A method for judging faults of a portal device based on vehicle passing data quality

Abstract

The application discloses a method for judging the fault of a tollgate device based on vehicle passing data quality. The method judges whether the tollgate device has a fault by analyzing the vehicle passing data collected by the tollgate device and combining data quality evaluation indexes. The application can judge whether the quality of the vehicle passing data meets the expected requirements by analyzing the numerical value and change trend of the vehicle passing indexes, thereby guaranteeing the normal operation of a traffic monitoring system, improving the data quality and optimizing resource utilization.

Description

Technical Field

[0001] This invention relates to a method for judging the fault of checkpoint equipment based on the quality of vehicle passage data. It is a technology applied in traffic monitoring systems. In this technology, the fault or abnormality of checkpoint equipment is automatically determined by evaluating and analyzing the quality of vehicle passage data. Background Technology

[0002] With the continuous development and increasing intelligence of urban transportation, traffic monitoring systems play a vital role in traffic management and safety assurance. As a core component of these systems, checkpoint equipment's normal operation and the accuracy of its data collection are crucial for the overall system functioning. However, due to prolonged operation, harsh environments, and equipment aging, checkpoint equipment may malfunction or experience abnormalities, leading to a decline in the quality of collected vehicle data. To promptly detect and resolve checkpoint equipment failures and ensure the normal operation of the traffic monitoring system, a fault diagnosis method based on vehicle data quality is needed. The key to this method is to assess the quality of vehicle data to determine if checkpoint equipment is malfunctioning. During the assessment process, by analyzing the values ​​and trends of these indicators, the system can automatically determine whether the quality of the vehicle data meets the expected requirements. This is of great significance for ensuring the normal operation of the traffic monitoring system, improving data quality, and optimizing resource utilization. Summary of the Invention

[0003] The purpose of this invention is to identify checkpoint equipment malfunctions based on the quality of vehicle passage data.

[0004] To achieve the above objectives, the technical solution of the present invention provides a method for judging the fault of checkpoint equipment based on the quality of vehicle passage data, characterized by comprising the following steps:

[0005] Step 1: Collect vehicle passage data from the checkpoint equipment at each checkpoint;

[0006] Step 2: Divide the day into different time periods according to a preset time length, and define the i-th time period as t. i For each checkpoint device, the corresponding vehicle passage data is segmented and aggregated according to time periods to obtain the checkpoint vehicle passage record data for different checkpoint devices in each time period. Specifically, the current checkpoint device e in the i-th time period t... i The checkpoint vehicle passage record data is defined as s i , then s i =n, where n is the value of the time interval t in the i-th time period. i The number of vehicles passing through the current checkpoint device e;

[0007] Step 3: Based on the i-th time period t of the previous N days for the current checkpoint device e. iHistorical vehicle passage records at checkpoints, and upstream checkpoint devices of the current checkpoint device e. up And the upstream checkpoint device e of the current checkpoint device e down The i-th time period t of the previous N days i The historical vehicle passage data at the checkpoint is used to determine whether the current checkpoint device (e) is abnormal, and this further includes the following steps:

[0008] Step 301: Calculate the current checkpoint device e in the i-th time period t. i historical average In the formula, s dnti For the current checkpoint device e, the i-th time period t is the n-th day of the previous N days. i Historical checkpoint vehicle passage records;

[0009] Step 302: Calculate the upstream checkpoint device e of the current checkpoint device e. up In the i-th time period t i Historical averages uti , In the formula, s udnti For the upstream checkpoint device e of the current checkpoint device e up The i-th time period t on the n-th day of the previous N days i Historical checkpoint vehicle passage records;

[0010] The downstream checkpoint device e of the current checkpoint device e is calculated. down In the i-th time period t i Historical averages dti , In the formula, s ddnti For the current checkpoint device e, the downstream checkpoint device e down The i-th time period t on the n-th day of the previous N days i Historical checkpoint vehicle passage records;

[0011] Step 303: Calculate the current checkpoint device e in the i-th time period t. i historical variance This leads to the historical standard deviation.

[0012] Step 304: Calculate the i-th time period t of the current checkpoint device e on the same day. i Standard deviation v ti , In the formula, s dti For the current checkpoint device e, in the i-th time period t of the day i Real-time vehicle passage record data at checkpoints;

[0013] If v ti ≤s ti Then the i-th time period t i The data is normal, and the current checkpoint device is functioning correctly.

[0014] If v ti >s ti Then proceed to step 305;

[0015] Step 305, v ti >(v uti ×α) and v ti >(v dti ×α), then the i-th time period t i The data is abnormal; the current checkpoint device e is malfunctioning, including: s uti For the upstream checkpoint device e of the current checkpoint device e up In the i-th time period t of the day i Real-time vehicle passage record data at checkpoints;

[0016] s dti For the current checkpoint device e, the downstream checkpoint device e down In the i-th time period t of the day i Real-time vehicle passage record data at checkpoints.

