Real-time synchronization and dynamic exception checking platform for un-uploaded data of road network toll exit

Abstract

The application discloses a real-time synchronization and dynamic exception checking platform for un-uploaded data of a road network toll exit and relates to the technical field of data checking management. The real-time synchronization and dynamic exception checking platform for un-uploaded data of a road network toll exit comprises a vehicle speed monitoring module, an un-uploaded data integrity monitoring module, an un-uploaded data matching monitoring module and an un-uploaded data statistical module. After the vehicle speed influence result is obtained, the un-uploaded data complete deviation result is obtained, the un-uploaded data matching qualified result is obtained after the un-uploaded data complete deviation result is qualified, and finally, the un-uploaded data statistical qualifiedness evaluation is executed, so that the PASSID matching exit flow and the terminal gantry flow accuracy of the road network toll exit vehicle are improved, and the problem of low PASSID matching exit flow and terminal gantry flow accuracy of the road network toll exit vehicle caused by single vehicle speed interference in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of data verification and management technology, and in particular to a real-time synchronization and dynamic anomaly verification platform for data not uploaded at toll exits of a road network. Background Technology

[0002] During the real-time synchronization of unuploaded data at tollbooth exits on the road network, firstly, in the unuploaded data collection phase, PASSIDs (pass numbers) are collected in real-time through the tollbooth exit gantry system and transmitted to the big data platform for aggregation. PASSID represents the vehicle's journey number from entering to exiting the highway, including information such as entry flow, gantry flow, and exit flow. Next, in the unuploaded data processing phase, successfully transacted gantry flows are filtered based on PASSIDs and grouped and aggregated on the big data platform. Successfully transacted gantry flows represent gantry flows that have actually been successfully transacted. The big data platform retrieves the passage time of the last gantry and the passage time of the first gantry in each PASSID group, along with the corresponding gantry code, vehicle information (license plate, vehicle type), and entry information (entrance tollbooth). Different maximum tolerable waiting times are set according to different road network areas. Within a given time period, each group's PASSID is matched against exit traffic and end-point gantry traffic. Unmatched PASSIDs are filtered out, and the road network toll exits corresponding to these unmatched PASSIDs are marked as abnormal road network toll exit records for which no data has been uploaded. Here, the end-point gantry refers to the last gantry that can be reached within the preset road network range. Abnormal road network toll exit records include: date of loss, lost gantry, and road segment to which the lost gantry belongs. Finally, during the data upload statistics phase, the abnormal road network toll exit records are grouped and summarized, the number of lost vehicles is calculated, and the vehicles are sorted according to the number of lost vehicles. Road segments with a large number of lost vehicles are notified first, and detailed road network toll exit records are provided, including: PASSID, entrance information, vehicle information, lost gantry, and time of loss. This is set as a scheduled task on the big data platform for automatic execution, automatically generating visual charts and detailed road network toll exit data.

[0003] For example, Chinese invention patent application CN117350703A discloses a method, system, and device for synchronous verification of master and backup parameters. This invention includes extracting the master execution operation record number and the backup execution operation record number to be verified from the execution operation record information, generating corresponding master execution operation record number tables and backup execution operation record number tables; performing delayed verification on the master execution operation record number tables and backup execution operation record number tables at preset time intervals, generating verification results; determining abnormal information of master and backup data parameters based on the verification results and generating an operating system inspection page in conjunction with alarm information; and sending the operating system inspection page to the front end for display.

[0004] For example, Chinese invention patent application CN119398755A discloses a method, device, equipment, and medium for verifying parameters of power electronic equipment based on a power distribution management system. The method includes: receiving a user's data synchronization instruction, obtaining the first device parameter to be synchronized, accessing and parsing the data synchronization instruction to obtain the target power distribution management system for the data to be synchronized; then, for each target power distribution management system, calling its corresponding target database, comparing the first device parameter with historical device parameters, and when it is determined that the first device parameter is consistent with the historical device parameter, marking the first device parameter as a redundant parameter and rejecting it from entering the target database.

[0005] In existing technologies, during the data collection phase before data upload, traffic segments typically use existing gantry transaction records. At the settlement time corresponding to the transaction day, details are settled through manual auditing of each record. Based on the exit transaction records uploaded by individual exit toll stations, it is determined whether any toll exits on the road network failed to upload transaction records for a certain period. Finally, the toll traffic flow is calculated at the exit lanes, generating a total count, which is compared with the uploaded exit transaction records. The continuity of ETC (Electronic Toll Collection System) transaction sequence numbers in the uploaded exit transaction records of the road segment is used to determine whether any ETC transactions have not been uploaded.

[0006] The above-mentioned technology has at least the following technical problems:

[0007] In existing technologies, during the data collection phase before data upload, due to interference from the speed of individual vehicles, when a vehicle passes through an adjacent gantry at a speed exceeding a threshold, the gantry system experiences a response delay. This causes the gantry system to be unable to collect the unuploaded data in real time within the sampling time interval, resulting in inaccurate position coordinates of the vehicle within the gantry system during the sampling time interval. Consequently, the gantry system collects incomplete unuploaded data, leading to incomplete gantry system flow. Due to the deviation in the vehicle's positioning within the gantry system, the same vehicle may be identified as different targets at different gantries, resulting in inaccurate PASSID matching of the vehicle at the exit flow and the end gantry flow. This further leads to the problem of low accuracy in matching the PASSID of vehicles at the road network toll exit and the end gantry flow due to interference from the speed of individual vehicles. Summary of the Invention

[0008] To address the technical problem of low accuracy in matching vehicle PASSID at tollbooth exits and the accuracy of exit flow and end-gantry flow caused by individual vehicle speed interference in existing technologies, this invention provides a real-time synchronization and dynamic anomaly verification platform for unuploaded data at tollbooth exits. The technical solution is as follows:

[0009] A platform for real-time synchronization and dynamic anomaly verification of unuploaded data at tollbooth exits on the road network is provided, including the following modules: vehicle speed monitoring module, unuploaded data integrity monitoring module, unuploaded data matching monitoring module, and unuploaded data statistics module. The vehicle speed monitoring module, during real-time synchronization of unuploaded data, acquires the vehicle speed impact result reflecting the vehicle's passability through the gantry and determines whether it meets the vehicle speed compliance requirements. If it does, it acquires the unuploaded data integrity deviation result reflecting the completeness of the unuploaded data; otherwise, it acquires the average unuploaded data positioning deviation result to verify the unuploaded data positioning compliance. The average unuploaded data positioning deviation result verifies the unuploaded data positioning compliance and determines whether to perform unuploaded data positioning adjustments. Unuploaded data positioning adjustments include: precise positioning adjustments and positioning capture frequency adjustments. The system adjusts the rate to increase the frequency of capturing vehicle dynamic positions, thereby improving the accuracy of vehicle positioning. The unuploaded data integrity monitoring module, after the vehicle speed impact result meets the vehicle speed qualification conditions, determines whether to perform deduplication based on the unuploaded data integrity deviation result. Deduplication ensures the uniqueness of the unuploaded data corresponding to the PASSID. The unuploaded data matching monitoring module, after the unuploaded data integrity deviation result meets the unuploaded data integrity conditions, obtains the unuploaded data matching qualification result to determine whether to perform the unuploaded data statistical qualification assessment. The unuploaded data statistics module, after the unuploaded data matching qualification result meets the unuploaded data matching qualification conditions, performs the unuploaded data statistical qualification assessment, obtains the verification waiting time assessment result, and determines whether to perform the verification waiting time adjustment operation.

