A small rail transit train noise prediction method and system

Abstract

The application provides a small rail transit train noise prediction method and system, and relates to the technical field of rail transit, comprising the following steps: constructing a speed correction model according to the positional relationship among a prediction point, a measurement point and a track surface; constructing a geometric divergence correction model according to train speed; constructing an air absorption attenuation model according to a pure tone attenuation coefficient and the position of the measurement point; constructing a ground effect attenuation model according to the average ground clearance of the propagation path and the position of the measurement point; constructing a line type correction model according to track information; calculating the single-event sound level of a single train passing through the prediction point according to the maximum noise source intensity, the speed correction model, the geometric divergence correction model, the air absorption attenuation model, the ground effect attenuation model and the line type correction model; and calculating the equivalent continuous sound level according to the time of the single train passing, the single-event sound level and the train logarithm. The application decomposes the noise propagation process into quantifiable physical links for correction, and outputs the equivalent sound level index meeting the engineering application requirements.

Description

Technical Field

[0001] This invention relates to the field of rail transit technology, specifically to a method and system for predicting noise in small rail transit trains. Background Technology

[0002] Noise from elevated urban rail transit exhibits unique characteristics in terms of frequency and propagation. Regarding frequency characteristics, traction noise is generally the primary noise source during startup or low-speed operation; when the speed is between 50 km / h and 160 km / h, wheel-rail noise becomes the main noise source. The noise source intensity test for rail transit primarily targets the noise level (Leq) during train passage, mainly recording time-domain data such as sound pressure levels. The evaluation metric used for a single vehicle during the test is the equivalent sound level during the train's passage, defined as the equivalent continuous A-weighted sound level from the moment the train's front arrives at the test location until the train's rear leaves the test location.

[0003] Small rail transit trains are characterized by a small number of trains, short vehicle length, and short transit time. Therefore, the noise source intensity measured at different speeds varies greatly and the accuracy is poor. Traditional noise monitoring and prediction methods cannot obtain accurate results. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for predicting noise from small rail transit trains, thereby improving the aforementioned problems. To achieve this objective, the technical solution adopted by this invention is as follows:

[0005] In a first aspect, this application provides a method for predicting noise from small rail transit trains, comprising:

[0006] A velocity correction model is constructed based on the positional relationship between the prediction point, the measurement point, and the track surface;

[0007] Construct a corresponding geometric divergence correction model based on the train speed;

[0008] An air absorption attenuation model is constructed based on the pure tone attenuation coefficient and the measurement point location.

[0009] A ground effect attenuation model is constructed based on the average ground clearance of the propagation path and the location of the measurement point.

[0010] Construct a line type correction model based on track information;

[0011] The sound level of a single event when a train passes through a prediction point is calculated based on the maximum noise source intensity, speed correction model, geometric divergence correction model, air absorption attenuation model, ground effect attenuation model, and line type correction model.

[0012] The equivalent continuous sound level is calculated based on the time it takes for a single train to pass, the sound level of a single event, and the number of train pairs.

[0013] Secondly, this application provides a noise prediction system for small rail transit trains, comprising:

[0014] The first module is for a method of predicting noise from small rail transit trains, characterized by comprising:

[0015] The second module is used to construct a velocity correction model based on the positional relationship between the prediction point, the measurement point and the track surface;

[0016] The third module is used to construct the corresponding geometric divergence correction model based on the train speed;

[0017] The fourth module is used to construct an air absorption attenuation model based on the pure tone attenuation coefficient and the measurement point location.

[0018] The fifth module is used to construct a ground effect attenuation model based on the average height above the ground of the propagation path and the location of the measurement point;

[0019] The sixth module is used to construct a line type correction model based on track information;

[0020] The seventh module is used to calculate the sound level of a single event when a train passes through a prediction point, based on the maximum noise source intensity, speed correction model, geometric divergence correction model, air absorption attenuation model, ground effect attenuation model, and line type correction model.

[0021] The eighth module is used to calculate the equivalent continuous sound level based on the time it takes for a single train to pass, the sound level of a single event, and the number of train pairs.

