A method and system for dynamic visual monitoring of noise

Abstract

The application discloses a noise dynamic visual monitoring method and system, and relates to the technical field of noise monitoring. The method comprises the following steps: acquiring the position coordinates of noise monitoring points in a to-be-monitored area, and collecting noise intensity sequences of the noise monitoring points in real time; acquiring the position coordinates and noise intensity of an estimated noise source based on the noise intensity sequences; dividing the to-be-monitored area into a plurality of sub-areas, and acquiring a target area of a to-be-interpolated position; calculating the interpolation weight of each noise monitoring point and the estimated noise source in the target area based on the position coordinates of the noise monitoring points and the estimated noise source; performing bimodal interpolation on the to-be-interpolated position to obtain the noise intensity of the to-be-interpolated position; and mapping the noise intensity of each to-be-interpolated position with a color to obtain a noise intensity heat map of the to-be-monitored area. The application can accurately interpolate the noise intensity of unknown positions, enhance the accuracy of noise monitoring, and realize the dynamic visual effect of noise monitoring.

Description

Technical Field

[0001] This invention relates to the field of noise monitoring technology, and in particular to a dynamic visualization monitoring method and system for noise. Background Technology

[0002] With the acceleration of urbanization and the increase in human activities, noise pollution has become an increasingly serious environmental problem in areas such as transportation planning and industrial supervision. Prolonged exposure to noise can cause not only irritability, poor sleep quality, and dizziness, but also heart disease, learning disabilities, and tinnitus. Therefore, monitoring and controlling urban noise pollution is both necessary and of practical significance. Noise monitoring is the activity of monitoring sounds and their sources that interfere with people's learning, work, and life. This includes noise monitoring in various functional areas of the city, road traffic noise monitoring, regional environmental noise monitoring, and noise source monitoring. Noise monitoring results are generally expressed as weighted sound levels, and the main instruments used are sound level meters and spectrum analyzers. The results of noise monitoring are used to analyze the current status and changing trends of noise pollution, and also provide basic data for the planning, management, and comprehensive control of noise pollution.

[0003] Traditional noise monitoring methods typically rely on a limited number of fixed monitoring points, making it impossible to provide real-time feedback on noise changes across different areas. Furthermore, since noise sensors are usually distributed in various locations, existing technologies fuse and interpolate scattered monitoring data to estimate the noise distribution across the entire area. However, most traditional noise monitoring methods employ Inverse-Distance-Weighting (IDW) spatial interpolation, relying solely on the distance between the interpolation location and the noise sensor, neglecting the influence of noise source distribution and sensor density differences within the monitoring area. This results in interpolation results that cannot exceed the maximum noise intensity measured by the sensor, leading to an underestimation of noise intensity in areas near the noise source. Consequently, traditional noise monitoring methods are prone to significant deviations in noise intensity distribution, resulting in poor accuracy. Moreover, traditional noise monitoring methods cannot directly observe noise intensity, making them less intuitive. Summary of the Invention

[0004] To accurately interpolate noise intensity at unknown locations, enhance the precision of noise monitoring, achieve dynamic visualization of noise monitoring, and address the shortcomings of traditional noise monitoring methods that often exhibit significant deviations in noise intensity distribution during spatial interpolation, this invention provides a dynamic visualization monitoring method and system for noise. The technical solution is as follows:

[0005] In a first aspect, the present invention provides a dynamic visualization monitoring method for noise, comprising the following steps: acquiring the location coordinates of noise monitoring points within a monitoring area, and collecting noise intensity sequences of each noise monitoring point in real time; clustering the noise intensity sequences to obtain several clusters, setting an equal number of particles according to the number of clusters, iteratively updating the parameters of the particles, and obtaining the location coordinates and noise intensity of several estimated noise sources; dividing the monitoring area into several sub-regions to obtain the target area for interpolation; calculating the interpolation weights of each noise monitoring point and estimated noise source within the target area based on the location coordinates of the noise monitoring points and estimated noise sources; performing bimodal interpolation on the interpolation location based on the interpolation weights of the noise monitoring points and estimated noise sources to obtain the noise intensity of the interpolation location; mapping the noise intensity of each interpolation location to a color to obtain a noise intensity heatmap of the monitoring area at the current time;

[0006] Specifically, based on the location coordinates of noise monitoring points and estimated noise sources within the target area, the density interpolation weights and distance interpolation weights of all noise monitoring points and estimated noise sources within the target area are calculated and obtained; the product of the density interpolation weight and distance interpolation weight of each noise monitoring point is used as the interpolation weight of the corresponding noise monitoring point, and the interpolation weight of each estimated noise source is obtained in the same way.

