Urban road traffic noise analysis method and system

Abstract

The invention discloses an urban road traffic noise analysis method and system, and relates to the technical field of noise analysis. Comprising the steps of setting noise monitoring equipment and continuously collecting road traffic noise data; constructing a noise annoyance degree prediction model, and predicting the noise annoyance degree of residents in each region; the high annoyance rate of each region is counted, a dynamic time-phased high annoyance rate calculation method is adopted, different time periods are analyzed respectively, and a time correction term and a region condition correction term are introduced; and proposing suggestions and measures for urban road traffic noise treatment based on the noise annoyance degree prediction model and the high annoyance rate calculation result. According to the invention, accurate adjustment can be carried out according to noise levels and regional conditions in different time periods, and a basis with higher reference value is provided for noise prevention and treatment decisions.

Description

Technical Field

[0001] The present invention relates to the technical field of noise analysis, and in particular to a method and system for analyzing urban road traffic noise. Background Art

[0002] In recent years, the problem of urban environmental noise pollution has received increasing attention from all sectors of society, and noise pollution prevention and control has become an indispensable part of ecological civilization construction and environmental protection. As the main noise source affecting the quality of urban sound environment, road traffic noise has the characteristics of wide coverage, high noise intensity, and high difficulty in control. It seriously affects the quality of life of residents and is frequently complained and reported by residents. Road traffic noise prevention and control has become a top priority for improving the quality of urban sound environment and building a "quiet home".

[0003] Existing technologies lack an accurate annoyance evaluation mechanism during noise analysis. Residents’ perception of traffic noise varies greatly, and residents in different time periods and different areas are also affected by noise in different ways. Traditional methods are difficult to accurately capture this dynamic change. Most existing technologies only evaluate the level of urban noise pollution through static analysis of noise data, and lack consideration of residents’ subjective noise annoyance. Noise control lacks personalized and detailed means, and many control solutions cannot meet the specific needs of residents in actual operations, which seriously limits the effectiveness of existing technologies in practical applications. Summary of the invention

[0004] In view of the existing problems in noise analysis, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is how to evaluate the noise impact in different time periods and areas.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: In the first aspect, an embodiment of the present invention provides a method for analyzing urban road traffic noise, which includes setting up noise monitoring equipment to continuously collect road traffic noise data; constructing a noise annoyance prediction model to predict the noise annoyance of residents in each area; counting the high annoyance rate of each area, using a dynamic time-divided high annoyance rate calculation method, analyzing different time periods respectively, and introducing time correction terms and regional condition correction terms; based on the noise annoyance prediction model and the high annoyance rate calculation results, proposing suggestions and measures for urban road traffic noise control.

[0007] As a preferred solution of the urban road traffic noise analysis method of the present invention, the construction of the noise annoyance prediction model to predict the noise annoyance of residents in each area includes the following steps: obtaining the noise time series data of the monitoring point and extracting the noise characteristic values ​​of different time periods; collecting population structure data and dividing the area according to population density to generate a regional population characteristic set; ; Combine the noise feature value with the population feature set, establish a preliminary correspondence between noise and population characteristics according to different time periods t and regions x, and form an input data matrix: ; Use fuzzy neural network to analyze the data matrix Based on the noise level and crowd sensitivity in different time periods, a noise annoyance prediction model is trained, where the output of the fuzzy neural network is the noise annoyance ; Optimize the noise annoyance prediction model, calculate the annoyance scores of different resident groups in a specific noise environment, and obtain the noise annoyance scores of residents in each area; according to the noise annoyance scores and time series changes, make time corrections to the noise annoyance of each area to obtain dynamic prediction results of noise annoyance.

[0008] As a preferred solution of the urban road traffic noise analysis method of the present invention, wherein: the noise characteristic values ​​extracted in different time periods include: A sound level The formula is:

[0009] in, is the noise pressure level at time t, T is the length of the time period; day and night equivalent sound level The formula is:

[0010] in, and Represents the A sound level during the day and at night respectively.

[0011] As a preferred solution of the urban road traffic noise analysis method of the present invention, the calculation process of the annoyance score is as follows:

[0012] in, , , All are regression coefficients; is a time-related correction term; the noise annoyance degree of each area is corrected for time, wherein the dynamic noise annoyance degree prediction formula is:

[0013] in, is the time variation, is the time correction factor.

