Full-period intelligent early warning method and system for ETC service abnormity

Abstract

The invention provides a full-period intelligent early warning method and system for ETC service abnormity, and the method comprises the steps: obtaining the vehicle-mounted unit information of a user based on the interaction between a vehicle-mounted unit of a user side and a road side unit, and the vehicle-mounted unit information comprises a user identifier and an account balance; determining a corresponding user account based on the user identifier, and performing automatic fee deduction based on the account balance; after automatic fee deduction is completed, user account data are called based on the user identifier, and the account data comprise account behavior data and account historical transactions; judging whether to trigger renewal reminding judgment or not based on the account balance of the user, judging whether to carry out renewal reminding or not based on the historical transaction data of the user in the processing process of the renewal reminding judgment, and if the renewal reminding is carried out, adding the renewal reminding into the account behavior data; and extracting user behavior data in a preset time period, performing predictive analysis, determining a user experience value through the predictive analysis, and judging whether to perform early warning on the user or not.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic toll collection systems, and in particular to a full-cycle intelligent early warning method and system for ETC service anomalies. Background Art

[0002] Electronic Toll Collection System (ETC), also known as non-stop toll collection system, is an automated toll collection method using advanced information and communication technology. The system installs antennas and vehicle type recognition systems at the entrances and exits of toll stations such as toll roads, highways, bridges, and tunnels, and installs on-board devices (such as ETC on-board devices or special electronic cards) on vehicles. When vehicles pass through these toll stations, there is no need to stop and pay manually. The system can automatically identify vehicle information and deduct the corresponding toll from the pre-deposited fees.

[0003] Compared with manual toll collection, the electronic toll collection system has many significant advantages. First, in terms of traffic efficiency, the electronic toll collection system greatly improves the speed of vehicle traffic. With the ETC system, vehicles do not need to slow down or stop when passing through toll stations. The toll collection time for each vehicle is usually less than two seconds, and the traffic capacity of its toll channel is 5 to 10 times that of the manual toll collection channel. This effectively alleviates traffic congestion in front of toll stations, reduces the waiting time of vehicles in queues, and also reduces energy consumption and exhaust emissions caused by stopping and starting, which is beneficial to environmental protection. Secondly, the electronic toll collection system has obvious advantages in toll accuracy. Since ETC toll collection is automatically completed by electronic equipment, it avoids problems such as human operating errors or negligence that may occur in manual toll collection, thereby ensuring the accuracy of toll collection. This not only reduces disputes caused by toll errors, but also improves the transparency and fairness of toll collection.

[0004] However, the electronic toll collection systems in the prior art are often designed only for toll collection and cannot manage user accounts. The prior art lacks intelligent management of user accounts. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides a full-cycle intelligent early warning method for ETC service anomalies to eliminate or improve one or more defects existing in the prior art.

[0006] One aspect of the present invention provides a full-cycle intelligent early warning method for ETC service anomalies, the method comprising the steps of: Acquire the user's onboard unit information based on the interaction between the onboard unit of the user terminal and the roadside unit, wherein the onboard unit information includes a user identifier and an account balance; Determine a corresponding user account based on the user identifier, and automatically deduct fees based on the account balance; After the automatic deduction is completed, calling the user account data based on the user identifier, the account data including account behavior data and account historical transactions; Determine whether to trigger a renewal reminder based on the user's account balance. During the renewal reminder determination process, determine whether to make a renewal reminder based on the user's historical transaction data. If a renewal reminder is made, add the renewal reminder to the account behavior data; Extract user behavior data for a preset time period, perform predictive analysis based on the user behavior data for the preset time period, determine the user experience value through predictive analysis, and decide whether to issue an early warning to the user.

[0007] By adopting the above scheme, this scheme can first complete the automatic deduction of users based on the interaction between the on-board unit and the roadside unit. When the automatic deduction is completed, the user's account data can be analyzed. First, the account balance and the account's recent transactions are used to determine whether to issue a renewal reminder, and the user's experience is determined through user behavior data over a period of time. Early warning can be achieved to ensure user experience.

[0008] In some embodiments of the present invention, in the step of determining whether to trigger a renewal reminder based on the user's account balance, the account balance is compared with a renewal threshold, and if the account balance is lower than the renewal threshold, a renewal reminder is triggered.

