique mapping relation between the on-chain data index and the auxiliary data path by adopting the on-chain data index to call the under-chain data after the intelligent contract verification passes.
Specifically, in order to optimize auxiliary data management, firstly, auxiliary data (such as surrounding vehicle tracks, road environment information and the like) of a blockchain mapping area are encrypted, and data are encrypted field by field or file by adopting a high-strength encryption algorithm such as AES-256 and the like, so that confidentiality of under-chain storage is ensured. For example, after encrypting the surrounding vehicle identification information in the traffic event by the AES algorithm, the surrounding vehicle identification information is stored in the form of ciphertext in an under-chain distributed database, such as IPFS or CouchDB, so as to prevent the data from being illegally read in the storage process.
Then, a unique mapping relationship of the on-chain data index and the off-chain auxiliary data path is established in the blockchain network. Specifically, each auxiliary data file generates a unique path identifier, such as a file hash value or a database address, when stored under the chain, and records the mapping relationship between the path identifier and a data index (such as a unique key value formed by combining event ID and data type) on the chain.
And after the intelligent contract passes verification, the authority verification of the data access party is successful, the auxiliary data path under the chain is quickly positioned by utilizing the data index on the chain, and the encrypted stored auxiliary data is called and decrypted, so that the compliance and the instantaneity of the data use are ensured. For example, when a traffic management department initiates a data access request, the intelligent contract verifies the authority of the traffic management department, finds an off-link data path according to the on-link index after passing the permission, invokes encrypted peripheral vehicle track data, and performs association analysis with on-link core data after decryption.
Through the flow, the safe storage and efficient calling of the auxiliary data are realized, and the effects of improving the data access efficiency and the system expansibility and supporting the real-time collaborative processing of the multidimensional data in the intelligent traffic scene are achieved on the premise of ensuring the safety of the auxiliary data.
Further, step a510 in the method provided by the embodiment of the present application includes:
And A511, after an access request is initiated, comparing the identity of the current visitor with the data access setting permission through the intelligent contract, and carrying out calling behavior authorization matching judgment by combining the access behavior, wherein the intelligent contract comprises a multi-factor access policy matching mechanism of the access identity, the data access permission and the access history behavior.
A512, when the matching is successful, allowing the consistency verification of the auxiliary data under the calling chain and the core data on the chain, and simultaneously, enabling the calling access behavior to be written into the blockchain network to form an audit log for subsequent data tracking and backtracking analysis.
In one embodiment, to achieve refined rights management, a multi-factor access policy mechanism is built through intelligent contracts deployed in a blockchain network. When a data access party (such as a traffic police department, a scientific research institution and the like) initiates a request, the intelligent contract firstly extracts the identity information (such as an institution code and a user ID) of the visitor, compares the identity information with a predefined authority list (such as real-time accident data accessible by the traffic police department), and simultaneously retrieves the historical behavior record (such as the data calling frequency and the operation type in the past 30 days) of the visitor to form a multi-dimensional verification parameter. For example, when a traffic police department applies to call congestion data of a certain road section, an intelligent contract can check whether the traffic police department has real-time traffic data access rights or not, and analyze whether the historical access record has abnormal call behaviors or not.
In the matching judging stage, the intelligent contract adopts a rule engine to carry out weighted matching on the access identity, the authority and the historical behavior. If the visitor identity meets the permission requirement and the history behavior is normal (if no override record exists), the matching is judged to be successful, and the visitor identity is allowed to call the auxiliary data under the chain and the core data on the chain. For example, after matching is successful, the data index is used for retrieving the encrypted and stored surrounding vehicle track data (auxiliary data) under the chain, and consistency verification is carried out on the surrounding vehicle track data and the hash value of the core data stored on the chain, so that the data is ensured not to be tampered. After the verification is passed, the access party acquires complete traffic data, and the intelligent contract writes the access behavior (such as access time, data type and operation main body) into the blockchain in a transaction form to form an tamper-proof audit log.
Through the process, the intelligent contract realizes the full-process automatic management from the right verification, the data verification and the operation audit, achieves the effects of realizing the precise management and control of the traffic data access and the credible traceability of the operation behavior through the multi-factor verification and the blockchain storage of the intelligent contract, and improves the standardization and auditability of the data security management.