[0014] As a preferred solution of the urban road traffic noise analysis method of the present invention, the method for calculating the high annoyance rate using dynamic time periods includes the following steps: dividing 24 hours a day into a night rest period, a day rest period and a day work period; fitting the slope of the straight line between the high annoyance rate of residents in the night rest period and the high annoyance rate in the work period Take 3, and fit the slope of the straight line of the difference between the high annoyance rate during the daytime rest period and the work period Take 1 and get the time correction term as follows:

[0015] in, It is the correction item for the high annoyance rate time; is the correction rate.

[0016] As a preferred solution of the urban road traffic noise analysis method of the present invention, the regional condition correction item includes the following contents: the regional condition correction item is composed of the sound function zoning and the regional resident age structure; the resident age structure correction amount is shown in the following formula:

[0017] in, is the correction item for the age structure of high annoyance rate; m is the regional sensitive population; the correction amount of the acoustic function zoning is shown in the following formula:

[0018]

[0019] in, It is the correction value of acoustic function zoning when the area to be predicted is a Class 1 acoustic function zone; It is the correction value of the sound function zoning when the area to be predicted is a Class 4a sound function zone; L is the continuous equivalent A sound level of road noise.

[0020] As a preferred solution of the urban road traffic noise analysis method of the present invention, the corrected high annoyance rate model is as follows: .

[0021] In the second aspect, an embodiment of the present invention provides an urban road traffic noise analysis system, which includes: a noise monitoring module, which is used to set up noise monitoring equipment and continuously collect road traffic noise data; a noise annoyance prediction module, which is used to construct a noise annoyance prediction model to predict the noise annoyance of residents in each area; a high annoyance rate statistical analysis module, which is used to count the high annoyance rate of each area, adopt a dynamic time-divided high annoyance rate calculation method, analyze different time periods respectively, and introduce time correction items and regional condition correction items; a noise control suggestion generation module, which is used to propose suggestions and measures for urban road traffic noise control based on the noise annoyance prediction model and the high annoyance rate calculation results, and summarize the analysis results.

[0022] In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the urban road traffic noise analysis method as described in the first aspect of the present invention are implemented.

[0023] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the urban road traffic noise analysis method as described in the first aspect of the present invention are implemented.

[0024] The beneficial effects of the present invention are as follows: the present invention not only focuses on the physical intensity of noise, but also introduces a subjective evaluation index - annoyance, and combines regional characteristics, time characteristics and other factors to dynamically predict the noise annoyance of residents in different regions and different time periods. Compared with the existing static noise analysis method, the prediction model constructed by the present invention can more accurately reflect the actual feelings of residents about traffic noise. In addition, the dynamic calculation method of the high annoyance rate of the present invention can be accurately adjusted according to the noise level and regional conditions in different time periods, providing a more valuable reference basis for noise prevention and control decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0026] Figure 1 Flow chart of the urban road traffic noise analysis method.

[0027] Figure 2 Comparison of fitting curves for high annoyance rate (measured data and EU data).

[0028] Figure 3It is the difference between the high worry rate during rest time and working time.

[0029] Figure 4 It is the difference of high annoyance rate in different areas.

[0030] Figure 5 Forecast the noise level and high annoyance rate accepted by residents. DETAILED DESCRIPTION

[0031] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0032] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0033] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0034] Example 1 Reference Figure 1~Figure 4 , which is the first embodiment of the present invention, and provides a method for analyzing urban road traffic noise, such as Figure 1 As shown, including, S1: Noise monitoring equipment is installed at multiple monitoring points around urban roads to continuously collect road traffic noise data, including the continuous equivalent sound level of noise in different time periods.

[0035] First, according to the characteristics of urban roads, areas with strong noise sources (such as main roads with heavy traffic, intersections, congested sections, etc.) are selected to set up noise monitoring points. At the same time, considering the comprehensiveness of monitoring, it is necessary to evenly arrange monitoring points at different locations such as both sides of the road, near residential areas, commercial areas, green belts, etc., in order to obtain the noise impact of different areas.