[0009] In some embodiments of the present invention, in the step of determining whether to issue a renewal reminder based on the user's historical transaction data, the number of transactions of the user in the historical transaction data of a second preset time period is counted, and the number of transactions is compared with a threshold number. If the number of transactions is greater than the threshold number, a renewal reminder is issued.

[0010] In some embodiments of the present invention, the step of performing predictive analysis based on user behavior data in a preset time period and determining the user experience value through predictive analysis includes: Counting the user behavior data in a preset time period, and determining the behavior number corresponding to each piece of data in the user behavior data based on a preset comparison table; Combine the behavior numbers corresponding to the user behavior data in the preset time period to obtain a behavior vector; The behavior vector is input into a pre-trained neural network model, and the neural network model outputs a user experience value.

[0011] In the specific implementation process, the pre-trained neural network model is a convolutional neural network model.

[0012] In some embodiments of the present invention, the user behavior data includes primary behaviors and secondary behaviors, the comparison table includes a primary comparison table and multiple secondary comparison tables, the primary behaviors correspond one-to-one to the items in the primary comparison table, each of the secondary comparison tables corresponds to an item in the primary comparison table, and the items in the secondary comparison table correspond one-to-one to the secondary behaviors. In the step of determining the behavior number corresponding to each piece of data in the user behavior data based on a preset comparison table, the primary number is determined based on the primary comparison table in the comparison table to determine the primary behavior of each piece of user behavior data, and the secondary number is determined based on the secondary comparison table in the comparison table to determine the secondary behavior of each piece of user behavior data. The primary number and the secondary number of each piece of user behavior data are combined to obtain the behavior number.

[0013] In some embodiments of the present invention, in the step of inputting the behavior vector into a pre-trained neural network model and the neural network model outputting a user experience value, the neural network model inputs a user experience vector, and the value of each dimension in the user experience vector is a user experience value of an item of user experience.

[0014] In some embodiments of the present invention, in the step of determining the user experience value through predictive analysis and determining whether to provide early warning to the user, the user experience value of each dimension in the user experience vector is compared with the corresponding experience threshold. If the user experience value of any dimension is lower than the corresponding experience threshold, it is determined that an early warning is provided for the project that triggers this experience.

[0015] In some embodiments of the present invention, the method further comprises: The data in the vehicle-mounted unit information is compared with the data in the server to determine whether the data in the two correspond. If any item in the data in the two does not correspond, an alarm is issued.

[0016] In some embodiments of the present invention, when comparing the data in the on-board unit information with the data in the server to determine whether the data of the two correspond, if any of the data of the two do not correspond, in the step of issuing an alarm reminder, the deduction information corresponding to a transaction is extracted, if there is no deduction information for a transaction, it is determined that a deduction is missed; if there is one-to-one corresponding deduction information for a transaction, it is determined that the transaction is normal; if there are multiple deduction information for a transaction, it is determined that duplicate deductions occur, and an alarm reminder is issued for the cases of missed and duplicate deductions.

[0017] The second aspect of the present invention also provides a full-cycle intelligent early warning system for ETC service anomalies, the system including a computer device, the computer device including a processor and a memory, the memory storing computer instructions, the processor being used to execute the computer instructions stored in the memory, and when the computer instructions are executed by the processor, the system implements the steps implemented by the method described above.

[0018] The third aspect of the present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, it implements the steps implemented by the aforementioned full-cycle intelligent early warning method for ETC service anomalies.

[0019] Additional advantages, purposes, and features of the present invention will be described in part in the following description, and will become apparent to those skilled in the art after studying the following, or may be learned from the practice of the present invention. The purposes and other advantages of the present invention can be specifically pointed out and obtained in the specification and the accompanying drawings.

[0020] Those skilled in the art will appreciate that the objectives and advantages that can be achieved with the present invention are not limited to the above specific description, and the above and other objectives that can be achieved by the present invention will be more clearly understood from the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The drawings described herein are used to provide a further understanding of the present invention, constitute a part of this application, and do not constitute a limitation of the present invention.

