Highway cloud toll collection system data transmission method

Abstract

The invention provides a highway cloud toll collection system data transmission method, and relates to the technical field of data transmission. The highway cloud toll collection system data transmission method specifically comprises the following steps: S1, data acquisition; s2, data preprocessing; s3, transmitting visible light communication data; s4, road side unit data receiving and processing; s5, encrypting the data and transmitting the data to an edge computing node; s6, edge computing node data processing; s7, uploading the data to a cloud platform; and S8, carrying out deep analysis on the cloud platform data. The invention provides an expressway cloud toll collection system data transmission method which has the advantages of efficient data transmission, high safety, high reliability, intelligent data processing, high expansibility, cost effectiveness and the like. The overall performance and the user experience of the highway toll collection system can be obviously improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data transmission, and in particular to a data transmission method for a cloud-based highway toll collection system. Background Art

[0002] Highway Cloud-based Toll Collection System is a highway toll collection system based on cloud computing and Internet of Things (IoT) technology. It automatically identifies and tracks vehicles through sensors and cameras installed at highway toll stations, and processes and settles data through cloud servers to achieve automated highway toll collection. Highway Cloud-based Toll Collection System can improve the efficiency, accuracy and security of highway toll collection, while reducing costs and improving user experience.

[0003] Existing highway toll collection systems, such as the ETC system, mainly use on-board equipment (OBU) and roadside units (RSU) for wireless communication, so that vehicles can be automatically deducted without stopping when passing through toll stations. ETC systems usually use DSRC (dedicated short-range communication) technology, which has a short communication range and requires the vehicle to be close to the RSU to achieve data transmission. Failure of the OBU or RSU equipment will cause the system to malfunction and require frequent maintenance. Affected by weather, obstacles, etc., the communication quality is unstable. The installation of OBU and RSU equipment requires high costs and high maintenance costs. During peak hours, ETC lanes are prone to queues, affecting traffic efficiency. The video recognition toll collection system uses video surveillance and image recognition technology to identify vehicle license plate information and realize automatic toll collection. The license plate recognition technology has low recognition accuracy in bad weather, at night or when the license plate is damaged. Low, video recognition requires a lot of data processing, and there may be processing delays. Video surveillance involves a lot of vehicle and personnel information, and there is a risk of privacy leakage. High-performance cameras and data processing equipment are expensive and require frequent maintenance. Some areas may have stricter laws and regulations on video surveillance, affecting technology promotion. Mobile payment and QR code toll collection systems are charged through mobile payment or QR code scanning. When the vehicle passes through the toll station, the driver manually scans the QR code or uses the mobile phone to pay, which requires manual operation by the driver, which is inconvenient and affects driving safety. The operation process takes time and may lead to low traffic efficiency, especially during peak hours. Mobile payment relies on the network, and poor network signals affect payment efficiency. Equipment and QR codes are easily affected by environmental factors, such as sunlight, rain and snow. Mobile payment and QR codes may involve payment risks and fraud.

[0004] Therefore, the present invention proposes a data transmission method for a highway cloud toll collection system to solve the above-mentioned problems. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a data transmission method for a highway cloud toll collection system, which solves the problems of limited communication range, equipment failure, multiple interferences, high cost and low recognition accuracy.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a data transmission method for a highway cloud toll collection system, specifically comprising the following steps:

[0007] S1. Data collection

[0008] An on-board unit (OBU) is installed on each vehicle. The OBU can be integrated into the ETC for installation. The OBU needs to be equipped with GPS and acceleration sensors to obtain vehicle location and speed information, and an embedded processor to process and store data in real time. During the driving process of the vehicle, the OBU generates and records relevant vehicle data in real time.

[0009] S2. Data Preprocessing

[0010] Perform preliminary data filtering on the OBU to remove redundant data and reduce the amount of transmission. Use the built-in encryption module of the OBU to perform preliminary encryption on the data to ensure the security of the data transmission process.

[0011] S3. Visible light communication data transmission

[0012] Initialize VLC communication. When the vehicle approaches the RSU, the OBU starts the visible light communication module and establishes a connection with the RSU. The pre-processed and encrypted data is transmitted to the RSU through the visible light signal of the vehicle headlights. After receiving the data, the RSU performs signal verification to ensure the integrity of the data transmission.