[0010] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0011] 1. By obtaining the average deviation result of the unuploaded data positioning, it is determined whether the positioning meets the qualification conditions. If not, the average deviation result is obtained to verify the qualification status. Based on the average deviation result, it is determined whether the positioning meets the qualification conditions and whether adjustments are needed. This helps to accurately assess the qualification level of the unuploaded data positioning, quickly identify anomalies or delays in the unuploaded data, enhance the gantry's fault tolerance and real-time monitoring capabilities, and avoid inaccurate real-time vehicle positioning due to delays in unuploaded data acquisition, thereby improving the accuracy of unuploaded data positioning. Obtaining the complete deviation result of the unuploaded data helps to determine whether the positioning meets the qualification conditions and whether adjustments are needed. This helps to accurately assess the completeness of the unuploaded data and avoids issues caused by missing or duplicated exit flow data. This approach helps to avoid biases in assessing the completeness of missing data, preventing non-compliance due to excessively large gaps in exit flow information. It improves the ability to identify abnormal breakpoints and potential risk zones, ensuring the completeness and timeliness of missing data. Obtaining the matching results of missing data to determine whether to perform a statistical compliance assessment helps to accurately evaluate the matching compliance of gantry flow and exit flow, preventing non-compliance due to errors in data collection, thus improving the effectiveness and reliability of missing data matching. Furthermore, performing a statistical compliance assessment of missing data to obtain verification waiting time evaluation results and determine whether to adjust the verification waiting time helps to accurately evaluate the compliance of abnormal missing data matching, preventing non-compliance due to mismatches between preset verification scope and verification waiting time, thereby improving the efficiency and accuracy of verification.

[0012] 2. Obtaining the average result of the positioning deviation of the unuploaded data to measure the passability of the unuploaded data positioning. Compared with the existing technology, due to the interference of individual vehicle speeds, when a vehicle passes through an adjacent gantry at a speed exceeding the threshold, the gantry system experiences a response delay, resulting in inaccurate vehicle positioning. This helps to accurately assess the accuracy of the unuploaded data positioning of the vehicle, further enhancing the gantry's ability to capture instantaneous changes in vehicle position, improving the accuracy of the gantry's vehicle positioning data acquisition, avoiding vehicle trajectory distortion caused by untimely vehicle position updates, thereby improving the reliability and passability of the gantry tracking the entire driving process of the vehicle, and ultimately improving the accuracy of the unuploaded data positioning.

[0013] 3. Obtaining complete deviation results for unuploaded data to measure the completeness of unuploaded data: Compared with existing technologies, the inaccurate position coordinates of vehicles on the gantry during the sampling time interval result in incomplete data collection by the gantry. This helps to accurately assess the deviation of exit flow at toll intersections on the road network, thereby promptly identifying missing, delayed, or incorrect unuploaded data, avoiding unqualified matching of unuploaded data due to incomplete data upload, ensuring the accuracy and qualification of unuploaded data, and providing a reliable basis for subsequent data supplementation and verification, thus improving the consistency and completeness of unuploaded data. Attached Figure Description

[0014] Figure 1 A schematic diagram of the structure of the real-time synchronization and dynamic anomaly verification platform for road network toll exits that have not uploaded data, provided in an embodiment of this application.

[0015] Figure 2 A schematic diagram of the architecture of the real-time synchronization and dynamic anomaly verification platform for road network toll exits that have not uploaded data, provided in the embodiments of this application;

[0016] Figure 3 A schematic diagram illustrating the framework for real-time synchronization and dynamic anomaly verification platform for road network toll exits that have not uploaded data, as provided in this application embodiment, for locating and adjusting the location of data that has not been uploaded.

[0017] Figure 4 This is a screenshot of the interface for querying the outlet flow information of the gantry flow calibration management system.

[0018] Figure 5 This is a screenshot of the interface for the visualization report of the gantry flow calibration management system. Detailed Implementation

[0019] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0020] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0021] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0022] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0023] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0024] This invention provides a real-time synchronization and dynamic anomaly verification platform for non-uploaded data at road network toll exits, such as... Figure 1 The diagram shown illustrates the structure of a real-time synchronization and dynamic anomaly verification platform for non-uploaded data at road network toll exits. It includes the following modules:

[0025] Vehicle speed monitoring module: During real-time synchronization of unuploaded data, it acquires the vehicle speed impact result reflecting the vehicle's passability in gantry identification and determines whether the vehicle speed meets the passability conditions. If it does, it acquires the unuploaded data integrity deviation result to verify the integrity of the unuploaded data. Conversely, it acquires the average unuploaded data positioning deviation result to verify the unuploaded data positioning passability and determines whether to adjust the unuploaded data positioning. Vehicle speed monitoring helps enhance the gantry's fault tolerance and real-time monitoring capabilities, avoiding inaccurate real-time vehicle positioning due to gantry response time delays, thereby improving the accuracy of gantry vehicle positioning.

[0026] The module for monitoring the integrity of unuploaded data: After the vehicle speed impact result meets the vehicle speed qualification conditions, it determines whether to perform deduplication operation based on the deviation result of the unuploaded data integrity. By monitoring the integrity of unuploaded data, it helps to improve the qualification of unuploaded data and avoid inaccurate uploaded data due to duplicate or missing exit flow data, thereby improving the integrity of unuploaded data.

[0027] The missing data matching monitoring module: After the missing data integrity deviation result meets the missing data integrity condition, it obtains the missing data matching qualification result to reflect the missing data matching qualification status, so as to determine whether to perform the missing data statistical qualification assessment. By conducting missing data matching monitoring, it helps to improve the matching qualification of gantry flow and outlet flow, avoids the non-qualification of missing data matching due to incomplete missing data, and thus improves the qualification of missing data matching.

[0028] The Unuploaded Data Statistics Module: After the unuploaded data matching results meet the unuploaded data matching qualification conditions, the module performs an unuploaded data statistics qualification assessment to obtain the verification waiting time assessment results and determine whether to perform verification waiting time adjustment operations. By performing unuploaded data statistics, the module helps to improve the verification efficiency and quality of abnormal unuploaded data, thereby improving the qualification of abnormal unuploaded data matching.

[0029] like Figure 2The diagram shows the architecture of the real-time synchronization and dynamic anomaly verification platform for unuploaded data at road network toll exits provided in this embodiment of the application. First, the vehicle speed impact result is obtained. Based on the monitored vehicle speed impact result, it is determined whether the vehicle speed meets the qualification conditions. If it does, the complete deviation result of the unuploaded data is obtained; otherwise, the average positioning deviation result of the unuploaded data is obtained. Based on the average positioning deviation result of the monitored unuploaded data, it is determined whether the positioning meets the qualification conditions. If it does, the complete deviation result of the unuploaded data is obtained; otherwise, positioning adjustment is performed. Based on the average positioning deviation result of the monitored unuploaded data, it is re-determined whether the positioning meets the qualification conditions. If it does, the complete deviation result of the unuploaded data is obtained; otherwise, an anomaly notification for positioning adjustment is sent to a preset personnel. The complete deviation result of the unuploaded data is obtained again. Based on the monitored complete deviation result of the unuploaded data, it is determined whether the completeness conditions of the unuploaded data are met. If it does, the matching qualification result of the unuploaded data is obtained; otherwise, deduplication is performed. Based on the monitored complete deviation result of the unuploaded data... Reassess whether the incomplete data upload condition is met. If it is, obtain the qualified result of the incomplete data upload. Otherwise, send a deduplication operation exception prompt to the preset personnel and obtain the qualified result of the incomplete data upload. Based on the monitored qualified result of the incomplete data upload, determine whether the incomplete data upload matching condition is met. If it is met, mark the corresponding incomplete data as qualified incomplete data and perform incomplete data statistics. Otherwise, mark the corresponding incomplete data as abnormal incomplete data and perform an incomplete data statistics qualification assessment. Based on the monitored verification waiting time assessment result, determine whether the incomplete data upload statistics qualification condition is met. If it is met, mark the corresponding abnormal incomplete data as data to be input and perform incomplete data statistics. Otherwise, perform a verification waiting time adjustment operation. Based on the monitored verification waiting time assessment result, reassess whether the incomplete data upload statistics qualification condition is met. If it is met, mark the corresponding abnormal incomplete data as data to be input and perform incomplete data statistics. Otherwise, mark the corresponding abnormal incomplete data as key abnormal data and send a statistical anomaly alarm.