[0007] Preferably, multiple locations are randomly or equidistantly selected as noise monitoring points within the same horizontal plane of the area to be monitored, based on the actual scenario. A noise sensor is placed at each noise monitoring point. A two-dimensional plane coordinate system is established based on the area to be monitored, and the position coordinates of each noise monitoring point are recorded. The noise intensity of the noise monitoring points is collected in real time through the noise sensor, and the noise intensity data within a preset time period is used as the noise intensity sequence of the corresponding noise monitoring point at the current moment.

[0008] Preferably, all noise intensity sequences are differentially processed to obtain a differential sequence corresponding to each noise monitoring point. The DBSCAN algorithm is used to cluster the differential sequences to obtain several clusters. Several location points in the monitoring area are randomly selected as particles according to the number of clusters. The number of particles is the same as the number of clusters. Each particle corresponds to an estimated noise source. The initial parameters of the particles are obtained and set, including the initial position coordinates and the initial noise intensity. The set initial noise intensity is greater than or equal to the maximum value in all noise intensity sequences.

[0009] Preferably, based on the distance between the noise monitoring point and the particle, the ideal attenuated noise intensity of each particle at each noise monitoring point is calculated using the point source noise attenuation formula. Based on the principle of multi-noise decibel superposition, the ideal superimposed noise intensity at each noise monitoring point is calculated. Based on the noise intensity sequence and ideal superimposed noise intensity collected in real time from the noise monitoring point, the objective function of particle swarm optimization is constructed. The initial parameters of the particles are optimized using the particle swarm optimization algorithm. The initial parameters of the particles are iteratively updated to obtain the optimized parameters of all particles, and then the location coordinates and noise intensity of all estimated noise sources are obtained.

[0010] Preferably, the area to be monitored is divided into multiple sub-regions of equal size. For a certain interpolation location, the sub-region where the interpolation location is located is merged with all its adjacent sub-regions and is called the target area of ​​the interpolation location. Each target area of ​​the interpolation location contains at least one noise monitoring point and one estimated noise source.

[0011] Preferably, the location coordinates of all noise monitoring points and estimated noise sources within the target area are obtained. The average Euclidean distance between a noise monitoring point and the other noise monitoring points within the target area is taken as the average distribution distance of the noise monitoring point. The average Euclidean distance between a noise monitoring point and the locations of all estimated noise sources within the target area is taken as the average monitoring distance of the noise monitoring point. The ratio between the average distribution distance and the average monitoring distance is taken as the density interpolation weight of the noise monitoring point. Similarly, the average Euclidean distance between a noise source and the other estimated noise sources within the target area is taken as the average distribution distance of the estimated noise source. The average Euclidean distance between the noise source and the locations of all noise monitoring points within the target area is taken as the average emission distance of the estimated noise source. The ratio between the average distribution distance and the average emission distance is taken as the density interpolation weight of the estimated noise source. The density interpolation weights of all noise monitoring points and estimated noise sources within the target area are calculated sequentially for each noise monitoring point and estimated noise source.

[0012] Preferably, the Euclidean distance between each noise monitoring point location and the location to be interpolated within the target area is calculated, and the square of the Euclidean distance corresponding to each noise monitoring point is used as the distance interpolation weight for that noise monitoring point. Similarly, the Euclidean distance between each estimated noise source location and the location to be interpolated within the target area is calculated, and the square of the Euclidean distance corresponding to each estimated noise source is used as the distance interpolation weight for that estimated noise source. By combining the density interpolation weights and distance interpolation weights of the noise monitoring points and estimated noise sources, the interpolation weights for each noise monitoring point and estimated noise source within the target area are obtained.

[0013] Preferably, the ratio of the interpolation weight of each noise monitoring point in the target area to the sum of the interpolation weights of all noise monitoring points in the target area is used as the normalized interpolation weight of the corresponding noise monitoring point. Similarly, the ratio of the interpolation weight of each estimated noise source in the target area to the sum of the interpolation weights of all estimated noise sources in the target area is used as the normalized interpolation weight of the corresponding estimated noise source. The product of the normalized interpolation weight of each noise monitoring point in the target area and the average value of the corresponding noise intensity sequence is used as the weight component of each noise monitoring point. The weight components of all noise monitoring points in the target area are accumulated to obtain the noise monitoring point interpolation value at the corresponding interpolation location. Similarly, the product of the normalized interpolation weight of each estimated noise source in the target area and the noise intensity of the corresponding estimated noise source is used as the weight component of each estimated noise source. The weight components of all estimated noise sources in the target area are accumulated to obtain the estimated noise source interpolation value at the corresponding interpolation location. Weights are set for the noise monitoring point interpolation and the estimated noise source interpolation. The sum of the products of the noise monitoring point interpolation and the estimated noise source interpolation with their respective weights is used as the noise intensity at the corresponding interpolation location.