[0022] The beneficial effects of this invention are as follows:

[0023] This invention, based on extensive field-measured noise data, analyzes the noise attenuation patterns and influencing factors at typical cross-sections of rail transit systems. It constructs a multi-factor coupled and corrected equivalent line source model, simplifying rail transit noise sources into linear sources with clearly defined spatial distribution characteristics, thus significantly improving the accuracy of miniaturized rail transit noise prediction. This scheme deconstructs the complex noise propagation process into multiple quantifiable and correctable physical components, ultimately outputting an equivalent sound level index that meets engineering application requirements.

[0024] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. Attached Figure Description

[0025] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained from these drawings without creative effort.

[0026] Figure 1 This is a schematic diagram of the track section and source strength measurement location in an embodiment of this application;

[0027] Figure 2 This is a schematic diagram showing the noise measurement locations at different cross-sections in an embodiment of this application;

[0028] Figure 3 This is a flowchart illustrating the noise prediction method for small rail transit trains according to an embodiment of this application.

[0029] Figure 4 This is a graph showing the prediction results for a speed of 70 km / h in an embodiment of this application.

[0030] Figure 5 This is a graph showing the prediction results for a speed of 60 km / h in an embodiment of this application.

[0031] Figure 6 This is a graph showing the prediction results for a speed of 50 km / h in an embodiment of this application.

[0032] Figure 7 This is a diagram of a noise prediction device for small rail transit trains according to an embodiment of this application.

[0033] Symbol explanation: 800 - Prediction device for noise of small rail transit trains; 801 - Processor; 802 - Memory; 803 - Multimedia component; 804 - I / O interface; 805 - Communication component. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0035] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0036] The method described in this application requires the placement of measurement points near the line; for details, please refer to... Figures 1-2 Four vertical test sections were set up at distances of 7.5m, 15m, 30m, and 60m from the track beam. Six measuring points were set up at each of the two farther sections, and eleven measuring points were set up at each of the two closer sections. The measuring points on these sections included source strength measuring points 1.5m above and 1.5m below the track surface. Noise levels of a single vehicle passing through at different speeds (including constant speed, acceleration, and deceleration) from 30km / h to 70km / h were tested.

[0037] Based on noise source intensity data measured near the line, the noise level of the predicted point (residential building, school, etc.) can be calculated.

[0038] Example 1:

[0039] See Figure 3 This embodiment provides a method for predicting noise of small rail transit trains, including steps S100, S200, S300, S400, S500, S600 and S700.

[0040] S100. Construct a velocity correction model based on the positional relationship between the prediction point, the measurement point, and the track surface;

[0041] Based on the characteristics of rail transit, it is considered as a single-line sound source. According to the relationship between the calculation point and the track surface, speed corrections are made separately for the track surface and above / below, as detailed below:

[0042] S110. Determine the positional relationship between the predicted point and the track surface. If the predicted point is above the track surface, obtain the first coefficient. If the predicted point is above the orbital plane, then the second coefficient is obtained, which is... ;

[0043] S120. Obtain the train's speed when it passes the measurement point. The measurement points include measurement points on the track and measurement points below the track;

[0044] S130. Obtain the measured speed of the train when it passes the predicted point. ;

[0045] S140. Based on the running speed, measured speed, and first coefficient at the measurement points on the track, a first speed correction model is constructed:

[0046] ;

[0047] in, This is the speed correction value. This is the measured speed of the train when it passes the predicted point. The train's speed when it passes the measurement point;

[0048] S150. Based on the running speed, measured speed, and second coefficient at the measurement points under the track, a second speed correction model is constructed:

[0049] ;

[0050] in, This is the speed correction value. This is the measured speed of the train when it passes the predicted point. The train's speed when it passes the measurement point;

[0051] S200. Construct a corresponding geometric divergence correction model based on the train speed, including:

[0052] S210. Obtain the third coefficient, the fourth coefficient, and the fifth coefficient, wherein the third coefficient, the fourth coefficient, and the fifth coefficient correspond to different train speeds from small to large;

[0053] At a speed of 50 km / h, the corresponding third coefficient is: ;

[0054] At a speed of 60 km / h, the corresponding fourth coefficient is: ;

[0055] At a speed of 70 km / h, the corresponding fourth coefficient is: ;

[0056] S220. By performing interpolation between the third and fourth coefficients and between the fourth and fifth coefficients respectively, a coefficient curve is constructed; the horizontal axis of the coefficient curve is the train speed, and the vertical axis is the coefficient value.

