Traffic noise testing system and method based on statistical passing method

Abstract

The invention relates to a traffic noise testing system and method based on a statistical passing method, and belongs to the field of traffic noise testing, and the system comprises a visual and radar device which is used for collecting and recognizing vehicle information; the noise recognition device is used for collecting noise signals of a current vehicle and generating noise data of the current vehicle. The upper computer is used for communicating the vision and radar device and the noise recognition device through the switch, controlling the vision and radar device and the noise recognition device to start collection at the same time, obtaining and analyzing vehicle information and noise data at the same time, calculating A-weighted sound pressure level data according to the noise data, calculating a sound pressure level-vehicle speed regression straight line, and calculating a sound pressure level-vehicle speed regression straight line. And selecting a weighting factor according to the category of the vehicle average speed and the vehicle type, obtaining a sound level at the reference speed according to the regression straight line, calculating a statistical pass index SPBI, and generating a test report. According to the system and the method, the test efficiency can be improved, and errors caused by manual operation are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic noise testing, and in particular to a traffic noise testing system and method based on a statistical passage method. Background Art

[0002] In the field of road traffic, different road surface characteristics lead to significant differences in road traffic noise, which has a serious impact on the environment along the roads. To measure this noise, specific measurement methods are defined in "GB / T 20243.1 Acoustic Road Surface Effects on Traffic Noise" and "JTG 3450-2019 Highway Roadbed and Pavement Field Test Procedure." Accurately measuring road traffic noise and assessing its impact on the surrounding environment are of great significance for traffic planning, road design, and noise control.

[0003] However, existing traffic noise testing methods rely primarily on manual operations, which present the following drawbacks: (1) In traditional manual testing, vehicle information (such as vehicle model and speed) and noise data are collected independently, requiring manual matching. This lacks integrated synchronous monitoring methods, resulting in low data matching accuracy. (2) Noise analysis relies on manual processing, making it impossible to automatically calculate the Statistical Passage Index (SPBI), resulting in low efficiency and susceptibility to human error. Summary of the Invention

[0004] The object of the present invention is to solve at least one of the technical drawbacks.

[0005] Therefore, the purpose of the present invention is to provide a traffic noise testing system and method based on the statistical pass method, which can improve the testing efficiency and reduce the errors caused by human operation.

[0006] To achieve the above objectives, an embodiment of one aspect of the present invention provides a traffic noise testing system based on a statistical pass method, comprising:

[0007] Vision and radar devices, noise identification devices, switches, host computers and mobile power supplies, among which,

[0008] The vision and radar devices are used to collect and identify vehicle information;

[0009] The noise recognition device is used to collect the noise signal of the current vehicle and perform analog-to-digital conversion on the noise signal to generate noise data of the current vehicle;

[0010] The host computer is used to communicate with the visual and radar device and the noise recognition device through the switch, control the visual and radar device and the noise recognition device to start collection at the same time, obtain vehicle information and noise data at the same time and analyze them, calculate A-weighted sound pressure level data based on the noise data, calculate the sound pressure level-vehicle speed regression line, select a weighting factor based on the category and vehicle model of the average speed of the vehicle, obtain the sound level at a reference speed based on the regression line, calculate the statistical passing index SPBI, and generate a test report;

[0011] The mobile power supply is used to supply power to the vision and radar devices, the noise recognition device and the host computer.

[0012] Furthermore, the vision and radar device includes: a vision module and a radar speed measurement module, wherein:

[0013] The visual module is used to photograph the vehicle, identify the vehicle type and license plate based on the photographed image, and calculate and record the estimated time and speed of the vehicle arriving at the test point;

[0014] The radar speed measurement module is used to identify the speed of the vehicle.

[0015] Furthermore, the host computer obtains the sound level at the reference speed according to the regression line and calculates the statistical passing index SPBI, including:

[0016] The corresponding sound pressure level at the reference speed is obtained according to the regression line, and three sound pressure levels are obtained, one for each vehicle type. The above factors are substituted into the formula to calculate the statistical passing index SPBI, which is as follows:

[0017] SPBI=10lg[W1×10 L1 / 10 +W 2a (v1 / v 2a )×10 L2a / 10 +W 2b (v1 / v 2b )×10 L2b / 10 ]

[0018] Among them, L1, L 2a , L 2b The sound pressure levels corresponding to vehicle types 1, 2a, and 2b, W1, W 2a 、W 2b are weighting factors, v1, v 2a 、v 2b Reference speeds corresponding to vehicle types 1, 2a, and 2b respectively.

