Method for predicting maximum noise influence degree of train, electronic equipment and medium

Abstract

The invention discloses a method for predicting the maximum noise influence degree of a train, electronic equipment and a medium, and the method comprises the steps: selecting a plurality of positions corresponding to different train speeds in an adjacent rail region as verification points, and collecting a first maximum A sound pressure level when a single train passes through each verification point; establishing and correcting a three-dimensional urban area model and a sound source model when a single train passes; according to the corrected sound source model, calculating a second maximum A sound pressure level when the single train passes through each verification point; verifying the accuracy of the corrected sound source model according to the error between the first maximum A sound pressure level and the second maximum A sound pressure level; and according to the three-dimensional urban area model and the corrected sound source model, drawing a maximum noise influence degree equal-sound-level line graph. Therefore, the method for predicting and evaluating the maximum noise influence degree of the rail train is provided from the dimension of the maximum noise, and the defects of an existing prediction and evaluation method can be overcome.

Description

Technical Field

[0001] The invention relates to the field of noise testing methods, and in particular to a method for predicting the maximum noise impact of a train, electronic equipment and a medium. Background Art

[0002] With the increasing development of urban rail transit, it can effectively reduce urban traffic pressure, but it also brings serious noise pollution to surrounding residents. Complaints about noise nuisance in residential areas near the rails are becoming more and more prominent.

[0003] The method for predicting the maximum noise value of residential areas adjacent to tracks when trains pass by is to establish a set of sound source modeling methods and use sound propagation models to predict the maximum noise value of residential areas adjacent to tracks when trains pass by. The prediction results can be used to intuitively determine the maximum noise radiation impact of trains passing by on residential areas adjacent to tracks.

[0004] In the current noise prediction and evaluation standards, the equivalent continuous sound level within a time period is generally used to evaluate the noise exceeding the standard and the degree of impact. There is a lack of prediction and evaluation methods for the maximum noise impact of high-frequency, intermittent, and high-intensity sound sources such as rail trains.

[0005] Therefore, it often happens that the evaluation indicators during the day, night or hour do not exceed the standard, but cause serious noise radiation impact on residents living near the track, resulting in a large number of noise nuisance complaints. Therefore, it is necessary to scientifically predict and analyze the maximum noise impact on residential areas near the track when trains pass. Summary of the invention

[0006] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention proposes a method for predicting the maximum noise impact of a train, which can supplement the deficiencies of existing prediction and evaluation methods and provide a method for predicting and evaluating the maximum noise impact of a high-frequency, intermittent, and high-intensity sound source such as a rail train.

[0007] The present invention also proposes an electronic device for implementing the method for predicting the maximum noise impact degree of the train.

[0008] The present invention also proposes a storage medium for implementing the method for predicting the maximum noise impact degree of the train.

[0009] According to a first aspect of the present invention, a method for predicting the maximum noise impact of a train includes:

[0010] Select several positions corresponding to different vehicle speeds in the adjacent track area as verification points, and collect the first maximum A sound pressure level when a single train passes through each verification point;

[0011] Build a three-dimensional urban area model based on the geospatial data of the area adjacent to the railway;

[0012] Establish a sound source model for a single train passing through;

[0013] Modifying the sound source model to obtain a modified sound source model;

[0014] Calculating the second maximum A sound pressure level when a single train passes through each verification point according to the modified sound source model;

[0015] verifying the accuracy of the modified sound source model according to an error between the first maximum A sound pressure level and the second maximum A sound pressure level;

[0016] A sound level line diagram of the maximum noise impact degree is drawn based on the three-dimensional urban area model and the corrected sound source model.

[0017] According to some embodiments of the present invention, establishing a sound source model when a single train passes by includes:

[0018] Select a sound source intensity reference point in the track-adjacent area, and record the horizontal distance and vertical distance from the sound source intensity reference point to the track centerline;

[0019] Collecting the maximum sound pressure level spectrum characteristics when the train passes through the sound source intensity reference point;

[0020] Based on noise propagation attenuation parameters at different frequencies, the maximum sound pressure level spectrum characteristics are corrected;

[0021] Calculating the maximum sound power level when a train passes according to the horizontal distance, the vertical distance and the corrected maximum sound pressure level spectrum characteristics;

[0022] A sound source model when a train passes by is established according to the maximum sound power level and the corrected spectrum characteristics of the maximum sound pressure level.

