Traffic database acquisition system and method based on business field

Abstract

The invention relates to the technical field of traffic database collection, and discloses a traffic database collection system and method based on the business field, and the system comprises a data collection planning module which is responsible for the overall planning and design of urban traffic data collection, and comprises the layout of data collection point locations, the setting of data collection frequency, and the formulation of a data structure standard; an AI auxiliary planning technology is introduced, and data acquisition point positions and frequency are intelligently adjusted according to multi-dimensional data of traffic flow, road conditions and weather conditions. Through an AI auxiliary planning technology, data acquisition point location and frequency can be intelligently adjusted, data are ensured to be comprehensive and accurate, resource waste is avoided, a machine learning algorithm is introduced to carry out accuracy verification, errors and abnormal values in the data are automatically detected and corrected, and data quality is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic database collection, and in particular to a traffic database collection system and method based on a business field. Background Art

[0002] The traffic database collection system based on business fields is an important part of the intelligent transportation system. It focuses on collecting, organizing and analyzing relevant data in the transportation field to support traffic planning, management, optimization and decision-making.

[0003] In the existing technology, the traffic database collection system based on the business field still has some shortcomings:

[0004] 1. Lack of overall planning and design: Urban traffic data collection often lacks directional overall planning and design, resulting in inconsistent data structure standards, strong discreteness, and uneven quality after collection. Various intelligent information systems are in the initial stage and lack in-depth engineering design and refined design, resulting in low overall data collection efficiency and accuracy;

[0005] 2. Data quality is difficult to guarantee: Due to the limitations of early hardware equipment, the established systems are often not robust enough, making it difficult to judge the data quality on their own. The integrity and accuracy of the data are questionable, and the value of the data itself cannot be fully utilized;

[0006] 3. Data security threats: The entire process of traffic big data collection, transmission, storage and analysis is carried out through the Internet and cloud computing platforms, which poses data security threats. Traditional security mechanisms including firewalls and anti-virus software are not sufficient to ensure the security of big data.

[0007] Therefore, it is necessary to design a traffic database collection system and method based on the business field to solve the above problems. Summary of the invention

[0008] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a traffic database collection system and method based on the business field.

[0009] In order to achieve the above object, the present invention adopts the following technical solutions:

[0010] A traffic database collection system based on business fields, including the following modules:

[0011] Data collection planning module: responsible for the overall planning and design of urban traffic data collection, including the layout of data collection points, the setting of data collection frequency, the formulation of data structure standards, and the introduction of AI-assisted planning technology to intelligently adjust data collection points and frequencies based on multi-dimensional data such as traffic flow, road conditions, and weather conditions;

[0012] Engineering in-depth design and refined design module: Detailed design and optimization of data acquisition equipment, transmission network, and storage system, adopting modular design concept to achieve rapid deployment and flexible adjustment of data acquisition equipment, and introduce Internet of Things technology;

[0013] Data integrity verification module: During the data collection process, the data integrity is verified, and blockchain technology is used to achieve distributed storage and non-tamperability of data;

[0014] Data accuracy verification module: Verify the accuracy of the collected data, including data range check, outlier detection, introduce machine learning algorithms, establish a data quality assessment model, and automatically detect and correct errors and outliers in the data;

[0015] Data encryption transmission module: During data collection and transmission, encryption algorithms are used to encrypt data, combined with quantum encryption technology to provide a high level of data protection;

[0016] Data security monitoring and emergency response module: monitors the security status of data in real time. Once abnormal behavior or potential threats are found, the emergency response mechanism is immediately triggered. AI security monitoring technology is introduced to achieve intelligent early warning and automatic defense, and establish a rapid response mechanism.

[0017] Cross-domain collaborative application module: provides unified interfaces and standards, supports integration and collaborative application with systems in other business areas, and adopts microservice architecture to achieve modularity and scalability of the system;

[0018] Intelligent application expansion module: Based on the collected traffic data, develop intelligent applications, including autonomous driving and intelligent scheduling, introduce deep learning artificial intelligence technology, and improve the accuracy and reliability of intelligent applications.

[0019] As a preferred technical solution of the present invention, in the data collection planning module, the module not only focuses on the reasonable layout of data collection points and the scientific setting of data collection frequency, but also goes deep into the rigorous formulation of data structure standards, introduces AI-assisted planning technology, and can comprehensively consider real-time data in multiple dimensions of traffic flow, road conditions, and weather conditions. Through deep learning algorithms, traffic data is mined and analyzed, and data collection points and frequencies are adjusted. During peak traffic flow or severe weather conditions, the system automatically increases data collection points and frequencies, and reduces data collection points and frequencies during periods of stable traffic flow or good weather conditions. The module also has the ability of self-learning and optimization. According to the actual data collection effect and feedback, the module adjusts and optimizes the data collection planning strategy to ensure continuous optimization and improvement of data collection work.