[0017] Preferably, in step 1, the vehicle data includes the time the vehicle passed through the current checkpoint, the location where the vehicle passed through the current checkpoint, the device number of the checkpoint device, the vehicle type, and the vehicle license plate number.

[0018] Preferably, in step 2, the day is divided into 144 time periods, each lasting 10 minutes.

[0019] Preferably, in step 2, the data is cleaned before performing the segmentation and aggregation calculation.

[0020] The method disclosed in this invention analyzes vehicle passage data collected by checkpoint equipment and combines it with data quality assessment indicators to determine whether the checkpoint equipment is malfunctioning. By analyzing the values ​​and trends of vehicle passage indicators at checkpoints, this invention can determine whether the quality of vehicle passage data meets expected requirements, thereby ensuring the normal operation of the traffic monitoring system and improving data quality and optimizing resource utilization. Attached Figure Description

[0021] Figure 1 This invention provides the steps for determining a checkpoint device malfunction.

[0022] Figure 2 This is a flowchart illustrating the process of determining whether a checkpoint device is malfunctioning, as described in this invention. Detailed Implementation

[0023] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be understood that after reading the teachings of this invention, those skilled in the art can make various alterations or modifications to the invention, and these equivalent forms also fall within the scope defined by the appended claims.

[0024] This invention discloses a method for determining checkpoint equipment malfunctions based on vehicle passage data quality, comprising the following steps:

[0025] Step 1: Collect vehicle passage data at the checkpoint, including information such as the time, location, equipment number, vehicle type, and license plate number of the vehicle passing through the checkpoint.

[0026] Step 2: Segment and aggregate the collected vehicle passage data at checkpoints by time period, which further includes the following steps:

[0027] Step 2.1: Clean the data to improve the quality of basic data. Clean the collected vehicle passage data from checkpoints, such as filtering out data with equipment numbers that do not conform to the numbering standard, checkpoint times that are empty, and license plate numbers that do not conform to the standard.

[0028] Step 2.2: Perform segmentation and aggregation calculations on the checkpoint vehicle passage data according to three dimensions: device code, time period, and checkpoint vehicle passage record. Divide the day into 144 time periods, each lasting 10 minutes, resulting in a time period code value t. The range of the daily value i is [1, 2, 3, 4…144], corresponding to the time periods t1: 00:00:00~00:10:00, t2: 00:10:00~00:20:00, ..., L. 144 23:50:00~00:00:00. The device is defined as e, and the number of vehicle passage records at the checkpoint during the i-th time period is defined as s. i If the number of vehicles passing through device e in the i-th time period is n, then s i =n.

[0029] Step 2.3: Similarly, divide the upstream and downstream vehicle data at the checkpoint into 144 time periods of 10 minutes each, obtaining a time period code value t. The range of values ​​i for a day is [1, 2, 3, 4...144], corresponding to the time periods t1:00:00:00~00:10:00, t2:00:10:00~00:20:00, ..., t 144 : 23:50:00~00:00:00.

[0030] Step 3: Based on the distribution pattern of the checkpoint data identified in Step 2, calculate the standard deviation of the checkpoint data for the first 30 days (t1), further including the following steps:

[0031] Step 3.1: Based on the data obtained in Step 2, count the number of vehicles passing through the checkpoint over the previous 30 days, divided into 10-minute time intervals. The value range d for the previous 30 days is represented as [1, 2, ..., 30]. Divide the data into 144 time intervals of 10 minutes each, resulting in a time interval code value t. The value range i for a day is [1, 2, 3, 4…144], corresponding to the time intervals t1: 00:00:00~00:10:00, t2: 00:10:00~00:20:00, ..., t 144 23:50:00~00:00:00. The formula for calculating the average value of the current device over the previous 30 days in the t1 time period is:

[0032] This gives the average number of vehicles passing through the checkpoint in the time period t1 of the sampled data from the previous 30 days.

[0033] Step 3.2: Based on the sampling data of the upstream and downstream checkpoints for the previous 30 days, calculate the average number of vehicles passing through the checkpoints during the t_1 time period of the previous 30 days:

[0034] Upstream

[0035] Downstream

[0036] Step 3.3: Calculate the variance based on the average value of each time period of the checkpoint obtained in Step 3.2. The formula for calculating the variance of time period t1 in the first 30 days is:

[0037]

[0038] This allows us to obtain the standard deviation s of the first 30 days, specifically the time period t1. t1 As a prerequisite for judging whether the vehicle data is normal, the calculation formula is:

[0039] Step 4: Taking time period t1 as an example, we calculate the standard deviation of the vehicle passage data at the checkpoint and compare it with the standard deviation s calculated in step 3.3. t1 The smaller the difference, the more stable the data, indicating that the equipment is functioning normally. A large difference indicates unstable data, abnormal data, or equipment malfunction, requiring personnel to troubleshoot. This includes the following steps:

[0040] Step 4.1: Calculate the standard deviation v of the vehicle passage data at the checkpoint in the current time period t1. t1 The calculation formula is as follows:

[0041]

[0042] Step 4.2: Compare the standard deviations of Step 3.3 and Step 4.1. If the standard deviation of the current vehicle data is less than or equal to the standard deviation of the previous 30 days, i.e., v t1 ≤s t1 If the data for the current time period is normal, then the checkpoint equipment is not faulty.