[0030] It should be added that, prior to the design of the real-time synchronization and dynamic anomaly verification platform for the road network toll exits that did not upload data in this application, a database storing various set data was established. The database includes, but is not limited to, preset vehicle speed impact results, preset vehicle trigger gantry response time, preset incomplete data deviation results, etc., and the various values ​​are directly set by technical personnel.

[0031] In this embodiment, a step-by-step judgment and optimization process is implemented through a vehicle speed monitoring module, an unuploaded data integrity monitoring module, an unuploaded data matching monitoring module, and an unuploaded data statistics module. These modules are closely interconnected. First, the vehicle speed monitoring module assesses the accuracy of vehicle positioning, ensuring the qualification of unuploaded data positioning. Next, the unuploaded data integrity monitoring module assesses the completeness of unuploaded data, including vehicle license plate information, vehicle speed, and vehicle entry / exit times, ensuring its integrity. Then, the unuploaded data matching monitoring module assesses the qualification of unuploaded data matching. If the matching is unqualified, the unuploaded data statistics module assesses the qualification of the unuploaded data verification, helping to improve the matching qualification of unuploaded data. This improves the qualification and completeness of unuploaded data positioning and collection, ultimately enhancing the accuracy of PASSID matching for vehicles at tollbooth exits and the accuracy of end-point gantry data.

[0032] 32. Further, the specific process for obtaining the vehicle speed influence result reflecting the vehicle's passability through the gantry and determining whether it meets the vehicle speed passability conditions is as follows: After weighted analysis of the individual vehicle speed deviation result and the first unuploaded data positioning influence degree, a coupled analysis is performed with the preset speed influence, i.e., an additive operation is performed to obtain the vehicle speed deviation index. The preset speed influence constant is pre-set by preset personnel to prevent the vehicle speed deviation index from being meaningless; after weighted analysis of the gantry response time deviation result and the second unuploaded data positioning influence degree, a coupled analysis is performed with the preset response to obtain the gantry response time deviation index. The preset response constant is pre-set by preset personnel. To prevent the gantry response time deviation index from being meaningless, the vehicle speed deviation index and the gantry response time deviation index are harmonicly averaged to obtain the vehicle speed influence result. The vehicle speed deviation index and the gantry response time deviation index are correlated; a larger vehicle speed deviation index indicates excessive vehicle speed. When passing the gantry, the gantry cannot react in time, causing nonlinear fluctuations in the gantry system's response delay, thus increasing the gantry response time deviation index. This helps to accurately assess the accuracy and qualification of vehicle positioning. Based on the monitored vehicle speed influence result, it is determined whether the vehicle speed qualification conditions are met. If the vehicle speed influence result meets the vehicle speed qualification conditions, then... The completeness of the missing data deviation result is verified by checking the completeness of the missing data; otherwise, the average result of the missing data positioning deviation is obtained to verify the positioning qualification of the missing data. The vehicle speed qualification condition indicates that the vehicle speed impact result is less than the preset vehicle speed impact result, which is represented by the average of the vehicle speed impact results over a historical time period. The missing data positioning qualification condition indicates that the average result of the missing data positioning deviation is less than the preset average result of the missing data positioning deviation, which is represented by the average of the preset average result of the missing data positioning deviation over a historical time period. The vehicle speed impact result is used to reflect the vehicle's passage through the gantry. The identification qualification status; the first non-uploaded data positioning impact is determined based on the proportion of the corresponding vehicle speed deviation index in the vehicle speed impact result; the vehicle speed at the preset position point of the gantry during the preset vehicle passage time period is monitored by the radar speed measuring instrument as the individual vehicle speed, and its proportion with the preset vehicle speed is quantified, that is, the result of the ratio calculation is used as the individual vehicle speed deviation result. The preset vehicle speed is set in advance by preset personnel. The second non-uploaded data positioning impact is determined based on the proportion of the corresponding gantry response time deviation index in the vehicle speed impact result. The preset vehicle passage time period represents the preset time period corresponding to the acquisition of the vehicle speed impact result set in advance by preset personnel.The gantry response time deviation is represented by a quantified ratio of the vehicle-triggered gantry response time to the preset vehicle-triggered gantry response time. The vehicle-triggered gantry response time is defined as the time from when the gantry detects the vehicle at a preset position within a preset vehicle passage time period to when the gantry initiates vehicle information collection. This preset vehicle-triggered gantry response time is then represented by the average of historical vehicle-triggered gantry response times over a given period.

[0033] It should be added that by inputting the vehicle speed deviation index and the gantry response time deviation result into the mapping set respectively, the corresponding unuploaded data positioning influence degree can be obtained. Here, the mapping set is the result representation constructed by preset personnel after mapping the vehicle speed deviation index and the gantry response time deviation result with the corresponding unuploaded data positioning influence degree one by one through preset mapping relationship. In this embodiment, the value range of the unuploaded data positioning influence degree is 0 to 1, and the unuploaded data positioning influence degree includes: the first unuploaded data positioning influence degree and the second unuploaded data positioning influence degree.

[0034] In this embodiment, obtaining the vehicle speed influence results helps to accurately assess the real-time and synchronous nature of vehicle positioning, and helps to improve the matching degree between vehicle position changes and gantry acquisition frequency, thereby improving the accuracy and consistency of gantry vehicle positioning, and further improving the reliability and stability of gantry vehicle identification. This avoids deviations in gantry vehicle positioning caused by problems such as signal processing lag and acquisition response time delay, and helps to improve the accuracy of gantry monitoring vehicle position, thus providing multi-dimensional basis for vehicle positioning.

[0035] Furthermore, the specific process for obtaining the average result of the unuploaded data positioning deviation to verify the qualification of unuploaded data positioning is as follows: The vehicle's collected deviation distance is quantified by proportionally comparing it with the preset vehicle collected deviation distance to obtain the unuploaded data positioning deviation result; the average value of the unuploaded data positioning deviation results within the preset vehicle positioning time period is obtained to obtain the average result of the unuploaded data positioning deviation; based on the monitored average result of the unuploaded data positioning deviation, it is determined whether the qualification conditions for unuploaded data positioning are met; the preset vehicle positioning time period refers to the preset time period corresponding to the acquisition of the average result of the unuploaded data positioning deviation set in advance by the preset personnel; the unuploaded data positioning deviation result is used to evaluate the vehicle's position in... The completeness of the position coordinates in the gantry; wherein, the vehicle acquisition distance is monitored by millimeter-wave radar during a preset vehicle positioning time period, and the difference between the vehicle acquisition distance and the preset vehicle acquisition distance is taken as the vehicle acquisition deviation distance. The preset vehicle acquisition distance is represented by the average of the vehicle acquisition distances over a historical time period. The vehicle acquisition distance represents the distance between the vehicle positioning acquisition point and the gantry; if the average result of the unuploaded data positioning deviation meets the qualified condition for unuploaded data positioning, the unuploaded data at the corresponding road network toll exit is marked as positioning synchronization unuploaded data, and the completeness deviation result of the unuploaded data is obtained to verify the completeness of the unuploaded data; otherwise, the unuploaded data positioning adjustment is performed.

[0036] In this embodiment, obtaining the average result of the unuploaded data positioning deviation helps to accurately measure the degree of consistency between the gantry's output of vehicle positioning coordinates and the vehicle's actual position, thereby improving the accuracy of capturing changes in the vehicle's position over time. It further measures the reliability and stability of the sampling sensors in the gantry under different conditions, avoids irregular and large fluctuations in the gantry position results, ensures that the gantry positioning results are consistent with the vehicle's actual coordinates, improves the accuracy of vehicle positioning, thereby improving the accuracy of unuploaded data positioning and ultimately improving the overall performance of the gantry.