[0014] Preferably, based on the actual requirements for noise intensity in the scenario, the range of noise intensity values ​​is divided into several levels, and different levels of noise intensity are mapped to different colors. Noise intensity within the same level range corresponds to one color. The noise intensity of all locations to be interpolated in the monitoring area at the current moment is mapped to the corresponding level color to obtain the noise intensity heat map of the monitoring area at the current moment. The noise intensity heat map of the monitoring area is updated in real time to achieve dynamic and visual monitoring of noise intensity.

[0015] Secondly, the present invention provides a dynamic visual monitoring system for noise, used to implement the above-mentioned dynamic visual monitoring method for noise, comprising: a processor, a memory, a communication interface, a display screen and a noise sensor, wherein the processor stores computer program instructions for implementing the above-mentioned dynamic visual monitoring method for noise, and multiple noise sensors are provided and installed on the same horizontal plane, and the communication interface is communicatively connected to the display screen and the noise sensors.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0017] This invention acquires the location information and noise intensity of each noise monitoring point and estimated noise source, calculates the density interpolation weight and distance interpolation weight of each location to be interpolated within the target area, and then obtains the noise monitoring point interpolation and estimated noise source interpolation for each location to be interpolated. Finally, it combines the interpolation weights of the noise monitoring points and estimated noise sources to perform bimodal interpolation on the location to be interpolated to obtain the noise intensity of each location. By using bimodal interpolation to accurately interpolate the noise intensity at unknown locations, the accuracy of noise monitoring can be effectively enhanced.

[0018] Compared to traditional noise monitoring methods that rely solely on IDW interpolation, this invention addresses the shortcomings of existing IDW interpolation methods. These methods, which depend solely on distance-based weighting, prevent interpolation from exceeding the maximum noise intensity measured by the noise sensor, resulting in locally optimal interpolated data with significant global bias. This solves the problem of large discrepancies between the monitored noise intensity distribution and the actual noise intensity distribution in traditional noise monitoring methods. Furthermore, by mapping the noise intensity of each interpolation location to color and displaying it as a heatmap, the invention provides real-time updates to the noise intensity heatmap of the monitored area, achieving dynamic visualization of noise monitoring results and making the noise intensity monitoring results more intuitive. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the implementation of a dynamic visualization monitoring method for noise according to an embodiment of this application.

[0020] Figure 2 This is a structural block diagram of a dynamic visualization monitoring system for noise according to an embodiment of this application. Detailed Implementation

[0021] The technical features of the present invention will be further described in detail below with reference to the accompanying drawings so that those skilled in the art can understand them.

[0022] A dynamic visualization monitoring method for noise, the implementation process is as follows: Figure 1 As shown, the specific implementation steps are as follows:

[0023] Step S1: Obtain the location coordinates of noise monitoring points within the area to be monitored, and collect the noise intensity sequence of each noise monitoring point in real time;

[0024] Specifically, based on the actual scenario, multiple locations are randomly or equidistantly selected as noise monitoring points on the same horizontal plane in the area to be monitored. A noise sensor is placed at each noise monitoring point, and each noise sensor corresponds to a noise monitoring point. A two-dimensional plane coordinate system is established based on the area to be monitored, and the position coordinates of each noise monitoring point are recorded. The noise intensity of the noise monitoring points is collected in real time through the noise sensor, and the noise intensity data within a preset time period is used as the noise intensity sequence of the corresponding noise monitoring point at the current moment.

[0025] The noise intensity sequence specifically refers to the noise intensity data within a window that includes the current moment and several previous moments. The preset time period is the duration of noise intensity data collection, which can be set to 10 seconds to 10 minutes according to the real-time requirements of noise monitoring in actual application scenarios. The noise is monitored in real time by collecting the noise intensity sequence rather than by the noise intensity value at the current moment. This is because the time delay of noise propagation needs to be considered to avoid accidental errors caused by environmental factors.

[0026] Step S2: Cluster the noise intensity sequence to obtain several clusters. Set an equal number of particles according to the number of clusters, and iteratively update the parameters of the particles to obtain several estimated noise source location coordinates and noise intensity.