[0020] As a preferred technical solution of the present invention, in the engineering in-depth design and refined design module, a modular design concept is adopted to split the equipment into multiple independent and interchangeable modules, including sensor modules, data processing modules, and communication modules. At intersections with heavy traffic, the number and types of sensor modules are increased, and at sections with light traffic, the number of sensor modules is reduced to reduce costs and power consumption. In the design of the transmission network, redundant design and fault switching mechanism are adopted to ensure that data will not be lost or delayed during transmission. At the same time, Internet of Things technologies are introduced, including LoRa and NB-IoT low-power wide area network communication technologies. In the design of the storage system, distributed storage and cloud storage technologies are adopted to store data on multiple physical nodes to achieve redundant backup and load balancing of data.

[0021] As a preferred technical solution of the present invention, blockchain technology is introduced in the data integrity verification module. As a decentralized, distributed ledger database technology, blockchain has the characteristics of data immutability and traceability. During the data collection process, whenever new data is generated, the data integrity verification module will immediately perform a hash operation on the data to generate a unique data fingerprint, i.e., a hash value. This data fingerprint is then recorded in a new block of the blockchain and linked to the previous block through the hash value to form a complete data chain. Due to the distributed storage characteristics of the blockchain, these data fingerprints are stored in multiple copies. Even if a node fails or the data is tampered with, other nodes can still maintain the integrity and consistency of the data. When the integrity of the data needs to be verified, it is only necessary to recalculate the hash value of the data and compare it with the hash value recorded in the blockchain. If the two are consistent, it means that the data has not been damaged or tampered with during the collection and transmission process.

[0022] As a preferred technical solution of the present invention, in the data accuracy verification module, in the data range check stage, the system compares each data according to the preset reasonable value range. Data beyond this range is regarded as potentially inaccurate or erroneous data, which triggers further review. Outlier detection relies on statistical analysis and pattern recognition technology, and can automatically identify isolated points that are different from most data points in the data set. This module integrates machine learning algorithms. The algorithm learns the inherent laws and characteristics of the data by training historical data samples, and constructs a data quality assessment model. The model automatically detects errors and outliers in the data, and based on contextual information and the correlation between data, intelligently judges the error type and provides corresponding correction suggestions.

[0023] A traffic database collection method based on business domain includes the following steps:

[0024] Step 1: Data collection planning and design

[0025] a. Demand analysis: clarify the business needs of traffic data collection, including but not limited to traffic flow analysis, road condition monitoring, and weather impact assessment.

[0026] b. Point layout: Based on the characteristics of the urban transportation network and combined with AI-assisted planning technology, data collection points are intelligently selected to ensure that the points are reasonably distributed and can fully cover key sections and traffic nodes.

[0027] c. Frequency setting: Set the data collection frequency according to the changing pattern of traffic flow and road conditions to ensure that the data can reflect the real-time traffic conditions without causing data redundancy.

[0028] d. Data structure standards: Establish unified data structure standards, including data formats and naming rules, to facilitate subsequent data processing and analysis.

[0029] Step 2: Engineering in-depth design and refined design

[0030] a. Equipment selection: Select appropriate data collection equipment, such as cameras and sensors, based on data collection requirements, and consider the durability, accuracy, and compatibility of the equipment.

[0031] b. Network design: Design an efficient and stable transmission network to ensure that data acquisition equipment can transmit data to the data center in real time and reliably.

[0032] c. Storage system optimization: Adopt a modular design concept to design an expandable storage system to ensure that it can store large amounts of long-term data and support rapid retrieval and analysis.

[0033] d. Internet of Things technology integration: Introduce Internet of Things technology to realize remote monitoring and management of data acquisition equipment and improve the flexibility of equipment deployment and adjustment.

[0034] Step 3: Data collection and integrity verification

[0035] a. Data collection: Start the data collection equipment according to the set points and frequencies to collect traffic data in real time.

[0036] b. Integrity verification: Blockchain technology is used to verify the integrity of data to ensure that the data has not been tampered with or lost during the collection process.

