ETC system based on Internet of Things mode

Abstract

The invention relates to an ETC (Electronic Toll Collection) system based on an Internet of Things mode, which comprises a distributed database, a processing center, a service registration center, a service governance center, a monitoring platform and an application center, the AI middle station comprises a data access unit, a data processing unit, a data storage unit, a label extraction unit, a model training unit and a model application unit. According to the system, multi-scene and full-process application of ETC data is realized, the ETC value can be deeply mined, the data utilization rate is high, the data analysis effect is ideal, the usability of ETC is improved, and the value increment of the ETC data is promoted.

Description

Technical Field

[0001] This patent application belongs to the field of ETC system reuse technology, and more specifically, it relates to an ETC system based on the Internet of Things model. Background Art

[0002] ETC itself is a means of communication. It not only has the ability to publish information in one direction, but also can conduct two-way information exchange. Upgrading from narrowband communication to broadband communication can enable more information exchange. At present, there are 28,000 ETC gantries, 250,000 sets of antennas and license plate recognition devices in the national road network. Therefore, ETC can be relied on to achieve vehicle-road collaboration. Through ETC, beyond-line-of-sight information is transmitted to the vehicle. At the same time, the information within the line of sight of the vehicle itself is intelligently perceived to achieve collaborative autonomous driving. In recent years, the gradual development of the smart car industry has been conducive to promoting ETC industry companies to expand the vehicle-road collaboration market.

[0003] On July 27, 2022, at the "24th Highway Informatization Conference", the Road Network Center of the Ministry of Transport proposed the "TraffiCatcher" solution, which uses the ETC system as the cornerstone to build a highway operation service system. AI technology is fully applied to improve the accuracy of identifying vehicle information, road conditions, road disasters, etc., and roadside sensing equipment is used to monitor and analyze congestion events, traffic accidents, average vehicle speed, highway flow, highway weather, illegal parking, vehicles driving in the opposite direction, pedestrian intrusions, etc. The gantry system is used to issue early warnings for events, and accident coordination and rapid emergency response can be carried out through information sharing, effectively driving the application of new technologies and industrial development while driving development through innovation.

[0004] At the same time, the ETC system records the user's travel data. Based on the huge user scale and relevant data, the ETC issuing service agency can first provide scientific data support for the operation and management of the expressways of the province and the group through big data analysis. It can also build a customer portrait of the ETC customer group through user travel path information and consumption information. Combined with the promotion and application of ETC expansion scenarios, the richer the application scenarios, the more detailed the data obtained, and the more detailed and accurate the ETC customer group portrait will be. Then, the user customer group portrait will be used to achieve precision marketing, deepen cooperation with financial institutions, support ETC to start from a single scenario, leverage the huge application space, and achieve profit model innovation. On the basis of improving service quality and user experience, further increase the data scale, enhance data value, and use data value to feed back ETC promotion and operation.

[0005] The current ETC system has limited space for the use of ETC transaction data, and has problems such as low data utilization and unsatisfactory data analysis. It is impossible to collect ETC transaction data in detail, accurately calculate, safely store and reasonably process it, nor is it possible to deeply explore the intrinsic value of ETC data, let alone promote the value-added of ETC data.

[0006] In order to better serve ETC users, it is necessary to develop an ETC system based on the Internet of Things model, which can actively promote the comprehensive application of ETC transaction data in multiple scenarios, explore new models and mechanisms for smart transportation, and provide strong support for the realization of modern comprehensive transportation infrastructure construction. Summary of the invention

[0007] The technical problem to be solved by the present invention is to provide an ETC system based on the Internet of Things model, which can deeply explore the value of ETC and promote the added value of ETC data.

[0008] In order to solve the above problems, the technical solution adopted by the present invention is:

[0009] An ETC system based on the Internet of Things model, including a distributed database, a processing center, a service registration center, a service governance center, a monitoring platform, and an application center;

[0010] The distributed database is connected to the processing center and stores ETC data;

[0011] The processing center is connected to the service registration center and is used to implement business processing, data calls, and AI model applications;

[0012] The service registration center is used to register ETC users and is connected to the service governance center;

[0013] The service governance center connects the service registration center, application center and monitoring platform to transmit ETC users' service adjustment, service discovery, service call and service monitoring commands;

[0014] The monitoring platform is connected to the distributed database, which is matched with the processing center and is used to perform service monitoring on the distributed database;

[0015] The application center is connected to the service governance center, which implements specific data applications through service calls, such as parking lot payment, car insurance payment and claims, and financial product sales.