[0027] Because there may be multiple noise sources in the noise monitoring area, and each noise monitoring point is at a different distance from the noise source and is affected by different noise sources in different ways, the noise distribution in the area is complex and diverse. Existing technology uses IDW interpolation to interpolate unknown locations in the monitoring area. However, IDW interpolation only relies on distance to calculate weights. The closer the noise monitoring point is, the greater its influence on the interpolation location. When multiple noise monitoring points are close to the location to be interpolated, the closer monitoring points dominate the interpolation magnitude, causing the interpolation influence of monitoring points at slightly farther distances to be over-smoothed, resulting in the noise intensity deviating from the true distribution. Therefore, it is necessary to obtain the estimated location coordinates and noise intensity of the noise source based on the particle swarm optimization algorithm, so as to calculate the noise intensity of the location to be interpolated more accurately in subsequent steps.

[0028] Specifically, all noise intensity sequences are differentially processed to obtain the differential sequence corresponding to each noise monitoring point. The DBSCAN algorithm is a density-based spatial clustering algorithm. The DBSCAN (Density-Based Spatial-Clustering-of-Applications-with-Noise) algorithm is used to cluster the differential sequences to obtain several clusters. Several location points in the monitoring area are randomly selected as particles according to the number of clusters. The number of particles is the same as the number of clusters. Each particle corresponds to an estimated noise source. The initial parameters of the particles are obtained and set, including the initial position coordinates and the initial noise intensity. Each particle contains three parameters: x-axis coordinate, y-axis coordinate, and noise intensity. The set initial noise intensity is greater than or equal to the maximum value in all noise intensity sequences.

[0029] Furthermore, based on the distance between the noise monitoring points and the particles, the ideal attenuated noise intensity of each particle at each noise monitoring point is calculated using the point source noise attenuation formula. Based on the principle of multi-noise decibel superposition, the ideal superimposed noise intensity at each noise monitoring point is calculated. Both point source noise attenuation and multi-noise decibel superposition are existing technologies. Based on the real-time noise intensity sequence and ideal superimposed noise intensity collected from the noise monitoring points, a particle swarm optimization objective function is constructed. The process involves calculating the difference between the ideal superimposed noise intensity and the average noise intensity sequence value at each noise monitoring point within the monitoring area. The squares of the differences at each noise monitoring point are then summed to form the objective function for particle swarm optimization. The initial parameters of the particles are optimized using a particle swarm optimization algorithm. The process involves setting a preset number of iterations based on the real-time requirements of noise monitoring in the actual application scenario, setting the update step size for the particle position parameters and the noise intensity parameters, and then iteratively updating the initial parameters of the particles based on the particle swarm optimization algorithm and the objective function until the preset number of iterations is reached. This yields the optimized parameters for all particles, and ultimately, the estimated location coordinates and noise intensities of all noise sources.

[0030] The objective function formula for particle swarm optimization is as follows:

[0031]

[0032] In the formula, This represents the objective function of particle swarm optimization. Indicates the number of noise monitoring points. Indicates the first The ideal superimposed noise intensity at each noise monitoring point Indicates the first The average value of the noise intensity sequence corresponding to each noise monitoring point;

[0033] The particle swarm optimization algorithm is used to update the parameters of the particles. The update step size of the position parameter can be set to 1, and the update step size of the noise intensity parameter is not less than the average value of the noise intensity sequence of the nearest noise monitoring point. The particle parameters are updated iteratively, and the ideal attenuated noise intensity of each particle at each noise monitoring point and the ideal superimposed noise intensity at each noise monitoring point are repeatedly calculated to obtain the corresponding objective function value. The iteration stops when a preset number of iterations is reached. An example preset number of iterations is 50. The particle parameters corresponding to the minimum objective function value during the iteration process are selected as the estimated location coordinates and noise intensity data of the noise source. Each particle corresponds to an estimated noise source.

[0034] Step S3: Divide the area to be monitored into several sub-regions and obtain the target area for the interpolation position;

[0035] Specifically, the area to be monitored is divided into multiple sub-regions of equal size. For a given interpolation location, the sub-region containing that location is merged with all its adjacent sub-regions to form the target area for that location. Each target area contains at least one noise monitoring point and one estimated noise source. There are three cases for the target area: when the location to be interpolated is in a corner region, the number of its adjacent sub-regions is 3; when the location to be interpolated is in an edge region, the number of its adjacent sub-regions is 5; and when the location to be interpolated is in a middle region, the number of its adjacent sub-regions is 8.