[0037] Step 4: Data accuracy verification and encrypted transmission

[0038] a. Accuracy verification: Introduce machine learning algorithms, establish data quality assessment models, and perform accuracy verification on the collected data, including data range checks and outlier detection, to automatically detect and correct errors and outliers in the data.

[0039] b. Data encryption: During data collection and transmission, encryption algorithms are used to encrypt data, combined with quantum encryption technology to provide a high level of data protection.

[0040] Step 5: Data security monitoring and emergency response

[0041] a. Security monitoring: Real-time monitoring of the security status of data, using AI security monitoring technology to achieve intelligent early warning and automatic defense.

[0042] b. Emergency response: Once abnormal behavior or potential threats are detected, the emergency response mechanism will be triggered immediately and necessary measures will be taken to protect data security.

[0043] Step 6: Cross-domain collaborative applications and intelligent application expansion

[0044] a. Cross-domain collaboration: Provide unified interfaces and standards to support integration and collaborative applications with systems in other business areas to achieve data sharing and interoperability.

[0045] b. Intelligent applications: Based on the collected traffic data, develop intelligent applications such as autonomous driving and intelligent scheduling, introduce advanced deep learning technologies, and improve the accuracy and reliability of applications.

[0046] The present invention has the following beneficial effects:

[0047] 1. Improve data collection efficiency and accuracy: AI-assisted planning technology can intelligently adjust data collection points and frequencies to ensure that data is both comprehensive and accurate, avoid wasting resources, introduce machine learning algorithms for accuracy verification, automatically detect and correct errors and outliers in the data, and significantly improve data quality;

[0048] 2. Enhance data security and integrity: Use blockchain technology for integrity verification, realize distributed storage and immutability of data, ensure the authenticity and credibility of data, apply encryption algorithms in the process of data collection and transmission, and combine quantum encryption technology to provide high-level data protection and effectively prevent data leakage and tampering;

[0049] 3. Optimize system flexibility and scalability: The engineering in-depth design and refined design modules adopt a modular design concept to facilitate the rapid deployment and flexible adjustment of data acquisition equipment. The cross-domain collaborative application module provides a unified interface and standard, supports system integration with other business areas, realizes data sharing and interoperability, and facilitates system expansion and upgrading. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A schematic diagram of the structure of a traffic database collection system based on a business field proposed by the present invention;

[0051] Figure 2 This is a schematic diagram of the steps of a traffic database collection method based on a business field proposed by the present invention. DETAILED DESCRIPTION

[0052] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0053] Reference Figure 1-2 , a traffic database collection system based on business fields, including the following modules:

[0054] Data collection planning module: responsible for the overall planning and design of urban traffic data collection, including the layout of data collection points, the setting of data collection frequency, the formulation of data structure standards, and the introduction of AI-assisted planning technology. According to the multi-dimensional data of traffic flow, road conditions, and weather conditions, the data collection points and frequency are intelligently adjusted. In the data collection planning module, this module not only focuses on the reasonable layout of data collection points and the scientific setting of data collection frequency, but also goes deep into the rigorous formulation of data structure standards. AI-assisted planning technology is introduced, which can comprehensively consider the real-time data of multiple dimensions of traffic flow, road conditions, and weather conditions. Through deep learning algorithms, traffic data is mined and analyzed, and data collection points and frequencies are adjusted. During peak traffic flow or bad weather conditions, the system automatically increases data collection points and frequencies, and reduces data collection points and frequencies during periods of stable traffic flow or good weather conditions. This module also has the ability of self-learning and optimization. According to the actual data collection effect and feedback, the data collection planning strategy is adjusted and optimized to ensure the continuous optimization and progress of data collection work;

[0055] Engineering deepening design and refined design module: Detailed design and optimization of data acquisition equipment, transmission network, and storage system. The modular design concept is adopted to realize the rapid deployment and flexible adjustment of data acquisition equipment. The Internet of Things technology is introduced. In the engineering deepening design and refined design module, the modular design concept is adopted to split the equipment into multiple independent and interchangeable modules, including sensor modules, data processing modules, and communication modules. At intersections with heavy traffic, the number and types of sensor modules are increased, and at sections with light traffic, the number of sensor modules is reduced to reduce costs and power consumption. In the design of the transmission network, redundant design and fault switching mechanism are adopted to ensure that data will not be lost or delayed during transmission. At the same time, the Internet of Things technology is introduced, including LoRa and NB-IoT low-power wide area network communication technologies. In the design of the storage system, distributed storage and cloud storage technologies are adopted to store data on multiple physical nodes to achieve redundant backup and load balancing of data.