[0037] Furthermore, adjusting the location without uploading data means simultaneously adjusting the precise location and the location acquisition frequency. This helps to comprehensively improve the gantry's acquisition frequency of vehicle signals, thereby enhancing the accuracy and reliability of vehicle positioning. Precise location adjustment involves transmitting vehicle signals to the gantry to capture the distance and orientation between the vehicle and the toll road intersection. Based on a pre-set big data platform, precise vehicle positioning is calculated. For example, when a vehicle enters the road network coverage area, its onboard terminal automatically transmits a radio frequency signal containing an encrypted identifier, timestamp, and signal feature code. This radio frequency signal is captured by the multi-mode receiving antenna array in the gantry system deployed along the road. The multi-mode receiving antenna array uses beamforming technology to lock the strongest path direction and performs synchronous sampling, converting the analog signal into digital intermediate frequency data and encapsulating it into a standardized frame structure before uploading it to the edge computing node. The edge server, using a pre-loaded network topology map and differential GNSS (Global Navigation Satellite System) correction parameters, combined with the arrival delay difference and phase change reported by multiple adjacent gantries, initiates a TDOA (Time Difference) based adjustment. A hybrid algorithm-based position calculation engine, combining Time Difference of Arrival (FDOA) and Frequency Difference of Arrival (FDOA), eliminates observation biases caused by ionospheric disturbances through carrier phase smoothing. This generates centimeter-level precision 3D coordinate information, which is then linked to a vehicle dynamics database. This enables continuous and stable vehicle tracking in high-speed scenarios. The positioning conversion, based on a polygonal positioning algorithm and considering the geometric distribution of multiple adjacent gantries, calculates the vehicle's lateral offset and longitudinal travel distance relative to the road coordinate system. Positioning acquisition frequency adjustment involves progressively increasing the vehicle positioning acquisition frequency within a preset time period, using a step size corresponding to a preset ratio of the original frequency. This improves the continuity of the vehicle trajectory and enhances the vehicle's tracking performance. The positioning is real-time and accurate; the vehicle positioning acquisition frequency is less than the preset maximum vehicle positioning acquisition frequency. Specifically, the vehicle positioning acquisition frequency in the preset vehicle positioning area within the preset vehicle positioning time period is monitored by the GNSS receiver module. The preset maximum vehicle positioning acquisition frequency is set in advance by preset personnel. The preset vehicle positioning acquisition frequency ratio is obtained by inputting the unuploaded data positioning deviation result and the vehicle positioning coordinates into the acquisition frequency mapping set in the database. The acquisition frequency mapping set represents the mapping set between the unuploaded data positioning deviation result and the vehicle positioning location point and the preset vehicle positioning acquisition frequency ratio. The acquisition frequency mapping set is used to reflect the mapping relationship between the unuploaded data positioning deviation result and the vehicle positioning location point and the preset vehicle positioning acquisition frequency ratio.Adjusting the location of vehicles without uploaded data helps improve the tracking density of vehicle dynamic positions, thereby reducing the positioning deviation caused by a single gantry measurement. If the average result of the re-acquired positioning deviation after adjusting the location of vehicles without uploaded data does not meet the qualified conditions for positioning without uploaded data, an abnormal positioning adjustment prompt will be sent to the designated personnel. Conversely, the unuploaded data at the corresponding tollbooth exit of the road network will be marked as positioning synchronization failure data.

[0038] It should be added that, such as Figure 3 The diagram illustrates the framework of a real-time synchronization and dynamic anomaly verification platform for non-uploaded data at road network toll exits, involving adjustments to the location of data not uploaded. It obtains the impact of vehicle speed, determines whether the vehicle speed meets the acceptable conditions, and if so, obtains the complete deviation result of the non-uploaded data. Otherwise, it obtains the average positioning deviation result of the non-uploaded data, and determines whether the location meets the acceptable conditions. If so, it obtains the complete deviation result of the non-uploaded data; otherwise, it performs positioning adjustments, including precise positioning adjustments and positioning capture frequency adjustments. Based on the average positioning deviation result of the non-uploaded data, it re-determines whether the location meets the acceptable conditions. If so, it obtains the complete deviation result of the non-uploaded data; otherwise, it sends an anomaly alert for non-uploaded data positioning adjustments to designated personnel.

[0039] In this embodiment, adjusting the positioning of unuploaded data helps improve the capture frequency, real-time performance, and continuity of vehicle positioning, ensuring the accuracy of capturing changes in vehicle position. This reduces the time interval of vehicle position information, ensuring the continuity of the vehicle's trajectory and avoiding gaps or blurred areas in the vehicle trajectory due to a mismatch between the vehicle capture interval and the vehicle's speed. This allows for accurate reflection of the vehicle's positional movement between preset adjacent gantries within a preset vehicle positioning time period, including details such as speed changes and direction adjustments. Ultimately, this improves the accuracy of positioning without uploaded data and reduces vehicle tracking deviations caused by delays in gantry receiving vehicle information.

[0040] Furthermore, the specific process for obtaining the incompleteness deviation result of the unuploaded data to reflect the completeness of the unuploaded data is as follows: Based on the monitored incompleteness deviation result of the unuploaded data, it is determined whether the incompleteness condition of the unuploaded data is met; the incompleteness condition of the unuploaded data indicates that the incompleteness deviation result of the unuploaded data is less than the preset incompleteness deviation result of the unuploaded data, wherein the preset incompleteness deviation result of the unuploaded data is represented by the average value of the incompleteness deviation results of the unuploaded data over a historical time period; the incompleteness deviation result of the unuploaded data is used to reflect the completeness of the unuploaded data; if the incompleteness deviation result of the unuploaded data meets the incompleteness condition of the unuploaded data, then a qualified matching result of the unuploaded data is obtained to verify the qualified matching status of the unuploaded data; otherwise, a deduplication operation is performed.

[0041] Specifically, the process for obtaining the incomplete deviation results of the missing data is as follows: The gantry flow deviation index and the preset gantry flow deviation index are quantified by proportion to obtain the gantry flow deviation result. The preset gantry flow deviation index is represented by the average value of the gantry flow deviation index over a historical time period. After weighted analysis of the gantry flow deviation result and the gantry flow evaluation rate, it is coupled with a preset flow constant to obtain the gantry flow deviation data. The preset flow constant is pre-set by designated personnel to prevent the gantry flow deviation data from being meaningless. The gantry flow evaluation rate is based on the proportion of the corresponding gantry flow deviation result in the incomplete deviation results of the missing data. The continuous verification deviation data is determined by weighting the continuous verification deviation results and the continuous verification evaluation rate, and then adding them to a preset verification constant. The preset verification constant is pre-set by designated personnel to prevent the continuous verification deviation data from being meaningless. The continuous verification evaluation rate is determined based on the proportion of the corresponding continuous verification deviation result in the complete deviation results of the data not uploaded. The complete deviation results of the data not uploaded are obtained by harmonic averaging the gantry flow deviation data and the continuous verification deviation data. The gantry flow deviation data and the continuous verification deviation data are correlated; a larger continuous verification deviation index means... A longer gantry flow sampling time indicates a larger amount of gantry flow data sampled, resulting in greater gantry flow data deviation. This helps to accurately assess the qualification level of unuploaded data, thereby improving the completeness of unuploaded data. The gantry flow deviation index is represented by the difference between the exit flow sampling time and the preset exit flow sampling time when the exit flow sampling time is longer than the preset exit flow sampling time. Conversely, it is represented by the difference between the preset exit flow sampling time and the preset exit flow sampling time when the exit flow sampling time is shorter than the preset exit flow sampling time. The preset data integrity assessment time period for the preset road network toll exits is monitored by the gantry system. The duration corresponding to the exit flow data collected at the toll exit of the road network is used as the exit flow data collection time. The preset exit flow data collection time is represented by the average of the exit flow data collection times over a historical period. The gantry system is a dedicated system and supporting facilities built along the cross-section of the highway, equipped with functions such as segmented toll calculation and license plate image recognition. The continuity verification deviation result is represented by the proportion quantified by the time window verification index and the preset time window verification index. Specifically, the time recorded when a vehicle passes through the preset exit point and the time recorded when a vehicle passes through the preset entrance point are monitored by the clock module of the gantry, and the duration corresponding to the difference is used as the time window verification index.