In summary, the traffic data security transmission method based on the blockchain provided by the embodiment of the application has the following technical effects:
According to the application, a traffic data monitoring network is connected to collect a traffic data set, an association relation between the data set and traffic events is established, hash signing and uplink recording are carried out on core data through event grade classification, core and auxiliary data analysis, encryption partition and other processing, auxiliary data is encrypted and stored under a chain, a mapping relation is established, data transmission is realized based on a blockchain network and under-chain mapping, authority verification and data consistency verification are carried out through an intelligent contract, so that transmission and management of traffic data are safely and efficiently completed, the traffic data safe transmission method is more reliable, and the technical effects of improving safety, integrity and traceability of data transmission are achieved.
In a second embodiment, as shown in fig. 2, based on the same inventive concept as the previous embodiment, the embodiment of the present application provides a traffic data security transmission system based on a blockchain, where the system includes:
The traffic collection data set acquisition module 1 is used for connecting a traffic data monitoring network to obtain a traffic collection data set, and establishing an association relationship between the traffic collection data set and traffic events.
The traffic collection data set partitioning module 2 is used for configuring encryption data partitioning rules according to the association relation, and identifying and partitioning the traffic collection data set to obtain a blockchain processing area and a blockchain mapping area.
And the traffic collection data set positioning module 3 is used for carrying out data positioning on the traffic collection data set based on the blockchain processing area, extracting target collection data for carrying out hash processing, generating a data abstract and carrying out digital signature by a private key used by a data source node.
And the blockchain network recording module 4 is used for submitting the data abstract, the digital signature, the data index and the access right information to a blockchain network for carrying out on-chain recording, and establishing a mapping relation under the chain with the blockchain mapping area.
And the traffic collection data set transmission module 5 is used for carrying out traffic collection data set transmission based on the block chain network and the under-chain mapping relation by the traffic collection data set transmission module 5.
Further, the traffic collection data set transmission module 5 is configured to perform the following steps:
And when the data access party passes the authority management verification, invoking the data under the chain through the data index and carrying out consistency verification based on hash on the chain, and obtaining the traffic transmission data after the consistency verification.
Further, the traffic collection data set obtaining module 1 is configured to perform the following steps:
the traffic collection data set comprises one or more of vehicle position, vehicle speed, vehicle direction, road identification information, traffic light status, and surrounding vehicle identification information.
Further, the traffic collection data set partitioning module 2 is configured to perform the following steps:
the method comprises the steps of obtaining a traffic event library, classifying traffic events according to the influence range, timeliness and related entity levels of the traffic event library, analyzing event core influence data and auxiliary influence data aiming at the traffic events classified in each level, establishing a mapping relation between the traffic events and a traffic collection data set, dividing and identifying characteristics of core data and auxiliary data of each traffic event, and setting encryption data partitioning rules according to the dividing and identifying characteristics of the core data and the auxiliary data, wherein the core data corresponds to a block chain processing area, and the auxiliary data corresponds to a block chain mapping area.
Further, the traffic collection data set partitioning module 2 is configured to perform the following steps:
Setting cross relation dimension of core influence data and auxiliary influence data according to the grade, wherein the cross relation dimension is higher as the traffic event grade is higher, analyzing the core influence data and auxiliary influence data relation of the traffic event according to the cross relation dimension, and determining extraction rules of the core influence data and the auxiliary influence data.
Further, the blockchain network recording module 4 is configured to perform the following steps:
the auxiliary data is stored in the under-chain distributed database in an encryption mode, and a unique mapping relation is established between the on-chain data index and the auxiliary data path and is used for calling the under-chain data after the intelligent contract verification passes.
Further, the traffic collection data set transmission module 5 is configured to perform the following steps:
When the access request is initiated, the identity of the current visitor is compared with the data access setting authority through the intelligent contract, and the calling behavior authorization matching judgment is carried out by combining the access behavior, wherein the intelligent contract comprises a multi-factor access strategy matching mechanism of the access identity, the data access authority and the access history behavior, when the matching is successful, the consistency verification is carried out on the auxiliary data under the calling chain and the core data on the chain, and meanwhile, the calling behavior is written into a blockchain network to form an audit log for subsequent data tracking and backtracking analysis.
The traffic data safety transmission system based on the blockchain provided by the embodiment of the invention can execute the traffic data safety transmission method based on the blockchain provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Although the present application makes various references to certain modules in a system according to an embodiment of the present application, any number of different modules may be used and run on a user terminal and/or a server, and each unit and module included are merely divided according to functional logic, but are not limited to the above-described division, so long as the corresponding functions can be implemented, and in addition, specific names of each functional unit are only for convenience of distinguishing from each other, and are not intended to limit the scope of protection of the present application.
The above embodiments do not limit the scope of the present application. It will be apparent to those skilled in the art that various modifications, combinations, and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the scope of the present application. In some cases, the acts or steps recited in the present application may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
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