[0023] According to some embodiments of the present invention, calculating the maximum sound power level when a train passes according to the horizontal distance, the vertical distance and the corrected maximum sound pressure level spectrum characteristics includes:

[0024] Calculate the surface area of ​​the sound wave passing vertically through the sound source intensity reference point according to the horizontal distance and the vertical distance;

[0025] Obtaining a maximum sound pressure level according to the maximum sound pressure level spectrum characteristics;

[0026] The maximum sound power level is calculated according to the surface area and the maximum sound pressure level.

[0027] According to some embodiments of the present invention, the step of modifying the sound source model to obtain a modified sound source model includes:

[0028] According to the actual speed of the train when passing through the verification point, the sound source model is corrected for the speed;

[0029] The sound source model is modified according to the actual vehicle condition when the train passes through the verification point.

[0030] According to some embodiments of the present invention, the step of correcting the speed of the sound source model according to the actual speed of the train when the train passes through the verification point includes:

[0031] Obtaining the actual speed and reference speed of the train when it passes the verification point;

[0032] Calculating a speed correction amount according to the actual vehicle speed and the reference vehicle speed;

[0033] The vehicle speed is corrected for the sound source model according to the speed correction amount.

[0034] According to some embodiments of the present invention, the performing vehicle condition correction on the sound source model according to the actual situation when the train passes through the verification point includes:

[0035] Calculating a first average value of the first maximum A sound pressure level when a plurality of trains pass through the verification points;

[0036] Subtracting the first maximum sound pressure level A measured when any train of the train passes through each verification point from the first average value to obtain a plurality of difference values;

[0037] Calculating a second average value of the plurality of differences as a vehicle condition correction value;

[0038] The vehicle condition correction is performed on the sound source model according to the vehicle condition correction amount.

[0039] According to some embodiments of the present invention, verifying the accuracy of the modified sound source model according to the error between the first maximum A sound pressure level and the second maximum A sound pressure level includes:

[0040] Calculate the absolute error between the first maximum A sound pressure level and the second maximum A sound pressure level of each verification point;

[0041] If all the absolute errors are less than or equal to the threshold, it is determined that the modified sound source model is accurate.

[0042] According to some embodiments of the present invention, drawing a sound level line diagram of the maximum noise impact degree according to the three-dimensional urban area model and the modified sound source model includes:

[0043] Calculate the predicted maximum A sound pressure level at all two-dimensional ground and three-dimensional building facade spatial positions in the three-dimensional urban area model according to the modified sound source model;

[0044] According to different sound pressure levels of the predicted maximum sound pressure level A, the three-dimensional urban area model is drawn and rendered using different colors to obtain a sound level line diagram of the maximum noise impact degree.

[0045] An electronic device according to an embodiment of the second aspect of the present invention includes:

[0046] one or more processors;

[0047] Memory;

[0048] One or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and when the programs are executed by the processors, the steps of the method for predicting the maximum noise impact degree of a train as described in any one of the above embodiments are implemented.

[0049] According to a third aspect of an embodiment of the present invention, a computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for predicting the maximum noise impact degree of a train as described in any one of the above embodiments.

[0050] The method for predicting the maximum noise impact of a train according to an embodiment of the present invention has at least the following beneficial effects: constructing and correcting a sound source model by collecting the maximum A sound pressure level, calculating the predicted maximum A noise level when a certain train passes through each verification point based on the sound source model, and comparing the predicted maximum A noise level when a train passes through each verification point, and verifying the accuracy of the model by comparing the predicted maximum noise impact level with the actual value; if the model is accurate, the model is used in combination with a three-dimensional urban area model to draw a sound level contour map of the maximum noise impact, thereby providing a method for predicting the maximum noise impact when a train passes, and a method for verifying the accuracy of the method. Therefore, compared with the conventional equivalent continuous sound level prediction method based on time periods, the solution used in the present technology can effectively predict the maximum noise impact of a train on the adjacent track area when it passes, and is an important supplement to the current rail transit noise prediction and evaluation methods. In addition, this method constructs a sound source model and only collects the maximum A noise level of a limited number of reference points to verify the accuracy of the constructed sound source model, rather than directly collecting the maximum A noise level over a large area to directly evaluate the maximum noise impact. This reduces work costs and workload, improves the efficiency of predicting and evaluating the maximum noise impact, avoids the huge workload and high cost of using measured data to evaluate the impact over a large area, and does not require actual installation of equipment at each evaluation scale of buildings in residential areas adjacent to the track to actually measure the maximum A noise level. Instead, the sound source model is used for calculation, thus avoiding the cost of equipment on the order of hundreds of thousands.