[0056] Data integrity verification module: During the data collection process, the data is checked for integrity, and blockchain technology is used to achieve distributed storage and non-tamperability of data. In the data integrity verification module, blockchain technology is introduced. As a decentralized, distributed ledger database technology, blockchain has the characteristics of data non-tamperability and traceability. During the data collection process, whenever new data is generated, the data integrity verification module will immediately perform a hash operation on the data to generate a unique data fingerprint, i.e., a hash value. This data fingerprint is then recorded in a new block of the blockchain and linked to the previous block through the hash value to form a complete data chain. Due to the distributed storage characteristics of the blockchain, these data fingerprints are stored in multiple copies. Even if a node fails or the data is tampered with, other nodes can still maintain the integrity and consistency of the data. When the integrity of the data needs to be verified, it is only necessary to recalculate the hash value of the data and compare it with the hash value recorded in the blockchain. If the two are consistent, it means that the data has not been damaged or tampered with during the collection and transmission process;

[0057] Data accuracy verification module: performs accuracy verification on the collected data, including data range check and outlier detection, introduces machine learning algorithms, establishes a data quality assessment model, and automatically detects and corrects errors and outliers in the data. In the data accuracy verification module, during the data range check phase, the system compares each data item based on a preset reasonable value range. Data outside this range is considered as potentially inaccurate or erroneous data, which triggers further review. Outlier detection relies on statistical analysis and pattern recognition technology, and can automatically identify isolated points that are different from most data points in the data set. This module integrates machine learning algorithms. The algorithm learns the inherent laws and characteristics of the data by training historical data samples, and constructs a data quality assessment model. The model automatically detects errors and outliers in the data, and intelligently determines the error type and provides corresponding correction suggestions based on contextual information and the correlation between data;

[0058] Data encryption transmission module: During data collection and transmission, encryption algorithms are used to encrypt data, combined with quantum encryption technology to provide a high level of data protection;

[0059] Data security monitoring and emergency response module: monitors the security status of data in real time. Once abnormal behavior or potential threats are found, the emergency response mechanism is immediately triggered. AI security monitoring technology is introduced to achieve intelligent early warning and automatic defense, and establish a rapid response mechanism.

[0060] Cross-domain collaborative application module: provides unified interfaces and standards, supports integration and collaborative application with systems in other business areas, and adopts microservice architecture to achieve modularity and scalability of the system;

[0061] Intelligent application expansion module: Based on the collected traffic data, develop intelligent applications, including autonomous driving and intelligent scheduling, introduce deep learning artificial intelligence technology, and improve the accuracy and reliability of intelligent applications.

[0062] A traffic database collection method based on a business domain comprises the following steps:

[0063] Step 1: Data collection planning and design

[0064] a. Demand analysis: clarify the business needs of traffic data collection, including but not limited to traffic flow analysis, road condition monitoring, and weather impact assessment.

[0065] b. Point layout: Based on the characteristics of the urban transportation network and combined with AI-assisted planning technology, data collection points are intelligently selected to ensure that the points are reasonably distributed and can fully cover key sections and traffic nodes.

[0066] c. Frequency setting: Set the data collection frequency according to the changing pattern of traffic flow and road conditions to ensure that the data can reflect the real-time traffic conditions without causing data redundancy.

[0067] d. Data structure standards: Establish unified data structure standards, including data formats and naming rules, to facilitate subsequent data processing and analysis.

[0068] Step 2: Engineering in-depth design and refined design

[0069] a. Equipment selection: Select appropriate data collection equipment, such as cameras and sensors, based on data collection requirements, and consider the durability, accuracy, and compatibility of the equipment.

[0070] b. Network design: Design an efficient and stable transmission network to ensure that data acquisition equipment can transmit data to the data center in real time and reliably.

[0071] c. Storage system optimization: Adopt a modular design concept to design an expandable storage system to ensure that it can store large amounts of long-term data and support rapid retrieval and analysis.

[0072] d. Internet of Things technology integration: Introduce Internet of Things technology to realize remote monitoring and management of data acquisition equipment and improve the flexibility of equipment deployment and adjustment.

[0073] Step 3: Data collection and integrity verification

[0074] a. Data collection: Start the data collection equipment according to the set points and frequencies to collect traffic data in real time.

[0075] b. Integrity verification: Blockchain technology is used to verify the integrity of data to ensure that the data has not been tampered with or lost during the collection process.

