A method for online calibration of a taximeter

Abstract

The application discloses a kind of taximeter online verification method for taxi, it is related to target verification technical field, including deployment roadside sensing device response vehicle and carry out target tracking;Dynamic matching algorithm is constructed to analyze and match the trajectory data collected in real time;Roadside ETC equipment is deployed and interacts with the taximeter in taxi;Through optical fiber, the trajectory data collected and taximeter data are updated to global data computing device in real time;From the trajectory data stored, all trajectory data are extracted and are subjected to mileage verification and compensation.The application can realize accurate dynamic detection of vehicle under various traffic conditions by deploying roadside sensing and ETC equipment, constructing dynamic matching algorithm and combining vehicle identity information with trajectory and taximeter data through global data computing device for mileage verification, quickly identify and correct the anomaly in trajectory data, ensure the high-precision calculation of taximeter verification mileage, significantly improve the accuracy and real-time performance of the verification method.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of target detection, in particular to an online calibration method for a taxi meter. BACKGROUND

[0002] The calibration of a taxi meter is a mandatory calibration work in China. The existing calibration method for a taxi meter mainly adopts a rolling wheel distance measurement method and a driving distance measurement method. The taxi needs to go to a specific calibration site for calibration. The traditional method relies on manual operation. These methods are time-consuming, high in cost and low in efficiency, and have problems such as easy congestion of taxis, influence on normal operation of taxis, too much carbon emission, and no support for remote price adjustment. Moreover, they are difficult to cope with complex and changeable traffic conditions in the implementation process and are easily affected by environmental factors, and cannot meet the needs of efficient, accurate and real-time calibration. With the development of intelligent network connection technology, the application of laser radar sensor detection, real-time trajectory tracking positioning technology, and car-road cooperation provides a reliable technical basis for online calibration of a taxi meter. Based on the above situation, the present application provides an online calibration method for a taxi meter. SUMMARY

[0003] In view of the problems existing in the above-mentioned online calibration method for a taxi meter, the present application is proposed.

[0004] Therefore, the problem to be solved by the present application is that the traditional method relies on manual operation, these methods are time-consuming, high in cost and low in efficiency, and have problems such as easy congestion of taxis, influence on normal operation of taxis, too much carbon emission, and no support for remote price adjustment. Moreover, they are difficult to cope with complex and changeable traffic conditions in the implementation process and are easily affected by environmental factors, and cannot meet the needs of efficient, accurate and real-time calibration.

[0005] To solve the above technical problems, the present application provides the following technical scheme: an online calibration method for a taxi meter, comprising: deploying a roadside sensing device to sense a vehicle and perform target tracking; constructing a dynamic matching algorithm to analyze and match real-time collected trajectory data; deploying a roadside ETC device to interact with a meter in a taxi; updating the collected trajectory data and meter data to a global data calculation device in real time through an optical fiber; extracting all trajectory data from the stored trajectory data and performing mileage calibration and compensation.

[0006] As a preferred scheme of the online calibration method for a taxi meter, wherein: the deployment of the roadside sensing device to sense the vehicle is to deploy the roadside sensing device on a selected road section, deploy a license plate capture camera in the roadside sensing device at the first starting point, and capture the basic information of the target when the vehicle passes through the starting point position after the roadside sensing device senses the vehicle.

[0007] As a preferred embodiment of the online verification method for taxi meters described in this invention, the target tracking involves unifying the identity of the target information perceived at previous and subsequent times, assigning a unique ID to the perceived target, triggering a capture command when the target passes through the capture area, acquiring a photo of the front of the vehicle and identifying the license plate, and binding the perceived target ID with the license plate information to obtain the perceived target ID-license plate number.

[0008] In a preferred embodiment of the online verification method for taxi meters according to the present invention, the dynamic matching algorithm is used to analyze and match the real-time collected trajectory data to select the optimal trajectory.