[0022] Figure 1 A schematic diagram of an implementation method of the full-cycle intelligent early warning method for ETC service abnormalities of the present invention; Figure 2 It is a schematic diagram of the overall framework of the present invention. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments and the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0024] It should also be noted that, in order to avoid obscuring the present invention due to unnecessary details, only structures and / or processing steps closely related to the solutions according to the present invention are shown in the accompanying drawings, while other details that are not closely related to the present invention are omitted.

[0025] The disadvantages of the existing technology include: 1. Manual monitoring system: Currently, many ETC service anomalies rely on manual monitoring and intervention. This method is inefficient and prone to missing problems, and cannot achieve real-time monitoring and rapid response; 2. Simple automation tools: Some automation tools can handle specific exception types, such as automatic refunds or blacklist management, but these tools usually lack comprehensive analysis and prediction capabilities; 3. Static data analysis: Some systems use periodic data analysis to identify anomalies, but this method cannot capture and respond to the latest transaction anomalies in real time; 4. Single-function system: Existing systems are often designed for a single problem, such as a separate refund processing system or blacklist management system, which lacks integration and cannot fully solve various anomalies in ETC services; 5. Customer Service System: The customer service system usually intervenes after the user complains and provides solutions, but this method is passive and the response time is long.

[0026] 6. Lack of real-time performance: Existing systems are often unable to achieve real-time monitoring and rapid response to ETC service anomalies, resulting in delays in problem discovery and resolution.

[0027] 7. Lack of predictiveness: Many existing solutions lack the ability to use data analysis to predict potential future anomalies and mainly rely on passive responses after problems occur.

[0028] 8. Low processing efficiency: The existing exception handling process usually involves multiple departments and manual operations, resulting in low processing efficiency and long waiting time for users.

[0029] 9. Insufficient personalized services: Existing systems often adopt a universal processing method and lack personalized services for different user characteristics.

[0030] 10. Low level of automation: After anomaly identification, existing systems often require manual intervention and lack automated processing procedures, which limits the speed and consistency of processing.

[0031] 11. Poor user experience: Due to the above limitations, users often have a poor experience when encountering ETC service anomalies and need to wait a long time for the problem to be resolved.

[0032] The advantages of this solution include: 1. Improve real-time performance: The present invention uses real-time monitoring technology to quickly identify abnormal situations in ETC services, thereby achieving immediate response.

[0033] 2. Enhanced prediction capabilities: Using advanced data analysis and machine learning algorithms, the present invention can predict potential service anomalies and provide proactive early warnings.

[0034] 3. Improve processing efficiency: Through the automated exception handling process, the present invention can reduce manual intervention and improve processing speed and efficiency.

[0035] 4. Realize personalized services: The present invention can provide customized warning and processing solutions based on the user's behavior patterns and historical data to enhance the user experience.

[0036] 5. Automated processing: The automated processing mechanism of the present invention is intended to reduce manual operations, ensure consistency and reduce errors.

[0037] 6. Improve user experience: Through fast, personalized and automated services, the invention aims to increase user satisfaction and loyalty.

[0038] like Figure 1 and 2 As shown, the present invention proposes a full-cycle intelligent early warning method for ETC service abnormalities, the steps of the method comprising: Step S100, obtaining the user's onboard unit information based on the interaction between the user's onboard unit and the roadside unit, wherein the onboard unit information includes a user identifier and an account balance; In the specific implementation process, OBU (On-Board Unit) is the ETC device installed on the vehicle, which is used to communicate with the roadside unit (RSU) to realize automatic deduction; RSU (Road-Side Unit) is the device installed at the toll station, which communicates with OBU and processes transaction information.

[0039] During the specific implementation process, there is bound card signature information between the ETC user account and the OBU. The card signature information includes the card number and signature number, which are used to identify and verify the user's identity.

[0040] Specifically, in the ETC system, certain ETC accounts will be marked as blacklisted due to arrears, violations or other reasons, and will be prohibited from using ETC services.

[0041] Step S200, determining a corresponding user account based on the user identifier, and automatically deducting fees based on the account balance; During the specific implementation process, in the step of automatic deduction based on the account balance, deductions are made from the corresponding bank card account based on the account balance, and before the deductions are made, the balance of the bank card account is compared with the account balance in the vehicle-mounted unit information to see if they are consistent. If they are inconsistent, the card account discrepancy is recorded; if they are consistent, automatic deductions are made.