[0042] It should be added that by inputting the gantry flow deviation data and the continuity verification deviation results into the mapping set respectively, the corresponding non-uploaded data integrity evaluation rate can be obtained. The mapping set is a result representation constructed by preset personnel through preset mapping relationships to map the gantry flow deviation data and the continuity verification deviation results to the corresponding non-uploaded data integrity evaluation rate one by one. In this embodiment, the value range of the non-uploaded data integrity evaluation rate is 0 to 1, and the non-uploaded data integrity evaluation rate includes: gantry flow evaluation rate and continuity verification evaluation rate.

[0043] In this embodiment, obtaining the completeness deviation results of the unuploaded data helps to accurately measure the completeness of the unuploaded data, as well as the misalignment or gap between the time range involved in the unuploaded data and the actual passage time series. This reflects the missing data situation and avoids the inconsistency of the unuploaded data in time, which may lead to the incomplete capture of the unuploaded data and the low completeness of the overall unuploaded data. This improves the accuracy of defining the scope and content not covered in the unuploaded data and provides a clear direction for assessing the missing data and supplementing the unuploaded data.

[0044] Harmonic averaging of gantry flow deviation data and continuity check deviation data helps to accurately assess the degree of difference between unuploaded data and standard data, improves the ability to capture unuploaded data, and thus reflects the loss or anomalies of unuploaded data. This avoids unuploaded data being unqualified due to missing information fields or incompatible formats, thereby improving the qualification of valid unuploaded data uploads and ultimately improving the completeness of unuploaded data in terms of quantity and timing.

[0045] Further, the specific process for deduplication is as follows: A prompt is sent to designated personnel to remove duplicate PASSIDs from the uploaded gantry and exit transaction records, and to filter out successful gantry transactions from the unuploaded PASSIDs' corresponding gantry and exit transaction records. Successful gantry transactions represent the gantry transaction record corresponding to a vehicle successfully paying tolls when exiting the highway through a toll station. For example, gantry transactions are grouped and summarized by PASSID, and the passage time of the last (longest passage time) gantry, the passage time of the first (shortest passage time) gantry, the corresponding gantry code, vehicle information (license plate, vehicle type), entrance information (entrance toll station), and gantry transaction details are obtained for each PASSID group. The criteria for determining a successful transaction in the gantry system's transaction determination logic can be based on specific success identification rules set for different types of payment media: when using an OBU as the payment method (corresponding field media_type=1), if its exclusive transaction status code obu_trade_result=0, it means that the transaction has been successfully completed; while for scenarios where CPC cards are used for payment (corresponding field media_type=2), it is necessary to check the general transaction result field trade_result=0 to confirm the transaction success. Combining the two cases, the complete transaction success judgment expression can be expressed as: (media_type=1 and obu_trade_result=0) or (media_type=2 and trade_result=0). This transaction judgment logic can accurately identify and process transaction verification results based on different communication media, thereby ensuring the precise execution of the toll collection process. Deduplication is performed to verify the integrity of the vehicle's journey, ensuring that each transaction corresponds to a unique node in the vehicle's passage process. This avoids statistical biases caused by duplicate exit transaction data corresponding to the PASSID, which could result in unuploaded data. Exit transaction data refers to the data generated after a vehicle pays toll when exiting the highway at a toll station. The passage medium refers to the medium used to record passage information while the vehicle is traveling on the highway. Currently, there are three main types: OBU (on-board unit), also known as an electronic tag, which allows passage through ETC lanes after inserting the corresponding ETC card; CPC (Contactless Passenger Card); and paper tickets. OBUs and CPCs can be used for transactions with gantries, while paper tickets cannot. This mainly includes vehicle information (license plate, vehicle type) and entrance / exit information (entrance / exit station, entrance / exit time) for the toll-paid trip. If the result of the missing data integrity deviation obtained after the deduplication operation meets the condition of missing data integrity, then the missing data matching result is obtained to verify the missing data matching status. Otherwise, a deduplication operation error message is sent to the preset personnel.

[0046] In this embodiment, the deduplication operation helps improve the clarity of the outbound transaction data corresponding to the PASSID, avoids interference from duplicate unuploaded data, and improves the accuracy of the statistical results of unuploaded data. This reduces the PASSID deviation caused by repeated calculation of unuploaded data, ensures the uniqueness and accuracy of the PASSID, and helps improve the validity of the outbound transaction data corresponding to the PASSID. This improves the quality and reliability of the PASSID. Selecting successfully completed transactions ensures the validity of unuploaded data, thereby ensuring the integrity of the PASSID and further improving the efficiency of unuploaded data processing.

[0047] Furthermore, the specific process for obtaining the "unuploaded data matching qualification result" to reflect the qualification status of unuploaded data matching is as follows: Based on the monitored unuploaded data matching qualification result, determine whether it meets the unuploaded data matching qualification condition; after matching the PASSID corresponding to the outflow flow and the PASSID corresponding to the terminal gantry flow within the preset matching time period, the proportion of identical PASSIDs is represented as the PASSID overlap. Using the Spark distributed computing framework, perform millisecond-level cross-comparison of PASSID records to obtain the proportion of identical PASSIDs; the PASSID corresponding to the outflow flow and the PASSID corresponding to the terminal gantry flow within the preset matching time period... The PASSID overlap obtained through PASSID matching represents a qualified result for unuploaded data matching. The preset matching time period indicates a pre-defined time period for obtaining qualified results for unuploaded data matching. The qualified condition for unuploaded data matching indicates that the qualified result for unuploaded data matching equals 1. If the qualified result for unuploaded data matching meets the qualified condition, the corresponding unuploaded data is marked as qualified unuploaded data and statistical analysis is performed. Conversely, if it does not meet the condition, the corresponding unuploaded data is marked as abnormal unuploaded data, and a qualified assessment of unuploaded data statistical analysis is performed. The end gantry refers to the gantry at the boundary between the inspected object and other non-inspected objects. For example, if the inspected object is all highways within Guangdong Province, then all provincial border gantries on Guangdong highways are end gantries. For instance, if the target is limited to road segment A, the end gantry is the gantry at the boundary between road segment A and other road segments. If the target is limited to Guangdong Province, the end gantry is the provincial border gantry at the boundary of Guangdong Province. If the target is Guangdong and Guangxi provinces / autonomous regions, then the end-point gantry refers to the gantry at the provincial border between Guangdong and Guangxi and other provinces / autonomous regions. If the target is the national road network, then there is no end-point gantry. The selection of the end-point gantry depends on the object of data verification (one or more road segments), and the end-point gantry is selected at the boundary between the data verification object and other non-verification objects.

[0048] It should be added that the millimeter-wave radar of the roadside unit monitors the speed vector of vehicles in the lane in real time, and predicts the spatiotemporal coordinates of the vehicles that are about to reach the gantry area by combining historical trajectories. Subsequently, the front-end sensor array synchronously increases the transmission power and switches to a high-speed mode modulation scheme. Beamforming technology is used to lock onto the target vehicle to form a stable energy focusing area. At the edge computing node, a multi-source data fusion engine is started to perform three-dimensional matching and verification of radar ranging data, video stream target tracking algorithm and vehicle model outline model in the prior database. When the initial PASSID identification is completed, the adaptive interaction protocol stack is immediately triggered to dynamically adjust the downlink signal bandwidth and coding redundancy according to the relative speed. Redundancy ensures that the complete interaction of PASSID identifier, passage medium type and transaction certificate is completed in a very short time. If the first communication fails, the backup frequency band is immediately activated for a second attempt. At the same time, the cooperative positioning mechanism of adjacent gantries is started, and virtual reference anchor points are built through the historical vehicle records of adjacent nodes to achieve cross-verification. Throughout the process, signal quality indicators are continuously monitored. When the signal-to-noise ratio is detected to be lower than the threshold, the infrared supplementary imaging mode is automatically switched. Finally, a reliable vehicle recognition result is output through multi-dimensional confidence weighted decision, and the event log is synchronously uploaded to the dynamic anomaly verification platform for analysis, thereby solving the problem of gantry recognition anomalies caused by excessive vehicle speed.