[0019] Furthermore, the workflow of the host computer is as follows:

[0020] (1) Receive user instructions to start testing;

[0021] (2) sending a start acquisition instruction to the vision and radar device and the noise recognition device to control the vision and radar device and the noise recognition device to start acquisition at the same time;

[0022] (3) The visual and radar device takes a photo of the vehicle entering the identification range, identifies the vehicle information of the vehicle, and calculates the estimated time to reach the location of the test equipment; the vehicle information includes: license plate, speed, vehicle model information, speed and photo taking time,

[0023] (4) While the vision and radar devices are working, the noise recognition device collects the noise signal of the current vehicle and performs analog-to-digital conversion on the noise signal to generate noise data of the current vehicle;

[0024] (5) When the host computer determines that the vehicle information does not meet the requirement that the vehicle speed is less than 50 km / h and the vehicle deviates from the test lane, the host computer writes the vehicle information data into the database;

[0025] (6) The host computer reads the contents of the database in real time. When a new vehicle information is added, the noise data is saved and processed according to the photo-taking time and the estimated time, and the sound pressure level data of the current time period is calculated based on the noise data; if the sound pressure level data meets the preset conditions, the average of the maximum sound pressure levels of the two microphones is taken as the sound pressure level corresponding to the current vehicle, and the sound pressure level is written into the database;

[0026] (7) According to the above judgment process, if the current data is valid, the number of vehicles is accumulated, and the vehicle information, time domain diagram and spectrum diagram of the current vehicle are displayed on the software interface of the host computer. If the upper limit of the number of vehicles set by the user is met, the test is stopped;

[0027] (8) After the test is completed, the data in the database is processed, and regression analysis is performed based on the vehicle and sound pressure level data, and finally the statistical passing index SPBI is calculated;

[0028] (9) The host computer receives the wind speed information and temperature and humidity information input by the user and generates a test report.

[0029] Furthermore, the preset conditions are as follows: the difference between the peak and trough of the sound pressure level curves formed by the current vehicle, the preceding vehicle, and the following vehicle is not less than 6dB; and the difference between the current vehicle and the background noise is not less than 10dB.

[0030] Furthermore, the host computer includes: a test module and an analysis module, wherein:

[0031] The test module is used to collect vehicle information and noise data and save them;

[0032] The analysis module is used to perform regression analysis on the stored vehicle type, vehicle speed and vehicle passing noise data, and calculate the vehicle statistical passing index according to the relevant algorithm.

[0033] Furthermore, the test module is used to create a new test project and receive test information entered by the user, the test information including: basic information, main instruments and equipment, vehicle information, and audio information;

[0034] After entering the project information and test information, wait for the device to connect. When the device is connected successfully, start the test.

[0035] Furthermore, during the test, the type and speed of the current passing vehicles, the number of different types of vehicles passing, and the sound pressure and sound pressure level of the passing vehicles are displayed in real time.

[0036] Furthermore, the analysis module loads the test data window, selects the project name and test name, and views the vehicle speed and sound pressure level data of different types of vehicles, as well as the contents of the database; after receiving the user's regression analysis instruction, a linear regression analysis window pops up, and under each audio channel, different vehicle types are selected in turn, and the regression analysis curve is saved for each vehicle type, and then the statistical pass index of each audio channel is calculated; after the calculation is completed, a generate test report window pops up, and the user enters the test report related information through the generate test report window to generate a test report for each audio channel, wherein the test report related information includes: commissioning unit, test item, test category, report number and audio channel information.

[0037] Another embodiment of the present invention provides a traffic noise testing method based on a statistical pass method, comprising the following steps:

[0038] S1: The host computer controls the sending of a start acquisition instruction to the visual and radar device and the noise recognition device, controlling the visual and radar device and the noise recognition device to simultaneously start acquisition. The visual and radar device collects and recognizes vehicle information, and the noise recognition device collects noise data of the current vehicle. The host computer obtains the vehicle information and noise data at the same time, calculates A-weighted sound pressure level data based on the noise data, displays it, and saves it.

[0039] S2, the host computer calculates the sound pressure level-vehicle speed regression line, selects a weighting factor according to the vehicle average speed category and vehicle model, obtains the sound level at the reference speed based on the regression line, and calculates the statistical passing index SPBI;

[0040] S3, the host computer receives the wind speed information and temperature and humidity information input by the user and generates a test report.

[0041] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0042] 1) The software controls the simultaneous collection of vehicle and noise data, and automatically aligns the two, breaking down the information barrier between vehicle information and vehicle noise data. The two parts of data can be automatically aligned to improve matching efficiency;

[0043] 2) Data display and analysis are performed on the software side, SPBI is automatically calculated based on the collected data, and test reports are exported, which improves test efficiency and reduces errors caused by human operation.

[0044] 3) The software platform deployed on the host computer has standardized and efficient information management, small program space occupation, powerful functions, convenient operation, easy to learn and use; information retrieval is fast, efficient and accurate; it uses a lightweight database with low resource consumption, easy deployment and high flexibility.