[0042] Step S300, after the automatic deduction is completed, calling the user account data based on the user identifier, the account data including the account behavior data and the account historical transactions; Step S400, determining whether to trigger a renewal reminder based on the user's account balance. During the renewal reminder determination process, determining whether to perform a renewal reminder based on the user's historical transaction data. If a renewal reminder is performed, the renewal reminder is added to the account behavior data; Step S500, extracting user behavior data for a preset time period, performing predictive analysis based on the user behavior data for the preset time period, determining a user experience value through predictive analysis, and determining whether to issue an early warning to the user.

[0043] In the specific implementation process, this plan covers the entire service cycle from user registration of ETC account, to transaction processing during use, to handling of possible abnormal situations, to user cancellation of account.

[0044] The steps of this solution also include data integration and cleaning: by integrating source data from different business systems, including the complaint system, ETC business processing system, card signing system, and recharge and consumption refund system, this system can provide a unified data view. The data cleaning and processing module is responsible for ensuring the accuracy and consistency of the data, providing a high-quality data foundation for subsequent analysis.

[0045] By adopting the above scheme, this scheme can first complete the automatic deduction of users based on the interaction between the on-board unit and the roadside unit. When the automatic deduction is completed, the user's account data can be analyzed. First, the account balance and the account's recent transactions are used to determine whether to issue a renewal reminder, and the user's experience is determined through user behavior data over a period of time. Early warning can be achieved to ensure user experience.

[0046] In some embodiments of the present invention, in the step of determining whether to trigger a renewal reminder based on the user's account balance, the account balance is compared with a renewal threshold, and if the account balance is lower than the renewal threshold, a renewal reminder is triggered.

[0047] In some embodiments of the present invention, in the step of determining whether to issue a renewal reminder based on the user's historical transaction data, the number of transactions of the user in the historical transaction data of a second preset time period is counted, and the number of transactions is compared with a threshold number. If the number of transactions is greater than the threshold number, a renewal reminder is issued.

[0048] In some embodiments of the present invention, the step of performing predictive analysis based on user behavior data in a preset time period and determining the user experience value through predictive analysis includes: Counting the user behavior data in a preset time period, and determining the behavior number corresponding to each piece of data in the user behavior data based on a preset comparison table; Combine the behavior numbers corresponding to the user behavior data in the preset time period to obtain a behavior vector; The behavior vector is input into a pre-trained neural network model, and the neural network model outputs a user experience value.

[0049] In some embodiments of the present invention, the data acquisition module is used as the data input layer of the entire system, which is mainly responsible for real-time data acquisition and unified management in a multi-source heterogeneous environment, supporting the efficient operation and data governance requirements of the big data system. The module covers the extraction, transmission and preprocessing functions of the original data of multiple business systems, laying the foundation for subsequent data governance, analysis, and application. These data include customer complaint records, ETC transaction records, card signing status, account balance, etc.

[0050] Specifically, the data acquisition module architecture includes the following functional components: a.Multi-source data interface manager: Provides standardized interfaces to support data acquisition from different business systems (such as 12345 complaint system and ETC processing system).

[0051] Supports multiple data access methods such as RESTful API, JDBC, file system, etc.

[0052] Automatically adapt the connection and configuration of heterogeneous data sources according to the characteristics of different business systems.

[0053] b.Data collection engine: Data collection is realized based on the DataX platform, supporting parallel extraction and loading of multi-source data.

[0054] Provides flexible data conversion capabilities to standardize data into a unified format (such as JSON or table format).

[0055] c.Data cleaning module: Perform automated cleaning on collected data, including redundant data removal, field completion, data type conversion, etc.

[0056] Use the rule engine to verify the data quality to ensure that the data meets the predefined quality standards.

[0057] d.Data synchronization mechanism: Based on incremental synchronization technology, it supports efficient collection and transmission of real-time updated data.

[0058] Real-time data synchronization is achieved by comparing the change logs of the source system (such as MySQL Binlog or Kafka).

[0059] e. Scheduling and management module: Use Dolphin Scheduling to automate the scheduling of collection tasks.

[0060] It supports task dependency management, multi-task parallel processing, and failure retry mechanism to ensure high availability of data collection.