[0013] S4. Roadside unit data reception and processing

[0014] RSU receives data from OBU through visible light communication and wireless communication. RSU parses the received data, performs further verification and processing, and uses the built-in AI chip of RSU to conduct preliminary analysis of the data.

[0015] S5. Data encryption is transmitted to the edge computing node

[0016] RSU and edge computing nodes generate encryption keys through quantum key distribution (QKD) technology. RSU uses the key generated by QKD to re-encrypt the processed data and transmit the data to the edge computing node through the encrypted secure channel;

[0017] S6. Edge computing node data processing

[0018] The edge computing node temporarily stores the data uploaded from the RSU, performs cache management, and uses the high-performance processor of the edge computing node to perform advanced analysis on the data. After the processing is completed, the data is encrypted again using QKD technology and prepared to be uploaded to the cloud platform;

[0019] S7. Upload data to the cloud platform

[0020] An encrypted channel is established between the edge computing node and the cloud platform through QKD technology. Through the encrypted channel, the processed data is securely uploaded to the cloud platform. The cloud platform stores and backs up the uploaded data to ensure the integrity and security of the data.

[0021] S8. In-depth analysis of cloud platform data

[0022] The data uploaded from the edge computing nodes are classified and stored to facilitate subsequent analysis. Big data and AI technology are used to conduct in-depth analysis of the data, and the analysis results are displayed to relevant departments and users to provide decision support.

[0023] Preferably, the data collection in step S1 includes collecting the vehicle's license plate number, speed, and location data.

[0024] Preferably, the preliminary analysis of data in the roadside unit data reception and processing in step S4 includes vehicle type recognition and abnormal behavior detection.

[0025] Preferably, the advanced data analysis in the edge computing node data processing in step S6 includes traffic flow prediction and toll calculation.

[0026] Preferably, the in-depth analysis of cloud platform data in step S8 includes user behavior analysis, traffic flow prediction, and toll optimization through visualization tools.

[0027] Preferably, in step S5, the data is encrypted and transmitted to the edge computing node using symmetric encryption AES for high-speed encryption of large amounts of data, and asymmetric encryption RSA for key exchange and encryption of small amounts of data.

[0028] The present invention provides a data transmission method for a highway cloud toll collection system. It has the following beneficial effects:

[0029] The present invention provides a data transmission method for a cloud-based toll collection system of a highway. Through visible light communication (VLC) and 5G high-bandwidth communication technology, the data transmission speed is significantly improved and the delay is reduced. Vehicle information can be collected and transmitted in real time to ensure the system's real-time response capability to traffic conditions. When a vehicle passes through a toll station, VLC can achieve fast data exchange to ensure timely data upload. In areas that VLC cannot cover, the 5G network provides backup communication support to ensure seamless data transmission. Data is initially encrypted between the OBU and the RSU, and is secondary encrypted between the RSU and the edge computing node through quantum key distribution (QKD) to ensure data security. The QKD technology provides an ultra-high security key distribution method to effectively prevent the key from being cracked. The principle of quantum mechanics is used to ensure the secure transmission of the key, which cannot be decrypted even if intercepted. From data collection to transmission, the encryption state is always maintained to prevent data leakage and tampering. Multiple communication methods are combined, such as VLC and 5G, to ensure that the system can still work normally when a single communication method fails. The edge computing node is used to perform preliminary processing and caching of the data to improve the system's fault tolerance and data processing efficiency. When the VLC signal is blocked, the 5G network automatically Switching ensures uninterrupted data transmission, moves some data processing tasks from the cloud to the edge, reduces latency, improves system response speed and reliability, embeds AI chips in RSU and edge computing nodes, processes and analyzes data in real time, improves the intelligence level of the system, and uses the AI ​​technology of the cloud platform for in-depth data analysis, such as traffic flow prediction and user behavior analysis, to provide more accurate services. Edge computing nodes can analyze and process data in real time, detect anomalies and optimize traffic management in a timely manner, and provide decision support through AI analysis results, such as optimizing charging strategies and predicting traffic congestion. The system adopts a modular design, which is easy to expand and upgrade to adapt to future technological development and changes in business needs. Edge computing nodes and RSUs of different sizes can be flexibly deployed according to needs to adapt to different road conditions and traffic. By combining edge computing and cloud computing, resource utilization is optimized and overall costs are reduced. The high degree of automation of the system reduces the need for manual intervention and reduces operation and maintenance costs. The data transmission method of the highway cloud toll system of the present invention has the advantages of efficient data transmission, high security, high reliability, intelligent data processing, high scalability and cost-effectiveness, and can significantly improve the overall performance and user experience of the highway toll system. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a schematic diagram of the system architecture of the present invention. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] like Figure 1 As shown, an embodiment of the present invention provides a data transmission method for a highway cloud toll collection system, which specifically includes the following steps:

