Traceable method and system for wide-area traffic trusted positioning multi-source heterogeneous data

Abstract

This invention provides a traceable method and system for reliable positioning of multi-source heterogeneous data in wide-area traffic, comprising: constructing a standard-structured metadata entity information table for traffic service information; creating a Bayesian classification algorithm for the metadata entity information table; integrating data using association rules and intelligent clustering to create a data table; establishing multiple heterogeneous data sources using a heterogeneous blockchain algorithm and providing a physical database for the data sources; mapping the physical database to obtain a logical database; using TPM or vTPM to create a trusted cloud platform to store the logical database and establish a blockchain; reducing the dimensionality of spatiotemporal data in the blockchain to spatiotemporal phase points; and constructing a Skiplist using JSON format files for search operations. This invention, based on a mapped metadata model, sorts out key connections from massive, diverse, and heterogeneous big data and establishes a standard-structured metadata entity information table for the metadata.

Description

Technical Field

[0001] This invention relates to the technical field of traceability technology, specifically to a traceability method and system for reliable positioning of multi-source heterogeneous data in wide-area traffic. Background Technology

[0002] Currently, the amount of location data and the speed of data transmission for vehicles are very fast. Due to the complexity of the data itself, it is difficult to simply classify it according to the type of vehicle. Instead, it is necessary to combine timestamps, location data, vehicle terminals, and roadside units to classify, integrate, and map the data.

[0003] Patent document CN111507394A discloses a multi-domain spatial data fusion method and fusion device. The method includes: acquiring positioning data of multiple vehicles, wherein the vehicles are at least two of the following: water vehicles, air vehicles, and land vehicles; converting the positioning data of the multiple vehicles into positioning data in the same coordinate system, denoted as first data; and displaying the first data on the same display screen. Existing data classification and conversion methods are ill-suited for integrating and indexing large amounts of data in complex situations. During the positioning data fusion process, time is inevitably wasted and efficiency is reduced, and information is easily lost, making subsequent traceability impossible.

[0004] Therefore, a new technical solution is needed to improve the above-mentioned technical problems. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the purpose of this invention is to provide a traceable method and system for reliable positioning of multi-source heterogeneous data in wide-area traffic.

[0006] According to the present invention, a traceability method for reliable positioning of multi-source heterogeneous data in wide-area traffic is provided, the method comprising the following steps:

[0007] Step S1: Construct a standard structure metadata entity information table for transportation service information;

[0008] Step S2: Create a Bayesian classification algorithm for the metadata entity information table;

[0009] Step S3: Using the Bayesian classification algorithm, data is integrated through association rules and intelligent clustering to create a data table;

[0010] Step S4: Using data tables, establish multiple heterogeneous data sources using heterogeneous blockchain algorithms, and provide a physical library for the data sources;

[0011] Step S5: Map the physical library to obtain the logical library;

[0012] Step S6: Use TPM or vTPM to create a trusted cloud platform to store the logic library and establish a blockchain;

[0013] Step S7: Reduce the spatiotemporal data in the blockchain to spatiotemporal phase points;

[0014] Step S8: Construct a Skiplist using a JSON file and perform a search operation.

[0015] Preferably, the traffic service information in step S1 includes the creation of a metadata model from information modules such as trusted navigation and positioning signals, vehicle-mounted terminals, roadside units, and mileage tolling modules.

[0016] Preferably, the blockchain in step S6 includes the blockchain's inherent algorithm.

[0017] Preferably, step S7 uses the phase point partial order relation "≤" to construct phase point order branches, and compares the size relationship between different nodes with the phase point partial order relation of phase-point. Multi-level indexes are added to the original ordered linked list, and a JSON format file is formed by searching through the indexes.

[0018] Preferably, the Skiplist index is constructed using the Skiplist data structure and the partial order relationship "≤"; a traceability mechanism is obtained, and a metadata model based on semantic mapping is established.

[0019] This invention also provides a traceability system for reliable positioning of multi-source heterogeneous data in wide-area traffic, the system comprising the following modules:

[0020] Module M1: Constructs a standard structure metadata entity information table for transportation service information;

[0021] Module M2: Creates a Bayesian classification algorithm for the metadata entity information table;

[0022] Module M3: Relying on the Bayesian classification algorithm, it integrates data using association rules and intelligent clustering to create data tables;

[0023] Module M4: Utilizes data tables and heterogeneous blockchain algorithms to establish multiple heterogeneous data sources and provides a physical library for the data sources;

[0024] Module M5: Maps the physical library to obtain the logical library;

[0025] Module M6: Use TPM or vTPM to create a trusted cloud platform to store the logic library and establish a blockchain;

[0026] Module M7: Reduces the dimensionality of spatiotemporal data in the blockchain and transforms it into spatiotemporal phase points;

[0027] Module M8: Constructs a Skiplist using a JSON file and performs search operations.