[0016] Due to the adoption of the above technical solution, the beneficial effects achieved by the present invention are:

[0017] (1) The present invention realizes the multi-scenario and full-process application of ETC data, can deeply explore the value of ETC, has high data utilization rate and ideal data analysis effect, improves the availability of ETC, promotes the added value of ETC data, and ensures the efficient use of massive ETC data. Since the processing center includes a business middle platform, a data middle platform, and an AI middle platform, the AI ​​middle platform plays the role of big data analysis. Taking the ETC parking lot as an example, the ETC parking history data can be subjected to ELT (extract-transform-load) operation and imported into the data warehouse through a third-party cooperation unit. At the same time, the relevant technologies such as big data and artificial intelligence are used to deeply explore the value of ETC data, build a customer portrait of the ETC customer group, and then use the user customer portrait to achieve precision marketing, deepen cooperation with financial institutions, and support ETC to start from a single scenario, leverage a huge application space, and achieve profit model innovation.

[0018] (2) The present invention has a simple configuration, a simple transaction process, and low access difficulty. It does not require frequent remote or even on-site maintenance by personnel, thereby improving maintenance efficiency and having greater promotion and application value.

[0019] (3) The present invention can realize automated operation management, resulting in less workload, simpler data search, and higher intelligence. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is the overall architecture diagram of the present invention.

[0021] Figure 2 This is a business architecture diagram of the present invention.

[0022] Figure 3 This is a connection block diagram of the AI ​​middle station of the present invention.

[0023] Figure 4 It is a connection block diagram of the data processing unit of the present invention.

[0024] Figure 5 This is a flow chart of obtaining label data corresponding to a target user in the present invention.

[0025] Figure 6 This is a timing diagram of ETC parking transactions according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The present invention is further described in detail below with reference to the embodiments.

[0027] An ETC system based on the Internet of Things model, such as Figure 1 As shown, it includes distributed database, processing center, service registration center, service governance center, monitoring platform, and application center;

[0028] The distributed database is connected to the processing center and stores ETC data;

[0029] The processing center is connected to the service registration center and is used to implement business processing, data calls, and AI model applications;

[0030] The service registration center is used to register ETC users and connect them to the service governance center; the service registration center allows registration via mobile phone number, email or WeChat.

[0031] The service governance center connects the service registration center, application center and monitoring platform to transmit ETC users' service adjustment, service discovery, service call and service monitoring commands;

[0032] The monitoring platform is connected to the distributed database, which is matched with the processing center and is used to perform service monitoring on the distributed database;

[0033] The application center is connected to the service governance center, and it implements specific data applications through service calls, such as parking lot payment, insurance claims, financial product sales, etc. The application center is one or more of APP, customer service, mini program, and H5 link to meet the intelligent operation needs of young people.

[0034] When building a distributed database, the distributed database includes but is not limited to one or more of a relational database, a NoSQL database, an HBase database, an openGauss database, and an oceanBase database.

[0035] exist Figure 1 In the middle of the system, the processing center includes three major platforms: business platform, data platform, and AI platform. The business platform, data platform, and AI platform are connected to the distributed database downward and to the service registration center upward. As the most critical part, they play a central role in connecting customers and databases.

[0036] Combination Figure 1 , Figure 2 The business middle platform executes specific business, including but not limited to one or more of customer business, transaction process business, equipment business, and monitoring business; during the design, the business middle platform will at least cover one or more of the customer center, transaction center, equipment center, and monitoring center. In addition, there are reconciliation center, clearing center, settlement center, refund center, etc., which overall covers payment, products, customers, real-time marketing, channels, grid operations and many other contents.

[0037] The data center stores a variety of data, including but not limited to one or more of customer data, transaction data, and marketing data;

[0038] The AI ​​middle platform is used to develop subsequent applications for the collected ETC data, thereby building a customer profile based on the ETC data.

[0039] Focus on the AI ​​middle station, such as Figure 3 The AI ​​middle station includes data access unit, data processing unit, data storage unit, label extraction unit, model training unit, and model application unit.