[0033] S1. Data collection

[0034] An on-board unit (OBU) is installed on each vehicle and can be integrated into the ETC for installation. The on-board unit needs to be equipped with GPS and acceleration sensors to obtain vehicle location and speed information, and an embedded processor to process and store data in real time. During the driving process of the vehicle, the on-board unit generates and records the vehicle's license plate number, speed, and location data in real time.

[0035] During the driving process of the vehicle, the OBU generates and records relevant data of the vehicle in real time. The OBU performs preliminary processing on the data, such as data filtering and formatting. The processed data is temporarily stored in the internal memory of the OBU and is waiting to be transmitted. GPS and acceleration sensors are used to obtain vehicle position and speed information. The embedded processor processes and stores the data in real time. The roadside unit is installed at toll booths or other key locations. It is responsible for communicating with the OBU and collecting the transmitted data. When the vehicle approaches the RSU, the OBU establishes a communication connection with the RSU. The OBU transmits the data to the RSU through wireless communication (such as VLC or DSRC). The RSU receives the data and performs preliminary verification and processing.

[0036] VLC (Visible Light Communication) uses visible light between car lights and street lights for data communication, which is fast and has low latency. DSRC (Dedicated Short Range Communication) is a short-range wireless communication technology used for data exchange between OBU and RSU.

[0037] Edge computing nodes are installed near toll booths or other key locations and are responsible for processing and storing data collected by RSUs. Edge computing nodes receive data from RSUs. Received data is temporarily stored in local storage. Preliminary analysis and processing of data, such as data cleaning and anomaly detection, is performed. Processed data is cached at edge nodes to reduce the frequency and amount of data transmitted to the cloud. Computation and storage are performed close to the data source to reduce latency and improve efficiency. Embedded AI chips are used to analyze and process data in real time to improve the level of intelligence.

[0038] Using the 5G high-speed communication network, data is transmitted from the edge computing node to the cloud data center. The edge computing node establishes a stable communication connection with the cloud data center. Using quantum key distribution (QKD) technology, the transmitted data is encrypted to ensure data security. The encrypted data is transmitted to the cloud via the 5G network. The cloud data center receives the data and stores and further processes it.

[0039] A cloud computing platform is set up to store and process large amounts of data for in-depth analysis and mining. The cloud data center stores the received data in a large-scale distributed storage system. Using big data analysis and AI technology, in-depth analysis of the data is performed, such as traffic flow prediction and user behavior analysis. The analysis results are fed back to the edge computing nodes and RSU for system optimization and decision support.

[0040] S2. Data Preprocessing

[0041] Perform preliminary data filtering on the OBU to remove redundant data and reduce the transmission volume. Use the built-in encryption module of the OBU to perform preliminary encryption on the data to ensure the security of the data transmission process.

[0042] Perform data cleaning to remove noise, errors, and incomplete information from the data to ensure data accuracy and consistency. First, identify missing values ​​in the data set. Use mean filling, interpolation, or machine learning algorithms to fill missing values, or delete missing data records. Use moving average and regression analysis methods to smooth the data and remove noise. Use mean, median, and standard deviation statistics to analyze and process data. Use algorithms such as KNN (K-NearestNeighbors) to fill missing values ​​or detect outliers.

[0043] Scale the data to a fixed range (such as [0,1]) to improve the convergence speed and stability of the algorithm. Convert the data to a distribution with a mean of 0 and a standard deviation of 1, which is suitable for some algorithms that need to assume that the data conforms to a normal distribution. Convert categorical variables to numerical variables, such as using the One-Hot Encoding method. Convert timestamps to time series features, such as hours, days of the week, and months. Extract useful features from raw data, such as speed change rate and travel time. Use correlation analysis or machine learning algorithms to select important features, reduce data dimensions, and improve model performance.