[0045] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments with reference to the accompanying drawings, in which:

[0047] Figure 1 Schematic diagram of a traffic noise testing system based on a statistical pass method according to an embodiment of the present invention;

[0048] Figure 2 This is a flowchart of the upper computer of an embodiment of the present invention;

[0049] Figure 3 This is a test flow chart of a traffic noise test system based on a statistical pass method according to an embodiment of the present invention;

[0050] Figure 4 This is an interface diagram of a test window according to an embodiment of the present invention;

[0051] Figure 5 This is an interface diagram of a new project in an embodiment of the present invention;

[0052] Figure 6 This is an interface diagram for creating a new test according to an embodiment of the present invention;

[0053] Figure 7 This is an interface diagram of device connections according to an embodiment of the present invention;

[0054] Figure 8 This is a diagram of the interface for starting a test according to an embodiment of the present invention;

[0055] Figure 9 This is a diagram of a test interface of a currently passing vehicle according to an embodiment of the present invention;

[0056] Figure 10 This is an interface diagram of the analysis window of an embodiment of the present invention;

[0057] Figure 11 This is an interface diagram for loading data according to an embodiment of the present invention;

[0058] Figure 12 This is a diagram showing the speed and sound pressure level data interface for a Class 1 vehicle according to an embodiment of the present invention;

[0059] Figure 13 This is a diagram showing the speed and sound pressure level data interface for a Class 2a vehicle according to an embodiment of the present invention;

[0060] Figure 14 This is a diagram showing the speed and sound pressure level data interface for a Class 2b vehicle according to an embodiment of the present invention;

[0061] Figure 15 This is an interface diagram of regression analysis according to an embodiment of the present invention;

[0062] Figure 16 This is an interface diagram for calculating SPBI according to an embodiment of the present invention;

[0063] Figure 17 This is an interface diagram of a test report generation window according to an embodiment of the present invention;

[0064] Figure 18 This is an interface diagram of a test report according to an embodiment of the present invention;

[0065] Figure 19 Schematic diagram of a traffic noise testing method based on the statistical pass method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0066] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and are not to be construed as limiting the present invention.

[0067] This invention proposes a traffic noise testing system and method based on the statistical pass-by method. The system consists of a video recognition and radar speed measurement device and a noise measurement device. These devices recognize vehicle speed, vehicle type, and license plate information, measure the passing noise of the vehicle under test, automatically acquire the speed of vehicles on the road and the noise generated by the passing vehicle, establish a relationship between speed and noise, and calculate the Statistical Pass-By Index (SPBI) based on a relevant algorithm, completing efficient automated testing. The system can measure the noise and speed of passing vehicles, classify vehicles, and calculate the SPBI, offering visualization and intuitive data.

[0068] The traffic noise testing system and method based on the statistical pass method provided in the embodiments of the present invention can involve vehicles powered by new energy sources such as plug-in hybrid, pure electric, and fuel cell.

[0069] It should be noted that the system and method of the present invention refer to the following standards during the testing process:

[0070] (1) GB / T 20243.1 Acoustic road surface impact on traffic noise measurement;

[0071] (2) "JTG 3450-2019" Highway roadbed and pavement field testing procedures.

[0072] like Figure 1 As shown, the traffic noise testing system based on the statistical pass method according to an embodiment of the present invention includes: a vision and radar device 1, a noise recognition device 2, a switch 3, a host computer 4 and a mobile power supply 5.

[0073] Specifically, the vision and radar device 1 is used to collect and identify vehicle information. The vision and radar device 1 takes a photo of the vehicle and calculates and records the estimated time it takes for the vehicle to arrive at the test point based on the vehicle speed and the photo-taking time. Figure 1 The vision and radar device 1 includes a vision module 11 and a radar speed measurement module 12. The vision module 11 is used to capture a vehicle image, identify the vehicle type and license plate based on the captured image, and calculate and record the estimated time it will take for the vehicle to arrive at the test point. The radar speed measurement module 12 is used to identify the vehicle's speed.

[0074] Among them, vehicle types include cars, two-axle heavy-duty vehicles, and multi-axle heavy-duty vehicles, and the corresponding numbers are 1, 2a, and 2b respectively.

[0075] The noise recognition device 2 is used to collect the noise signal of the current vehicle, and perform analog-to-digital conversion on the noise signal to generate noise data of the current vehicle.

[0076] Specifically, the noise identification device 2 includes a microphone 21 and a data acquisition module 22. The microphone 21 converts the noise signal into an electrical signal, and the data acquisition module 22 converts the collected analog quantity into a digital quantity for output.

[0077] In the embodiment of the present invention, the microphone 21 is two 1 / 2-inch high-precision condenser free-field microphones 21 , which have the characteristics of high sensitivity and good stability.

[0078] The data acquisition module 22 uses a four-channel data collector with a high sampling rate. The higher the sampling rate, the more points are collected in one second. Therefore, the noise signal can be restored well. It has the characteristics of multi-channel and high sampling rate, and can realize multi-channel synchronous acquisition.

[0079] The mobile power supply 5 is used to supply power to the vision and radar device 1 , the noise recognition device 2 and the host computer 4 .