[0049] In addition, it should be noted that the toll collection exits use their internal verification procedures to conduct real-time and comprehensive checks on every toll record, ensuring the integrity of the exit transaction flow and verifying the logical consistency between core elements such as toll amount, vehicle type definition, and payment method. If a gap is found in the transaction sequence of collected fees or a hash verification failure is detected, the toll collection exit will immediately mark the anomaly and initiate a tracing process. It will call upon the surveillance video and ground loop signals within that time period to perform temporal and spatial correspondence analysis. If the uncollected passage is caused by card reader malfunction, network connection problems, or improper operation by staff, the toll collection exit will estimate the fee due based on the license plate number recognition information and route planning model. Furthermore, all anomalies are recorded in detail and transmitted to the dynamic anomaly verification platform to generate processing task orders, which are used to resolve the loss of collected transaction flow and the uncollected exit situations caused by monitoring software and hardware failures, toll collector errors, or vehicles maliciously evading tolls.

[0050] In this embodiment, obtaining the qualified results of matching the unuploaded data helps to accurately measure the qualification of the unuploaded data matching, avoid the occurrence of abnormal or missing unuploaded data, improve the uploading efficiency of unuploaded data, thereby improving the accuracy and qualification of unuploaded data matching, ensuring the high efficiency of unuploaded data flow, ensuring the stability and reliability of the dynamic anomaly verification platform, avoiding matching interruptions caused by unuploaded data issues, and further improving the quality and effectiveness of the dynamic anomaly verification platform.

[0051] Furthermore, the specific process for performing the non-uploaded data statistics compliance assessment is as follows: The time taken by a vehicle from the first gantry to the last gantry within the preset verification range, monitored by the gantry's clock module, is represented as the vehicle travel time; the difference between the vehicle travel time and the preset verification waiting time, i.e., the result of the subtraction operation, is represented as the verification waiting time assessment result; based on the monitored verification waiting time assessment result, it is determined whether it meets the non-uploaded data statistics compliance condition; the non-uploaded data statistics compliance condition indicates that the verification waiting time assessment result is less than or equal to 0; the preset verification waiting time represents the maximum allowable waiting time dynamically set within the preset statistical range, wherein the preset statistical range and the preset verification waiting time are preset by preset personnel; if If the verification waiting time assessment result meets the qualification criteria for non-uploaded data statistics, the corresponding abnormal non-uploaded data will be marked as pending input data and non-uploaded data statistics will be performed. Otherwise, the verification waiting time adjustment operation will be performed. Non-uploaded data statistics means that qualified non-uploaded data and pending input data are input into the preset data visualization model of the dynamic anomaly verification platform to output a visualization report and export detailed non-uploaded data. For example, qualified non-uploaded data, pending input data, and visualization reports are randomly divided into training set and validation set. The training set data is input into a multimodal large-scale language model for training to obtain a training model. The newly acquired qualified non-uploaded data and pending input data are input into the training model to output a visualization report and export detailed non-uploaded data.

[0052] In this embodiment, performing a non-uploaded data statistical compliance assessment helps to accurately measure the compliance level of abnormal non-uploaded data, ensuring the compliance of the non-uploaded data during the verification process. This ensures that the non-uploaded data corresponding to the vehicle's driving meets the prescribed time range, thereby improving the processing efficiency of abnormal non-uploaded data. It also helps to improve the ability of the dynamic anomaly verification platform to determine whether the vehicle is abnormal, stuck, or delayed, ensuring the timeliness and completeness of non-uploaded data matching, and thus improving the compliance of preventing abnormal non-uploaded data matching.

[0053] Furthermore, the specific process for adjusting the verification waiting time is as follows: The verification waiting time assessment result and the verification area area are input into the verification time mapping set in the database to obtain the preset verification radius ratio. The verification waiting time adjustment operation means increasing the current verification waiting time step by step based on the preset verification radius ratio. This helps to increase the waiting time when the initial verification is not completed, thereby improving the coverage and accuracy of a verification of data not uploaded, and reducing missed detections. The verification time mapping set reflects the mapping relationship between the verification waiting time assessment result, the verification area area, and the preset verification radius ratio. For example, the transaction time of the gantry flow is greater than or equal to the minimum value of the verification period minus N days, and less than the maximum value of the verification period plus M days. M and N are determined according to the verification scope. Because the overdue period for gantry data upload is generally 3 days, and if the verification scope is limited to within the province, it is generally set to 4 days; because the maximum travel time nationwide is generally 7 days, and if the verification scope crosses provinces, it is generally set to 8 days. If the verification waiting time assessment result obtained after the verification waiting time adjustment operation meets the qualified conditions for the statistics of unuploaded data, the corresponding abnormal unuploaded data will be marked as pending input data and the statistics of unuploaded data will be performed. Otherwise, the corresponding abnormal unuploaded data will be marked as key abnormal data and a statistical abnormality alert will be sent.

[0054] Simultaneously, exit traffic can also be matched using gantry travel traffic that is not marked as edge_gantry. The scope of exit traffic is: all exit toll stations within the verification range. The flag of the gantry travel traffic that matches the exit traffic is written as out_list. The exit traffic is matched using gantry travel traffic that is not marked as edge_gantry and is not marked as out_list. The transaction time of the exit traffic is greater than or equal to the minimum verification time and less than the maximum verification time. The matching rules are: the license plate (vehicle_id) of the gantry travel is the same as the license plate (vehicle_id) of the exit traffic, and the transaction time (ex_time) of the exit traffic is greater than the minimum transaction time (min_trans_time) of the gantry travel, and the entry time (en_time) of the exit traffic is less than the maximum transaction time (max_trans_time) of the gantry travel.

[0055] On the other hand, the license plate must comply with Chinese license plate regulations, as follows:

[0056] Non-new energy vehicles in mainland China:

[0057] Jingjin沪渝冀豫云辽黑湘皖鲁新苏浙赣鄂桂甘晋蒙陕吉闽贵粤青藏川宁琼Shi Ling][A-HJ-NP-Z][A-HJ-NP-Z0-9]{4,5}[A-HJ-NP-Z0-9Gua Xue Jing Gangao Aomen];

[0058] New energy vehicles in the Mainland:

[0059] ([Jingjin沪渝冀豫云辽黑湘皖鲁新苏浙赣鄂桂甘晋蒙陕吉闽贵粤青藏川宁琼Shi Ling A-Z]{1}(([0-9]{5}[DF])|([DABCEFGHJK]([A-HJ-NP-Z0-9])[0-9]{4})));

[0060] Hong Kong license plate, the regular expression is: ^(Yue Z|Gang Z)[A-Z]{1,2}\d{1,4};

[0061] Macau license plate, the regular expression is: ^(Yue Z)[A-Z]{1,2}\d{1,5};

[0062] Match the gantry travel water of the export water flow, mark the original flag as double_media, and import the gantry travel water (with and without marks) after the above marking process into the database of the dynamic exception verification platform. Group and summarize the imported data in the dynamic exception verification platform. Among them, the flag is empty (unmarked), which is the vehicle travel of the lost export water flow verified this time. The gantry number max_gantry_id corresponding to the maximum passing time is the last gantry before the lost export water flow, and the next exit toll station corresponding to this gantry is the toll station where the export water flow is lost. License plate vehicle_id, vehicle type vehicle_type, media type media_type, media number obusn, CPU card number cpucard_id, entrance toll station hex value en_toll_station_hex, entrance time en_time, etc. are the vehicle and its travel information corresponding to the travel of this lost export water flow. The total transaction amount sum_fee after accumulating the transaction amount of each gantry water flow is the amount lost in the verification range of the travel of this lost export water flow.