[0057] It should be noted that when the speed is below 50 km / h, the same third coefficient as in the 50 km / h condition is used, i.e. When the speed is above 70 km / h but below 80 km / h, the same third coefficient as for the 70 km / h operating condition shall be used, i.e. ;

[0058] S230. Find the corresponding target coefficient on the coefficient curve based on the train speed;

[0059] S240. Construct a geometric divergence correction model based on the target coefficients;

[0060] ;

[0061] in, This is the geometric divergence correction value. To predict the distance between the point and the orbit, To measure the distance between the point and the track; The coefficients are obtained by finding them based on the coefficient curve;

[0062] S300. An air absorption attenuation model is constructed based on the pure tone attenuation coefficient and the measurement point location:

[0063]

[0064] in, This is the air absorption attenuation value. The pure tone attenuation coefficient, This refers to the distance from the predicted point (such as a residential building or school) to the sound source.

[0065] S400. Construct a ground effect attenuation model based on the average ground clearance of the propagation path and the location of the measurement point;

[0066] Attenuation caused by ground effect The average height above the ground of the propagation path ( ), propagation distance ( Closely related, calculated as follows: :

[0067] ;

[0068] in, The above formula, representing the ground effect attenuation value, applies to sound waves propagating over soft surfaces (grass, soil, etc.) when they pass over reflecting surfaces (including paved roads, water surfaces, ice surfaces, and compacted ground). =0dB;

[0069] S500: Construct a line type correction model based on track information;

[0070] Line type correction value The correction is affected by factors such as the radius of the horizontal circular curve of the track line and the gradient. Specifically, when the radius of the track curve R > 500m, no correction is considered; when 300m ≤ R ≤ 500m, the correction value is... +3dBA, correction value for slope (uphill and gradient > 6‰) It is +2dBA.

[0071] S600 calculates the single-event sound level (SEL) of a single train passing through a prediction point based on the maximum noise source intensity, speed correction model, geometric divergence correction model, air absorption attenuation model, ground effect attenuation model, and line type correction model.

[0072] First, calculate:

[0073]

[0074] in, This is the total noise correction value. This is the speed correction value. This is the geometric divergence correction value. This is the air absorption attenuation value. This is the ground effect attenuation value. This is a correction value for the line type;

[0075] The sound level of a single event occurring when a train passes through a predicted point is calculated as follows:

[0076]

[0077] in, The sound level of a single event occurring when a train passes through a predicted point. This is the total noise correction value. This represents the maximum noise source strength.

[0078] The method for determining the maximum noise source intensity is as follows:

[0079] Acquire acoustic radiation measurement data at different locations beside the track;

[0080] Vertical directional measuring points at different heights (including on and below the rail surface) were set at distances of 7.5m and 15m from the rail, and a large amount of data was collected.

[0081] Based on acoustic radiation measurement data, a vertical directivity map is plotted, including:

[0082] Treat the train as a point sound source, and calculate its relative angle with the sound source based on the vertical directivity measurement point position of the sound radiation measurement data;

[0083] The ground reflection correction term is calculated based on the ground height and propagation distance of the sound source and the vertical directivity measuring point.

[0084]

[0085] in, For ground reflection correction, The propagation distance from the sound source to the vertically directional measuring point; The height of the sound source above the ground. The height of the measuring point above the ground;

[0086] The geometric divergence correction term is calculated based on the distance between the vertical directional measurement point and the track;

[0087]

[0088] in, The distance between the noise source intensity measurement point and the track. The distance between the vertically oriented measuring point and the track; These are preset coefficients;

[0089] The acoustic radiation measurement data are corrected based on the ground reflection correction term and the geometric divergence correction term;

[0090] A vertical directivity diagram was drawn based on the corrected acoustic radiation measurement data and its relative angle with the sound source.

[0091] Determine the target angle with the maximum acoustic radiation value based on the vertical directivity diagram;

[0092] The vertical directivity diagram is a polar coordinate graph: the angle is the polar angle, and the radial direction is the corrected sound level (dB (A)). Connecting the sound level points at different angles in the graph yields a curve. Analyzing the curve reveals the point of maximum sound radiation, thus determining the corresponding target angle.

[0093] Obtain the radiated sound level at the target angle, that is, measure the noise at the target angle to obtain the maximum noise source intensity.

[0094] S700: Calculate the equivalent continuous sound level based on the time of a single train's passage, the single event sound level (SEL), and the number of train pairs;

[0095] S710. Calculate the equivalent time of the train's passage period based on the train's operating speed, train length, and distance from the prediction point to the sound source.