[0044] Identify and collect data from different sources, such as OBU, RSU, and edge computing nodes. Match and merge data from different sources based on timestamps and vehicle ID key fields. Detect conflicts in data, such as duplicate records and inconsistent data formats, and resolve them using rules or algorithms. Delete duplicate data to ensure data uniqueness and consistency. Use relational or NoSQL databases for data storage and management. Use SQL queries and Python data processing libraries for data matching and fusion.

[0045] Randomly extract subsets from large data sets for pre-analysis and modeling. Use clustering algorithms to group similar data into one category to reduce the amount of data. Reduce the dimensionality of high-dimensional data through linear transformation and retain the main features. Use correlation analysis and LASSO regression methods to select important features and reduce the data dimension. Convert continuous variables to discrete variables and divide them into intervals. Summarize the data, calculate the average and sum, and reduce the amount of data.

[0046] Data preprocessing is an important step to ensure data quality and processing efficiency. Through data cleaning, conversion, integration and reduction, the accuracy, consistency and availability of data can be significantly improved, laying a solid foundation for subsequent data analysis and processing. In the highway cloud toll collection system, the efficient implementation of data preprocessing helps to achieve intelligent, efficient and reliable toll collection management.

[0047] S3. Visible light communication data transmission

[0048] Initialize VLC communication. When the vehicle approaches the RSU, the OBU starts the visible light communication module and establishes a connection with the RSU. The pre-processed and encrypted data is transmitted to the RSU through the visible light signal of the vehicle headlights. After receiving the data, the RSU performs signal verification to ensure the integrity of the data transmission.

[0049] VLC system components

[0050] Light source (transmitter): consists of car lights, street lights or special LED light sources.

[0051] Photoelectric detector (receiver): installed on the vehicle and RSU to receive optical signals.

[0052] Modem: Converts digital signals into optical signals for transmission, and converts received optical signals into digital signals.

[0053] The data collected by the OBU or RSU is ready for transmission. Pack the data in a predetermined format and add necessary header information and checksums. Select a suitable modulation method, such as pulse width modulation (PWM) and orthogonal frequency division multiplexing (OFDM). Modulate the digital signal into an optical signal so that it can be transmitted through a light source. Drive the LED light source or car lights to adjust the light intensity or frequency according to the changes in the modulation signal. The modulated optical signal is emitted into the air through the light source to form a visible light channel.

[0054] The photodetector receives the optical signal from the transmitter. The received optical signal is converted into an electrical signal for subsequent processing. The converted electrical signal is amplified and filtered to remove noise and interference. According to the selected modulation method, the corresponding demodulation processing is performed to convert the electrical signal into a digital signal. The demodulated digital signal is verified to ensure the integrity and correctness of the data. The data packet is unpacked to extract the original data.

[0055] Install the LED light source or retrofit lamp on the front or rear of the vehicle, making sure the light beam is directed toward the RSU. Install the light source and photodetector on the RSU, making sure the light beam is directed toward the lane.

[0056] Install an optical filter in front of the photodetector to filter out the interference of ambient light. Use modulation and mediation technology and algorithms to suppress the interference of background light and improve the signal-to-noise ratio.

[0057] S4. Roadside unit data reception and processing

[0058] RSU receives data from OBU through visible light communication and wireless communication. RSU parses the received data for further verification and processing, and uses the built-in AI chip of RSU to perform preliminary analysis of the data, including vehicle type recognition and abnormal behavior detection.

[0059] The RSU identifies approaching vehicles through DSRC or VLC communication. The RSU establishes a communication connection with the OBU on the vehicle and prepares to receive data. The data signal from the OBU is received through the antenna. The received signal is decoded and converted into digital data. A checksum (such as CRC) is used to perform an integrity check on the received data to ensure that there are no errors in the data.

[0060] Identify and process outliers or erroneous data in the data. Fill or mark missing data. Store received data in the local cache of the RSU as temporary storage. Store important data in the database system inside the RSU for subsequent processing and access. Aggregate data according to predetermined rules, such as counting the number of passing vehicles and average speed. Perform real-time analysis on data, such as detecting traffic congestion and identifying abnormal vehicles.