[0063] It should be added that the specific operation of marking the corresponding abnormal unuploaded data as key abnormal data and sending a statistical abnormal alarm is to group and summarize according to the loss time (date), lost gantry, and the section where the lost gantry belongs, and calculate the quantity. Sort according to the quantity, notify the section with a larger quantity first, and provide detailed data (passid, entrance information, vehicle information, lost gantry, lost time, etc.).

[0064] In this embodiment, adjusting the verification waiting time helps improve the accuracy of the increase in verification waiting time, further quantifies the quality of abnormal non-uploaded data uploads, avoids low verification efficiency due to long verification waiting times, or poor matching quality of non-uploaded data due to short verification waiting times, ensures consistency between abnormal non-uploaded data and uploaded data in the database, thereby improving the flexibility of verification waiting time, ensuring the stability and reliability of non-uploaded data matching, and ultimately improving the accuracy of abnormal non-uploaded data matching.

[0065] like Figure 4 The image shows the interface for querying exit traffic flow information in the gantry traffic flow verification management system. The left side is the navigation bar for the system, including: System User Management, Toll Type Management, Toll Station Management, Gantry Traffic Flow Management, Exit Traffic Flow Management, System Management, and Equipment Management. System User Management includes vehicle owner information query and addition; Toll Type Management includes toll type query and selection; Toll Station Management includes toll station query and toll station manager query; Gantry Traffic Flow Management includes gantry traffic flow information query and gantry traffic flow anomaly alerts; System Management includes visual reports. The upper right side features a search bar, time period selection bar, and road segment selection bar; the middle displays a geographical road segment map, with the right side showing today's total revenue, today's traffic flow, open lanes, and abnormal events; the lower right side is the automatic optimization area for vehicle capture frequency.

[0066] like Figure 5 The image shows the interface of the gantry flow verification management system's visual report, which includes a recent transaction record table. The recent transaction record table includes transaction time, lane, license plate number, payment method, amount, and status; abnormal events include the road segment to which the last gantry belongs, PASSID, license plate, 1-OBU 2-CPC, media code, ETC card number, entrance time at the entry station, maximum gantry time for intra-provincial trips, minimum gantry time for intra-provincial trips, last gantry, and estimated intra-provincial trip amount in cents.

[0067] In summary, this application embodiment determines whether the positioning conditions for unuploaded data are met by obtaining the average result of the positioning deviation of the unuploaded data. If not, it verifies the qualification status of the unuploaded data positioning by obtaining the average result of the positioning deviation of the unuploaded data. Based on the average result of the positioning deviation of the unuploaded data, it determines whether the conditions for qualification are met and whether adjustments to the unuploaded data positioning are necessary. This helps to accurately assess the qualification level of the unuploaded data positioning, thereby quickly identifying anomalies or delays in the unuploaded data, enhancing the fault tolerance and real-time monitoring capabilities of the gantry, and preventing inaccurate real-time vehicle positioning due to delays in unuploaded data acquisition, thus improving the accuracy of unuploaded data positioning. Furthermore, by obtaining the complete deviation result of the unuploaded data to determine whether the conditions for qualification are met and whether adjustments to the unuploaded data positioning are necessary, it helps to accurately assess the completeness of the unuploaded data, preventing inaccuracies in the unuploaded exit flow rate due to missing data. Missing or duplicated data can lead to deviations in assessing the completeness of unuploaded data. Avoiding unuploaded data non-compliance caused by excessively large gaps in exit flow information helps improve the ability to identify abnormal breakpoints and potential risk ranges, ensuring the completeness and timeliness of unuploaded data. Obtaining the matching results of unuploaded data to determine whether to perform an unuploaded data statistical compliance assessment helps accurately evaluate the matching compliance of gantry flow and exit flow, avoiding unuploaded data non-compliance due to errors in data collection, thereby improving the effectiveness and reliability of unuploaded data matching. Performing an unuploaded data statistical compliance assessment to obtain the verification waiting time assessment results and determine whether to adjust the verification waiting time helps accurately assess the compliance of abnormal unuploaded data matching, avoiding unuploaded data non-compliance due to mismatches between preset verification scope and verification waiting time, thereby improving the efficiency and accuracy of verification.

[0068] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A real-time synchronization and dynamic anomaly verification platform for data not uploaded at road network toll exits, characterized in that: It includes the following modules: vehicle speed monitoring module, unuploaded data integrity monitoring module, unuploaded data matching monitoring module, and unuploaded data statistics module. The vehicle speed monitoring module is used to obtain the vehicle speed impact result reflecting the qualification status of the vehicle through the gantry recognition during the real-time synchronization of unuploaded data and to determine whether the vehicle speed qualification conditions are met. If they are met, the module obtains the unuploaded data integrity deviation result to reflect the integrity of the unuploaded data. Otherwise, the module obtains the average result of the unuploaded data positioning deviation to verify the qualification status of the unuploaded data positioning and to determine whether to perform unuploaded data positioning adjustment. The unuploaded data positioning adjustment includes: performing positioning precision adjustment and performing positioning capture frequency adjustment. The unuploaded data integrity monitoring module is used to determine whether to perform deduplication operation based on the unuploaded data integrity deviation result after the vehicle speed influence result meets the vehicle speed qualification condition. The deduplication operation is used to ensure the uniqueness of the unuploaded data corresponding to PASSID. The unuploaded data matching monitoring module is used to obtain the unuploaded data matching qualification result to reflect the unuploaded data matching qualification status after the unuploaded data integrity deviation result meets the unuploaded data integrity condition, so as to determine whether to perform the unuploaded data statistical qualification assessment. The specific process for obtaining the matching result of the unuploaded data, which reflects the matching qualification status of the unuploaded data, is as follows: The PASSID corresponding to the outflow within the preset matching time period is matched with the PASSID corresponding to the end gantry flow. The proportion of PASSIDs that are the same is expressed as the PASSID overlap. The PASSID corresponding to the outgoing flow and the PASSID corresponding to the end gantry flow within the preset matching time period will be matched. The PASSID overlap rate obtained is represented as a qualified matching result for data not uploaded. Determine whether the conditions for matching non-uploaded data are met based on the qualified results of the monitored non-uploaded data matching. The "no uploaded data matching qualified condition" means that the no uploaded data matching qualified result is equal to 1; If the result of the unuploaded data matching meets the conditions for unuploaded data matching, the corresponding unuploaded data will be marked as qualified unuploaded data and unuploaded data statistics will be performed. Otherwise, the corresponding unuploaded data will be marked as abnormal unuploaded data and unuploaded data statistics qualification assessment will be performed. The specific process for performing the qualification assessment of statistics on data not uploaded is as follows: The time taken for a vehicle to travel from the first gantry to the last gantry within the preset verification scope will be expressed as the vehicle travel time. The result of the difference between the vehicle travel time and the preset verification waiting time is expressed as the verification waiting time evaluation result. The assessment results of the monitoring and verification waiting time determine whether the conditions for the statistics of unuploaded data are met. The condition for not uploading data to be considered as a valid condition is that the verification waiting time assessment result is less than or equal to 0. If the verification waiting time assessment result meets the qualified conditions for the statistics of unuploaded data, the corresponding abnormal unuploaded data will be marked as data to be input and the statistics of unuploaded data will be performed; otherwise, the verification waiting time adjustment operation will be performed. The aforementioned "statistics on unuploaded data" refers to inputting qualified unuploaded data and data to be input into the preset Alibaba Cloud model of the dynamic anomaly verification platform to output a visual report and export detailed unuploaded data. The unuploaded data statistics module is used to perform an unuploaded data statistics compliance assessment after the unuploaded data matching result meets the unuploaded data matching compliance conditions, obtain the verification waiting time assessment result, and determine whether to perform a verification waiting time adjustment operation.