[0051] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0053] Figure 1 A schematic diagram of the steps of a method for predicting the maximum noise impact of a train according to an embodiment of the present invention;

[0054] Figure 2 for Figure 1 The flowchart of the specific steps of step S300 of the method for predicting the maximum noise impact degree of a train is shown;

[0055] Figure 3 It is a schematic diagram of a specific process of a method for predicting the maximum noise impact degree of a train according to an embodiment of the present invention. DETAILED DESCRIPTION

[0056] Embodiments of the present invention are described in detail below, and examples of the embodiments 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 only used to explain the present invention, and cannot be understood as limiting the present invention.

[0057] In the description of the present invention, it should be understood that descriptions involving orientations, such as up, down, front, back, left, right, etc., and orientations or positional relationships indicated are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0058] In the description of the present invention, "several" means one or more, "more" means more than two, "greater than", "less than", "exceed" etc. are understood as not including the number itself, and "above", "below", "within" etc. are understood as including the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing the technical features, and cannot be understood as indicating or implying the relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0059] In the description of the present invention, unless otherwise clearly defined, terms such as setting, installing, connecting, etc. should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in the present invention based on the specific content of the technical solution.

[0060] The following is combined with Figure 1-3 , a method, electronic device and medium for predicting the maximum noise impact of a train proposed by the present invention are described in detail.

[0061] Reference Figure 1 The present invention proposes a method for predicting the maximum noise impact of a train, comprising the following steps:

[0062] S100: selecting a number of positions corresponding to different vehicle speeds in the track-adjacent area as verification points, and collecting the first maximum A sound pressure level when a single train passes through each verification point;

[0063] S200: Establish a three-dimensional urban area model based on the geospatial data of the adjacent rail area;

[0064] S300: establishing a sound source model when a single train passes;

[0065] S400: Correcting the sound source model to obtain a corrected sound source model;

[0066] S500: Calculate the second maximum A sound pressure level when a single train passes through each verification point according to the modified sound source model;

[0067] S600: verifying the accuracy of the corrected sound source model according to the error between the first maximum A sound pressure level and the second maximum A sound pressure level;

[0068] S700: Draw a sound level line diagram of the maximum noise impact based on the three-dimensional urban area model and the corrected sound source model.

[0069] Specifically, in this embodiment, refer to Figure 1 and Figure 3 , wherein step S100 specifically includes: firstly selecting a number of positions corresponding to different vehicle speeds in the adjacent track area as verification points, the number of which is at least three, and collecting the first maximum A sound pressure level L when a single train passes through each verification point Amax 1. As objective evaluation data for subsequent verification of whether the sound source model is accurate.

[0070] Step S200 specifically includes: establishing a three-dimensional urban area model based on the geographic spatial data of the area adjacent to the track, including information such as terrain, roads, tracks, buildings, and sound barriers, in combination with existing modeling techniques.

[0071] Step S300 specifically includes: (3.1) selecting a position in the adjacent track area that is close to the track and not affected by obstructions such as sound barriers as a sound source intensity reference point, and recording the horizontal distance and vertical distance from the sound source intensity reference point to the center line of the track; (3.2) collecting the octave or 1 / 3 frequency band data of the A-weighted or linearly weighted maximum sound pressure level when the train passes through the sound source intensity reference point as the maximum sound pressure level spectrum characteristics when the train passes through the sound source intensity reference point; (3.3) based on the noise propagation attenuation parameters at different frequencies, correcting the maximum sound pressure level spectrum characteristics when the train passes, including geometric attenuation correction, atmospheric absorption correction, etc.; (3.4) then according to the horizontal distance and vertical distance from the sound source intensity reference point to the center line of the track, based on commonly used geometric principles, calculate the surface area A of the sound wave vertically passing through the sound source intensity reference point, and according to the maximum sound pressure level spectrum characteristics, obtain the maximum sound pressure level L when the train passes through the sound source intensity reference point. Amax , the surface area A and the maximum sound pressure level L Amax Substituting into the formula L W,max =L Amax +10lgA, the maximum sound power level L when the train passes the sound source intensity reference point is calculated W,max ; (3.5) Finally, use the maximum sound power level L W,max and the corrected maximum sound pressure level spectrum characteristics, one or more linear sound sources are established at the verification point along the center line of the track as the sound source model when the train passes. The sound source model refers to the characteristic data for physically modeling the sound source radiation characteristics of the noise source when the train passes. In this embodiment, a line sound source is used to simulate the train sound source, which includes characteristic data such as the length, height, sound power level, and spectrum characteristics of the train sound source.