[0080] The host computer 4 is used to communicate with the visual and radar device 1 and the noise recognition device 2 through the switch 3, control the visual and radar device 1 and the noise recognition device 2 to start collection at the same time, obtain vehicle information and noise data at the same time and analyze them, calculate A-weighted sound pressure level data based on the noise data, calculate the sound pressure level-vehicle speed regression line, select the weighting factor according to the category and vehicle model of the average speed of the vehicle, obtain the sound level at the reference speed based on the regression line, calculate the statistical passing index SPBI, and generate a test report.

[0081] In the embodiment of the present invention, the software algorithm of the host computer 4 runs on a computer.

[0082] The vision and radar device 1 and the noise recognition device 2 are connected to the computer via the switch 3. The host computer 4 runs on the computer, which controls the vision and radar device 1 and the noise recognition device 2 to collect data simultaneously, summarizes the collected vehicle data and noise data through the switch 3 and transmits them to the host computer 4. The host computer 4 displays and saves the collected data in real time, analyzes the saved data, calculates statistics through the index SPBI, and finally generates a report for export.

[0083] Specifically, the host computer 4 obtains the sound level at the reference speed based on the regression line and calculates the statistical passing index SPBI, including the following steps:

[0084] The corresponding sound pressure level at the reference speed is obtained according to the regression line, and three sound pressure levels are obtained, one for each vehicle type. The above factors are substituted into the formula to calculate the statistical passing index SPBI, which is as follows:

[0085] SPBI=10lg[W1×10 L1 / 10 +W 2a (v1 / v 2a )×10 L2a / 10 +W 2b (v1 / v 2b )×10 L2b / 10 ]

[0086] Among them, L1, L 2a , L 2b The sound pressure levels corresponding to vehicle types 1, 2a, and 2b, W1, W 2a 、W 2b are weighting factors, v1, v 2a 、v 2b Reference speeds corresponding to vehicle types 1, 2a, and 2b respectively.

[0087] like Figure 2As shown, the process of data processing by the host computer 4 is as follows:

[0088] Step 1: Obtain vehicle information and noise data at the same time, calculate the A-weighted sound pressure level data based on the noise data, display it, and save it.

[0089] The host computer 4 triggers the vision and radar device 1 and the noise recognition device 2 to start collecting data simultaneously. The vision and radar device 1 starts to collect vehicle type, license plate, speed and calculates the estimated time for the vehicle to reach the test point. The noise recognition device 2 starts to collect noise. The host computer 4 aligns the vehicle and noise data based on the estimated time and displays them in real time on the interface. At the same time, the host computer 4 determines whether the collected data meets the following conditions:

[0090] 1) The difference between the peak and valley of the sound pressure level curve formed by the current vehicle, the preceding vehicle, and the following vehicle shall not exceed 6dB;

[0091] 2) The difference between the current vehicle and the background noise is no more than 10dB;

[0092] 3) The vehicle deviates from the test lane;

[0093] 4) The vehicle speed is less than 50 km / h;

[0094] If it meets the requirements, the current vehicle information and noise data are saved; otherwise, they are not saved and the system waits for the next vehicle test. The host computer 4 performs A-weighting on the saved noise data, calculates the maximum sound pressure level when the vehicle passes, and saves it.

[0095] Step 2: Calculate the sound pressure level-vehicle speed regression line. Select weighting factors based on the high / medium / low speed category and vehicle model of the vehicle's average speed. Obtain the sound level at the reference speed based on the regression line. Substitute the above factors into the formula to calculate SPBI.

[0096] First, the sound pressure level-vehicle speed regression line is calculated, and a linear regression analysis of the sound pressure level against the vehicle speed is performed using data pairs consisting of the maximum A-weighted sound pressure level relative to the logarithm of the speed of the current passing vehicle (base 10).

[0097] Next, we select a weighting factor based on the high / medium / low speed category and vehicle model, averaging the speed data. Averaged data is considered low speed if it falls between 45 km / h and 64 km / h, medium speed if it falls between 65 km / h and 99 km / h, and high speed if it exceeds 100 km / h. The weighting factors for different speed categories are shown in Table 1.

[0098] Table 1 Weighting factors corresponding to different vehicle speed categories

[0099]

[0100] Then, the corresponding sound pressure level at the reference speed is obtained based on the regression line. Three sound pressure levels can be obtained, one for each vehicle type. The above factors are substituted into the formula to calculate the statistical passing index SPBI. The specific formula is as follows:

[0101]

[0102] Among them, SPBI is the statistical passing index, the unit is decibel (dB), L1, L 2a , L 2b The sound pressure levels corresponding to vehicle types 1, 2a, and 2b, W1, W 2a 、W 2b are the weighting factors obtained from Table 1, v1, v 2a 、v 2b Reference speeds corresponding to vehicle types 1, 2a, and 2b respectively.

[0103] Step 3: Export the test report.

[0104] refer to Figure 3 ,The working process of the built-in software platform of host computer 4 is as follows:

[0105] (1) Receive user instructions to start testing.

[0106] (2) Sending a start acquisition instruction to the vision and radar device 1 and the noise recognition device 2 to control the vision and radar device 1 and the noise recognition device 2 to start acquisition at the same time.