[0043] Step 4.3: If the standard deviation of the current vehicle data is greater than the standard deviation v of the previous 30 days... t1 >s t1 If the vehicle passage data at the checkpoint is abnormal during the current time period, further calculations based on upstream and downstream checkpoint data are needed to determine whether the checkpoint equipment is malfunctioning.

[0044] Step 4.4: Calculate the standard deviation of vehicle passage data for the current time period t1 at the upstream and downstream checkpoints.

[0045] The standard deviation of vehicle passage data at the upstream checkpoint in the current time period t1 is represented by v. ut1 This indicates that the standard deviation of vehicle passage data at the downstream checkpoint in the current time period t1 is represented by v. dt1 The calculation formula is as follows:

[0046]

[0047]

[0048] Step 4.5: When the standard deviation of the vehicle passage data at the checkpoint in the current time period t1 exceeds the standard deviation of the upstream and downstream checkpoints by 25%, i.e., v t1 >(v ut1 ×1.25) and v t1 >(v dt1 If the value is ×1.25), then the checkpoint equipment is faulty in the current time period t1, and personnel need to troubleshoot the fault.

Claims

1. A method for judging the fault of checkpoint equipment based on the quality of vehicle passage data, characterized in that, Includes the following steps: Step 1: Collect vehicle passage data from the checkpoint equipment at each checkpoint; Step 2: Divide the day into different time periods according to a preset time length, and then divide the first... A time period is defined as For each checkpoint device, the corresponding vehicle passage data is segmented and aggregated according to time periods to obtain the checkpoint vehicle passage record data for each time period for different checkpoint devices. Among them, the current checkpoint device... In the Time period The checkpoint vehicle passage record data is defined as ,but , In the first Time period Through the current checkpoint equipment The number of vehicles passing by; Step 3: Based on the current checkpoint equipment forward The first day Time period Historical checkpoint vehicle passage records and current checkpoint equipment upstream checkpoint equipment and current checkpoint equipment Downstream gate equipment forward The first day Time period Historical checkpoint vehicle passage data is used to determine the current checkpoint equipment. Whether there is an anomaly, further steps include: Step 301: Calculate the current checkpoint device In the Time period historical average In the formula, For the current checkpoint equipment forward Tianzhongdi The first day Time period Historical checkpoint vehicle passage records; Step 302: Calculate the current checkpoint device. upstream checkpoint equipment In the Time period historical average , In the formula, For current checkpoint equipment upstream checkpoint equipment forward Tianzhongdi The first day Time period Historical checkpoint vehicle passage records; Calculate the current checkpoint device Downstream gate equipment In the Time period historical average , In the formula, For the current checkpoint equipment Downstream gate equipment forward Tianzhongdi The first day Time period Historical checkpoint vehicle passage records; Step 303: Calculate and obtain the current checkpoint device. In the Time period historical variance Thus, the historical standard deviation is obtained. ; Step 304: Calculate the current checkpoint device The day Time period Standard deviation , In the formula, For the current checkpoint equipment The day Time period Real-time vehicle passage record data at checkpoints; like Then the first Time period The data is normal; the current checkpoint equipment... No faults; like Then proceed to step 305; Step 305 and Then the first Time period The data is abnormal; the current checkpoint device... There is a fault, including: , For current checkpoint equipment upstream checkpoint equipment On the day Time period Real-time vehicle passage record data at checkpoints; , For current checkpoint equipment Downstream gate equipment On the day Time period Real-time vehicle passage record data at checkpoints.

2. The method for judging checkpoint equipment faults based on vehicle passage data quality as described in claim 1, characterized in that, In step 1, the vehicle data includes the time when the vehicle passes through the current checkpoint, the location where the vehicle passes through the current checkpoint, the device number of the checkpoint device, the vehicle type, and the vehicle license plate number.

3. The method for judging checkpoint equipment faults based on vehicle passage data quality as described in claim 1, characterized in that, In step 2, the day is divided into 144 time periods, each lasting 10 minutes.

4. The method for judging checkpoint equipment faults based on vehicle passage data quality as described in claim 1, characterized in that, In step 2, the data is cleaned before performing the splitting and aggregation calculations.

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