[0072] Step S400 specifically includes: (4.1) according to the actual speed of the train when it passes the verification point, the maximum sound power level L of the sound source model when the train passes W,max The speed correction method is: substitute the actual speed v when the train passes the verification point and the reference speed v0 of the train into the formula C t,v =20lg(v / v 0 ), calculate the speed correction C t,v ; Then according to the formula L W,max,v =L W,max +C t,v The vehicle speed is corrected for the sound source model. In the formula, L W,max is the maximum sound power level of the sound source model before velocity correction, L W,max,v is the maximum sound power level of the sound source model after speed correction. (4.2) The sound source model is corrected for the train condition when the train passes the verification point. The correction method is as follows: (4.2.1) First, calculate the first maximum A sound pressure level L when several trains pass each verification point.Amax Average value of 1 (4.2.2) Then, the maximum sound pressure level L measured when any train passes through each verification point Amax，n , respectively, with the first average Make a difference to get several difference values ​​ΔL Amax，n ; (4.2.3) Calculate several differences ΔL Amax，n The average As the vehicle condition correction amount C i , where n represents the nth verification point, i represents the i-th train, and finally according to the formula L W,max,i =L W,max +C i The vehicle condition is corrected for the sound source model. In the formula, L W,max is the maximum sound power level of the sound source model before vehicle condition correction, L W,max,i It is the maximum sound power level of the sound source model after correction for vehicle conditions.

[0073] Step S500 specifically includes: calculating the second maximum A sound pressure level L when a single train passes through each verification point according to the modified sound source model, including characteristic data such as the length, height, sound power level, and spectrum characteristics of the train sound source Amax 2 as the predicted value;

[0074] Step S600 specifically includes: calculating the first maximum A sound pressure level L of each verification point Amax 1 and predicted second maximum A sound pressure level L Amax 2 Absolute error |ΔL Amax |, to evaluate the prediction accuracy of the sound source model. The evaluation method is: if the absolute error |ΔL Amax If the value of | is less than or equal to 3dB, it means that the sound source model is accurate. If the prediction results of multiple verification points are all accurate, it means that the sound source model is universal and can be used as a noise source model. Figure 2 If the prediction result is poor or the accuracy of multiple verification points is inconsistent, it means that the sound source model is unreliable, the prediction error is relatively large, and it cannot be used as a noise source. Figure 2 3D and 4D network prediction and calculation of sound source models.

[0075] Step S700 specifically includes: calculating the predicted maximum A sound pressure level L of all ground two-dimensional and building facade three-dimensional grid space positions in the three-dimensional urban area model according to the modified sound source model Amax , that is, the predicted noise level; according to the predicted maximum A sound pressure level L AmaxThe 3D urban area model is rendered using the 3DGIS engine according to different colors according to different sound pressure levels, and the sound level line diagram of the maximum noise impact degree is obtained, which is the predicted result of the maximum noise impact degree when the train passes through the area.