[0009] Calculate trajectory similarity score Represented as:

[0010] ;

[0011] in, It is the similarity score between trajectory r and the preset trajectory r′. This represents the position vector of the real-time trajectory at time t. This represents the position vector of the preset trajectory at time t, where a and b represent the time periods considered. It is a real-time trajectory With preset trajectory The square of the Euclidean distance at time t;

[0012] Calculate the dynamic weights of the time window Represented as:

[0013] ;

[0014] in, It is a normalization factor. It is the amplitude of the nth Fourier component. It is the frequency of the nth component. It is the phase of the nth component. These are the coefficients of the m-th wavelet component. It is the translation parameter The m-th wavelet function centered at the center, It refers to the decay rate, where t is time. It is the current time point;

[0015] Calculate the weighted geographic information Represented as:

[0016] ;

[0017] in, and These represent the geographic coordinates of the real-time trajectory r and the preset trajectory r′, respectively. The representative considered the largest geographical location difference. and These represent the average altitude of the real-time trajectory and the preset trajectory, respectively. This is the maximum difference in altitude;

[0018] Analyze speed changes Represented as:

[0019] ;

[0020] in, Indicates speed, Indicates acceleration. Represents the rate of change of acceleration. Represents the standard deviation function. It is the hyperbolic tangent function;

[0021] Comprehensive construction of dynamic matching algorithm Represented as:

[0022] ;

[0023] in, It is a dynamic matching score. and These are weighting coefficients. It is a trajectory similarity score. It is a dynamic weight of the time window. It is a geographic information weighting, It involves velocity change analysis, selecting the trajectory with the highest matching score as the optimal trajectory.

[0024] As a preferred embodiment of the online verification method for taxi meters described in this invention, the interaction between the roadside ETC device and the taxi meter refers to the interaction between the roadside ETC device and the taxi meter device within a selected road segment. When the taxi passes through the ETC trigger area at the verification starting point, the taxi meter uploads its own data to the ETC device. The ETC antenna then begins to interact with the taxi meter passing by in real time, setting the meter's mileage calculation value to 0 and recalculating the mileage. This moment is then used as the starting point for verification, and a meter mileage information dictionary is established.

[0025] As a preferred embodiment of the online verification method for taxi meters described in this invention, the step of updating the collected trajectory data and meter data to the global data computing device in real time via optical fiber refers to receiving all roadside sensing device information via optical fiber, assigning a unique global ID to the sensing data at previous and subsequent times and under multiple different detection ranges through target tracking, obtaining the trajectory data under the global ID target, associating the real-time acquired trajectory data with the license plate number to obtain global target ID-sensing target ID-license plate number, saving the associated data and naming the folder global ID, and performing initialization operations on the roadside ETC device when the global data computing device starts, including setting the initial power of the ETC antenna, performing GPS timing on the ETC antenna, and receiving the meter data uploaded by the ETC device.

[0026] As a preferred embodiment of the online verification method for taxi meters according to the present invention, the step of extracting all trajectory data from the stored trajectory data includes: querying the taxi trajectory data stored in the full-domain data computing device based on the license plate number information in the meter data dictionary, finding the taxi trajectory data folder, traversing the time information Tt corresponding to the trajectory data, and comparing it with all mileage times in the meter data dictionary to find the trajectory time Tt corresponding to the mileage times, including T0, T1, T2, T3, and T4, and obtaining all trajectory data between the start times corresponding to the trajectory data in sequence according to T0-T1, T0-T2, T0-T3, and T0-T4.

[0027] As a preferred embodiment of the online verification method for taxi meters described in this invention, the mileage verification and compensation refer to performing a clustering operation on the trajectory data between calculation times, merging duplicate points into a single trajectory point, and calculating the verification mileage.

[0028] ;

[0029] Where L is the calibration mileage. It is the value of the i-th x-coordinate. It is the value of the i-th ordinate. It is the first The values ​​of the x-coordinates, It is the first The values ​​of the ordinates;

[0030] Mileage compensation verification:

[0031] ;

[0032] in, It is the time difference at the initial moment. It is the initial moment of the trajectory. It is the initial moment of the journey. It is the time difference of time n. It is time n of the trajectory. It is the nth moment of the mileage. It is the initial mileage compensation. It is the speed of the vehicle at point 0 on the trajectory. It is the mileage compensation for time n. It is the speed of the vehicle at point n on the trajectory. It is the compensated calibration mileage. It is the calibration mileage. It's the meter mileage. It is the percentage of error;

[0033] Judging the accuracy of a taximeter based on the percentage of error:

[0034] If the error percentage is less than the specified error range, it indicates that the meter is accurate. The meter is marked as qualified for calibration. The calibration results are recorded in detail and archived. A periodic calibration plan for the meter is formulated.