[0107] Specifically, a new project is created in the software of the host computer 4, relevant information of the project is filled in, and then the start acquisition button is clicked to send an instruction to start acquisition to the visual and radar device 1 and the noise recognition device 2, and the visual and radar device 1 and the noise recognition device 2 start acquisition at the same time.

[0108] (3) The vision and radar device 1 takes a photo of the vehicle that enters the identification range, identifies the vehicle information of the vehicle, and calculates the estimated time to reach the location of the test equipment. The vehicle information includes: license plate, speed, vehicle model information, speed, and photo taking time.

[0109] Specifically, the vision and radar device 1 identifies and starts collecting data; for vehicles entering the identification range, the vision and radar device 1 takes a photo of the vehicle, identifies the vehicle's license plate, speed, model information, speed and photo taking time, and calculates the estimated time to reach the location of the test equipment.

[0110] (4) While the vision and radar device 1 is working, the noise recognition device 2 collects the noise signal of the current vehicle and performs analog-to-digital conversion on the noise signal to generate noise data of the current vehicle.

[0111] (5) When the host computer 4 determines that the vehicle information does not meet the requirement that the vehicle speed is less than 50 km / h and the vehicle deviates from the test lane, the host computer 4 writes the vehicle information data into the database.

[0112] That is, the vision and radar device 1 makes a basic judgment on the vehicle. If the following conditions are not met, the vehicle information data is written into the database:

[0113] 1) The vehicle deviates from the test lane;

[0114] 2) The vehicle speed is less than 50km / h.

[0115] (6) The host computer 4 reads the contents of the database in real time. When a new vehicle information is added, the noise data is saved and processed based on the photo capture time and the estimated time, and the sound pressure level data for the current time period is calculated based on the noise data. If the sound pressure level data meets the preset conditions, the average of the maximum sound pressure levels of the two microphones 21 is taken as the sound pressure level corresponding to the current vehicle, and the sound pressure level is written into the database. The preset conditions are as follows: the difference between the peak and valley of the sound pressure level curve formed by the current vehicle, the preceding vehicle, and the following vehicle is not less than 6dB; and the difference between the current vehicle and the background noise is not less than 10dB.

[0116] Specifically, the noise identification device 2 begins a noise test. Initially, the system only collects data and reads the database contents in real time. If a new vehicle is added, the system saves and processes the noise data based on the time taken and the estimated time, and calculates the sound pressure level data for the current time period based on the noise data. If the sound pressure level data meets the following conditions, the average of the maximum sound pressure levels of the two microphones 21 is taken as the sound pressure level corresponding to the current vehicle, and this sound pressure level is written to the database:

[0117] 1) The difference between the peak and valley of the sound pressure level curve formed by the current vehicle, the preceding vehicle, and the following vehicle shall not be less than 6dB;

[0118] 2) The difference between the current vehicle and the background noise is not less than 10dB.

[0119] (7) According to the above judgment process, if the current data is valid, the number of vehicles is accumulated, and the vehicle information, time domain diagram and spectrum diagram of the current vehicle are displayed on the software interface of the host computer 4. If the upper limit of the number of vehicles set by the user is met, the test is stopped.

[0120] (8) After the test is completed, the data in the database is processed, and regression analysis is performed based on the vehicle and sound pressure level data. Finally, the statistical passing index SPBI is calculated.

[0121] (9) The host computer 4 receives the wind speed information, temperature and humidity information and other information input by the user and generates a test report.

[0122] The host computer 4 includes a test module and an analysis module. The test module is used to collect and store vehicle information and noise data. The analysis module is used to perform regression analysis on the stored vehicle type, speed, and vehicle pass-by noise data, and calculate the vehicle statistical pass-by index based on relevant algorithms.

[0123] Specifically, the test module creates a new test project and receives test information entered by the user, including basic information, key instruments and equipment, vehicle information, and audio information. After entering the project and test information, it waits for the device to connect. Once the device is successfully connected, the test begins.

[0124] During the test, the type and speed of the current passing vehicles, the number of different types of vehicles passing, and the sound pressure and sound pressure level of the passing vehicles are displayed in real time.

[0125] The analysis module loads the test data window. Select the project name and test name to view speed and sound pressure level data for different vehicle types, as well as the database contents. After receiving the user's regression analysis command, the linear regression analysis window pops up. Under each audio channel, select different vehicle types in turn, save the regression analysis curve for each vehicle type, and then calculate the statistical pass index for each audio channel. After the calculation is complete, the Generate Test Report window pops up, where the user enters the test report information to generate a test report for each audio channel.

[0126] In an embodiment of the present invention, the test report related information includes: the commissioning unit, the test item, the test category, the report number and the audio channel information.

[0127] The following describes the test analysis process in conjunction with the operating procedures of the host computer 4 software.

[0128] 1. Create a new project

[0129] Double-click the .exe file to open the software and enter the test interface by default. Figure 4 shown.