[0036] Step S4: Based on the location coordinates of the noise monitoring points and the estimated noise sources, calculate the interpolation weights of each noise monitoring point and the estimated noise source within the target area;

[0037] Specifically, the location coordinates of all noise monitoring points and estimated noise sources within the target area are obtained. The average Euclidean distance between a noise monitoring point and the other noise monitoring points within the target area is taken as the average distribution distance of the noise monitoring point. The average Euclidean distance between a noise monitoring point and the locations of all estimated noise sources within the target area is taken as the average monitoring distance of the noise monitoring point. The ratio between the average distribution distance and the average monitoring distance is taken as the density interpolation weight for that noise monitoring point. Similarly, the average Euclidean distance between a noise source and the other estimated noise sources within the target area is taken as the average distribution distance of the estimated noise source. The average Euclidean distance between the estimated noise source and the locations of all noise monitoring points within the target area is taken as the average emission distance of the estimated noise source. The ratio between the average distribution distance and the average emission distance is taken as the density interpolation weight for that estimated noise source. This process is repeated for each noise monitoring point and estimated noise source to obtain the density interpolation weight for all noise monitoring points and estimated noise sources within the target area.

[0038] The calculation logic for density interpolation weight is as follows: For a noise monitoring point, the smaller the Euclidean distance between it and other noise monitoring points in the target area, the greater the monitoring point density. Over-reliance on this monitoring point will cause the interpolation to fall into a local optimum. Therefore, the density interpolation weight should be proportional to the Euclidean distance between monitoring points.

[0039] Meanwhile, for a noise monitoring point, the larger the Euclidean distance between it and the estimated noise source in the target area, the greater the influence of the environment on the noise intensity measured by the monitoring point, and the more likely it is to cause measurement distortion. Over-reliance on the monitoring point will cause the interpolation to deviate from the true distribution. Therefore, the density interpolation weight should be inversely proportional to the Euclidean distance between the monitoring point and the estimated noise source.

[0040] The interpolation weighting of noise monitoring points within the target area is calculated using the following formula:

[0041]

[0042] In the formula, Indicates the first in the target area Density interpolation weights for each noise monitoring point Indicates the first The average Euclidean distance between the location of one noise monitoring point and the locations of other noise monitoring points within the target area. Indicates the first The average of the Euclidean distances between each noise monitoring point and the locations of all estimated noise sources within the target area;

[0043] Similarly, the interpolation weights for estimating the noise source density within the target area are calculated using the following formula:

[0044]

[0045] In the formula, Indicates the first in the target area The density interpolation weights of each estimated noise source Indicates the first The average of the Euclidean distances between the estimated noise source locations and the other estimated noise source locations within the target area. Indicates the first The average of the Euclidean distances between the estimated noise source locations and the locations of all noise monitoring points within the target area.

[0046] Furthermore, the Euclidean distance between each noise monitoring point and the interpolation point within the target area is calculated, and the square of the Euclidean distance for each noise monitoring point is used as the distance interpolation weight for that noise monitoring point. Similarly, the Euclidean distance between each estimated noise source and the interpolation point within the target area is calculated, and the square of the Euclidean distance for each estimated noise source is used as the distance interpolation weight for that estimated noise source. Combining the density interpolation weights and distance interpolation weights of the noise monitoring points and estimated noise sources, the product of the density interpolation weights and distance interpolation weights for each noise monitoring point is used as the interpolation weight for that noise monitoring point. Similarly, the product of the density interpolation weights and distance interpolation weights for each estimated noise source is used as the interpolation weight for that estimated noise source, thus obtaining the interpolation weights for each noise monitoring point and estimated noise source within the target area.

[0047] The interpolation weights of noise monitoring points within the target area are calculated using the following formula:

[0048]

[0049] In the formula, Indicates the first in the target area Interpolation weights for each noise monitoring point Indicates the first The Euclidean distance between each noise monitoring point and the location to be interpolated Indicates the first Distance interpolation weights for each noise monitoring point Indicates the first Density interpolation weights for each noise monitoring point;

[0050] Similarly, the interpolation weights for estimating noise sources within the target area are calculated using the following formula:

[0051]

[0052] In the formula, Indicates the first in the target area Interpolation weights for each estimated noise source, Indicates the first The estimated Euclidean distance between the noise source location and the location to be interpolated. Indicates the first The distance interpolation weights for each estimated noise source, Indicates the first Density interpolation weights for each estimated noise source;

[0053] The weight of monitoring points is measured by both density and distance. In dense areas, the excessive number of monitoring points can easily dominate the interpolation direction, so their weight should be reduced appropriately. In sparse areas, although the monitoring points are far apart, the noise intensity distribution range is larger, so their weight should be increased appropriately to avoid local interpolation optima.