[0076] This method constructs and corrects the sound source model by collecting the maximum A sound pressure level, calculates the predicted maximum A noise level when a certain train passes through each verification point based on the sound source model, and compares it with the actual value to verify the accuracy of the model. If it is accurate, the model is used in combination with the three-dimensional urban area model to draw a sound level contour map of the maximum noise impact degree, thereby providing a prediction method for the maximum noise impact degree when a train passes, and a verification method for the accuracy of the method. Therefore, compared with the conventional equivalent continuous sound level prediction method based on time period, the solution used in this technology can effectively predict the maximum noise impact degree of the train passing on the adjacent track area, which is an important supplement to the current rail transit noise prediction and evaluation methods. In addition, this method constructs a sound source model and only collects the maximum A noise level of a limited number of reference points to verify the accuracy of the constructed sound source model, rather than directly collecting the maximum A noise level over a large area to directly evaluate the maximum noise impact. This reduces work costs and workload, improves the efficiency of predicting and evaluating the maximum noise impact, avoids the huge workload and high cost of using measured data to evaluate the impact over a large area, and does not require actual installation of equipment at each evaluation scale of buildings in residential areas adjacent to the track to actually measure the maximum A noise level. Instead, the sound source model is used for calculation, thus avoiding the cost of equipment on the order of hundreds of thousands.

[0077] Reference Figure 2 Further, in some embodiments of the present invention, step S300: establishing a sound source model when a single train passes, includes:

[0078] S310: Select a sound source intensity reference point in the track-adjacent area, and record the horizontal distance and vertical distance from the sound source intensity reference point to the track centerline;

[0079] S320: Collect the maximum sound pressure level spectrum characteristics when the train passes through the sound source intensity reference point;

[0080] S330: based on noise propagation attenuation parameters at different frequencies, modifying the spectrum characteristics of the maximum sound pressure level;

[0081] S340: Calculate the maximum sound power level when the train passes according to the horizontal distance, the vertical distance and the corrected maximum sound pressure level spectrum characteristics;

[0082] S350: Establishing a sound source model when a train passes according to the maximum sound power level and the corrected maximum sound pressure level spectrum characteristics.

[0083] Specifically, in this embodiment, step S310 specifically includes: selecting a position in the track-adjacent area that is close to the track and not affected by obstructions such as sound barriers as a sound source intensity reference point, and recording the horizontal distance and vertical distance from the sound source intensity reference point to the center line of the track.

[0084] Step S320 specifically includes: collecting octave or 1 / 3 frequency band data of the A-weighted or linearly weighted maximum sound pressure level when the train passes through the sound source intensity reference point as the maximum sound pressure level spectrum characteristics when the train passes through the sound source intensity reference point.

[0085] Step S330 specifically includes: based on noise propagation attenuation parameters at different frequencies, correcting the maximum sound pressure level spectrum characteristics when the train passes, including geometric attenuation correction, atmospheric absorption correction, etc.

[0086] Step S340 specifically includes: calculating the surface area A of the sound wave passing vertically through the sound source intensity reference point based on the horizontal distance and vertical distance from the sound source intensity reference point to the center line of the track based on the commonly used geometric principles, and obtaining the maximum sound pressure level L when the train passes through the sound source intensity reference point based on the maximum sound pressure level spectrum characteristics. Amax , the surface area A and the maximum sound pressure level L Amax Substituting into the formula L W,max =L Amax +10lgA, the maximum sound power level L when the train passes the sound source intensity reference point is calculated W,max .

[0087] Step S350 specifically includes: using the maximum sound power level L W,max and the corrected maximum sound pressure level spectrum characteristics, one or more linear sound sources are established at the verification point along the center line of the track as the sound source model when the train passes. The sound source model refers to the characteristic data for physically modeling the sound source radiation characteristics of the noise source when the train passes. In this embodiment, a line sound source is used to simulate the train sound source, which includes characteristic data such as the length, height, sound power level, and spectrum characteristics of the train sound source.

[0088] Further, in some embodiments of the present invention, step S340: calculating the maximum sound power level when the train passes according to the horizontal distance, the vertical distance and the corrected maximum sound pressure level spectrum characteristics, includes:

[0089] (3.4.1) Based on the horizontal distance and vertical distance, calculate the surface area of ​​the sound wave passing vertically through the sound source intensity reference point;

[0090] (3.4.2) Obtain the maximum sound pressure level according to the maximum sound pressure level spectrum characteristics;

[0091] (3.4.3) Based on the surface area and the maximum sound pressure level, calculate the maximum sound power level.