[0035] If the percentage of error exceeds the specified error range, it indicates that the fare meter is inaccurate. The fare meter should be marked as unqualified and its use should be stopped immediately. The specific problem with the fare meter should be investigated, and the fare meter should be calibrated and repaired. After calibration and repair, the fare meter should be recalibrated.

[0036] A computer device includes: a memory and a processor; the memory stores a computer program, and the processor executes the computer program to implement the steps of an online verification method for taxi meters.

[0037] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of an online verification method for a taxi meter.

[0038] The beneficial effects of this invention are as follows: By deploying roadside sensing and ETC devices, constructing a dynamic matching algorithm, and combining vehicle identity information with trajectory and fare calculation data through a full-domain data calculation device for mileage verification, this invention can achieve accurate dynamic detection of vehicles under various traffic conditions, quickly identify and correct anomalies in trajectory data, ensure high-precision calculation of mileage for fare meter verification, and significantly improve the accuracy and real-time performance of the verification method. Attached Figure Description

[0039] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a flowchart illustrating the online verification method for taxi meters.

[0041] Figure 2 This is a schematic diagram illustrating the implementation of the online verification method for taxi meters. Detailed Implementation

[0042] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0043] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0044] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0045] Example 1

[0046] Reference Figure 1 and Figure 2 This is the first embodiment of the present invention, which provides an online verification method for taxi meters. The online verification method for taxi meters includes the following steps:

[0047] S1. Deploy roadside sensing devices to detect vehicles and track targets;

[0048] Specifically, deploying roadside sensing devices to detect vehicles involves deploying n roadside sensing devices on a selected road segment. A license plate capture camera is deployed in the roadside sensing device at the first starting point. When a vehicle passes the starting point, the roadside sensing device detects the vehicle and captures the vehicle's basic information as a target, including its location, speed, size, heading angle, type, and timestamp.

[0049] It should be noted that when deploying license plate capture cameras in the roadside sensing equipment at the first starting point, the target sensing range should exceed the license plate capture area to ensure that the vehicle is sensed first and then the license plate is captured. By deploying roadside sensing equipment to sense vehicles, automatic identification and data collection of vehicles when entering the inspection area are achieved. This provides an initial data capture point for the vehicle, captures basic vehicle information, and provides basic data for subsequent taximeter inspection. This improves the automation level of data collection, reduces the need for manual intervention, reduces the labor intensity of the inspection process, and also improves the speed and accuracy of data collection, laying the foundation for more accurate inspection.

[0050] Furthermore, target tracking involves unifying the identity of target information perceived at different times by assigning a unique ID to the perceived target. When the target passes through the capture area, a capture command is triggered to capture a photo of the front of the vehicle and identify the license plate. The perceived target ID is then bound to the license plate information to obtain the perceived target ID-license plate number.

[0051] Target tracking assigns a unique ID to each vehicle and triggers a capture command when the vehicle passes through the capture area to obtain a photo of the front of the vehicle and identify the license plate. This ensures continuous tracking of the vehicle throughout the entire inspection route. No matter how the vehicle moves, it can be accurately located using the unique ID, ensuring the consistency and traceability of vehicle data. By combining the license plate with the perceived target ID, the security of the inspection process and the tamper-proof capability of the data are guaranteed, thereby ensuring the fairness and accuracy of the inspection results.