[0040] The data access unit is connected to the distributed database, and ETC customer information is obtained through the data access unit;

[0041] The data processing unit processes the acquired ETC customer information to form target customer information;

[0042] The data storage unit is connected to the data processing unit and stores the target customer information therein;

[0043] The label extraction unit extracts customer label information from the target customer information and determines label data according to the customer label information;

[0044] The model application unit issues requirements, and the model training unit calculates and analyzes the label data according to the requirements to form a customer profile.

[0045] When accessing data, the data access unit accesses the ETC customer information in the distributed database through the distributed message queue Kafka. Through the ETL tool, the synchronous relational database is extracted in one of the full or timed increments. The ETL tool includes Sqoop and DataStage. The extracted synchronous relational database is imported in batches in the form of text files. It comes with HUE as a data extraction management tool, which provides a graphical interface to define data extraction rules and complete the data extraction workflow. The distributed file system HDFS provides files through FTP Over HDFS and transmits them to the HDFS channel through FTP. And through Flume, a large number of log files are aggregated to HDFS.

[0046] like Figure 4 The data processing unit includes a data cleaning module, a classification processing module, and a category classification execution module connected in sequence:

[0047] The data cleaning module deletes empty words in the acquired ETC customer information, detects errors and inconsistencies in the ETC customer information, and eliminates and corrects erroneous data;

[0048] The classification processing module divides ETC customer information and forms classification requirements;

[0049] Category classification execution module, ETC customer information is classified according to classification requirements to form target customer information.

[0050] Regarding the label extraction unit, Figure 5As shown in the figure, “the label extraction unit extracts the customer label information from the ETC customer information and determines the label data according to the customer label information” specifically refers to:

[0051] Obtaining behavior information from target customer information, obtaining behavior features based on the behavior information, and thus constructing a feature vector corresponding to the behavior features;

[0052] According to the constructed eigenvector, the eigenvalue corresponding to the eigenvector is calculated;

[0053] Collect multiple characteristic information of target customers in different dimensions and establish customer label information corresponding to target customers;

[0054] Based on the obtained customer label information and the calculated feature values, the label data corresponding to the target user is obtained.

[0055] For example, there are 1,000 customers who need to park, the behavior feature is parking, the feature vector is parking lot, and there are 8 parking lots. According to the parking demand, the number of customers in each parking lot is screened out based on the 8 parking lots, that is, the feature value:

[0056] category P1 P2 P3 P4 P5 P6 P7 P8 quantity 340 150 50 200 86 70 56 48

[0057] It can be seen that the parking lot numbered P1 has the largest number of customers, so P1 can be used as the analysis object first, and multiple feature information of the target customers can be collected using different dimensions. The label data is: age, driving car model, parking consumption amount, parking frequency, etc., and then the other parking lots P1 to P8 can be analyzed one by one.

[0058] Marketers can implement the following strategies for the eight parking lots mentioned above:

[0059] Parking lot P1 has a large flow of people, the people are young, the parking consumption is low, the parking frequency is high, the parking time is short. These users are young and lack cash flow, so bank loan products can be promoted to them in a targeted manner.

[0060] Parking lot P5 has a large flow of people, with an average age of the users, high parking consumption, high parking frequency, and long parking time. These users are middle-aged and have a certain cash flow, and can be targeted for financial management or auto insurance products.

[0061] Parking lot P4 has a large flow of people, the people are older, the parking consumption is relatively high, the parking frequency is low, and the parking time is long. These users are elderly people with certain savings, and we can promote high-end improvement properties, such as houses or villas, in a targeted manner.

[0062] There are so many things that I won’t go into detail. The final customer portraits include portraits based on basic attributes, portraits based on consumption behavior, portraits based on time and space, portraits based on usage motivation, and portraits based on usage behavior. Combined with the above portraits, targeted marketing strategies are designed and implemented.

[0063] Taking the ETC-compatible parking lot payment as an example, the ETC system provided by the present invention develops ETC priority payment based on the http protocol, and is compatible with a multi-channel payment routing algorithm for payment methods such as contactless payment and code scanning payment. For specific payment steps, see Figure 6 Timing diagram:

[0064] (1) Formulate a standard openAPI interface for connecting to the parking system in the parking lot, and design an ETC multi-scenario service platform that communicates with the parking system. The standard openAPI interface is the PC-RSU interface. The ETC multi-scenario service platform is designed by the openEuler operating system, and can also be designed by other operating systems, such as Debian. The programming language is: Golang / Rust / Python; the database is: oceanBase / openGauss / Redis. The front-end development technology is: Bootstrap / Vue; the back-end development technology is: Nginx / Kafka / microservices; the mobile development technology is: Harmony / Android / IOS, etc.