[0036] The number and distribution of monitoring points can be reasonably determined based on the length of the road, the height and density of surrounding buildings, and the population density. For example, a noise monitoring point can be set up every 500 meters to 1 kilometer. More frequent monitoring points can be set up in special areas (such as densely populated residential areas, hospitals, schools, etc.).

[0037] Use noise monitoring instruments that comply with national standards (such as GB 3096 "Noise Environment Quality Standard"). The equipment needs to be able to continuously measure A-level noise data and record the time series of noise to analyze noise changes in different time periods. The dynamic range of the equipment should cover the common sound levels of road traffic noise (50-100 dB(A)).

[0038] The noise data collected include the continuous equivalent A-level of noise at different time periods. and day and night equivalent sound levels .

[0039] S2: Based on noise data and regional population structure and other factors, a noise annoyance prediction model is constructed to predict the noise annoyance level of residents in each area.

[0040] The noise annoyance prediction model takes into account multiple influencing factors (such as sound level, time, resident age, etc.) and uses fuzzy neural network or regression analysis to calculate the noise annoyance of individual residents.

[0041] S2.1: Based on the noise data collected in step S1, obtain the noise time series data of the monitoring point and extract the noise characteristic values ​​of different time periods, including the A sound level. and day and night equivalent sound levels , the specific formula is as follows:

[0042] in, is the noise pressure level at time t, and T is the length of the time period.

[0043] Day and night equivalent sound level The formula is:

[0044] in, and Represents the A sound level during the day and at night respectively.

[0045] S2.2: Collect population structure data, divide regions according to population density, and generate regional population characteristic sets , represents the population characteristics of region x.

[0046] S2.3: Combine the noise feature value with the population feature set, establish a preliminary correspondence between noise and population characteristics according to different time periods t and regions x, and form an input data matrix: .

[0047] S2.4: Using Fuzzy Neural Networks to Analyze Data Matrix Processing is performed to train a noise annoyance prediction model based on noise levels and crowd sensitivity in different time periods, including: The fuzzy rule is defined as: if Higher and The higher the proportion of sensitive people, the higher the level of annoyance.

[0048] The output of the fuzzy neural network is the noise annoyance .

[0049] S2.5: Introduce regression analysis method to optimize the noise annoyance prediction model, calculate the annoyance scores of different resident groups in a specific noise environment, and obtain the noise annoyance scores of residents in each area.

[0050] Preferably, the calculation process of the annoyance score is as follows:

[0051] in, , , All are regression coefficients; is a time-related correction term, and the noise annoyance score of residents in each area is calculated based on area x and time t.

[0052] S2.6: Based on the noise annoyance score and the time series changes, the noise annoyance of each area is time-corrected to obtain a dynamic prediction result of the noise annoyance.

[0053] Specifically, according to the noise annoyance score of residents in the area The dynamic prediction result of noise annoyance is obtained by performing time correction based on the time series change. The dynamic noise annoyance prediction formula is:

[0054] in, is the time variation, It is the time correction factor, which reflects the changing trend of noise annoyance in different time periods.

[0055] S3: Count the high annoyance rate in each area. Taking into account the differences in noise impact in different time periods, a dynamic time-division high annoyance rate calculation method is adopted to analyze the daytime working period, daytime rest period and nighttime rest period respectively, and introduce time correction items and regional condition correction items.

[0056] S3.1: Calculate the high annoyance rate of residents in different areas.

[0057] The high annoyance rate curve of the present invention is fitted according to the logistic function specified in the "Evaluation and Prediction Method of Acoustic Noise Annoyance Degree" (GB / T 42473-2023), and the formula is as follows:

[0058] Among them, HA% is the high annoyance rate of residents in the area, and L is the continuous equivalent sound level of road noise.

[0059] Comparing the high annoyance rate curve of residents obtained by fitting the results of the residents’ annoyance survey in this study with the high annoyance rate fitting curve officially released by the European Commission, similar noise-high annoyance rate dose curves and change trends were found. Therefore, this study continued to use the 10% high annoyance rate threshold. Roads or areas with a high annoyance rate exceeding 10% can be regarded as high annoyance areas that require governance measures, such as Figure 2 shown.