[0052] S2. Construct a dynamic matching algorithm to analyze and match the trajectory data collected in real time;

[0053] Specifically, a dynamic matching algorithm is constructed to analyze and match the real-time collected trajectory data to select the optimal trajectory:

[0054] Calculate trajectory similarity score Represented as:

[0055] ;

[0056] in, This is the similarity score between trajectory r and the preset trajectory r′, with a value range of 0 to 1. A value close to 1 indicates that the two trajectories are very similar, while a value close to 0 indicates that the two trajectories are quite different. This represents the position vector of the real-time trajectory at time t. represents the position vector of the preset trajectory at time t, and a, b represent the time intervals considered. It is a real-time trajectory With preset trajectory The square of the Euclidean distance at time t is used to quantify the difference between the two trajectories at that moment;

[0057] Calculate the dynamic weights of the time window Represented as:

[0058] ;

[0059] in, It is a normalization factor used to ensure that the weights are within an appropriate range and to maintain the overall balance of the formula. It represents the amplitude of the nth Fourier component, indicating the importance or contribution of that frequency component in the time series. It is the frequency of the nth component, representing the periodic variation in the time series, such as a daily cycle or a weekly cycle. It is the phase of the nth component, which determines the position of the periodic change in time. These are the coefficients of the m-th wavelet component, representing the importance or contribution of this local feature in the time series. It is the translation parameter The m-th wavelet function centered at the time series is, in this embodiment, preferably the Morlet wavelet function, used to capture local temporal features in the time series. It is the decay rate, which determines how quickly the weights change over time. The further away the simulation time is from the current moment, the smaller the effect of the weights. t refers to time. It is the current time point, used to determine the position of the weighting function relative to the actual time, so as to implement time decay;

[0060] Calculate the weighted geographic information Represented as:

[0061] ;

[0062] in, and These represent the geographic coordinates of the real-time trajectory r and the preset trajectory r′, respectively. This indicates the geographical location difference between the real-time trajectory and the preset trajectory. This represents the maximum geographical location difference considered, used in the calculation of normalized geographical location difference. and These represent the average altitude of the real-time trajectory and the preset trajectory, respectively. This represents the absolute value of the altitude difference between two trajectories. It represents the maximum altitude difference, used for calculating the normalized altitude difference.

[0063] Analyze speed changes Represented as:

[0064] ;

[0065] in, The velocity represents the instantaneous velocity along the trajectory r, which changes with time t. This represents acceleration, specifically the first derivative of velocity v with respect to time t. This represents the rate of change of acceleration, i.e., the second derivative of velocity with respect to time. The standard deviation function is used to calculate the standard deviation of acceleration, which represents the degree of dispersion of acceleration changes. It is the hyperbolic tangent function;

[0066] Comprehensive construction of dynamic matching algorithm Represented as:

[0067] ;

[0068] in, It is a dynamic matching score. and These are weighting coefficients. It is a trajectory similarity score. It is a dynamic weight of the time window. It is a geographic information weighting, It involves velocity change analysis, selecting the trajectory with the highest matching score as the optimal trajectory.

[0069] A dynamic matching algorithm is constructed to analyze and match the real-time collected trajectory data to select the optimal trajectory, realizing real-time and high-precision tracking of vehicle driving paths. It also enables accurate evaluation of the differences between real-time trajectories and preset trajectories, and can adjust weights to optimize the matching results. It effectively identifies the actual path that is closest to the expected driving trajectory, significantly improving the accuracy of trajectory matching and the system's adaptability to complex road conditions. In particular, under changing traffic environments and different geographical conditions, this dynamic algorithm enables the verification system to adapt to various actual situations, improving the accuracy and reliability of taximeter verification.

[0070] S3. Deploy roadside ETC devices to interact with taxi meters;

[0071] Specifically, deploying roadside ETC devices to interact with taxi meters means that within a selected road segment, roadside ETC devices interact with taxi meters. When a taxi passes through the ETC trigger area at the verification starting point, the taxi meter uploads its own data to the ETC device. The ETC antenna then begins to interact with the taxi meters passing by in real time, setting the meter's mileage calculation value to 0 and recalculating the mileage. This moment is used as the verification starting point to establish a meter mileage information dictionary.