[0054] Step S5: Combine the interpolation weights of the noise monitoring points and the estimated noise sources to perform bimodal interpolation at the location to be interpolated, and obtain the noise intensity at the location to be interpolated;

[0055] Specifically, the ratio of the interpolation weight of each noise monitoring point in the target area to the sum of the interpolation weights of all noise monitoring points in the target area is used as the normalized interpolation weight of the corresponding noise monitoring point. Similarly, the ratio of the interpolation weight of each estimated noise source in the target area to the sum of the interpolation weights of all estimated noise sources in the target area is used as the normalized interpolation weight of the corresponding estimated noise source.

[0056] Among them, the The normalized interpolation weights for each noise monitoring point are: The normalization formula is as follows:

[0057]

[0058] n represents the number of noise monitoring points within the target area. Similarly, we obtain the number of noise monitoring points. The normalized interpolation weights of the estimated noise sources are: ;

[0059] Furthermore, the product of the normalized interpolation weight of each noise monitoring point within the target area and the average value of the corresponding noise intensity sequence is used as the weight component of each noise monitoring point. The weight components of all noise monitoring points within the target area are accumulated to obtain the noise monitoring point interpolation value at the corresponding interpolation location. Similarly, the product of the normalized interpolation weight of each estimated noise source within the target area and the noise intensity of the corresponding estimated noise source is used as the weight component of each estimated noise source. The weight components of all estimated noise sources within the target area are accumulated to obtain the estimated noise source interpolation value at the corresponding interpolation location. Weights are set for the noise monitoring point interpolation and the estimated noise source interpolation. The sum of the products of the noise monitoring point interpolation and the estimated noise source interpolation with their respective weights is used as the noise intensity at the corresponding interpolation location.

[0060] The formula for calculating the noise intensity at the location to be interpolated is as follows:

[0061]

[0062] In the formula, This represents the noise intensity at a specific interpolation location. This indicates the number of noise monitoring points within the target area. Indicates the first Normalized interpolation weights for each noise monitoring point Indicates the first The average value of the noise intensity sequence at each noise monitoring point. This indicates the estimated number of noise sources within the target area. Indicates the first The estimated noise source normalized interpolation weights Indicates the first The noise intensity of an estimated noise source, This indicates the interpolation value of the noise monitoring points. This represents the estimated noise source interpolation. This indicates the weight of the noise monitoring point interpolation. This indicates the weight of the noise source interpolation estimation. The weights for noise monitoring point interpolation and noise source interpolation estimation are typically set to 0.5.

[0063] Since the noise sources within the monitored area are unknown, the selected noise monitoring points may not necessarily be the locations of the noise sources, resulting in noise intensities collected by the monitoring points often being lower than those of the noise sources. Existing technologies obtain the noise intensity at unknown locations by using the noise intensity at known monitoring points and IDW interpolation. However, IDW interpolation relies on weighted fusion interpolation based on the distance between the noise monitoring point and the unknown location, leading to interpolation results that do not exceed the noise intensity at the monitoring points. This masks the noise intensity distribution near the noise sources within the area, resulting in underestimated noise intensity and delayed early warnings. Therefore, this invention obtains the noise monitoring point interpolation value by using the normalized interpolation weights of the noise monitoring points, and obtains the estimated noise source interpolation value by estimating the normalized interpolation weights of the noise sources. The noise monitoring point interpolation value and the estimated noise source interpolation value are then weighted and fused to obtain the noise intensity at the location to be interpolated through bimodal interpolation. By performing bimodal interpolation using the estimated noise sources and noise monitoring points, the noise intensity under the superimposed influence of multiple estimated noise sources can be analyzed, obtaining a more accurate noise intensity distribution. Furthermore, it is adaptable to dynamic visualization monitoring of moving noise sources, avoiding the phenomenon where the interpolation value cannot exceed that of known monitoring points.

[0064] Step S6: Map the noise intensity of each location to be interpolated to the color to obtain a heat map of the noise intensity of the area to be monitored at the current time;

[0065] Specifically, based on the noise intensity limitations of the actual scenario, the noise intensity range is divided into several levels, and different levels of noise intensity are mapped to different colors. Noise intensity within the same level range corresponds to one color. The noise intensity of all locations to be interpolated within the monitoring area at the current moment is mapped to the corresponding level color to obtain a noise intensity heatmap of the monitoring area at the current moment. The noise intensity heatmap of the monitoring area is updated in real time to achieve dynamic and visual monitoring of noise intensity. In addition, a noise intensity threshold can be set, and locations within the monitoring area where the noise intensity exceeds the threshold are marked and flashed as a warning.