[0061] Encrypt the data to ensure data security during transmission. Use AES and RSA encryption algorithms. Pack the data into a suitable format and add necessary header information and checksums. Transmit the data to the edge computing node or cloud data center through 5G or other high-speed communication networks. After the edge computing node or cloud confirms receipt of the data, it sends a confirmation message to the RSU. If an error occurs during data transmission, the RSU will retransmit the erroneous data packet.

[0062] S5. Data encryption is transmitted to the edge computing node

[0063] RSU and edge computing nodes generate encryption keys through quantum key distribution (QKD) technology. RSU uses the key generated by QKD to re-encrypt the processed data and transmit the data to the edge computing node through the encrypted secure channel.

[0064] Symmetric encryption AES (Advanced Encryption Standard) is used to encrypt large amounts of data at high speed. Asymmetric encryption RSA (Rivest-Shamir-Adleman) is used to exchange keys and encrypt small amounts of data.

[0065] Generate encryption and decryption keys. Securely distribute keys to data senders and receivers, usually through a secure key exchange protocol such as Diffie-Hellman. Organize and package the data to be transmitted in preparation for encryption. Use the selected encryption algorithm and key to encrypt the data and generate an encrypted data packet.

[0066] Format the encrypted data into a suitable transmission format, and add necessary header information and checksums. For large amounts of data, package the data into pieces so that they can be processed in segments during transmission. Select a suitable data transmission protocol, such as TCP / IP, UDP, or HTTP / HTTPS, to ensure the reliability and efficiency of data transmission. Set transmission parameters, such as transmission rate and window size, to optimize transmission performance.

[0067] Establish a network connection between the RSU and the edge computing node through 5G, Wi-Fi or wired network. Use the transmission protocol to perform handshakes and establish a secure transmission channel. Send the packaged and encrypted data to the edge computing node through the network connection. Monitor the data transmission process in real time and handle packet loss, retransmission and delay issues. After receiving the data, the edge computing node sends a confirmation message to the RSU to confirm that the data transmission was successful. If an error occurs during the transmission process, the RSU resends the unsuccessful data packet to ensure data integrity.

[0068] After receiving the data packet, the edge computing node reassembles and verifies the data packet to ensure data integrity. Prepare the key and algorithm required for decryption. Use the predetermined decryption algorithm and key to decrypt the received data and restore the original data. After decryption, perform integrity check on the data to ensure that the data has not been tampered with. Store the decrypted data in the database of the edge computing node. Perform real-time analysis and processing of the data as needed, such as vehicle identification and toll calculation.

[0069] S6. Edge computing node data processing

[0070] The edge computing node temporarily stores the data uploaded from the RSU and performs cache management. It uses the high-performance processor of the edge computing node to perform advanced analysis of the data, including traffic flow prediction and toll calculation. After processing, it uses QKD technology to encrypt the data again and prepare to upload it to the cloud platform.

[0071] Hardware and software configuration of edge computing nodes

[0072] hardware

[0073] Computing unit: high-performance server or industrial computer equipped with multi-core CPU and large memory.

[0074] Storage device: high-speed solid-state drive (SSD) or large-capacity mechanical hard disk (HDD).

[0075] Network interface: Support 5G, Wi-Fi, and Ethernet high-speed network connections.

[0076] software

[0077] Operating system: such as Linux, Windows Server.

[0078] Virtualization platforms: such as Docker and Kubernetes, used to manage and deploy application containers.

[0079] Data processing frameworks: such as Apache Kafka and Apache Spark, for stream data processing and batch processing.

[0080] Listen to data transmission from the RSU through the network interface. Temporarily store the received data in memory or local cache for further processing. Prepare the keys and algorithms required for decryption. Decrypt the received encrypted data and restore the original data. Use a checksum (such as CRC) to perform integrity check on the data to ensure that the data has not been tampered with or damaged. Check the data format to confirm the correctness and integrity of the data packet.

[0081] Parse data packets according to the predetermined communication protocol and extract valid data. Convert data into a format suitable for further processing, such as JSON, XML. Identify and process outliers or erroneous data in the data. Fill or mark missing data. Aggregate data according to predetermined rules, such as counting the number of passing vehicles and average speed. Perform real-time statistical analysis on data, such as traffic flow and vehicle classification.