[0061] The collection process of the data collection module includes: a. Access data source: Configure multi-source data interfaces according to the characteristics of different business systems.

[0062] Define the mapping relationship between the source system and the target storage (field name, type, etc.).

[0063] b. Data collection: Start the DataX task, collect raw data and load it into storage (such as HDFS).

[0064] c. Data cleaning and conversion: After data collection is completed, the data is cleaned through the rule engine.

[0065] Convert data formats (such as JSON to table).

[0066] d. Data synchronization and governance: Real-time data synchronization is achieved based on Dolphin scheduling.

[0067] Generate log records for collected data to facilitate auditing and tracking.

[0068] Specifically, we mainly analyze various abnormal behaviors that may cause complaints from users, including 12345 complaints, unsettled orders and complaints, abnormal account closure and refund, etc. The big data processing platform is scheduled every day, and the spark framework extracts full data from the dwd layer and ads layer of the hive data warehouse to cover the ads layer. Then the big data processing platform continues to schedule, and incrementally writes the clickhouse result table from the hive mapping table in the clickhouse library in etl mode, and then merges small files and deletes the data in the clickhouse result table except for the analysis of the day.

[0069] Real-time data analysis: Real-time guessing your complaints use pseudo real-time scheduling analysis, which analyzes the business data written to the real-time table by the materialized view in ClickHouse. After the analysis is completed, the results are written to the result table of ClickHouse.

[0070] Technical implementation: 1.Spark framework scheduled scheduling: Use the Spark framework for scheduled scheduling to extract data from the DWD (data workspace layer) and ADS (analytical data service layer) of the Hive data warehouse.

[0071] The DWD layer stores cleaned data, and the ADS layer stores processed and summarized data suitable for analysis.

[0072] Use Spark to extract full data from these dimension tables and fact tables to ensure that all data in the HiveADS layer is covered and complete data synchronization.

[0073] 2. Data ETL processing: Perform ETL (Extract, Transform, Load) processing through the big data processing platform to write the incremental data of Hive's mapping table into ClickHouse.

[0074] 3. Incremental data writing: Extract data from the ClickHouse mapping table and write it incrementally to the ClickHouse result table to ensure the latest historical data, reduce repeated writing of full data, and improve efficiency.

[0075] 4. Small file merging operation A large number of small files may be generated during the incremental writing process. Merging small files can optimize disk storage and query performance.

[0076] ClickHouse supports efficient distributed queries, and merge operations can significantly improve query efficiency.

[0077] 5. Data deletion operation Delete non-current data in the ClickHouse result table to avoid the accumulation of useless data, save storage space and optimize query efficiency.

[0078] 6. Pseudo real-time scheduling A near-real-time scheduling scheme is adopted to reduce data delay and resource consumption by processing small batches at regular intervals (for example, scheduling every minute), thus achieving near-real-time data updates.

[0079] 7. Efficient Query ClickHouse's distributed query architecture and columnar storage make user ID-based queries very efficient.

[0080] Data is stored in columns, and data can be read by user number, which improves page response speed.

[0081] In some embodiments of the present invention, the user behavior data includes primary behaviors and secondary behaviors, the comparison table includes a primary comparison table and multiple secondary comparison tables, the primary behaviors correspond one-to-one to the items in the primary comparison table, each of the secondary comparison tables corresponds to an item in the primary comparison table, and the items in the secondary comparison table correspond one-to-one to the secondary behaviors. In the step of determining the behavior number corresponding to each piece of data in the user behavior data based on a preset comparison table, the primary number is determined based on the primary comparison table in the comparison table to determine the primary behavior of each piece of user behavior data, and the secondary number is determined based on the secondary comparison table in the comparison table to determine the secondary behavior of each piece of user behavior data. The primary number and the secondary number of each piece of user behavior data are combined to obtain the behavior number.

[0082] In some embodiments of the present invention, the first-level behaviors include deduction errors, equipment failure and poor customer service experience; the second-level behaviors corresponding to the deduction errors include system misdeductions, repeated deductions and inconsistent amounts; the second-level behaviors corresponding to equipment failures include failure and inability to identify the on-board OBU; the second-level behaviors corresponding to poor customer service experience include difficulty in getting through the hotline, templated responses and poor attitude.