[0028] Preferably, the traffic service information in module M1 includes a metadata model created by the information module of the trusted navigation and positioning signal, vehicle terminal, roadside unit, and mileage toll module.

[0029] Preferably, the blockchain in module M6 includes the blockchain's inherent algorithm.

[0030] Preferably, module M7 uses the phase point partial order relation "≤" to construct phase point order branches, and compares the size relationship between different nodes with the phase point partial order relation of phase-point. Multi-level indexes are added to the original ordered linked list, and a JSON format file is formed by searching through the indexes.

[0031] Preferably, the Skiplist index is constructed using the Skiplist data structure and the partial order relationship "≤"; a traceability mechanism is obtained, and a metadata model based on semantic mapping is established.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] 1. This invention sorts out key threads from massive, diverse, and heterogeneous big data by using a metadata model based on mapping, and establishes a standard structure metadata entity information table for the metadata;

[0034] 2. This invention solves the problem of heterogeneous storage of various types of data by researching a new hash method to map data, achieving efficient storage and meeting the data storage requirements of wide-area transportation.

[0035] 3. This invention solves the problem of malicious users modifying data by adopting a unique blockchain self-inspection method, thus achieving the effects of ensuring data security, reliable location and traceability.

[0036] 4. By adopting the Skiplist+Phase-Point structure, this invention solves the problems of reducing the dimensionality of spatiotemporal data and enabling integrated querying of spatiotemporal data, thereby shortening query time and reducing memory usage. Attached Figure Description

[0037] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0038] Figure 1 This is a schematic diagram of a traceability system;

[0039] Figure 2 A flowchart for intelligent clustering;

[0040] Figure 3 A flowchart of the cloud platform technology process;

[0041] Figure 4 A schematic diagram of the internal algorithm of blockchain;

[0042] Figure 5 A logic diagram for spatiotemporal phase point indexing;

[0043] Figure 6 The intent to represent metadata entity information;

[0044] Figure 7 This is a schematic diagram of the physical data source and nodes for a blockchain algorithm. Detailed Implementation

[0045] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0046] Example 1:

[0047] According to the present invention, a traceability method for reliable positioning of multi-source heterogeneous data in wide-area traffic is provided, the method comprising the following steps:

[0048] Step S1: Construct a standard structure metadata entity information table for traffic service information; the traffic service information includes information modules such as trusted navigation and positioning signals, vehicle terminals, roadside units, and mileage tolling modules, and create a metadata model.

[0049] Step S2: Create a Bayesian classification algorithm for the metadata entity information table;

[0050] Step S3: Using the Bayesian classification algorithm, data is integrated through association rules and intelligent clustering to create a data table;

[0051] Step S4: Using data tables, establish multiple heterogeneous data sources using heterogeneous blockchain algorithms, and provide a physical library for the data sources;

[0052] Step S5: Map the physical library to obtain the logical library;

[0053] Step S6: Use TPM or vTPM to create a trusted cloud platform to store the logic library and establish a blockchain; the blockchain includes the blockchain's inherent algorithm.

[0054] Step S7: Reduce the spatiotemporal data in the blockchain to spatiotemporal phase points; construct phase point order branches using the phase point partial order relationship "≤", and compare the size relationship between different nodes with the phase point partial order relationship of phase-point. Add multi-level indexes to the original ordered linked list, and form a JSON format file by searching through the index.

[0055] Step S8: Construct a Skiplist using a JSON file and perform a search operation.

[0056] By utilizing the Skiplist data structure and the partial order relation "≤", a Skiplist index is constructed; a traceability mechanism is obtained, and a metadata model based on semantic mapping is established.

[0057] The present invention also provides a traceability system for multi-source heterogeneous data of trusted positioning in wide-area traffic. The traceability system for multi-source heterogeneous data of trusted positioning in wide-area traffic can be implemented by executing the process steps of the traceability method for multi-source heterogeneous data of trusted positioning in wide-area traffic. That is, those skilled in the art can understand the traceability method for multi-source heterogeneous data of trusted positioning in wide-area traffic as a preferred embodiment of the traceability system for multi-source heterogeneous data of trusted positioning in wide-area traffic.