[0096]

[0097] in, The equivalent time for the train to pass through, in seconds; This refers to the train length, in meters (m). The train's speed is expressed in m / s. To predict the distance from the point to the sound source.

[0098] S720. Obtain the preset evaluation time and calculate the number of train pairs passing through within the evaluation time;

[0099] Evaluation time is expressed as , for Number of trains passing through within a time period

[0100] S730. The equivalent continuous sound level is calculated based on the equivalent time, evaluation time, number of train pairs, and single event sound level (SEL).

[0101]

[0102] in, The equivalent continuous sound level during time interval T is expressed in dB(A). The sound level of a single event occurring when a train passes through a predicted point; To evaluate time, for Number of trains passing through within a time period This is the equivalent time for the period during which the train passes through.

[0103] The above method was used to predict and calculate rail transit noise, and measured data from typical cross-sections were selected as the verification benchmark.

[0104] At a speed of 70 km / h, the predicted curve is as follows: Figure 4 As shown;

[0105] At a speed of 60 km / h, the predicted curve is as follows: Figure 5 As shown;

[0106] At a speed of 50 km / h, the predicted curve is as follows: Figure 6 As shown;

[0107] Under various working conditions, measured data were obtained at distances of 7.5m, 15m, 30m, and 60m from the track beam for comparison.

[0108] Comparative analysis shows that the absolute difference between the predicted and measured noise values ​​for all calculated sections is less than 2 dB(A), with 54% of the control points having a difference ≤ 1 dB(A). This result demonstrates that the proposed calculation method has high accuracy in predicting rail transit noise and matches the measured data well, verifying the feasibility and reliability of the method.

[0109] Example 2:

[0110] This embodiment provides a noise prediction system for small rail transit trains, including:

[0111] The first module is for a method of predicting noise from small rail transit trains, characterized by comprising:

[0112] The second module is used to construct a velocity correction model based on the positional relationship between the prediction point, the measurement point and the track surface;

[0113] The third module is used to construct the corresponding geometric divergence correction model based on the train speed;

[0114] The fourth module is used to construct an air absorption attenuation model based on the pure tone attenuation coefficient and the measurement point location.

[0115] The fifth module is used to construct a ground effect attenuation model based on the average height above the ground of the propagation path and the location of the measurement point;

[0116] The sixth module is used to construct a line type correction model based on track information;

[0117] The seventh module is used to calculate the sound level of a single event when a train passes through a prediction point, based on the maximum noise source intensity, speed correction model, geometric divergence correction model, air absorption attenuation model, ground effect attenuation model, and line type correction model.

[0118] The eighth module is used to calculate the equivalent continuous sound level based on the time of a single train passing, the single event sound level (SEL), and the number of train pairs.

[0119] As an optional implementation, the second module includes:

[0120] The first unit is used to determine the positional relationship between the prediction point and the track surface. If the prediction point is above the track surface, the first coefficient is obtained; if the prediction point is above the track surface, the second coefficient is obtained.

[0121] The second unit is used to obtain the train's speed when it passes the measurement points, which include measurement points on the track and measurement points under the track.

[0122] The third unit is used to obtain the measured speed of the train when it passes the predicted point;

[0123] The fourth unit is used to construct the first speed correction model based on the running speed, measured speed and first coefficient of the measurement points on the track;

[0124] The fifth unit is used to construct the second speed correction model based on the running speed, measured speed and second coefficient of the measurement point under the track.

[0125] As an optional implementation, the system further includes:

[0126] The ninth module is used to acquire acoustic radiation measurement data at different locations beside the track;

[0127] Module 10 is used to draw vertical directivity maps based on acoustic radiation measurement data;

[0128] Module 11 is used to determine the target angle with the maximum acoustic radiation value based on the vertical directivity diagram;

[0129] The twelfth module is used to obtain the radiated sound level at the target angle and to obtain the maximum noise source intensity.