[0130] like Figure 5 As shown, click New Project, a project information window will pop up, fill in the project name, set the project path, fill in the creator, project notes and other information in sequence, and click "OK" after filling in to create a new project.

[0131] Click New Test and a test information window will pop up. The test information includes four parts: basic information, main instruments and equipment, vehicle information, and audio information.

[0132] like Figure 6As shown, in the basic information, you must first select an appropriate project name (such as "project name") in the "Project Name" drop-down menu, then enter the name of this test (such as "test name") in the "Test Name" input box, click the selection box to select the date of the test (such as 2025 / 05 / 08) at "Test Date", enter the section identifier (such as "ld") in the "Test Section" input box, select the direction (such as "fx") in the "Test Direction" drop-down menu, enter the stake number (such as "zg") in the "Test Stake Number" input box, enter the standard on which the test is based (such as GB / T 20243.1) in the "Test Basis" input box, and if necessary, fill in relevant instructions (such as "bz") in the "Remarks" input box.

[0133] In the main instruments and equipment section, enter the actual instrument and equipment information in the "Name", "Model" and "Number" input boxes corresponding to "Device 1-Device 5" in turn. Enter the number of different types of vehicles in the vehicle information setting section. For the audio information configuration section, select the audio collection duration for each vehicle (such as 4s) in the "Audio Collection Duration" drop-down menu, and select the appropriate sampling rate (such as 16384Hz) in the "Sampling Rate" drop-down menu. According to the actual connection and test requirements, check the corresponding microphone 21 channels (such as Channel 1 and Channel 2 are already checked, Channel 3 and Channel 4 can be checked as needed), and confirm or adjust the sensitivity parameters corresponding to each channel (such as Channel 1 is 46.60mV / Pa, etc.).

[0134] After completing all the above information filling and setting, check carefully and click the "OK" button to submit the test information. If you need to reset it, click the "Cancel" button to cancel the operation.

[0135] like Figure 7 As shown, after completing the project information and test information, wait for the device to connect until the device connection window pops up, indicating that the device is connected successfully and you can start the test.

[0136] 2. Testing

[0137] like Figure 8 As shown, after the device is successfully connected, click "Start Test". Then find the vehicle_detection folder in the same directory as the .exe file. Follow the path of vehicle_detection>logs>app.log to find the app.log file and run it in the form of Notepad. Then return to the software interface and wait for the graphics to appear on the interface. Figure 9 You can see the type and speed of the current passing vehicle, the number of different types of vehicles passing, and the sound pressure and sound pressure level of the passing vehicles. After the test is completed, click "Stop Test".

[0138] 3. Analysis

[0139] After the test is completed, click "Open Analysis Window" to enter the analysis interface. Figure 10 shown.

[0140] like Figure 11 As shown, click "Load Data", the load test data window pops up, select the project name and test name, and then click "OK", you can view the 1st type of vehicle (such as Figure 12 As shown), 2a class vehicles (such as Figure 13 As shown), 2b type vehicles (such as Figure 14 Vehicle speed and sound pressure level data (as shown), as well as database contents.

[0141] like Figure 15 As shown, in the database interface, click "Regression Analysis" to pop up the linear regression analysis window. Under each audio channel, select vehicle types 1, 2a, and 2b in turn. Save the regression analysis curve for each vehicle type, and then calculate the statistical passing index of each audio channel, as shown in the following figure. Figure 16 shown.

[0142] like Figure 17 As shown in the figure, after the calculation is completed, click "Generate Report" to pop up the test report generation window, fill in the commissioning unit, test items, test category, report number, audio channel information, and then click "Generate Test Report" to generate test reports for audio channel 1 and audio channel 2 respectively. Figure 18 As shown, the generated test report can be viewed in the project folder.

[0143] like Figure 19 As shown, the present invention also proposes a traffic noise testing method based on the statistical pass method, comprising the following steps:

[0144] S1: The host computer sends a start acquisition instruction to the visual and radar devices and the noise recognition device, controlling them to start acquisition simultaneously. The visual and radar devices collect and identify vehicle information, and the noise recognition device collects noise data of the current vehicle. The host computer obtains vehicle information and noise data at the same time, calculates the A-weighted sound pressure level data based on the noise data, and displays and saves it.

[0145] Specifically, the host computer triggers the vision and radar device and the noise recognition device to start collecting data simultaneously. The vision and radar device begins to collect vehicle type, license plate, speed and calculates the estimated time it takes for the vehicle to reach the test point. The noise recognition device begins to collect noise. The host computer aligns vehicle and noise data based on the estimated time and displays them in real time on the interface. At the same time, the host computer determines whether the collected data meets the following conditions:

[0146] 1) The difference between the peak and valley of the sound pressure level curve formed by the current vehicle, the preceding vehicle, and the following vehicle shall not exceed 6dB;

[0147] 2) The difference between the current vehicle and the background noise is no more than 10dB;

[0148] 3) The vehicle deviates from the test lane;

[0149] 4) The vehicle speed is less than 50 km / h;

[0150] If it meets the requirements, the current vehicle information and noise data will be saved. Otherwise, they will not be saved and the system will wait for the next vehicle test. The host computer will perform A-weighting on the saved noise data, calculate the maximum sound pressure level when the vehicle passes, and save it.