[0065] Regarding the interface or functions of the ETC multi-scenario service platform, you can Figure 1 , Figure 2 As shown, based on the distributed service framework, it covers the database, business middle platform / center, data middle platform / center, AI middle platform / center, service registration center, service governance, WEB application, and monitoring platform. It is thus possible to structure the customer center, transaction center, equipment center, monitoring center, reconciliation center, clearing center, settlement center, and refund center, and obtain customer data, transaction data, marketing data, equipment data, monitoring data, reconciliation data, clearing data, settlement data, and refund data. Through big data analysis and processing, AI model training and model application can be realized. With the help of AI model applications, ETC data can be used for secondary applications to realize predictive analysis, behavioral analysis and other functions.

[0066] (2) Users log in to the ETC Multi-Scenario Service Platform to register. They can register via mobile phone number, email or WeChat, and enter information such as license plate, owner, and phone number. After successful registration, they can log in to the ETC Multi-Scenario Service Platform to query / modify details, fee lists, and refund services.

[0067] (3) In actual use, when a successfully registered user drives out of the parking system exit, the parking system performs license plate recognition and calculates the fee, and then initiates an ETC transaction request to the ETC multi-scenario service platform through the standard openAPI interface. The standard openAPI interface supports the MQTT Internet of Things protocol, TCP / IP protocol, or http protocol. In other words, the parking system combines the standard openAPI interface and initiates a transaction request with the ETC multi-scenario service platform through the MQTT Internet of Things protocol, TCP / IP protocol, or http protocol, indicating that an ETC transaction is about to be carried out.

[0068] (4) The ETC multi-scenario service platform first performs authorization authentication and sends a request to read vehicle information to the "ETC+" lane control system. The "ETC+" lane control system controls the RSU to read the vehicle information and returns the result of reading the vehicle information. After the authorization authentication is passed, the ETC transaction is processed. After the ETC transaction is processed, the ETC multi-scenario service platform feedbacks the ETC transaction result to the parking system. If the ETC transaction processing time is within the agreed time interval, S5 is executed, otherwise the user makes other payments. Generally speaking, the time interval is less than or equal to 3s, and other payments include one or more of contactless payment, code scanning payment, and cash payment. That is, when the ETC multi-scenario service platform executes ETC transaction processing, it gives priority to deducting parking fees through ETC. If the ETC transaction time is greater than 3s, it proves that there is a fault in the ETC transaction, and the user makes other payments.

[0069] (5) Regardless of whether it is an ETC transaction or other transaction, after the user pays the fee, the parking system lifts the barrier to allow the user to pass and records the final transaction to form the final transaction result. At this time, the consumption information can be sent to the user via WeChat, SMS, or email.

[0070] (6) The parking system sends the final transaction result to the ETC multi-scenario service platform until the ETC multi-scenario service platform feeds back the processing result to the parking system, recording details such as whether an ETC transaction occurred, parking time, location, license plate number, amount, remarks, etc.

[0071] (7) Reconciliation: The parking system transmits reconciliation data to the ETC multi-scenario service platform according to the agreed cycle. The ETC multi-scenario service platform feeds back the reconciliation results to the parking system after performing data verification, error handling and other operations. The cycle can be one week, half a month, one month or one quarter, etc., without any requirement. Through reconciliation, the data between the parking lot and the ETC multi-scenario service platform are kept consistent, and potential deviations are discovered in a timely manner.

[0072] (8) Clearing / settlement processing: The ETC multi-scenario service platform uploads ETC clearing data and settlement data according to the interfaces provided by the superior settlement center and the bank to query the clearing results and settlement results. The bank transfers the parking fee after deducting the settlement service fee to the parking lot operator based on the settlement data. Since there are many cooperating units or departments, it can directly reach the superior settlement center and the various banks to maintain data uniformity.