[0072] In this embodiment, due to the limited coverage of ETC devices and the need for interaction at different mileage points during taxi travel, a number of ETC devices are placed on the roadside (e.g., 5 ETC devices are needed for mileages of 0m, 200m, 400m, 600m, and 800m). The meter mileage information dictionary is used to record the taxi's mileage information at 0m and the mileage information at each subsequent interaction with the ETC, up to the destination. The dictionary format is as follows:

[0073] License plate number: {Taxi meter number: xxxxxxxxxx (used to uniquely bind the nationally unified taxi meter number to the license plate number, preventing the occurrence of cloned license plates);

[0074] Mileage 0: [0, mileage 0 at time];

[0075] Mileage 1: [Mileage 1 value, Mileage 1 time];

[0076] Mileage 2: [Mileage 2 value, Mileage 2 time];

[0077] Mileage 3: [Mileage 3 value, Mileage 3 time];

[0078] Mileage 4: [Mileage 4 value, Mileage 4 time].

[0079] By deploying roadside ETC devices to interact with taxi meters, this invention achieves automated zero-point calibration of the initial mileage of taxi meters. This ensures that the measurement benchmark of all meters is unified and traceable from the starting point of the verification, thus laying a solid foundation for accurate mileage measurement, reducing the possibility of human error, improving the standardization of verification, and enhancing the accuracy and efficiency of taxi meter verification.

[0080] S4. The collected trajectory data and fare meter data are updated in real time to the global data computing device via optical fiber;

[0081] Specifically, the collected trajectory data and meter data are updated in real time to the global data computing device via optical fiber. This means that all roadside sensing device information is received via optical fiber, and sensing data from previous and subsequent times and multiple different detection ranges are assigned a unique global ID through target tracking. The trajectory data under the global ID target is then obtained. The target information includes global ID, sensing target ID, location, size, heading angle, type, speed, and timestamp. The real-time acquired trajectory data is associated with the license plate number to obtain global target ID-sensing target ID-license plate number. The global target information includes license plate number, global ID, sensing target ID, location, size, heading angle, type, speed, and timestamp. The associated data is saved and the folder is named global ID. When the global data computing device starts, it performs initialization operations on the roadside ETC devices, including setting the initial power of the ETC antenna, performing GPS timing on the ETC antenna, and receiving meter data uploaded by the ETC devices. The meter data includes the vehicle license plate number, meter number, target mileage value, and mileage time.

[0082] By updating the trajectory data and fare meter data in the global data computing device in real time through the fiber optic network, the high-speed transmission advantage of fiber optic communication is utilized to achieve rapid and stable information flow under large data volume conditions. This real-time data update mechanism enhances the timeliness and reliability of data processing, allowing the system to monitor and analyze fare meter data at near real-time speed. No matter what changes occur during vehicle operation, the system can react quickly and make corresponding data adjustments, significantly improving the response speed and data processing efficiency of the verification system.

[0083] S5. Extract all trajectory data from the stored trajectory data and perform mileage verification and compensation.

[0084] Specifically, extracting all trajectory data from the stored trajectory data includes querying the taxi trajectory data stored in the full-domain data computing device based on the license plate number information in the fare meter data dictionary, locating the taxi trajectory data folder, traversing the time information Tt corresponding to the trajectory data, and comparing it with all mileage times in the fare meter data dictionary to find the trajectory time Tt corresponding to the mileage time, including T0, T1, T2, T3, and T4. Then, based on T0-T1, T0-T2, T0-T3, and T0-T4, all trajectory data corresponding to the start time of the trajectory data are obtained.

[0085] Extracting all trajectory data from the stored trajectory data enables in-depth analysis of the vehicle's actual driving path, including refined identification of vehicle stop points and driving routes. By comprehensively utilizing multi-dimensional information such as location and speed, it enhances the insight into the vehicle's motion state, thereby providing detailed raw data for mileage verification. It aggregates scattered single-point data into a complete driving trajectory, providing accurate data support for subsequent verification calculations, improving the accuracy of trajectory data, and ensuring the comprehensiveness and accuracy of the verification results.

[0086] Furthermore, mileage verification and compensation involves clustering the trajectory data between calculation times, merging duplicate points into a single trajectory point, and calculating the verification mileage.