[0082] S7. Upload data to the cloud platform

[0083] An encrypted channel is established between the edge computing node and the cloud platform through QKD technology. The processed data is securely uploaded to the cloud platform through the encrypted channel. The cloud platform stores and backs up the uploaded data to ensure the integrity and security of the data.

[0084] S8. In-depth analysis of cloud platform data

[0085] The data uploaded from the edge computing nodes are classified and stored to facilitate subsequent analysis. Big data and AI technology are used to conduct in-depth analysis of the data, including user behavior analysis, traffic flow prediction, and toll optimization. The analysis results are displayed to relevant departments and users through visualization tools to provide decision support.

[0086] Aggregate data processed by edge computing nodes to the cloud platform. Integrate other relevant data sources, such as weather data, traffic flow data, and road condition data. Use distributed storage systems (such as HDFS) to store large-scale data. Use relational databases (such as MySQL) or NoSQL databases (such as Cassandra) to manage structured and unstructured data.

[0087] Use relational databases (such as MySQL) or NoSQL databases (such as Cassandra) to manage structured and unstructured data. Perform descriptive statistical analysis to understand the basic characteristics of the data. Select features that are useful for analysis and modeling. Extract new and useful features from the original data. Select appropriate machine learning or deep learning models, such as regression models, classification models, and clustering models, based on the analysis objectives. Use training data to train the model and adjust parameters to optimize model performance. Use validation data sets to validate the model and evaluate the accuracy and stability of the model. Based on the evaluation results, adjust model parameters to further improve model performance.

[0088] Use real-time data stream processing frameworks (such as Apache Kafka and Apache Flink) to process real-time data streams. Perform real-time data analysis to monitor traffic flow and vehicle behavior. Establish a real-time early warning system to trigger early warnings based on analysis results. Automatically adjust traffic management strategies based on early warning results, such as signal light control and lane allocation. Use visualization tools to generate various charts, such as line charts, bar charts, and pie charts. Establish a data dashboard to display key data and analysis results in real time. Generate regular reports, such as daily, weekly, and monthly reports, to display data analysis results and trends. Generate customized reports based on user needs to provide in-depth analysis of specific data.

[0089] System framework of the present invention

[0090] 1. On-board terminal equipment (OBU)

[0091] The OBU has multiple built-in sensors and communication modules for collecting and transmitting static and dynamic information of the vehicle. It supports multiple communication methods, including visible light communication (VLC) and conventional wireless communication (DSRC, 5G), and performs preliminary processing and encryption on the collected data to ensure the accuracy and security of the data.

[0092] Hardware Composition

[0093] High-precision sensors: GPS, IMU, used to collect vehicle dynamic information.

[0094] Communication module: VLC transmitter, wireless communication module (such as DSRC, 5G module).

[0095] Processor: used for data preprocessing and encryption.

[0096] Power management: ensure low power consumption and long-term operation of the device.

[0097] 2. Roadside Unit (RSU)

[0098] The data sent by the OBU is received through VLC and wireless communication, the built-in AI chip is used to perform preliminary verification, filtering and processing of the data, the quantum key distribution (QKD) technology is used to encrypt the data, and the data is transmitted to the edge computing node.

[0099] Hardware Composition

[0100] Communication receiving module: VLC receiver, wireless communication receiving module.

[0101] AI processing chip: used for data analysis and processing.

[0102] Quantum Key Module: used to generate and manage encryption keys.

[0103] 3. Edge computing nodes

[0104] Temporarily store the data uploaded from the RSU, perform cache management, and use high-performance processors to perform complex data analysis, such as traffic flow prediction and vehicle identification. After processing, encrypt the data through QKD technology and prepare to upload it to the cloud platform.

[0105] Hardware Composition

[0106] High-performance processor: used to handle large amounts of data and complex calculations.

[0107] Storage unit: A high-speed storage device used for data caching and management.

[0108] Quantum Key Module: used for the generation and management of encryption keys.

[0109] 4. Quantum Key Distribution (QKD) Network

[0110] Through quantum key distribution technology, secure encryption keys are generated, distributed and managed, ensuring the security of communications between nodes, providing high-intensity encryption services, and ensuring the security of data during transmission.