[0073] (9) Since a large amount of ETC data has been obtained, the parking lot can also upload supporting data through the ETC multi-scenario service platform according to the interface provided by the cooperation unit to query the required results. In this step, the supporting data is ETC big data, and the cooperation unit analyzes ETC big data through a deep learning model to form deep learning data. The deep learning model is one or more of Kafka, Flume, and pytorch, or other models. With this step, continuing the above operation, you can directly go to various cooperation units, such as non-bank financial institutions, insurance, shopping malls, online shopping platforms, etc., and build ETC customer profiles through user travel path information and consumption information. Combined with the promotion and application of ETC expansion scenarios, the richer the application scenarios, the more detailed the data obtained, and the more detailed and accurate the ETC customer profile will be. Then use the user customer profile to achieve precision marketing, deepen cooperation with financial institutions, and support ETC to start from a single scenario, leverage a huge application space, and achieve profit model innovation.

[0074] For example, it can be clear who the ETC customers are and gain access to high-quality customers. ETC customers usually have a certain level of spending power and a good credit record, and are a rare high-quality group for banks. According to age and spending power, most of the bank's existing ETC customers are young people born in the 1980s and 1990s. They have three major characteristics: First, the Internet penetration rate is high, and their lives and work are greatly Internet-based, and they are also highly dependent on the Internet. They pursue new technology products, pay attention to the sense of technology, and advocate convenient and fast user experience. Second, they are open-minded, pursue individuality while paying attention to diversity. They like innovation and can quickly accept new things. Third, they have a high degree of education and strong learning ability, especially for the emergence of new products and new concepts.

[0075] In terms of spending power, most ETC customers have a certain spending power and are key marketing customers for banks. According to QuestMobile industry data, more than 50% of "car owners" are aged between 25 and 35, more than 30% have online spending power of more than 1,000 yuan, more than 70% have a medium or above willingness to spend, and nearly 40% use terminals worth more than 3,000 yuan. At the same time, the TGI of these dimensions is also quite high, and its specific value and imagination space are very huge.

[0076] It can also provide access to traffic and data. The competition among merchants for ETC is not for the profits brought by the high-speed toll business, but for the next payment traffic and data access, aiming to build a comprehensive service platform integrating "payment access + data collection + operation management + financial services". Through this platform, it can deepen the in-depth exploration of the high-quality group of car owners and further expand its own high-value businesses such as credit cards, financial management, insurance, and loans.

[0077] In other words, ETC is just a "traffic entrance" for banks, which can divert traffic to other businesses of the bank. ETC not only has the value of retail finance, but also contains market opportunities for supply chain finance and industrial chain finance. The ETC card itself does not belong to the business of retail finance, but because it is related to automobiles, the customer stickiness brought about by the service contact with customers on this basis can explore more market space from the automobile industry. The data generated by the ETC business can provide a more complete user portrait of car owners and vehicles, which will greatly help to solve the current common incomplete data in the automobile finance industry.

[0078] Next, we need to understand the real needs of customers from four aspects: consumer finance needs for major consumer goods, advanced consumption needs for pursuing quality life, enthusiasm for Internet financial services, and independent and flexible financial services.

[0079] Then use products to reach customers, analyze customer behavior preferences and transaction habits through big data analysis and mobile Internet platforms, track and grasp customers' usage trends of banking products in a timely and dynamic manner, predict customers' next needs based on analysis, and use corresponding marketing strategies to accurately locate customers through labeling. Effective cross-marketing is carried out for potential customers, and customers and products are accurately matched together. Personalized experience is given through data analysis, and product services are upgraded or eliminated in a timely manner through feedback and evaluation to maintain customer vitality, avoid unnecessary waste of money and time, reduce redundant procedures, and increase customer stickiness.

[0080] For example, to address the financial management pain points of a certain type of customer group, such as over-consumption and low savings, we match them with small financial management products. By setting up small deposits in the bank, earmarking funds for specific purposes, and connecting them to profit-enhancing products, we can gradually cultivate users' financial management habits. This fundamentally solves the customer's pain points, is easy to operate, and can be completed through mobile banking, connecting customers and financial management.

[0081] We can also build an auto finance ecosystem, combining maintenance, fines, insurance, and refueling, and integrate shared bicycles, city libraries, and parks to meet customer needs in multiple ways. We can also combine with third parties to achieve breakthroughs in cooperation with mobile Internet platforms related to car rentals and taxis, embed financial services, and expand application scenarios.