[0151] In step S2, the host computer calculates the sound pressure level-vehicle speed regression line, selects a weighting factor based on the vehicle's average speed and type, obtains the sound level at the reference speed based on the regression line, and calculates the statistical passing index SPBI.

[0152] First, the host computer calculates the sound pressure level-vehicle speed regression line and uses the data pair consisting of the maximum A-weighted sound pressure level relative to the logarithm of the speed of the current passing vehicle (base 10) to perform a linear regression analysis of the sound pressure level against the vehicle speed.

[0153] Secondly, the vehicle speed data is averaged based on the high / medium / low speed categories and vehicle model selection weighting factors. The averaged data is considered low speed if it is in the range of 45km / h to 64km / h, medium speed if it is in the range of 65km / h to 99km / h, and high speed if it is greater than or equal to 100km / h.

[0154] Then, the corresponding sound pressure level at the reference speed is obtained based on the regression line. Three sound pressure levels can be obtained, one for each vehicle type. The above factors are substituted into the formula to calculate the statistical passing index SPBI. The specific formula is as follows:

[0155] SPBI=10lg[W1×10 L1 / 10 +W 2a (v1 / v 2a )×10 L2a / 10 +W 2b (v1 / v 2b )×10 L2b / 10 ]

[0156] Among them, SPBI is the statistical passing index, the unit is decibel (dB), L1, L 2a , L 2b The sound pressure levels corresponding to vehicle types 1, 2a, and 2b, W1, W 2a 、W 2b are weighting factors, v1, v 2a 、v2b Reference speeds corresponding to vehicle types 1, 2a, and 2b respectively.

[0157] S3, the host computer receives the wind speed information and temperature and humidity information input by the user and generates a test report.

[0158] The traffic noise testing system and method based on the statistical pass method according to the embodiment of the present invention has the following software and hardware requirements:

[0159] (1) Hardware environment

[0160] The computer must have at least 2GB of memory and 5GB of free hard disk space.

[0161] (2) Software environment

[0162] Operating platform: Windows

[0163] Windows: Windows 10 64-bit and above.

[0164] In summary, the traffic noise testing system and method based on the statistical pass-by method in the embodiments of the present invention triggers the video recognition device to identify vehicle information when a vehicle passes, and simultaneously triggers the noise measurement device to continuously monitor the noise, analyzing and displaying the sound pressure level data of the vehicle's pass-by noise in real time. Based on the data judgment basis specified in the standard, the noise curve is screened and analyzed. After the test is completed, the operator fills in information such as the test date, temperature and humidity, and generates a test report.

[0165] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0166] Those skilled in the art will readily understand that the present invention encompasses any combination of the components described in the Summary and Detailed Description of the Invention and the accompanying drawings. Due to space limitations and for the sake of clarity, not all of the various solutions resulting from these combinations are described. Any modifications, equivalent substitutions, and improvements within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

[0167] Although the embodiments of the present invention have been shown and described above, it should be understood that the above embodiments are illustrative and are not to be construed as limiting the present invention. Those skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments without departing from the principles and intent of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A traffic noise testing system based on statistical passing method, characterized in that: include: Vision and radar devices, noise identification devices, switches, host computers and mobile power supplies, among which, The vision and radar devices are used to collect and identify vehicle information; The noise recognition device is used to collect the noise signal of the current vehicle and perform analog-to-digital conversion on the noise signal to generate noise data of the current vehicle; The host computer is used to communicate with the visual and radar device and the noise recognition device through the switch, control the visual and radar device and the noise recognition device to start collection at the same time, obtain vehicle information and noise data at the same time and analyze them, calculate A-weighted sound pressure level data based on the noise data, calculate the sound pressure level-vehicle speed regression line, select a weighting factor based on the category and vehicle model of the average speed of the vehicle, obtain the sound level at a reference speed based on the regression line, calculate the statistical passing index SPBI, and generate a test report; The mobile power supply is used to supply power to the vision and radar devices, the noise recognition device and the host computer.

2. The traffic noise testing system based on the statistical pass method according to claim 1, characterized in that: The vision and radar device includes: a vision module and a radar speed measurement module, wherein: The visual module is used to photograph the vehicle, identify the vehicle type and license plate based on the photographed image, and calculate and record the estimated time the vehicle will arrive at the test point; The radar speed measurement module is used to identify the speed of the vehicle.