[0083] In the specific implementation process, the user behavior data includes repeated deductions, abnormal refunds, and card signatures about to expire. This solution uses key element data, and the intelligent algorithm analysis module can identify and predict potential business anomalies, such as repeated deductions, abnormal refunds, and card signatures about to expire. This prediction mechanism enables the system to take proactive measures before problems occur, thereby improving customer satisfaction and business processing efficiency.

[0084] Specifically, this solution also includes a user contact module, which is designed to communicate effectively with customers and promptly inform them of the status of ETC cards and OBU tags, as well as any abnormal transactions that require their attention. At the same time, this module is closely linked with the business system to ensure that once an abnormality is detected, it can respond and take action quickly. Furthermore, this solution can automate the processing process: by designing automated business processing processes, such as automatically initiating refunds, card signing extension operations, recharge and supplementary payments, etc., it reduces manual intervention and improves processing speed and accuracy.

[0085] As the hub of the entire ETC service abnormal intelligent warning and processing system, the user access module establishes a connection between the intelligent analysis module and the actual business execution system. When the intelligent analysis module accurately determines the abnormal situation in the ETC service based on data and algorithms, the business system access module converts these warning information into practical and specific business operation instructions. Accurately identify the business processes corresponding to different types of abnormal warnings, and ensure that these abnormal warning information is delivered to the back-end business execution system, so as to achieve timely processing of ETC service abnormalities and ensure the stable operation of the entire ETC service system.

[0086] Technical implementation: 1. Design business rule engine: Build a business rule library that includes various ETC service exception handling rules, such as overdue payment warning rules (when the user account balance is lower than the threshold and the number of consecutive uses reaches a certain amount, automatic renewal reminders will be triggered, which can be pushed via SMS and in-app notifications).

[0087] The rule engine technology is used to realize dynamic configuration management of rules, and a visual editing interface is provided to facilitate business personnel to modify rules according to needs. For example, when the ETC service policy changes, the rules related to the arrears threshold can be quickly updated.

[0088] The rule engine matches rules and executes operations based on real-time warning information. For example, if a warning is received that a card signature is about to expire, the reminder process is triggered and the notification method is selected according to user habits.

[0089] 2. Implement API interface with back-end business system: Design standardized and secure API interface specifications and adopt a RESTful architecture style to ensure smooth communication with the backend ETC-related business systems.

[0090] Develop API client libraries for different business systems, encapsulate interaction details, and provide concise calling methods. For example, when interacting with the account management system, the client library provides functions such as balance query, status update, and renewal.

[0091] We focus on the security and integrity of API interface data transmission, use HTTPS protocol encryption, perform identity authentication and authorization management to ensure system security.

[0092] 3. Design artificial intervention mechanism: A manual intervention operation platform is built for auditors to view detailed information of abnormal warnings and related business data. The platform provides an intuitive interface to display key information, historical data and processing suggestions to assist decision-making.

[0093] Establish a manual intervention process and clarify the steps and responsibilities of auditors in handling abnormal warnings. Auditors verify evidence according to the process and record the handling process and results to ensure traceable auditing.

[0094] Achieve seamless integration of manual intervention and automated processing flow, update business data with manual processing results, and feed back to the intelligent analysis module. At the same time, mark and record the processing status for statistical analysis to improve the mechanism.

[0095] In some embodiments of the present invention, in the step of inputting the behavior vector into a pre-trained neural network model and the neural network model outputting a user experience value, the neural network model inputs a user experience vector, and the value of each dimension in the user experience vector is a user experience value of an item of user experience.

[0096] In some embodiments of the present invention, in the step of determining the user experience value through predictive analysis and determining whether to provide early warning to the user, the user experience value of each dimension in the user experience vector is compared with the corresponding experience threshold. If the user experience value of any dimension is lower than the corresponding experience threshold, it is determined that an early warning is provided for the project that triggers this experience.

[0097] In the specific implementation process, the user contact module is responsible for proactively communicating with users when an exception is detected in the ETC service exception handling, providing exception details and solutions. Whether the ETC card, OBU tag or transaction is abnormal, users can be informed in a timely manner to enhance user trust and satisfaction. Considering user scenarios and convenience, it can be pushed to users through APP, SMS or email.