[0058] Example 2:

[0059] This invention also provides a traceability system for reliable positioning of multi-source heterogeneous data in wide-area traffic, the system comprising the following modules:

[0060] Module M1: Constructs a standard structure metadata entity information table for traffic service information; the traffic service information includes trusted navigation and positioning signals, vehicle terminals, roadside units, and information modules for mileage tolling modules, creating a metadata model.

[0061] Module M2: Creates a Bayesian classification algorithm for the metadata entity information table;

[0062] Module M3: Relying on the Bayesian classification algorithm, it integrates data using association rules and intelligent clustering to create data tables;

[0063] Module M4: Utilizes data tables and heterogeneous blockchain algorithms to establish multiple heterogeneous data sources and provides a physical library for the data sources;

[0064] Module M5: Maps the physical library to obtain the logical library;

[0065] Module M6: Uses TPM or vTPM to create a trusted cloud platform to store the logic library and establish a blockchain; the blockchain includes the blockchain's internal algorithm.

[0066] Module M7: Reduces the spatiotemporal data in the blockchain to spatiotemporal phase points; constructs phase point order branches using the phase point partial order relationship "≤", and compares the size relationship between different nodes with the phase point partial order relationship of phase-point. Adds multi-level indexes to the original ordered linked list, and forms a JSON format file by searching through the index.

[0067] Module M8: Constructs a Skiplist using a JSON file and performs search operations.

[0068] By utilizing the Skiplist data structure and the partial order relation "≤", a Skiplist index is constructed; a traceability mechanism is obtained, and a metadata model based on semantic mapping is established.

[0069] Example 3:

[0070] This invention addresses the demands and desires of consumers in intelligent transportation toll collection scenarios, clarifies the responsible units in wide-area reliable navigation and positioning mileage-based toll collection services based on a traceability technology mechanism, and utilizes the fields required by big data analysis to collect data from reliable navigation and positioning signals, vehicle terminals, roadside units, and mileage-based toll collection modules. Information such as vehicle ID, location stamp, timestamp, video, and images is abstracted into a metadata model, constructing a standard-structured metadata entity information table to achieve efficient management of massive heterogeneous data. Simultaneously, a Bayesian classification algorithm for different information fields is constructed based on probability statistics. Then, association rules and intelligent clustering are used for data integration, providing a clear and traceable data form. Subsequently, this invention constructs an efficient and secure storage method based on a unique database format and heterogeneous storage indexing method, enabling large-capacity storage and providing high-speed data interfaces. Furthermore, a Skiplist index is constructed using the Skiplist data structure and the partial order relationship (analogous to the partial order relationship between different nodes) "≤". This achieves a traceability technology mechanism and establishes a metadata model based on semantic mapping; including the following steps:

[0071] Step 1: Create metadata models for traffic service information modules, such as reliable navigation and positioning signals, vehicle-mounted terminals, roadside units, mileage-based toll collection modules, etc., and construct standard structure metadata entity information tables (see...). Figure 1 , Figure 6 ).

[0072] Step 2: Create a unique Bayesian classification algorithm for the metadata entity information table.

[0073] Step 3: Relying on a unique Bayesian classification algorithm, data is integrated using association rules and intelligent clustering to create an efficient data table (see...). Figure 2).

[0074] Step 4: Utilize data tables and a unique heterogeneous blockchain algorithm to establish multiple heterogeneous data sources, and provide the physical repository for these data sources (see...). Figure 7 ).

[0075] Step 5: Map the physical library to obtain the logical library.

[0076] Step 6: Use TPM or vTPM to create a trusted cloud platform to store the logic library and establish a blockchain. (See...) Figure 3 ).

[0077] Step 6.1: The Intrinsic Algorithm of Blockchain. (See...) Figure 4 ).

[0078] Step 7: Reduce the spatiotemporal data in the blockchain to spatiotemporal phase points to improve indexing efficiency. Use the phase point partial order relationship "≤" to construct phase point order branches. Analogize the size relationship between different nodes to the phase point partial order relationship of Phase-Point. Add multi-level indexes to the original ordered linked list to quickly search through the index and form a JSON format file.