3. The traffic noise testing system based on the statistical pass method according to claim 1, characterized in that: The host computer obtains the sound level at the reference speed based on the regression line and calculates the statistical passing index SPBI, including: The corresponding sound pressure level at the reference speed is obtained according to the regression line, and three sound pressure levels are obtained, one for each vehicle type. The above factors are substituted into the formula to calculate the statistical passing index SPBI, which is as follows: SPBI=10lg[W1×10 L1 / 10 +W 2a (v1 / v 2a )×10 L2a / 10 +W 2b (v1 / v 2b )×10 L2b / 10 ] Among them, L1, L 2a , L 2b The sound pressure levels corresponding to vehicle types 1, 2a, and 2b, W1, W 2a 、W 2b are weighting factors, v1, v 2a 、v 2b Reference speeds corresponding to vehicle types 1, 2a, and 2b respectively.

4. The traffic noise testing system based on the statistical pass method according to claim 1, characterized in that: The workflow of the host computer is as follows: (1) Receive user instructions to start testing; (2) sending a start acquisition instruction to the vision and radar device and the noise recognition device to control the vision and radar device and the noise recognition device to start acquisition at the same time; (3) The visual and radar device takes a photo of the vehicle entering the identification range, identifies the vehicle information of the vehicle, and calculates the estimated time to reach the location of the test equipment; the vehicle information includes: license plate, speed, vehicle model information, speed and photo taking time, (4) While the vision and radar devices are working, the noise recognition device collects the noise signal of the current vehicle and performs analog-to-digital conversion on the noise signal to generate noise data of the current vehicle; (5) When the host computer determines that the vehicle information does not meet the requirement that the vehicle speed is less than 50 km / h and the vehicle deviates from the test lane, the host computer writes the vehicle information data into the database; (6) The host computer reads the contents of the database in real time. When a new vehicle information is added, the noise data is saved and processed according to the photo-taking time and the estimated time, and the sound pressure level data of the current time period is calculated based on the noise data; if the sound pressure level data meets the preset conditions, the average of the maximum sound pressure levels of the two microphones is taken as the sound pressure level corresponding to the current vehicle, and the sound pressure level is written into the database; (7) According to the above judgment process, if the current data is valid, the number of vehicles is accumulated, and the vehicle information, time domain diagram and spectrum diagram of the current vehicle are displayed on the software interface of the host computer. If the upper limit of the number of vehicles set by the user is met, the test is stopped; (8) After the test is completed, the data in the database is processed, and regression analysis is performed based on the vehicle and sound pressure level data, and finally the statistical passing index SPBI is calculated; (9) The host computer receives the wind speed information and temperature and humidity information input by the user and generates a test report.

5. The traffic noise testing system based on the statistical pass method according to claim 4, characterized in that: The preset conditions are as follows: the difference between the peak and trough of the sound pressure level curves formed by the current vehicle, the preceding vehicle, and the following vehicle is not less than 6dB; and the difference between the current vehicle and the background noise is not less than 10dB.

6. The traffic noise testing system based on statistical pass method according to claim 1, characterized in that: The host computer includes: a test module and an analysis module, wherein: The test module is used to collect vehicle information and noise data and save them; The analysis module is used to perform regression analysis on the stored vehicle type, vehicle speed and vehicle passing noise data, and calculate the vehicle statistical passing index according to the relevant algorithm.

7. The traffic noise testing system based on the statistical pass method according to claim 6, characterized in that: The test module is used to create a new test project and receive test information entered by the user, the test information including: basic information, main instruments and equipment, vehicle information, and audio information; After entering the project information and test information, wait for the device to connect. When the device is connected successfully, start the test.

8. The traffic noise testing system based on statistical pass method according to claim 7, characterized in that: During the test, the type and speed of the current passing vehicles, the number of different types of vehicles passing, and the sound pressure and sound pressure level of the passing vehicles are displayed in real time.

9. The traffic noise testing system based on statistical pass method according to claim 6, characterized in that: The analysis module loads a test data window, selects a project name and a test name, and views the speed and sound pressure level data of different types of vehicles, as well as the contents of the database; after receiving the user's regression analysis instruction, a linear regression analysis window pops up, selects different vehicle types in each audio channel, saves the regression analysis curve for each vehicle type, and then calculates the statistical passing index of each audio channel; After the calculation is completed, a Generate Test Report window pops up. The user enters test report related information through the Generate Test Report window to generate a test report for each audio channel. The test report related information includes: commissioning unit, test item, test category, report number and audio channel information.

10. A traffic noise testing method based on statistical passing method, characterized in that: The steps include: S1, the host computer controls the sending of a start acquisition instruction to the visual and radar device and the noise recognition device, controlling the visual and radar device and the noise recognition device to start acquisition simultaneously, the visual and radar device collecting and identifying vehicle information, and the noise recognition device collecting noise data of the current vehicle; The host computer obtains vehicle information and noise data at the same time, calculates A-weighted sound pressure level data based on the noise data, and displays and saves the data; S2, the host computer calculates the sound pressure level-vehicle speed regression line, selects a weighting factor according to the vehicle average speed category and vehicle model, obtains the sound level at the reference speed based on the regression line, and calculates the statistical passing index SPBI; S3, the host computer receives the wind speed information and temperature and humidity information input by the user and generates a test report.

Images