[0098] Technical implementation: 1. Integrated communication interface: Develop a communication interface framework to support SMS, email, and application notifications (WeChat public account messages, mobile APP push). For SMS, work with providers to access the gateway, accurately set parameters, and monitor status. Email configuration server connection, compatible with common clients, and set content in multiple formats. Application notifications are developed for different platforms, using relevant technologies to accurately push, and customized content to guide user operations.

[0099] 2. Design user interaction process: Draw a detailed interactive flow chart, covering the operation process after the user receives the warning. The interactive design follows the principles of habit and convenience, such as clicking on the notification to jump to the details page to display key information and operation buttons, using responsive design to ensure multi-device experience, real-time prompt operation feedback, and recording and analyzing behavior to optimize the process.

[0100] 3. Implement personalized notification strategy: Build a user portrait database and integrate multi-dimensional information to analyze user needs and preferences. Formulate strategy rules based on the analysis results, such as selecting notification time based on travel habits, providing detailed cost explanations for cost-sensitive users, and customizing channel combinations based on user notification method preferences.

[0101] With the rapid development of intelligent transportation systems, ETC services, as an important part of intelligent transportation management, play a key role in improving road utilization efficiency and reducing operating costs with their efficiency and convenience. However, the complexity of the ETC system and the growth of the user base have also brought a series of challenges, especially in the monitoring, early warning and handling of service anomalies. Service anomalies not only affect user experience, but may also lead to economic losses and management difficulties. This solution analyzes the abnormal data in the user portrait and predicts the items with poor user experience, that is, the possible complaint points of users, so that customer service can prepare in advance or proactively solve the problem.

[0102] In some embodiments of the present invention, the method further comprises: The data in the vehicle-mounted unit information is compared with the data in the server to determine whether the data in the two correspond. If any item in the data in the two does not correspond, an alarm is issued.

[0103] By adopting the above solution, the system realizes real-time monitoring of ETC transactions and account status, and can promptly capture problems such as transaction information not being uploaded and card account funds not matching, and provide a feedback mechanism to quickly adjust and optimize services.

[0104] In some embodiments of the present invention, when comparing the data in the on-board unit information with the data in the server to determine whether the data of the two correspond, if any of the data of the two do not correspond, in the step of issuing an alarm reminder, the deduction information corresponding to a transaction is extracted, if there is no deduction information for a transaction, it is determined that a deduction is missed; if there is one-to-one corresponding deduction information for a transaction, it is determined that the transaction is normal; if there are multiple deduction information for a transaction, it is determined that duplicate deductions occur, and an alarm reminder is issued for the cases of missed and duplicate deductions.

[0105] An embodiment of the present invention also provides a full-cycle intelligent early warning system for ETC service anomalies, the system comprising a computer device, the computer device comprising a processor and a memory, the memory storing computer instructions, the processor being used to execute the computer instructions stored in the memory, and when the computer instructions are executed by the processor, the system implements the steps implemented by the method described above.

[0106] The embodiment of the present invention also provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps implemented by the above-mentioned full-cycle intelligent early warning method for ETC service anomalies are implemented. The computer-readable storage medium can be a tangible storage medium, such as a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a floppy disk, a hard disk, a removable storage disk, a CD-ROM, or any other form of storage medium known in the technical field.

[0107] It should be understood by those skilled in the art that the exemplary components, systems and methods described in conjunction with the embodiments disclosed herein can be implemented in hardware, software or a combination of the two. Whether it is performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention. When implemented in hardware, it can be, for example, an electronic circuit, an application-specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present invention are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link via a data signal carried in a carrier.

[0108] It should be clear that the present invention is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present invention.

[0109] In the present invention, features described and / or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, and / or combined with features of other embodiments or replace features of other embodiments.

[0110] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the embodiments of the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A full-cycle intelligent early warning method for ETC service anomalies, characterized in that: The steps of the method include: Acquire the user's onboard unit information based on the interaction between the onboard unit of the user terminal and the roadside unit, wherein the onboard unit information includes a user identifier and an account balance; Determine a corresponding user account based on the user identifier, and automatically deduct fees based on the account balance; After the automatic deduction is completed, calling the user account data based on the user identifier, the account data including account behavior data and account historical transactions; Determine whether to trigger a renewal reminder based on the user's account balance. During the renewal reminder determination process, determine whether to make a renewal reminder based on the user's historical transaction data. If a renewal reminder is made, add the renewal reminder to the account behavior data; Extract user behavior data for a preset time period, perform predictive analysis based on the user behavior data for the preset time period, determine the user experience value through predictive analysis, and decide whether to issue an early warning to the user.