[0066] This invention also discloses a dynamic visualization monitoring system for noise, used to implement the above-mentioned dynamic visualization monitoring method for noise, the system structure of which is as follows: Figure 2 As shown, it includes: a processor, a memory, a communication interface, a display screen, and a noise sensor. The processor stores computer program instructions for implementing the above-mentioned dynamic visual monitoring method for noise. Multiple noise sensors are provided and installed on the same horizontal plane. The communication interface is communicatively connected to the display screen and the noise sensors.

[0067] The embodiments included in this invention are merely descriptions of preferred embodiments of the invention and are not limited to the precise structures described above and shown in the accompanying drawings. Various modifications and changes can be made without departing from the scope of protection. Any variations and improvements made by those skilled in the art to the technical solutions of this invention without departing from the design concept of this invention should fall within the scope of protection of this invention.

Claims

1. A method for dynamic visual monitoring of noise, characterized in that: The system acquires the location coordinates of noise monitoring points within the monitored area and collects the noise intensity sequence of each monitoring point in real time. It then clusters the noise intensity sequences to obtain several clusters, sets an equal number of particles based on the number of clusters, and iteratively updates the particle parameters to obtain the location coordinates and noise intensity of several estimated noise sources. The monitored area is divided into several sub-regions to obtain the target region for interpolation. Based on the location coordinates of the noise monitoring points and estimated noise sources, the interpolation weights for each noise monitoring point and estimated noise source within the target region are calculated. The interpolation weights of the noise monitoring points and estimated noise sources are combined to perform bimodal interpolation on the interpolation location to obtain the noise intensity at the interpolation location. Finally, the noise intensity at each interpolation location is mapped to a color to obtain a heatmap of the noise intensity in the monitored area at the current time. Specifically, based on the location coordinates of noise monitoring points and estimated noise sources within the target area, the density interpolation weights and distance interpolation weights of all noise monitoring points and estimated noise sources within the target area are calculated and obtained; the product of the density interpolation weight and distance interpolation weight of each noise monitoring point is used as the interpolation weight of the corresponding noise monitoring point, and the interpolation weight of each estimated noise source is obtained in the same way.

2. The dynamic visualization monitoring method for noise according to claim 1, characterized in that, The process of obtaining the location coordinates of noise monitoring points within the area to be monitored and collecting the noise intensity sequence of each noise monitoring point in real time includes: randomly selecting or equidistantly selecting multiple locations as noise monitoring points on the same horizontal plane within the area to be monitored according to the actual scene; placing a noise sensor at each noise monitoring point; establishing a two-dimensional plane coordinate system based on the area to be monitored; recording the location coordinates of each noise monitoring point; collecting the noise intensity of the noise monitoring points in real time through the noise sensor; and using the noise intensity data within a preset time period as the noise intensity sequence of the corresponding noise monitoring point at the current moment.

3. The dynamic visualization monitoring method for noise according to claim 1, characterized in that, The process of clustering the noise intensity sequences to obtain several clusters, and setting an equal number of particles based on the number of clusters, includes: performing differential processing on all noise intensity sequences to obtain a differential sequence corresponding to each noise monitoring point; using the DBSCAN algorithm to cluster the differential sequences to obtain several clusters; randomly selecting several location points within the monitoring area as particles based on the number of clusters, with the number of particles being the same as the number of clusters, and each particle corresponding to an estimated noise source; and obtaining and setting the initial parameters of the particles, including the initial location coordinates and the initial noise intensity, wherein the set initial noise intensity is greater than or equal to the maximum value among all noise intensity sequences.

4. The dynamic visualization monitoring method for noise according to claim 3, characterized in that, The iterative update of particle parameters to obtain the location coordinates and noise intensity of several estimated noise sources includes: calculating the ideal attenuated noise intensity of each particle at each noise monitoring point based on the distance between the noise monitoring point and the particle using the point source noise attenuation formula; calculating the ideal superimposed noise intensity at each noise monitoring point based on the principle of multi-noise decibel superposition; constructing a particle swarm optimization objective function based on the noise intensity sequence and ideal superimposed noise intensity collected in real time from the noise monitoring point; optimizing the initial parameters of the particles using the particle swarm optimization algorithm; iteratively updating the initial parameters of the particles to obtain the optimized parameters of all particles; and finally obtaining the location coordinates and noise intensity of all estimated noise sources.

5. The dynamic visualization monitoring method for noise according to claim 1, characterized in that, The step of dividing the area to be monitored into several sub-regions and obtaining the target area of ​​the interpolation position includes: dividing the area to be monitored into multiple sub-regions of equal size; for a certain interpolation position, merging the sub-region where the interpolation position is located with all its adjacent sub-regions, which is called the target area of ​​the interpolation position; and each target area of ​​the interpolation position contains at least one noise monitoring point and one estimated noise source.