[0076] Step 4: Data accuracy verification and encrypted transmission

[0077] a. Accuracy verification: Introduce machine learning algorithms, establish data quality assessment models, and perform accuracy verification on the collected data, including data range checks and outlier detection, to automatically detect and correct errors and outliers in the data.

[0078] b. Data encryption: During data collection and transmission, encryption algorithms are used to encrypt data, combined with quantum encryption technology to provide a high level of data protection.

[0079] Step 5: Data security monitoring and emergency response

[0080] a. Security monitoring: Real-time monitoring of the security status of data, using AI security monitoring technology to achieve intelligent early warning and automatic defense.

[0081] b. Emergency response: Once abnormal behavior or potential threats are detected, the emergency response mechanism will be triggered immediately and necessary measures will be taken to protect data security.

[0082] Step 6: Cross-domain collaborative applications and intelligent application expansion

[0083] a. Cross-domain collaboration: Provide unified interfaces and standards to support integration and collaborative applications with systems in other business areas to achieve data sharing and interoperability.

[0084] b. Intelligent applications: Based on the collected traffic data, develop intelligent applications, such as autonomous driving and intelligent scheduling, introduce advanced deep learning technologies, and improve the accuracy and reliability of applications.

[0085] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can replace or change the technical solution and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A traffic database collection system based on business fields, characterized in that: Includes the following modules: Data collection planning module: responsible for the overall planning and design of urban traffic data collection, including the layout of data collection points, the setting of data collection frequency, the formulation of data structure standards, and the introduction of AI-assisted planning technology to intelligently adjust data collection points and frequencies based on multi-dimensional data such as traffic flow, road conditions, and weather conditions; Engineering in-depth design and refined design module: Detailed design and optimization of data acquisition equipment, transmission network, and storage system, adopting modular design concept to achieve rapid deployment and flexible adjustment of data acquisition equipment, and introduce Internet of Things technology; Data integrity verification module: During the data collection process, the data integrity is verified, and blockchain technology is used to achieve distributed storage and non-tamperability of data; Data accuracy verification module: Verify the accuracy of the collected data, including data range check, outlier detection, introduce machine learning algorithms, establish a data quality assessment model, and automatically detect and correct errors and outliers in the data; Data encryption transmission module: During data collection and transmission, encryption algorithms are used to encrypt data, combined with quantum encryption technology to provide a high level of data protection; Data security monitoring and emergency response module: monitors the security status of data in real time. Once abnormal behavior or potential threats are found, the emergency response mechanism is immediately triggered. AI security monitoring technology is introduced to achieve intelligent early warning and automatic defense, and establish a rapid response mechanism. Cross-domain collaborative application module: provides unified interfaces and standards, supports integration and collaborative application with systems in other business areas, and adopts microservice architecture to achieve modularity and scalability of the system; Intelligent application expansion module: Based on the collected traffic data, develop intelligent applications, including autonomous driving and intelligent scheduling, introduce deep learning artificial intelligence technology, and improve the accuracy and reliability of intelligent applications.

2. A traffic database collection system based on business domain according to claim 1, characterized in that: In the data collection planning module, this module not only focuses on the reasonable layout of data collection points and the scientific setting of data collection frequency, but also goes deep into the rigorous formulation of data structure standards, and introduces AI-assisted planning technology. It can comprehensively consider real-time data in multiple dimensions such as traffic flow, road conditions, and weather conditions. It mines and analyzes traffic data through deep learning algorithms, adjusts data collection points and frequencies, and automatically increases data collection points and frequencies during peak traffic flow or severe weather conditions. It reduces data collection points and frequencies during periods of stable traffic flow or good weather conditions. This module also has the ability of self-learning and optimization. According to the actual data collection results and feedback, it adjusts and optimizes the data collection planning strategy to ensure continuous optimization and improvement of data collection work.

3. The traffic database collection system based on business domain according to claim 1 is characterized in that: In the engineering in-depth design and refined design modules, a modular design concept is adopted to split the equipment into multiple independent and interchangeable modules, including sensor modules, data processing modules, and communication modules. At intersections with heavy traffic, the number and types of sensor modules are increased, and at sections with light traffic, the number of sensor modules is reduced to reduce costs and power consumption. In the design of the transmission network, redundant design and fault switching mechanism are adopted to ensure that data will not be lost or delayed during transmission. At the same time, Internet of Things technologies are introduced, including LoRa and NB-IoT low-power wide area network communication technologies. In the design of the storage system, distributed storage and cloud storage technologies are adopted to store data on multiple physical nodes to achieve redundant backup and load balancing of data.