[0092] Reference Figure 3 Further, in some embodiments of the present invention, S400: modifying the sound source model to obtain a modified sound source model includes:

[0093] (4.1) According to the actual speed of the train when it passes the verification point, the sound source model is corrected for the speed;

[0094] (4.2) According to the actual condition of the train when it passes the verification point, the sound source model is corrected.

[0095] Specifically, in this embodiment, (4.1) is calculated based on the actual speed of the train when it passes the verification point, and the maximum sound power level L of the sound source model when the train passes W,max The speed correction method is: substitute the actual speed v when the train passes the verification point and the reference speed v0 of the train into the formula C t,v =20lg(v / v 0 ), calculate the speed correction C t,v ; Then according to the formula L W,max,v =L W,max +C t,v The vehicle speed is corrected for the sound source model. In the formula, L W,max is the maximum sound power level of the sound source model before velocity correction, L W,max,v is the maximum sound power level of the sound source model after speed correction. (4.2) The sound source model is corrected for the train condition when the train passes the verification point. The correction method is as follows: (4.2.1) First, calculate the first maximum A sound pressure level L when several trains pass each verification point. Amax Average value of 1 (4.2.2) Then, the maximum sound pressure level L measured when any train passes through each verification point Amax，n , respectively, with the first average Make a difference to get several difference values ​​ΔL Amax，n ; (4.2.3) Calculate several differences ΔL Amax，n The second average As the vehicle condition correction amount C i , where n represents the nth verification point and i represents the i-th train; (4.2.4) Finally, according to the formula L W,max,i =L W,max +C i The vehicle condition is corrected for the sound source model. In the formula, L W,max is the maximum sound power level of the sound source model before vehicle condition correction, L W,max,i It is the maximum sound power level of the sound source model after correction for vehicle conditions.

[0096] Reference Figure 3Further, in some embodiments of the present invention, step (4.1): performing speed correction on the sound source model according to the actual speed of the train when the train passes the verification point, comprises:

[0097] (4.1.1) Obtain the actual speed and reference speed of the train when it passes the verification point;

[0098] (4.1.2) Calculate the speed correction value based on the actual vehicle speed and the reference vehicle speed;

[0099] (4.1.3) According to the speed correction value, the sound source model is corrected for vehicle speed.

[0100] Specifically, in this embodiment, according to the actual speed of the train when it passes the verification point, the maximum sound power level L of the sound source model when the train passes W,max The speed correction method is: substitute the actual speed v when the train passes the verification point and the reference speed v0 of the train into the formula C t,v =20lg(v / v 0 ), calculate the speed correction C t,v ; Then according to the formula L W,max,v =L W,max +C t,v The vehicle speed is corrected for the sound source model. In the formula, L W,max is the maximum sound power level of the sound source model before velocity correction, L W,max,v is the maximum sound power level of the sound source model after velocity correction.

[0101] Reference Figure 3 Further, in some embodiments of the present invention, step (4.2) corrects the vehicle condition of the sound source model according to the actual situation when the train passes through the verification point, including:

[0102] (4.2.1) Calculate the first average value of the first maximum A sound pressure level when several trains pass through each verification point;

[0103] (4.2.2) Subtract the first maximum A sound pressure level measured when any train passes through each verification point from the first average value to obtain a number of difference values;

[0104] (4.2.3) Calculate a second average value of the plurality of differences as a vehicle condition correction value;

[0105] (4.2.4) According to the vehicle condition correction value, the vehicle condition correction is performed on the sound source model.

[0106] Specifically, in this embodiment, the sound source model of the train passing through the verification point is corrected for the vehicle condition. The vehicle condition correction method is as follows: (4.2.1) First, calculate the first maximum A sound pressure level L when several trains pass through each verification point. AmaxAverage value of 1 (4.2.2) Then, the maximum sound pressure level L measured when any train passes through each verification point Amax，n , respectively, with the first average Make a difference to get several difference values ​​ΔL Amax，n ; (4.2.3) Calculate several differences ΔL Amax，n The second average As the vehicle condition correction amount C i , where n represents the nth verification point and i represents the i-th train; (4.2.4) Finally, according to the formula L W,max,i =L W,max +C i The vehicle condition is corrected for the sound source model. In the formula, L W,max is the maximum sound power level of the sound source model before vehicle condition correction, L W,max,i It is the maximum sound power level of the sound source model after correction for vehicle conditions.