6. The dynamic visualization monitoring method for noise according to claim 1, characterized in that, The calculation of interpolation weights for each noise monitoring point and estimated noise source within the target area, based on the location coordinates of noise monitoring points and estimated noise sources, includes: obtaining the location coordinates of all noise monitoring points and estimated noise sources within the target area; using the average Euclidean distance between a noise monitoring point and the other noise monitoring points within the target area as the average distribution distance of the noise monitoring point; using the average Euclidean distance between a noise monitoring point and the locations of all estimated noise sources within the target area as the average monitoring distance of the noise monitoring point; and using the ratio between the average distribution distance and the average monitoring distance as the density interpolation weight of the noise monitoring point. Similarly, using the average Euclidean distance between a noise source and the other estimated noise sources within the target area as the average distribution distance of the estimated noise source; using the average Euclidean distance between the estimated noise source and the locations of all noise monitoring points within the target area as the average transmission distance of the estimated noise source; and using the ratio between the average distribution distance and the average transmission distance as the density interpolation weight of the estimated noise source. This process is repeated for each noise monitoring point and estimated noise source to obtain the density interpolation weights for all noise monitoring points and estimated noise sources within the target area.

7. The dynamic visualization monitoring method for noise according to claim 6, characterized in that, The calculation of interpolation weights for each noise monitoring point and estimated noise source within the target area, based on the location coordinates of noise monitoring points and estimated noise sources, further includes: calculating the Euclidean distance between the location of each noise monitoring point within the target area and the location to be interpolated, and using one-squared of the Euclidean distance corresponding to each noise monitoring point as the distance interpolation weight for that noise monitoring point; similarly, calculating the Euclidean distance between the location of each estimated noise source within the target area and the location to be interpolated, and using one-squared of the Euclidean distance corresponding to each estimated noise source as the distance interpolation weight for that estimated noise source; and combining the density interpolation weights and distance interpolation weights of the noise monitoring points and estimated noise sources to obtain the interpolation weights for each noise monitoring point and estimated noise source within the target area.

8. The method for dynamic visual monitoring of noise according to any one of claims 1 to 7, characterized in that, The method of combining the interpolation weights of noise monitoring points and estimated noise sources to perform bimodal interpolation at the location to be interpolated, thereby obtaining the noise intensity at the location to be interpolated, includes: using the ratio of the interpolation weight of each noise monitoring point in the target area to the sum of the interpolation weights of all noise monitoring points in the target area as the normalized interpolation weight of the corresponding noise monitoring point; similarly, using the ratio of the interpolation weight of each estimated noise source in the target area to the sum of the interpolation weights of all estimated noise sources in the target area as the normalized interpolation weight of the corresponding estimated noise source; and using the product of the normalized interpolation weight of each noise monitoring point in the target area and the average value of the corresponding noise intensity sequence as... The weighted components of each noise monitoring point are summed, and the sum of the weighted components of all noise monitoring points within the target area is used as the noise monitoring point interpolation value for the corresponding interpolation location. Similarly, the product of the normalized interpolation weight of each estimated noise source within the target area and the noise intensity of the corresponding estimated noise source is used as the weighted component of each estimated noise source. The sum of the weighted components of all estimated noise sources within the target area is used as the estimated noise source interpolation value for the corresponding interpolation location. Weights are set for the noise monitoring point interpolation value and the estimated noise source interpolation value. The sum of the products of the noise monitoring point interpolation value and the estimated noise source interpolation value with their respective weights is used as the noise intensity for the corresponding interpolation location.

9. The dynamic visualization monitoring method for noise according to claim 1, characterized in that, The step of mapping the noise intensity of each interpolation location to a color to obtain a noise intensity heatmap of the monitored area at the current moment includes: dividing the noise intensity range into several levels according to the actual scene's noise intensity constraints; mapping different levels of noise intensity to different colors, with noise intensity within the same level range corresponding to one color; mapping the noise intensity of all interpolation locations in the monitored area at the current moment to the corresponding level of color to obtain a noise intensity heatmap of the monitored area at the current moment; and updating the noise intensity heatmap of the monitored area in real time to achieve dynamic and visual monitoring of noise intensity.

10. A dynamic visual monitoring system for noise, characterized in that: The device includes a processor, a memory, a communication interface, a display screen, and a noise sensor. The processor stores computer program instructions for implementing the dynamic visual monitoring method for noise according to any one of claims 1 to 9. Multiple noise sensors are provided and installed on the same horizontal plane. The communication interface is communicatively connected to the display screen and the noise sensors.

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