2. The real-time synchronization and dynamic anomaly verification platform for non-uploaded data at road network toll exits according to claim 1, characterized in that, The specific process for obtaining the vehicle speed impact result reflecting the vehicle's passability through the gantry and determining whether it meets the vehicle speed passability conditions is as follows: After weighted analysis of the speed deviation results of individual vehicles and the location influence of the first unuploaded data, the vehicle speed deviation index is obtained by coupling analysis with the preset speed influence. After weighting and analyzing the gantry response time deviation results and the impact of the second unuploaded data on positioning, a coupled analysis with the preset response is performed to obtain the gantry response time deviation index. The effect of vehicle speed is obtained by harmonic averaging the vehicle speed deviation index and the gantry response time deviation index. Determine whether the vehicle speed meets the acceptable conditions based on the monitoring results of the vehicle speed impact. If the vehicle speed impact result meets the vehicle speed qualification conditions, then obtain the complete deviation result of the unuploaded data to verify the completeness of the unuploaded data; otherwise, obtain the average positioning deviation result of the unuploaded data to verify the positioning qualification of the unuploaded data. The vehicle speed qualification condition means that the impact of the vehicle speed is less than the preset impact of the vehicle speed. The vehicle speed impact result is used to reflect the vehicle's passability when identified by the gantry; The speed deviation of a single vehicle is represented by a quantified result of the ratio between the speed of the single vehicle and the preset speed of a vehicle. The speed of a single vehicle refers to the speed of a single vehicle when it passes through a preset position point on the gantry. The gantry response time deviation result is represented by the ratio of the vehicle-triggered gantry response time to the preset vehicle-triggered gantry response time. The vehicle trigger gantry response time refers to the time when the gantry detects the vehicle and initiates vehicle information collection.

3. The real-time synchronization and dynamic anomaly verification platform for non-uploaded data at road network toll exits according to claim 2, characterized in that, The specific process for obtaining the average result of the positioning deviation of the unuploaded data to verify the qualification of the positioning of the unuploaded data is as follows: The unuploaded data positioning deviation result is obtained by quantifying the ratio of the vehicle data acquisition deviation distance to the preset vehicle data acquisition deviation distance. The average value of the positioning deviation results of the unuploaded data within the preset vehicle positioning time period is used to obtain the average positioning deviation result of the unuploaded data. Determine whether the location deviation of the unuploaded data meets the qualified conditions for location without uploaded data based on the average result of the monitored unuploaded data location deviation. The qualified condition for location without uploaded data means that the average deviation of the location without uploaded data is less than the preset average deviation of the location without uploaded data. The unuploaded data positioning deviation results are used to assess the completeness of the vehicle's position coordinates in the gantry; The vehicle acquisition deviation distance is represented by the difference between the vehicle acquisition distance and the preset vehicle acquisition distance; The vehicle data collection distance refers to the distance between the vehicle positioning data collection point and the gantry; If the average result of the unuploaded data positioning deviation meets the qualified conditions for unuploaded data positioning, the unuploaded data of the corresponding road network toll exit will be marked as positioning synchronization unuploaded data, and the complete deviation result of the unuploaded data will be obtained to verify the completeness of the unuploaded data. Otherwise, positioning adjustment will be performed based on the unuploaded data.

4. The real-time synchronization and dynamic anomaly verification platform for non-uploaded data at road network toll exits according to claim 3, characterized in that, The phrase "adjusting the location without uploaded data" means simultaneously adjusting the positioning precision and adjusting the positioning capture frequency. The precise positioning adjustment refers to obtaining the vehicle's precise location by transmitting vehicle signals to the gantry to capture the distance and orientation between the vehicle and the toll road intersection, and then performing positioning calculations based on a big data platform. The relationship between the unuploaded data positioning deviation result and the vehicle positioning coordinates input into the capture frequency mapping set in the database is used to obtain the preset vehicle positioning capture frequency ratio. The term "adjusting the positioning capture frequency" means increasing the vehicle positioning capture frequency step by step within a preset vehicle positioning time period, with the adjustment step size corresponding to the preset vehicle positioning capture frequency ratio. If the average result of the unuploaded data positioning deviation obtained after the positioning adjustment does not meet the qualified conditions for unuploaded data positioning, an abnormal prompt for unuploaded data positioning adjustment will be sent to the preset personnel. Otherwise, the unuploaded data of the corresponding road network toll exit will be marked as positioning synchronization unuploaded data.

5. The real-time synchronization and dynamic anomaly verification platform for non-uploaded data at road network toll exits according to claim 1, characterized in that, The specific process for obtaining the incompleteness deviation result of the unuploaded data, which reflects the completeness of the unuploaded data, is as follows: Determine whether the condition for completeness of unuploaded data is met based on the monitored deviation results of the incompleteness of unuploaded data. The condition for completeness of unuploaded data indicates that the deviation result of the incompleteness of unuploaded data is less than the preset deviation result of the incompleteness of unuploaded data. The incompleteness deviation result of the unuploaded data is used to reflect the degree of completeness of the unuploaded data; If the deviation result of the missing data meets the condition of missing data, then obtain the qualified result of the missing data matching to verify the qualified status of the missing data matching; otherwise, perform the deduplication operation.

6. The real-time synchronization and dynamic anomaly verification platform for non-uploaded data at road network toll exits according to claim 5, characterized in that, The specific process for obtaining the complete deviation result of the unuploaded data is as follows: The gantry flow deviation result is obtained by quantifying the ratio of the gantry flow deviation index and the preset gantry flow deviation index. After weighted analysis of the gantry flow deviation results and gantry flow evaluation rate, they are coupled with the preset flow constant to obtain gantry flow deviation data. After weighting and analyzing the continuity verification deviation results and the continuity verification evaluation rate, they are added to the preset verification constant to obtain the continuity verification deviation data. The complete deviation result of the unuploaded data is obtained by harmonic averaging the gantry flow deviation data and the continuity verification deviation data; The gantry flow deviation index is represented by the difference between the outlet flow sampling time and the preset outlet flow sampling time when the outlet flow sampling time is greater than the preset outlet flow sampling time. The exit flow data collection time refers to the duration corresponding to the collection of exit flow data at the toll exits of the road network. The continuity verification deviation result is represented by a ratio quantified using a time window verification index and a preset time window verification index. The time window verification index is represented by the duration corresponding to the difference between the time recorded when the vehicle passes the exit preset point and the time recorded when the vehicle passes the entrance preset point in the gantry.

7. The real-time synchronization and dynamic anomaly verification platform for non-uploaded data at road network toll exits according to claim 5, characterized in that, The specific process for performing the deduplication operation is as follows: Send a notification to the preset personnel to remove the gantry flow and exit flow corresponding to the duplicate PASSID that has been uploaded, and filter the successful transaction gantry flow in the gantry flow and exit flow corresponding to the PASSID that has not been uploaded. The deduplication operation is used to verify the integrity of the vehicle journey and avoid statistical deviations caused by duplicate exit flow data corresponding to PASSID, which may result in unuploaded data. If the deviation result of the unuploaded data obtained after the deduplication operation meets the condition of complete unuploaded data, then the result of the unuploaded data matching is obtained to verify the matching status of the unuploaded data; otherwise, an abnormal deduplication operation prompt is sent to the preset personnel.

8. The real-time synchronization and dynamic anomaly verification platform for non-uploaded data at road network toll exits according to claim 1, characterized in that, The specific process for adjusting the verification waiting time is as follows: The relationship between the verification waiting time assessment results and the verification area area is input into the verification time mapping set in the database to obtain the preset verification radius ratio; The operation of adjusting the verification waiting time means increasing the current verification waiting time step by step based on the preset verification waiting time, with the adjustment step size corresponding to the preset verification radius ratio. If the verification waiting time assessment result obtained again after the verification waiting time adjustment operation meets the qualification conditions for the non-uploaded data statistics, the corresponding abnormal non-uploaded data will be marked as pending input data and the non-uploaded data statistics will be performed. Otherwise, the corresponding abnormal non-uploaded data will be marked as key abnormal data and a statistical abnormality alert will be sent.

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