[0060] S3.2: Divide the time periods, calculate the high annoyance rate for each time period separately, and apply the time correction term to make adjustments based on the noise level in different time periods.

[0061] Among them, at lower sound levels, the noise annoyance of residents in different time periods does not change much; when the noise level reaches 64dB(A) or above, the annoyance of residents in the morning, afternoon and evening rest periods gradually increases compared to other time periods, and the greater the noise level, the more significant this difference is. Therefore, the time correction term should follow the following principles: When the sound level is lower than 64dB(A), the correction term should be close to 0; When the sound level reaches 64dB(A) and above, the correction term should increase as the sound level increases; In order to simplify the calculation, the 24 hours a day are divided into the evening rest period, the daytime rest period and the daytime working period. Taking the daytime working period as the basis, the correction item within the daytime working period should be close to 0.

[0062] Draw the difference curve of annoyance between rest period and working period above 63dB(A) Figure 3 As shown. Figure 3 It can be seen that as the sound level increases, the effect of time on the high annoyance rate of residents changes roughly linearly. According to the fitting, the slope of the fitting line of the difference between the high annoyance rate of residents during the evening rest period and the high annoyance rate during the working period is is 3.1674, and the slope of the fitting line of the difference between the high annoyance rate during the daytime rest period and the working period is is 0.8846. Therefore, according to the principle of convenient approximation, Approximately 3, Approximately taking 1, the time correction term is as follows:

[0063] in, It is the correction item for the high annoyance rate time; is the correction rate, and according to the fitting results, it is taken as follows:

[0064] Wherein, L is the continuous equivalent A-sound level of road noise.

[0065] Night rest period: the period from 22:00 at night to 6:00 in the morning of the next day; Daytime rest periods: 6:00~8:00, 12:00~14:00, 20:00~22:00; Daytime working hours: 8:00~12:00 and 14:00~20:00.

[0066] S3.3: Calculate regional condition corrections based on regional conditions, including acoustic functional zoning and age structure.

[0067] The regional condition correction item is mainly composed of acoustic functional zoning and the age structure of regional residents. The high annoyance curves of regions with the same functional zoning but different age compositions are similar in shape, and the differences in high annoyance rates mainly come from the left and right shifts of the curves; while the regional differences in different acoustic functional zoning with the same age composition are similar to the differences caused by time.

[0068] Based on this concept, in terms of the age composition of regional residents, the impact caused by the age structure of residents is similar to the sound level impact of 6 dB(A), so the correction amount of the age structure of residents is shown as follows:

[0069] in, is the correction item for the age structure of high worry rate; m is the proportion of sensitive population in the area (residents under 18 years old and over 50 years old).

[0070] Furthermore, in terms of acoustic function zoning, the commonly used Class 2 acoustic function zones are used as the benchmark, and the difference in high annoyance rate between Class 1, Class 4a and Class 2 zones is calculated, and a curve is drawn as follows: Figure 4 shown.

[0071] Depend on Figure 4 It can be seen that the difference in high annoyance rate between different areas increases linearly between 60 and 70 dB(A), reaches a peak at 70 dB(A), and then slowly decreases. Considering that 70 dB(A) is the minimum standard that road noise should meet, only the part below 70 dB(A) is considered based on linear fitting.

[0072] According to the fitting results, the correction value of acoustic function zoning can be obtained as shown in the following formula:

[0073]

[0074] in, It is the correction value of acoustic function zoning when the area to be predicted is a Class 1 acoustic function zone; It is the correction value of the sound function zoning when the area to be predicted is a Class 4a sound function zone; L is the continuous equivalent A sound level of road noise.

[0075] S3.4: Determine the corrected high annoyance rate model as follows:

[0076] S4: Based on the noise annoyance prediction model and the high annoyance rate calculation results, put forward suggestions and measures for urban road traffic noise control, and summarize the analysis results.

[0077] Specifically, based on the noise annoyance prediction model established in S2 and the high annoyance rate statistics in S3, the noise annoyance and high annoyance rate data of each area are summarized to generate a regional noise impact analysis report, with key governance areas where the high annoyance rate exceeds a threshold (such as 10%) being marked.