2. The full-cycle intelligent early warning method for ETC service abnormality according to claim 1 is characterized in that: In the step of determining whether to trigger a renewal reminder based on the user's account balance, the account balance is compared with a renewal threshold, and if the account balance is lower than the renewal threshold, a renewal reminder is triggered.

3. The full-cycle intelligent early warning method for ETC service abnormality according to claim 2 is characterized in that: In the step of determining whether to issue a renewal reminder based on the user's historical transaction data, the number of transactions of the user in the historical transaction data of a second preset time period is counted, and the number of transactions is compared with a threshold number. If the number of transactions is greater than the threshold number, a renewal reminder is issued.

4. The full-cycle intelligent early warning method for ETC service abnormality according to any one of claims 1 to 3, characterized in that: The step of performing predictive analysis based on user behavior data in a preset time period and determining the user experience value through predictive analysis includes: Counting the user behavior data in a preset time period, and determining the behavior number corresponding to each piece of data in the user behavior data based on a preset comparison table; Combine the behavior numbers corresponding to the user behavior data in the preset time period to obtain a behavior vector; The behavior vector is input into a pre-trained neural network model, and the neural network model outputs a user experience value.

5. The full-cycle intelligent early warning method for ETC service anomalies according to claim 4 is characterized in that: The user behavior data includes primary behaviors and secondary behaviors, the comparison table includes a primary comparison table and multiple secondary comparison tables, the primary behaviors correspond one-to-one to the items in the primary comparison table, each of the secondary comparison tables corresponds to an item in the primary comparison table, and the items in the secondary comparison table correspond one-to-one to the secondary behaviors. In the step of determining the behavior number corresponding to each piece of data in the user behavior data based on the preset comparison table, the primary number is determined based on the primary behavior of each piece of user behavior data determined in the primary comparison table in the comparison table, and the secondary number is determined based on the secondary behavior of each piece of user behavior data determined in the secondary comparison table in the comparison table. The primary number and the secondary number of each piece of user behavior data are combined to obtain the behavior number.

6. The full-cycle intelligent early warning method for ETC service anomalies according to claim 5 is characterized in that: In the step of inputting the behavior vector into a pre-trained neural network model and the neural network model outputting a user experience value, the neural network model inputs a user experience vector, and the value of each dimension in the user experience vector is a user experience value of an item of user experience.

7. The full-cycle intelligent early warning method for ETC service anomalies according to claim 6 is characterized in that: In the step of determining the user experience value through predictive analysis and determining whether to issue an early warning to the user, the user experience value of each dimension in the user experience vector is compared with the corresponding experience threshold. If the user experience value of any dimension is lower than the corresponding experience threshold, an early warning is determined for the project that triggers this experience.

8. The full-cycle intelligent early warning method for ETC service anomalies according to claim 1 is characterized in that: The method further comprises the steps of: The data in the vehicle-mounted unit information is compared with the data in the server to determine whether the data in the two correspond. If any item in the data in the two does not correspond, an alarm is issued.

9. The full-cycle intelligent early warning method for ETC service anomalies according to claim 8 is characterized in that: In the step of comparing the data in the vehicle-mounted unit information with the data in the server to determine whether the data in the two correspond, if any item of the data in the two does not correspond, in the step of issuing an alarm reminder, extracting the deduction information for the corresponding transaction, if there is no deduction information for a transaction, it is determined that the deduction is missed; if there is one-to-one corresponding deduction information for a transaction, it is determined that the transaction is normal; If there are multiple deduction information for a transaction, it will be determined that duplicate deductions have occurred, and an alarm will be issued for missed and duplicate deductions.

10. A full-cycle intelligent early warning system for ETC service anomalies, characterized in that: The system includes a computer device, which includes a processor and a memory, wherein the memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the system implements the steps implemented by the method as described in any one of claims 1 to 9.

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