[0087] ;

[0088] Where L is the calibration mileage. It is the value of the i-th x-coordinate. It is the value of the i-th ordinate. It is the first The values ​​of the x-coordinates, It is the first The values ​​of the ordinates;

[0089] Mileage compensation verification:

[0090] ;

[0091] in, It is the time difference at the initial moment. It is the initial moment of the trajectory. It is the initial moment of the journey. It is the time difference of time n. It is time n of the trajectory. It is the nth moment of the mileage. It is the initial mileage compensation. It is the speed of the vehicle at point 0 on the trajectory. It is the mileage compensation for time n. It is the speed of the vehicle at point n on the trajectory. It is the compensated calibration mileage. It is the calibration mileage. It's the meter mileage, read from the meter itself. It is the percentage of error;

[0092] Judging the accuracy of a taximeter based on the percentage of error:

[0093] If the error percentage is less than the specified error range, it indicates that the meter is accurate. The meter is marked as qualified for calibration. The calibration results are recorded in detail and archived. A periodic calibration plan for the meter is formulated.

[0094] If the percentage of error exceeds the specified error range, it indicates that the fare meter is inaccurate. The fare meter should be marked as unqualified and its use should be stopped immediately. The specific problem with the fare meter should be investigated, and the fare meter should be calibrated and repaired. After calibration and repair, the fare meter should be recalibrated.

[0095] In this embodiment, the specification refers to the explicit provisions in common general knowledge. Through a precise calculation process, deviations in the mileage calibration are corrected, and possible data errors are compensated. This is particularly crucial for correcting data distortions caused by sensor errors, signal obstruction, and non-standard vehicle movement. It ensures that the mileage calibration reflects the actual distance traveled by the vehicle, guarantees the calibration accuracy of the meter, enhances trust between consumers and service providers, and provides a reliable fare calculation basis for the taxi industry.

[0096] Example 2

[0097] This is the second embodiment of the present invention, which differs from the previous embodiment in that:

[0098] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0099] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0100] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0101] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

Claims

1. A method for online verification of taxi meters, characterized in that: include, Deploy roadside sensing devices to detect vehicles and track targets; A dynamic matching algorithm is constructed to analyze and match trajectory data collected in real time; Deploy roadside ETC devices to interact with taxi meters; The collected trajectory data and fare meter data are updated in real time to the global data computing device via optical fiber; Extract all trajectory data from the stored trajectory data and perform mileage verification and compensation; The aforementioned dynamic matching algorithm analyzes and matches real-time collected trajectory data to select the optimal trajectory. Calculate trajectory similarity score Represented as: in, It is the similarity score between trajectory r and the preset trajectory r′. This represents the position vector of the real-time trajectory at time t. This represents the position vector of the preset trajectory at time t, where a and b represent the time periods considered. It is a real-time trajectory With preset trajectory The square of the Euclidean distance at time t; Calculate the dynamic weights of the time window Represented as: in, It is a normalization factor. It is the amplitude of the nth Fourier component. It is the frequency of the nth component. It is the phase of the nth component. These are the coefficients of the m-th wavelet component. It is the translation parameter The m-th wavelet function centered at... It refers to the decay rate, where t is time. It is the current time point; Calculate the weighted geographic information Represented as: in, and These represent the geographic coordinates of the real-time trajectory r and the preset trajectory r′, respectively. The representative considered the largest geographical location difference. and These represent the average altitude of the real-time trajectory and the preset trajectory, respectively. This is the maximum difference in altitude; Analyze speed changes Represented as: in, Indicates speed, Indicates acceleration. Represents the rate of change of acceleration. Represents the standard deviation function. It is the hyperbolic tangent function; Comprehensive construction of dynamic matching algorithm Represented as: in, It is a dynamic matching score. and These are weighting coefficients. It is a trajectory similarity score. It is a dynamic weight of the time window. It is a geographic information weighting, It involves velocity change analysis, selecting the trajectory with the highest matching score as the optimal trajectory.

2. The online verification method for taxi meters as described in claim 1, characterized in that: The deployment of roadside sensing devices to detect vehicles involves deploying roadside sensing devices on selected road sections. At the first starting point, a license plate capture camera is deployed in the roadside sensing device. When a vehicle passes the starting point, the roadside sensing device detects the vehicle and captures the vehicle's basic information as a target.