[0078] Based on the high annoyance rate statistics in S3 and the annoyance degree prediction results for each time period in S2, analyze whether the current noise control measures (such as speed limit, sound insulation facilities, etc.) have achieved the expected results. If the high annoyance rate area is still large, it means that the existing measures are insufficient and need further optimization and adjustment.

[0079] Furthermore, based on the feedback results, the existing noise control strategies can be optimized. For example, more targeted control measures can be taken for areas with high annoyance rates, such as adding soundproof walls, adjusting traffic flow, or implementing noise control in specific areas at night. Different noise management policies can be implemented according to regional characteristics, focusing on areas with more noise sources and dense populations.

[0080] Furthermore, this embodiment also provides an urban road traffic noise analysis system, comprising: The noise monitoring module is used to set up noise monitoring equipment and continuously collect road traffic noise data. The noise annoyance prediction module is used to build a noise annoyance prediction model to predict the noise annoyance of residents in each area. The high annoyance rate statistical analysis module is used to calculate the high annoyance rate of each area, using a dynamic time-divided high annoyance rate calculation method to analyze different time periods, and introduce time correction items and regional condition correction items. The noise control suggestion generation module is used to propose suggestions and measures for urban road traffic noise control based on the noise annoyance prediction model and high annoyance rate calculation results, and summarize the analysis results.

[0081] This embodiment also provides a computer device, which is applicable to the case of the urban road traffic noise analysis method, and includes a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions to implement the urban road traffic noise analysis method proposed in the above embodiment.

[0082] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0083] This embodiment further provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for analyzing urban road traffic noise proposed in the above embodiment is implemented.

[0084] In summary, the present invention not only focuses on the physical intensity of noise, but also introduces a subjective evaluation index - annoyance, and combines regional characteristics, time characteristics and other factors to dynamically predict the noise annoyance of residents in different regions and different time periods. Compared with the existing static noise analysis method, the prediction model constructed by the present invention can more accurately reflect the actual feelings of residents about traffic noise. In addition, the dynamic calculation method of the high annoyance rate of the present invention can be accurately adjusted according to the noise level and regional conditions in different time periods, providing a more valuable reference basis for noise prevention and control decisions.

[0085] Example 2 Reference Figure 5 , which is the second embodiment of the present invention, and this embodiment provides a method for analyzing urban road traffic noise. In order to verify the beneficial effects of the present invention, a scientific demonstration is carried out through simulation experiments.

[0086] There is noise nuisance in the neighborhood around a road. In order to understand the impact of traffic noise generated by the road on the surrounding residents and formulate targeted noise reduction measures, the high annoyance rate of residents around the road is predicted. It is known that the building with complaints is 30m away from the road boundary line and is classified as a Class 2 acoustic environment functional area. There are many noise-sensitive residents such as middle-aged and elderly people and minors.

[0087] The noise generated by the road in one day was monitored and the noise propagation attenuation was predicted to be about 11dB(A) based on the distance between the building and the road. The road noise change curve received by residents was obtained by subtracting the distance between the building and the road.

[0088] According to the "Noise Environment Quality Standard", the noise limit in this area is 60dB(A) during the day and 50dB(A) at night. The period of exceeding the standard is from 11:00 a.m. to 6:00 a.m. the next day, with the maximum exceeding the standard by 9 dB(A) during the day and 17 dB(A) at night.

[0089] The high annoyance rate of residents in this community is calculated using the prediction formula for high annoyance rate of residents. The results are as follows: Figure 5 As shown: It can be seen that the period of exceeding the standard is from 10 am to 3 am the next day, the maximum high annoyance rate during the day is about 30%, and the maximum high annoyance rate at night is 60%, which is around 22:00 to 2:00 am. It can be seen that the noise reduction countermeasure should be formulated with the night noise at 22:00 as the noise reduction control point, and the noise reduction amount is 11 dB(A). Finally, the maximum value of the noise limit noise reduction amount and the high annoyance rate noise reduction amount is selected as the target noise reduction amount.

[0090] Taking into account the changes in road traffic noise at different heights of buildings, for high-rise buildings, it is recommended to predict the high noise annoyance rate by region based on the noise distribution. In the area between 6 and 10 floors, 4dB(A) can be added to the ground noise intensity for prediction.