[0107] Further, in some embodiments of the present invention, step S600: verifying the accuracy of the modified sound source model according to the error between the first maximum A sound pressure level and the second maximum A sound pressure level, comprises:

[0108] (6.1) Calculate the absolute error between the first maximum A sound pressure level and the second maximum A sound pressure level of each verification point;

[0109] (6.2) If all absolute errors are less than or equal to the threshold, the modified sound source model is judged to be accurate.

[0110] Specifically, in this embodiment, by calculating the first maximum A sound pressure level L of each verification point Amax 1 and predicted second maximum A sound pressure level L Amax 2 Absolute error |ΔL Amax |, to evaluate the prediction accuracy of the sound source model. The evaluation method is: if the absolute error |ΔL Amax If the value of | is less than or equal to 3dB, it means that the sound source model is accurate. If the prediction results of multiple verification points are all accurate, it means that the sound source model is universal and can be used as a noise source model. Figure 2 If the prediction result is poor or the accuracy of multiple verification points is inconsistent, it means that the sound source model is unreliable, the prediction error is relatively large, and it cannot be used as a noise source. Figure 2 3D and 4D network prediction and calculation of sound source models.

[0111] Further, in some embodiments of the present invention, step S700: drawing a sound level contour diagram of the maximum noise impact degree according to the three-dimensional urban area model and the corrected sound source model, comprises:

[0112] (7.1) Based on the modified sound source model, calculate the predicted maximum A sound pressure level at all ground 2D and building facade 3D spatial locations in the 3D urban area model;

[0113] (7.2) According to the different sound pressure levels of the predicted maximum A sound pressure level, the three-dimensional urban area model is rendered using different colors to obtain the sound level line diagram of the maximum noise impact degree.

[0114] Specifically, in this embodiment, according to the modified sound source model, the predicted maximum A sound pressure level L of all ground two-dimensional and building facade three-dimensional grid space positions in the three-dimensional urban area model is calculated. Amax , that is, the predicted noise level; according to the predicted maximum A sound pressure level L Amax The 3D urban area model is rendered using the 3DGIS engine according to different colors according to different sound pressure levels, and the sound level line diagram of the maximum noise impact degree is obtained, which is the predicted result of the maximum noise impact degree when the train passes through the area.

[0115] The present invention also provides an electronic device, comprising:

[0116] one or more processors;

[0117] Memory;

[0118] One or more programs, wherein the one or more programs are stored in a memory and are configured to be executed by one or more processors, and when the programs are executed by the processors, the steps of the method for predicting the maximum noise impact degree of a train as in any one of the above embodiments are implemented.

[0119] The present invention also proposes a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the method for predicting the maximum noise impact degree of a train as described in any one of the above embodiments are implemented.

[0120] It will be appreciated by those skilled in the art that all or part of the steps for implementing the above-mentioned embodiments can be accomplished by hardware, or by a program to instruct the relevant hardware to accomplish, and the program can be stored in a computer-readable storage medium. In the context of the present invention, the computer-readable medium can be considered to be tangible and non-temporary. Non-limiting examples of non-temporary tangible computer-readable media include non-volatile memory circuits (e.g., flash memory circuits, erasable programmable read-only memory circuits, or mask read-only memory circuits), volatile memory circuits (e.g., static random access memory circuits or dynamic random access memory circuits), magnetic storage media (e.g., analog or digital tapes or hard disk drives), and optical storage media (e.g., CDs, DVDs, or Blu-ray discs), etc. The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

[0121] In addition, although each operation is described in a specific order, this should be understood as requiring such operation to be performed in the specific order shown or in a sequential order, or requiring that all illustrated operations should be performed to obtain desired results. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be interpreted as limiting the scope of the present invention. Some features described in the context of a separate embodiment can also be implemented in a single implementation in combination. On the contrary, the various features described in the context of a single implementation can also be implemented in multiple implementations individually or in any suitable sub-combination mode.

[0122] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge scope of ordinary technicians in the technical field without departing from the purpose of the present invention.