[0130] As an optional implementation, the tenth module includes:

[0131] The sixth unit is used to calculate the relative angle between the measuring point and the sound source based on the location of the measuring point in the sound radiation measurement data;

[0132] The seventh unit is used to calculate the ground reflection correction term based on the ground height and propagation distance of the sound source and the measuring point;

[0133] Unit 8 is used to calculate the geometric divergence correction term based on the distance between the measuring point and the track;

[0134] Unit 9 is used to correct acoustic radiation measurement data based on ground reflection correction terms and geometric divergence correction terms;

[0135] Unit 10 is used to draw a vertical directivity diagram based on the corrected acoustic radiation measurement data and its relative angle with the sound source.

[0136] Example 3:

[0137] Corresponding to the above method embodiments, this embodiment also provides a device for predicting the noise of small rail transit trains. The device for predicting the noise of small rail transit trains described below can be referred to in correspondence with the method for predicting the noise of small rail transit trains described above.

[0138] Figure 7 This is a block diagram illustrating a small rail transit train noise prediction device 800 according to an exemplary embodiment. Figure 7As shown, the miniature rail transit train noise prediction device 800 includes a processor 801 and a memory 802. The miniature rail transit train noise prediction device 800 may also include one or more of the following: a multimedia component 803, an input / output (I / O) interface 804, and a communication component 805. The processor 801 controls the overall operation of the miniature rail transit train noise prediction device 800 to complete all or part of the steps in the aforementioned miniature rail transit train noise prediction method. The memory 802 stores various types of data to support the operation of the miniature rail transit train noise prediction device 800. This data may include, for example, commands for any application or method operating on the miniature rail transit train noise prediction device 800, and application-related data such as contact data, sent and received messages, pictures, audio, video, etc. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0139] Multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals.

[0140] The received audio signal can be further stored in memory 802 or transmitted via communication component 805. The audio component also includes at least one speaker for outputting audio signals. I / O interface 804 provides an interface between processor 801 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons can be virtual or physical. Communication component 805 is used for wired or wireless communication between the small rail transit train noise prediction device 800 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof, is used; therefore, the corresponding communication component 805 may include a Wi-Fi module, a Bluetooth module, or an NFC module.

[0141] Example 4:

[0142] Corresponding to the above embodiment of the method for predicting noise of small rail transit trains, this embodiment also provides a readable storage medium. The readable storage medium described below can be referred to in relation to the method for predicting noise of small rail transit trains described above.

[0143] A readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described method for predicting noise in small rail transit trains.

[0144] Specifically, the readable storage medium can be a USB flash drive, external hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, or any other readable storage medium capable of storing program code.

[0145] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0146] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for predicting noise from small rail transit trains, characterized in that, include: A velocity correction model is constructed based on the positional relationship between the prediction point, the measurement point, and the track surface; Construct a corresponding geometric divergence correction model based on the train speed; An air absorption attenuation model is constructed based on the pure tone attenuation coefficient and the measurement point location; A ground effect attenuation model is constructed based on the average ground clearance of the propagation path and the location of the measurement point. Construct a line type correction model based on track information; The sound level of a single event when a train passes through a prediction point is calculated based on the maximum noise source intensity, speed correction model, geometric divergence correction model, air absorption attenuation model, ground effect attenuation model, and line type correction model. The equivalent continuous sound level is calculated based on the time it takes for a single train to pass, the sound level of a single event, and the number of train pairs.

2. The method for predicting noise from small rail transit trains according to claim 1, characterized in that, A velocity correction model is constructed based on the positional relationship between the predicted point, the measurement point, and the track surface, including: Determine the positional relationship between the predicted point and the track surface. If the predicted point is above the track surface, obtain the first coefficient; if the predicted point is above the track surface, obtain the second coefficient. The train's speed is obtained when it passes a measurement point, which includes measurement points on the track and measurement points under the track. Obtain the measured speed of the train when it passes the predicted point; Based on the running speed, measured speed and first coefficient of the measurement points on the track, a first speed correction model is constructed. Based on the running speed, measured speed, and second coefficient at the measurement point under the track, a second speed correction model is constructed.

3. The method for predicting noise from small rail transit trains according to claim 1, characterized in that, The method includes: Acquire acoustic radiation measurement data at different locations beside the track; A vertical directivity diagram was drawn based on acoustic radiation measurement data; Determine the target angle with the maximum acoustic radiation value based on the vertical directivity diagram; Obtain the radiated sound level at the target angle to get the maximum noise source intensity.