[0111] Hardware Composition

[0112] Quantum communication module: used for key generation and distribution.

[0113] Encryption module: Encrypts data using the generated key.

[0114] 5. Cloud Platform

[0115] The data uploaded from the edge computing nodes are classified, stored and backed up. Big data and AI technology are used to conduct in-depth analysis of the data, such as user behavior analysis and traffic flow prediction. The analysis results are displayed to relevant departments and users through visualization tools to provide decision support, monitor the operating status of the entire system in real time, and ensure stable system operation.

[0116] composition

[0117] Cloud Server: A high-performance server cluster used for data storage and computing.

[0118] Big data processing platform: supports the storage, processing and analysis of massive data.

[0119] AI analysis module: provides deep learning and machine learning AI technical support for data analysis and prediction.

[0120] Visualization tools: used for result presentation and report generation.

[0121] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A data transmission method for a highway cloud toll collection system, characterized in that: The specific steps include: S1. Data collection An on-board unit (OBU) is installed on each vehicle. The OBU can be integrated into the ETC for installation. The OBU needs to be equipped with GPS and acceleration sensors to obtain vehicle location and speed information, and an embedded processor to process and store data in real time. During the driving process of the vehicle, the OBU generates and records relevant vehicle data in real time. S2. Data Preprocessing Perform preliminary data filtering on the OBU to remove redundant data and reduce the amount of transmission. Use the built-in encryption module of the OBU to perform preliminary encryption on the data to ensure the security of the data transmission process. S3. Visible light communication data transmission Initialize VLC communication. When the vehicle approaches the RSU, the OBU starts the visible light communication module and establishes a connection with the RSU. The pre-processed and encrypted data is transmitted to the RSU through the visible light signal of the vehicle headlights. After receiving the data, the RSU performs signal verification to ensure the integrity of the data transmission. S4. Roadside unit data reception and processing RSU receives data from OBU through visible light communication and wireless communication. RSU parses the received data, performs further verification and processing, and uses the built-in AI chip of RSU to conduct preliminary analysis of the data. S5. Data encryption is transmitted to the edge computing node RSU and edge computing nodes generate encryption keys through quantum key distribution (QKD) technology. RSU uses the key generated by QKD to re-encrypt the processed data and transmit the data to the edge computing node through the encrypted secure channel; S6. Edge computing node data processing The edge computing node temporarily stores the data uploaded from the RSU, performs cache management, and uses the high-performance processor of the edge computing node to perform advanced analysis on the data. After the processing is completed, the data is encrypted again using QKD technology and prepared to be uploaded to the cloud platform; S7. Upload data to the cloud platform An encrypted channel is established between the edge computing node and the cloud platform through QKD technology. Through the encrypted channel, the processed data is securely uploaded to the cloud platform. The cloud platform stores and backs up the uploaded data to ensure the integrity and security of the data. S8. In-depth analysis of cloud platform data The data uploaded from the edge computing nodes are classified and stored to facilitate subsequent analysis. Big data and AI technology are used to conduct in-depth analysis of the data, and the analysis results are displayed to relevant departments and users to provide decision support.

2. According to claim 1, a data transmission method for a highway cloud toll collection system is characterized by: The data collection in step S1 includes collecting the vehicle's license plate number, speed, and location data.

3. According to claim 1, a data transmission method for a highway cloud toll collection system is characterized by: The preliminary data analysis in the roadside unit data reception and processing in step S4 includes vehicle type recognition and abnormal behavior detection.

4. According to claim 1, a data transmission method for a highway cloud toll collection system is characterized by: The advanced data analysis in the edge computing node data processing in step S6 includes traffic flow prediction and toll calculation.

5. According to claim 1, a data transmission method for a highway cloud toll collection system is characterized by: The step S8 cloud platform data in-depth analysis includes user behavior analysis, traffic flow prediction, and toll optimization through visualization tools.

6. The data transmission method for a highway cloud toll collection system according to claim 1 is characterized by: In step S5, the data is encrypted and transmitted to the edge computing node using symmetric encryption AES to encrypt large amounts of data at high speed, and asymmetric encryption RSA to exchange keys and encrypt small amounts of data.

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