Claims

1. A method for predicting the maximum noise impact of a train, characterized in that: include: Select several positions corresponding to different vehicle speeds in the adjacent track area as verification points, and collect the first maximum A sound pressure level when a single train passes through each verification point; Build a three-dimensional urban area model based on the geospatial data of the area adjacent to the railway; Establish a sound source model for a single train passing through; Modifying the sound source model to obtain a modified sound source model; Calculating the second maximum A sound pressure level when a single train passes through each verification point according to the modified sound source model; verifying the accuracy of the modified sound source model according to an error between the first maximum A sound pressure level and the second maximum A sound pressure level; A sound level line diagram of the maximum noise impact degree is drawn based on the three-dimensional urban area model and the corrected sound source model.

2. The method for predicting the maximum noise impact of a train according to claim 1, characterized in that: The step of establishing a sound source model when a single train passes by includes: Select a sound source intensity reference point in the track-adjacent area, and record the horizontal distance and vertical distance from the sound source intensity reference point to the track centerline; Collecting the maximum sound pressure level spectrum characteristics when the train passes through the sound source intensity reference point; Based on noise propagation attenuation parameters at different frequencies, the maximum sound pressure level spectrum characteristics are corrected; Calculating the maximum sound power level when a train passes according to the horizontal distance, the vertical distance and the corrected maximum sound pressure level spectrum characteristics; A sound source model when a train passes by is established according to the maximum sound power level and the corrected spectrum characteristics of the maximum sound pressure level.

3. The method for predicting the maximum noise impact of a train according to claim 2, characterized in that: The calculating, according to the horizontal distance, the vertical distance and the corrected maximum sound pressure level spectrum characteristics, the maximum sound power level when the train passes, comprises: Calculate the surface area of ​​the sound wave passing vertically through the sound source intensity reference point according to the horizontal distance and the vertical distance; Obtaining a maximum sound pressure level according to the maximum sound pressure level spectrum characteristics; The maximum sound power level is calculated according to the surface area and the maximum sound pressure level.

4. The method for predicting the maximum noise impact of a train according to claim 1, characterized in that: The step of correcting the sound source model to obtain a corrected sound source model includes: According to the actual speed of the train when passing through the verification point, the sound source model is corrected for the speed; The sound source model is modified according to the actual vehicle condition when the train passes through the verification point.

5. The method for predicting the maximum noise impact of a train according to claim 4, characterized in that: The performing speed correction on the sound source model according to the actual speed of the train when the train passes through the verification point includes: Obtaining the actual speed and reference speed of the train when it passes the verification point; Calculating a speed correction amount according to the actual vehicle speed and the reference vehicle speed; The vehicle speed is corrected for the sound source model according to the speed correction amount.

6. The method for predicting the maximum noise impact of a train according to claim 4, characterized in that: The step of modifying the sound source model according to the actual situation when the train passes through the verification point includes: Calculating a first average value of the first maximum A sound pressure level when a plurality of trains pass through the verification points; Subtracting the first maximum sound pressure level A measured when any train of the train passes through each verification point from the first average value to obtain a plurality of difference values; Calculating a second average value of the plurality of differences as a vehicle condition correction value; The vehicle condition correction is performed on the sound source model according to the vehicle condition correction amount.

7. The method for predicting the maximum noise impact of a train according to claim 1, characterized in that: The verifying the accuracy of the modified sound source model according to the error between the first maximum A sound pressure level and the second maximum A sound pressure level includes: Calculate the absolute error between the first maximum A sound pressure level and the second maximum A sound pressure level of each verification point; If all the absolute errors are less than or equal to the threshold, it is determined that the modified sound source model is accurate.

8. The method for predicting the maximum noise impact of a train according to claim 1, characterized in that: The step of drawing a sound level line diagram of the maximum noise impact degree according to the three-dimensional urban area model and the corrected sound source model comprises: Calculate the predicted maximum A sound pressure level at all two-dimensional ground and three-dimensional building facade spatial positions in the three-dimensional urban area model according to the modified sound source model; According to different sound pressure levels of the predicted maximum sound pressure level A, the three-dimensional urban area model is drawn and rendered using different colors to obtain a sound level line diagram of the maximum noise impact degree.

9. An electronic device, characterized in that: include: one or more processors; Memory; One or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and when the programs are executed by the processors, the steps of the method for predicting the maximum noise impact degree of a train as described in any one of claims 1-8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for predicting the maximum noise impact degree of a train as described in any one of claims 1 to 8 are implemented.

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