[0091] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for analyzing urban road traffic noise, characterized in that: include, Install noise monitoring equipment to continuously collect road traffic noise data; Construct a noise annoyance prediction model to predict the noise annoyance of residents in each area; Statistics on the high annoyance rate in each area, using a dynamic time-division high annoyance rate calculation method, analyzing different time periods separately, and introducing time correction items and regional condition correction items; Based on the noise annoyance prediction model and high annoyance rate calculation results, suggestions and measures for urban road traffic noise control are put forward.

2. The urban road traffic noise analysis method according to claim 1, characterized in that: The construction of the noise annoyance prediction model to predict the noise annoyance of residents in each area includes the following steps: Obtain the noise time series data of the monitoring point and extract the noise characteristic values ​​in different time periods; Collect population structure data, divide regions according to population density, and generate regional population characteristic sets ; The noise characteristic values ​​are combined with the population characteristic set, and the preliminary correspondence between noise and population characteristics is established according to different time periods t and regions x to form the input data matrix: ; Using fuzzy neural network to train data matrix Based on the noise level and crowd sensitivity in different time periods, a noise annoyance prediction model is trained, where the output of the fuzzy neural network is the noise annoyance ; The noise annoyance prediction model was optimized to calculate the annoyance scores of different resident groups in a specific noise environment, and the noise annoyance scores of residents in each area were obtained; According to the noise annoyance score and time series changes, the noise annoyance of each area is time-corrected to obtain the dynamic prediction result of the noise annoyance.

3. The urban road traffic noise analysis method according to claim 2, characterized in that: The extracting of noise characteristic values ​​in different time periods comprises: A-sound level The formula is: in, is the noise pressure level at time t, T is the length of the time period; Day and night equivalent sound level The formula is: in, and Represents the A sound level during the day and at night respectively.

4. The urban road traffic noise analysis method according to claim 3, characterized in that: The calculation process of the annoyance score is as follows: in, , , All are regression coefficients; is the time-related correction term; The noise annoyance level of each area is corrected over time, wherein the dynamic noise annoyance level prediction formula is: in, is the time variation, is the time correction factor.

5. The urban road traffic noise analysis method according to claim 4, characterized in that: The method for calculating the high annoyance rate using dynamic time periods comprises the following steps: Divide the 24 hours of a day into night rest period, daytime rest period and daytime work period; The slope of the straight line fitting the difference between the high annoyance rate of residents during the evening rest period and the high annoyance rate during the working period Take 3, and fit the slope of the straight line of the difference between the high annoyance rate during the daytime rest period and the work period Take 1 and get the time correction term as follows: in, It is the correction item for the high annoyance rate time; is the correction rate.

6. The urban road traffic noise analysis method according to claim 5, characterized in that: The regional status modifiers include the following: The regional condition correction item is composed of the acoustic function zoning and the age structure of regional residents; The resident age structure correction amount is as follows: in, is the correction item for the age structure of high trouble rate; m is the sensitive population in the region; The correction amount of the acoustic function zoning is shown in the following formula: in, It is the correction value of acoustic function zoning when the area to be predicted is a Class 1 acoustic function zone; It is the correction value of the sound function zoning when the area to be predicted is a Class 4a sound function zone; L is the continuous equivalent A sound level of road noise.

7. The urban road traffic noise analysis method according to claim 6, characterized in that: The corrected high annoyance rate model is as follows: 。 8. An urban road traffic noise analysis system, based on the urban road traffic noise analysis method according to any one of claims 1 to 7, characterized in that: Also includes, Noise monitoring module, used to set up noise monitoring equipment and continuously collect road traffic noise data; A noise annoyance prediction module is used to construct a noise annoyance prediction model to predict the noise annoyance of residents in each area; The high annoyance rate statistical analysis module is used to count the high annoyance rate in each area. It adopts a dynamic time-divided high annoyance rate calculation method to analyze different time periods and introduces time correction items and regional condition correction items. The noise control suggestion generation module is used to put forward suggestions and measures for urban road traffic noise control based on the noise annoyance prediction model and high annoyance rate calculation results, and summarize the analysis results.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the urban road traffic noise analysis method according to any one of claims 1 to 7 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 urban road traffic noise analysis method according to any one of claims 1 to 7 are implemented.

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