[0079] Step 8: Construct a Skiplist using a JSON file to implement search operations, supporting dynamic insertion and deletion operations, while maintaining low time complexity (see...). Figure 5 ).

[0080] Those skilled in the art can understand this embodiment as a more specific description of Embodiment 1 and Embodiment 2.

[0081] Those skilled in the art will understand that, besides implementing the system and its various devices, modules, and units provided by this invention in the form of purely computer-readable program code, the same functions can be achieved entirely through logical programming of the method steps, making the system and its various devices, modules, and units of this invention function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, the system and its various devices, modules, and units provided by this invention can be considered as a hardware component, and the devices, modules, and units included therein for implementing various functions can also be considered as structures within the hardware component; alternatively, the devices, modules, and units for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0082] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A traceable method for wide-area traffic trusted positioning multi-source heterogeneous data, characterized in that, The method includes the following steps: Step S1: Construct a standard structure metadata entity information table for transportation service information; Step S2: Create a Bayesian classification algorithm for the metadata entity information table; Step S3: Using the Bayesian classification algorithm, data is integrated by employing association rules and intelligent clustering to create a data table; Step S4: Using data tables, establish multiple heterogeneous data sources using heterogeneous blockchain algorithms, and provide a physical library for the data sources; Step S5: Map the physical library to obtain the logical library; Step S6: Use TPM or vTPM to create a trusted cloud platform to store the logic library and establish a blockchain; Step S7: Reduce the spatiotemporal data in the blockchain to spatiotemporal phase points; Step S8: Construct a Skiplist using a JSON file and perform a search operation; The Skiplist index is constructed using the Skiplist data structure and the phase point partial order relationship "≤"; We will obtain traceability technology mechanisms and establish a metadata model based on semantic mapping.

2. The traceable method for wide-area traffic trusted positioning multi-source heterogeneous data according to claim 1, characterized in that, The information for the traffic services in step S1 includes the creation of a metadata model from information modules such as trusted navigation and positioning signals, vehicle-mounted terminals, roadside units, and mileage tolling modules.

3. The traceable method for wide-area traffic trustable positioning multi-source heterogeneous data according to claim 1, characterized in that, The blockchain in step S6 includes the blockchain's inherent algorithm.

4. The traceability method for reliable multi-source heterogeneous data of wide-area traffic positioning according to claim 1, characterized in that, Step S7 utilizes the Phase Point partial order relation "≤" to construct a phase point order branch, analogizing the size relationship between different nodes to the phase point partial order relation of Phase-Point, adding multi-level indexes to the original ordered linked list, and forming a JSON format file through index lookup.

5. A traceable system for reliable positioning of multi-source heterogeneous data in wide-area traffic, characterized in that, The system includes the following modules: Module M1: Constructs a standard structure metadata entity information table for transportation service information; Module M2: Creates a Bayesian classification algorithm for the metadata entity information table; Module M3: Relying on the Bayesian classification algorithm, it integrates data using association rules and intelligent clustering to create data tables; Module M4: Utilizes data tables and heterogeneous blockchain algorithms to establish multiple heterogeneous data sources and provides a physical library for the data sources; Module M5: Maps the physical library to obtain the logical library; Module M6: Use TPM or vTPM to create a trusted cloud platform to store the logic library and establish a blockchain; Module M7: Reduces the dimensionality of spatiotemporal data in the blockchain and transforms it into spatiotemporal phase points; Module M8: Constructs a Skiplist using a JSON file and performs search operations; The Skiplist index is constructed using the Skiplist data structure and the phase point partial order relation "≤"; We will obtain traceability technology mechanisms and establish a metadata model based on semantic mapping.

6. The traceability system for reliable positioning of multi-source heterogeneous data in wide-area traffic according to claim 5, characterized in that, The traffic service information in module M1 includes reliable navigation and positioning signals, vehicle terminals, roadside units, and information modules for mileage toll collection, creating a metadata model.

7. The traceability system for reliable positioning of multi-source heterogeneous data in wide-area traffic according to claim 5, characterized in that, The blockchain in module M6 includes the blockchain's inherent algorithm.

8. The traceability system for reliable positioning of multi-source heterogeneous data in wide-area traffic according to claim 5, characterized in that, The module M7 uses the "≤" partial order relation of the phase points to construct phase point order branches, and compares the size relationship between different nodes with the partial order relation of the phase points of the phase points. It adds multi-level indexes to the original ordered linked list, and forms a JSON format file by searching through the indexes.

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