4. The traffic database collection system based on business domain according to claim 1 is characterized in that: In the data integrity verification module, blockchain technology is introduced. As a decentralized, distributed ledger database technology, blockchain has the characteristics of data immutability and traceability. During the data collection process, whenever new data is generated, the data integrity verification module will immediately perform a hash operation on the data to generate a unique data fingerprint, i.e., a hash value. This data fingerprint is then recorded in a new block of the blockchain and linked to the previous block through the hash value to form a complete data chain. Due to the distributed storage characteristics of the blockchain, these data fingerprints are stored in multiple copies. Even if a node fails or the data is tampered with, other nodes can still maintain the integrity and consistency of the data. When the integrity of the data needs to be verified, it is only necessary to recalculate the hash value of the data and compare it with the hash value recorded in the blockchain. If the two are consistent, it means that the data has not been damaged or tampered with during the collection and transmission process.

5. The traffic database collection system based on business domain according to claim 1 is characterized in that: In the data accuracy verification module, during the data range check phase, the system compares each data item against a preset reasonable value range. Data outside this range is considered potentially inaccurate or erroneous data, which in turn triggers further review. Outlier detection relies on statistical analysis and pattern recognition techniques, and can automatically identify isolated points that are different from most data points in the data set. This module integrates machine learning algorithms, which learn the inherent laws and characteristics of the data by training historical data samples, and constructs a data quality assessment model. The model automatically detects errors and outliers in the data, and based on contextual information and the correlation between data, intelligently determines the type of error and provides corresponding correction suggestions.

6. A traffic database collection method based on a business domain, based on a traffic database collection system based on a business domain as claimed in any one of claims 1 to 5, characterized in that: The following steps are involved: Step 1: Data collection planning and design a. Demand analysis: clarify the business needs of traffic data collection, including but not limited to traffic flow analysis, road condition monitoring, and weather impact assessment. b. Point layout: Based on the characteristics of the urban transportation network and combined with AI-assisted planning technology, data collection points are intelligently selected to ensure that the points are reasonably distributed and can fully cover key sections and traffic nodes. c. Frequency setting: Set the data collection frequency according to the changing pattern of traffic flow and road conditions to ensure that the data can reflect the real-time traffic conditions without causing data redundancy. d. Data structure standards: Establish unified data structure standards, including data formats and naming rules, to facilitate subsequent data processing and analysis. Step 2: Engineering in-depth design and refined design a. Equipment selection: Select appropriate data collection equipment, such as cameras and sensors, based on data collection requirements, and consider the durability, accuracy, and compatibility of the equipment. b. Network design: Design an efficient and stable transmission network to ensure that data acquisition equipment can transmit data to the data center in real time and reliably. c. Storage system optimization: Adopt a modular design concept to design an expandable storage system to ensure that it can store large amounts of long-term data and support rapid retrieval and analysis. d. Internet of Things technology integration: Introduce Internet of Things technology to realize remote monitoring and management of data acquisition equipment and improve the flexibility of equipment deployment and adjustment. Step 3: Data collection and integrity verification a. Data collection: Start the data collection equipment according to the set points and frequencies to collect traffic data in real time. b. Integrity verification: Blockchain technology is used to verify the integrity of data to ensure that the data has not been tampered with or lost during the collection process. Step 4: Data accuracy verification and encrypted transmission a. Accuracy verification: Introduce machine learning algorithms, establish data quality assessment models, and perform accuracy verification on the collected data, including data range checks and outlier detection, to automatically detect and correct errors and outliers in the data. b. Data encryption: During data collection and transmission, encryption algorithms are used to encrypt data, combined with quantum encryption technology to provide a high level of data protection. Step 5: Data security monitoring and emergency response a. Security monitoring: Real-time monitoring of the security status of data, using AI security monitoring technology to achieve intelligent early warning and automatic defense. b. Emergency response: Once abnormal behavior or potential threats are detected, the emergency response mechanism will be triggered immediately and necessary measures will be taken to protect data security. Step 6: Cross-domain collaborative applications and intelligent application expansion a. Cross-domain collaboration: Provide unified interfaces and standards to support integration and collaborative applications with systems in other business areas to achieve data sharing and interoperability. b. Intelligent applications: Based on the collected traffic data, develop intelligent applications such as autonomous driving and intelligent scheduling, introduce advanced deep learning technology, and improve the accuracy and reliability of applications.

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