3. The online calibration method for taxi meters as described in claim 2, characterized in that: The target tracking process involves unifying the identity of the target information sensed at previous and subsequent moments, assigning a unique ID to the sensed target, and triggering a capture command when the target passes through the capture area to obtain a photo of the front of the vehicle and identify the license plate. The sensed target ID is then bound to the license plate information to obtain the sensed target ID-license plate number.

4. The online calibration method for taxi meters as described in claim 3, characterized in that: The interaction between the roadside ETC device and the taxi meter refers to the interaction between the roadside ETC device and the taxi meter device within a selected road segment. When a taxi passes through the ETC trigger area at the verification starting point, the taxi meter uploads its own data to the ETC device. The ETC antenna then begins to interact with the taxi meter that is passing by in real time, setting the meter's mileage calculation value to 0 and recalculating the mileage. This moment is used as the starting point of the verification, and a meter mileage information dictionary is established.

5. The online verification method for taxi meters as described in claim 4, characterized in that: The process of updating the collected trajectory data and fare meter data to the global data computing device in real time via optical fiber refers to receiving information from all roadside sensing devices via optical fiber, assigning a unique global ID to the sensing data at previous and subsequent times and under multiple different detection ranges through target tracking, obtaining the trajectory data under the global ID target, associating the real-time acquired trajectory data with the license plate number to obtain global target ID-sensing target ID-license plate number, saving the associated data and naming the folder global ID, and performing initialization operations on the roadside ETC device when the global data computing device starts up, including setting the initial power of the ETC antenna, performing GPS timing on the ETC antenna, and receiving the fare meter data uploaded by the ETC device.

6. The online verification method for taxi meters as described in claim 5, characterized in that: The step of extracting all trajectory data from the stored trajectory data includes querying the taxi trajectory data stored in the full-domain data computing device based on the license plate number information in the fare meter data dictionary, locating the taxi trajectory data folder, traversing the time information Tt corresponding to the trajectory data, and comparing it with all mileage times in the fare meter data dictionary to find the trajectory time Tt corresponding to the mileage time, including T0, T1, T2, T3, and T4. Then, based on T0-T1, T0-T2, T0-T3, and T0-T4, all trajectory data between the start times corresponding to the trajectory data are obtained.

7. The online verification method for taxi meters as described in claim 6, characterized in that: The mileage verification and compensation process refers to performing clustering operations on the trajectory data between calculation times, merging duplicate points into a single trajectory point, and calculating the verification mileage. Where L is the calibration mileage. It is the value of the i-th x-coordinate. It is the value of the i-th ordinate. It is the first The values ​​of the x-coordinates, It is the first The values ​​of the ordinates; Mileage compensation verification: in, It is the time difference at the initial moment. It is the initial moment of the trajectory. It is the initial moment of the journey. It is the time difference of time n. It is time n of the trajectory. It is the nth moment of the mileage. It is the initial mileage compensation. It is the vehicle's speed at point 0 on the trajectory. It is the mileage compensation for time n. It is the speed of the vehicle at point n on the trajectory. It is the compensated calibration mileage. It is the calibration mileage. It's the meter mileage. It is the percentage of error; Judging the accuracy of a taximeter based on the percentage of error: If the error percentage is less than the specified error range, it indicates that the meter is accurate. The meter is marked as qualified for calibration. The calibration results are recorded in detail and archived. A periodic calibration plan for the meter is formulated. If the percentage of error exceeds the specified error range, it indicates that the fare meter is inaccurate. The fare meter should be marked as unqualified and its use should be stopped immediately. The specific problem with the fare meter should be investigated, and the fare meter should be calibrated and repaired. After calibration and repair, the fare meter should be recalibrated.

8. A computer device, comprising: Memory and processor; The memory stores a computer program, characterized in that: when the processor executes the computer program, it implements the steps of the online verification method for taxi meters according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the online verification method for taxi meters according to any one of claims 1 to 7.

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