[0082] Therefore, the ETC system based on the Internet of Things model provided by the present invention has broad application prospects because it can deeply explore the value of ETC and promote the value-added of ETC data. Taking parking lot payment as an example, the ETC payment process in the parking scene is optimized, which not only meets the parking needs, improves the system maintenance efficiency, and standardizes and automates the processing of business such as clearing and settlement, but also uses big data to manage ETC customers in a labeled manner, and through data analysis, accurately profiles individual user resources, cross-marketing, and deep mining, thereby transforming ETC customers into long-term and stable customer resources, achieving a win-win situation for customers and banks or third-party institutions. Therefore, it has significant technical advantages and application prospects.

Claims

1. An ETC system based on the Internet of Things model, characterized by: Including distributed database, processing center, service registration center, service governance center, monitoring platform, and application center; The distributed database is connected to the processing center and stores ETC data; The processing center is connected to the service registration center and is used to implement business processing, data calls, and AI model applications; The service registration center is used to register ETC users and is connected to the service governance center; The service governance center connects the service registration center, application center and monitoring platform to transmit ETC users' service adjustment, service discovery, service call and service monitoring commands; The monitoring platform is connected to the distributed database, which is matched with the processing center and is used to perform service monitoring on the distributed database; The application center is connected to the service governance center, which implements specific data applications through service calls.

2. According to claim 1, an ETC system based on the Internet of Things mode is characterized in that: The distributed database includes, but is not limited to, one or more of a relational database, a NoSQL database, an HBase database, an openGauss database, and an oceanBase database.

3. The ETC system based on the Internet of Things mode according to claim 1 is characterized in that: The processing center includes the business center, data center, and AI center. The business center, data center, and AI center are all connected to the distributed database. The business center, data center, and AI center are also connected to the service registration center. The business platform executes specific business, including but not limited to one or more of customer business, transaction process business, equipment business, and monitoring business; The data center stores a variety of data, including but not limited to one or more of customer data, transaction data, and marketing data; The AI ​​middle platform is used to develop subsequent applications for the collected ETC data, thereby building a customer profile based on the ETC data.

4. The ETC system based on the Internet of Things mode according to claim 3 is characterized in that: The AI ​​middle station includes data access unit, data processing unit, data storage unit, label extraction unit, model training unit, and model application unit. The data access unit is connected to the distributed database, and ETC customer information is obtained through the data access unit; The data processing unit processes the acquired ETC customer information to form target customer information; The data storage unit is connected to the data processing unit and stores the target customer information therein; The label extraction unit extracts customer label information from the target customer information and determines label data according to the customer label information; The model application unit issues requirements, and the model training unit calculates and analyzes the label data according to the requirements to form a customer profile.

5. The ETC system based on the Internet of Things mode according to claim 4 is characterized in that: The data access unit accesses the ETC customer information in the distributed database through the distributed message queue Kafka.

6. The ETC system based on the Internet of Things mode according to claim 4 is characterized in that: The data processing unit includes a data cleaning module, a classification processing module, and a category classification execution module which are connected in sequence: The data cleaning module deletes empty words in the acquired ETC customer information, detects errors and inconsistencies in the ETC customer information, and eliminates and corrects erroneous data; The classification processing module divides ETC customer information and forms classification requirements; Category classification execution module, ETC customer information is classified according to classification requirements to form target customer information.

7. The ETC system based on the Internet of Things mode according to claim 6 is characterized in that: "The label extraction unit extracts customer label information from the ETC customer information and determines label data based on the customer label information", specifically refers to: Obtaining behavior information from target customer information, obtaining behavior features based on the behavior information, and thus constructing a feature vector corresponding to the behavior features; According to the constructed eigenvector, the eigenvalue corresponding to the eigenvector is calculated; Collect multiple characteristic information of target customers in different dimensions and establish customer label information corresponding to target customers; Based on the obtained customer label information and the calculated feature values, the label data corresponding to the target user is obtained.

8. An ETC system based on the Internet of Things mode according to any one of claims 3 to 7, characterized in that: The customer portraits formed include portraits of basic attributes, portraits based on consumption behavior, portraits based on time and space, portraits based on usage motivations, and portraits based on usage behavior.

9. An ETC system based on the Internet of Things mode according to any one of claims 1 to 7, characterized in that: The service registration center allows registration via mobile phone number, email or WeChat.

10. An ETC system based on the Internet of Things mode according to any one of claims 1 to 7, characterized in that: The application center is one or more of APP, customer service, mini program, and H5 link.

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