4. The method for predicting noise from small rail transit trains according to claim 3, characterized in that, Based on acoustic radiation measurement data, a vertical directivity map is plotted, including: Calculate the relative angle between the measuring point and the sound source based on the location of the measuring point in the sound radiation measurement data; Calculate the ground reflection correction term based on the ground height of the sound source and the measuring point, and the propagation distance; Calculate the geometric divergence correction term based on the distance between the measuring point and the track; The acoustic radiation measurement data are corrected based on the ground reflection correction term and the geometric divergence correction term; A vertical directivity diagram was drawn based on the corrected acoustic radiation measurement data and its relative angle with the sound source.

5. The method for predicting noise from small rail transit trains according to claim 1, characterized in that, Based on the train speed, a corresponding geometric divergence correction model is constructed, including: Obtain the third, fourth, and fifth coefficients, which correspond to different train speeds from smallest to largest; By interpolating the third and fourth coefficients, and the fourth and fifth coefficients respectively, coefficient curves are constructed; the horizontal axis of the coefficient curves represents the train speed, and the vertical axis represents the coefficient values. The target coefficient is obtained by searching on the coefficient curve based on the train speed. Construct a geometrically divergent correction model based on the target coefficients.

6. The method for predicting noise from small rail transit trains according to claim 1, characterized in that, The equivalent continuous sound level is calculated based on the time it takes for a single train to pass, the sound level of a single event, and the number of train pairs, including: The equivalent time of the train's passage is calculated based on the train's speed, train length, and the distance from the prediction point to the sound source. Obtain the preset evaluation time and calculate the number of train pairs passing through within the evaluation time; The equivalent continuous sound level is calculated based on the equivalent time, evaluation time, number of train pairs, and sound level of a single event.

7. A noise prediction system for small rail transit trains, characterized in that, include: The first module is for a method of predicting noise from small rail transit trains, characterized by comprising: The second module is used to construct a velocity correction model based on the positional relationship between the prediction point, the measurement point and the track surface; The third module is used to construct the corresponding geometric divergence correction model based on the train speed; The fourth module is used to construct an air absorption attenuation model based on the pure tone attenuation coefficient and the measurement point location. The fifth module is used to construct a ground effect attenuation model based on the average height above the ground of the propagation path and the location of the measurement point; The sixth module is used to construct a line type correction model based on track information; The seventh module is used to calculate the sound level of a single event when a train passes through a prediction point, based on the maximum noise source intensity, speed correction model, geometric divergence correction model, air absorption attenuation model, ground effect attenuation model, and line type correction model. The eighth module is used to calculate the equivalent continuous sound level based on the time it takes for a single train to pass, the sound level of a single event, and the number of train pairs.

8. The noise prediction system for small rail transit trains according to claim 7, characterized in that, The second module includes: The first unit is used to determine the positional relationship between the prediction point and the track surface. If the prediction point is above the track surface, the first coefficient is obtained; if the prediction point is above the track surface, the second coefficient is obtained. The second unit is used to obtain the train's speed when it passes the measurement points, which include measurement points on the track and measurement points under the track. The third unit is used to obtain the measured speed of the train when it passes the predicted point; The fourth unit is used to construct the first speed correction model based on the running speed, measured speed and first coefficient of the measurement points on the track; The fifth unit is used to construct the second speed correction model based on the running speed, measured speed and second coefficient of the measurement point under the track.

9. The noise prediction system for small rail transit trains according to claim 1, characterized in that, The system also includes: The ninth module is used to acquire acoustic radiation measurement data at different locations beside the track; Module 10 is used to draw vertical directivity maps based on acoustic radiation measurement data; Module 11 is used to determine the target angle with the maximum acoustic radiation value based on the vertical directivity diagram; The twelfth module is used to obtain the radiated sound level at the target angle and to obtain the maximum noise source intensity.

10. The noise prediction system for small rail transit trains according to claim 9, characterized in that, The tenth module includes: The sixth unit is used to calculate the relative angle between the measuring point and the sound source based on the location of the measuring point in the sound radiation measurement data; The seventh unit is used to calculate the ground reflection correction term based on the ground height and propagation distance of the sound source and the measuring point; Unit 8 is used to calculate the geometric divergence correction term based on the distance between the measuring point and the track; Unit 9 is used to correct acoustic radiation measurement data based on ground reflection correction terms and geometric divergence correction terms; Unit 10 is used to draw a vertical directivity diagram based on the corrected acoustic radiation measurement data and its relative angle with the sound source.

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